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Lithium and quetiapine are effective treatments for bipolar disorder, but their potential neuroprotective effects in humans remain unclear. A single blinded equivalence randomized controlled maintenance trial was conducted in a prospective cohort of first-episode mania (FEM) patients (n=26) to longitudinally compare the putative protective effects of lithium and quetapine on grey and white matter volume. A healthy control sample was also collected (n=20). Using structural MRI scans, voxel-wise grey and white matter volumes at baseline and changes over time in response to treatment were investigated. Patients were assessed at three time points (baseline, 3 and 12-month follow-up), whereas healthy controls were assessed at two time points (baseline and 12-month follow-up). Patients were randomized to lithium (serum level 0.6 mmol l−1, n=20) or quetiapine (flexibly dosed up to 800 mg per day, n=19) monotherapy. At baseline, compared with healthy control subjects, patients with FEM showed reduced grey matter in the orbitofrontal cortex, anterior cingulate, inferior frontal gyrus and cerebellum. In addition, patients had reduced internal capsule white matter volume bilaterally (t1,66>3.20, P2,112=8.54, PPost hoc testing showed that, compared with baseline, lithium was more effective than quetiapine in slowing the progression of white matter volume reduction after 12 months (t1,24=3.76, P

Neuroimaging as endpoints in clinical trials: Are we there yet? Perspective from the first Provence workshop

Schizophrenia is widely regarded as a disorder of abnormal brain connectivity. Magnetic resonance imaging (MRI) suggests that patients show robust reductions of structural connectivity. However, corresponding changes in functional connectivity do not always follow, with increased functional connectivity being reported in many cases. Here, we consider different methodological and mechanistic accounts that might reconcile these apparently contradictory findings and argue that increased functional connectivity in schizophrenia likely represents a pathophysiological dysregulation of brain activity arising from abnormal neurodevelopmental wiring of structural connections linking putative hub regions of association cortex to other brain areas. Elucidating the pathophysiological significance of connectivity abnormalities in schizophrenia will be contingent on better understanding how network structure shapes and constrains function.

Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.

The psychotomimetic effect of the N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine is thought to arise from a functional modulation of the brain's fronto-striato-thalamic (FST) circuits. Animal models suggest a pronounced effect on ventral 'limbic' FST systems, although recent work in patients with psychosis and high-risk individuals suggests specific alterations of dorsal 'associative' FST circuits. Here, we used functional magnetic resonance imaging to investigate the effects of a subanesthetic dose of ketamine on measures of functional connectivity as indexed by the temporal coherence of spontaneous neural activity in both dorsal and ventral FST circuits, as well as their symptom correlates. We adopted a placebo-controlled, double-blind, randomized, repeated-measures design in which 19 healthy participants received either an intravenous saline infusion or a racemic mixture of ketamine (100 ng/ml) separated by at least 1 week. Compared with placebo, ketamine increased functional connectivity between the dorsal caudate and both the thalamus and midbrain bilaterally. Ketamine additionally increased functional connectivity of the ventral striatum/nucleus accumbens and ventromedial prefrontal cortex. Both connectivity increases significantly correlated with the psychosis-like and dissociative symptoms under ketamine. Importantly, dorsal caudate connectivity with the ventrolateral thalamus and subthalamic nucleus showed inverse correlation with ketamine-induced symptomatology, pointing to a possible resilience role to disturbances in FST circuits. Although consistent with the role of FST in mediating psychosis, these findings contrast with previous research in clinical samples by suggesting that acute NMDAR antagonism may lead to psychosis-like experiences via a mechanism that is distinct from that implicated in frank psychotic illness.

Cannabis is the most widely used illicit drug worldwide, though it is unclear whether its regular use is associated with persistent alterations in brain morphology. This review examines evidence from human structural neuroimaging investigations of regular cannabis users and focuses on achieving three main objectives. These include examining whether the literature to date provides evidence that alteration of brain morphology in regular cannabis users: i) is apparent, compared to non-cannabis using controls; ii) is associated with patterns of cannabis use; and with iii) measures of psychopathology and neurocognitive performance. The published findings indicate that regular cannabis use is associated with alterations in medial temporal, frontal and cerebellar brain regions. Greater brain morphological alterations were evident among samples that used at higher doses for longer periods. However, the evidence for an association between brain morphology and cannabis use parameters was mixed. Further, there is poor evidence for an association between measures of brain morphology and of psychopathology symptoms/neurocognitive performance. Overall, numerous methodological issues characterize the literature to date. These include investigation of small sample sizes, heterogeneity across studies in sample characteristics (e.g., sex, comorbidity) and in employed imaging techniques, as well as the examination of only a limited number of brain regions. These factors make it difficult to draw firm conclusions from the existing findings. Nevertheless, this review supports the notion that regular cannabis use is associated with alterations of brain morphology, and highlights the need to consider particular methodological issues when planning future cannabis research.

Background Major depressive disorder (MDD) has been shown to be associated with a disrupted topological organization of functional brain networks. However, little is known regarding whether these changes have a structural basis. Diffusion tensor imaging (DTI) enables comprehensive whole-brain mapping of the white matter tracts that link regions distributed throughout the entire brain, the so-called human connectome. Methods We examined whole-brain structural networks in a cohort of 95 MDD outpatients and 102 matched control subjects. Structural networks were represented by an 84 × 84 connectivity matrix representing probabilistic white matter connections between 84 parcellated cortical and subcortical regions using DTI tractography. Network-based statistics were used to assess differences in the interregional connectivity matrix between the two groups, and graph theory was used to examine overall topological organization. Results Our network-based statistics analysis demonstrates lowered structural connectivity within two distinct brain networks that are present in depression: the first primarily involves the regions of the default mode network and the second comprises the frontal cortex, thalamus, and caudate regions that are central in emotional and cognitive processing. These two altered networks were observed in the context of an overall preservation of topology as reflected as no significant group differences for the graph-theory measures. Conclusions This is the first report to use DTI to show the structural connectomic alterations present in MDD. Our findings highlight that altered structural connectivity between nodes of the default mode network and the frontal-thalamo-caudate regions are core neurobiological features associated with MDD.

SummaryWe investigated the morphology of multiple brain regions in a rare sample of 15 very heavy cannabis users with minimal psychiatric comorbidity or significant exposure to other substances (compared with 15 age- and IQ-matched non-cannabis-using controls) using manual techniques. Heavy cannabis users demonstrated smaller hippocampus and amygdala volumes, but no alterations of the orbitofrontal and anterior- and paracingulate cortices, or the pituitary gland. These findings indicate that chronic cannabis use has a selective and detrimental impact on the morphology of the mediotemporal lobe.

The human brain is characterized by a remarkable ability to adapt its information processing based on current goals. This ability, which is encompassed by the psychological construct of cognitive control, involves activity throughout large-scale, specialized brain systems that support segregated functions at rest and during active task performance. Based on recent research, we propose an account in which control functions rely on transitory changes in patterns of cooperation and competition between neural systems. This account challenges current conceptualizations of control as relying on segregated or antagonistic activity of specialized brain systems. Accordingly, we argue that the study of transitory task-based interactions between brain systems is critical to understanding the flexibility of normal cognitive control and its disruption in pathological conditions.

Recent studies using diffusion weighted magnetic resonance imaging (DW-MRI) have provided evidence of abnormal white matter microstructure in adults with substance use disorders (SUDs). While there is a growing body of research using DW-MRI to examine the impact of heavy substance use during adolescence, this literature has not been systematically reviewed. Online databases were searched for DW-MRI studies of adolescent substance users, and 10 studies fulfilled the inclusion and exclusion criteria. We identified consistent evidence for abnormal white matter microstructure in neocortical association pathways as well as in projection and thalamic pathways. Dose-dependent relationships between DW-MRI measures and patterns of substance use were also observed. The consistency of these findings with DW-MRI research in adults suggests that white matter microstructure is impacted in the early stages of heavy substance use. However, given the largely cross-sectional nature of the available data, important questions remain regarding the extent to which white matter abnormalities are a consequence of adolescent exposure to alcohol and other drugs of abuse or reflect pre-existing differences that increase risk for SUDs.

The human brain is a complex, interconnected network par excellence. Accurate and informative mapping of this human connectome has become a central goal of neuroscience. At the heart of this endeavor is the notion that brain connectivity can be abstracted to a graph of nodes, representing neural elements (e.g., neurons, brain regions), linked by edges, representing some measure of structural, functional or causal interaction between nodes. Such a representation brings connectomic data into the realm of graph theory, affording a rich repertoire of mathematical tools and concepts that can be used to characterize diverse anatomical and dynamical properties of brain networks. Although this approach has tremendous potential - and has seen rapid uptake in the neuroimaging community - it also has a number of pitfalls and unresolved challenges which can, if not approached with due caution, undermine the explanatory potential of the endeavor. We review these pitfalls, the prevailing solutions to overcome them, and the challenges at the forefront of the field.

Network-based analysis of structural and functional connections has provided a new technique to study the brains of healthy people and patients with neurological and psychiatric disorders. Graph theory provides a powerful method to quantitatively describe the topological organisation of brain connectivity. With such a framework, the brain can be depicted as a set of nodes connected by edges. Distinct modifications of network topological organisation in the brain have been identified during development and normal ageing, whereas disrupted functional and structural connectivities have been associated with several neurological and psychiatric disorders, including dementia, amyotrophic lateral sclerosis, multiple sclerosis, and schizophrenia. These assessments have improved understanding of the clinical manifestations noted in these patients, including disability and cognitive impairment. Future network-based research might enable indentification of different stages of disorders, subtypes for cognitive impairment, and connectivity profiles associated with different clinical outcomes.

Despite nearly three decades of neuroimaging research in psychiatry, no imaging study to date has identified a single site of pathology in the brain that represents a causal factor in the emergence of any of the major mental illnesses described in current nosological systems. Rather, the available evidence indicates that these disorders are associated with subtle abnormalities distributed throughout the brain (e.g., Fornito et al., 2009, 2012; Bora et al., 2010, 2012), implying that they arise from disordered interactions between connected neural systems rather than damage to any individual brain region. Accordingly, current hopes for the development of more targeted interventions in psychiatry are being placed on research attempting to map the molecular determinants and clinical correlates of neural circuit abnormalities in mental disorders (Insel and Scolnick, 2006; Meyer-Lindenberg, 2010). The impact of this thinking on the field is evident in the emergence of new scientific journals devoted to the study of brain connectivity1; increasingly popular workshops devoted to the topic2; recent large-scale collaborative initiatives such as the Human Connectome Project3,4; freely available software packages dedicated to connectivity analyses5,6; and the establishment data-sharing initiatives such as the 1000 connectomes project7. These developments offer the potential to greatly enhance our understanding of brain connectivity abnormalities in psychiatric disorders. This Special Topic of Frontiers in Psychiatry presents work illustrating the application of connectomic techniques to study brain network changes in mental disorders. It brings together researchers working to understand brain network abnormalities in disorders as diverse as schizophrenia, attention-deficit hyperactivity disorder (ADHD), major depression, bipolar disorder, post-traumatic stress disorder (PTSD), and Alzheimer's disease, and showcases the diverse range of methods used to interrogate these brain changes. The article by Fornito and Bullmore (2012) overviews these techniques and reviews their application to the study of genetic influences on brain networks. They conclude that the available literature, albeit preliminary, indicates that risk genes show greater penetrance at the level of distributed brain networks rather than individual regions, and connectomic measures may thus provide more sensitive intermediate phenotypes for psychiatric disorders. The remaining articles illustrate the application of specific techniques to study different disorders. Independent component analysis (ICA), a popular data-driven technique for decomposing fMRI data into distinct components with characteristic anatomy and temporal dynamics (Calhoun et al., 2004), is applied by Calhoun et al. (2011) to demonstrate phenotypic continuities and discontinuities in the brain functional networks of patients with schizophrenia and bipolar disorder performing an auditory oddball task. Seed-based techniques are featured in the work of Mills et al. (2012), Mennes et al. (2011), Rabinak et al. (2011), and Davey et al. (2012). The first two articles use multiple seeds to characterize how individual differences in functional connectivity relate to cognitive performance on tests of executive function in children with ADHD and typically developing controls. This work offers welcome new evidence supporting the behavioral relevance of resting-state measures for psychiatric disorders, which is critical for validating any case-control differences observed under such experimental conditions (Fornito and Bullmore, 2010). In war veterans with combat-related PTSD, Rabinak et al. (2011) report a prominent alteration of resting-state functional connectivity between an amygdala seed region and posterior insular cortex, extending prior evidence from task-based activation studies in this patient group. Finally, Davey et al. (2012) provide evidence that adaptive changes in the functional connectivity of the subgenual cingulate cortex may be necessary to support intact executive function early in the course of major depressive disorder (MDD), prior to the emergence of characteristic cognitive deficits. In a second study of MDD, Almeida et al. (2011) use dynamic causal modeling (DCM) to characterize changes in the effective connectivity (i.e., causal functional interactions) of a circuit linking ventral frontal cortical regions and the amygdala during emotional processing. Their findings shed light on how sex differences in the dynamics of this network may relate to the clinical phenomenology of female compared to male MDD. Graph analytic techniques are featured in the work of Xie and He (2011) and Collin et al. (2011). Xie and He provide a primer on the basic principles of graph theoretic analysis of neuroimaging data, and comprehensively review the application of these techniques to study disturbances of structural and functional brain network connectivity and topology in Alzheimer's disease. In a resting-state fMRI study, Collin and colleagues use graph analytic techniques to provide some of the first evidence that functional connectivity between cerebellar and cortical and subcortical regions is associated with genetic risk for schizophrenia. Finally, Lungu and Stip (2012) present an interesting case study of a schizophrenia patient with partial agenesis of the corpus callosum. They report intact activation to perception of visual stimuli but abnormal neural activation during emotional processing in this patient. They interpret this finding as implicating callosal connectivity in patients’ emotional and psychotic disturbances, a conclusion consistent with morphometric studies of callosal abnormalities in schizophrenia (Walterfang et al., 2008). Collectively, the research presented in this Special Topic provides the novice reader with exposure to the diverse array of methods available for interrogating brain network structure and function in health and disease, and offers the expert reader prime examples of cutting-edge applications of these methods to understand psychiatric disorders. The continued development of novel connectivity mapping techniques (Bassett et al., 2011; Fornito et al., 2011a; Friston et al., 2011; Sui et al., 2012; Zalesky et al., 2012a), improvements in available algorithms and network models (Friston et al., 2011; Rubinov and Sporns, 2011; Zalesky et al., 2012b), and the elucidation of genetic influences on brain connectivity (Esslinger et al., 2009; Fornito et al., 2011b), will be critical for advancing work in this field. These advances, coupled with the valuable data to emerge from initiatives such as the Human Connectome Project, will enable researchers to move beyond localized, segregationist pathophysiological models of mental illness to characterize the full complexity of distributed neural circuit disruptions that underlie these conditions.

Background: Obsessive-compulsive disorder (OCD) is associated with a range of emotional abnormalities linked to its defining symptoms, comorbid illnesses and cognitive deficits. The aim of this preliminary study was to examine functional changes in the brain that are associated with experimentally induced sad mood in patients with OCD compared with healthy controls in a frontolimbic circuit relevant to both OCD and mood regulation. Methods: Participants underwent a validated sad mood induction procedure during functional magnetic resonance imaging. Analyses focused on mapping changes in the functional connectivity of the subgenual anterior cingulate cortex (ACC) within and between the 2 groups in response to successfully induced sadness. Results: We enrolled 11 patients with OCD and 10 age-, sex-and IQ-matched controls in our study. Unlike controls, patients with OCD did not demonstrate predicted increases in functional connectivity between the subgenual ACC and other frontal regions during mood induction. Instead, patients demonstrated heightened connectivity between the subgenual ACC and ventral caudate/nucleus accumbens region and the hypothalamus. Limitations: Our study included a small, partially medicated patient cohort that precluded our ability to investigate sex or drug effects, evaluate behavioural differences between the groups and perform a whole-brain analysis. Conclusion: The ventral striatum and ventral frontal cortex were distinctly and differentially modulated in their connectivity with the subgenual ACC during the experience of sad mood in patients with OCD. These results suggest that, in patients with OCD, induced sadness appears to have provoked a primary subcortical component of the hypothesized "OCD circuit," which may offer insights into why OCD symptoms tend to develop and worsen during disturbed emotional states.

Psychiatric disorders are phenotypically heterogeneous entities with a complex genetic basis. To mitigate this complexity, many investigators study so-called intermediate phenotypes that putatively provide a more direct index of the physiological effects of candidate genetic risk variants than overt psychiatric syndromes. Magnetic resonance imaging (MRI) is a particularly popular technique for measuring such phenotypes because it allows interrogation of diverse aspects of brain structure and function in vivo. Much of this work however, has focused on relatively simple measures that quantify variations in the physiology or tissue integrity of specific brain regions in isolation, contradicting an emerging consensus that most major psychiatric disorders do not arise from isolated dysfunction in one or a few brain regions, but rather from disturbed interactions within and between distributed neural circuits; i.e., they are disorders of brain connectivity. The recent proliferation of new MRI techniques for comprehensively mapping the entire connectivity architecture of the brain, termed the human connectome, has provided a rich repertoire of tools for understanding how genetic variants implicated in mental disorder impact distinct neural circuits. In this article, we review research using these connectomic techniques to understand how genetic variation influences the connectivity and topology of human brain networks. We highlight recent evidence from twin and imaging genetics studies suggesting that the penetrance of candidate risk variants for mental illness, such as those in SLC6A4, MAOA, ZNF804A and APOE, may be higher for intermediate phenotypes characterised at the level of distributed neural systems than at the level of spatially localised brain regions. The findings indicate that imaging connectomics provides a powerful framework for understanding how genetic risk for psychiatric disease is expressed through altered structure and function of the human connectome.

Cannabis use is highly prevalent among people with schizophrenia, and coupled with impaired cognition, is thought to heighten the risk of illness onset. However, while heavy cannabis use has been associated with cognitive deficits in long-term users, studies among patients with schizophrenia have been contradictory. This article consists of 2 studies. In Study I, a meta-analysis of 10 studies comprising 572 patients with established schizophrenia (with and without comorbid cannabis use) was conducted. Patients with a history of cannabis use were found to have superior neuropsychological functioning. This finding was largely driven by studies that included patients with a lifetime history of cannabis use rather than current or recent use. In Study II, we examined the neuropsychological performance of 85 patients with first-episode psychosis (FEP) and 43 healthy nonusing controls. Relative to controls, FEP patients with a history of cannabis use (FEP + CANN; n = 59) displayed only selective neuropsychological impairments while those without a history (FEP − CANN; n = 26) displayed generalized deficits. When directly compared, FEP + CANN patients performed better on tests of visual memory, working memory, and executive functioning. Patients with early onset cannabis use had less neuropsychological impairment than patients with later onset use. Together, these findings suggest that patients with schizophrenia or FEP with a history of cannabis use have superior neuropsychological functioning compared with nonusing patients. This association between better cognitive performance and cannabis use in schizophrenia may be driven by a subgroup of “neurocognitively less impaired” patients, who only developed psychosis after a relatively early initiation into cannabis use.

Objective: previous research suggested that structural and functional abnormalities within the amygdala and orbitofrontal cortex contribute to the pathophysiology of Conduct Disorder (CD). Here, we investigated whether the integrity of the white-matter pathways connecting these regions is abnormal and thus may represent a putative neurobiological marker for CD.Methods: diffusion Tensor Imaging (DTI) was used to investigate white-matter microstructural integrity in male adolescents with childhood-onset CD, compared with healthy controls matched in age, sex, intelligence, and socioeconomic status. Two approaches were employed to analyze DTI data: voxel-based morphometry of fractional anisotropy (FA), an index of white-matter integrity, and virtual dissection of white-matter pathways using tractography.Results: adolescents with CD displayed higher FA within the right external capsule relative to controls (T?=?6.08, PConclusions: these results provide evidence that CD is associated with white-matter microstructural abnormalities in the anatomical tract that connects the amygdala and orbitofrontal cortex, the uncinate fascicle. These results implicate abnormal maturation of white-matter pathways which are fundamental in the regulation of emotional behavior in CD

Ketamine is a non-competitive anatognist of the N-methyl-D-aspartate (NMDA) receptor commonly used as an anesthetic and analgesic. In sub-anesthetic doses, it can induce temporary psychotic symptoms and has served as a pharmacological model for schizophrenia. While its acute effects on brain and behavior have been studied, the effects of long-term exposure to ketamine on brain activity have been largely unexplored. In this study, we aimed to examine such effects on spontaneous brain dynamics measure using resting-state functional magnetic resonance imaging (fMRI). Forty-one patients with ketamine dependence and forty-four healthy control subjects were imaged with BOLD fMRI using a 3.0-Tesla Siemens scanner at the Magnetic Resonance Center of Hunan Provincial People's Hospital, analyzed with the regional homogeneity (ReHo) method. Compared with healthy controls, decreased ReHo was found in ketamine users in the right anterior cingulate cortex and increased ReHo was found in left precentral frontal gyrus (p

Numerous studies have demonstrated that brain networks derived from neuroimaging data have nontrivial topological features, such as small-world organization, modular structure and highly connected hubs. In these studies, the extent of connectivity between pairs of brain regions has often been measured using some form of statistical correlation. This article demonstrates that correlation as a measure of connectivity in and of itself gives rise to networks with non-random topological features. In particular, networks in which connectivity is measured using correlation are inherently more clustered than random networks, and as such are more likely to be small-world networks. Partial correlation as a measure of connectivity also gives rise to networks with non-random topological features. Partial correlation networks are inherently less clustered than random networks. Network measures in correlation networks should be benchmarked against null networks that respect the topological structure induced by correlation measurements. Prevalently used random rewiring algorithms do not yield appropriate null networks for some network measures. Null networks are proposed to explicitly normalize for the inherent topological structure found in correlation networks, resulting in more conservative estimates of small-world organization. A number of steps may be needed to normalize each network measure individually and control for distinct features (e.g. degree distribution). The main conclusion of this article is that correlation can and should be used to measure connectivity, however appropriate null networks should be used to benchmark network measures in correlation networks.

Much recent interest has centered on understanding the relationship between brain structure variability and individual differences in cognition, but there has been little progress in identifying specific neuroanatomical bases of such individual differences. One cognitive ability that exhibits considerable variability in the healthy population is reality monitoring; the cognitive processes used to introspectively judge whether a memory came from an internal or external source (e.g., whether an event was imagined or actually occurred). Neuroimaging research has implicated the medial anterior prefrontal cortex (PFC) in reality monitoring, and here we sought to determine whether morphological variability in a specific anteromedial PFC brain structure, the paracingulate sulcus (PCS), might underlie performance. Fifty-three healthy volunteers were selected on the basis of MRI scans and classified into four groups according to presence or absence of the PCS in their left or right hemisphere. The group with absence of the PCS in both hemispheres showed significantly reduced reality monitoring performance and ability to introspect metacognitively about their performance when compared with other participants. Consistent with the prediction that sulcal absence might mean greater volume in the surrounding frontal gyri, voxel-based morphometry revealed a significant negative correlation between anterior PFC gray matter and reality monitoring performance. The findings provide evidence that individual differences in introspective abilities like reality monitoring may be associated with specific structural variability in the PFC.

While schizophrenia is frequently characterized as a disorder of disturbed functional connectivity, the causes and pathophysiological origins of such disturbances remain unclear. The aim of this study was to better elucidate the mechanistic causes of abnormal functional connectivity in schizophrenia, measured as the extent of temporal correlation between endogenous fluctuations recorded at anatomically discrete brain regions during resting‐state functional MRI. An approach was developed to perform whole‐brain connectivity mapping at the resolution of individual pairs of voxels, without the need for arbitrary parcellation of the cerebrum. Between‐group connectivity reductions in 12 people diagnosed with schizophrenia and 15 age‐, IQ‐, and gender‐matched healthy volunteers were localized to a distributed network including frontoparietal and occipitoparietal connections. The gray‐matter regions comprising this disturbed network showed evidence of local reductions in both intra‐regional homogeneity (29%–33% reduction) and signal power (40%–60% reduction). The extent to which inter‐regional correlation was reduced between a pair of gray matter regions was found to be strongly correlated with the extent of local decoherence evident within the gray matter regions per se. This suggests measurement of aberrant functional connectivity in schizophrenia is both a measurement of altered coupling between regions as well as a measurement of local decoherence within regions. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.

Schizophrenia is frequently characterized as a disorder of brain connectivity. Neuroimaging has played a central role in supporting this view, with nearly two decades of research providing abundant evidence of structural and functional connectivity abnormalities in the disorder. In recent years, our understanding of how schizophrenia affects brain networks has been greatly advanced by attempts to map the complete set of inter-regional interactions comprising the brain's intricate web of connectivity; i.e., the human connectome. Imaging connectomics refers to the use of neuroimaging techniques to generate these maps which, combined with the application of graph theoretic methods, has enabled relatively comprehensive mapping of brain network connectivity and topology in unprecedented detail. Here, we review the application of these techniques to the study of schizophrenia, focusing principally on magnetic resonance imaging (MRI) research, while drawing attention to key methodological issues in the field. The published findings suggest that schizophrenia is associated with a widespread and possibly context-independent functional connectivity deficit, upon which are superimposed more circumscribed, context-dependent alterations associated with transient states of hyper- and/or hypo-connectivity. In some cases, these changes in inter-regional functional coupling dynamics can be related to measures of intra-regional dysfunction. Topological disturbances of functional brain networks in schizophrenia point to reduced local network connectivity and modular structure, as well as increased global integration and network robustness. Some, but not all, of these functional abnormalities appear to have an anatomical basis, though the relationship between the two is complex. By comprehensively mapping connectomic disturbances in patients with schizophrenia across the entire brain, this work has provided important insights into the highly distributed character of neural abnormalities in the disorder, and the potential functional consequences that these disturbances entail.

Inhalants are frequently among the first drugs abused by adolescents; however, little is known about how chronic inhalant abuse affects cognition (e.g. executive functioning). Several studies have examined cognitive deficits among inhalant users; however, no study has thoroughly addressed the confounding issues frequently associated with inhalant users (e.g. polysubstance use). The aim of the current study was to examine possible deficits in cognitive control among young, regular inhalant users and explore the relationship between inhalant use and executive functioning.Three groups (n = 19) of young people (aged 14-24) were recruited: an inhalant-using group, a drug-using control group and a community control group. The inhalant and drug-using controls were matched on demographic, clinical and substance use measures. All three groups were matched on age, sex and education. Cognitive control was assessed using Stroop and Go/No-Go tasks.There were no significant differences in performance between the groups on any measure. However, three measures (incongruent reaction times and congruent errors for the Stroop and omission errors for the Go/No-Go) were significantly correlated with inhalant use measures, suggesting inhalant use was associated with poorer performance.The lack of significant differences between the groups is surprising; however, it raises important questions regarding cognitive deficits among chronic inhalant users. Further longitudinal studies using well-matched control participants are required to delineate the nature and timing of cognitive and neurobiological pathology among adolescent inhalant users.

Background Voxel-based morphometry (VBM) has been widely used to quantify structural brain changes associated with Major Depressive Disorder (MDD). While some consistent findings have been reported, individual studies have also varied with respect to the key brain regions affected by the illness, and how these abnormalities are related to patients' clinical characteristics. Here, we aimed to identify those brain regions that most consistently showed gray matter anomalies in MDD, and their clinical correlates, using meta-analytic techniques. Methods A systematic search of VBM studies was applied in MDD. Signed differential mapping, a new coordinate based neuroimaging meta-analysis technique, was applied to data collated from a total of 23 studies comparing regional gray matter volumes of 986 MDD patients and 937 healthy controls. Results Gray matter was significantly reduced in a confined cluster located in the rostral anterior cingulate cortex (ACC). There were also gray matter reductions in dorsolateral and dorsomedial prefrontal cortex and decrease in the latter region was evident in patients with multiple-episodes. Amygdala and parahippocampal gray matter volumes were significantly reduced in studies including patients with comorbid anxiety disorders, as well as in first-episode/drug free samples. Conclusions Gray matter reduction in rostral ACC was the most consistent finding in VBM studies of MDD. The evidence for reductions in other regions within fronto-subcortical and limbic regions was less consistent. The associations between these gray matter anomalies and clinical characteristics, particularly measures relating to illness duration, suggest that chronic MDD has a robust and deleterious, albeit spatially focal, effect on brain structure.

Background: Previous diffusion tensor imaging (DTI) studies in patients with obsessive-compulsive disorder (OCD) have reported inconsistent findings, and it is not known whether observed findings are related to abnormalities in axonal structure or myelination. Methods: In this DTI study, we investigated fractional anisotropy, as well as axial and radial diffusivity, in 21 patients with OCD and 29 healthy controls. Results: We found decreased fractional anisotropy in the body of the corpus callosum in the OCD group, which was underpinned by increased radial diffusivity. Limitations: The cross-sectional design was the main limitation. Conclusion: Our findings of increased radial diffusivity provide preliminary evidence for abnormal myelination in patients with OCD.

Heroin addiction has been associated with impaired neuronal connectivity and cognitive deficits. One mechanism that potentially explains these findings is alterations in white matter connectivity secondary to chronic opiate use. However, few studies have quantitavely examined white matter deficits in opiate addiction (OA). Here, we investigated white matter microstructure in OA using diffusion tensor imaging (DTI). We performed voxel-wise analysis of fractional anisotropy (FA) in 24 participants with OA and 29 healthy controls. The OA group showed reduced FA in multiple pathways including the corpus callosum, thalamic radiation and inferior longitudinal fasciculus. This FA reduction was mainly the result of increased radial diffusivity (λ(⊥)), indicative of myelin pathology. Longer duration of OA was also associated with axonal diffusivity (λ(1)), most robustly in superior longitudinal fasciculi and right frontal white matter suggesting axonal injury in long-term users. Together, the findings indicate that chronic OA use has widespread and diverse effects on neuronal connectivity and function.

Background: There is growing evidence that inhalants are neurotoxic to white matter, yet limited work has been conducted to inves tigate the neurobiologic effects of long-term exposure among adolescent users, despite inhalant use being most prominent during this developmental period. Methods: We used diffusion tensor imaging to examine white-matter integrity in 11 adolescents who used in halants, 11 matched cannabis users and 8 drug-naive controls. Results: Although both groups of drug users had white-matter abnormalities (i.e., lower fractional anisotropy), abnormalities were more pronounced in the inhalant group, particularly among early-onset users. Limitations: The findings of this study should be considered in light of its small sample size, cross-sectional design and the complex psychosocial background of long-term inhalant users. Conclusion: White-matter abnormalities may underpin long-term behavioural and mental health problems seen in individuals with long-term inhalant use.

Background Schizophrenia is believed to result from abnormal functional integration of neural processes thought to arise from aberrant brain connectivity. However, evidence for anatomical dysconnectivity has been equivocal, and few studies have examined axonal fiber connectivity in schizophrenia at the level of whole-brain networks. Methods Cortico-cortical anatomical connectivity at the scale of axonal fiber bundles was modeled as a network. Eighty-two network nodes demarcated functionally specific cortical regions. Sixty-four direction diffusion tensor-imaging coupled with whole-brain tractography was performed to map the architecture via which network nodes were interconnected in each of 74 patients with schizophrenia and 32 age- and gender-matched control subjects. Testing was performed to identify pairs of nodes between which connectivity was impaired in the patient group. The connectional architecture of patients was tested for changes in five network attributes: nodal degree, small-worldness, efficiency, path length, and clustering. Results Impaired connectivity in the patient group was found to involve a distributed network of nodes comprising medial frontal, parietal/occipital, and the left temporal lobe. Although small-world attributes were conserved in schizophrenia, the cortex was interconnected more sparsely and up to 20% less efficiently in patients. Intellectual performance was found to be associated with brain efficiency in control subjects but not in patients. Conclusions This study presents evidence of widespread dysconnectivity in white-matter connectional architecture in a large sample of patients with schizophrenia. When considered from the perspective of recent evidence for impaired synaptic plasticity, this study points to a multifaceted pathophysiology in schizophrenia encompassing axonal as well as putative synaptic mechanisms.

The 2nd Schizophrenia International Research Society Conference, was held in Florence, Italy, April 10–15, 2010. Student travel awardees served as rapporteurs of each oral session and focused their summaries on the most significant findings that emerged from each session and the discussions that followed. The following report is a composite of these reviews. It is hoped that it will provide an overview for those who were present, but could not participate in all sessions, and those who did not have the opportunity to attend, but who would be interested in an update on current investigations ongoing in the field of schizophrenia research.

Background Cognitive control impairments in schizophrenia are thought to arise from dysfunction of interconnected networks of brain regions, but interrogating the functional dynamics of large-scale brain networks during cognitive task performance has proved difficult. We used functional magnetic resonance imaging to generate event-related whole-brain functional connectivity networks in participants with first-episode schizophrenia and healthy control subjects performing a cognitive control task. Methods Functional connectivity during cognitive control performance was assessed between each pair of 78 brain regions in 23 patients and 25 control subjects. Network properties examined were region-wise connectivity, edge-wise connectivity, global path length, clustering, small-worldness, global efficiency, and local efficiency. Results Patients showed widespread functional connectivity deficits in a large-scale network of brain regions, which primarily affected connectivity between frontal cortex and posterior regions and occurred irrespective of task context. A more circumscribed and task-specific connectivity impairment in frontoparietal systems related to cognitive control was also apparent. Global properties of network topology in patients were relatively intact. Conclusions The first episode of schizophrenia is associated with a generalized connectivity impairment affecting most brain regions but that is particularly pronounced for frontal cortex. Superimposed on this generalized deficit, patients show more specific cognitive-control-related functional connectivity reductions in frontoparietal regions. These connectivity deficits occur in the context of relatively preserved global network organization.

Despite an increasing number of published voxel based morphometry studies of schizophrenia, there has been no adequate attempt to examine gray (GM) and white matter (WM) abnormalities and the heterogeneity of published findings. In the current article, we used a coordinate based meta-analysis technique to simultaneously examine GM and WM abnormalities in schizophrenia and to assess the effects of gender, chronicity, negative symptoms and other clinical variables. 79 studies meeting our inclusion criteria were included in the meta-analysis. Schizophrenia was associated with GM reductions in the bilateral insula/inferior frontal cortex, superior temporal gyrus, anterior cingulate gyrus/medial frontal cortex, thalamus and left amygdala. In WM analyses of volumetric and diffusion-weighted images, schizophrenia was associated with decreased FA and/or WM in interhemispheric fibers, anterior thalamic radiation, inferior longitudinal fasciculi, inferior frontal occipital fasciculi, cingulum and fornix. Male gender, chronic illness and negative symptoms were associated with more severe GM abnormalities and illness chronicity was associated with more severe WM deficits. The meta-analyses revealed overlapping GM and WM structural findings in schizophrenia, characterized by bilateral anterior cortical, limbic and subcortical GM abnormalities, and WM changes in regions including tracts that connect these structures within and between hemispheres. However, the available findings are biased towards characteristics of schizophrenia samples with poor prognosis. (C) 2011 Elsevier B.V. All rights reserved.

The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules”) decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.

Background We conducted a meta-analysis of gray matter abnormalities in bipolar disorder (BD) using voxel-based morphometry studies to help clarify the structural abnormalities underpinning this condition. Methods A systematic review was conducted for voxel-based morphometry studies of patients with BD. Meta-analyses of gray matter differences between BD and control subjects were undertaken using “signed differential mapping,” a novel method that, in contrast to previously used techniques, allows inclusion of negative findings and ensures that single studies do not exert undue influence on the results. Meta-regression and subgroup analyses were used to examine the effect of moderator variables on gray matter abnormalities. Results A total of 21 studies comparing gray matter volumes of 660 BD patients and 770 healthy control subjects were included. Gray matter reduction in left rostral anterior cingulate cortex (ACC) and right fronto-insular cortex was associated with BD. Fronto-insular cortex abnormality was not evident in early phases of the illness. In chronic patients, longer duration of illness was associated with increased gray matter in a cluster that included basal ganglia, subgenual ACC, and amygdala. Lithium treatment was associated with enlargement of ACC gray matter volumes, which overlapped with the region where gray matter was reduced in BD. Conclusions The most robust gray matter reductions in BD occur in anterior limbic regions, which may be related to the executive control and emotional processing abnormalities seen in this patient population. Clinical factors such as illness duration and lithium treatment also impact on case-control comparisons of gray matter volume.

Whole-brain anatomical connectivity in living humans can be modeled as a network with diffusion-MRI and tractography. Network nodes are associated with distinct grey-matter regions, while white-matter fiber bundles serve as interconnecting network links. However, the lack of a gold standard for regional parcellation in brain MRI makes the definition of nodes arbitrary, meaning that network nodes are defined using templates employing either random or anatomical parcellation criteria. Consequently, the number of nodes included in networks studied by different authors has varied considerably, from less than 100 up to more than 10(4). Here, we systematically and quantitatively assess the behavior, structure and topological attributes of whole-brain anatomical networks over a wide range of nodal scales, a variety of grey-matter parcellations as well as different diffusion-MRI acquisition protocols. We show that simple binary decisions about network organization, such as whether small-worldness or scale-freeness is evident, are unaffected by spatial scale, and that the estimates of various organizational parameters (e.g. small-worldness, clustering, path length, and efficiency) are consistent across different parcellation scales at the same resolution (i.e. the same number of nodes). However, these parameters vary considerably as a function of spatial scale; for example small-worldness exhibited a difference of 95% between the widely-used automated anatomical labeling (AAL) template (approximately 100 nodes) and a 4000-node random parcellation (sigma(AAL)=1.9 vs. sigma(4000)=53.6+/-2.2). These findings indicate that any comparison of network parameters across studies must be made with reference to the spatial scale of the nodal parcellation.

In this review, we describe neuropsychological and brain imaging findings in the early stages of psychosis and schizophrenia. We focus on recent clinical high-risk studies and consider whether the evidence supports these as ‘endophenotypes’ of a vulnerability to the illness or as ‘biomarkers’ of illness onset and transition. The findings suggest that there are a number of processes at psychosis onset that may represent biomarkers of incipient illness. These neurobiological indices particularly implicate the integrity of frontal and temporal cortices, which may or may not be related to the genetics of psychosis (i.e. potential ‘endophenotypes’). However, these brain regions are dynamically changing during normal maturation, meaning that any putative neurobiological markers identified at the earliest stages of illness may be relatively unstable. We suggest that, while such measures may be readily identified as potential neurobiological markers of established illness, they are inconsistent at (or around) the time of illness onset when assessed cross-sectionally. Instead, identification of more valid risk markers may require longitudinal assessment to ascertain normal or abnormal trajectories of neurodevelopment. Accordingly, we assert that the current conceptualisations of potential biomarkers and/or ‘endophenotypes’ for schizophrenia may need to be reconsidered in the context of normal and abnormal brain maturational processes at the time of onset of psychotic disorders.

Although structural magnetic resonance imaging (sMRI) and neuropathological investigations offer complementary information that can be used to formulate and test hypotheses about pathophysiological mechanisms in psychiatric disorders, the findings from these two fields are seldom integrated in a systematic manner. In this study, we overview recent sMRI findings in schizophrenia and bipolar disorder and consider how they relate to neuropathological data.sMRI research indicates that schizophrenia is associated with volumetric reductions in a network of frontal, temporal, limbic, striatal, and thalamic regions. Some of these abnormalities are apparent prior to psychosis onset and may progress with ongoing illness. sMRI findings in bipolar disorder have been more variable, with both volumetric increases and decreases being reported across several brain regions at different illness stages. Neuropathological studies of both patient groups suggest the cellular changes associated with these volumetric differences affect diverse tissue compartments in a regionally heterogeneous way.These findings suggest that any putative pathophysiological mechanism in schizophrenia or bipolar disorder should account for the dynamic, complex, and regionally heterogeneous brain abnormalities seen in these patients. We contend that greater integration of the findings from these two fields will facilitate more targeted and hypothesis-driven research in the future.

We arm researchers with a simple method to chart a macroscopic cortico-cortical connectivity network in living human subjects. The researcher provides a diffusion-magnetic resonance imaging (MRI) data set and N cortical regions of interest. In return, we provide an N times N structural adjacency matrix (SAM) quantifying the relative connectivity between all cortical region pairs. We also return a connectivity map for each pair to enable visualization of interconnecting fiber bundles. The measure of connectivity we devise is: 1) free of length bias, 2) proportional to fiber bundle cross-sectional area, and 3) invariant to an exchange of seed and target. We construct a 3-D lattice scaffolding (graph) for white-matter by drawing a link between each pair of voxels in a 26-voxel neighborhood for which their two respective principal eigenvectors form a sufficiently small angle. The connectivity between a cortical region pair is then measured as the maximum number of link-disjoint paths that can be established between them in the white-matter graph. We devise an efficient Edmonds-Karp-like algorithm to compute a conservative bound on the maximum number of link-disjoint paths. Using both simulated and authentic diffusion-tensor imaging data, we demonstrate that the number of link-disjoint paths as a measure of connectivity satisfies properties 1)-3), unlike the fraction of intersecting streamlines-the measure intrinsic to most existing probabilistic tracking algorithms. Finally, we present connectivity maps of some notoriously difficult to track longitudinal and contralateral fasciculi.

A key challenge for systems neuroscience is the question of how to understand the complex network organization of the brain on the basis of neuroimaging data. Similar challenges exist in other specialist areas of systems biology because complex networks emerging from the interactions between multiple non-trivially interacting agents are found quite ubiquitously in nature, from protein interactomes to ecosystems. We suggest that one way forward for analysis of brain networks will be to quantify aspects of their organization which are likely to be generic properties of a broader class of biological systems. In this introductory review article we will highlight four important aspects of complex systems in general: fractality or scale-invariance; criticality; small-world and related topological attributes; and modularity. For each concept we will provide an accessible introduction, an illustrative data-based example of how it can be used to investigate aspects of brain organization in neuroimaging experiments, and a brief review of how this concept has been applied and developed in other fields of biomedical and physical science. The aim is to provide a didactic, focussed and user-friendly introduction to the concepts of complexity science for neuroscientists and neuroimagers.

Voxel-based morphometry (VBM) is a popular tool for mapping neuroanatomical changes in schizophrenia patients. Several recent meta-analyses have identified the brain regions in which patients most consistently show grey matter reductions, although they have not examined whether such changes reflect differences in grey matter concentration (GMC) or grey matter volume (GMV). These measures assess different aspects of grey matter integrity, and may therefore reflect different pathological processes. In this study, we used the Anatomical Likelihood Estimation procedure to analyse significant differences reported in 37 VBM studies of schizophrenia patients, incorporating data from 1646 patients and 1690 controls, and compared the findings of studies using either GMC or GMV to index grey matter differences. Analysis of all studies combined indicated that grey matter reductions in a network of frontal, temporal, thalamic and striatal regions are among the most frequently reported in literature. GMC reductions were generally larger and more consistent than GMV reductions, and were more frequent in the insula, medial prefrontal, medial temporal and striatal regions. GMV reductions were more frequent in dorso-medial frontal cortex, and lateral and orbital frontal areas. These findings support the primacy of frontal, limbic, and subcortical dysfunction in the pathophysiology of schizophrenia, and suggest that the grey matter changes observed with MRI may not necessarily result from a unitary pathological process.

Objective: Evidence related to overlapping clinical and genetic risk factors in schizophrenia and bipolar disorder (BD) have raised concerns about the validity of ‘Kraepelinian dichotomy’. As controversies mainly arise in mixed psychoses that occupy the intermediate zone between schizophrenia and BD, investigating neurobiological markers of mixed psychoses may be relevant to understanding the nature of psychotic disorders. Method: In this article, we review studies comparing magnetic resonance imaging, neuropsychological and electrophysiological findings in mixed psychoses with each other, as well as with more prototypical cases of schizophrenia and BD. Results: The evidence reviewed suggests that mixed psychoses may be associated with different genetic and neurobiological markers compared with prototypical forms of schizophrenia and BD. Conclusion: These findings may be compatible with more sophisticated versions of dimensional and continuum models or, alternatively, they may suggest that there is an intermediate third category between prototypical schizophrenia and BD.

Background While there is evidence to suggest that major depressive disorder (MDD) is associated with structural brain abnormalities, the precise nature of these abnormalities remains unclear. Aims To review recent structural magnetic resonance imaging (MRI) research findings in MDD while considering the potential influence of key clinical and demographic variables. Method A selective review of all T1-weighted structural MRI studies published between 2000 and 2007 in adult samples of MDD patients. Results Volumetric reductions of the hippocampus, basal ganglia and OFC and SGPFC are consistently found in MDD patients, with more persistent forms of MDD (e.g., multiple episodes or repeated relapses, longer illness duration) being associated with greater impact on regional brain volumes. Gender, medication, stage of illness, and family history all affect the nature of the findings in a regionally specific manner. Limitations Overall, differences between the samples in factors such as illness severity, medication, gender and family history of mental illness makes difficult to identify their confounding effects on the observed neuroanatomical changes. Also, the tracing protocols used for particular brain regions were different amongst the reviewed studies, making difficult to compare their findings. Conclusions The data support the notion that MDD involves pathological alterations of limbic and cortical structures, and that they are generally more apparent in patients with more severe or persistent forms of the illness.

Cannabis is the most widely used illicit drug in the developed world. Despite this, there is a paucity of research examining its long-term effect on the human brain.To determine whether long-term heavy cannabis use is associated with gross anatomical abnormalities in 2 cannabinoid receptor-rich regions of the brain, the hippocampus and the amygdala.Cross-sectional design using high-resolution (3-T) structural magnetic resonance imaging.Participants were recruited from the general community and underwent imaging at a hospital research facility.Fifteen carefully selected long-term (10 years) and heavy (5 joints daily) cannabis-using men (mean age, 39.8 years; mean duration of regular use, 19.7 years) with no history of polydrug abuse or neurologic/mental disorder and 16 matched nonusing control subjects (mean age, 36.4 years).Volumetric measures of the hippocampus and the amygdala combined with measures of cannabis use. Subthreshold psychotic symptoms and verbal learning ability were also measured.Cannabis users had bilaterally reduced hippocampal and amygdala volumes (P = .001), with a relatively (and significantly [P = .02]) greater magnitude of reduction in the former (12.0% vs 7.1%). Left hemisphere hippocampal volume was inversely associated with cumulative exposure to cannabis during the previous 10 years (P = .01) and subthreshold positive psychotic symptoms (P.001). Positive symptom scores were also associated with cumulative exposure to cannabis (P = .048). Although cannabis users performed significantly worse than controls on verbal learning (P.001), this did not correlate with regional brain volumes in either group.These results provide new evidence of exposure-related structural abnormalities in the hippocampus and amygdala in long-term heavy cannabis users and corroborate similar findings in the animal literature. These findings indicate that heavy daily cannabis use across protracted periods exerts harmful effects on brain tissue and mental health.

The anterior cingulate cortex (ACC) is a functionally heterogeneous region involved in diverse cognitive and emotional processes that support goal-directed behaviour. Structural magnetic resonance imaging (MRI) and neuropathological findings over the past two decades have converged to suggest abnormalities in the region may represent a neurobiological basis for many of the clinical manifestations of schizophrenia. However, while each approach offers complimentary information that can provide clues regarding underlying patholophysiological processes, the findings from these 2 fields are seldom integrated. In this article, we review structural neuroimaging and neuropathological studies of the ACC, focusing on the unique information they provide. The available imaging data suggest grey matter reductions in the ACC precede psychosis onset in some categories of high-risk individuals, show sub-regional specificity, and may progress with illness duration. The available post-mortem findings indicate these imaging-related changes are accompanied by reductions in neuronal, synaptic, and dendritic density, as well as increased afferent input, suggesting the grey matter differences observed with MRI arise from alterations in both neuronal and non-neuronal tissue compartments. We discuss the potential mechanisms that might facilitate integration of these findings and consider strategies for future research.

Background Abnormalities of the anterior cingulate cortex (ACC) are frequently implicated in the pathophysiology of psychotic disorders, but whether such changes are apparent before psychosis onset remains unclear. In this study, we characterized prepsychotic ACC abnormalities in a sample of individuals at ultra-high-risk (UHR) for psychosis. Methods Participants underwent baseline magnetic resonance imaging and were followed-up over 12–24 months to ascertain diagnostic outcomes. Baseline ACC morphometry was then compared between UHR individuals who developed psychosis (UHR-P; n = 35), those who did not (UHR-NP; n = 35), and healthy control subjects ( n = 33). Results Relative to control subjects, UHR-P individuals displayed bilateral thinning of a rostral paralimbic ACC region that was negatively correlated with negative symptoms, whereas UHR-NP individuals displayed a relative thickening of dorsal and rostral limbic areas that was correlated with anxiety ratings. Baseline ACC differences between the two UHR groups predicted time to psychosis onset, independently of symptomatology. Subdiagnostic comparisons revealed that changes in the UHR-P group were driven by individuals subsequently diagnosed with a schizophrenia spectrum psychosis. Conclusions These findings indicate that anatomic abnormalities of the ACC precede psychosis onset and that baseline ACC differences distinguish between UHR individuals who do and do not subsequently develop frank psychosis. They also indicate that prepsychotic changes are relatively specific to individuals who develop a schizophrenia spectrum disorder, suggesting they may represent a diagnostically specific risk marker.