s2fieldsofstudy: Biology Psychology

text:
The effect of cannabis use on memory function: an update

Investigating the effects of cannabis use on memory function appears challenging. While early observational investigations aimed to elucidate the longer-term effects of cannabis use on memory function in humans, findings remained equivocal and pointed to a pattern of interacting factors impacting on the relationship between cannabis use and memory function, rather than a simple direct effect of cannabis. Only recently, a clearer picture of the chronic and acute effects of cannabis use on memory function has emerged once studies have controlled for potential confounding factors and started to investigate the acute effects of delta-9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD), the main ingredients in the extract of the cannabis plant in pharmacological challenge experiments. Relatively consistent findings have been reported regarding the acute impairments induced by a single dose of Δ9-THC on verbal and working memory. It is unclear whether they may persist beyond the intoxication state. In the long-term, these impairments seem particularly likely to manifest and may also persist following abstinence if regular and heavy use of cannabis strains high in Δ9-THC is started at an early age. Although still at an early stage, studies that employed advanced neuroimaging techniques have started to model the neural underpinnings of the effects of cannabis use and implicate a network of functional and morphological alterations that may moderate the effects of cannabis on memory function. Future experimental and epidemiological studies that take into consideration individual differences, particularly previous cannabis history and demographic characteristics, but also the precise mixture of the ingredients of the consumed cannabis are necessary to clarify the magnitude and the mechanisms by which cannabis-induced memory impairments occur and to elucidate underlying neurobiological mechanisms.

Introduction
Marijuana or Cannabis sativa (C. sativa) is the most widely used illicit drug, 1,2 and its use often starts during teenage years. 3 Cannabis contains more than 600 ingredients, including over 60 different cannabinoids, 4 which are now recognized for both their toxic and potential therapeutic effects, 5 and that are mediated through their effects on the endogenous cannabinoid system. 6 Delta-9-tetrahydrocannabinol (commonly known as ∆9-THC) is thought to be the principal psychoactive ingredient present in cannabis that is responsible for the acute and adverse effects of cannabis on various cognitive functions including memory and the induction of psychotic symptoms. [7][8][9][10][11][12][13][14][15] In contrast, the other major cannabinoid that has attracted attention in recent years, cannabidiol (CBD), does not impair cognition, 16 and may have anxiolytic and antipsychotic effects. [17][18][19][20] The potency of cannabis that is available on the street can vary and seems to have changed over time in many countries, [21][22][23] with strains that are high in ∆9-THC and low in CBD currently dominating the market. 24,25 Hence, the acute effects as well as the effects of long-term use of cannabis on neurocognitive processes and psychological state are likely to vary depending on the precise mix of different cannabinoids present in the cannabis used.
Cannabis has historically been considered as a relatively harmless drug. However, the age of onset of cannabis use is decreasing and young people start to use cannabis at an earlier age, 26,27 a time when they are likely to be more vulnerable to its impairing effects. 28,29 Additionally, considering that the effects of ∆9-THC on memory appear to be dosedependent, 9,12,30,31 and increasingly potent forms of cannabis with higher ∆9-THC content are available on the street than before, 25 the modern cannabis user may be at a particular risk for its adverse cognitive effects. Several factors are likely to influence the impact of cannabis on memory, particularly the duration and frequency of previous cannabis use, [31][32][33] as well as the strain of cannabis used, 20 which complicate the interpretation of early observational studies investigating memory function in cannabis users. Only recently have the effects of cannabis been studied, employing controlled experimental designs in conjunction with neuroimaging techniques that allow one to draw conclusions regarding the acute effects of cannabis and the neurobiological mechanisms that may underlie the effects of cannabis on memory function. This update is organized into sections addressing the acute and chronic effects of cannabis on memory function at a behavioral level, but also provides an overview regarding the potential neural mechanisms underlying these processes, reflecting the focus of the recent literature.

Acute effects of cannabis on memory
When studying the effects of cannabis on memory function, it is important to be mindful that human memory is not a unitary measurable concept. Rather, it is a construct that considers multiple subsystems with different specializations and processes being involved -all of which are localized in overlapping but different brain regions. [34][35][36] In one approach, the stage model of memory refers to a temporal categorization that distinguishes between sensory memory, short-term memory, and long-term memory, and includes the processes of encoding (receiving and processing of information), followed by storage (creation of a permanent record of the encoded information), and recall (retrieval or recollection of information). As an alternative approach, the memory system is subdivided depending on its content, which refers to the discrimination between declarative and procedural memory systems. Procedural (implicit) memory is the storage of elements which are inaccessible to the consciousness (eg, the memory of skills such as playing a guitar). Most of the memory tests that have been employed when investigating the acute effects of cannabis assessed the declarative (explicit) memory system, which requires the conscious recollection of events and facts and is further divided into episodic (facts about the world) and semantic memory (storage of elements that have an autobiographical context), 37 while working memory is defined as the temporary storage and manipulation of episodic information. 38 The challenge to measure this complex construct is reflected in the variety of different memory tests that are proposed in the literature to measure similar domains (see Table 1). Therefore, it remains a matter of debate whether test performance is comparable across the different tests being employed, a point that needs to be considered when evaluating the studies that investigated the effects of cannabis on memory function.
The acute effects of the different cannabinoids on memory function have been studied by directly administering cannabis or its different ingredients in order to examine its potential adverse or therapeutic effects. 39 Several pharmacological challenge studies have consistently reported that the administration of ∆9-THC disrupted working and episodic memory in humans and animals. 13,[40][41][42][43][44][45][46][47][48] However, when examining the literature regarding the acute effects of ∆9-THC, several factors that limit the direct comparability of the findings need to be considered. For instance, chronic exposure of cannabinoid drugs may result in the development of tolerance to the adverse effects of ∆9-THC or WIN 55,212-2 (a cannabinoid receptor agonist) on memory and executive functioning in humans and animals. 45,[49][50][51][52][53] To illustrate, while 500 µg of ∆9-THC per kg body weight acutely decreased performance in the Tower of London Task (executive functioning) in occasional users, 54 no such performance deficits were reported in heavy smokers following administration of the same dose. 45 In accordance, acute ∆9-THC exposure has been associated with fewer impairments in working memory in heavy cannabis users than in occasional users, 55 which is consistent with studies that reported no acute effects of ∆9-THC on memory function in frequent users. 49,56 As summarized in Table 2, the majority of memory domains tested in heavy cannabis smokers were not affected by the acute administration of ∆9-THC or were only affected if the dose was high (eg, more than 3.9% ∆9-THC concentration in the smoked cigarette). In contrast, occasional users and regular users seem to show memory impairments after both high doses and low doses of ∆9-THC, with ∆9-THC impacting on memory function in a dose-dependent manner. 8,9,12,40,49,54,57 For instance, increases in ∆9-THC concentration were associated with a linear decrease in performance in short-term memory tasks. 9 Therefore, infrequent users may be more susceptible than heavy users to the acute effects of ∆9-THC on behavior, 58 and hence it is important to take into account the history of previous cannabis use when investigating the acute effects of ∆9-THC on memory functioning. 59 Encouragingly, despite the acute impairments caused by ∆9-THC, especially at high doses, a single dose of ∆9-THC is unlikely to have persistent effects on memory function. For instance, Curran et al 8 found that ∆9-THC significantly impaired episodic memory and verbal recall 2 hours after the oral administration of ∆9-THC,     ″∆9-THC did not induce deficits in performance; a see Table 1.
with no residual effects being present 24 or 48 hours after administration.
The other major component in the cannabis extract is CBD, a cannabinoid that has received less attention in pharmacological challenge studies investigating the effect of cannabis on memory function -probably due to its known nonharmful effects on cognition. 43,60 However, considering that CBD may be protective for some aspects of memory function by inhibiting the ∆9-THC-induced impairments in episodic memory in humans and spatial memory in animals, 20,61,62 but not in working memory, 60,61 CBD may have therapeutic potential in reversing certain cognitive impairments induced by cannabis. Therefore, further experimental studies will be useful to understand the effects of the two main ingredients on memory function separately and in combination.
Studies have also investigated whether genetic variations may moderate variable sensitivity to the effects of ∆9-THC. For instance, a polymorphism in the catechol-O-methyltransferase (COMT) gene has been shown to moderate the effect of ∆9-THC on verbal memory. 42 Genetic variations may thus underlie some of the inconsistencies in results across studies depending on the genetic makeup of study participants. As indicated in Table 2, the overrepresentation of mainly adult male subjects in some of these study samples may also limit the generalizability of the results, especially because there are some suggestions that gender may be an important factor in mediating variability in the response to cannabinoids in animals and humans; 63-68 preliminary evidence also suggests an effect of gender on ∆9-THC-induced impairments in working memory and learning in humans and animals. [69][70][71] In summary, based on the recent literature, a relatively robust picture regarding the acute effects of cannabis emerged (ie, there is convincing evidence that the acute administration ∆9-THC impairs memory function), with several factors potentially moderating its effects, including previous exposure to cannabis, the dose of ∆9-THC administered, the precise ∆9-THC:CBD ratio, a genetic vulnerability to its effects, and the particular assessment of the multifactorial nature of memory function.

Long-term effects of cannabis on memory
In contrast to the acute effects, less consistency exists regarding the long-term effects of cannabis use on neurocognitive functioning. A major difficulty that appears when interpreting studies that compared cannabis users with control subjects regarding their memory performance submit your manuscript | www.dovepress.com Dovepress Dovepress is the heterogeneity of the selected samples. For instance, studies used different criteria to categorize their subjects (ie, allocated them to distinct but at times overlapping categories such as former cannabis users, current regular cannabis users, or heavy cannabis users, long-term and short-term cannabis users, or early-onset and late-onset cannabis users). Based on the methodological approaches in the literature, Table 3 summarizes the studies in terms of the subject selection criteria employed and the confounding factors that have been considered.
One crucial factor that has been suggested to interfere with the accuracy and interpretability of the results is the abstinence period. For instance, a previous meta-analysis implicated that regular cannabis users present with selective long-term memory impairments, including the domains of learning and retrieval, 72 although the magnitude of the pooled effect sizes were modest, and uncertainty exists as to whether these deficits persist beyond the acute intoxication state. The question regarding the irreversibility of the effects of cannabis has specifically been addressed by Pope et al, 73 who compared adult heavy and long-term cannabis users (smoked at least 5000 times in their lives and were daily smokers at study entry) and controls (who had smoked no more than 50 times in their lives) regarding their performance on neurocognitive measures at three different time-points after the first assessment. On days 0, 1, and 7, cannabis users significantly differed in terms of task performance compared to controls, and the learning and recall deficits across time points were related to ∆9-THC-creatinine ratios at first assessment. However, by day 28, the two groups did not differ any longer, and initial ∆9-THC concentrations were not related to task performance, suggesting that memory impairments in long-term cannabis users may not persist beyond the impact of ∆9-THC-induced "residual effects." This is in line with more recent studies implicating that the severity of deficits in memory and immediate recall associated with cannabis use decreased over time following abstinence; 58,74-76 moreover, studies reported that normal memory functioning was observed in regular cannabis users when they were abstinent for between 48 hours up to a month. [77][78][79][80] In contrast, a more recent systematic review concluded that long-term cannabis use impairs memory, particularly verbal and working memory, 81 reflecting the evidence from another set of studies that reported impairments in working memory and learning that persisted beyond the acute intoxication state in heavy cannabis users. 30,[82][83][84] This body of evidence points to the idea that some individuals may recover from their memory impairments after a period of abstinence, while others continue exhibiting the memory impairments after the use of cannabis, implicating that additional factors (other than intoxication state) need to be identified in order to explain the memory deficits associated with cannabis use. To illustrate, it was reported that heavy smokers (who smoked on average 94 joints per week) that were abstinent for 28 days showed persistent impairments in a range of memory functions. 30 In addition, a recent study suggested that neuropsychological deficits did not recover completely even 1 year after the cessation of cannabis use in those who started using it regularly before the age of 18 years, 29 indicating that age of onset and frequency of cannabis use may be important moderators that need to be considered.
In fact, accumulating evidence suggests that deficits in memory performance in abstinent adolescent and adult cannabis users depend on the weekly dose of cannabis used, 30 as well as on the frequency, duration, and age of onset of cannabis use. 29,[31][32][33]81,83,85 For instance, in a group of adolescent cannabis users, the frequency of use explained 24.2% of the variance in word recall and a combined predictive model that included quantity and age of onset of cannabis use accounted for 31.5% of the variance in memory performance. 31 In accordance, Meier et al 29 showed that the decline in memory performance in cannabis users who began smoking in adolescence was independent of the frequency of cannabis use, whereas the decline in adult-onset users was dependent on the frequency of use. Finally, at a neurobiological level, long-term and earlyonset cannabis use appear to cause greater morphological and functional alterations in the still developing brain rather than in the mature human and animal brain. 71,[86][87][88][89][90][91] Therefore, as summarized in Table 3, it appears that impairments in memory are unlikely to persist above and beyond the intoxication state in late-onset users and short-term users, 74,79,80,82,85,92 and if used only in an occasional manner. 74 In contrast, early-onset and heavy cannabis use over the long-term seem to have a particularly adverse effect on memory function and may lead to persistent impairments in a range of memory domains beyond the acute intoxication state. This is also consistent with evidence from animal studies where it has been possible to investigate this issue under controlled experimental conditions with greater methodological rigor; it was found that the adverse effects of ∆9-THC on learning are more severe in adolescents than in adults. 71,91 Similarly, chronic treatment with the cannabinoid receptor agonist, WIN55,212-2 (WIN), was associated with long-lasting impairments in object recognition memory in pubertal but not in adult rats. 93 However, not all studies have reported memory impairments in heavy and early-onset cannabis users. 86,[95][96][97] As indicated by pharmacological challenge studies, an important determinant of the long-term effects of cannabis use may be the precise mixture of the ingredients of the consumed cannabis. Although a naturalistic study reported that cannabis users who smoked cannabis high in CBD and low in ∆9-THC were protected from the memory-impairing effects associated with cannabis strains high in ∆9-THC, 20 most of the observational studies to date did not include the ∆9-THC/ CBD ratio (eg, by employing hair analysis as a proxy estimate of the amount of ∆9-THC or CBD in the cannabis used) as a potential confounder in statistical reporting (see Table 3). Given the impact of the ∆9-THC/CBD ratio on brain structures implicated in memory function and executive functions in cannabis users (while taking into account the variability of this ratio in street cannabis 25 ), 98,99 consideration of this factor in the design and analysis of studies may improve the replicability and interpretability of the results, particularly because simple and valid methods are now applicable for the quantitative detection of ∆9-THC and CBD. 100,101 In light of the complexities in the assessment of memory function, some of the inconsistencies in results across studies may be related to the different memory paradigms that have been used in these studies, which may have investigated related but not completely overlapping aspects of memory and employed tasks with differing levels of complexity. For instance, studies that have employed functional neuroimaging techniques to investigate the neural substrates underlying memory impairments related to cannabis use have often used tasks that are fairly simple and specific to a memory-domain rather than more complex paradigms that can assess multiple domains. While this strategy has generally been employed to avoid potential confounding effects associated with differences in task performance, which can cloud the interpretation of brain activation patterns related to specific task demands, this restricts a direct comparison of the results of these studies with those employing more complex paradigms. Interestingly, neuroimaging studies have consistently reported similar performance levels in memory tasks (eg, throughout short delay response tasks, face-name cued recall tasks, and pictorial memory tasks) in heavy cannabis users when compared to controls, 77,78,80,86,95,97,[102][103][104] suggesting perhaps that more challenging and multidimensional memory tasks may be required to discriminate chronic cannabis users from occasional users or nonusers. This is consistent with recent evidence from animal literature, which suggests that persistence of the adverse effects of chronic cannabinoid exposure [eg, the agonist WIN55,212-2 (WIN)] beyond the immediate withdrawal period may depend on the specific memory domain being tested, with some forms of hippocampal-dependent short-term memory (such as that measured by the object location task) being particularly sensitive, while deficits in other measures of short-term memory (such as the object recognition and water maze tasks) recover following a short period of abstinence. 105 As shown in Table 1, a commonly employed and wellvalidated task is the Rey Auditory Verbal Learning Test (RAVLT), 106-108 a test that assesses a range of memory domains, including immediate and delayed recall, learning rate, recognition, proactive and retroactive interference, and primacy and recency effects. There is a greater degree of consistency with regard to poorer performance using the RAVLT in adolescent and adult heavy cannabis users when compared to controls, [29][30][31]33,73 unlike some of the other paradigms such as omitted numbers test, short-delay response task, or paced auditory serial addition test -all of which were tasks that were employed to measure working memory -with inconclusive results (see Table 3). 8,33,96 Similarly, current long-term and heavy cannabis users, but not former or short-term cannabis users, performed poorer in the Buschke Selective Reminding Test (BSRT), 73,85,109 a test that assesses multiple cognitive processes including learning and verbal memory, sensitivity to interference, recognition, and retrieval. 106,110 In sum, early observational studies pointed to the idea of cannabis-induced impairments in memory and learning, 72 although the extent of the irreversible neurotoxic effects of cannabis on working memory, verbal learning, and memory functions remained a matter of contention due to a number of potential confounders that have variably been considered. Although some subsequent studies are still of limited interpretability due to a lack of consideration of crucial confounding factors, 96,111-113 the majority of recent studies have generally attempted to address these methodological issues by considering factors such as the acute intoxication state, interindividual variability in past and current cannabis use, as well as the psychiatric and medical status of study participants (see Table 3), leading to a clearer picture of the long-term effects. There is now a growing body of evidence that tends to suggest that persistent memory deficits in a range of domains are more likely to occur in frequent and long-term cannabis users who start using cannabis early in life and consume cannabis strains high in ∆9-THC, while impairments become particularly salient when memory is measured as a complex multifactorial contract. These findings may be particularly worrying, given the increasing prevalence of submit your manuscript | www.dovepress.com  cannabis use especially in young populations, 3 particularly with high ∆9-THC concentration. 25

Underlying neural mechanisms
Over the last couple of decades, advanced neuroimaging techniques have allowed investigation of the neural mechanisms underlying the effects of acute and chronic cannabis exposure in humans in vivo. While structural magnetic resonance imaging allows for the display of brain morphology in vivo, functional imaging techniques allow an indirect estimation of regional neural activity by utilizing the fact that neuronal activation results in regionally increased blood flow and metabolism. The application of functional magnetic resonance imaging (fMRI), single photon emission tomography, and positron emission tomography (PET) allow the investigation of brain activity changes in response to a study participant performing a specific cognitive task.
In pharmacological challenge studies, the acute effects of cannabinoids on memory function can be analyzed, while imaging studies comparing subjects with and without a history of cannabis use allow the investigation of changes in neural function associated with chronic cannabis exposure in humans. 17,114 Cannabinoids exert their effects by binding to specific endogenous cannabinoid receptors, including the CB1 receptor and the CB2 receptor, 24 both of which are G-protein-coupled receptors for cannabinoid ligands. However, the receptors differ in their structure, affinity for ligands, and distribution in the human body. CB2 receptors are mainly expressed in the immune system and in peripheral nerve tissues, and are not considered to affect cognition. 115 Given the high densities of the CB1 receptors present in brain regions that are critically involved in learning and memory functions, particularly the prefrontal cortex (PFC), hippocampus, basal ganglia, anterior cingulate, and cerebellum, 116,117 the endogenous cannabinoid (eCB) system is thought to modulate the neural substrates accompanying memory functioning after exposure to cannabis. 118 Most of the major cannabinoids present in the extract of the cannabis plant act presynaptically as agonists at CB1 receptors, 119 and thereby cause alterations in neuromodulator systems (eg, dopaminergic, cholinergic, serotonergic, GABAergic, and glutamatergic systems) that in turn affect molecular mechanisms relevant to cognitive processes and prevent long-term potentiation -a process that is widely believed to underlie learning and memory. [120][121][122][123] In contrast, CB1 antagonists/inverse agonists such as CBD or rimonabant may reverse some of the neurobiological and behavioral effects of ∆9-THC in animals and humans. 19,20,[124][125][126] The particular vulnerability of the adolescent brain to the adverse cognitive effects of cannabis use may also be related to the increased sensitivity of the eCB system during this developmental stage due to the synaptic pruning and myelination that still occur, particularly in the frontal regions of the brain. 118  have shown that both the acute and chronic administration of ∆9-THC or CB1 agonists caused morphological and functional changes in the hippocampus in rats, 127,128 and the modulation of hippocampal CB1 receptors after the administration of ∆9-THC significantly disrupted memory performance in rats. 129 Moreover, it was found that microinjection of a CB1 receptor antagonist into the hippocampus blocked spatial memory deficits caused by ∆9-THC administration, indicating that the disruptive effects of ∆9-THC on memory function are mediated through its effects on hippocampal CB1 receptors. 130 Studies that have investigated human cannabis users employing structural imaging have reported morphological alterations in the parahippocampal, hippocampal, and thalamic areas, 131,132 while larger hippocampus volumes have been related to higher memory scores in controls, but not in frequent cannabis users. 97 Despite these structural imaging findings, there is modest evidence of a major effect of cannabis use on the brain structures relevant to memory function, 114,133 and structural imaging studies lack the ability to display the dynamic neural processes underlying memory function. In this context, functional neuroimaging (single photon emission tomography, PET, and fMRI) studies of cannabis users have been used to assess neural activation during the resting state or during the performance of memory tasks. Resting state neuroimaging studies provide evidence of alterations in regional cerebral blood flow (rCBF) and cerebral metabolism related to acute and chronic cannabis exposure, 114,134-136 while brain activation may normalize after a period of abstinence. 137 Conversely, fMRI imaging studies using paradigms that engage memory and learning processes in cannabis users have reported both hypoactivation and hyperactivation in parahippocampal regions, 78,96,102,103,138 despite nonsignificant differences in task performance between cannabis users and noncannabis users.
Another fMRI study reported working memory-related hypoactivation in the inferior frontal and temporal regions, including the anterior cingulate cortex (ACC) and right inferior frontal and superior temporal areas; however, hyperactivation was noted in the medial temporal areas in cannabis users when compared to nonusers, 79 despite similar task performance. Similarly, in a PET study, Eldreth et al 139 found hypoactivation in the left perigenual ACC and the left lateral prefrontal cortex (LPFC) and bilateral hyperactivity in the hippocampus. While on the one hand, these studies support the notion of "hyperactivity," indicating that increased brain activation reflects a higher neurophysiological effort or a compensatory mechanism to achieve similar performance levels, on the other hand they also tend to suggest that such compensatory mechanisms may no longer be sufficient during the performance of more challenging tasks. 96,140 Moreover, parahippocampal activity appeared to be dependent on the frequency of previous cannabis use when performing an activation task. 86 Jacobsen et al 141 found that adolescent submit your manuscript | www.dovepress.com Dovepress Dovepress cannabis users performed poorer in the working memory task compared to controls, and that the former group failed to deactivate the right hippocampus, as opposed to controls. It has been suggested that this failure may reflect dysfunction of inhibitory interneurons within the hippocampus in cannabis users during mnemonic processing.
The acute administration of ∆9-THC and its neurocognitive correlates have only been studied recently, using sophisticated neuroimaging techniques with better spatial resolution. 39 Bhattacharyya et al 16 provided the first human evidence that memory impairments induced by ∆9-THC were mediated through its acute effects on brain activation in the medial temporal and prefrontal areas. In this study, a modest dose of orally administered ∆9-THC was found to augment task-related activity in the parahippocampal cortex during the encoding condition of a verbal learning task, thereby disrupting the linear decremental change in neural activity associated with repeated presentations of verbal stimuli and the relationship between parahippocampal activity and task performance in healthy occasional cannabis users, indicating that increased neurophysiological efforts are necessary under the acute effects of ∆9-THC in order to meet the demands of the task during encoding.
During the recall condition, ∆9-THC attenuated the taskrelated activity in the LPFC and ACC. These findings are in line with an electrophysiological study that reported an attenuation of stimulus-locked event related potentials (ERPs) in response to increased memory-task difficulty and impaired task performance after the acute administration of marijuana; additionally, a higher degree of intoxication (measured in terms of heart rate and subjective ratings) was related to a more dramatic reduction in ERP amplitude. 142 In a recent fMRI study, Bossong et al 143 reported that ∆9-THC attenuated activity in the insula and inferior frontal gyrus on the right side and in the middle occipital gyrus on the left side during the encoding condition of an associative memory task. These effects were unrelated to task performance, implicating that neural activity patterns associated with ∆9-THC administration could not be explained by differences in task performance and suggest that ∆9-THC may affect neural activity in brain regions critical to learning new information. During the recall condition, ∆9-THC resulted in a network-wide increase in activation, with the strongest effects being observed in the cuneus and precuneus. Given the opposite effects of ∆9-THC on brain activity during the encoding and recall conditions, the authors suggested that ∆9-THC affects memory more directly at an early stage (ie, during encoding), while changes during recall were more likely to be reflective of a mechanism to compensate for the effect of the drug during encoding.
Moreover, Bossong et al 144 showed that a rising working memory load was related to a linear increase in activity in brain areas that mediate working memory in subjects under the placebo condition. In this way, ∆9-THC disrupted this linear relationship by inducing hyperactivity and decreasing task performance at low working memory load. In keeping with behavioral evidence suggesting differential vulnerability to the adverse effects of cannabis, preliminary evidence has emerged pointing to the neural mechanisms that may underlie this genetically mediated variable sensitivity to the effects of ∆9-THC. 39 This study reported that the effects of ∆9-THC on medial temporal, striatal, and midbrain function during the encoding and recall conditions of a verbal memory task were moderated by variations in the AKT1 and the dopamine transporter (DAT1) genes that regulate central dopaminergic neurotransmission. Furthermore, the effects of ∆9-THC on brain activation during the encoding and recall condition were greater in those carrying the risk variants of both genes compared to the rest, indicating that the impact of ∆9-THC on the neural activity that may underlie memory function might depend on individual genetic profiles.
Another factor that potentially determines the level of vulnerability to the harmful effects of cannabis on cognitive functioning is the relative proportion of the different cannabinoids, particularly the relative proportion of ∆9-THC to CBD. While, at a behavioral level, the acute administration of ∆9-THC is thought to have adverse effects on memory and induces anxiety and psychotic-like symptoms in humans, 100 CBD has not been associated with these adverse effects, 39,60 and might even reverse the deficits induced by ∆9-THC. 126 For instance, Morgan et al 20 reported that impairments in episodic memory induced by ∆9-THC were prevented by a higher level of CBD. Moreover, at a neurobiological level, Bhattacharyya et al 19 reported opposite effects of ∆9-THC and CBD on activation in the ACC, medial PFC, and the LPFC during verbal recall in healthy men with minimal previous cannabis exposure. These findings further implicate the involvement of CB1-rich brain regions on memory through the potential adverse effects of cannabinoid agonists such as ∆9-THC, and also propose protective effects of cannabinoid antagonists/inverse agonists such as CBD on memory function and learning in humans.

Conclusion
In conclusion, evidence regarding the acute impairments in memory function induced by cannabis is generally robust, particularly for those using cannabis with a lower proportion of CBD and higher proportion of ∆9-THC. In other words, the effects are likely to depend on the type of cannabis used submit your manuscript | www.dovepress.com Dovepress Dovepress (ie, the greater the dose of ∆9-THC, the greater the memory impairment). 9 However, whether those with a history of frequent cannabis use may develop tolerance to the acute impairments caused by ∆9-THC is not entirely clear. Although some studies have reported recovery from cannabis-induced cognitive impairments in long-term users, 73,74 deficits in verbal and working memory, as well as alterations in brain function and structure associated with cannabis use, are likely to persist beyond the acute intoxication state, particularly when heavy cannabis use is started at an early age. These findings emphasize the need for further investigations regarding the role of early-onset cannabis use on neurodevelopmental processes.
Since higher ∆9-THC concentrations are now dominating the market, 23 and given that high prevalence rates exist particularly among teenagers, 1,3 its consumption represents a major public health concern. Although the precise neurocognitive and neurochemical mechanisms underlying the marijuana-induced memory impairments remain to be elucidated, cognitive neurobiological studies suggest that cannabinoids may affect functioning in memory-relevant brain areas by interfering with the homeostatic role of the eCB system. While the hippocampus appears to have the highest density of CB1 receptors, and since this has been shown to play an important role in the disruptive effects of ∆9-THC on memory, 130,145 studies have highlighted that these effects may also be related to the effects of ∆9-THC on prefrontal and parahippocampal function, 16,46 implicating the involvement of a network of brain areas affected by ∆9-THC. 143 In this context, the increased brain activation noted in cannabis users while performing hippocampus-dependent memory tasks may reflect compensatory mechanisms that could be activated in order to achieve similar performance levels comparable to those observed in nonusers. Moreover, these effects may depend on individual genetic profiles, 39,42 suggesting interesting avenues for future genetic studies to investigate the modulatory role of susceptibility genes with regard to the memory impairments caused by cannabis.
Overall, further research elucidating the precise neural mechanisms underlying the heterogeneous and sometimes opposite effects of cannabinoids will be useful, not only in order to facilitate the development of treatments that may prevent the deleterious effects of cannabis, the most widely used illicit drug worldwide, 2 but also to identify potential candidate targets for memory impairments in conditions such as schizophrenia or dementia. Given the therapeutic potential of cannabinoids such as ∆9-THC in the management of medical conditions including neuropathic pain or spasticity due to multiple sclerosis (for a review see Grant et al 146 ), an understanding of the neural mechanisms underlying the effects of cannabis on memory function also has a wider implication beyond psychiatric disorders, as it may lead to the identification or synthesis of related molecules with the desired therapeutic effects, but without the undesired side effects.

Disclosure
The authors report no conflicts of interest in this work.

Substance Abuse and Rehabilitation
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extfieldsofstudy: Biology Medicine

text:
COI barcode versus morphological identification of Culex ( Culex ) (Diptera: Culicidae) species: a case study using samples from Argentina and Brazil

Sequences of the cytochrome c oxidase subunit I (COI) mitochondrial gene from adults of 22 Culex ( Culex ) species from Argentina and Brazil were employed to assess species identification and to test the usefulness of COI for barcoding using the best close match (BCM) algorithm. A pairwise Kimura two-parameter distance matrix including the mean intra and interspecific distances for 71 COI barcode sequences was constructed. Of the 12 COI lineages recovered in the Neighbour-joining topology, five confirmed recognised morphological species ( Cx. acharistus , Cx. chidesteri , Cx. dolosus , Cx. lygrus and Cx. saltanensis ) with intraspecific divergences lower than 1.75%. Cx. bilineatus is formally resurrected from the synonymy of Cx. dolosus . Cx. maxi , Cx. surinamensis and the Coronator group species included were clustered into an unresolved lineage. The intraspecific distance of Cx. pipiens (3%) was almost twice the interspecific between it and Cx. quinquefasciatus (1.6%). Regarding the BCM criteria, the COI barcode successfully identified 69% of all species. The rest of the sequences, approximately 10%, 18% and 3%, remained as ambiguously, mis and unidentified, respectively. The COI barcode does not contain enough information to distinguish Culex ( Cux. ) species.

Mem Inst Oswaldo Cruz, Rio de Janeiro, Vol. 108(Suppl. I): 110-122, 2013 online | memorias.ioc.fiocruz.br The genus Culex is one of the largest groups of the family Culicidae, containing 768 species subdivided into 26 subgenera (Harbach 2011). The subgenus Culex includes 198 species, some potentially involved in the transmission of lymphatic filariasis nematodes (Wuchereria bancrofti, Brugia malayi and Brugia timori) (Fontes et al. 1998(Fontes et al. , 2005 and several arboviruses (Hubálek 2000, Komar 2003. Cx. quinquefasciatus is the primary vector of W. bancrofti in Northeast Brazil (Fontes et al. 2005), whereas Cx. coronator and Cx. declarator were recorded as potential vectors of the Saint Louis encephalitis virus (SLEV) (Vasconcelos et al. 1991). In Argentina, Cx. quinquefasciatus and Cx. interfor are primary and secondary vectors of the SLEV, respectively (Spinsanti et al. 2009), while the Rio Negro virus from the Venezuelan equine encephalitis virus complex was isolated from both Cx. coronator and Cx. maxi, among other species (Pisano et al. 2010). Species of the genus Culex were also implicated as vectors of the West Nile virus (Kramer et al. 2008) in North America.
The current subgeneric classification of the genus Culex is based on characters of females and males (Bram 1967, Forattini 2002. Species of the subgenus Culex are subdivided into six groups, several subgroups and species complex (Harbach 2011). The Neotropical species were included in the Coronator and Pipiens groups (Harbach 2011). Morphological traits of the male genitalia usually allow an accurate identification of the species. Contrasting, external morphological characteristics of the females are also employed for identification. However, most of the available identification keys should be used with caution because female morphological characters may be either polymorphic or overlap among distinct species. Characters of the fourth-instar larva are also employed for identification; however, it is also possible to have either some overlap or absence of differences among species. The presence of unknown species complexes also makes species identification difficult. Complexes of morphologically similar species were demonstrated to be present in several groups of invertebrates, i.e., in anopheline mosquitoes (Foster et al. 2013), wasps (Bickford et al. 2007), butterflies (Hebert et al. 2004), aquatic gastropod mollusks (de Aranzamendi et al. 2009), among several others.
According to Hebert et al. (2004), the mean interspecific genetic divergence should be at least 10 times higher than the average intraspecific genetic distance in order to define the presence of species complexes. Although the patterns of intra and interspecific variation in COI are similar in various animal groups (Hebert et al. 2004), Ruiz-Lopez et al. (2012) suggested for mosquitoes, a mean intraspecific Kimura two-parameter (K2P) distance varying from 0.2-1.4% and a mean interspecific variation between 2-5.6%.
One of the criticisms of using the COI DNA barcode is the ambiguous identification or the absence of clusters in trees of recently diverged species (Meyer & Paulay 2005, Kaila & Ståhls 2006, Lou & Golding 2010. In order to improve the power of the COI barcode datasets to correctly identify recently diverged species, new algorithms were developed recently by Meier et al. (2006) and van Velzen et al. (2012). The best close match (BCM) algorithm was developed by Meier et al. (2006) to identify the best barcode matches of a query and only assigns the species name of that barcode to the query if the barcode is sufficiently similar. To determine how similar the sequences are, a threshold similarity value has to be estimated for a given dataset by obtaining a frequency distribution of all intraspecific pairwise distances and determining the distance below which 95% of all intraspecific distances are found (Meier et al. 2006).
In a recent study carried out by Demari-Silva et al. (2011), a 478 base pair fragment of the COI gene was employed to differentiate Culex species from Brazil (9 of which belong to the subgenus Culex), to verify phylogenetic relationships of the genus Lutzia relative to Culex and the taxonomic status of the subgenus Phenacomyia within the genus Culex. Results of the analyses showed high intraspecific variation, revealing the presence of species complexes within Culex. In the present study, the COI barcode region was employed to assess both the species identification and subgroups/complexes within Culex (Cx.) from the Neotropics. Classification and nomenclature adopted herein were compiled by Harbach (2011).

MATERIALS AND METHODS
Mosquito sampling -Adults of 22 species of the Pipiens and Coronator groups (Table I)  and employed in this study (Supplementary data 1). Females and males were obtained from individually reared fourth-instar larvae or pupae. Species identification was based on male genitalia using descriptions and redescriptions of the species and taxonomic keys (Forattini 2002). Females and males from Argentina were obtained from the same larval habitats.
DNA extraction, amplification and sequencing -DNA extractions were obtained either from whole adult specimens (14 individuals) or one or two legs (57 individuals) from each mosquito; most of them, preserved dry over silica gel. Genomic DNA was extracted using 50/10 µL of NaCl and 240/20 µL of Chelex-100 5% (w/v). The extract solution was vortexed and incubated at 99ºC for 10 min. After centrifugation at 13,000 rpm for 15 min, at 25ºC, the supernatant was recovered and an aliquot of 12 µL, frozen at -20ºC, was used for the polymerse chain reactions (PCRs). The remaining Chelex-DNA was stored at -80ºC in Entomological Collection Reference, School of Public Health, University of São Paulo, Brazil.
The primer pairs of Folmer et al. (1994) LCO1490 (5'-GGTCAACAAATCATAAAGATATTGG-3') and HCO2198 (5'-TAAACTTCAGGGTGACCAAAAAAT-CA-3') were used to amplify the ~658 bp fragments of COI which were trimmed between 615-654 bp. The PCR was performed in a total volume of 25 μL containing 1-6 μL of Chelex-DNA, 1 x PCR buffer (Invitrogen), 1.5 mM MgCl2 (Invitrogen), 0.1 μM each primer, 200 mM each dNTPs (Amresco), 0.625 U Taq Platinum polymerase (Invitrogen) and the remaining volume of ddH2O. The PCR thermal regime consisted of 94ºC for 3 min, 40 cycles of 94ºC for 1 min, 55ºC for 1 min and 72ºC for 1 min and a final extension at 72ºC for 7 min. For 22 samples that could not be amplified using the previously described protocol (museum samples), the reaction proceeded under the following temperature profile taken from Zapata et al. (2012): 94ºC for 3 min, 5 cycles of 94ºC for 30 sec, 45ºC for 90 sec, 68ºC for 60 sec, 35 cycles of 94ºC for 30 sec, 51ºC for 30 sec, 68ºC for 60 sec and a final extension at 68ºC for 10 sec.
The PCR products of the COI gene were electrophoresed in 1% TAE agarose gels stained with GelRedTM Nucleic Acid Gel Stain (Biotium Inc, Hayward, USA). All sequencing reactions were carried out in both directions using an ABI Big Dye Terminator Kit v.3.1 (PE Applied Biosystems, Warrington, England) with the same set of PCR primers.
The sequencing reactions were carried in a total volume of 10 μL containing 40-50 ng of the PEG purified PCR product, 0.5 μL BigDye Terminator Ready Reaction Mix, 1 x Sequencing Buffer (Applied Biosystems), 3.6 pmol of R (HCO2198) or F (LCO1490) primers and the remaining volume of ddH2O. The sequencing reactions were purified on Sephadex G50 columns (GE Healthcare) and analysed in an Applied Biosystems 3130 DNA Analyser (PE Applied Biosystems). The sequences were edited in Sequencher v.4.9 (Genes Codes Corporation, Ann Arbor, MI, USA), primer regions were removed from sequences. Comparisons with available sequences using Basic Local Alignment Search Tool (blast.ncbi.nlm.nih. gov/Blast.cgi) were performed to check for sequence homology and species identification. DNA sequences were aligned by nucleotides using Muscle algorithm (Edgar 2004) in SeaView v.4 (Gouy et al. 2010) and by amino acids using TranslatorX (Abascal et al. 2010).
Barcode clusters -Pairwise nucleotide sequence divergences and the mean intraspecific and interspecific distances were estimated using K2P distance (Kimura 1980) implemented in MEGA v5 (Tamura et al. 2011. From the 71 sequences, 63 unique sequences were recovered in DAMBE v.5.3.2 (Xia & Xie 2001) and subsequently used to generate a neighbour-joining (NJ) tree, using MEGA v.5 (Tamura et al. 2011) to evaluate the clustering pattern between species. The statistical support for the clusters obtained in the NJ tree was estimated using bootstrap support values (BSV) obtained with 1,000 bootstrap replicates.
Maximum parsimony (MP) was also employed to verify if MP lineages corroborate results obtained in the NJ topology. An. darlingi and Stegomyia aegypti (L.) were used as outgroups. MP analysis was carried out in MEGA v.5 (Tamura et al. 2011), using the default parameters. Statistical support for the clades was estimated using BSV obtained in 1,000 bootstrap replicates.
COI and species identification -The usefulness of the COI gene for barcoding was tested using the BCM algorithm in TaxonDNA (taxondna.sf.net/). This algorithm involves matching the query sequence to the most similar barcode within a specified species threshold. TaxonDNA estimates the frequency distribution of the intraspecific distances to determine the threshold value below which 95% of all intraspecific distances are found. Queries without barcode match below the threshold value remained unidentified. Contrasting, those queries with match above the threshold value were considered a successful, ambiguous or incorrect identification. A correct identification was achieved if both names were identical, when at least two equally good best matches were found the identification was ambiguous and when the names were mismatched, the identification was a failure.

RESULTS
Barcode clusters -A pairwise K2P distance matrix was constructed for the 71 COI barcode sequences. K2P distances and means, between and within groups, are in Supplementary data 2, Table II, respectively. Twelve COI lineages were recovered ( Fig. 1), which did not entirely corroborate previously identified species and subgroups. COI K2P distance between sequences generated from individuals preliminarily identified as Cx. lygrus form 1 and Cx. lygrus form 2 of the Lygrus lineage ( Fig.  1) ranged from 0.2-0.9% (Supplementary data 2). The split leading to both individuals was strongly supported (100% BSV) (Fig. 1) and the divergence with the closest  (Table II), Cx. eduardoi, was 8.7 times greater than the average intraspecific distance. The Pipiens lineage included two individuals of each Cx. pipiens and Cx. quinquefasciatus from Argentina. The average K2P distance between these species was 1.6% (0-3%) (Supplementary data 2, Table II), approximately 0.5 the value of mean distance between specimens of Cx. pipiens (3%). The COI sequences generated from individual M044 of Cx. quinquefasciatus from Formosa province and M026 of Cx. pipiens from Córdoba province share 100% similarity. In contrast, K2P distances between these two individuals and the remaining Cx. pipiens and Cx. quinquefasciatus sequences ranged between 0.3-3% (Supplementary data 2).
The split leading to species of the Coronator group (Cx. coronator s.s., Cx. camposi and Cx. usquatus) (Table  I), plus Cx. maxi and Cx. surinamensis from the Tarsalis subgroup (Table I), was supported by 91% BSV (Fig. 1), whereas the K2P distance ranged from 0-1.2% (0.54%). The Saltanensis lineage ( Fig. 1) included two sequences generated from individuals morphologically identified as Cx. saltanensis and genetic distance within the cluster was 0.2% (Supplementary data 2). The COI K2P distance between the Saltanensis lineage and the Coronator group, Cx. maxi and Cx. surinamensis ranged from 1.6-2.3% (1.94%) and was 9.7 times greater than any intraspecific distances. Fig. 1: bootstrapped neighbour-joining tree of 71 cytochrome c oxidase subunit I (COI) sequences generated from specimens belonging to 22 Culex (Culex) species from Argentina and Brazil based on the Kimura two-parameter distance algorithm and 1,000 replicates. Bootstrap values less than 70% are not shown. The 12 COI lineages recovered are named.
The Acharistus lineage ( Fig. 1) was supported by 99% BSV. It contained sequences generated from individuals identified as Cx. acharistus that were collected in both Argentina and Brazil. Intraspecific COI K2P distance varied from 0.2-1.7% (1.21%), whereas the distance between the Cx. acharistus cluster and Cx. spinosus M107 ranged from 3.3-4.2% (3.64%). These values are threefold the intraspecific COI sequence distance within Cx. acharistus. The COI K2P distance between the Acharistus and Dolosus lineages from Argentina and the Pico do Itapeva lineage ranged from 3.8-5.1% (4.46%), which are 3.25 and 3.68 times higher than intraspecific variability estimated for Cx. acharistus, respectively, whereas between the Acharistus and Bilineatus lineages the distance is higher than 4.9%.
The Mollis lineage ( Fig. 1) was supported by 100% BSV and included sequences generated from representatives of Cx. mollis from Argentina and Brazil. Sequences obtained from Cx. mollis M089 and Cx. mollis SP05-100 were unique COI haplotypes. The K2P distance between the Mollis and the closest lineage formed by Cx. declarator, Cx. nigripalpus and Cx. bidens was lower than 2% (Table II). The Nigripalpus grouping was moderately supported by 82% BSV (Fig. 1) and contains individuals that were morphologically identified as Cx. nigripalpus. The intraspecific K2P distance ranged from 0-0.3% (0.18%). The closest species to the Nigripalpus grouping are Cx. declarator and Cx. bidens, with a mean COI K2P distance between them of 0.9% (Table II). Interspecific distance values were, respectively, 4.86 and 5.14 times higher than the variability within Cx. nigripalpus.
The sequences from to Cx. bidens, Cx. declarator and Cx. tatoi were not included in a cluster (Fig. 1). The mean variability within Cx. declarator (0.51%) (Table II) was higher than the mean divergence between Cx. declarator and Cx. bidens (0.4%) (Supplementary data 2). The greatest interspecific divergence was between Cx. bidens and Cx. tatoi, which varied from 0.7-1.9% (1.02%) (Supplementary data 2, Table II) and represents 3.92 and 1.11 times of the variability within Cx. bidens and Cx. tatoi, respectively. The COI sequences from Cx. tatoi collected in the state of Rondônia clustered together (Fig. 1).
The MP tree topology (Fig. 2) and the NJ topology ( Fig. 1) are consistent. The 12 COI lineages were recovered with both analyses with similar BSV.
COI species identification -Ninety-five percent of intraspecific K2P distances were found within the interval between 0-2.8%, the higher value was used as cut-off to define the limit for species identification. Seventy-one mosquitoes were sequenced, 69.01% of which (49 sequences) were successfully identified in accordance with the BCM, whereas 9.85% (7 sequences) of all sequences were ambiguously identified, 18.3% (13 sequences) misidentified and the remaining 2.81% (Cx. pipiens F004 and Cx. spinosus M107) had no match below 2.95% and remained unidentified (Table III).

DISCUSSION
Morphological identification of Culex (Cux.) species, particularly from females, is problematic because of both polymorphism and overlap of characters state. Characteristics of fourth-instar larva are largely employed for species identification. However, similar to females, there are overlapping and absence of morphological differences among some species. Consequently, characteristics of the male genitalia usually allow a more accurate species identification. The major objective of the current study was to determine if the COI barcode fragment contains information for the identification of the species and species complexes within the subgenus Culex of the genus Culex, employing specimens from Argentina and Brazil.
Results of all analyses recovered 12 clusters, five of which confirm currently recognised species (Cx. acharistus, Cx. chidesteri, Cx. dolosus, Cx. lygrus and Cx. saltanensis). The remaining specimens were not correctly recovered as individuals of the morphologically identified species. Cx. lygrus seems to be a monophyletic species because the mean intraspecific COI K2P divergence is less than 2% and the degree of divergence with the closest species is higher than 4%. According to Ruiz-Lopez et al. (2012), intraspecific divergence within anopheline species may vary from 0-< 2%, whereas Hebert et al. (2004) proposed that the divergence with the closest species should be at least 10 times higher than the average intraspecific genetic distance. The four Cx. lygrus sequences were successfully identified according to the BCM criterion. Based on morphological traits of the male genitalia, specimens of Cx. lygrus can be subdivided into two subgroups: Cx. lygrus form 1 (Cx. lygrus SP74-24 and Cx. lygrus SP78-6) and Cx. lygrus form 2 (Cx. lygrus SP56-25 and Cx. lygrus SP56-26). COI barcode sequences generated from Cx. lygrus form 1 clustered together with Cx. lygrus form 2 because the intergroup distance varied between 0.3-0.9% (0.6%) and the variability between both subgroups was only 0.86 and three times greater than the variation within Cx. lygrus form 1 and Cx. lygrus form 2, respectively (Supplementary data 2). Cx. lygrus was described by Root (1927). The type-locality is Magé, state of Rio de Janeiro, Brazil. Comparing both ventral and dorsal divisions of the lateral plate of the male genitalia of Cx. lygrus forms 1 and 2 with that illustrated by Root (1927), it seems that form 1 is morphologically more similar to the Magé specimen than form 2. However, in considering COI barcode sequence similarity, we can infer that specimens may belong to a single species and thus differences observed represent polymorphisms or COI barcode does not contain enough information to indicate Cx. lygrus forms 1 and 2 are distinct species.
The Pipiens subgroup belongs to the Pipiens group compiled by Harbach (2011) (Brewer et al. 1987), an established intergradation area which also include Santa Fe province (Almirón et al. 1995). Due to our questionable results about the Pipiens lineage, we agree with Reddy et al. (2012) that comparative genomic studies will help to understand the incipient speciation in this species complex. The cluster leading to Cx. apicinus and Cx. interfor is supported by 100% BSV, with the latter species forming a lineage with Cx. apicinus M007b. Cx. interfor is a species of the Tarsalis subgroup (Table I), whereas Cx. apicinus belongs to the Apicinus subgroup (Table I), both subgroups of the Pipiens group. Cx. interfor is morphologically similar to Cx. bidens. Identification of these species is problematic when employing only characteristics of the fourth-instar larva and female. However, Cx. apicinus is easily distinguished from other members of the subgenus based on features of the male genitalia (Harbach et al. 1986, Sallum et al. 1996, including Cx. inerfor based also on features of females and larvae (Rossi et al. 2008). Consequently, having both species clustered together was a totally unexpected result that might be caused by both the limited information of the COI barcode fragment and poor resolution of the K2P distance analysis. Results of the BCM analyses show that Cx. apicinus is a good species.
The grouping herein designated as the Dolosus lineage ( Fig. 1) (Table I). Two individuals of Cx. saltanensis formed a strongly supported lineage (99% BSV). Intraspecific COI K2P distance within Cx. saltanensis is on average lower than 2%. Furthermore, the interspecific distance ratio between Cx. saltanensis and the cluster composed of the Coronator lineage, Cx. maxi and Cx. surinamensis is approximately the value proposed by Hebert et al. (2004). The BCM criterion supports Cx. saltanensis as a valid species that can be identified based on COI barcode sequence (Table III). Females of the three species share some morphological similarities, which make it difficult to distinguish these species. However, morphological characteristics of male genitalia and fourth-instar larvae, as well as COI barcode sequence, can be useful for an accurate identification. The remaining species are clustered into a moderately supported, unresolved lineage (91% BSV). Results of the analyses revealed that the COI barcode fragment does not contain enough information to identify these species. Moreover, it seems that the low COI K2P genetic distances observed among species from Argentina and Brazil do not reflect the conspicuous morphological differences observed in male genitalia and fourth-instar larvae. Despite that, the grouping consisting of Cx. coronator, Cx. maxi, Cx. surinamensis and Cx. saltanensis was not unexpected due to the fact that Laurito and Almirón (2013) found a close relationship between them and an unresolved polytomy comprised species of the Coronator group (Cx. camposi, Cx. coronator, Cx. ousqua, Cx. usquatissimus and Cx. usquatus) in their phylogenetic study of the subgenus Culex from Argentina based on morphological characters. In that work, the Pipiens group was recovered as polyphyletic relative to the Sitiens and the Coronator groups in contrast to the study of Harbach et al. (2012) in which the Pipiens group was recovered as monophyletic in the absence, however, of species of the Coronator group.
According to the criterion of Ruiz-Lopez et al. (2012) for the species status, Cx. acharistus seems to be a single species because the average intraspecific COI K2P divergence is less than 2% and the divergence from the closest species is between 2-5.6%. The five Cx. acharistus sequences were successfully identified according to the BCM criterion (Table III). Morphological features of the male genitalia, pupa and fourth-instar larva specimens were compared following Laurito et al. (2009). This analysis showed that specimens from remote localities (Supplementary data 1) have the apical third of the gonostylus with minute annulations, the absence of dorsal and lateral arms of the phallosome, the typical shapes of dorsomentum and siphon and the seta 1-A near the middle of the antenna in larvae.
The mean interspecific divergence between Cx. mollis and the closest species is lower than 2%, but at least 25 times the divergence within the species. All the sequences of Cx. mollis were successfully identified based on the BCM criterion (Table III). Demari-Silva et al. (2011) distinguished and established relationships between 17 Culex species, using a shorter fragment than the one assessed in the present study: 478 bp of the COI gene, which includes part of the barcode region. The authors found a high intraspecific variation (0%-2.3%) between Cx. mollis sequences, indicating they comprise a complex. This idea was also supported by a deeper morphological analysis with striking differences in the male genitalia. The low intraspecific divergence (0.08%) and the correct identification of the Cx. mollis sequences included here leads us to conclude that the samples belong to one of the two complexes found by Demari-Silva et al. (2011). The specimens are also similar morphologically and were collected in remote localities from Argentina and Brazil (Supplementary data 1).
Even though mean divergence between the Chidesteri and Nigripalpus lineages is 2.10%, the upper limit of the criterion of intraspecific distance of Ruiz-Lopez et al. (2012), the intercluster divergence is 11 times higher than the intracluster divergence of the Nigripualpus lineage. The morphological differences of both male genitalia and fourth-instar larvae allow an unequivocal identification, as well as the COI barcode region included in the BCM analyses, which show that Cx. chidesteri and Cx. nigripalpus are separate species (Table III).
The remainder of the included sequences, which correspond to morphological specimens identified as Cx. bidens, Cx. declarator and Cx. tatoi, were not included in a cluster. The criteria of neither Ruiz-Lopez et al.
(2012) nor Hebert et al. (2004) could be used to differentiate these morphological groups. On the other hand, all Cx. tatoi and most of the Cx. bidens sequences were successfully identified based on the BCM criterion, but not the Cx. declarator sequences which were ambiguously identified. As has been mentioned for other lineages in this study, the COI K2P genetic distances observed between the sequences of Cx. bidens, Cx. declarator and Cx. tatoi do not reflect the conspicuous morphological differences observed in male genitalia, revealing that the COI barcode fragment does not contain enough information to identify these species.
In accordance with Dai et al. (2012) and Bourke et al. (2013), a single molecular marker can not successfully resolve and identify all the species included in a group or subgroup. However, in the latter study, a multilocus barcode composed of COI and ITS2 resolved all species in a NJ tree and successfully identified all specimens to species using the BCM approach. The use of combined datasets, including both multilocus and morphological strategies for phylogenetic analyses is proposed in order to identify those species which remain unclear, as was suggested for other dipteran families, such as Muscidae (Renaud et al.

2012) and Sarcophagidae (Meiklejohn et al. 2013).
In our study, only 42% of the samples were clustered with their conspecifics in the NJ tree and 69.01% of the sequences were successfully identified in accordance with the BCM criterion. Because the COI barcode fragment does not contain enough information to distinguish between morphologically well-defined species of the sub- revealed that COI fails to distinguish some tephritid sibling species, which may reflect a very recent ancestry. Regarding mosquito identification in the study of Kumar et al. (2007), two closely related species of the genus Ochlerotatus could not be identified by the COI barcode region, which showed a negligible genetic divergence.
Regarding the current classification of the subgenus Culex, Harbach (2011) pointed out that the two groups which comprise the subgenus are both complex assemblages of species that do not fit readily into either group. Furthermore, Harbach et al. (2012) noted that evidence from their study and previous studies indicates that the subgenus is polyphyletic and has been retained as a taxon of convenience. The last classification review, on a worldwide sense, of the subgenus was done more than 80 years ago (Edwards 1932). During this period, a lot of taxonomic studies carried out that diminish the number of unknown life stages of mosquito species, new molecular markers proved to be informative tools (Miller et al. 1996, Mukabayire et al. 1999, Hebert et al. 2003a, Chen et al. 2004, Yao et al. 2010, Dai et al. 2012, Morais et al. 2012) and stronger statistical methodologies have been developed that engender trust in morphologically based classifications (NJ, parsimony, maximum likelihood, bayesian inference). A natural classification will be achieved when further phylogenetic studies include integrated morphological and molecular character treatments, as many group members as possible and broader sampling of each species is undertaken to evaluate inherent polymorphism.