Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach

Frequency dependent alterations in regional homogeneity of baseline brain activity in schizophrenia

Low frequency oscillations are essential in cognitive function impairment in schizophrenia. While functional connectivity can reveal the synchronization between distant brain regions, the regional abnormalities in task-independent baseline brain activity are less clear, especially in specific frequency bands. Here, we used a regional homogeneity (ReHo) method combined with resting-state functional magnetic resonance imaging to investigate low frequency spontaneous neural activity in the three different frequency bands (slow-5:0.01-0.027 Hz; slow-4:0.027-0.08 Hz; and typical band: 0.01-0.08 Hz) in 69 patients with schizophrenia and 62 healthy controls. Compared with controls, schizophrenia patients exhibited decreased ReHo in the precentral gyrus, middle occipital gyrus, and posterior insula, whereas increased ReHo in the medial prefrontal cortex and anterior insula. Significant differences in ReHo between the two bands were found in fusiform gyrus and superior frontal gyrus (slow-4> slow-5), and in basal ganglia, parahippocampus, and dorsal middle prefrontal gyrus (slow-5> slow-4). Importantly, we identified significant interaction between frequency bands and groups in the inferior occipital gyrus and caudate body. This study demonstrates that ReHo changes in schizophrenia are widespread and frequency dependent.

Functional MRI in the investigation of blast-related traumatic brain injury

This review focuses on the application of functional magnetic resonance imaging (fMRI) to the investigation of blast-related traumatic brain injury (bTBI). Relatively little is known about the exact mechanisms of neurophysiological injury and pathological and functional sequelae of bTBI. Furthermore, in mild bTBI, standard anatomical imaging techniques (MRI and computed tomography) generally fail to show focal lesions and most of the symptoms present as subjective clinical functional deficits. Therefore, an objective test of brain functionality has great potential to aid in patient diagnosis and provide a sensitive measurement to monitor disease progression and treatment. The goal of this review is to highlight the relevant body of blast-related TBI literature and present suggestions and considerations in the development of fMRI studies for the investigation of bTBI. The review begins with a summary of recent bTBI publications followed by discussions of various elements of blast-related injury. Brief reviews of some fMRI techniques that focus on mental processes commonly disrupted by bTBI, including working memory, selective attention, and emotional processing, are presented in addition to a short review of resting state fMRI. Potential strengths and weaknesses of these approaches as regards bTBI are discussed. Finally, this review presents considerations that must be made when designing fMRI studies for bTBI populations, given the heterogeneous nature of bTBI and its high rate of comorbidity with other physical and psychological injuries.

Brain connectivity studies in schizophrenia: unravelling the effects of antipsychotics

Impaired brain connectivity is a hallmark of schizophrenia brain dysfunction. However, the effect of drug treatment and challenges on the dysconnectivity of functional networks in schizophrenia is an understudied area. In this review, we provide an overview of functional magnetic resonance imaging studies examining dysconnectivity in schizophrenia and discuss the few studies which have also attempted to probe connectivity changes with antipsychotic drug treatment. We conclude with a discussion of possible avenues for further investigation.

Aberrant connectivity of areas for decoding degraded speech in patients with auditory verbal hallucinations

Auditory verbal hallucinations (AVH) are a hallmark of psychotic experience. Various mechanisms including misattribution of inner speech and imbalance between bottom-up and top-down factors in auditory perception potentially due to aberrant connectivity between frontal and temporo-parietal areas have been suggested to underlie AVH. Experimental evidence for disturbed connectivity of networks sustaining auditory-verbal processing is, however, sparse. We compared functional resting-state connectivity in 49 psychotic patients with frequent AVH and 49 matched controls. The analysis was seeded from the left middle temporal gyrus (MTG), thalamus, angular gyrus (AG) and inferior frontal gyrus (IFG) as these regions are implicated in extracting meaning from impoverished speech-like sounds. Aberrant connectivity was found for all seeds. Decreased connectivity was observed between the left MTG and its right homotope, between the left AG and the surrounding inferior parietal cortex (IPC) and the left inferior temporal gyrus, between the left thalamus and the right cerebellum, as well as between the left IFG and left IPC, and dorsolateral and ventrolateral prefrontal cortex (DLPFC/VLPFC). Increased connectivity was observed between the left IFG and the supplementary motor area (SMA) and the left insula and between the left thalamus and the left fusiform gyrus/hippocampus. The predisposition to experience AVH might result from decoupling between the speech production system (IFG, insula and SMA) and the self-monitoring system (DLPFC, VLPFC, IPC) leading to misattribution of inner speech. Furthermore, decreased connectivity between nodes involved in speech processing (AG, MTG) and other regions implicated in auditory processing might reflect aberrant top-down influences in AVH.

Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia

Altered brain connectivity has emerged as a central feature of schizophrenia. Low frequency oscillations and connectivity strength (CS) of resting state brain networks are altered in patients with schizophrenia (SZs). However, the relationship between these two measures has not yet been studied. Such work may be helpful in understanding the so-called "rich club" organization (i.e. high-CS nodes are more densely connected among themselves than are nodes of a lower CS in the human brain) in healthy controls (HCs) and SZs. Here we present a study of HCs and SZs examining low frequency oscillations and CS by first decomposing resting state fMRI (R-fMRI) data into independent components (ICs) using group independent component analysis (ICA) and computing the low frequency power ratio (LFPR) of each ICA time course. Weighted brain graphs consisting of ICs were built based on correlations between ICA time courses. Positive CS and negative CS of each node in the brain graphs were then examined. The correlations between LFPR and CSs as well as "rich club" coefficients of group mean brain graphs were assessed. Results demonstrate that the LFPR of some ICs were lower in SZs compared to HCs. In addition, LFPR was correlated with positive CS in HCs, but to a lesser extent in SZs. HCs showed higher normalized rich club parameter than SZs. The findings provide new insight into disordered intrinsic brain graphs in schizophrenia.

Hierarchical alteration of brain structural and functional networks in female migraine sufferers

BACKGROUND

Little is known about the changes of brain structural and functional connectivity networks underlying the pathophysiology in migraine. We aimed to investigate how the cortical network reorganization is altered by frequent cortical overstimulation associated with migraine.

METHODOLOGY/PRINCIPAL FINDINGS

Gray matter volumes and resting-state functional magnetic resonance imaging signal correlations were employed to construct structural and functional networks between brain regions in 43 female patients with migraine (PM) and 43 gender-matched healthy controls (HC) by using graph theory-based approaches. Compared with the HC group, the patients showed abnormal global topology in both structural and functional networks, characterized by higher mean clustering coefficients without significant change in the shortest absolute path length, which indicated that the PM lost optimal topological organization in their cortical networks. Brain hubs related to pain-processing revealed abnormal nodal centrality in both structural and functional networks, including the precentral gyrus, orbital part of the inferior frontal gyrus, parahippocampal gyrus, anterior cingulate gyrus, thalamus, temporal pole of the middle temporal gyrus and the inferior parietal gyrus. Negative correlations were found between migraine duration and regions with abnormal centrality. Furthermore, the dysfunctional connections in patients' cortical networks formed into a connected component and three dysregulated modules were identified involving pain-related information processing and motion-processing visual networks.

CONCLUSIONS

Our results may reflect brain alteration dynamics resulting from migraine and suggest that long-term and high-frequency headache attacks may cause both structural and functional connectivity network reorganization. The disrupted information exchange between brain areas in migraine may be reshaped into a hierarchical modular structure progressively.

Functional and structural MR imaging in neuropsychiatric disorders, part 2: application in schizophrenia and autism

SUMMARY

During the past decade, the application of advanced MR imaging techniques in neuropsychiatric disorders has seen a rapid increase. Disease-specific alterations in brain function can be assessed by fMRI. Structural GM and WM properties are increasingly investigated by DTI and voxel-based approaches like VBM. These methods provide neurobiologic correlates for brain architecture and function, evaluation tools for therapeutic approaches, and potential early markers for diagnosis. Having provided insight into principles of functional and structural imaging and delineated common findings in mild cognitive impairment and Alzheimer disease in Part 1 of this review, we will now focus on autism and schizophrenia as common psychiatric disorders covering different stages of the life span. This review concludes by summarizing current applications, limitations, and future prospects in the field of MR imaging-based neuroimaging.

Delayed white matter growth trajectory in young nonpsychotic siblings of patients with childhood-onset schizophrenia

CONTEXT Nonpsychotic siblings of patients with childhood-onset schizophrenia (COS) share cortical gray matter abnormalities with their probands at an early age; these normalize by the time the siblings are aged 18 years, suggesting that the gray matter abnormalities in schizophrenia could be an age-specific endophenotype. Patients with COS also show significant white matter (WM) growth deficits, which have not yet been explored in nonpsychotic siblings. OBJECTIVE To study WM growth differences in nonpsychotic siblings of patients with COS. DESIGN Longitudinal (5-year) anatomic magnetic resonance imaging study mapping WM growth using a novel tensor-based morphometry analysis. SETTING National Institutes of Health Clinical Center, Bethesda, Maryland. PARTICIPANTS Forty-nine healthy siblings of patients with COS (mean [SD] age, 16.1 [5.3] years; 19 male, 30 female) and 57 healthy persons serving as controls (age, 16.9 [5.3] years; 29 male, 28 female). INTERVENTION Magnetic resonance imaging. MAIN OUTCOME MEASURE White matter growth rates. RESULTS We compared the WM growth rates in 3 age ranges. In the youngest age group (7 to <14 years), we found a significant difference in growth rates, with siblings of patients with COS showing slower WM growth rates in the parietal lobes of the brain than age-matched healthy controls (false discovery rate, q = 0.05; critical P = .001 in the bilateral parietal WM; a post hoc analysis identified growth rate differences only on the left side, critical P = .004). A growth rate difference was not detectable at older ages. In 3-dimensional maps, growth rates in the siblings even appeared to surpass those of healthy individuals at later ages, at least locally in the brain, but this effect did not survive a multiple comparisons correction. CONCLUSIONS In this first longitudinal study of nonpsychotic siblings of patients with COS, the siblings showed early WM growth deficits, which normalized with age. As reported before for gray matter, WM growth may also be an age-specific endophenotype that shows compensatory normalization with age.

Multimodal functional and structural imaging investigations in psychosis research

Substantial pathophysiological questions about the relationship of brain pathologies in psychosis can only be answered by multimodal neuroimaging approaches combining different imaging modalities such as structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), positron emission tomography (PET) and magnetic-resonance spectroscopy. In particular, the multimodal imaging approach has the potential to shed light on the neuronal mechanisms underlying the major brain structural and functional pathophysiological features of schizophrenia and high-risk states such as prefronto-temporal gray matter reduction, altered higher-order cognitive processing, or disturbed dopaminergic and glutamatergic neurotransmission. In recent years, valuable new findings have been revealed in these fields by multimodal imaging studies mostly reflecting a direct and aligned correlation of brain pathologies in psychosis. However, the amount of multimodal studies is still limited, and further efforts have to be made to consolidate previous findings and to extend the scope to other pathophysiological parameters contributing to the pathogenesis of psychosis. Here, investigating the genetic foundations of brain pathology relationships is a major challenge for future multimodal imaging applications in psychosis research.

Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia

Multimodal fusion is an effective approach to better understand brain diseases. However, most such instances have been limited to pair-wise fusion; because there are often more than two imaging modalities available per subject, there is a need for approaches that can combine multiple datasets optimally. In this paper, we extended our previous two-way fusion model called "multimodal CCA+joint ICA", to three or N-way fusion, that enables robust identification of correspondence among N data types and allows one to investigate the important question of whether certain disease risk factors are shared or distinct across multiple modalities. We compared "mCCA+jICA" with its alternatives in a 3-way fusion simulation and verified its advantages in both decomposition accuracy and modal linkage detection. We also applied it to real functional Magnetic Resonance Imaging (fMRI)-Diffusion Tensor Imaging (DTI) and structural MRI fusion to elucidate the abnormal architecture underlying schizophrenia (n=97) relative to healthy controls (n=116). Both modality-common and modality-unique abnormal regions were identified in schizophrenia. Specifically, the visual cortex in fMRI, the anterior thalamic radiation (ATR) and forceps minor in DTI, and the parietal lobule, cuneus and thalamus in sMRI were linked and discriminated between patients and controls. One fMRI component with regions of activity in motor cortex and superior temporal gyrus individually discriminated schizophrenia from controls. Finally, three components showed significant correlation with duration of illness (DOI), suggesting that lower gray matter volumes in parietal, frontal, and temporal lobes and cerebellum are associated with increased DOI, along with white matter disruption in ATR and cortico-spinal tracts. Findings suggest that the identified fractional anisotropy changes may relate to the corresponding functional/structural changes in the brain that are thought to play a role in the clinical expression of schizophrenia. The proposed "mCCA+jICA" method showed promise for elucidating the joint or coupled neuronal abnormalities underlying mental illnesses and improves our understanding of the disease process.

White matter tract abnormalities in first-episode psychosis

Fibers connecting fronto-temporal and fronto-medial structures that pass through the anterior limb of the internal capsule (ALIC) subserve executive and psychomotor functioning. Both of these functions are adversely affected in schizophrenia, and may be abnormal at illness onset. In a study of first-episode psychosis, we used diffusion tensor imaging (DTI) and cognitive testing to examine ALIC integrity. Fourteen early psychosis patients and 29 healthy volunteers were included. Symptoms were assessed with the Positive and Negative Syndromes Scale (PANSS). All structural and diffusion scans were acquired on a GE Signa 1.5T scanner. A T1-weighted 3D FSPGR Inversion Recovery imaging series was acquired for manual seeding in structural space. Diffusion tensor imaging (DTI) was performed, and all DTI images were co-registered to structural space. Seeds were manually drawn bilaterally on the coronal plane at a specified location. Diffusion images were post-processed for subsequent Tract-based Spatial Statistics (TBSS) analysis. First-episode psychosis patients had significantly smaller fronto-medial and fronto-temporal AIC tract volumes compared to healthy volunteers on the left and the right (p-values<0.04). No differences in mean fractional anisotropy (FA) were seen within either left or right tracts (p-values>0.05), nor did TBSS reveal any other differences in FA values between groups in other regions. Relationships between tract volumes and symptom severity were not observed in this study.

Functional and anatomical connectivity abnormalities in cognitive division of anterior cingulate cortex in schizophrenia

Introduction

Current pathophysiological theories of schizophrenia highlight the role of altered brain functional and anatomical connectivity. The cognitive division of anterior cingulate cortex (ACC-cd) is a commonly reported abnormal brain region in schizophrenia for its importance in cognitive control process. The aim of this study was to investigate the functional and anatomical connectivity of ACC-cd and its cognitive and clinical manifestation significance in schizophrenia by using the resting-state functional magnetic resonance imaging (fMRI) and the diffusion tensor imaging (DTI).

Methods

Thirty-three medicated schizophrenics and 30 well-matched health controls were recruited. Region-of-interest (ROI)-based resting-state functional connectivity analysis and Tract-Based Spatial Statistics (TBSS) were performed on 30 patients and 30 controls, and 24 patients and 29 controls, respectively. The Pearson correlation was performed between the imaging measures and the Stroop performance and scores of the Positive and Negative Syndrome Scale (PANSS), respectively.

Results

Patients with schizophrenia showed significantly abnormal in the functional connectivity and its hemispheric asymmetry of the ACC-cd with multiple brain areas, e.g., decreased positive connectivity with the bilateral putamen and caudate, increased negative connectivity with the left posterior cingulated cortex (PCC), increased asymmetry of connectivity strength with the contralateral inferior frontal gyrus (IFG). The FA of the right anterior cingulum was significantly decreased in patients group (p = 0.014). The abnormal functional and structural connectivity of ACC-cd were correlated with Stroop performance and the severity of the symptoms in patients.

Conclusions

Our results suggested that the abnormal connectivity of the ACC-cd might play a role in the cognitive impairment and clinical symptoms in schizophrenia.

Constrained source-based morphometry identifies structural networks associated with default mode network

We present constrained source-based morphometry (SBM), a multivariate semiblind data-driven approach, to explore a possible brain-wide structural network in both gray matter (GM) and white matter (WM) associated with the functional default mode network (DMN). With this approach, we utilize seed regions associated with the DMN as constraints on GM maps and derive a joint GM and WM structural network automatically through a multivariate data-driven approach. In this article, we first provide a simulation to validate the constrained SBM approach. The approach was then applied to structural magnetic resonance imaging and diffusion tensor imaging data obtained from 102 healthy controls. Regions that have consistently reported to be associated with the DMN were used to create an a priori mask that was integrated within an independent component analysis framework to derive the structural network associated with the DMN. We identified a set of GM and corresponding WM regions contributing to a structural network underlying the functional DMN. The GM regions consisted mainly of the precuneus, superior and medial frontal gyri, middle temporal gyrus, hippocampus, cuneus, and cerebellum. The WM regions included the cingulum, corpus callosum, corona radiata, association fibers, and middle cerebellar peduncle. Significant gender differences in the relationship between intelligence quotient (IQ) and the identified structural network were observed. Our findings suggest that the functional DMN is underpinned by a corresponding brain-wide structural network. The constrained SBM approach is additionally applicable to a wide variety of problems identifying structural networks from seed regions.

Reduced connectivity in the self-processing network of schizophrenia patients with poor insight

Lack of insight (unawareness of illness) is a common and clinically relevant feature of schizophrenia. Reduced levels of self-referential processing have been proposed as a mechanism underlying poor insight. The default mode network (DMN) has been implicated as a key node in the circuit for self-referential processing. We hypothesized that during resting state the DMN network would show decreased connectivity in schizophrenia patients with poor insight compared to patients with good insight. Patients with schizophrenia were recruited from mental health care centers in the north of the Netherlands and categorized in groups having good insight (n= 25) or poor insight (n = 19). All subjects underwent a resting state fMRI scan. A healthy control group (n = 30) was used as a reference. Functional connectivity of the anterior and posterior part of the DMN, identified using Independent Component Analysis, was compared between groups. Patients with poor insight showed lower connectivity of the ACC within the anterior DMN component and precuneus within the posterior DMN component compared to patients with good insight. Connectivity between the anterior and posterior part of the DMN was lower in patients than controls, and qualitatively different between the good and poor insight patient groups. As predicted, subjects with poor insight in psychosis showed decreased connectivity in DMN regions implicated in self-referential processing, although this concerned only part of the network. This finding is compatible with theories implying a role of reduced self-referential processing as a mechanism contributing to poor insight.

A framework for interpreting functional networks in schizophrenia

Some promising genetic correlates of schizophrenia have emerged in recent years but none explain more than a small fraction of cases. The challenge of our time is to characterize the neuronal networks underlying schizophrenia and other neuropsychiatric illnesses. Early models of schizophrenia have been limited by the ability to readily evaluate large-scale networks in living patients. With the development of resting state and advanced structural magnetic resonance imaging, it has become possible to do this. While we are at an early stage, a number of models of intrinsic brain networks have been developed to account for schizophrenia and other neuropsychiatric disorders. This paper reviews the recent voxel-based morphometry (VBM), diffusion tensor imaging (DTI), and resting functional magnetic resonance imaging literature in light of the proposed networks underlying these disorders. It is suggested that there is support for recently proposed models that suggest a pivotal role for the salience network. However, the interactions of this network with the default mode network and executive control networks are not sufficient to explain schizophrenic symptoms or distinguish them from other neuropsychiatric disorders. Alternatively, it is proposed that schizophrenia arises from a uniquely human brain network associated with directed effort including the dorsal anterior and posterior cingulate cortex (PCC), auditory cortex, and hippocampus while mood disorders arise from a different brain network associated with emotional encoding including the ventral anterior cingulate cortex (ACC), orbital frontal cortex, and amygdala. Both interact with the dorsolateral prefrontal cortex and a representation network including the frontal and temporal poles and the fronto-insular cortex, allowing the representation of the thoughts, feelings, and actions of self and others across time.

Modeling the outcome of structural disconnection on resting-state functional connectivity

A growing body of experimental evidence suggests that functional connectivity at rest is shaped by the underlying anatomical structure. Furthermore, the organizational properties of resting-state functional networks are thought to serve as the basis for an optimal cognitive integration. A disconnection at the structural level, as occurring in some brain diseases, would then lead to functional and presumably cognitive impairments. In this work, we propose a computational model to investigate the role of a structural disconnection (encompassing putative local/global and axonal/synaptic mechanisms) on the organizational properties of emergent functional networks. The brain's spontaneous neural activity and the corresponding hemodynamic response were simulated using a large-scale network model, consisting of local neural populations coupled through white matter fibers. For a certain coupling strength, simulations reproduced healthy resting-state functional connectivity with graph properties in the range of the ones reported experimentally. When the structural connectivity is decreased, either globally or locally, the resultant simulated functional connectivity exhibited a network reorganization characterized by an increase in hierarchy, efficiency and robustness, a decrease in small-worldness and clustering and a narrower degree distribution, in the same way as recently reported for schizophrenia patients. Theoretical results indicate that most disconnection-related neuropathologies should induce the same qualitative changes in resting-state brain activity.

Less efficient information transfer in Cys-allele carriers of DISC1: a brain network study based on diffusion MRI

Previous neuroimaging studies of brain networks have revealed less efficient information transfer in patients with schizophrenia. However, the underlying genetic basis remains largely unexplored. In this study, we investigated the brain anatomical networks of 278 healthy volunteers with different genotypes in the common missense variant (Ser704Cys) of the Disrupted-in-Schizophrenia-1 (DISC1) gene, which is one of the main susceptibility genes of schizophrenia and other psychiatric disorders. The anatomical brain network for each individual was constructed using fiber tractography technique based on diffusion magnetic resonance imaging (dMRI). The properties of this network were then calculated using graph theory. This revealed that Cys-allele carriers showed significantly lower global efficiency of their brain networks than Ser homozygotes, thereby supporting our hypothesis that genetic variation in DISC1 may relate to the risk of schizophrenia by affecting the efficiency of brain network. Additional dMRI analyses were also performed at different levels, with a convergent trend towards decreased white matter integrity being consistently observed in Cys-allele carriers. Together these findings not only provide new clues for understanding DISC1 function, but also suggest that network analyses based on graph theory combined with neuroimaging techniques may reveal structural disruptions related to genetic risk in the brains of healthy individuals.

A chemical genetic approach identifies piperazine antipsychotics as promoters of CNS neurite growth on inhibitory substrates

Injury to the central nervous system (CNS) can result in lifelong loss of function due in part to the regenerative failure of CNS neurons. Inhibitory proteins derived from myelin and the astroglial scar are major barriers for the successful regeneration of injured CNS neurons. Previously, we described the identification of a novel compound, F05, which promotes neurite growth from neurons challenged with inhibitory substrates in vitro, and promotes axonal regeneration in vivo (Usher et al., 2010). To identify additional regeneration-promoting compounds, we used F05-induced gene expression profiles to query the Broad Institute Connectivity Map, a gene expression database of cells treated with >1300 compounds. Despite no shared chemical similarity, F05-induced changes in gene expression were remarkably similar to those seen with a group of piperazine phenothiazine antipsychotics (PhAPs). In contrast to antipsychotics of other structural classes, PhAPs promoted neurite growth of CNS neurons challenged with two different glial derived inhibitory substrates. Our pharmacological studies suggest a mechanism whereby PhAPs promote growth through antagonism of calmodulin signaling, independent of dopamine receptor antagonism. These findings shed light on mechanisms underlying neurite-inhibitory signaling, and suggest that clinically approved antipsychotic compounds may be repurposed for use in CNS injured patients.

Investigation of long-term reproducibility of intrinsic connectivity network mapping: a resting-state fMRI study

BACKGROUND AND PURPOSE

Connectivity mapping based on resting-state fMRI is rapidly developing, and this methodology has great potential for clinical applications. However, before resting-state fMRI can be applied for diagnosis, prognosis, and monitoring treatment for an individual patient with neurologic or psychiatric diseases, it is essential to assess its long-term reproducibility and between-subject variations among healthy individuals. The purpose of the study was to quantify the long-term test-retest reproducibility of ICN measures derived from resting-state fMRI and to assess the between-subject variation of ICN measures across the whole brain.

MATERIALS AND METHODS

Longitudinal resting-state fMRI data of 6 healthy volunteers were acquired from 9 scan sessions during >1 year. The within-subject reproducibility and between-subject variation of ICN measures, across the whole brain and major nodes of the DMN, were quantified with the ICC and COV.

RESULTS

Our data show that the long-term test-retest reproducibility of ICN measures is outstanding, with >70% of the connectivity networks showing an ICC > 0.60. The COV across 6 healthy volunteers in this sample was >0.2, suggesting significant between-subject variation.

CONCLUSIONS

Our data indicate that resting-state ICN measures (eg, the correlation coefficients between fMRI signal-intensity profiles from 2 different brain regions) are potentially suitable as biomarkers for monitoring disease progression and treatment effects in clinical trials and individual patients. Because between-subject variation is significant, it may be difficult to use quantitative ICN measures in their current state as a diagnostic tool.

Auditory hallucinations: expectation-perception model

In this paper, we aimed to present a hypothesis that would explain the mechanism of auditory hallucinations, one of the main symptoms of schizophrenia. We propose that auditory hallucinations arise from abnormalities in the predictive coding which underlies normal perception, specifically, from the absence or attenuation of prediction error. The suggested deficiencies in processing prediction error could arise from (1) abnormal modulation of thalamus by prefrontal cortex, (2) absence or impaired transmission of external input, (3) dysfunction of the auditory and association cortex, (4) neurotransmitter dysfunction and abnormal connectivity, and (5) hyperactivity activity in auditory cortex and broad prior probability. If there is no prediction error, the initially vague prior probability develops into an explicit percept in the absence of external input, as a result of a recursive pathological exchange between auditory and prefrontal cortex. Unlike existing explanations of auditory hallucinations, we propose concrete mechanisms which underlie the imbalance between perceptual expectation and external input. Impaired processing of prediction error is reflected in reduced mismatch negativity and increased tendency to report non-existing meaningful language stimuli in white noise, shown by those suffering from auditory hallucinations. We believe that the expectation-perception model of auditory hallucinations offers a comprehensive explanation of the underpinnings of auditory hallucinations in both patients and those not diagnosed with mental illness. Therefore, our hypothesis has the potential to fill the gaps in the existing knowledge about this distressing phenomenon and contribute to improved effectiveness of treatments, targeting specific mechanisms.

Potential therapeutic avenues to tackle social cognition problems in schizophrenia

Therapeutic strategies for improving social cognition in patients with schizophrenia have shown much promise in improving social functioning, as well as remediating core psychotic symptoms. However, the efficacy of previous interventions has often been limited by the ambiguity and inconsistency of the categorized subdomains of social cognition, including theory of mind, emotion processing, social perception and attributional bias. Recent research in social and cognitive neuroscience has revealed many new issues that could contribute to the development of more integrated approaches for improving social functioning. The application of such neuroscientific work to a therapeutic and diagnostic context is likely to encourage more effective transference of learned skills to real-world social functioning. This article seeks to provide a comprehensive review of previous social cognitive interventions for schizophrenia, highlight some crucial limitations of these and present the relevance of recent advances in neuroscientific research in possible future treatment strategies. It is emphasized that a more integrated and naturalistic approach for improving social functioning with greater sensitivity for neuroscientific findings related to the psychopathology of schizophrenia is warranted.

Correspondence between structure and function in the human brain at rest

To further understanding of basic and complex cognitive functions, previous connectome research has identified functional and structural connections of the human brain. Functional connectivity is often measured by using resting-state functional magnetic resonance imaging (rs-fMRI) and is generally interpreted as an indirect measure of neuronal activity. Gray matter (GM) primarily consists of neuronal and glia cell bodies; therefore, it is surprising that the majority of connectome research has excluded GM measures. Therefore, we propose that by exploring where GM corresponds to function would aid in the understanding of both structural and functional connectivity and in turn the human connectome. A cohort of 603 healthy participants underwent structural and functional scanning on the same 3 T scanner at the Mind Research Network. To investigate the spatial correspondence between structure and function, spatial independent component analysis (ICA) was applied separately to both GM density (GMD) maps and to rs-fMRI data. ICA of GM delineates structural components based on the covariation of GMD regions among subjects. For the rs-fMRI data, ICA identified spatial patterns with common temporal features. These decomposed structural and functional components were then compared by spatial correlation. Basal ganglia components exhibited the highest structural to resting-state functional spatial correlation (r = 0.59). Cortical components generally show correspondence between a single structural component and several resting-state functional components. We also studied relationships between the weights of different structural components and identified the precuneus as a hub in GMD structural network correlations. In addition, we analyzed relationships between component weights, age, and gender; concluding that age has a significant effect on structural components.

Schizophrenia, neuroimaging and connectomics

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.

A selective review of multimodal fusion methods in schizophrenia

Schizophrenia (SZ) is one of the most cryptic and costly mental disorders in terms of human suffering and societal expenditure (van Os and Kapur, 2009). Though strong evidence for functional, structural, and genetic abnormalities associated with this disease exists, there is yet no replicable finding which has proven accurate enough to be useful in clinical decision making (Fornito et al., 2009), and its diagnosis relies primarily upon symptom assessment (Williams et al., 2010a). It is likely in part that the lack of consistent neuroimaging findings is because most models favor only one data type or do not combine data from different imaging modalities effectively, thus missing potentially important differences which are only partially detected by each modality (Calhoun et al., 2006a). It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships, is an important tool to help unravel the black box of schizophrenia. In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may help us understand the disorder in a more comprehensive and integrated manner. We also provide a table that characterizes these applications by the methods used and compare these methods in detail, especially for multivariate models, which may serve as a valuable reference that helps readers select an appropriate method based on a given research question.

Structure-function relationship of working memory activity with hippocampal and prefrontal cortex volumes

A rapidly increasing number of studies are quantifying the system-level network architecture of the human brain based on structural-to-structural and functional-to-functional relationships. However, a largely unexplored area is the nature and existence of "cross-modal" structural-functional relationships, in which, for example, the volume (or other morphological property) of one brain region is related to the functional response to a given task either in that same brain region, or another brain region. The present study investigated whether the gray matter volume of a selected group of structures (superior, middle, and inferior frontal gyri, thalamus, and hippocampus) was correlated with the fMRI response to a working memory task, within a mask of regions previously identified as involved with working memory. The subjects included individuals with schizophrenia, their siblings, and healthy controls (n = 154 total). Using rigorous permutation testing to define the null distribution, we found that the volume of the superior and middle frontal gyri was correlated with working memory activity within clusters in the intraparietal sulcus (i.e., dorsal parietal cortex) and that the volume of the hippocampus was correlated with working memory activity within clusters in the dorsal anterior cingulate cortex and left inferior frontal gyrus. However, we did not find evidence that the identified structure-function relationships differed between subject groups. These results show that long-distance structural-functional relationships exist within the human brain. The study of such cross-modal relationships represents an additional approach for studying systems-level interregional brain networks.

Graph-based network analysis in schizophrenia

Over the last few years, many studies have been published using modern network analysis of the brain. Researchers and practical doctors alike should understand this method and its results on the brain evaluation at rest, during activation and in brain disease. The studies are noninvasive and usually performed with elecroencephalographic, magnetoencephalographic, magnetic resonance imaging and diffusion tensor imaging brain recordings. Different tools for analysis have been developed, although the methods are in their early stages. The results of these analyses are of special value. Studies of these tools in schizophrenia are important because widespread and local network disturbances can be evaluated by assessing integration, segregation and several structural and functional properties. With the help of network analyses, the main findings in schizophrenia are lower optimum network organization, less efficiently wired networks, less local clustering, less hierarchical organization and signs of disconnection. There are only about twenty five relevant papers on the subject today. Only a few years of study of these methods have produced interesting results and it appears promising that the development of these methods will present important knowledge for both the preclinical signs of schizophrenia and the methods’ therapeutic effects.

Reduced anterior cingulate cognitive activation is associated with prefrontal-temporal cortical thinning in schizophrenia

BACKGROUND

The anterior cingulate cortex plays a central role in altered processes of cognitive control in schizophrenia. However, the cortical foundations of disturbed anterior cingulate cognitive activation are poorly understood. Therefore, this study investigated the association of anterior cingulate cognitive activation and cortical thickness in schizophrenia combining functional magnetic resonance imaging (fMRI) and surface-based morphometry.

METHODS

Fifty-three patients with schizophrenia according to DSM-IV and 53 age- and sex-matched healthy subjects were included and underwent fMRI and high-resolution T1-weighted MRI. fMRI data was analyzed using SPM5. Cortical thickness was calculated using an automated computerized algorithm (Freesurfer Software). Statistical cortical maps were created correlating anterior cingulate activation and cortical thickness on a node-by-node basis covering the entire cortex in schizophrenia and healthy control subjects.

RESULTS

Patients demonstrated a significantly reduced anterior cingulate cognitive activation. Significantly differing associations of anterior cingulate activation and cortical thickness were found in a pattern of dorsolateral prefrontal, superior frontal-anterior cingulate, and superior temporal cortical regions, where patients but not healthy control subjects demonstrated a significant association of reduced anterior cingulate activation and cortical thinning. A direct comparison of cortical thickness between the diagnostic groups revealed a significantly reduced cortical thickness of these prefrontotemporal regions in schizophrenia.

CONCLUSIONS

To our best knowledge, this is the first study indicating that prefrontotemporal cortical thinning constitutes a relevant cortical pathomechanism for altered cognitive activation in schizophrenia. Our data additionally reveal a profound disruption of structural and functional integration in the prefrontotemporal system in schizophrenia.

Default mode network activity and connectivity in psychopathology

Neuropsychiatric disorders are associated with abnormal function of the default mode network (DMN), a distributed network of brain regions more active during rest than during performance of many attention-demanding tasks and characterized by a high degree of functional connectivity (i.e., temporal correlations between brain regions). Functional magnetic resonance imaging studies have revealed that the DMN in the healthy brain is associated with stimulus-independent thought and self-reflection and that greater suppression of the DMN is associated with better performance on attention-demanding tasks. In schizophrenia and depression, the DMN is often found to be hyperactivated and hyperconnected. In schizophrenia this may relate to overly intensive self-reference and impairments in attention and working memory. In depression, DMN hyperactivity may be related to negative rumination. These findings are considered in terms of what is known about psychological functions supported by the DMN, and alteration of the DMN in other neuropsychiatric disorders.

Dominant component analysis of electrophysiological connectivity networks

Connectivity matrices obtained from various modalities (DTI, MEG and fMRI) provide a unique insight into brain processes. Their high dimensionality necessitates the development of methods for population-based statistics, in the face of small sample sizes. In this paper, we present such a method applicable to functional connectivity networks, based on identifying the basis of dominant connectivity components that characterize the patterns of brain pathology and population variation. Projection of individual connectivity matrices into this basis allows for dimensionality reduction, facilitating subsequent statistical analysis. We find dominant components for a collection of connectivity matrices by using the projective non-negative component analysis technique which ensures that the components have non-negative elements and are non-negatively combined to obtain individual subject networks, facilitating interpretation. We demonstrate the feasibility of our novel framework by applying it to simulated connectivity matrices as well as to a clinical study using connectivity matrices derived from resting state magnetoencephalography (MEG) data in a population of subjects diagnosed with autism spectrum disorder (ASD).

Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction

The insular cortex is one of the brain regions that show consistent abnormalities in both structural and functional neuroimaging studies of schizophrenia. In healthy individuals, the insula has been implicated in a myriad of physiologic functions. The anterior cingulate cortex (ACC) and insula together constitute the salience network, an intrinsic large-scale network showing strong functional connectivity. Considering the insula as a functional unit along with the ACC provides an integrated understanding of the role of the insula in information processing. In this review, we bring together evidence from imaging studies to understand the role of the salience network in schizophrenia and propose a model of insular dysfunction in psychosis.

Hierarchical structural mapping for globally optimized estimation of functional networks

In this study, we propose a framework to map functional MRI (fMRI) activation signals using DTI-tractography. This framework, which we term functional by structural hierarchical (FSH) mapping, models the regional origin of fMRI brain activation to construct "N-step reachable structural maps". Linear combinations of these N-step reachable maps are then used to predict the observed fMRI signals. Additionally, we constructed a utilization matrix, which numerically estimates whether the inclusion of a specific structural connection better predicts fMRI, using simulated annealing. We applied this framework to a visual fMRI task in a sample of body dysmorphic disorder (BDD) subjects and comparable healthy controls. Group differences were inferred by comparing the observed utilization differences against 10,000 permutations under the null hypothesis. Results revealed that BDD subjects under-utilized several key local connections in the visual system, which may help explain previously reported fMRI findings and further elucidate the underlying pathophysiology of BDD.

A review of multivariate methods for multimodal fusion of brain imaging data

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

Global functional connectivity deficits in schizophrenia depend on behavioral state

Schizophrenia is a devastating psychiatric illness characterized by deterioration of cognitive and emotional processing. It has been hypothesized that aberrant cortical connectivity is implicated in the disease (Friston, 1998), yet previous studies of functional connectivity (FC) in schizophrenia have shown mixed results (Garrity et al., 2007; Jafri et al., 2008; Lynall et al., 2010). We measured FC using fMRI in human schizophrenia patients and healthy controls during two different tasks and a rest condition, and constructed a voxel-based global FC index. We found a striking FC decrease in patients compared with controls. In the task conditions, relatively weaker FC was specific to regions of cortex not active during the task. In the rest condition, the FC difference between patients and controls was larger and allowed a case-by-case separation between individuals of the two groups. The results suggest that the relative reduction of FC in schizophrenia is dependent on the state of cortical activity, with voxels not activated by the task showing higher levels of FC deficiency. This novel finding may shed light on previous reports of FC in schizophrenia. Whether this neural characteristic is related to the development of the disorder remains to be established.

Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy

We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls.

Biological motion processing: the left cerebellum communicates with the right superior temporal sulcus

The cerebellum is thought to be engaged not only in motor control, but also in the neural network dedicated to visual processing of body motion. However, the pattern of connectivity within this network, in particular, between the cortical circuitry for observation of others' actions and the cerebellum remains largely unknown. By combining functional magnetic resonance imaging (fMRI) with functional connectivity analysis and dynamic causal modelling (DCM), we assessed cerebro-cerebellar connectivity during a visual perceptual task with point-light displays depicting human locomotion. In the left lateral cerebellum, regions in the lobules Crus I and VIIB exhibited increased fMRI response to biological motion. The outcome of the connectivity analyses delivered the first evidence for reciprocal communication between the left lateral cerebellum and the right posterior superior temporal sulcus (STS). Through communication with the right posterior STS that is a key node not only for biological motion perception but also for social interaction and visual tasks on theory of mind, the left cerebellum might be involved in a wide range of social cognitive functions.

Altered resting state complexity in schizophrenia

The complexity of the human brain's activity and connectivity varies over temporal scales and is altered in disease states such as schizophrenia. Using a multi-level analysis of spontaneous low-frequency fMRI data stretching from the activity of individual brain regions to the coordinated connectivity pattern of the whole brain, we investigate the role of brain signal complexity in schizophrenia. Specifically, we quantitatively characterize the univariate wavelet entropy of regional activity, the bivariate pairwise functional connectivity between regions, and the multivariate network organization of connectivity patterns. Our results indicate that univariate measures of complexity are less sensitive to disease state than higher level bivariate and multivariate measures. While wavelet entropy is unaffected by disease state, the magnitude of pairwise functional connectivity is significantly decreased in schizophrenia and the variance is increased. Furthermore, by considering the network structure as a function of correlation strength, we find that network organization specifically of weak connections is strongly correlated with attention, memory, and negative symptom scores and displays potential as a clinical biomarker, providing up to 75% classification accuracy and 85% sensitivity. We also develop a general statistical framework for the testing of group differences in network properties, which is broadly applicable to studies where changes in network organization are crucial to the understanding of brain function.

Understanding aberrant white matter development in schizophrenia: an avenue for therapy?

Although historically gray matter changes have been the focus of neuropathological and neuroradiological studies in schizophrenia, in recent years an increasing body of research has implicated white matter structures and its constituent components (axons, their myelin sheaths and supporting oligodendrocytes). This article summarizes this body of literature, examining neuropathological, neurogenetic and neuroradiological evidence for white matter pathology in schizophrenia. We then look at the possible role that antipsychotic medication may play in these studies, examining both its role as a potential confounder in studies examining neuronal density and brain volume, but also the possible role that these medications may play in promoting myelination through their effects on oligodendrocytes. Finally, the role of potential novel therapies is discussed.

Anatomical insights into disrupted small-world networks in schizophrenia

Schizophrenia is characterized by lowered efficiency in distributed information processing, as indicated by research that identified a disrupted small-world functional network. However, whether the dysconnection manifested by the disrupted small-world functional network is reflected in underlying anatomical disruption in schizophrenia remains unresolved. This study examined the topological properties of human brain anatomical networks derived from diffusion tensor imaging in patients with schizophrenia and in healthy controls. We constructed the weighted brain anatomical network for each of 79 schizophrenia patients and for 96 age and gender matched healthy subjects using diffusion tensor tractography and calculated the topological properties of the networks using a graph theoretical method. The topological properties of the patients' anatomical networks were altered, in that global efficiency decreased but local efficiency remained unchanged. The deleterious effects of schizophrenia on network performance appear to be localized as reduced regional efficiency in hubs such as the frontal associative cortices, the paralimbic/limbic regions and a subcortical structure (the left putamen). Additionally, scores on the Positive and Negative Symptom Scale correlated negatively with efficient network properties in schizophrenia. These findings suggest that complex brain network analysis may potentially be used to detect an imaging biomarker for schizophrenia.

Large-scale brain networks and psychopathology: a unifying triple network model

The science of large-scale brain networks offers a powerful paradigm for investigating cognitive and affective dysfunction in psychiatric and neurological disorders. This review examines recent conceptual and methodological developments which are contributing to a paradigm shift in the study of psychopathology. I summarize methods for characterizing aberrant brain networks and demonstrate how network analysis provides novel insights into dysfunctional brain architecture. Deficits in access, engagement and disengagement of large-scale neurocognitive networks are shown to play a prominent role in several disorders including schizophrenia, depression, anxiety, dementia and autism. Synthesizing recent research, I propose a triple network model of aberrant saliency mapping and cognitive dysfunction in psychopathology, emphasizing the surprising parallels that are beginning to emerge across psychiatric and neurological disorders.

Sex steroids and connectivity in the human brain: a review of neuroimaging studies

Our brain operates by the way of interconnected networks. Connections between brain regions have been extensively studied at a functional and structural level, and impaired connectivity has been postulated as an important pathophysiological mechanism underlying several neuropsychiatric disorders. Yet the neurobiological mechanisms contributing to the development of functional and structural brain connections remain to be poorly understood. Interestingly, animal research has convincingly shown that sex steroid hormones (estrogens, progesterone and testosterone) are critically involved in myelination, forming the basis of white matter connectivity in the central nervous system. To get insights, we reviewed studies into the relation between sex steroid hormones, white matter and functional connectivity in the human brain, measured with neuroimaging. Results suggest that sex hormones organize structural connections, and activate the brain areas they connect. These processes could underlie a better integration of structural and functional communication between brain regions with age. Specifically, ovarian hormones (estradiol and progesterone) may enhance both cortico-cortical and subcortico-cortical functional connectivity, whereas androgens (testosterone) may decrease subcortico-cortical functional connectivity but increase functional connectivity between subcortical brain areas. Therefore, when examining healthy brain development and aging or when investigating possible biological mechanisms of 'brain connectivity' diseases, the contribution of sex steroids should not be ignored.

Reality distortion is related to the structure of the salience network in schizophrenia

BACKGROUND

An intrinsic cerebral network comprising the anterior cingulate and anterior insula (the salience network) is considered to play an important role in salience detection in healthy volunteers. Aberrant salience has been proposed as an important mechanism in the production of psychotic symptoms such as delusions and hallucinations (reality distortion). We investigated whether structural deficits in the salience network are associated with the reality distortion seen in schizophrenia.

METHOD

A sample of 57 patients in a clinically stable state of schizophrenia and 41 controls were studied with high-resolution magnetic resonance imaging.

RESULTS

Bilateral volume reduction was seen in the anterior cingulate and anterior insula in patients with schizophrenia. Reduced volume in the two left-sided regions of the salience network was significantly correlated with the severity of reality distortion.

CONCLUSIONS

These findings suggest that a deficit of grey matter in the salience network leads to an impaired attribution of salience to stimuli that is associated with delusions and hallucinations in schizophrenia.

Functional resting-state networks are differentially affected in schizophrenia

Neurobiological theories posit that schizophrenia relates to disturbances in connectivity between brain regions. Resting-state functional magnetic resonance imaging is a powerful tool for examining functional connectivity and has revealed several canonical brain networks, including the default mode, dorsal attention, executive control, and salience networks. The purpose of this study was to examine changes in these networks in schizophrenia. 42 patients with schizophrenia and 61 healthy subjects completed a RS-fMRI scanning session. Seed-based region-of-interest correlation analysis was used to identify the default mode, dorsal attention, executive control, and salience networks. Compared to healthy subjects, individuals with schizophrenia demonstrated greater connectivity between the posterior cingulate cortex, a key hub of the default mode, and the left inferior gyrus, left middle frontal gyrus, and left middle temporal gyrus. Interestingly, these regions were more strongly connected to the executive control network in healthy control subjects. In contrast to the default mode, patients demonstrated less connectivity in the executive control and dorsal attention networks. No differences were observed in the salience network. The results indicate that resting-state networks are differentially affected in schizophrenia. The alterations are characterized by reduced segregation between the default mode and executive control networks in the prefrontal cortex and temporal lobe, and reduced connectivity in the dorsal attention and executive control networks. The changes suggest that the process of functional specialization is altered in schizophrenia. Further work is needed to determine if the alterations are related to disturbances in white matter connectivity, neurodevelopmental abnormalities, and genetic risk for schizophrenia.

Imaging of thalamocortical dysrhythmia in neuropsychiatry

Abnormal brain activity dynamics, in the sense of a thalamocortical dysrhythmia (TCD), has been proposed as the underlying mechanism for a subset of disorders that bridge the traditional delineations of neurology and neuropsychiatry. In order to test this proposal from a psychiatric perspective, a study using magnetoencephalography (MEG) was implemented in subjects with schizophrenic spectrum disorder (n = 14), obsessive–compulsive disorder (n = 10), or depressive disorder (n = 5) and in control individuals (n = 18). Detailed CNS electrophysiological analysis of these patients, using MEG, revealed the presence of abnormal theta range spectral power with typical TCD characteristics, in all cases. The use of independent component analysis and minimum-norm-based methods localized such TCD to ventromedial prefrontal and temporal cortices. The observed mode of oscillation was spectrally equivalent but spatially distinct from that of TCD observed in other related disorders, including Parkinson's disease, central tinnitus, neuropathic pain, and autism. The present results indicate that the functional basis for much of these pathologies may relate most fundamentally to the category of calcium channelopathies and serve as a model for the cellular substrate for low-frequency oscillations present in these psychiatric disorders, providing a basis for therapeutic strategies.

Structural integrity of the uncinate fasciculus and resting state functional connectivity of the ventral prefrontal cortex in late life depression

Background

Neuroimaging studies in late life depression have reported decreased structural integrity of white matter tracts in the prefrontal cortex. Functional studies have identified changes in functional connectivity among several key areas involved in mood regulation. Few studies have combined structural and functional imaging. In this study we sought to examine the relationship between the uncinate fasciculus, a key fronto-temporal tract and resting state functional connectivity between the ventral prefrontal cortex ((PFC) and limbic and striatal areas.

Methods

The sample consisted of 24 older patients remitted from unipolar major depression. Each participant had a magnetic resonance imaging brain scan using standardized protocols to obtain both diffusion tensor imaging and resting state functional connectivity data. Our statistical approach compared structural integrity of the uncinate fasciculus and functional connectivity data.

Results

We found positive correlations between left uncinate fasciculus (UF) fractional anisotropy (FA) and resting state functional connectivity (rsFC) between the left ventrolateral PFC and left amygdala and between the left ventrolateral PFC and the left hippocampus. In addition, we found a significant negative correlation between left ventromedial PFC-caudate rsFC and left UF FA. The right UF FA did not correlate with any of the seed region based connectivity.

Conclusions

These results support the notion that resting state functional connectivity reflects structural integrity, since the ventral PFC is structurally connected to temporal regions by the UF. Future studies should include larger samples of patients and healthy comparison subjects in which both resting state and task-based functional connectivity are examined.

Fractal analysis of spontaneous fluctuations of the BOLD signal in rat brain

Analysis of task-evoked fMRI data ignores low frequency fluctuations (LFF) of the resting-state the BOLD signal, yet LFF of the spontaneous BOLD signal is crucial for analysis of resting-state connectivity maps. We characterized the LFF of resting-state BOLD signal at 11.7T in α-chloralose and domitor anesthetized rat brain and modeled the spontaneous signal as a scale-free (i.e., fractal) distribution of amplitude power (|A|²) across a frequency range (f) compatible with an |A(f)|² ∝ 1/f(β) model where β is the scaling exponent (or spectral index). We compared β values from somatosensory forelimb area (S1FL), cingulate cortex (CG), and caudate putamen (CPu). With α-chloralose, S1FL and CG β values dropped from ~0.7 at in vivo to ~0.1 at post mortem (p<0.0002), whereas CPu β values dropped from ~0.3 at in vivo to ~0.1 at post mortem (p<0.002). With domitor, cortical (S1FL, CG) β values were slightly higher than with α-chloralose, while subcortical (CPu) β values were similar with α-chloralose. Although cortical and subcortical β values with both anesthetics were significantly different in vivo (p<0.002), at post mortem β values in these regions were not significantly different and approached zero (i.e., range of -0.1 to 0.2). Since a water phantom devoid of susceptibility gradients had a β value of zero (i.e., random), we conclude that deoxyhemoglobin present in voxels post-sacrifice still impacts tissue water diffusion. These results suggest that in the anesthetized rat brain the LFF of BOLD signal at 11.7T follow a general 1/f(β) model of fractality where β is a variable responding to physiology. We describe typical experimental pitfalls which may elude detection of fractality in the resting-state BOLD signal.

General and specific functional connectivity disturbances in first-episode schizophrenia during cognitive control performance

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.