Underlying event measurements in $p$+$p$ collisions at $\sqrt{s}= 200 $ GeV at RHIC

Particle production sensitive to non-factorizable and non-perturbative processes that contribute to the underlying event associated with a high transverse momentum ($p_{T}$) jet in proton+proton collisions at $\sqrt{s}$=200 GeV is studied with the STAR detector. Each event is divided into three regions based on the azimuthal angle with respect to the highest-$p_{T}$ jet direction: in the leading jet direction ("Toward"), opposite to the leading jet ("Away"), and perpendicular to the leading jet ("Transverse"). In the Transverse region, the average charged particle density is found to be between 0.4 and 0.6 and the mean transverse momentum, $\langle p_{T}\rangle$, between 0.5-0.7 GeV/$c$ for particles with $p_{T}$$>$0.2 GeV/$c$ at mid-pseudorapidity ($|\eta|$$<$1) and jet $p_{T}$$>$15 GeV/$c$. Both average particle density and $\langle p_{T}\rangle$ depend weakly on the leading jet $p_{T}$. Closer inspection of the Transverse region hints that contributions to the underlying event from initial- and final-state radiation are significantly smaller in these collisions than at the higher energies, up to 13 TeV, recorded at the LHC. Underlying event measurements associated with a high-$p_{T}$ jet will contribute to our understanding of QCD processes at hard and soft scales at RHIC energies, as well as provide constraints to modeling of underlying event dynamics.

Particle production sensitive to non-factorizable and non-perturbative processes that contribute to the underlying event associated with a high transverse momentum (pT ) jet in proton+proton collisions at √ s=200 GeV is studied with the STAR detector. Each event is divided into three regions based on the azimuthal angle with respect to the highest-pT jet direction: in the leading jet direction ("Toward"), opposite to the leading jet ("Away"), and perpendicular to the leading jet ("Transverse"). In the Transverse region, the average charged particle density is found to be between 0.4 and 0.6 and the mean transverse momentum, pT , between 0.5-0.7 GeV/c for particles with pT >0.2 GeV/c at mid-pseudorapidity (|η|<1) and jet pT >15 GeV/c. Both average particle density and pT depend weakly on the leading jet pT . Closer inspection of the Transverse region hints that contributions to the underlying event from initial-and final-state radiation are significantly smaller in these collisions than at the higher energies, up to 13 TeV, recorded at the LHC. Underlying event measurements associated with a high-pT jet will contribute to our understanding of QCD processes at hard and soft scales at RHIC energies, as well as provide constraints to modeling of underlying event dynamics. Understanding the underlying physics of hadronic collisions requires detailed characterization of the particle production processes. Proton+proton (p+p) collisions include elastic and inelastic scatterings, with inelastic p+p scatterings consisting of single diffractive, double diffractive and non-diffractive processes. In nondiffractive events, when a hard scattering occurs with large momentum transfer (p T ≥2 GeV/c) from the longitudinal to the transverse plane, other processes could occur in addition to the production of a high energy dijet. These additional processes include softer secondary hard scatterings or multiple parton interactions (MPI), gluon radiation or quark-antiquark splittings from the initial-or final-state partons of the primary hard scattering (ISR/FSR), and color reconnections with the beamremnant partons (BR). Cumulatively, these result in what is referred to, in experiments, as the underlying event. The partons produced in these processes reduce their virtuality and finally fragment mainly into lowenergy particles. The properties of the dijet produced in the initial hard scattering can be calculated by perturbative QCD, with good agreement between experimental measurements and theoretical calculations after careful correction for the underlying event activity [1][2][3]. Modeling the soft physics, which dominates the underlying event activity, is challenging since it does not factorize and requires non-perturbative calculations [4,5]. Experimental studies of the underlying event activity, spanning non-perturbative and perturbative QCD and including sensitivities to multi-scale physics, can help us to improve theoretical modeling and understand the QCD processes. Underlying event activity is often experimentally accessed through topological structure observables, such as particle production away from the primary hard scattering reference direction. The CDF collaboration used the highest-p T charged particle, leading jet, or Drell-Yan pair in each proton+antiproton event to define the hard scattering reference [6,7]. At LHC energies, the ALICE, ATLAS, and CMS collaborations have used the highestp T charged particle, charged jet, jet with both charged and neutral particles, or Z boson as the hard scattering references [8][9][10][11][12]. In this analysis, we use the highest-p T jet as our hard scattering reference.
At TeV collision energy scales, the average mid-pseudorapidity charged multiplicity density and mean transverse momentum, both sensitive to the underlying event activity, were observed to be positively correlated with observables sensitive to the hard scattering energy [7][8][9][10][11][12]. These positive correlations are understood as resulting from increasing contributions from wide angle ISR/FSR, as the transferred momentum scale, Q 2 , of the hard scattering increases. However, at forward rapidity, CMS found that the relationship between underlying event particle production and leading jet p T depends on collision energy [13]: at √ s = 0.9 TeV, the underlying event particle production activity and leading jet p T were negatively correlated, while at 7 TeV they were mostly positively correlated for leading charged jets with 1<p T <15 GeV/c. The negative correlations reported at the lower collision energy could be due to energy conservation constraining the underlying event production. For these reasons, it is worth exploring the underlying event at RHIC with even lower energies to search for its relationship with the leading jet energy at mid-pseudorapidity.

II. EXPERIMENT AND DATA ANALYSIS
In this paper, we report measurements of underlying event activity in p+p collisions at √ s = 200 GeV by the STAR experiment at RHIC. The data used in this analysis were collected in 2012 with an integrated luminosity of ∼23 pb −1 . The major subsystems used for the analysis were the Time Projection Chamber (TPC) [14], and the Barrel Electromagnetic Calorimeter (BEMC) [15]. The TPC provides charged particle tracking with good momentum resolution, while the 4800 isolated towers of the BEMC record the energy deposited by photons, electrons, π 0 , and η mesons. Both cover mid-pseudorapidity (|η|<1) with full azimuthal angle (φ) coverage.
This analysis used both a minimum bias and a calorimeter-triggered dataset. The minimum bias data required a coincidence of signals from the Vertex Position Detectors [16], which measure photons from π 0 decays at forward and backward pseudorapidity, 4.2≤|η|≤5.1. The calorimeter jet-patch triggered data required a minimum transverse energy (E T ) in a ∆η × ∆φ region of ≈ 1 × 1 of the BEMC. In this analysis, three E T trigger thresholds were used: 3.5, 5.4, and 7.3 GeV. Jets reconstructed from the calorimeter-triggered dataset are therefore biased towards higher neutral energy fractions [17]. Corrections for this electromagnetic calorimetertrigger bias are determined via a data-driven technique using the minimum bias data, and only the corrected calorimeter-triggered data are reported in the final results. Event-level and particle-level selections were applied to the recorded data. The events were required to have a reconstructed primary event vertex, z vtx , within 30 cm of the center of the TPC along the beam axis (z) in order to ensure nearly uniform detector acceptance. Events containing a charged particle with p T >20 GeV/c were discarded to avoid events with low tracking momentum reconstruction resolution. Events with deposited energy in a single BEMC tower of E T >20 GeV were also discarded for symmetry. The charged particle tracks reconstructed in the TPC were required to satisfy the following conditions: 0.2<p T <20 GeV/c for high tracking efficiency; a distance of closest approach (dca) to the event vertex of |dca|<1 cm to ensure particles are from the primary collision vertex; a number of fit points along the track greater than 20 out of a maximum of 45; and a ratio of the number of fit points to the maximum number of possible fit points larger than 0.52 for good primary track reconstruction [14,18]. The neutral energy deposits in each BEMC tower were required to have 0.2<E T <20 GeV. For the particles used in this paper, the pseudorapidity region was restricted to |η|<1.
The simulation sample used for detector response and background correction in this analysis was generated using PYTHIA 6.4.28 [19] with the CTEQ6L1 PDF [20] and the Perugia 2012 tune [4] with the PARP(90) parameter changed to 0.213, which is, throughout this article, referred to as PYTHIA 6 (STAR) [3]. PYTHIA 6 (STAR) is tuned with STAR published minimum bias identified particle spectra [21,22] resulting in agreement with the inclusive identified charged pion cross-sections at the 10% level [23]. The PYTHIA parameter PARP(90) is related to the energy scaling of the minimum bias and underlying event phenomena [4]. PYTHIA 6 (STAR) generated events are processed through the STAR GEANT3 [24] detector simulation with π 0 , η, and Σ 0 decays and weak decays handled by GEANT and set as stable in PYTHIA . The simulated GEANT output events were further embedded into zero bias (randomly triggered) experimental data. This embedding procedure simulates a similar background, such as pile-up, beam-gas interactions and cosmic rays, as exists for the experimental measurements [17]. The default PYTHIA 6.4.28 Perugia 2012 tune with the CTEQ6L1 PDF, and the default PYTHIA 8.215 Monash 2013 tune [25] simulations were also used for comparison with corrected data, later referred to as PYTHIA 6 and PYTHIA 8, respectively.
All charged tracks with p T >0.2 GeV/c and all BEMC towers with deposited E T >0.2 GeV, within |η|<1 were used to reconstruct jets.
Jet reconstruction used the anti-k T algorithm [26] with a resolution parameter R anti−k T =0.6 as implemented in the FastJet pack-age [27]. The reconstructed jets were restricted to |η jet |<0.4 to ensure that all tracks/neutral energy deposits of the jets were inside our detector acceptance. The jet neutral energy fraction [17] was required to be less than 90% to minimize non-collision backgrounds such as beam-gas interactions and cosmic rays. Jet axes were further required to be within ∆R = ∆η 2 + ∆φ 2 =0.6 distance from the jet-patch center for calorimeter-triggered data [17]. The reported jet energies include contributions from the underlying event and are corrected for detector effects including pile-up via the unfolding procedure described below.
The analysis followed the CDF topological structure method [6]. For each collision event, the leading, highest p T , jet azimuthal angle (φ jet ) is defined as the reference angle. The reconstructed charged particles are then categorized into different regions by the relative difference of their azimuthal angle (φ i ) to the jet's reference angle, ∆φ ≡ φ i − φ jet . The "Toward" region contains particles with |∆φ|<60 • , while the "Away" region, which is not required to contain a reconstructed jet, is defined as those particles with |∆φ-180 • |<60 • . Finally, the "Transverse" region covers 60 o ≤|∆φ|≤120 • . The activity in each region is integrated over |η|<1, which is inside of the TPC acceptance. The underlying event activity is then accessed through the η integrated Transverse region and reported for charged tracks only. The average charged particle multiplicity density, dN ch dηdφ , and the mean transverse momentum, p T , are studied as a function of the leading jet p T .
Each event has two Transverse regions, 60 • ≤∆φ≤120 • and -120 • ≤∆φ≤-60 • . For each event, the Transverse region with larger charged multiplicity density was defined as "TransMax", and the other as "TransMin". This binning procedure will cause the results to differ, even if the TransMax and TransMin distributions originate from the same parent distribution, due to region-to-region statistical fluctuations. Since the MPI and BR processes are potentially unrelated to the hard scattering jet angle, both processes make comparable contributions to both the TransMax and TransMin regions. However, ISR/FSR may produce a wide angle third jet, the fragmentation particles of which have a high probability of being recorded in only one of the Transverse regions. ISR/FSR can therefore also result in a significant difference in the two regions' multiplicity densities. Comparisons of TransMax and TransMin regions may therefore reveal details of the various processes contributing to the underlying event. The TransMin region is sensitive to particle production from BR and MPI, while the Trans-Max region also contains signal from ISR/FSR.
The measured data were corrected for trigger bias, detector inefficiency, cosmic-ray background, and pile-up effects. The trigger bias was corrected for by weighting calorimeter-triggered data to have the same jet neutral fraction distribution as minimum bias data. The trigger bias corrections were p T dependent and were less than 15% for the multiplicity density results and less than 20% for p T .
The calorimeter-triggered data were then unfolded using a detailed simulation of the trigger to correct for inefficiencies as a function of jet p T . Differences to the previously mentioned bias-corrected result were accounted for as part of the systematic uncertainty estimate. The detector inefficiency, resolution effects, and background corrections were performed using 2-dimensional Bayesian unfolding [28]. The response matrices were constructed from matched generator-level and detector-level observables [29]. The generator-level is from PYTHIA 6 (STAR) and the detector-level is from the embedded sample to include the backgrounds to the signal, such as the pile-up contribution. A jet pair was considered matched if the distance of a leading jet at the detectorlevel to a leading or sub-leading jet at the generator-level was less than the jet resolution parameter, R anti−k T =0.6. When there was a match, the leading jet p T at the generator-level was used to create the response matrices. For the track level observable p T , a matched track pair was also required. The track pair is considered matched when more than ten hits are matched [18]. The embedding sample had the same track and jet quality requirements applied as the experimental data which included requiring the particle p T >0.2 GeV/c, |η|<1 and leading jet |η jet |<0.4. The negative bins represent the probability for no match to be identified for the concerned variable. Although the two figures exhibit a fairly linear correlation between the detector-level and generator-level variables, their correlations are smeared. To ensure closure of the unfolding correction procedure, the simulation data were divided into two subsets, one was used for training and one for the unfolding closure test. This successful closure test also demonstrated that the uncertainty due to the response matrices is negligible. The unfolding method uncertainty was estimated by varying the Bayesian iterations (with 4 as the default), the unfolding prior, and the maximum tower energy cut of 20 GeV. The effects on the unfolded results due to the uncertainty on the TPC tracking efficiency of 4% and the BEMC tower energy calibration uncertainty of 4% were also estimated. For both cases the response matrices were recalculated after varying the simulations by the appropriate uncertainty and the data unfolded with the new matrix. The systematic errors obtained from these variations were added quadratically with the unfolding uncertainty to form the total systematic uncertainty. The total correction uncertainties were found to be less than 15%.  Figure 2 shows the fully corrected average charged particle multiplicity densities for particles with p T >0.2 GeV/c and |η|<1 as a function of the leading jet p T . In this and the following figures, data points are placed at the center of each p T bin. The statistical uncertainties are shown as vertical bars, which are smaller than the data symbols. The box heights are systematic uncertainties, while their widths correspond to the p T bin sizes. The Toward and Away average multiplicity densities, dN ch dηdφ , both show a rise with leading jet p T . For the Transverse region, dN ch dηdφ tends to slightly decrease as the leading jet p T increases. In contrast, at LHC energies the mid-pseudorapidity transverse multiplicity was observed to quickly increase and then saturate or slightly increase with increasing leading jet/track p T [8,9]. PYTHIA 6 (STAR), PYTHIA 6 default Perugia 2012 and PYTHIA 8 default Monash 2013 simulations are also shown as curves with widths from widest to thinnest, respectively. Deviations for all the simulations from data for jet p T >15 GeV/c are observed in the Transverse region, with PYTHIA 6 (STAR), which was tuned with STAR published minimum bias spectra, closest to the measured results. However, while the agreement between data and PYTHIA 6 (STAR) is reasonable, additional improvements to the tuning or modeling itself would still be appropriate at RHIC energies. To allow comparison with results obtained at facilities with higher collision energies, analyses for particle p T > 0.5 GeV/c were also performed. Figure 3 compares the fully corrected dN ch dηdφ in the Transverse region as a function of the leading jet p T for particle p T >0.2 GeV/c and p T >0.5 GeV/c. Similar trends are observed for these two p T cases, with mismatch between data and PYTHIA.

III. RESULTS AND DISCUSSION
The p T was measured to further profile the characteristics of the underlying event. Figure 4 shows the fully corrected charged particle p T as a function of the leading jet p T for the three regions, with particle p T >0.2 GeV/c. The Transverse region p T slightly increases as the leading jet p T increases. Both the Toward and Away regions show linearly increasing trends. PYTHIA simulations, shown as curves, provide a better description of the p T measurements than the average multiplicity density.  Figure 5 shows the fully corrected transverse region charged particle p T as a function of the leading jet p T for p T >0.2 GeV/c and p T >0.5 GeV/c. Similar trends are observed for these two p T cases. Figure 6 shows the uncorrected detector-level Trans-Max and TransMin average charged particle multiplic- ity densities as a function of the leading jet p T . At √ s = 200 GeV, we observe that while the multiplicity densities in the TransMax and TransMin regions have different overall magnitudes, they both exhibit slightly decreasing trends with increasing leading jet p T with the difference in the densities staying roughly constant.
Meanwhile, experiments at the LHC have reported an increasing difference in the charged particle multiplicities of these regions with increasing leading trigger p T [8,9]. Increasing contributions to the TransMax region from wide angle third-jet production via ISR/FSR is understood as the main physical cause of the LHC results. To further investigate our results, we tested the hypothesis that the TransMax and TransMin charged particle density distributions are drawn, statistically independently, from the same parent probability distribution, f (x). In such a case the probability distribution functions for the measured charged particle multiplicities in the Trans-Max and TransMin regions, f max (x) and f min (x) respectively, can be expressed as f max (x) = 2f (x)F (x) and f min ( The calculated averages of f max (x) and f min (x) as a function of leading jet p T are shown as curves in Fig. 6, when f (x) is taken as the average of the measured Trans-Max and TransMin distributions.
These calculated distributions account for most of the features of the measured data, suggesting any net contribution from additional physical sources is small. Detector-level variables were compared here to avoid the impact of additional fluctuations from the unfolding procedure, with the caveat that any detector response asymmetries could enlarge the TransMax and TransMin difference at the detector-level. This suggests that there are less ISR/FSR contributions to p+p events at RHIC energies than at LHC energies.
Besides the underlying event activity's dependence on the hard scattering reference energy scale, studying the collision energy dependence also contributes to our understanding of how to model these low momentum processes. Detailed underlying event measurements have been reported at a variety of collision energies, which makes it possible to study the energy scaling of the underlying event phenomena, including a possible collision energy dependence of the transverse momentum cut-off between hard and soft scatterings [30]. A world-data comparison with our RHIC measurements is done with jets or charged particles as the hard scattering reference to avoid any possible complexity that different hard references may introduce to the underlying event activity.
Since the underlying event multiplicities at the Tevatron and LHC energies show the same general trend of a rapid rise with reference p T before reaching a plateau [8], comparison points were chosen from the region where the plateau is just reached. An exception is the ATLAS full jet measurement at 7 TeV, where the general trend of the underlying event is not observed due to limited kinematic reach at lower p T . In this case, the lowest p T data point was chosen for comparison [31]. Figure 7 shows the collision energy dependence of the charged particle density extracted for these Transverse regions and Table I details the beam energy, trigger type and p T range, and Transverse region's η coverage for each experiment. Note all the reported results use charged particles with p T >0.5 GeV/c to measure the underlying event (UE) activity and that the LHC and STAR results are from p+p collisions while those of CDF are from p+p events.
A near-linear increase with the log of the collision energy is observed in Fig. 7, despite the different references and pseudorapidity coverage for the various measurements. It has been shown in [4] (for example), that it remains unclear how to model correctly the collision energy scaling of the p+p data; a tension emerges when trying to simulate minimum bias and high multiplicity data with the same tune. This study of the collision energy dependence of the underlying event's average charged particle density provides additional information that may aid in resolving this tension.

IV. SUMMARY
We have reported several observables sensitive to the underlying event activity in p+p collisions at √ s = 200 GeV as recorded by the STAR experiment.
The results used full jets reconstructed at mid-pseudorapidity |η jet |<0.4 with R anti−k T =0.6 and 5<p jet T <45 GeV/c. The charged particles used for the underlying event measurements were required to have |η|<1, 0.2<p T <20 GeV/c, and 60 • ≤|∆φ|≤120 • . The reported observables were corrected to the generator-level to enable direct comparison with theoretical calculations. The detector response corrections were performed via a data-driven trigger correction and embedded PYTHIA 6 (STAR)+GEANT simulations into zero-bias experimental data. The uncertainties from the detector response corrections are included in the systematic uncertainties.
The corrected observables were reported for three topological regions, Toward, Away, and Transverse, based on the azimuthal angle of the particles relative to that of the highest p T jet. The average charged particle multiplicity densities and their mean transverse momenta in these regions were studied as functions of the leading jet p T . These correlations characterize the relationship between the underlying event activity and a hard scattering in p+p collisions. The Transverse charged particle density was observed to slightly decrease with the leading jet p T , while the Transverse p T slightly increases with the leading jet p T . The slight negative correlation of dN ch dηdφ is consistent with energy conservation restricting particle production in the Transverse region as the leading jet becomes more energetic.
The Transverse regions were further split into Trans-Max and TransMin areas. The observation of similar charged particle production in the TransMax and Trans-Min regions at RHIC, which is not observed at the LHC, suggests that ISR/FSR contributes less to the underlying event at √ s = 200 GeV than at TeV collision energies.


extfieldsofstudy:
Medicine
Computer Science

text:
Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety)

Objective Resting-state functional magnetic resonance imaging (rs-fMRI) has become an essential measure to investigate the human brain’s spontaneous activity and intrinsic functional connectivity. Several studies including our own previous work have shown that the brain controls the regulation of energy expenditure and food intake behavior. Accordingly, we expected different metabolic states to influence connectivity and activity patterns in neuronal networks. Methods The influence of hunger and satiety on rs-fMRI was investigated using three connectivity models (local connectivity, global connectivity and amplitude rs-fMRI signals). After extracting the connectivity parameters of 90 brain regions for each model, we used sequential forward floating selection strategy in conjunction with a linear support vector machine classifier and permutation tests to reveal which connectivity model differentiates best between metabolic states (hunger vs. satiety). Results We found that the amplitude of rs-fMRI signals is slightly more precise than local and global connectivity models in order to detect resting brain changes during hunger and satiety with a classification accuracy of 81%. Conclusion The amplitude of rs-fMRI signals serves as a suitable basis for machine learning based classification of brain activity. This opens up the possibility to apply this combination of algorithms to similar research questions, such as the characterization of brain states (e.g., sleep stages) or disease conditions (e.g., Alzheimer’s disease, minimal cognitive impairment).

INTRODUCTION
Resting-state functional magnetic resonance imaging (rs-fMRI) has been increasingly applied to study activity and connectivity of the resting brain and involves the recording of the bloodoxygen-level-dependent (BOLD) signal without imposing a task (Biswal et al., 2010;X.-N. Zuo and Xing, 2014). This approach can be used to assess intrinsic and spontaneous brain activity. Analysis techniques of rs-fMRI have evolved rapidly over the past few years and are based on correlation methods (Bullmore and Sporns, 2009;Sporns, 2011;Wee et al., 2012;Jie et al., 2014), partial correlation (Salvador et al., 2005;Marrelec et al., 2006Marrelec et al., , 2007, graph theory based analysis (Chen and Herskovits, 2007;Bullmore and Sporns, 2009) and sparse representation methods (Lee et al., 2011;Wee et al., 2014), among others. Because of its simplicity (short scan time, no stimulation equipment needed, no task requirements), the rs-fMRI method has become particularly popular for the characterization of clinical conditions, for example pinpointing to differences between healthy participants and patients with Parkinson's disease (Göttlich et al., 2013;Tahmasian et al., 2015), Alzheimer's disease (AD) (Sheline and Raichle, 2013;Dennis and Thompson, 2014), bilateral vestibular failure (Göttlich et al., 2014a), schizophrenia (Alderson-Day et al., 2016;Hu et al., 2017), and obsessive-compulsive disorder (Göttlich et al., 2014b;Gürsel et al., 2018), to name but a few targeted neuropsychiatric conditions.
In neuroimaging, machine learning classifier (MLC) methods are applied to fMRI data to detect model-free brain activity and to use these brain activity patterns to differentiate between groups or conditions (Cox and Savoy, 2003;Pereira et al., 2009). The application of MLC to fMRI data is often referred to as multi-voxel (i.e., analyzing more than one voxel at once) pattern analysis (MVPA). MVPA is a helpful tool to investigate how a pattern of brain activity is related to different cognitive states (Haynes and Rees, 2006;Norman et al., 2006;Mahmoudi et al., 2012). The process of applying the MVPA approach to fMRI data can be broken down into three stages (Pereira et al., 2009;Poldrack et al., 2011). First, feature extraction, which converts the BOLD fMRI signals to the relevant variables, i.e., features, which will be used to train and to test the classifier. Second, feature selection, which determines features that have to be included in the classifier analysis in order to improve the classification. Third, cross-validation methods that divide the data into training and testing samples and determine the accuracy of the classifier in generalization to new data. Support vector machine (SVM; Cortes and Vapnik, 1995;Vapnik, 2013) is a powerful method available to perform MVPA. In contrast to alternative MVPA methods (such as linear discriminant analyses), SVM provides better prediction accuracy, having the advantage of being relatively insensitive to the sample size of the training dataset (O'Toole et al., 2007;Mountrakis et al., 2011). Furthermore, SVM has additional advantages regarding efficiency, simplicity, robustness and is less susceptible to noise (Mountrakis et al., 2011;Meier et al., 2012).
The application of SVM to fMRI data at group level has several advantages over traditional univariate (i.e., individual) voxel-based methods, like the general linear model (GLM). For instance, SVM allows to identify voxels or brain regions of interest that are informative for classifying groups by accumulating the information in an efficient way across many spatial locations. While in GLM analysis, these voxels or brain regions could appear statistically insignificant, although they might carry some information about differences between states or groups (Haynes and Rees, 2006;Norman et al., 2006). Thus, SVM provides insight into the defining differences between the two states or groups (Cox and Savoy, 2003;Orru et al., 2012).
In many cases, fMRI data have a small number of samples and a large number of variables or features. This often leads to overfitting in classification, which in turn leads to deceptive diagnostic results and poor generalization performance (Pereira et al., 2009;Wang, 2009). To avoid the danger of overfitting, most of the MVPA-based fMRI studies applied both methodologies, feature-selection algorithms to remove redundant information and MLC methods that are less sensitive to a high dimensionality, such as linear SVM. Finally, cross-validation analyses are performed to evaluate the classification accuracy and generalizability for unseen data (Cox and Savoy, 2003;Mitchell et al., 2004;Kamitani and Tong, 2005;Zhang et al., 2005).
Rs-fMRI yields data comprise multiple data points per subject and/or condition, among other things, raising the question of whether it might be possible to distinguish between different conditions (e.g., disease present or not) using classification algorithms from the realm of machine learning. Indeed, several recent publications have tackled this question. For example, Abós et al. (2017) obtained functional connectomes from the rs-fMRI in healthy controls (HC) and 70 Parkinson's disease patients [of which one third had a mild cognitive impairment (MCI)]. Using a SVM trained on features selected through randomized logistic regression with leave-one-out cross-validation (LOOCV), they could separate patients with MCI from those not having MCI with an accuracy of about 83% in the training sample. In a smaller validation sample of 25 Parkinson patients (8 MCI), classification accuracy with regard to MCI was 80% using the features found in the training sample (Abós et al., 2017). This suggests that SVM classification based on metrics obtained from rs-fMRI can indeed yield meaningful results. Likewise, applying a graph theoretical approach to rs-fMRI to characterize functional connectivity in patients with MCI, AD and age-matched HC (total sample n = 168), followed by SVM based classification, Khazaee et al. (2015a) were able to accurately classify the subjects into three groups (HC, MCI, and AD) with 88.4% accuracy. The same research group (Hojjati et al., 2017) tried to distinguish patients with MCI who later converted to an AD from MCI patients who did not. Again a SVM, using features derived from local and global graph measures, was used. This approach yielded a specificity of 91.4% and sensitivity of 83.2% regarding the conversion to the AD. Bi et al. (2018) attempted to classify patients with autism spectrum disorder (ASD) from HC using random SVM cluster and reported classification accuracy based on the optimal feature to be 96%. These are just a few examples illustrating that rs-fMRI derived features can be used for classification of conditions using machine learning algorithms. What it is less clear, however, is which method of rs-fMRI analysis delivering the most discriminating features might be best in distinguishing different metabolic states (hunger vs. satiety).
In the present investigation, we therefore sought to compare the accuracies of three different connectivity parameters or features (the predictor variables used for classification) extracted from rs-fMRI fluctuations. These features assess local and global functional connectivity as well as changes in the brain activity as indicated by the amplitude of the BOLD signal, i.e., regional homogeneity (ReHo), degree of centrality (DC), and fractional amplitude of low-frequency fluctuations (fALFF), respectively. Briefly, ReHo characterizes the local connectivity of a brain voxel to its nearest neighboring brain voxels (Zang et al., 2004;Jiang and Zuo, 2015) by determining the coherence among spontaneous BOLD signals that might reflect spontaneous neuronal activity (Sato et al., 2012). ReHo has been applied to widely differing neuropsychiatric conditions (Cao et al., 2006;Liu et al., 2006;He et al., 2007;Wu et al., 2009;Paakki et al., 2010). DC is derived from graph theory based analysis and describes the global connectivity (global connectedness) of a given voxel with the voxels in the entire brain, by computing the number of connections above a certain threshold (Buckner et al., 2009;Bullmore and Sporns, 2009;Zuo et al., 2012). Again, DC has seen widespread application in neuropsychiatric conditions (Buckner et al., 2009;Beucke et al., 2013;Di Martino et al., 2013;Göttlich et al., 2013;Hou et al., 2014). Finally, to quantify spontaneous local brain activity, the amplitude of the BOLD signals has been used. This can be assessed by the amplitude of lowfrequency fluctuations (ALFF) and its derivative fALFF Zou et al., 2008). While ALFF describes the local spontaneous brain activity across the whole brain, by assessing the amplitude in a given voxel or brain area in the low-frequency range (0.01-0.08 Hz), fALFF is a normalized derivation of ALFF representing the ratio of low-frequency range amplitudes (0.01-0.08 Hz) relative to the entire frequency range (e.g., 0-0.25 if TR=2 s) amplitudes. Both ALFF and fALFF have high temporal stability (Küblböck et al., 2014) and test-retest reliability (X. N. Zuo and Xing, 2014). In contrast to ALFF, fALFF has been reported to have higher specificity in detecting local spontaneous brain activities, especially in gray matter (Zou et al., 2008;Zuo et al., 2010). Moreover, fALFF is recommended to be used instead of ALFF (X.-N. Zuo and Xing, 2014), since it is more robust against non-specific signal components, such as physiological noise . In the present study, we performed fALFF on rs-fMRI data to describe the local spontaneous brain activities.
The aim of feature selection algorithms is to reduce the dimensionality of feature space and computation time, as well as to enhance the accuracy of optimization methods by ignoring redundant, irrelevant or noisy features (Tang et al., 2014;Jović et al., 2015). In general, the feature selection algorithms are classified in two categories, according to the type of objective functions that one chooses to work with: filter methods and wrapper methods (Pereira et al., 2009;Mwangi et al., 2014). Filter methods select the feature subsets based on statistical properties (such as interclass distance, mutual information, entropy or statistical independence) of the features to filter out poorly informative ones without employing any classification algorithm. In contrast, wrapper methods rate the feature subsets based on their predictive accuracy to improve the performance of classification when applying a particular classifier (such as SVM or the k-nearest neighbor). Filter methods are advantageous because they perform quickly, afford a more general solution and tend to select large feature subsets. Wrapper methods are expensive because they need more time to train the classifier of each subject many times (i.e., cross-validation), but often do not deteriorate from the problem of overfitting (Burrell et al., 2007) and provide more accurate results comparable to filter methods (Guyon and Elisseeff, 2003;Maldonado and Weber, 2009).
There are several strategies to apply wrapper methods (Mwangi et al., 2014). For instance, sequential forward selection (SFS) and sequential forward floating selection (SFFS) are easy to execute and are assumed to provide useful results. Although the SFFS strategy requires massive computational resources, it performs better and is more effective for solving small-and medium-scale problems than simpler strategies like SFS (Kudo and Sklansky, 2000). However, the SFS strategy reduces the computational costs for the feature subset selection. Accordingly, Burrell et al. (2007) concluded that SFS was a reasonable alternative to select a small subset of features for fMRI data. In this work, we compared between SFS and SFFS strategies for creating feature subsets to distinguish different metabolic states.
The emphasis of the present work is on the ability to classify the metabolic states (hunger vs. satiety) by the MVPA approach. Therefore, we first estimate and compare the prediction accuracy of classification (hunger vs. satiety) based on different features of rs-fMRI data (ReHo, DC and fALFF). Second, we identify brain regions containing discriminating information between different metabolic states. To this end, we apply supporting linear SVM as a classifier and two feature selection strategies (SFS and SFFS) to identify those brain regions that most efficiently differentiate between hungry and satiated states based on rs-fMRI data. Finally, we employ a cross-validation scheme and permutation tests to validate the reliability of classifier and significance testing, respectively (see Figure 1 for classification procedure).

Experimental Design
We investigated 24 healthy male volunteers aged 20 to 30 years (mean ± SEM: 24.5 ± 0.6 years) with a body mass index (BMI) within the normal weight range of 20-25 kg/m 2 (mean ± SEM in kg/m 2 : 23.4 ± 0.3), recruited from the University and the local population of Lübeck, Germany.
Each subject was investigated in two sessions (i.e., two metabolic states), once under fasting (36 h fasting) and once under standardized eating conditions (five meals throughout 36 h). The order of the two sessions was randomized across participants, with a break of at least 1 week between sessions.
In the hunger condition, subjects fasted (no food or beverages, except water) from 11 pm the night before the examination started until the end of the rs-fMRI recording. In the satiety condition, three standardized meals per day were provided. Standardized meals were served according to the recommendations of the clinical diabetes counseling department at the University Medical Campus Schleswig-Holstein (UMCSH): Breakfast (25% protein, 50% carbohydrate and 25% fat), lunch (20% protein, 63% carbohydrate and 17% fat) and dinner (22% protein, 60% carbohydrate and 18% fat) were provided at 9 am, 12 pm and 7 pm, respectively. In both sessions, subjects arrived at the sleep lab at 8 am. The first blood sample for defining the blood glucose levels was taken at 8:45 am. More details on the exact timing for obtaining blood samples can be found in our previous work (please see the experimental design and Figure 1 in Al-Zubaidi et al., 2018). All subjects stayed and slept overnight in the laboratory at the UMCSH. On the next morning, blood samples were drawn at 8:45 am and at fixed time points throughout noon until the MRI was performed at 1 pm for the rs-fMRI recording (duration 6 min). Subjects were instructed to lie still inside the scanner having the eyes closed and trying to avoid any particular cognitive activity. For each condition, subjects rated hunger feelings 20 min before the MRI sessions started. This was done by using a visual analog scale from 0 (not hungry at all) to 9 (very hungry).
Blood sugar levels were lower while feelings of hunger were more intense during hunger compared to satiety before the rs-fMRI scans (Figure 2). These findings confirm the success of our fasting treatment. Results of blood sugar levels and hunger ratings can be found in Al-Zubaidi et al. (2018).

Image Acquisition
We used a 3-T Philips Achieva scanner (Philips Medical Systems, Best, Netherlands) and a standard eight-channel phased array head coil to record structural and functional images. Anatomical scans consisting of 180 sagittal slices were acquired by applying a T1-weighted 3D turbo gradient-echo sequence with SENSE (image matrix 240 × 240; voxel dimensions 1 × 1 × 1 mm; field of view 240 × 240 mm 2 ; 1 mm slice thickness; 9 • flip FIGURE 2 | Average blood glucose levels (A) and rating of hunger feeling (B) per experimental condition. * and * * * represent the significant differences between conditions, at a threshold of p < 0.01 and p < 0.0001, respectively.
Frontiers in Human Neuroscience | www.frontiersin.org angle). For the rs-fMRI recording (duration 6 min), subjects were instructed to lie still inside the scanner with their eyes closed and to not engage in any particular cognitive activity. Further, 178 whole-brain fMRI volumes were acquired in an interleaved fashion with a T2 * -weighted single-shot gradient-echo EPI sequence (TR = 2000 ms; TE = 28 ms; voxel dimensions 3 × 3 × 3 mm; field of view 192 × 192 mm 2 ; 80 • flip angle; 40 slices).

Preprocessing
Part of the preprocessing of the functional images was carried out using FSLv5.0 1 to implement independent component analysis (ICA)-based strategy for automatic removal of motion artifacts (ICA-AROMA) for head motion correction (Pruim et al., 2015a). It has been reported that ICA-AROMA improves the specificity (i.e., the signals of interest) and sensitivity (i.e., motionrelated noise) of rs-fMRI activation and connectivity analyses (Pruim et al., 2015a). To improve inter-subject alignment (Klein et al., 2009;McLaren et al., 2010), the spatial preprocessing of the data was performed with the statistical parametric mapping 12b (SPM12b).
The rs-fMRI images were preprocessed as follows: (i) we discarded the first eight functional volumes from each participant's two runs to allow steady-state tissue magnetization; (ii) we manually reoriented all functional volumes to the anterior commissure; (iii) we implemented head movement correction on data by realigning all volumes to the middle time-point volume using MCFLIRT (Jenkinson et al., 2002); (iv) we applied ICA-AROMA to the data in order to identify and remove motion-related components using four spatial and temporal features (Pruim et al., 2015b). Briefly, functional images were submitted to the MELODIC toolbox for running a probabilistic independent component analysis (PICA) with automatic estimation of the number of hidden independent components (i.e., source signals) to find a set of components for each participant per recording individually (Beckmann and Smith, 2004). A predetermined classifier was then applied on independent components to represent the motion-related artifact components, which were identified at least by assessing each component to one of the following criteria: (1) maximum correlation with realignment parameters, (2) high-frequency temporal content >35%, (3) spatial content in edge voxels and cerebrospinal fluid (CSF) >10%. Finally, we removed the motionrelevant components from rs-fMRI data that had been realigned by using a linear regression approach. The structural and denoised functional images were further preprocessed with data processing assistant for resting-state fMRI (DPARSF) toolbox as follows (Yan, 2010): (v) we co-registered the T1 structural image to the mean functional image; (vi) we ran a segmentation protocol to distinguish gray matter, white matter and CSF; (vii) we applied bias correction and spatial normalization of the T1 structural image and adjusted them to the MNI template using DARTEL algorithm (Ashburner and Friston, 2005); (viii) we performed nuisance regression (including white matter and CSF signals) to reduce the impact of undefined physiological effects 1 http://fsl.fmrib.ox.ac.uk/fsl/ on rs-fMRI signals (Liu, 2016); (ix) we spatially normalized functional images and gray matter images to the MNI-template using the normalization parameters estimated by DARTEL algorithm with a 3 mm isotropic voxel size; (x) we performed spatial smoothing with a 6 mm full width at half maximum (FWHM) Gaussian kernel. Rs-fMRI signals were smoothed after calculating ReHo (Zang et al., 2004) not before (see ReHo paragraph); (xi) we applied temporal band-pass filtering (0.01-0.08 Hz) on the rs-fMRI signal to reduce the effect of lowfrequency drift, such as respiratory, and high-frequency noise, such as heart activity. As suggested by Zou et al. (2008) no such band-pass filter was used when computing the fALFF (see fALFF paragraph); (xii) finally, we masked all functional images with a gray matter mask. The gray matter mask was calculated by averaging the grey matter images of all subjects. To generate the binary mask, we defined the common voxels between the average gray matter image and the gray matter template (without cerebellar lobules) derived from the Automated-anatomicallabeling (AAL) atlas (Tzourio-Mazoyer et al., 2002) by using the xjView toolbox 2 . In some subjects we were not able to measure the whole cerebellum. Therefore, the cerebellar cortex was excluded from the gray matter mask and regions of interest (ROIs).

Feature Extraction From rs-fMRI Data
Feature extraction is used to reduce the dimension of the original data space to a new feature space. This new feature space helps to minimize the training time taken by the classifier (Chen et al., 2010). To encompass different aspects of rs-fMRI fluctuations, we extracted and compared three of the most common features according to their accuracy to select those brain regions that best distinguish different metabolic states by using linear SVM together with feature selection strategies. In the beginning, we submitted the preprocessed data to the DPARSF toolbox (Yan, 2010) and extracted three features from each subject per section as described in the following paragraphs. Then, those features were analyzed as explained in Section of Feature Selection.
First, local connectivity of brain regions was described using ReHo (for regional homogeneity), which is a measure of the temporal homogeneity among brain voxels and the neighboring brain voxels within the low-frequency range of rs-fMRI signals (Zang et al., 2004). The rationale behind ReHo is based on the assumption that the BOLD signal has characteristics that depend on neuronal activities, and therefore, the time series of neighboring voxels in a functional brain area will be highly similar or synchronized when that area supports specific goals or representations (Jiang and Zuo, 2015). The ReHo index for a particular voxel is calculated by using Kendall's coefficient concordance (KCC) approach. In this study, the KCC was calculated using the following formula (Zang et al., 2004): where KCC is the ranging coefficient, from 0 to 1 (no to maximal coherence), of a given voxel in relation to its nearest neighbors, R i represents the rank sum of i th time point as R i = k j=1 r ij and r ij is the rank of the i th time point of the j th voxel. R refers to the average of the R i and n represents the length of the time series (here n = 170 time points). K is the number of voxels within the targeted clusters (here K = 27, the given voxel (which is the center voxel) plus its 26 immediate neighbors). The KCC value was then put to the center voxel of the respective cluster. From the KCC of all voxels, the so-called ReHo map can be constructed. Thus, an individual ReHo map was computed for each subject per session.
Second, DC (for degree centrality) is used to investigate the global connectivity of brain regions, which is defined as the number of connections of one voxel in the brain to whole brain voxels (Buckner et al., 2009). This measure depends on graph theoretical approaches. The individual DC map was generated by correlating the time course of each voxel in the brain with all other voxels in the brain and calculating the number of connections above a definite threshold (Buckner et al., 2009;Al-Zubaidi et al., 2018). The temporal connection between two voxels was defined by applying Pearson's correlation coefficient (r) approach. Then, the individual correlation coefficients were used to generate a correlation matrix =    where N is the number of voxels within the whole-brain mask and r ij is the temporal Pearson's correlation of time series between i th and j th voxels measuring the similarity between two voxels. The correlation matrix was thresholded at 0.25 to build a binary undirected and unweighted network matrix d ij as follows The binary connectivity matrix d ij was used to define the degree centrality of voxel D i by the following Third, fALFF was employed to provide information on the magnitude of the BOLD signals, which reflects the neural activity of each brain voxel or region within a network of interest (Fujino et al., 2017;Al-Zubaidi et al., 2018). It refers to the ratio of rs-fMRI signal fluctuation in the low-frequency range proportional to the entire frequency range (Zou et al., 2008). DPARSF toolbox has a built-in fast Fourier transform (FFT) to convert time series for each voxel to the frequency domain and compute the power spectrum. This procedure estimates the amplitude of each frequency as the square root of the power spectrum. The total amplitude of the low-frequency range (0.01-0.08 Hz) was divided by that of the full frequency range 0-0.25 Hz.

Feature Selection for Hunger/Satiety Status Classification
After generating ReHo, DC and fALFF maps from rs-fMRI data for each subject per condition, we used the AAL atlas to define the ROIs. The AAL atlas is a well-established anatomical parcellation of the brain into 45 ROIs per hemisphere when excluding the cerebellar lobules. Mean values of ReHo, DC and fALFF were calculated for each ROI and used to create a feature (region) vector, i.e., R[1, ..., 90], with 90 dimensions for each map. Those features are listed in Table 1.
In the classical classification problem, the goal of feature selection is to automatically search and select the best feature subset for the classification purpose. Here, we applied sequential feature selection algorithms to select the optimal feature subset (region subset) that best captured differences between hunger and satiety. This type of selection algorithm contains two components. The first element is a sequential search strategy to select and establish the best future subset, which evaluates additional features by a criterion function. In this study we used two strategies, namely SFS and SFFS. The SFS procedure starts by identifying the first feature with the highest classification rate and feeds it to a new empty candidate set. Other features are selected sequentially by adding a local feature to the first feature or the last subset of features in the candidate set, and testing a new feature combination until the highest classification rate (objective function) is achieved. The processing continues until further features do not enhance the objective function. However, the SFS algorithm is suboptimal and suffers from the "nesting effect" (Pudil et al., 1994), while SFFS offers the flexibility to discard features that were earlier selected and to re-evaluate features that had been discarded previously. This theoretical advantage notwithstanding, Burrell et al. (2007) showed that the computationally less demanding SFS could provide a reasonable alternative to SFFS to select features for discriminating between epileptic and non-epileptic activity of epileptic patients, indicating that both strategies had similar difficulties to separate patterns of functional and dysfunctional brain activities in epileptic patients. In this study, we compared SFS and SFFS strategies in order to figure out if SFS already provides near-optimal results. SFS and SFFS strategies were performed using the "sequential" function in MATLAB and sequential floating feature selection toolbox 3 , respectively.
The second component in the feature selection strategy is an objective (criterion) function to evaluate over all possible feature subsets. In this work, the misclassification rate of the linear SVM classifier was set as an objective function (Guyon and Elisseeff, 2003). The combination of SFFS and SVMs has previously been used, for example to assess Gabor features for classification of Parkinson's disease risk assessment based on transcranial sonography images (Al-Zubaidi et al., 2013). To evaluate the feature subset, the data were divided into test and training samples using a LOOCV scheme. Accordingly, independent samples were used for training and testing. For each LOOCV loop, the training samples were submitted to train an SVM model, and the test sample was applied to that model to evaluate the feature subset. In the end, the average of the values returned by LOOCV loops was calculated and used to assess each candidate's feature subset (Kohavi and John, 1997). The classification accuracy (CA) was derived using a LOOCV strategy with confusion matrix (CM) and calculation of classification error rate (ER). In our study, the CM comprises information about the actual and predicted classifications generated by linear SVM. By comparing the results of the SVM classifier (hunger or satiety) with the reference data, we documented the outcomes of the CM in the present study as given in Table 2. For significance testing (Pereira et al., 2009), we estimated the empirical distribution by calculating the error rate 10,000 times for random label permutations in a cross-validation procedure. P < 0.05 implies that classification results differ significantly from chance.

RESULTS
The experiments showed that fALFF was marginally better than ReHo and DC in distinguishing between hunger and satiety states in the healthy brain ( Table 3). The region subset obtained by SFFS resulted in higher classification accuracy than SFS, both higher than no feature selection (90 regions). Using a linear SVM classifier with an LOOCV strategy, we observed that the fALFF region subset selected by SFFS identified the hunger state with the highest classification accuracy of 81% and with the most balanced overall performance. In our SFFS results (Table 3), the regions 45 and 46 are the left and right inferior occipital lobe (Table 1), respectively, and region 50 is the right superior occipital lobe. Also, regions 5 and 35 are medial (Calcarine) and inferior (Fusiform) surfaces of the occipital lobe, respectively. Furthermore, regions 17 and 18 are left and right frontal gyrus, respectively, region 52 is the right Olfactory cortex, region 56 is the right Paracentral lobule and region 73 is the left Rolandic operculum. SFFS-identified brain regions, which distinguished best between hunger and satiety for each rs-fMRI feature, are shown in Figure 3. Furthermore, SFFS fALFF (Figure 4) was most stable in the permutation test (ER = 0.19 / p = 0.0001) closely followed by DC (ER = 0.21/p = 0.0004) and ReHo (ER = 0.29/p = 0.0068).

DISCUSSION
The primary goal of the present research was to assess the ability to classify different brain states by applying a MVPA approach, i.e., feature selection strategies and linear SVM, on various features, i.e., connectivity parameters, derived from rs-fMRI data. This approach was carried out on a data set comprising two conditions (hungry and satiated) in a repeated measures design. As the two metabolic states, i.e., hunger and satiety, were induced for a rather long time (36 h), experimental conditions can be treated as the ground truth to compare and evaluate the classification scheme. An advanced preprocessing, including ICA-AROMA, was carefully applied, ensuring the removal of motion artifacts and other structured noise from the data [e.g., cardiac pulsation artifacts (Pruim et al., 2015b)]. Thus, classification in the current case is deemed to reflect true brain differences rather than extracerebral differences (e.g., motion) between the conditions. A critical question with regard to classification is the selection of the best approach for feature selection. The inclusion of all possible features and the computation of all possible combinations of features is computationally not feasible at present. Therefore, sequential search techniques have gained some popularity. These work by choosing the best individual feature and then adding a second feature that yields the best FIGURE 3 | Brain regions that provided relevant information to distinguish between hunger and satiety states in healthy lean participants. The performance of these regions was evaluated by linear SVM classifier and SFFS algorithm. All images are in neurological orientation, i.e., right = right and left = left.
Frontiers in Human Neuroscience | www.frontiersin.org classification accuracy in combination with the first feature. This procedure is repeated for a third and any subsequent features until the addition of further features does not yield an improvement of classification rates. Alternatively, the user can predetermine the maximum number of features. The SFFS method tries to optimize feature selection by adding an elimination step to this sequential search process. Concretely, at each level, it is examined whether the classification rates improve, if any of the selected features are eliminated. If this happens, the feature set is reduced by this feature. Then, the search continues based on the new set. Jain and Zongker (1997), who compared 14 different methods for feature selection applying them to the problem of handwriting recognition. In this case, the SFFS method outperformed other feature selection schemes. It has to be pointed out, however, that conventional feature selection approaches, including SFS and SFFS as well as filter approaches (Kira and Rendell, 1992;Almuallim and Dietterich, 1994), have recently been supplemented by metaheuristic methods for feature selection. In this regard, Zhu et al. (2007) have suggested a genetic algorithm combined with local search in a hybrid wrapper and filter feature selection algorithms. Others, like Neshatian and Zhang (2009) and Gu et al. (2018), proposed new optimizations methods including such advanced feature selection procedures. Applying genetic algorithms and new optimization functions on rs-fMRI data are beyond the scope of this study and will be considered in future work.
In the present study, the subset of regions obtained by the SFFS algorithm provided the highest classification rate for all rs-fMRI maps ( Table 3). Using fALFF, SFFS and SVM classification, we were able to demonstrate that patterns of amplitude BOLD signals in five brain regions [paracentral lobule, Rolandic operculum, olfactory cortex, lateral occipital (inferior division) gyrus, and inferior frontal (opercular) gyrus; Figure 3] can distinguish between metabolic states (hunger vs. satiety) with 81% accuracy.
The Rolandic operculum, which belongs to somatosensory regions, is activated during the anticipation and consumption of food (Stice et al., 2008;Stice and Yokum, 2016), in response to palatable food receipt (Stice et al., 2013) and has been associated with the processing of high-and low-caloric food pictures (Stice et al., 2010). Among many functions, paracentral gyrus is known to respond to highly rewarding stimuli (Stice and Yokum, 2016). A study using independent component analysis to estimate functional connectivity (FC) parameters showed that the connectivity strength of the paracentral gyrus in the sensorimotor network was increased during hypoglycemia relative to euglycemia (Bolo et al., 2015). Furthermore, Van Duinkerken et al. (2012) reported that the change of sensorimotor FC was associated with basal glycemic levels in type 1 diabetes mellitus patients. Thus, the paracentral lobule seems to be part of the reward system and the sensorimotor network. The olfactory cortex (OLFC), whose activity was modulated by metabolic states as well, is involved in the experience and processing of negative affective states, including anxiety and depression (Krusemark et al., 2013). Consistent with that, a rs-fMRI study in rodents demonstrated that ReHo of the OLFC is increased in stress-exposed rats compared to a control group (Li et al., 2018). In our study, the fasting for 36 h might have led to a stress increase, which might be reflected in an increase in OLFC activity. Moreover, the satiety state might have reduced peripheral hunger signals compared to the hunger state, and accordingly, might have influenced brain regions related to somatosensory processes, such as Rolandic operculum, and parts of the sensorimotor network like the paracentral lobule.
The inferior frontal gyrus (IFG) has been suggested to be involved in cognitive control (Hare et al., 2009;Sundermann and Pfleiderer, 2012). IFG activation during response inhibition has been associated with a reduced desire for food and with successful impulse regulation (Gabrieli et al., 2015;Hollmann et al., 2012;Lopez et al., 2014). In addition, stronger IFG activity in response to orosensory stimulation was found in successful weight loss maintainers compared to people who were obese or normal weighted (Sweet et al., 2012). In our experiment, participants had to refrain from eating during the hunger state and from overfeeding during the satiety state, which may have contributed to the finding that the IFG is partially important for classifying between different metabolic states.
The lateral occipital cortex (LOC) is part of the visual association cortex and is activated in response to the perception of emotionally salient stimuli, such as food, which is thought to be a correlate of heightened attention (Killgore and Yurgelun-Todd, 2007;van der Laan et al., 2011). For instance, a recent rs-fMRI study using SVM on graph theory analysis indicates that the LOC is partly important for classification between high-caloric (potato chips) vs. low-caloric (zucchini) food ingestion on the brain of healthy subjects (Mendez-Torrijos et al., 2018). Furthermore, it has been suggested that the processing of visual salience of a stimulus depends on the affective state of the individual and the motivational value of a stimulus (Killgore and Yurgelun-Todd, 2007). Considering the general role of the LOC in the visual processing of food stimuli, this region might potentially facilitate the detection/perception of such cues in a deprived state. Note that these interpretations are based on reverse inference of resting-state data and should thus be taken with caution.
However, some studies have used rs-fMRI to investigate changes in baseline brain activity of lean or obese participants during both hunger and satiety states. For instance, Lohmann et al. (2010) showed increased centrality, which was measured by eigenvector centrality analysis, of the anterior precuneus (APCUN) during the hunger state relative to the satiety state of 22 normal volunteers. Consistent with that, our previous study (Al-Zubaidi et al., 2018) revealed that the fALFF was increased in the APCUN and posterior cingulate cortex (PCC) when comparing hunger against satiety states. Additionally, Wright et al. (2016) used seed-based connectivity analysis to estimate the FC parameters of 19 healthy participants. They reported that the FC between the posterior insula and superior frontal gyrus, and between the hypothalamus and IFG, were enhanced during the hunger state. Furthermore, it has been found that both 20 lean and 20 obese subjects had increased ReHo connectivity from hunger to the satiety state in the orbitofrontal cortex and inferior temporal lobe (Zhang et al., 2015). These studies reported different brain regions that might be related to the changes in the metabolic state compared to the results of the current study, except IFG. These inconsistencies here might be associated with the different experimental paradigms and neuroimaging modalities.
Importantly, the statistical analysis at group levels of the studies mentioned above was computed by using GLM approaches to define the significant brain regions at baseline activity that are sensitive to changes in metabolic state. In GLM approaches, the p-values are the successful statistical tests to represent significant brain regions that show different brain activities in the average sense of one or more brain features when compared between different groups and/or experimental conditions. On the other hand, SVM classifier aims to automatically classify each subject into one of the groups or experimental design in the study. Thus, the overall classification accuracy is usually used to measure the success of the studies. In general, it is easier to demonstrate group or experimental condition differences compared to predict a single subject (Arbabshirani et al., 2017). Furthermore, the significant variables or features that show the difference between group or experiment do not necessarily have high classification accuracy and vice versa (Lo et al., 2015;Arbabshirani et al., 2017). Hence, brain regions that showed significant differences between hunger and satiety states in previous studies do not necessarily mean that these regions can predict the subject class with high accuracy and vice versa.
Furthermore, our results indicate that fALFF analysis is more informative than ReHo and is slightly more precise than DC for classification of resting brain changes during hunger and satiety, probably because fALFF is an index of the power of the BOLD signal. Against this, ReHo and DC parameters refer to dynamics of BOLD connectivity, either with some (in this case, 26) neighboring voxels (ReHo) or with all voxels (DC) in the gray matter of the brain.
One other important question is, whether SFFS based classification is superior to simply trying to classify states using statistically significant group differences. Baker et al. (2012) answered this question on an EEG data set from AD patients, MCI patients and HC. They used an SFFS algorithm and a t-test to classify patients and found that the SFFS technique resulted in improved classification rates compared to the t-test for four feature types (average power, coherence, correlation, and phase synchrony). They concluded that the SFFS algorithm selects reliable features for classification where statistically significant features fail in classification.
The sample size of most fMRI studies is often relatively small due to the high costs of scanning time and subject stamina (Poldrack et al., 2017). However, sample size impacts the trade-off between accuracy and generalizability (Schnack and Kahn, 2016). For instance, in the context of rs-fMRI features and SVM classification methods, several studies reported high classification accuracies (92 ± 9%) with relatively small sample sizes (20 ± 5 subjects per group), when classifying groups of brain disease patients and healthy subjects (Lord et al., 2012;Tang et al., 2012;Fekete et al., 2013;Wang et al., 2013;Wei et al., 2013;Cheng et al., 2015;Khazaee et al., 2015b). Here, the high classification accuracy is driven by the heterogeneity between groups (Schnack and Kahn, 2016;Arbabshirani et al., 2017). In contrast, studies with large sample sizes are assumed to result in classification models with a higher degree of generalizability, allowing for a better prediction in samples drawn from other populations. Their classification models capture a more complete picture of disease patterns, but at the cost of lower accuracy, which is likely due to the within-group heterogeneity (Anderson and Cohen, 2013;Krystal et al., 2013;Guo et al., 2014;Schnack and Kahn, 2016;Arbabshirani et al., 2017).
One limitation of the present study is the ability to generalize, since the sample size of 24 subjects is relatively small. Accordingly, larger samples are needed to confirm our findings. However, we would like to argue that our results are not driven by the heterogeneity between samples, because we have chosen a within-subject design in a well-controlled experimental setting. Also, we evaluated whether the rs-fMRI features in conjunction with sequential feature selection strategies were sufficiently reliable to predict the subject's metabolic state using the LOOCV scheme. Thus, independent training and testing samples were used to estimate the SVM model parameters and to validate the classification model. In this case, the CA was derived by averaging the resulting classification accuracies over all samples. All in all, the homogeneity of our samples and the high CA results in an increased validity of our findings, determining brain patterns that are able to discriminate between different metabolic states.
As rs-fMRI has received widespread attention over the past 10 years, the possibility of reliable classification of disease conditions or subject states (e.g., sleep stages) paves the way for using rs-fMRI as a diagnostic tool on an individual patient/subject level. In addition the applications mentioned in the introduction, such as the prediction of conversion of MCI to AD (Hojjati et al., 2017), many other diagnostic and research questions lend themselves to this approach, e.g., the differentiation of typical Parkinson's disease from atypical Parkinsonian syndromes (c.f., Tang et al., 2010). From our data, we conclude that fALFF, in combination with SFFS based feature selection, is a useful and straightforward way to proceed in tackling such research questions.

ETHICS STATEMENT
This study was carried out in accordance with the recommendations of the Declaration of Helsinki. The protocol was approved by the ethics committee of the University of Lübeck. Before participation, each participant gave written informed consent to the study.

AUTHOR CONTRIBUTIONS
KJ-C and TM designed the study and wrote the protocol. AA-Z, MH, and KJ-C participated in the data collection. AA-Z managed the literature searches, wrote the first draft of the manuscript and performed data analysis. AA-Z and AM designed the statistical analyses. MH and TM helped with interpretation of data. All authors contributed to the manuscript revision and approved the final version.


s2fieldsofstudy: Biology Medicine

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A multicenter, open‐label, single‐arm study of anamorelin (ONO‐7643) in advanced gastrointestinal cancer patients with cancer cachexia

Background Cancer cachexia is characterized by weight loss and is associated with increased morbidity and mortality in patients with cancer. Anamorelin (ONO‐7643; ANAM) is a novel and selective ghrelin receptor agonist that improves appetite, lean body mass (LBM), body weight, and anorexia. Methods This multicenter, open‐label, single‐arm study investigated the efficacy and safety of 100 mg anamorelin in 50 Japanese patients with advanced and unresectable gastrointestinal (colorectal, gastric, or pancreatic) cancer. ANAM was administered once daily over 12 weeks. The primary endpoint was the proportion of patients that maintained or gained LBM over the course of the study. Secondary endpoints included changes in LBM, body weight, quality of life (QoL), and nutritional status biomarkers. Results The proportion of patients who responded to treatment was 63.3% (95% CI, 48.3%‐76.6%), with a least square mean ± SE change in LBM and body weight from baseline of 1.89 ± 0.36 kg and 1.41 ± 0.61 kg, respectively. Appetite‐related questions on the QoL questionnaire showed that ANAM improved appetite. Adverse events occurred in 79.6% of patients, and the most common treatment‐related adverse events were increased γ‐glutamyl transpeptidase (8.2%), diabetes mellitus (6.1%), hyperglycemia (6.1%), and prolonged QRS complex (6.1%). Conclusions ANAM improved anorexia and patients' nutritional status, resulting in rapid increases in LBM and body weight in patients with advanced gastrointestinal cancer who had cancer cachexia. ANAM treatment was well tolerated over 12 weeks. ANAM is a potential clinically beneficial pharmacotherapeutic option for patients with advanced gastrointestinal cancer who have cancer cachexia.

INTRODUCTION
Up to 80% of patients with cancer develop cachexia, which is characterized by weight loss (mainly lean body mass [LBM]) and anorexia. 1 Cachexia reflects a negative protein and energy balance driven by reduced food intake and hypermetabolism. 2 This results in diminished physical function and a reduced ability to tolerate chemotherapy, and is associated with a poor prognosis and reduced quality of life (QoL). 3,4 Cachexia is prevalent in patients with gastrointestinal cancers and, despite its negative impact on patients, it is frequently under-recognized and insufficiently treated, partly because its underlying mechanisms are not fully understood. [5][6][7] Cachexia cannot be fully reversed by conventional nutritional support, and effective treatments are lacking, meaning that there is an unmet need for novel strategies to address the condition. Although corticosteroids Cancer December 1, 2019 and progestins can increase appetite and weight, they have been shown to be ineffective against cachexia, and their long-term use is associated with adverse drug reactions. 8,9 Cachexia is a growing problem in Asia, primarily because of the rapidly increasing incidence of cancer, and there is a paucity of data in terms of its prevalence and management. 10 In Western countries, megestrol acetate is administered to patients with acquired immunodeficiency syndrome-related and cancer-related cachexia but is associated with an increase in body weight mainly through increasing fat rather than LBM. 11 Megestrol acetate is also associated with a high incidence of adverse events (AEs), such as thromboembolic events and edema. 12 In addition, MABp1 (an interleukin 1α antibody) has shown some activity in phase 3 trials, although this agent is currently under development and has not yet been approved for use. 13 More recently, ghrelin, which stimulates growth hormone (GH) release and increases appetite, has been suggested as a viable treatment target. [14][15][16] Ghrelin receptor agonists have garnered interest for their potential effects on clinical conditions, such as anorexia and cancer-related cachexia. It has been suggested that ghrelin receptor agonists positively affect LBM through the increased secretion of GH, insulin-like growth factor 1 (IGF-1), and insulin-like growth factor-binding protein 3 (IGFBP-3) through activation of the ghrelin receptor. [17][18][19] Anamorelin (ANAM) is a novel, orally active, selective ghrelin receptor agonist with appetite-enhancing and anabolic activity. During clinical development in phase 1, 2, and 3 trials, it was confirmed that ANAM enhances appetite, increases LBM, and is well tolerated. 20-23 Two multinational phase 3 studies using 100 mg ANAM daily showed increases in LBM and body weight and improved anorexia/cachexia-specific QoL of non-small-cell lung cancer (NSCLC)-associated cachexia. 23,24 Furthermore, in two Japanese phase 2 studies, the activity of 100 mg ANAM daily was demonstrated by an increase in LBM and body weight while improving appetite loss in patients who had NSCLC with cachexia. 25,26 However, ANAM has not been evaluated in Japanese patients with gastrointestinal cancer who have cachexia.
The purpose of this clinical trial was to determine the efficacy and safety of ANAM for the treatment of cachexia by evaluating the change in LBM from baseline and tolerability in Japanese patients with gastrointestinal cancers who received treatment with ANAM for 12 weeks.

Study Design
This was an open-label, single-arm study performed from February 2017 to April 2018 at 19 centers across Japan. Patients were administered 100 mg ANAM once daily for 12 weeks during the treatment period, which was followed by a 4-week follow-up period for assessment of AEs. Ethical approval was provided by the institutional review board at each study site, and the study adhered to the principles outlined in the Declaration of Helsinki and good clinical practice guidelines. This trial is registered in the Japan Primary Registries Network database (JapicCTI-163426).

Patients
Patients who were eligible for inclusion in this study were Japanese patients (aged ≥20 years) with advanced, unresectable colorectal, gastric, or pancreatic cancer who were in the stage of cachexia by definition. Cachexia was defined as follows: involuntary weight loss ≥5% for 6 months before enrolling in the study, anorexia, and satisfaction of at least 2 of the following criteria: fatigue, generalized muscle weakness, arm muscle circumference (in centimeters) <10th percentile, and 1 or more of the following conditions: albumin <3.2 g/dL, C-reactive protein >5.0 mg/L, or hemoglobin <12 g/dL. Anorexia, fatigue, and generalized muscle weakness were defined as grade 1 or higher based on the NCI Common Terminology Criteria for Adverse Events, version 4.0 (Japan Clinical Oncology Group; Japanese version). An additional criterion was an Eastern Cooperative Oncology Group performance status from 0 to 2 (or from 0 to 1 for patients with pancreatic cancer). 27 Patients were excluded if they had an inability to orally ingest, digest, or absorb food and oral drugs; uncontrolled mental conditions; ascites, pleural effusions, or pericardial effusions requiring drainage or thoracentesis; uncontrolled edema; diminished cardiac function; or uncontrolled diabetes mellitus. All patients provided written informed consent before participation.

Prohibited Prior and Concomitant Therapies
Patients were requested not to take systemic corticosteroids (except when used to prevent anticancer drug-induced nausea, vomiting, or hypersensitivity or to prevent hypersensitivity reactions to contrast agents), GH preparations, medroxyprogesterone acetate, megestrol acetate, Chinese traditional medicines indicated for anorexia, antiarrhythmic drugs, anthracycline antineoplastic drugs, or cytochrome p450 3A4 inhibitors and inducers Cancer December 1, 2019 (including food and beverages containing St John's wort) during the study period. Anticancer agents other than those specifically excluded above were permitted. Radiotherapy, except to treat brain metastases as long as concomitant systemic corticosteroids were administered at ≤5 mg daily (prednisolone equivalent dose), and palliative radiotherapy for bone metastases also were prohibited.

Outcomes
The primary endpoint was the proportion of patients classified as LBM responders. Responders were defined as patients who maintained or gained LBM (≥0 kg) from baseline to all evaluation time points, as measured by dual energy x-ray absorptiometry (DEXA). Details of the models used for measurements and parameters measured have been described previously. 26 DEXA was performed at baseline and at weeks 3, 6, 9, and 12 of the treatment period.

Safety Assessment
Safety outcomes assessed included evaluation of all AEs, treatment-related AEs, clinical laboratory tests (hematology, biochemistry, and urinalysis), 12-lead electrocardiography, and vital signs. Version 4.03 of the National Cancer Institute Common Terminology Criteria for Adverse Events was used for toxicity and AE reporting. 28

Statistical Analysis
Previous Japanese 25 and non-Japanese 24 studies showed that the threshold and expected responder rates for the current study were estimated to be 30.7% and 54.9%, respectively, based on data from the placebo and 100 mg ANAM arms.
The planned sample size was calculated based on the probability (>90%) that the lower limit of the Clopper-Pearson 95% CI for the expected responder rate (54.9%) would exceed the threshold rate (30.7%), which was the placebo responder rate in 5 previous ANAM studies. Assuming <10% of patients would be excluded from the primary analysis set, a planned sample size of 50 was considered to be sufficient to assess the primary endpoint.
All eligible patients who received at least 1 dose of the study drug comprised the safety analysis set (SAF). The full analysis set (FAS) included all patients in the SAF who had undergone a minimum of 1 efficacy assessment after treatment was initiated.
The objective of the primary analysis was to calculate the proportion of patients who maintained or gained LBM measured by DEXA and to estimate the 95% CI using the Clopper-Pearson method. The primary endpoint criteria required the lower 95% CI value for LBM responders to be higher than the threshold responder rate of 30.7%. The least square (LS) mean change in LBM at week 12 was calculated using a model-based analysis for LBM, body weight, and QoL-ACD. The LS mean change at each time point was calculated using a model-based analysis for each endpoint. Descriptive summary values were calculated for quantitative variables, including demographics, patient characteristics, and baseline values. All statistical analyses were performed by qualified statisticians at Ono Pharmaceutical Company Ltd using SAS Version 9.3 software (SAS Institute Inc).

Lean Body Mass
During the study, ANAM treatment maintained or increased LBM over time in 31 of 49 patients, and the LBM responder rate was 63.3% (95% CI, 48.3%-76.6%). The lower limit of the 95% CI for the responder rate exceeded the threshold rate (30.7%).
The LS mean changes in LBM from baseline to week 12 are shown in Figure 2. The percent change for each patient in LBM from baseline to the final evaluation point, defined as week 12 of the treatment period or the last time point with available measurements for patients who discontinued, is shown in Figure 3. The LS mean ± SE change at week 12 was 1.89 ± 0.36 kg from baseline. When stratified by tumor type, 24 of 39 patients (61.5%) with colorectal tumors, 2 of 5 (40%) with gastric tumors, and 5 of 5 (100%) with pancreatic tumors were LBM responders.

Secondary Endpoints
The LS mean changes in body weight from baseline to week 12 are shown in Figure 4. The LS mean ± SE change at week 12 was 1.41 ± 0.61 kg from baseline.
The LS mean change in patients' answers to appetiterelated questions (8, 9, and 11) in the QoL-ACD is shown in Figure 5A-C and clearly demonstrates that 100 mg ANAM improved appetite over time. No apparent change in the QoL-ACD total scores was observed (data not shown).
The LS mean changes in IGF-1, IGFBP-3, and transthyretin levels from baseline are shown in Figure 6A-C. IGF-1, IGFBP-3, and transthyretin all increased after the administration of ANAM, and the mean levels were higher than baseline at all evaluation points except for transthyretin at week 12.

DISCUSSION
This is the first study to evaluate ANAM in Japanese patients with advanced gastrointestinal cancer and cancer cachexia. Patients with gastrointestinal cancers suffer from impaired food intake and absorption of nourishment, and a high percentage of patients experience more weight loss than patients with other types of cancer. 29 The lower limit of the 95% CI for the proportion of responders who maintained or increased LBM in response to ANAM treatment surpassed the threshold for demonstrating improvement (30.7%); therefore, the primary endpoint was achieved. The responder rate and actual changes in LBM were comparable to those observed in previous studies conducted in cachexic patients with advanced NSCLC. 25,26 The changes in the degree of anorexia and biomarkers, including IGF-1, IGFBP-3, and transthyretin, also showed patterns similar to those in previous reports. 25,26 ANAM rapidly increased LBM and body weight and improved anorexia. Moreover, patients' overall nutritional state improved, as indicated by the observed increases in transthyretin. These results suggest that the observed gains in weight were caused not only by increased protein synthesis but also by increased appetite, which led to increased food intake and, in turn, improved nutritional status overall.
Ghrelin receptor agonists increase the secretion of GH from the arcuate nucleus of the hypothalamus, which stimulates the release of IGF-1 from the liver. 30 Both nonclinical 31 and clinical studies 17,22 have shown that ANAM also promotes the secretion of GH and IGF-1 but does not promote tumor growth.
GH and IGF-1 both play important roles in anabolic pathways, which increase body weight primarily through increases in LBM rather than increasing stored fat. 32,33 In this regard, previous studies have shown that  ghrelin activation can enhance muscle protein synthesis and thus may prevent muscle atrophy. 34 Therefore, ghrelin agonists may help improve physical function in patients with cancer cachexia. 35 However, there has been no consistent evidence of an improvement in motor function in the cancer cachexia clinical studies conducted to date. 17,23,36,37 A more appropriate parameter for evaluating motor function in patients with cancer cachexia has not been established, despite observations of increased muscle mass. This may be because of the lack of a correlation between muscle mass and motor function in patients with chronic inflammatory disorders. [37][38][39] Furthermore, it would be a reasonable expectation that concurrent rehabilitation and appropriate daily exercise would be mandatory requirements to improve motor function, as even healthy people would experience diminished motor function if they remained at rest throughout the day without partaking in load-bearing activity. However, in general, rehabilitation has not been applied to patients enrolled in clinical studies of this area. Cancer cachexia is characterized by a decrease in muscle tissue, and the guidelines stipulate that treatment should maintain or increase body weight and LBM. 2 Therefore, LBM, and not motor function, was selected as the primary endpoint in this study. Because cancer cachexia is a syndrome caused by multiple factors, including loss of general activity because of fatigue and poor oral intake, if the study design had included improvement in muscle function as a co-primary endpoint, then it should have included daily burden to muscle by rehabilitation and exercise. This comprehensive approach to investigating cancer cachexia syndrome is being carried out in Japan 40 and internationally. 41 It is hoped that the results will provide insights into the potential benefits of multimodal intervention for the management of cancer cachexia. This study showed that ANAM administration was well tolerated and that any treatment-related AEs were mostly mild in severity. The occurrence of grade 3 diabetes mellitus in this study (n = 3 patients) is believed to be consistent with the action of ghrelin. 42 The development of diabetes mellitus was also reported in both the ROMANA-1 and ROMANA-2 studies (Safety and Efficacy of Anamorelin HCl in Patients With Non-Small Cell Lung Cancer-Cachexia). 23 It is hypothesized that, in addition to the action of ghrelin, an increase in food intake because of increases in appetite may have been a factor in these metabolic AEs. However, these AEs were controlled with the administration of an oral hypoglycemic drug or insulin.

Limitations
Limitations of this study include the small sample size, especially for patients with gastric (n = 5) and pancreatic (n = 5) cancers, and the absence of a functional endpoint (eg, handgrip strength, which may not correlate well with muscle mass). 43 In addition, this study was limited by the open-label and single-arm design. However, no placebo was included in this study because previous studies have shown that patients with cancer cachexia receiving placebo appeared to have decreases in LBM; therefore, any increases observed in weight-related endpoints would be because of the study drug ANAM. 22,23 Although this study has limited generalizability because of the inclusion of only Japanese patients, increased LBM, body weight, and appetite have been consistently confirmed in both Japanese phase 2 studies 25,26 and non-Japanese phase 3 studies. 23,24 Therefore, it may be possible to apply this study's data to populations of non-Japanese patients with gastrointestinal cancer.

Conclusions
ANAM increased LBM, improved anorexia, and increased transthyretin, a marker of nutritional status, in patients with gastrointestinal cancer and cachexia. Patients who have advanced gastrointestinal cancer are more prone to weight loss because of impaired food intake and absorption of nutrients compared with patients who have NSCLC. However, the results of this study are consistent with the efficacy and safety profiles of ANAM in patients with NSCLC and cachexia. Thus, ANAM can be considered an important treatment option for cancer cachexia syndrome in combination with other modalities, including nutritional support, rehabilitation and exercise, and treatment to suppress inflammatory processes.

FUNDING SUPPORT
This work was funded by Ono Pharmaceutical Company Ltd (Osaka, Japan).

AUTHOR CONTRIBUTIONS
Satoshi Hamauchi: Investigation, writing-original draft, and writingreview and editing. Junji Furuse: Conceptualization, investigation, project administration, supervision, and writing-review and editing. Toshimi Takano: Investigation and writing-review and editing. Yoshinori Munemoto: Investigation and writing-review and editing. Ken Furuya: Investigation and writing-review and editing. Hideo Baba: Investigation and writing-review and editing. Manabu Takeuchi: Investigation and writing-review and editing. Yasuhiro Choda: Investigation and writing-review and editing. Takashi Higashiguchi: Conceptualization and writing-review and editing. Tateaki Naito: Conceptualization and writing-review and editing. Kei Muro: Conceptualization, project administration, supervision, and writing-review and editing. Koichi Takayama: Conceptualization, project administration, supervision, and writingreview and editing. Shusuke Oyama: Conceptualization, formal analysis, software, writing-original draft, and writing-review and editing. Toru Takiguchi: Conceptualization, project administration, writingoriginal draft, and writing-review and editing. Naoyuki Komura: Conceptualization, project administration, writing-original draft, and writing-review and editing. Kazuo Tamura: Conceptualization, project administration, supervision, and writing-review and editing.