General overview on the merits of multimodal neuroimaging data fusion
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paper_title: Magnetoencephalography: From SQUIDs to neuroscience Neuroimage 20th Anniversary Special Edition

paper_content:

Abstract Magnetoencephalography (MEG), with its direct view to the cortex through the magnetically transparent skull, has developed from its conception in physics laboratories to a powerful tool of basic and clinical neuroscience. MEG provides millisecond time resolution and allows real-time tracking of brain activation sequences during sensory processing, motor planning and action, cognition, language perception and production, social interaction, and various brain disorders. Current-day neuromagnetometers house hundreds of SQUIDs, superconducting quantum interference devices, to pick up signals generated by concerted action of cortical neurons. Complementary MEG measures of neuronal involvement include evoked responses, modulation of cortical rhythms, properties of the on-going neural activity, and interareal connectivity. Future MEG breakthroughs in understanding brain dynamics are expected through advanced signal analysis and combined use of MEG with hemodynamic imaging (fMRI). Methodological development progresses most efficiently when linked with insightful neuroscientific questions.
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paper_title: Imaging in vivo: watching the brain in action

paper_content:

Advances in cellular imaging have been crucial for improving our understanding of many aspects of neuroscience. Kerr and Denk describe how sophisticated optical imaging techniques allow us to image activity in single neurons or neuron populations in living animals.
---
paper_title: Non-invasive optical spectroscopy and imaging of human brain function

paper_content:

Abstract Brain activity is associated with changes in optical properties of brain tissue. Optical measurements during brain activation can assess haemoglobin oxygenation, cytochrome- c -oxidase redox state, and two types of changes in light scattering reflecting either membrane potential (fast signal) or cell swelling (slow signal), respectively. In previous studies of exposed brain tissue, optical imaging of brain activity has been achieved at high temporal and microscopical spatial resolution. Now, using near-infrared light that can penetrate biological tissue reasonably well, it has become possible to assess brain activity in human subjects through the intact skull non-invasively. After early studies employing single-site near-infrared spectroscopy, first near-infrared imaging devices are being applied successfully for low-resolution functional brain imaging. Advantages of the optical methods include biochemical specificity, a temporal resolution in the millisecond range, the potential of measuring intracellular and intravascular events simultaneously and the portability of the devices enabling bedside examinations.
---
paper_title: Principles of magnetic resonance assessment of brain function

paper_content:

MRI has advanced to being one of the major tools for the assessment of brain function. This review article examines the basic principles that underpin these measurements. The main emphasis is on the characteristics and detection of blood oxygen level dependent (BOLD) contrast. In the first part of the article the relationship between BOLD, blood flow, blood oxygen, and the rate of metabolic consumption of oxygen is described. The four contrast mechanisms that contribute to the BOLD signal change, namely extravascular static and dynamic dephasing, intravascular T2-like changes, and the intravascular frequency offset effect are described in terms of their spatial localization and relative contributions to the BOLD signal. The current model of changes in blood flow being an indirect consequence of synaptic input to a region is presented. The second section of the article deals with the imaging characteristics of BOLD in terms of the attainable spatial resolution and linear system characteristics. In the third section, practical BOLD imaging is examined for choice of pulse sequence, resolution, echo time (TE), repetition time (TR), and flip angle. The final section touches on other MRI approaches that are relevant to cognitive neuroimaging, in particular the measurement of blood flow, blood volume, resting state fluctuations in the BOLD signal, and measures of connectivity using diffusion tensor imaging and fiber-tracking.
---
paper_title: The Development, Past Achievements, and Future Directions of Brain PET

paper_content:

The early developments of brain positron emission tomography (PET), including the methodological advances that have driven progress, are outlined. The considerable past achievements of brain PET have been summarized in collaboration with contributing experts in specific clinical applications including cerebrovascular disease, movement disorders, dementia, epilepsy, schizophrenia, addiction, depression and anxiety, brain tumors, drug development, and the normal healthy brain. Despite a history of improving methodology and considerable achievements, brain PET research activity is not growing and appears to have diminished. Assessments of the reasons for decline are presented and strategies proposed for reinvigorating brain PET research. Central to this is widening the access to advanced PET procedures through the introduction of lower cost cyclotron and radiochemistry technologies. The support and expertize of the existing major PET centers, and the recruitment of new biologists, bio-mathematicians and chemists to the field would be important for such a revival. New future applications need to be identified, the scope of targets imaged broadened, and the developed expertize exploited in other areas of medical research. Such reinvigoration of the field would enable PET to continue making significant contributions to advance the understanding of the normal and diseased brain and support the development of advanced treatments.
---
paper_title: Towards the utilization of EEG as a brain imaging tool

paper_content:

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications.
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paper_title: K-space sampling strategies

paper_content:

The k-space algorithm offers a comprehensive way for classification and understanding of the imaging properties of all commonly used MR sequences. This presentation describes the basic concepts of k-space and its most relevant properties for MR imaging. The ramifications of k-space sampling is discussed for the most commonly used groups of MR sequences including gradient-echo techniques, echo-planar imaging, spin echo, and rapid acquisition relation enhanced imaging (e. g., turbo spin echo, fast spin echo). In addition, the basic problems and properties of sequences based on non-rectilinear k-space sampling, such as spiral imaging, are discussed. Their artifact behavior is significantly different from rectilinear scans, which project all imperfections along the phase-encoding directions, whereas the artifact produced by spirals are more complex and not always easily recognizable as such. An understanding of the k-space sampling offers important insight into the basic properties of a given sequence regarding signal-to-noise ratio, image distortion, resolution and contrast. It is demonstrated that the ultimate limitation in imaging speed is given by the loss of signal-to-noise ratio inherent to faster data sampling.
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paper_title: The current state, challenges and perspectives of MR-PET

paper_content:

Abstract Following the success of PET/CT during the last decade and the recent increasing proliferation of SPECT/CT, another hybrid imaging instrument has been gaining more and more interest: MR-PET. First combined, simultaneous PET and MR studies carried out in small animals demonstrated the feasibility of the new approach. Concurrently, some prototypes of an MR-PET scanner for simultaneous human brain studies have been built, their performance is being tested and preliminary applications have already been shown. Through this pioneering work, it has become clear that advances in the detector design are necessary for further optimization. Recently, the different issues related to the present state and future prospects of MR-PET were presented and discussed during an international 2-day workshop at the Forschungszentrum Julich, Germany, held after, and in conjunction with, the 2008 IEEE Nuclear Science Symposium and Medical Imaging Conference in Dresden, Germany on October 27–28, 2008. The topics ranged from small animal MR-PET imaging to human MR-BrainPET imaging, new detector developments, challenges/opportunities for ultra-high field MR-PET imaging and considerations of possible future research and clinical applications. This report presents a critical summary of the contributions made to the workshop.
---
paper_title: The Development and Application of Optogenetics

paper_content:

Genetically encoded, single-component optogenetic tools have made a significant impact on neuroscience, enabling specific modulation of selected cells within complex neural tissues. As the optogenetic toolbox contents grow and diversify, the opportunities for neuroscience continue to grow. In this review, we outline the development of currently available single-component optogenetic tools and summarize the application of various optogenetic tools in diverse model organisms.
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paper_title: Present and future of simultaneous EEG-fMRI

paper_content:

The brain's activity can be measured in numerous complementary ways, including electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The simultaneous acquisition of EEG and fMRI was originally developed to make the localization of the generators of often subtle pathological activity commonly observed in EEG recordings of patients with epilepsy more sensitive and spatially accurate by mapping their hemodynamic correlates. Now, the value of the information provided by simultaneous EEG-fMRI is being evaluated in a clinical context, while in parallel, more sophisticated data analysis techniques, e.g. with electrical source imaging or dynamic causal modeling, have begun to be applied to increase the technique's sensitivity and allow the study of brain network structure. Beyond its clinically oriented application in epilepsy, simultaneous EEG-fMRI recording has now gained interest as a tool for basic and systems human neuroscience, e.g. the study of neuro-vascular coupling and cognitive studies. In this review, we give an overview over the current use of simultaneous EEG-fMRI, its applications to the study of epilepsy as well as human cognition and systems neuroscience and ongoing and anticipated methodological developments.
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paper_title: Combination of MEG and MRI in one setup

paper_content:

R.J. Ilmoniemi, J. Dabek, F.-H. Lin, J.O. Nieminen, L.T. Parkkonen, P.T. Vesanen, K.C.J. Zevenhoven, A.V. Zhdanov, J. Luomahaara, J. Hassel, J. Penttila, J. Simola, A.I. Ahonen, and J.P. Makela Dept. Biomedical Engineering and Computational Science, Aalto University School of Science, Espoo, Finland; Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan; Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States; Elekta Oy, Helsinki, Finland; BioMag Laboratory, HUSLAB, Helsinki University Central Hospital, Helsinki, Finland; VTT Technical Research Centre of Finland, Espoo, Finland; Aivon Oy, Espoo, Finland
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paper_title: Imaging in vivo: watching the brain in action

paper_content:

Advances in cellular imaging have been crucial for improving our understanding of many aspects of neuroscience. Kerr and Denk describe how sophisticated optical imaging techniques allow us to image activity in single neurons or neuron populations in living animals.
---
paper_title: Non-invasive optical spectroscopy and imaging of human brain function

paper_content:

Abstract Brain activity is associated with changes in optical properties of brain tissue. Optical measurements during brain activation can assess haemoglobin oxygenation, cytochrome- c -oxidase redox state, and two types of changes in light scattering reflecting either membrane potential (fast signal) or cell swelling (slow signal), respectively. In previous studies of exposed brain tissue, optical imaging of brain activity has been achieved at high temporal and microscopical spatial resolution. Now, using near-infrared light that can penetrate biological tissue reasonably well, it has become possible to assess brain activity in human subjects through the intact skull non-invasively. After early studies employing single-site near-infrared spectroscopy, first near-infrared imaging devices are being applied successfully for low-resolution functional brain imaging. Advantages of the optical methods include biochemical specificity, a temporal resolution in the millisecond range, the potential of measuring intracellular and intravascular events simultaneously and the portability of the devices enabling bedside examinations.
---
paper_title: Combined PET/MRI: one step further in multimodality imaging.

paper_content:

Given the need for sophisticated in vivo detection techniques to better characterize the cellular and subcellular processes in animals and humans, molecular imaging has become an important discipline. Techniques in molecular imaging have developed from stand alone modalities to multimodality methods. Among these, the combination of positron emission tomography (PET) and computed tomography (CT) is a successful imaging method and has become an important tool in clinical practice. Technological approaches that combine magnetic resonance imaging (MRI) with diffuse optical tomography (DOT), fluorescence tomography (FT) and PET have now been introduced. PET/MRI and the resulting combination of molecular, morphological and functional information will pave the way for a better understanding of physiological and disease mechanisms in preclinical and clinical settings.
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paper_title: PET/MRI: challenges, solutions and perspectives.

paper_content:

Already from the start of PET/CT integrating positron emission tomography (PET) and computed tomography (CT) in one instrument, there have been considerations how to combine PET and magnetic resonance imaging (MRI) so that their complementary abilities can be utilized in a single investigation. Since classical PET electronics fail in an even weak magnetic field and PET signal processing might disturb high-frequency signals of MRI, it soon became clear that new solutions had to be found to avoid mutual interferences. During the last fifteen years a number of different approaches towards PET/MRI for small animal imaging have been developed by research groups which together with their specific features are summarized in this review. Recently, PET/MRI for human imaging became available as well - this time by industrial initiatives. First some prototypes of BrainPET/MRI were developed followed by commercial products for simultaneous and non-simultaneous whole-body PET/MRI. Although only PET/MRI integrated in one scanner offers the full diversity of complementary multiparametric imaging, there are also promising applications of non-simultaneous sequential PET/MRI. While describing the present instrumentation for human PET/MRI, this review discusses the challenges and promises related to this new imaging technology.
---
paper_title: The Development, Past Achievements, and Future Directions of Brain PET

paper_content:

The early developments of brain positron emission tomography (PET), including the methodological advances that have driven progress, are outlined. The considerable past achievements of brain PET have been summarized in collaboration with contributing experts in specific clinical applications including cerebrovascular disease, movement disorders, dementia, epilepsy, schizophrenia, addiction, depression and anxiety, brain tumors, drug development, and the normal healthy brain. Despite a history of improving methodology and considerable achievements, brain PET research activity is not growing and appears to have diminished. Assessments of the reasons for decline are presented and strategies proposed for reinvigorating brain PET research. Central to this is widening the access to advanced PET procedures through the introduction of lower cost cyclotron and radiochemistry technologies. The support and expertize of the existing major PET centers, and the recruitment of new biologists, bio-mathematicians and chemists to the field would be important for such a revival. New future applications need to be identified, the scope of targets imaged broadened, and the developed expertize exploited in other areas of medical research. Such reinvigoration of the field would enable PET to continue making significant contributions to advance the understanding of the normal and diseased brain and support the development of advanced treatments.
---
paper_title: Beyond Conventional Event-related Brain Potential (ERP): Exploring the Time-course of Visual Emotion Processing Using Topographic and Principal Component Analyses

paper_content:

Recent technological advances with the scalp EEG methodology allow researchers to record electric fields generated in the human brain using a large number of electrodes or sensors (e.g. 64-256) distributed over the head surface (multi-channel recording). As a consequence, such high-density ERP mapping yields fairly dense ERP data sets that are often hard to analyze comprehensively or to relate straightforwardly to specific cognitive or emotional processes, because of the richness of the recorded signal in both the temporal (millisecond time-resolution) and spatial (multidimensional topographic information) domains. Principal component analyses (PCA) and topographic analyses (combined with distributed source localization algorithms) have been developed and successfully used to deal with this complexity, now offering powerful alternative strategies for data-driven analyses in complement to more traditional ERP analyses based on waveforms and peak measures. In this paper, we first briefly review the basic principles of these approaches, and then describe recent ERP studies that illustrate how they can inform about the precise spatio-temporal dynamic of emotion processing. These studies show that the perception of emotional visual stimuli may produce both quantitative and qualitative changes in the electric field configuration recorded at the scalp level, which are not apparent when using conventional ERP analyses. Additional information gained from these approaches include the identification of a sequence of successive processing stages that may not fully be reflected in ERP waveforms only, and the segregation of multiple or partly overlapping neural events that may be blended within a single ERP waveform. These findings highlight the added value of such alternative analyses when exploring the electrophysiological manifestations of complex and distributed mental functions, as for instance during emotion processing.
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paper_title: Laminar differences in gamma and alpha coherence in the ventral stream.

paper_content:

Attention to a stimulus enhances both neuronal responses and gamma frequency synchrony in visual area V4, both of which should increase the impact of attended information on downstream neurons. To determine whether gamma synchrony is common throughout the ventral stream, we recorded from neurons in the superficial and deep layers of V1, V2, and V4 in two rhesus monkeys. We found an unexpected striking difference in gamma synchrony in the superficial vs. deep layers. In all three areas, spike-field coherence in the gamma (40-60 Hz) frequency range was largely confined to the superficial layers, whereas the deep layers showed maximal coherence at low frequencies (6-16 Hz), which included the alpha range. In the superficial layers of V2 and V4, gamma synchrony was enhanced by attention, whereas in the deep layers, alpha synchrony was reduced by attention. Unlike these major differences in synchrony, attentional effects on firing rates and noise correlation did not differ substantially between the superficial and deep layers. The results suggest that synchrony plays very different roles in feedback and feedforward projections.
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paper_title: Neurophysiological investigation of the basis of the fMRI signal

paper_content:

Functional magnetic resonance imaging (fMRI) is widely used to study the operational organization of the human brain, but the exact relationship between the measured fMRI signal and the underlying neural activity is unclear. Here we present simultaneous intracortical recordings of neural signals and fMRI responses. We compared local field potentials (LFPs), single- and multi-unit spiking activity with highly spatio-temporally resolved blood-oxygen-level-dependent (BOLD) fMRI responses from the visual cortex of monkeys. The largest magnitude changes were observed in LFPs, which at recording sites characterized by transient responses were the only signal that significantly correlated with the haemodynamic response. Linear systems analysis on a trialby-trial basis showed that the impulse response of the neurovascular system is both animal- and site-specific, and that LFPs yield a better estimate of BOLD responses than the multi-unit responses. These findings suggest that the BOLD contrast mechanism reflects the input and intracortical processing of a given area rather than its spiking output.
---
paper_title: EEG–fMRI Information Fusion: Biophysics and Data Analysis

paper_content:

Cerebral activity has many attributes: bioelectrical, metabolic, haemodynamic, hormonal, endogenous, exogenous, specialised, integrated, pathological, stable, dynamic, to mention but a few. The diverse nature of biological processes has been recognised for centuries. It seems obvious that moving from unimodal recordings to multimodal measurements will allow neuroscientists to better understand the nature and structure of cerebral activity. This means that fusing electrophysiological data and BOLD-related measurements represents an important methodological challenge. The realisation of any cognitive, motor or sensory process rests on cerebral dynamics and creates order in the bioelectric and haemodynamic signals measured with EEG and fMRI, respectively. To detect and interpret the relevant features of these signals, one typically describes processes at their own temporal and spatial scales. The main sources of scalp EEG signals are postsynaptic cortical currents associated with large pyramidal neurons, which are oriented perpendicular to the cortical surface (Nunez 1981). However, the scalp topology of measured electrical potentials does not, without additional (prior) information, uniquely specify the location of underlying bioelectric activity. This issue is referred to as the ill-posed nature of the EEG/MEG inverse problem. Conversely, even though fMRI discloses complementary features of neuronal activity (Nunez and Silberstein 2000; Mukamel et al. 2005), it is only an indirect measure, through metabolism, oxygenation and blood flow, where these slow mechanisms provide temporally smoothed correlates of neuronal activity.
---
paper_title: Left ear advantage in speech-related dichotic listening is not specific to auditory processing disorder in children: A machine-learning fMRI and DTI study

paper_content:

Dichotic listening (DL) tests are among the most frequently included in batteries for the diagnosis of auditory processing disorders (APD) in children. A finding of atypical left ear advantage (LEA) for speech-related stimuli is often taken by clinical audiologists as an indicator for APD. However, the precise etiology of ear advantage in DL tests has been a source of debate for decades. It is uncertain whether a finding of LEA is truly indicative of a sensory processing deficit such as APD, or whether attentional or other supramodal factors may also influence ear advantage. Multivariate machine learning was used on diffusion tensor imaging (DTI) and functional MRI (fMRI) data from a cohort of children ages 7–14 referred for APD testing with LEA, and typical controls with right-ear advantage (REA). LEA was predicted by: increased axial diffusivity in the left internal capsule (sublenticular region), and decreased functional activation in the left frontal eye fields (BA 8) during words presented diotically as compared to words presented dichotically, compared to children with right-ear advantage (REA). These results indicate that both sensory and attentional deficits may be predictive of LEA, and thus a finding of LEA, while possibly due to sensory factors, is not a specific indicator of APD as it may stem from a supramodal etiology.
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paper_title: Model driven EEG/fMRI fusion of brain oscillations

paper_content:

This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. Fusion is the combination of both imaging modalities based on a cascade of for- ward models from ensemble of post-synaptic potentials (ePSP) to net primary current densities (nPCD) to EEG; and from ePSP to vasomotor feed forward signal (VFFSS) to BOLD. In absence of a model, data driven fusion creates maps of correlations between EEG and BOLD or between estimates of nPCD and VFFS. A consistent finding has been that of positive correlations between EEG alpha power and BOLD in both frontal cortices and thalamus and of negative ones for the occipital region. For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. For exploratory simulations we show that the Local Linearization (LL) method for integrating stochastic differential equations is appropriate for highly nonlinear dynamics. It has been successfully applied to small and medium sized networks, reproducing the described EEG/BOLD correlations. A new LL-algebraic method allows simulations with hundreds of thousands of neural populations, with connectivities and conduction delays estimated from diffusion weighted MRI. For parameter and state estimation, Kalman filtering combined with the LL method estimates the innovations or prediction errors. From these the likelihood of models given data are obtained. The LL-innovation estimation method has been already applied to small and medium scale models. With improved Bayesian compu- tations the practical estimation of very large scale EEG/fMRI models shall soon be possible. Hum Brain Mapp 30:2701-2721, 2009. V C 2008 Wiley-Liss, Inc.
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paper_title: BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis

paper_content:

Multimodal neuroimaging studies have recently become a trend in the neuroimaging field and are certainly a standard for the future. Brain connectivity studies combining functional activation patterns using resting-state or task-related functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) tractography have growing popularity. However, there is a scarcity of solutions to perform optimized, intuitive, and consistent multimodal fMRI/DTI studies. Here we propose a new tool, brain connectivity analysis tool (BrainCAT), for an automated and standard multimodal analysis of combined fMRI/DTI data, using freely available tools. With a friendly graphical user interface, BrainCAT aims to make data processing easier and faster, implementing a fully automated data processing pipeline and minimizing the need for user intervention, which hopefully will expand the use of combined fMRI/DTI studies. Its validity was tested in an aging study of the default mode network (DMN) white matter connectivity. The results evidenced the cingulum bundle as the structural connector of the precuneus/posterior cingulate cortex and the medial frontal cortex, regions of the DMN. Moreover, mean fractional anisotropy (FA) values along the cingulum extracted with BrainCAT showed a strong correlation with FA values from the manual selection of the same bundle. Taken together, these results provide evidence that BrainCAT is suitable for these analyses.
---
paper_title: The current state, challenges and perspectives of MR-PET

paper_content:

Abstract Following the success of PET/CT during the last decade and the recent increasing proliferation of SPECT/CT, another hybrid imaging instrument has been gaining more and more interest: MR-PET. First combined, simultaneous PET and MR studies carried out in small animals demonstrated the feasibility of the new approach. Concurrently, some prototypes of an MR-PET scanner for simultaneous human brain studies have been built, their performance is being tested and preliminary applications have already been shown. Through this pioneering work, it has become clear that advances in the detector design are necessary for further optimization. Recently, the different issues related to the present state and future prospects of MR-PET were presented and discussed during an international 2-day workshop at the Forschungszentrum Julich, Germany, held after, and in conjunction with, the 2008 IEEE Nuclear Science Symposium and Medical Imaging Conference in Dresden, Germany on October 27–28, 2008. The topics ranged from small animal MR-PET imaging to human MR-BrainPET imaging, new detector developments, challenges/opportunities for ultra-high field MR-PET imaging and considerations of possible future research and clinical applications. This report presents a critical summary of the contributions made to the workshop.
---
paper_title: Evaluating gradient artifact correction of EEG data acquired simultaneously with fMRI

paper_content:

Abstract Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has become a widely used application in spite of EEG perturbations due to electromagnetic interference in the MR environment. The most prominent and disturbing artifacts in the EEG are caused by the alternating magnetic fields (gradients) of the MR scanner. Different methods for gradient artifact correction have been developed. Here we propose an approach for the systematic evaluation and comparison of these gradient artifact correction methods. Exemplarily, we evaluate different algorithms all based on artifact template subtraction — the currently most established means of gradient artifact removal. We introduce indices for the degree of gradient artifact reduction and physiological signal preservation. The combination of both indices was used as a measure for the overall performance of gradient artifact removal and was shown to be useful in identifying problems during artifact removal. We demonstrate that the evaluation as proposed here allows to reveal frequency-band specific performance differences among the algorithms. This emphasizes the importance of carefully selecting the artifact correction method appropriate for the respective case.
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paper_title: Simultaneous intracranial EEG and fMRI of interictal epileptic discharges in humans

paper_content:

Simultaneous scalp EEG–fMRI measurements allow the study of epileptic networks and more generally, of the coupling between neuronal activity and haemodynamic changes in the brain. Intracranial EEG (icEEG) has greater sensitivity and spatial specificity than scalp EEG but limited spatial sampling. We performed simultaneous icEEG and functional MRI recordings in epileptic patients to study the haemodynamic correlates of intracranial interictal epileptic discharges (IED). ::: ::: Two patients undergoing icEEG with subdural and depth electrodes as part of the presurgical assessment of their pharmaco-resistant epilepsy participated in the study. They were scanned on a 1.5 T MR scanner following a strict safety protocol. Simultaneous recordings of fMRI and icEEG were obtained at rest. IED were subsequently visually identified on icEEG and their fMRI correlates were mapped using a general linear model (GLM). ::: ::: On scalp EEG–fMRI recordings performed prior to the implantation, no IED were detected. icEEG–fMRI was well tolerated and no adverse health effect was observed. intra-MR icEEG was comparable to that obtained outside the scanner. In both cases, significant haemodynamic changes were revealed in relation to IED, both close to the most active electrode contacts and at distant sites. In one case, results showed an epileptic network including regions that could not be sampled by icEEG, in agreement with findings from magneto-encephalography, offering some explanation for the persistence of seizures after surgery. ::: ::: Hence, icEEG–fMRI allows the study of whole-brain human epileptic networks with unprecedented sensitivity and specificity. This could help improve our understanding of epileptic networks with possible implications for epilepsy surgery.
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paper_title: Simultaneous EEG–fMRI

paper_content:

Abstract Acquisition of electroencephalogram (EEG) during functional magnetic resonance imaging (fMRI) provides an additional monitoring tool for the analysis of brain state fluctuations. The exploration of brain responses following inputs or in the context of state changes is crucial for a better understanding of the basic principles governing large-scale neuronal dynamics. State-of-the-art techniques allow EEG activity—from DC (direct current) up to high frequencies in the gamma range—to be acquired simultaneously with fMRI data. In the interleaved mode, spiking activities can also be assessed during concurrent fMRI. The utilization of fMRI evidence to better constrain solutions of the inverse problem of source localization of EEG activity is an exciting possibility. Nonetheless, this approach should be applied cautiously since the degree of overlap between underlying neuronal activity sources is variable and, for the most part, unknown. The ultimate goal is to make joint inferences about the activity, dynamics, and functions by exploiting complementary information from multimodal data sets.
---
paper_title: EEG–fMRI Information Fusion: Biophysics and Data Analysis

paper_content:

Cerebral activity has many attributes: bioelectrical, metabolic, haemodynamic, hormonal, endogenous, exogenous, specialised, integrated, pathological, stable, dynamic, to mention but a few. The diverse nature of biological processes has been recognised for centuries. It seems obvious that moving from unimodal recordings to multimodal measurements will allow neuroscientists to better understand the nature and structure of cerebral activity. This means that fusing electrophysiological data and BOLD-related measurements represents an important methodological challenge. The realisation of any cognitive, motor or sensory process rests on cerebral dynamics and creates order in the bioelectric and haemodynamic signals measured with EEG and fMRI, respectively. To detect and interpret the relevant features of these signals, one typically describes processes at their own temporal and spatial scales. The main sources of scalp EEG signals are postsynaptic cortical currents associated with large pyramidal neurons, which are oriented perpendicular to the cortical surface (Nunez 1981). However, the scalp topology of measured electrical potentials does not, without additional (prior) information, uniquely specify the location of underlying bioelectric activity. This issue is referred to as the ill-posed nature of the EEG/MEG inverse problem. Conversely, even though fMRI discloses complementary features of neuronal activity (Nunez and Silberstein 2000; Mukamel et al. 2005), it is only an indirect measure, through metabolism, oxygenation and blood flow, where these slow mechanisms provide temporally smoothed correlates of neuronal activity.
---
paper_title: Multimodal imaging utilising integrated MR-PET for human brain tumour assessment

paper_content:

Objectives ::: The development of integrated magnetic resonance (MR)-positron emission tomography (PET) hybrid imaging opens up new horizons for imaging in neuro-oncology. In cerebral gliomas the definition of tumour extent may be difficult to ascertain using standard MR imaging (MRI) only. The differentiation of post-therapeutic scar tissue, tumour rests and tumour recurrence is challenging. The relationship to structures such as the pyramidal tract to the tumour mass influences the therapeutic neurosurgical approach.
---
paper_title: Model driven EEG/fMRI fusion of brain oscillations

paper_content:

This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. Fusion is the combination of both imaging modalities based on a cascade of for- ward models from ensemble of post-synaptic potentials (ePSP) to net primary current densities (nPCD) to EEG; and from ePSP to vasomotor feed forward signal (VFFSS) to BOLD. In absence of a model, data driven fusion creates maps of correlations between EEG and BOLD or between estimates of nPCD and VFFS. A consistent finding has been that of positive correlations between EEG alpha power and BOLD in both frontal cortices and thalamus and of negative ones for the occipital region. For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. For exploratory simulations we show that the Local Linearization (LL) method for integrating stochastic differential equations is appropriate for highly nonlinear dynamics. It has been successfully applied to small and medium sized networks, reproducing the described EEG/BOLD correlations. A new LL-algebraic method allows simulations with hundreds of thousands of neural populations, with connectivities and conduction delays estimated from diffusion weighted MRI. For parameter and state estimation, Kalman filtering combined with the LL method estimates the innovations or prediction errors. From these the likelihood of models given data are obtained. The LL-innovation estimation method has been already applied to small and medium scale models. With improved Bayesian compu- tations the practical estimation of very large scale EEG/fMRI models shall soon be possible. Hum Brain Mapp 30:2701-2721, 2009. V C 2008 Wiley-Liss, Inc.
---
paper_title: A review of multivariate methods for multimodal fusion of brain imaging data

paper_content:

Abstract The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multi-modal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous multimodal fusion reports, mostly fMRI with other modality, which were performed with or without prior information. A table for comparing optimization assumptions, purpose of the analysis, the need of priors, dimension reduction strategies and input data types is provided, which may serve as a valuable reference that helps readers understand the trade-offs of the 7 methods comprehensively. Finally, we evaluate 3 representative methods via simulation and give some suggestions on how to select an appropriate method based on a given research.
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paper_title: The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution

paper_content:

Abstract Functional magnetic resonance imaging (fMRI) is increasingly used to study functional connectivity in large-scale brain networks that support cognitive and perceptual processes. We face serious conceptual, statistical and data analysis challenges when addressing the combinatorial explosion of possible interactions within high-dimensional fMRI data. Moreover, we need to know, and account for, the physiological mechanisms underlying our signals. We argue here that (i) model selection procedures for connectivity should include consideration of more than just a few brain structures, (ii) temporal precedence – and causality concepts based on it – are essential in dynamic models of connectivity and (iii) undoing the effect of hemodynamics on fMRI data (by deconvolution) can be an important tool. However, it is crucially dependent upon assumptions that need to be verified.
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paper_title: Causal time series analysis of functional magnetic resonance imaging data

paper_content:

This review focuses on dynamic causal analysis of functional magnetic resonance (fMRI) data to infer brain connectivity from a time series analysis and dynamical systems perspective. Causal influence is expressed in the Wiener-Akaike-Granger-Schweder (WAGS) tradition and dynamical systems are treated in a state space modeling framework. The nature of the fMRI signal is reviewed with emphasis on the involved neuronal, physiological and physical processes and their modeling as dynamical systems. In this context, two streams of development in modeling causal brain connectivity using fMRI are discussed: time series approaches to causality in a discrete time tradition and dynamic systems and control theory approaches in a continuous time tradition. This review closes with discussion of ongoing work and future perspectives on the integration of the two approaches.
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paper_title: EEG–fMRI Information Fusion: Biophysics and Data Analysis

paper_content:

Cerebral activity has many attributes: bioelectrical, metabolic, haemodynamic, hormonal, endogenous, exogenous, specialised, integrated, pathological, stable, dynamic, to mention but a few. The diverse nature of biological processes has been recognised for centuries. It seems obvious that moving from unimodal recordings to multimodal measurements will allow neuroscientists to better understand the nature and structure of cerebral activity. This means that fusing electrophysiological data and BOLD-related measurements represents an important methodological challenge. The realisation of any cognitive, motor or sensory process rests on cerebral dynamics and creates order in the bioelectric and haemodynamic signals measured with EEG and fMRI, respectively. To detect and interpret the relevant features of these signals, one typically describes processes at their own temporal and spatial scales. The main sources of scalp EEG signals are postsynaptic cortical currents associated with large pyramidal neurons, which are oriented perpendicular to the cortical surface (Nunez 1981). However, the scalp topology of measured electrical potentials does not, without additional (prior) information, uniquely specify the location of underlying bioelectric activity. This issue is referred to as the ill-posed nature of the EEG/MEG inverse problem. Conversely, even though fMRI discloses complementary features of neuronal activity (Nunez and Silberstein 2000; Mukamel et al. 2005), it is only an indirect measure, through metabolism, oxygenation and blood flow, where these slow mechanisms provide temporally smoothed correlates of neuronal activity.
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paper_title: Dynamic causal modeling of evoked responses in EEG and MEG i

paper_content:

Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged. In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework.
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paper_title: Model driven EEG/fMRI fusion of brain oscillations

paper_content:

This article reviews progress and challenges in model driven EEG/fMRI fusion with a focus on brain oscillations. Fusion is the combination of both imaging modalities based on a cascade of for- ward models from ensemble of post-synaptic potentials (ePSP) to net primary current densities (nPCD) to EEG; and from ePSP to vasomotor feed forward signal (VFFSS) to BOLD. In absence of a model, data driven fusion creates maps of correlations between EEG and BOLD or between estimates of nPCD and VFFS. A consistent finding has been that of positive correlations between EEG alpha power and BOLD in both frontal cortices and thalamus and of negative ones for the occipital region. For model driven fusion we formulate a neural mass EEG/fMRI model coupled to a metabolic hemodynamic model. For exploratory simulations we show that the Local Linearization (LL) method for integrating stochastic differential equations is appropriate for highly nonlinear dynamics. It has been successfully applied to small and medium sized networks, reproducing the described EEG/BOLD correlations. A new LL-algebraic method allows simulations with hundreds of thousands of neural populations, with connectivities and conduction delays estimated from diffusion weighted MRI. For parameter and state estimation, Kalman filtering combined with the LL method estimates the innovations or prediction errors. From these the likelihood of models given data are obtained. The LL-innovation estimation method has been already applied to small and medium scale models. With improved Bayesian compu- tations the practical estimation of very large scale EEG/fMRI models shall soon be possible. Hum Brain Mapp 30:2701-2721, 2009. V C 2008 Wiley-Liss, Inc.
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paper_title: EEG-fMRI Fusion of Paradigm-Free Activity Using Kalman Filtering

paper_content:

We address here the use of EEG and fMRI, and their combination, in order to estimate the full spatiotemporal patterns of activity on the cortical surface in the absence of any particular assumptions on this activity such as stimulation times. For handling such a high-dimension inverse problem, we propose the use of (1) a global forward model of how these measures are functions of the "neural activity" of a large number of sources distributed on the cortical surface, formalized as a dynamical system, and (2) adaptive filters, as a natural solution to solve this inverse problem iteratively along the temporal dimension. This estimation framework relies on realistic physiological models, uses EEG and fMRI in a symmetric manner, and takes into account both their temporal and spatial information. We use the Kalman filter and smoother to perform such an estimation on realistic artificial data and demonstrate that the algorithm can handle the high dimensionality of these data and that it succeeds in solving this inverse problem, combining efficiently the information provided by the two modalities (this information being naturally predominantly temporal for EEG and spatial for fMRI). It performs particularly well in reconstructing a random temporally and spatially smooth activity spread over the cortex. The Kalman filter and smoother show some limitations, however, which call for the development of more specific adaptive filters. First, they do not cope well with the strong nonlinearity in the model that is necessary for an adequate description of the relation between cortical electric activities and the metabolic demand responsible for fMRI signals. Second, they fail to estimate a sparse activity (i.e., presenting sharp peaks at specific locations and times). Finally their computational cost remains high. We use schematic examples to explain these limitations and propose further developments of our method to overcome them.
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paper_title: Modeling the hemodynamic response to brain activation

paper_content:

Neural activity in the brain is accompanied by changes in cerebral blood flow (CBF) and blood oxygenation that are detectable with functional magnetic resonance imaging (fMRI) techniques. In this paper, recent mathematical models of this hemodynamic response are reviewed and integrated. Models are described for: (1) the blood oxygenation level dependent (BOLD) signal as a function of changes in cerebral oxygen extractionfraction(E) andcerebralbloodvolume(CBV);(2)theballoon model, proposed to describe the transient dynamics of CBVand deoxyhemoglobin (Hb) and how they affect the BOLD signal; (3) neurovascular coupling, relating the responses in CBF and cerebral metabolic rate of oxygen (CMRO2) to the neural activity response; and (4) a simple model for the temporal nonlinearity of the neural response itself. These models are integrated into a mathematical framework describing the steps linking a stimulus to the measured BOLD and CBF responses. Experimental results examining transient features of the BOLD response (post-stimulus undershoot and initial dip), nonlinearities of the hemodynamic response, and the role of the physiologic baseline state in altering the BOLD signal are discussed in the context of the proposed models. Quantitative modeling of the hemodynamic response, when combined with experimental data measuring both the BOLD and CBF responses,makes possible a more specific and quantitative assessment of brain physiology than is possible with standard BOLD imaging alone. This approach has the potential to enhance numerous studies of brain function in development, health, and disease.
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paper_title: Effective connectivity: Influence, causality and biophysical modeling

paper_content:

This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments.
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paper_title: Dynamic causal models of steady-state responses

paper_content:

In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.
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paper_title: Present and future of simultaneous EEG-fMRI

paper_content:

The brain's activity can be measured in numerous complementary ways, including electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The simultaneous acquisition of EEG and fMRI was originally developed to make the localization of the generators of often subtle pathological activity commonly observed in EEG recordings of patients with epilepsy more sensitive and spatially accurate by mapping their hemodynamic correlates. Now, the value of the information provided by simultaneous EEG-fMRI is being evaluated in a clinical context, while in parallel, more sophisticated data analysis techniques, e.g. with electrical source imaging or dynamic causal modeling, have begun to be applied to increase the technique's sensitivity and allow the study of brain network structure. Beyond its clinically oriented application in epilepsy, simultaneous EEG-fMRI recording has now gained interest as a tool for basic and systems human neuroscience, e.g. the study of neuro-vascular coupling and cognitive studies. In this review, we give an overview over the current use of simultaneous EEG-fMRI, its applications to the study of epilepsy as well as human cognition and systems neuroscience and ongoing and anticipated methodological developments.
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paper_title: Magnetoencephalography: From SQUIDs to neuroscience Neuroimage 20th Anniversary Special Edition

paper_content:

Abstract Magnetoencephalography (MEG), with its direct view to the cortex through the magnetically transparent skull, has developed from its conception in physics laboratories to a powerful tool of basic and clinical neuroscience. MEG provides millisecond time resolution and allows real-time tracking of brain activation sequences during sensory processing, motor planning and action, cognition, language perception and production, social interaction, and various brain disorders. Current-day neuromagnetometers house hundreds of SQUIDs, superconducting quantum interference devices, to pick up signals generated by concerted action of cortical neurons. Complementary MEG measures of neuronal involvement include evoked responses, modulation of cortical rhythms, properties of the on-going neural activity, and interareal connectivity. Future MEG breakthroughs in understanding brain dynamics are expected through advanced signal analysis and combined use of MEG with hemodynamic imaging (fMRI). Methodological development progresses most efficiently when linked with insightful neuroscientific questions.
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paper_title: Mapping the Organization of Axis of Motion Selective Features in Human Area MT Using High-Field fMRI

paper_content:

Functional magnetic resonance imaging (fMRI) at high magnetic fields has made it possible to investigate the columnar organization of the human brain in vivo with high degrees of accuracy and sensitivity. Until now, these results have been limited to the organization principles of early visual cortex (V1). While the middle temporal area (MT) has been the first identified extra-striate visual area shown to exhibit a columnar organization in monkeys, evidence of MT's columnar response properties and topographic layout in humans has remained elusive. Research using various approaches suggests similar response properties as in monkeys but failed to provide direct evidence for direction or axis of motion selectivity in human area MT. By combining state of the art pulse sequence design, high spatial resolution in all three dimensions (0.8 mm isotropic), optimized coil design, ultrahigh field magnets (7 Tesla) and novel high resolution cortical grid sampling analysis tools, we provide the first direct evidence for large-scale axis of motion selective feature organization in human area MT closely matching predictions from topographic columnar-level simulations.
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paper_title: Layer-Specific fMRI Reflects Different Neuronal Computations at Different Depths in Human V1

paper_content:

Recent work has established that cerebral blood flow is regulated at a spatial scale that can be resolved by high field fMRI to show cortical columns in humans. While cortical columns represent a cluster of neurons with similar response properties (spanning from the pial surface to the white matter), important information regarding neuronal interactions and computational processes is also contained within a single column, distributed across the six cortical lamina. A basic understanding of underlying neuronal circuitry or computations may be revealed through investigations of the distribution of neural responses at different cortical depths. In this study, we used T2-weighted imaging with 0.7 mm (isotropic) resolution to measure fMRI responses at different depths in the gray matter while human subjects observed images with either recognizable or scrambled (physically impossible) objects. Intact and scrambled images were partially occluded, resulting in clusters of activity distributed across primary visual cortex. A subset of the identified clusters of voxels showed a preference for scrambled objects over intact; in these clusters, the fMRI response in middle layers was stronger during the presentation of scrambled objects than during the presentation of intact objects. A second experiment, using stimuli targeted at either the magnocellular or the parvocellular visual pathway, shows that laminar profiles in response to parvocellular-targeted stimuli peak in more superficial layers. These findings provide new evidence for the differential sensitivity of high-field fMRI to modulations of the neural responses at different cortical depths.
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paper_title: Comparison of Cerebral Blood Flow Acquired by Simultaneous [15O]Water Positron Emission Tomography and Arterial Spin Labeling Magnetic Resonance Imaging

paper_content:

Until recently, no direct comparison between [15O]water positron emission tomography (PET) and arterial spin labeling (ASL) for measuring cerebral blood flow (CBF) was possible. With the introduction of integrated, hybrid magnetic resonance (MR)-PET scanners, such a comparison becomes feasible. This study presents results of CBF measurements recorded simultaneously with [15O]water and ASL. A 3T MR-BrainPET scanner was used for the simultaneous acquisition of pseudo-continuous ASL (pCASL) magnetic resonance imaging (MRI) and [15O]water PET. Quantitative CBF values were compared in 10 young healthy male volunteers at baseline conditions. A statistically significant (P<0.05) correlation was observed between the two modalities; the whole-brain CBF values determined with PET and pCASL were 43.3 ±6.1 mL and 51.9 ± 7.1 mL per 100 g per minute, respectively. The gray/white matter (GM/WM) ratio of CBF was 3.0 for PET and 3.4 for pCASL. A paired t-test revealed differences in regional CBF between ASL and PET with higher ASL-CBF than PET-CBF values in cortical areas. Using an integrated, hybrid MR-PET a direct simultaneous comparison between ASL and [15O]water PET became possible for the first time so that temporal, physiologic, and functional variations were avoided. Regional and individual differences were found despite the overall similarity between ASL and PET, requiring further detailed investigations.
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paper_title: Towards the utilization of EEG as a brain imaging tool

paper_content:

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications.
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paper_title: Simultaneous EEG and fMRI of the alpha rhythm

paper_content:

The alpha rhythm in the EEG is 8–12 Hz activity present when a subject is awake with eyes closed. In this study, we used simultaneous EEG and fMRI to make maps of regions whose MRI signal changed reliably with modulation in posterior alpha activity. We scanned 11 subjects as they rested with eyes closed. We found that increased alpha power was correlated with decreased MRI signal in multiple regions of occipital, superior temporal, inferior frontal, and cingulate cortex, and with increased signal in the thalamus and insula. These results are consistent with animal experiments and point to the alpha rhythm as an index of cortical inactivity that may be generated in part by the thalamus. These results also may have important implications for interpretation of resting baseline in fMRI studies.
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paper_title: Simultaneous EEG–fMRI

paper_content:

Abstract Acquisition of electroencephalogram (EEG) during functional magnetic resonance imaging (fMRI) provides an additional monitoring tool for the analysis of brain state fluctuations. The exploration of brain responses following inputs or in the context of state changes is crucial for a better understanding of the basic principles governing large-scale neuronal dynamics. State-of-the-art techniques allow EEG activity—from DC (direct current) up to high frequencies in the gamma range—to be acquired simultaneously with fMRI data. In the interleaved mode, spiking activities can also be assessed during concurrent fMRI. The utilization of fMRI evidence to better constrain solutions of the inverse problem of source localization of EEG activity is an exciting possibility. Nonetheless, this approach should be applied cautiously since the degree of overlap between underlying neuronal activity sources is variable and, for the most part, unknown. The ultimate goal is to make joint inferences about the activity, dynamics, and functions by exploiting complementary information from multimodal data sets.
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paper_title: Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest

paper_content:

We assessed the relation between hemodynamic and electrical indices of brain function by performing simultaneous functional MRI (fMRI) and electroencephalography (EEG) in awake subjects at rest with eyes closed. Spontaneous power fluctuations of electrical rhythms were determined for multiple discrete frequency bands, and associated fMRI signal modulations were mapped on a voxel-by-voxel basis. There was little positive correlation of localized brain activity with alpha power (8–12 Hz), but strong and widespread negative correlation in lateral frontal and parietal cortices that are known to support attentional processes. Power in a 17–23 Hz range of beta activity was positively correlated with activity in retrosplenial, temporo-parietal, and dorsomedial prefrontal cortices. This set of areas has previously been characterized by high but coupled metabolism and blood flow at rest that decrease whenever subjects engage in explicit perception or action. The distributed patterns of fMRI activity that were correlated with power in different EEG bands overlapped strongly with those of functional connectivity, i.e., intrinsic covariations of regional activity at rest. This result indicates that, during resting wakefulness, and hence the absence of a task, these areas constitute separable and dynamic functional networks, and that activity in these networks is associated with distinct EEG signatures. Taken together with studies that have explicitly characterized the response properties of these distributed cortical systems, our findings may suggest that alpha oscillations signal a neural baseline with “inattention” whereas beta rhythms index spontaneous cognitive operations during conscious rest.
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paper_title: Analysis and Visualization of Single-Trial Event-Related Potentials

paper_content:

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the “ERP image,” that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Hum. Brain Mapping 14:166–185, 2001. © 2001 Wiley-Liss, Inc.
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paper_title: Present and future of simultaneous EEG-fMRI

paper_content:

The brain's activity can be measured in numerous complementary ways, including electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The simultaneous acquisition of EEG and fMRI was originally developed to make the localization of the generators of often subtle pathological activity commonly observed in EEG recordings of patients with epilepsy more sensitive and spatially accurate by mapping their hemodynamic correlates. Now, the value of the information provided by simultaneous EEG-fMRI is being evaluated in a clinical context, while in parallel, more sophisticated data analysis techniques, e.g. with electrical source imaging or dynamic causal modeling, have begun to be applied to increase the technique's sensitivity and allow the study of brain network structure. Beyond its clinically oriented application in epilepsy, simultaneous EEG-fMRI recording has now gained interest as a tool for basic and systems human neuroscience, e.g. the study of neuro-vascular coupling and cognitive studies. In this review, we give an overview over the current use of simultaneous EEG-fMRI, its applications to the study of epilepsy as well as human cognition and systems neuroscience and ongoing and anticipated methodological developments.
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paper_title: Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 Tesla

paper_content:

Abstract This study investigated the spatio-temporal properties of blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) signals in gray matter, excluding the confounding, inaccurate contributions of large blood vessels. We quantified the spatial specificity of the BOLD response, and we investigated whether this specificity varies as a function of time from stimulus onset. fMRI was performed at 7 Tesla (T), where mapping signals of parenchymal origin are easily detected. Two abutting visual stimuli were adjusted to elicit responses centered on a flat gray matter region in V1. fMRI signals were sampled at high-resolution orthogonal to the retinotopic boundary between the representations of the stimuli. Signals from macro-vessels were masked out. Principal component analysis revealed that the first component in space accounted for 96.2 ± 1.6% of the variance over time. The spatial profile of this time-invariant response was fitted with a model consisting of the convolution of a step function and a Gaussian point-spread-function (PSF). The mean full-width at half-maximal-height of the fitted PSF was 2.34 ± 0.20 mm. Based on simulations of confounding effects, we estimate that BOLD PSF in human gray matter is smaller than 2 mm. A time-point to time-point analysis revealed that the PSF obtained during the 3rd (1.52 mm) and 4th (1.99 mm) seconds of stimulation were narrower than the mean PSF obtained from the 5th second on (2.42 ± 0.15 mm). The position of the edge of the responding region was offset (1.72 ± 0.07 mm) from the boundary of the stimulated region, indicating a spatial non-linearity. Simulations showed that the effective contrast between active and non-active columns is reduced 25-fold when imaged using a PSF whose width is equal to the cycle of the imaged columnar organization. Thus, the PSF of the hyper-oxygenated BOLD response in human gray matter is narrower than that reported at 1.5 T, where macro-vessels dominate the mapping signals. The initial phase of this response is more spatially specific than later phases. Data acquisition methods that suppress macro-vascular signals should increase the spatial specificity of BOLD fMRI. The choice of optimal stimulus duration represents a trade-off between the spatial specificity and the overhead associated with short stimulus duration.
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paper_title: Modeling the hemodynamic response to brain activation

paper_content:

Neural activity in the brain is accompanied by changes in cerebral blood flow (CBF) and blood oxygenation that are detectable with functional magnetic resonance imaging (fMRI) techniques. In this paper, recent mathematical models of this hemodynamic response are reviewed and integrated. Models are described for: (1) the blood oxygenation level dependent (BOLD) signal as a function of changes in cerebral oxygen extractionfraction(E) andcerebralbloodvolume(CBV);(2)theballoon model, proposed to describe the transient dynamics of CBVand deoxyhemoglobin (Hb) and how they affect the BOLD signal; (3) neurovascular coupling, relating the responses in CBF and cerebral metabolic rate of oxygen (CMRO2) to the neural activity response; and (4) a simple model for the temporal nonlinearity of the neural response itself. These models are integrated into a mathematical framework describing the steps linking a stimulus to the measured BOLD and CBF responses. Experimental results examining transient features of the BOLD response (post-stimulus undershoot and initial dip), nonlinearities of the hemodynamic response, and the role of the physiologic baseline state in altering the BOLD signal are discussed in the context of the proposed models. Quantitative modeling of the hemodynamic response, when combined with experimental data measuring both the BOLD and CBF responses,makes possible a more specific and quantitative assessment of brain physiology than is possible with standard BOLD imaging alone. This approach has the potential to enhance numerous studies of brain function in development, health, and disease.
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paper_title: Concordance between distributed EEG source localization and simultaneous EEG-fMRI studies of epileptic spikes

paper_content:

In order to analyze where epileptic spikes are generated, we assessed the level of concordance between EEG source localization using distributed source models and simultaneous EEG-fMRI which measures the hemodynamic correlates of EEG activity. Data to be compared were first estimated on the same cortical surface and two comparison strategies were used: (1) MEM-concordance: a comparison between EEG sources localized with the Maximum Entropy on the Mean (MEM) method and fMRI clusters showing a significant hemodynamic response. Minimal geodesic distances between local extrema and overlap measurements between spatial extents of EEG sources and fMRI clusters were used to quantify MEM-concordance. (2) fMRI-relevance: estimation of the fMRI-relevance index alpha quantifying if sources located in an fMRI cluster could explain some scalp EEG data, when this fMRI cluster was used to constrain the EEG inverse problem. Combining MEM-concordance and fMRI-relevance (alpha) indexes, each fMRI cluster showing a significant hemodynamic response (p<0.05 corrected) was classified according to its concordance with EEG data. Nine patients with focal epilepsy who underwent EEG-fMRI examination followed by EEG recording outside the scanner were selected for this study. Among the 62 fMRI clusters analyzed (7 patients), 15 (24%) found in 6 patients were highly concordant with EEG according to both MEM-concordance and fMRI-relevance. EEG concordance was found for 5 clusters (8%) according to alpha only, suggesting sources missed by the MEM. No concordance with EEG was found for 30 clusters (48%) and for 10 clusters (16%) alpha was significantly negative, suggesting EEG-fMRI discordance. We proposed two complementary strategies to assess and classify EEG-fMRI concordance. We showed that for most patients, part of the hemodynamic response to spikes was highly concordant with EEG sources, whereas other fMRI clusters in response to the same spikes were found distant or discordant with EEG sources.
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paper_title: EEG–fMRI Information Fusion: Biophysics and Data Analysis

paper_content:

Cerebral activity has many attributes: bioelectrical, metabolic, haemodynamic, hormonal, endogenous, exogenous, specialised, integrated, pathological, stable, dynamic, to mention but a few. The diverse nature of biological processes has been recognised for centuries. It seems obvious that moving from unimodal recordings to multimodal measurements will allow neuroscientists to better understand the nature and structure of cerebral activity. This means that fusing electrophysiological data and BOLD-related measurements represents an important methodological challenge. The realisation of any cognitive, motor or sensory process rests on cerebral dynamics and creates order in the bioelectric and haemodynamic signals measured with EEG and fMRI, respectively. To detect and interpret the relevant features of these signals, one typically describes processes at their own temporal and spatial scales. The main sources of scalp EEG signals are postsynaptic cortical currents associated with large pyramidal neurons, which are oriented perpendicular to the cortical surface (Nunez 1981). However, the scalp topology of measured electrical potentials does not, without additional (prior) information, uniquely specify the location of underlying bioelectric activity. This issue is referred to as the ill-posed nature of the EEG/MEG inverse problem. Conversely, even though fMRI discloses complementary features of neuronal activity (Nunez and Silberstein 2000; Mukamel et al. 2005), it is only an indirect measure, through metabolism, oxygenation and blood flow, where these slow mechanisms provide temporally smoothed correlates of neuronal activity.
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paper_title: The role of physiological noise in resting-state functional connectivity

paper_content:

Abstract Functional connectivity between different brain regions can be estimated from MRI data by computing the temporal correlation of low frequency (
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paper_title: The current state, challenges and perspectives of MR-PET

paper_content:

Abstract Following the success of PET/CT during the last decade and the recent increasing proliferation of SPECT/CT, another hybrid imaging instrument has been gaining more and more interest: MR-PET. First combined, simultaneous PET and MR studies carried out in small animals demonstrated the feasibility of the new approach. Concurrently, some prototypes of an MR-PET scanner for simultaneous human brain studies have been built, their performance is being tested and preliminary applications have already been shown. Through this pioneering work, it has become clear that advances in the detector design are necessary for further optimization. Recently, the different issues related to the present state and future prospects of MR-PET were presented and discussed during an international 2-day workshop at the Forschungszentrum Julich, Germany, held after, and in conjunction with, the 2008 IEEE Nuclear Science Symposium and Medical Imaging Conference in Dresden, Germany on October 27–28, 2008. The topics ranged from small animal MR-PET imaging to human MR-BrainPET imaging, new detector developments, challenges/opportunities for ultra-high field MR-PET imaging and considerations of possible future research and clinical applications. This report presents a critical summary of the contributions made to the workshop.
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paper_title: On the Relationship of Synaptic Activity to Macroscopic Measurements: Does Co-Registration of EEG with fMRI Make Sense?

paper_content:

A two-scale theoretical description outlines relationships between brain current sources and the resulting extracranial electric field, recorded as EEG. Finding unknown sources of EEG, the so-calledg “inverse problem”, is discussed in general terms, with emphasis on the fundamental non-uniqueness of inverse solutions. Hemodynamic signatures, measured with fMRI, are expressed as voxel integrals to facilitate comparisons with EEG. Two generally distinct cell groups (1 and 2), generating EEG and fMRI signals respectively, are embedded within the much broader class of synaptic action fields. Cell groups 1 and 2 may or may not overlap in specific experiments. Implications of this incomplete overlap for co-registration studies are considered. Each experimental measure of brain function is generally sensitive to a different kind of source activity and to different spatial and temporal scales. Failure to appreciate such distinctions can exacerbate conflicting views of brain function that emphasize either global integration or functional localization.
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paper_title: Multimodal imaging utilising integrated MR-PET for human brain tumour assessment

paper_content:

Objectives ::: The development of integrated magnetic resonance (MR)-positron emission tomography (PET) hybrid imaging opens up new horizons for imaging in neuro-oncology. In cerebral gliomas the definition of tumour extent may be difficult to ascertain using standard MR imaging (MRI) only. The differentiation of post-therapeutic scar tissue, tumour rests and tumour recurrence is challenging. The relationship to structures such as the pyramidal tract to the tumour mass influences the therapeutic neurosurgical approach.
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paper_title: Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity

paper_content:

Haemodynamic signals underlying functional brain imaging (for example, functional magnetic resonance imaging (fMRI)) are assumed to reflect metabolic demand generated by local neuronal activity, with equal increases in haemodynamic signal implying equal increases in the underlying neuronal activity. Few studies have compared neuronal and haemodynamic signals in alert animals to test for this assumed correspondence. Here we present evidence that brings this assumption into question. Using a dual-wavelength optical imaging technique that independently measures cerebral blood volume and oxygenation, continuously, in alert behaving monkeys, we find two distinct components to the haemodynamic signal in the alert animals' primary visual cortex (V1). One component is reliably predictable from neuronal responses generated by visual input. The other component-of almost comparable strength-is a hitherto unknown signal that entrains to task structure independently of visual input or of standard neural predictors of haemodynamics. This latter component shows predictive timing, with increases of cerebral blood volume in anticipation of trial onsets even in darkness. This trial-locked haemodynamic signal could be due to an accompanying V1 arterial pumping mechanism, closely matched in time, with peaks of arterial dilation entrained to predicted trial onsets. These findings (tested in two animals) challenge the current understanding of the link between brain haemodynamics and local neuronal activity. They also suggest the existence of a novel preparatory mechanism in the brain that brings additional arterial blood to cortex in anticipation of expected tasks.
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paper_title: Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters

paper_content:

Previous studies have shown that under some conditions, noise fluctuations in an fMRI time-course are dominated by physiological modulations of the image intensity with secondary contributions from thermal image noise and that these two sources scale differently with signal intensity, susceptibility weighting (TE) and field strength. The SNR of the fMRI time-course was found to be near its asymptotic limit for moderate spatial resolution measurements at 3 T with only marginal gains expected from acquisition at higher field strengths. In this study, we investigate the amplitude of image intensity fluctuations in the fMRI time-course at magnetic field strengths of 1.5 T, 3 T, and 7 T as a function of image resolution, flip angle and TE. The time-course SNR was a similar function of the image SNR regardless of whether the image SNR was modulated by flip angle, image resolution, or field strength. For spatial resolutions typical of those currently used in fMRI (e.g., 3 x 3 x 3 mm(3)), increases in image SNR obtained from 7 T acquisition produced only modest increases in time-course SNR. At this spatial resolution, the ratio of physiological noise to thermal image noise was 0.61, 0.89, and 2.23 for 1.5 T, 3 T, and 7 T. At a resolution of 1 x 1 x 3 mm(3), however, the physiological to thermal noise ratio was 0.34, 0.57, and 0.91 for 1.5 T, 3 T and 7 T for TE near T2*. Thus, by reducing the signal strength using higher image resolution, the ratio of physiologic to image noise could be reduced to a regime where increased sensitivity afforded by higher field strength still translated to improved SNR in the fMRI time-series.
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paper_title: fMRI and its interpretations: an illustration on directional selectivity in area V5/MT

paper_content:

fMRI is a tool to study brain function noninvasively that can reliably identify sites of neural involvement for a given task. However, to what extent can fMRI signals be related to measures obtained in electrophysiology? Can the blood-oxygen-level-dependent signal be interpreted as spatially pooled spiking activity? Here we combine knowledge from neurovascular coupling, functional imaging and neurophysiology to discuss whether fMRI has succeeded in demonstrating one of the most established functional properties in the visual brain, namely directional selectivity in the motion-processing region V5/MT+. We also discuss differences of fMRI and electrophysiology in their sensitivity to distinct physiological processes. We conclude that fMRI constitutes a complement, not a poor-resolution substitute, to invasive techniques, and that it deserves interpretations that acknowledge its stand as a separate signal.
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paper_title: Modeling the hemodynamic response to brain activation

paper_content:

Neural activity in the brain is accompanied by changes in cerebral blood flow (CBF) and blood oxygenation that are detectable with functional magnetic resonance imaging (fMRI) techniques. In this paper, recent mathematical models of this hemodynamic response are reviewed and integrated. Models are described for: (1) the blood oxygenation level dependent (BOLD) signal as a function of changes in cerebral oxygen extractionfraction(E) andcerebralbloodvolume(CBV);(2)theballoon model, proposed to describe the transient dynamics of CBVand deoxyhemoglobin (Hb) and how they affect the BOLD signal; (3) neurovascular coupling, relating the responses in CBF and cerebral metabolic rate of oxygen (CMRO2) to the neural activity response; and (4) a simple model for the temporal nonlinearity of the neural response itself. These models are integrated into a mathematical framework describing the steps linking a stimulus to the measured BOLD and CBF responses. Experimental results examining transient features of the BOLD response (post-stimulus undershoot and initial dip), nonlinearities of the hemodynamic response, and the role of the physiologic baseline state in altering the BOLD signal are discussed in the context of the proposed models. Quantitative modeling of the hemodynamic response, when combined with experimental data measuring both the BOLD and CBF responses,makes possible a more specific and quantitative assessment of brain physiology than is possible with standard BOLD imaging alone. This approach has the potential to enhance numerous studies of brain function in development, health, and disease.
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paper_title: The BOLD post-stimulus undershoot, one of the most debated issues in fMRI

paper_content:

Abstract This paper provides a brief overview of how we got involved in fMRI work and of our efforts to elucidate the mechanisms underlying BOLD signal changes. The phenomenon discussed here in particular is the post-stimulus undershoot (PSU), the interpretation of which has captivated many fMRI scientists and is still under debate to date. This controversy is caused both by the convoluted physiological origin of the BOLD effect, which allows many possible explanations, and the lack of comprehensive data in the early years. BOLD effects reflect changes in cerebral blood flow (CBF), volume (CBV), metabolic rate of oxygen (CMRO 2 ), and hematocrit fraction (Hct). However, the size of such effects is modulated by vascular origin such as intravascular, extravascular, macro and microvascular, venular and capillary, the relative contributions of which depend not only on the spatial resolution of the measurements, but also on stimulus duration, on magnetic field strength and on whether spin echo (SE) or gradient echo (GRE) detection is used. The two most dominant explanations of the PSU have been delayed vascular compliance (first venular, later arteriolar, and recently capillary) and sustained increases in CMRO 2 , while post-activation reduction in CBF is a distant third. MRI has the capability to independently measure CBF and arteriolar, venous, and total CBV contributions in humans and animals, which has been of great assistance in improving the understanding of BOLD phenomena. Using currently available MRI and optical data, we conclude that the predominant PSU origin is a sustained increase in CMRO 2 . However, some contributions from delayed vascular compliance are likely, and small CBF undershoot contributions that are difficult to detect with current arterial spin labeling technology can also not be excluded. The relative contribution of these different processes, which are not mutually exclusive and can act together, is likely to vary with stimulus duration and type.
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paper_title: Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study.

paper_content:

Signal fluctuations in functional magnetic resonance imaging (fMRI) can result from a number of sources that may have a neuronal, physiologic or instrumental origin. To determine the relative contribution of these sources, we recorded physiological (respiration and cardiac) signals simultaneously with fMRI in human volunteers at rest with their eyes closed. State-of-the-art technology was used including high magnetic field (7 T), a multichannel detector array and high-resolution (3 mm(3)) echo-planar imaging. We investigated the relative contribution of thermal noise and other sources of variance to the observed fMRI signal fluctuations both in the visual cortex and in the whole brain gray matter. The following sources of variance were evaluated separately: low-frequency drifts due to scanner instability, effects correlated with respiratory and cardiac cycles, effects due to variability in the respiratory flow rate and cardiac rate, and other sources, tentatively attributed to spontaneous neuronal activity. We found that low-frequency drifts are the most significant source of fMRI signal fluctuations (3.0% signal change in the visual cortex, TE=32 ms), followed by spontaneous neuronal activity (2.9%), thermal noise (2.1%), effects due to variability in physiological rates (respiration 0.9%, heartbeat 0.9%), and correlated with physiological cycles (0.6%). We suggest the selection and use of four lagged physiological noise regressors as an effective model to explain the variance related to fluctuations in the rates of respiration volume change and cardiac pulsation. Our results also indicate that, compared to the whole brain gray matter, the visual cortex has higher sensitivity to changes in both the rate of respiration and the spontaneous resting-state activity. Under the conditions of this study, spontaneous neuronal activity is one of the major contributors to the measured fMRI signal fluctuations, increasing almost twofold relative to earlier experiments under similar conditions at 3 T.
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paper_title: Coupling of neural activity and fMRI-BOLD in the motion area MT.

paper_content:

The fMRI-BOLD contrast is widely used to study the neural basis of sensory perception and cognition. This signal, however, reflects neural activity only indirectly, and the detailed mechanisms of neurovascular coupling and the neurophysiological correlates of the BOLD signal remain debated. Here we investigate the coupling of BOLD and electrophysiological signals in the motion area MT of the macaque monkey by simultaneously recording both signals. Our results demonstrate that a prominent neuronal response property of area MT, so-called motion opponency, can be used to induce dissociations of BOLD and neuronal firing. During the presentation of a stimulus optimally driving the local neurons, both field potentials [local field potentials (LFPs)] and spiking activity [multi-unit activity (MUA)] correlated with the BOLD signal. When introducing the motion opponency stimulus, however, correlations of MUA with BOLD were much reduced, and LFPs were a much better predictor of the BOLD signal than MUA. In addition, for a subset of recording sites we found positive BOLD and LFP responses in the presence of decreases in MUA, regardless of the stimulus used. Together, these results demonstrate that correlations between BOLD and MUA are dependent on the particular site and stimulus paradigm, and foster the notion that the fMRI-BOLD signal reflects local dendrosomatic processing and synaptic activity rather than principal neuron spiking responses.
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paper_title: The current state, challenges and perspectives of MR-PET

paper_content:

Abstract Following the success of PET/CT during the last decade and the recent increasing proliferation of SPECT/CT, another hybrid imaging instrument has been gaining more and more interest: MR-PET. First combined, simultaneous PET and MR studies carried out in small animals demonstrated the feasibility of the new approach. Concurrently, some prototypes of an MR-PET scanner for simultaneous human brain studies have been built, their performance is being tested and preliminary applications have already been shown. Through this pioneering work, it has become clear that advances in the detector design are necessary for further optimization. Recently, the different issues related to the present state and future prospects of MR-PET were presented and discussed during an international 2-day workshop at the Forschungszentrum Julich, Germany, held after, and in conjunction with, the 2008 IEEE Nuclear Science Symposium and Medical Imaging Conference in Dresden, Germany on October 27–28, 2008. The topics ranged from small animal MR-PET imaging to human MR-BrainPET imaging, new detector developments, challenges/opportunities for ultra-high field MR-PET imaging and considerations of possible future research and clinical applications. This report presents a critical summary of the contributions made to the workshop.
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paper_title: A calibration method for quantitative BOLD fMRI based on hyperoxia

paper_content:

The estimation of changes in CMRO2 using functional MRI involves an essential calibration step using a vasoactive agent to induce an isometabolic change in CBF. This calibration procedure is performed most commonly using hypercapnia as the isometabolic stimulus. However, hypercapnia possesses a number of detrimental side effects. Here, a new method is presented using hyperoxia to perform the same calibration step. This procedure requires independent measurement of PaO2, the BOLD signal, and CBF. We demonstrate that this method yields results that are comparable to those derived using other methods. Further, the hyperoxia technique is able to provide an estimate of the calibration constant that has lower overall intersubject and intersession variability compared to the hypercapnia approach.
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paper_title: A review of calibrated blood oxygenation level-dependent (BOLD) methods for the measurement of task-induced changes in brain oxygen metabolism.

paper_content:

The dynamics of the blood oxygenation level-dependent (BOLD) response are dependent on changes in cerebral blood flow, cerebral blood volume and the cerebral metabolic rate of oxygen consumption. Furthermore, the amplitude of the response is dependent on the baseline physiological state, defined by the haematocrit, oxygen extraction fraction and cerebral blood volume. As a result of this complex dependence, the accurate interpretation of BOLD data and robust intersubject comparisons when the baseline physiology is varied are difficult. The calibrated BOLD technique was developed to address these issues. However, the methodology is complex and its full promise has not yet been realised. In this review, the theoretical underpinnings of calibrated BOLD, and issues regarding this theory that are still to be resolved, are discussed. Important aspects of practical implementation are reviewed and reported applications of this methodology are presented.
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paper_title: Calibrated fMRI

paper_content:

Functional magnetic resonance imaging with blood oxygenation level-dependent (BOLD) contrast has had a tremendous influence on human neuroscience in the last twenty years, providing a non-invasive means of mapping human brain function with often exquisite sensitivity and detail. However the BOLD method remains a largely qualitative approach. While the same can be said of anatomic MRI techniques, whose clinical and research impact has not been diminished in the slightest by the lack of a quantitative interpretation of their image intensity, the quantitative expression of BOLD responses as a percent of the baseline T2*- weighted signal has been viewed as necessary since the earliest days of fMRI. Calibrated MRI attempts to dissociate changes in oxygen metabolism from changes in blood flow and volume, the latter three quantities contributing jointly to determine the physiologically ambiguous percent BOLD change. This dissociation is typically performed using a "calibration" procedure in which subjects inhale a gas mixture containing small amounts of carbon dioxide or enriched oxygen to produce changes in blood flow and BOLD signal which can be measured under well-defined hemodynamic conditions. The outcome is a calibration parameter M which can then be substituted into an expression providing the fractional change in oxygen metabolism given changes in blood flow and BOLD signal during a task. The latest generation of calibrated MRI methods goes beyond fractional changes to provide absolute quantification of resting-state oxygen consumption in micromolar units, in addition to absolute measures of evoked metabolic response. This review discusses the history, challenges, and advances in calibrated MRI, from the personal perspective of the author.
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paper_title: Modeling the hemodynamic response to brain activation

paper_content:

Neural activity in the brain is accompanied by changes in cerebral blood flow (CBF) and blood oxygenation that are detectable with functional magnetic resonance imaging (fMRI) techniques. In this paper, recent mathematical models of this hemodynamic response are reviewed and integrated. Models are described for: (1) the blood oxygenation level dependent (BOLD) signal as a function of changes in cerebral oxygen extractionfraction(E) andcerebralbloodvolume(CBV);(2)theballoon model, proposed to describe the transient dynamics of CBVand deoxyhemoglobin (Hb) and how they affect the BOLD signal; (3) neurovascular coupling, relating the responses in CBF and cerebral metabolic rate of oxygen (CMRO2) to the neural activity response; and (4) a simple model for the temporal nonlinearity of the neural response itself. These models are integrated into a mathematical framework describing the steps linking a stimulus to the measured BOLD and CBF responses. Experimental results examining transient features of the BOLD response (post-stimulus undershoot and initial dip), nonlinearities of the hemodynamic response, and the role of the physiologic baseline state in altering the BOLD signal are discussed in the context of the proposed models. Quantitative modeling of the hemodynamic response, when combined with experimental data measuring both the BOLD and CBF responses,makes possible a more specific and quantitative assessment of brain physiology than is possible with standard BOLD imaging alone. This approach has the potential to enhance numerous studies of brain function in development, health, and disease.
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paper_title: Quantification in PET.

paper_content:

Quantification provides the link between the concentrations of radioactivity measured in tissue and the underlying physiologic processes occurring in the organ. It relates the rate at which radioactivity levels in the body change over time to quantitative parameters such as absolute rate glucose metabolism, regional blood flow, or concentrations of receptors or other binding sites. Absolute measurement of physiologic parameters generally requires accurate measurement of activity concentrations in arterial blood, which provides the input function to the kinetic model. Although absolute quantification can be a difficult process, simplifications of these invasive techniques, involving reference tissues or normalization approaches (eg, SUV), have been applied with some success. Any simplified model of tracer behavior must be validated against the full model to test for bias and systematic errors.
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paper_title: Regional differences in the coupling of cerebral blood flow and oxygen metabolism changes in response to activation: Implications for BOLD-fMRI

paper_content:

Abstract Functional magnetic resonance imaging (fMRI) based on blood oxygenation level dependent (BOLD) signal changes is a sensitive tool for mapping brain activation, but quantitative interpretation of the BOLD response is problematic. The BOLD response is primarily driven by cerebral blood flow (CBF) changes, but is moderated by M, a scaling parameter reflecting baseline deoxyhemoglobin, and n, the ratio of fractional changes in CBF to cerebral metabolic rate of oxygen consumption (CMRO2). We compared M and n between cortical (visual cortex, VC) and subcortical (lentiform nuclei, LN) regions using a quantitative approach based on calibrating the BOLD response with a hypercapnia experiment. Although M was similar in both regions (∼ 5.8%), differences in n (2.21 ± 0.03 in VC and 1.58 ± 0.03 in LN; Cohen d = 1.71) produced substantially weaker (∼ 3.7x) subcortical than cortical BOLD responses relative to CMRO2 changes. Because of this strong sensitivity to n, BOLD response amplitudes cannot be interpreted as a quantitative reflection of underlying metabolic changes, particularly when comparing cortical and subcortical regions.
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paper_title: Accuracy of in vivo neuroreceptor quantification by PET and review of steady-state, transient, double injection, and equilibrium models.

paper_content:

The accuracy of in vivo dopamine D2 receptor quantification by positron emission tomography (PET) was determined for several models by means of singular-value decomposition, and some of the model assumptions were reviewed. These include steady-state, transient, double injection, and equilibrium approaches. All four modes are augmented by including a realistic kinetic interaction of the radioligand with the striatal serotonin S2 receptor. When a set of parameters derived specifically with reference to the equilibrium model was applied, it was found that a reversible radioligand used in conjunction with the equilibrium model permits accurate quantification. However, an assumption of the equilibrium model-that equilibrium is achieved during the time of measurement-is shown to be unsupported by the published experimental data. These results indicate that the equilibrium approach can provide an alternative to the kinetic approaches, but that additional experimental evidence is required to demonstrate the validity of the equilibrium assumption.
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paper_title: Assignment of functional activations to probabilistic cytoarchitectonic areas revisited

paper_content:

Probabilistic cytoarchitectonic maps in standard reference space provide a powerful tool for the analysis of structure-function relationships in the human brain. While these microstructurally defined maps have already been successfully used in the analysis of somatosensory, motor or language functions, several conceptual issues in the analysis of structure-function relationships still demand further clarification. In this paper, we demonstrate the principle approaches for anatomical localisation of functional activations based on probabilistic cytoarchitectonic maps by exemplary analysis of an anterior parietal activation evoked by visual presentation of hand gestures. After consideration of the conceptual basis and implementation of volume or local maxima labelling, we comment on some potential interpretational difficulties, limitations and caveats that could be encountered. Extending and supplementing these methods, we then propose a supplementary approach for quantification of structure-function correspondences based on distribution analysis. This approach relates the cytoarchitectonic probabilities observed at a particular functionally defined location to the areal specific null distribution of probabilities across the whole brain (i.e., the full probability map). Importantly, this method avoids the need for a unique classification of voxels to a single cortical area and may increase the comparability between results obtained for different areas. Moreover, as distribution-based labelling quantifies the "central tendency" of an activation with respect to anatomical areas, it will, in combination with the established methods, allow an advanced characterisation of the anatomical substrates of functional activations. Finally, the advantages and disadvantages of the various methods are discussed, focussing on the question of which approach is most appropriate for a particular situation.
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paper_title: Towards multimodal atlases of the human brain

paper_content:

Atlases of the human brain have an important impact on neuroscience. The emergence of ever more sophisticated imaging techniques, brain mapping methods and analytical strategies has the potential to revolutionize the concept of the brain atlas. Atlases can now combine data describing multiple aspects of brain structure or function at different scales from different subjects, yielding a truly integrative and comprehensive description of this organ. These integrative approaches have provided significant impetus for the human brain mapping initiatives, and have important applications in health and disease.
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paper_title: Functionally informed cortex based alignment: An integrated approach for whole-cortex macro-anatomical and ROI-based functional alignment

paper_content:

Abstract Due to anatomical variability across subjects many brain mapping experiments have analysis focused on a few particular regions of interest so as to circumvent the problem of sub-optimal statistics resulting from the lack of anatomical correspondence across subjects. Since the topographic distribution of experimental effects across the cortex is also often of interest, two separate analyses are often conducted, one on the regions of interest alone, as well as a separate ‘whole brain’ analysis with sub-optimal spatial correspondence across brains. In this paper we present a new group alignment procedure which incorporates, from each subject, both macro-anatomical (curvature) information and functional information from standard localizer experiments. After specifying appropriate parameters to weight anatomical and functional alignment forces, we were able to create a group cortical reconstruction which was well aligned in terms of both anatomical and functional areas. We observed an increase in the overlap of functional areas as well as an improvement in group statistics following this integrated alignment procedure. We propose that, using this alignment scheme, two separate analyses may not be necessary as both analyses can be integrated into a single procedure. After an integrated structural and functional alignment one is able to carry out a whole brain analysis with improved statistical sensitivity due to the reduction in spatial variation in the location of functional regions of interest which fCBA accomplishes. Furthermore, regions in the vicinity of localised and aligned regions-of-interest will also benefit from the integrated alignment.
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paper_title: A diffusion tensor imaging tractography atlas for virtual in vivo dissections

paper_content:

Diffusion tensor imaging (DTI) tractography allows perform virtual dissections of white matter pathways in the living human brain. In 2002, Catani et al. published a method to reconstruct white matter pathways using a region of interest (ROI) approach. The method produced virtual representations of white matter tracts faithful to classical post-mortem descriptions but it required detailed a priori anatomical knowledge. Here, using the same approach, we provide a template to guide the delineation of ROIs for the reconstruction of the association, projection and commissural pathways of the living human brain. The template can be used for single case studies and case-control comparisons. An atlas of the 3D reconstructions of the single tracts is also provided as anatomical reference in the Montreal Neurological Institute (MNI) space.
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paper_title: Assessing interactions in the brain with exact low-resolution electromagnetic tomography

paper_content:

Scalp electric potentials (electroencephalogram; EEG) are contingent to the impressed current density unleashed by cortical pyramidal neurons undergoing post-synaptic processes. EEG neuroimaging consists of estimating the cortical current density from scalp recordings. We report a solution to this inverse problem that attains exact localization: exact low-resolution brain electromagnetic tomography (eLORETA). This non-invasive method yields high time-resolution intracranial signals that can be used for assessing functional dynamic connectivity in the brain, quantified by coherence and phase synchronization. However, these measures are non-physiologically high because of volume conduction and low spatial resolution. We present a new method to solve this problem by decomposing them into instantaneous and lagged components, with the lagged part having almost pure physiological origin.
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paper_title: Spatial registration for functional near-infrared spectroscopy: From channel position on the scalp to cortical location in individual and group analyses

paper_content:

Abstract Functional near-infrared spectroscopy (fNIRS) has now become widely accepted as a common functional imaging modality. In order for fNIRS to achieve genuine neuroimaging citizenship, it would ideally be equipped with functional and structural image analyses. However, fNIRS measures cortical activities from the head surface without anatomical information of the object being measured. In this review article, we will present a methodological overview of spatial registration of fNIRS data to overcome this technical drawback of fNIRS. We first introduce and explore the use of standard stereotaxic space and anatomical labeling. Second, we explain different ways of describing scalp landmarks using 10–20 based systems. Third, we describe the simplest case of fNIRS data co-registration to a subject's own MRI. Fourth, we extend the concept to fNIRS data registration of group data. Fifth, we describe probabilistic registration methods, which use a reference-MRI database instead of a subject's own MRIs, and thus enable MRI-free registration for standalone fNIRS data. Sixth, we further extend the concept of probabilistic registration to three-dimensional image reconstruction in diffuse optical tomography. Seventh, we describe a 3D-digitizer-free method for the virtual registration of fNIRS data. Eighth, we provide practical guidance on how these techniques are implemented in software. Finally, we provide information on current resources and limitations for spatial registration of child and infant data. Through these technical descriptions, we stress the importance of presenting fNIRS data on a common platform to facilitate both intra- and inter-modal data sharing among the neuroimaging community.
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paper_title: Bias between MNI and Talairach coordinates analyzed using the ICBM‐152 brain template

paper_content:

MNI coordinates determined using SPM2 and FSL/FLIRT with the ICBM-152 template were compared to Talairach coordinates determined using a landmark-based Talairach registration method (TAL). Analysis revealed a clear-cut bias in reference frames (origin, orientation) and scaling (brain size). Accordingly, ICBM-152 fitted brains were consistently larger, oriented more nose down, and translated slightly down relative to TAL fitted brains. Whole brain analysis of MNI/Talairach coordi- nate disparity revealed an ellipsoidal pattern with disparity ranging from zero at a point deep within the left hemisphere to greater than 1-cm for some anterior brain areas. MNI/Talairach coordinate dis- parity was generally less for brains fitted using FSL. The mni2tal transform generally reduced MNI/ Talairach coordinate disparity for inferior brain areas but increased disparity for anterior, posterior, and superior areas. Coordinate disparity patterns differed for brain templates (MNI-305, ICBM-152) using the same fitting method (FSL/FLIRT) and for different fitting methods (SPM2, FSL/FLIRT) using the same template (ICBM-152). An MNI-to-Talairach (MTT) transform to correct for bias between MNI and Talairach coordinates was formulated using a best-fit analysis in one hundred high-resolution 3-D MR brain images. MTT transforms optimized for SPM2 and FSL were shown to reduced group mean MNI/Talairach coordinate disparity from a 5-13 mm to 1-2 mm for both deep and superficial brain sites. MTT transforms provide a validated means to convert MNI coordinates to Talairach compatible coordinates for studies using either SPM2 or FSL/FLIRT with the ICBM-152 template. Hum Brain Mapp
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paper_title: How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging

paper_content:

Diffusion tensor imaging is often performed by acquiring a series of diffusion-weighted spin-echo echo-planar images with different direction diffusion gradients. A problem of echo-planar images is the geometrical distortions that obtain near junctions between tissues of differing magnetic susceptibility. This results in distorted diffusion-tensor maps. To resolve this we suggest acquiring two images for each diffusion gradient; one with bottom-up and one with top-down traversal of k-space in the phase-encode direction. This achieves the simultaneous goals of providing information on the underlying displacement field and intensity maps with adequate spatial sampling density even in distorted areas. The resulting DT maps exhibit considerably higher geometric fidelity, as assessed by comparison to an image volume acquired using a conventional 3D MR technique.
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paper_title: Distortion correction in EPI at ultra-high-field MRI using PSF mapping with optimal combination of shift detection dimension

paper_content:

Despite its wide use, echo-planar imaging (EPI) suffers from geometric distortions due to off-resonance effects, i.e., strong magnetic field inhomogeneity and susceptibility. This article reports a novel method for correcting the distortions observed in EPI acquired at ultra-high-field such as 7 T. Point spread function (PSF) mapping methods have been proposed for correcting the distortions in EPI. The PSF shift map can be derived either along the nondistorted or the distorted coordinates. Along the nondistorted coordinates more information about compressed areas is present but it is prone to PSFghosting artifacts induced by large k-space shift in PSF encoding direction. In contrast, shift maps along the distorted coordinates contain more information in stretched areas and are more robust against PSF-ghosting. In ultra-high-field MRI, an EPI contains both compressed and stretched regions depending on the B0 field inhomogeneity and local susceptibility. In this study, we present a new geometric distortion correction scheme, which selectively applies the shift map with more information content. We propose a PSF-ghost elimination method to generate an artifact-free pixel shift map along nondistorted coordinates. The proposed method can correct the effects of the local magnetic field inhomogeneity induced by the susceptibility effects along with the PSF-ghost artifact cancellation. We have experimentally demonstrated the advantages of the proposed method in EPI data acquisitions in phantom and human brain using 7-T MRI. Magn Reson Med 68:1239–1246, 2012. V C 2011 Wiley Periodicals, Inc.
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paper_title: SENSE: Sensitivity encoding for fast MRI

paper_content:

New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced. The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k-space sampling patterns. Special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density. For this case the feasibility of the proposed methods was verified both in vitro and in vivo. Scan time was reduced to one-half using a two-coil array in brain imaging. With an array of five coils double-oblique heart images were obtained in one-third of conventional scan time. Magn Reson Med 42:952-962, 1999.
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paper_title: Characterization and 3D correction of geometric distortion in low-field open-magnet MRI

paper_content:

In this paper, we present a method to characterize and correct geometric image distortion in 0.2 T MR images. A large 3D phantom with spherical balls was used to characterize geometric distortion on an AIRIS Mate 0.2 T MR Scanner (Hitachi). MR images of the phantom were acquired in axial, sagittal and coronal planes using 2D Fast Spin Echo (FSE) sequence and distortions were measured at each control point. Two piecewise interpolation methods were then applied to correct geometric distortion. Distortion was characterized and corrected in any axial, sagittal or coronal slice within an effective FOV of 330(LR) x 180(AP) x 210(HF) mm(3). The distortion was reduced from 16 mm to 1.2 mm at 180 mm from the magnet center. A fast and accurate method for correction of geometric distortion was performed within large distances from the magnet isocenter.
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paper_title: Simultaneous recording of EEG and BOLD responses: a historical perspective.

paper_content:

Electromagnetic fields as measured with electroencephalogram (EEG) are a direct consequence of neuronal activity and feature the same timescale as the underlying cognitive processes, while hemodynamic signals as measured with functional magnetic resonance imaging (fMRI) are related to the energy consumption of neuronal populations. It is obvious that a combination of both techniques is a very attractive aim in neuroscience, in order to achieve both high temporal and spatial resolution for the non-invasive study of cognitive brain function. During the last decade a number of research groups have taken up this challenge. Here, we review the development of the combined EEG-fMRI approach. We summarize the main data integration approaches developed to achieve such a combination, discuss the current state-of-the-art in this field and outline challenges for the future success of this promising approach.
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paper_title: Present and future of simultaneous EEG-fMRI

paper_content:

The brain's activity can be measured in numerous complementary ways, including electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). The simultaneous acquisition of EEG and fMRI was originally developed to make the localization of the generators of often subtle pathological activity commonly observed in EEG recordings of patients with epilepsy more sensitive and spatially accurate by mapping their hemodynamic correlates. Now, the value of the information provided by simultaneous EEG-fMRI is being evaluated in a clinical context, while in parallel, more sophisticated data analysis techniques, e.g. with electrical source imaging or dynamic causal modeling, have begun to be applied to increase the technique's sensitivity and allow the study of brain network structure. Beyond its clinically oriented application in epilepsy, simultaneous EEG-fMRI recording has now gained interest as a tool for basic and systems human neuroscience, e.g. the study of neuro-vascular coupling and cognitive studies. In this review, we give an overview over the current use of simultaneous EEG-fMRI, its applications to the study of epilepsy as well as human cognition and systems neuroscience and ongoing and anticipated methodological developments.
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paper_title: How Ongoing Neuronal Oscillations Account for Evoked fMRI Variability

paper_content:

Variability of evoked single-trial responses despite constant input or task is a feature of large-scale brain signals recorded by fMRI. Initial evidence signified relevance of fMRI signal variability for perception and behavior. Yet the underlying intrinsic neuronal sources have not been previously substantiated. Here, we address this issue using simultaneous EEG–fMRI and real-time classification of ongoing alpha-rhythm states triggering visual stimulation in human subjects. We investigated whether spontaneous neuronal oscillations—as reflected in the posterior alpha rhythm—account for variability of evoked fMRI responses. Based on previous work, we specifically hypothesized linear superposition of fMRI activity related to fluctuations of ongoing alpha rhythm and a visually evoked fMRI response. We observed that spontaneous alpha-rhythm power fluctuations largely explain evoked fMRI response variance in extrastriate, thalamic, and cerebellar areas. For extrastriate areas, we confirmed the linear superposition hypothesis. We hence linked evoked fMRI response variability to an intrinsic rhythm9s power fluctuations. These findings contribute to our conceptual understanding of how brain rhythms can account for trial-by-trial variability in stimulus processing.
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paper_title: Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI

paper_content:

Abstract The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Multimodal imaging techniques rely on the assumption of a common neuronal source for the different recorded signals. In order to maximally exploit the combination of these techniques, one needs to understand the coupling (i.e., the relation) between electroencephalographic (EEG) and fMRI blood oxygen level-dependent (BOLD) signals. Recently, simultaneous EEG-fMRI measurements have been used to investigate the relation between the two signals. Previous attempts at the analysis of simultaneous EEG-fMRI data reported significant correlations between regional BOLD activations and modulation of both event-related potential (ERP) and oscillatory EEG power, mostly in the alpha but also in other frequency bands. Beyond the correlation of the two measured brain signals, the relevant issue we address here is the ability of predicting the signal in one modality using information from the other modality. Using multivariate machine learning-based regression, we show how it is possible to predict EEG power oscillations from simultaneously acquired fMRI data during an eyes-open/eyes-closed task using either the original channels or the underlying cortically distributed sources as the relevant EEG signal for the analysis of multimodal data.
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paper_title: Modulation of Visually Evoked Cortical fMRI Responses by Phase of Ongoing Occipital Alpha Oscillations

paper_content:

Using simultaneous electroencephalography as a measure of ongoing activity and functional magnetic resonance imaging (fMRI) as a measure of the stimulus-driven neural response, we examined whether the amplitude and phase of occipital alpha oscillations at the onset of a brief visual stimulus affects the amplitude of the visually evoked fMRI response. When accounting for intrinsic coupling of alpha amplitude and occipital fMRI signal by modeling and subtracting pseudo-trials, no significant effect of prestimulus alpha amplitude on the evoked fMRI response could be demonstrated. Regarding the effect of alpha phase, we found that stimuli arriving at the peak of the alpha cycle yielded a lower blood oxygenation level-dependent (BOLD) fMRI response in early visual cortex (V1/V2) than stimuli presented at the trough of the cycle. Our results therefore show that phase of occipital alpha oscillations impacts the overall strength of a visually evoked response, as indexed by the BOLD signal. This observation complements existing evidence that alpha oscillations reflect periodic variations in cortical excitability and suggests that the phase of oscillations in postsynaptic potentials can serve as a mechanism of gain control for incoming neural activity. Finally, our findings provide a putative neural basis for observations of alpha phase dependence of visual perceptual performance.
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Title: General Overview on the Merits of Multimodal Neuroimaging Data Fusion

Section 1: What is multimodal neuroimaging?
Description 1: Describe the concept and importance of multimodal neuroimaging, including its definition in both the narrow and wide sense.

Section 2: Physical interactions
Description 2: Discuss the physical principles underlying different imaging techniques and how these interactions determine the measured physiological processes and structures.

Section 3: Narrow and wide sense multimodal imaging, definition
Description 3: Explain the distinction between narrow and wide sense multimodal imaging, including examples of each.

Section 4: Data acquisition and fusion in multimodal imaging
Description 4: Describe the specialized post-processing tools and methods required for merging multimodal datasets, highlighting the benefits and challenges.

Section 5: Simultaneous and separate data acquisition
Description 5: Discuss the pros and cons of simultaneous versus separate data acquisition, including considerations for data quality and subject comfort.

Section 6: Symmetric and asymmetric data fusion
Description 6: Explain the differences between symmetric and asymmetric data fusion approaches, including examples and methodologies for each.

Section 7: Generative models
Description 7: Describe the role of generative models in achieving model-driven symmetric fusion and their importance in multimodal data analysis.

Section 8: Benefits of multimodal imaging
Description 8: Outline the general benefits of multimodal imaging, such as improving spatial and temporal resolution and providing a comprehensive physiological view on brain processes and structures.

Section 9: Effective and nominal resolution
Description 9: Discuss the difference between nominal and effective resolution and how multimodal imaging can enhance the effective resolution through data fusion.

Section 10: Comprehensive physiological view on brain processes and structures
Description 10: Explain how combining different imaging methods provides a more holistic view of brain processes by leveraging the unique sensitivities of each method.

Section 11: Quantification
Description 11: Describe how multimodal imaging, combined with generative models, allows for the quantification and normalization of imaging signals.

Section 12: Coordinate systems and Generalization
Description 12: Discuss the use of standardized coordinate systems and atlases in multimodal data integration to facilitate data comparison and generalization across studies.

Section 13: Improving data quality
Description 13: Explain how multimodal imaging can enhance the data quality of one modality using information from another modality.

Section 14: Biomarkers
Description 14: Describe how data from different modalities can serve as biomarkers to inform the interpretation of the primary data and assess brain tissue states.

Section 15: Conclusion
Description 15: Summarize the merits and challenges of multimodal neuroimaging, emphasizing its importance in providing a comprehensive understanding of brain structure and function.