Disruptive changes of cerebellar functional connectivity with the default mode network in schizophrenia

A whole-brain neuromark resting-state fMRI analysis of first-episode and early psychosis: Evidence of aberrant cortical-subcortical-cerebellar functional circuitry

Psychosis (including symptoms of delusions, hallucinations, and disorganized conduct/speech) is a main feature of schizophrenia and is frequently present in other major psychiatric illnesses. Studies in individuals with first-episode (FEP) and early psychosis (EP) have the potential to interpret aberrant connectivity associated with psychosis during a period with minimal influence from medication and other confounds. The current study uses a data-driven whole-brain approach to examine patterns of aberrant functional network connectivity (FNC) in a multi-site dataset comprising resting-state functional magnetic resonance images (rs-fMRI) from 117 individuals with FEP or EP and 130 individuals without a psychiatric disorder, as controls. Accounting for age, sex, race, head motion, and multiple imaging sites, differences in FNC were identified between psychosis and control participants in cortical (namely the inferior frontal gyrus, superior medial frontal gyrus, postcentral gyrus, supplementary motor area, posterior cingulate cortex, and superior and middle temporal gyri), subcortical (the caudate, thalamus, subthalamus, and hippocampus), and cerebellar regions. The prominent pattern of reduced cerebellar connectivity in psychosis is especially noteworthy, as most studies focus on cortical and subcortical regions, neglecting the cerebellum. The dysconnectivity reported here may indicate disruptions in cortical-subcortical-cerebellar circuitry involved in rudimentary cognitive functions which may serve as reliable correlates of psychosis.

Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography

BACKGROUND

Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles.

METHODS

We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT).

RESULTS

The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10-6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18-0.96, all p < 10-4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 404, surface: 570, PINT: 628, FDR corrected).

CONCLUSION

Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification

The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting-state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting-state fMRI-based measures called dynamic functional network connectivity (dFNC) using state-of-the-art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the B-SNIP biomarker-based (Biotype) approach. Statistical group differences and cross-validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM-IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM-IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM-IV and biology-based categories while also emphasizing the importance of future work in this direction, including employing further data types.

Widespread Intra- and Inter-Network Dysconnectivity among Large-Scale Resting State Networks in Schizophrenia

Schizophrenia is characterized by the distributed dysconnectivity of resting-state multiple brain networks. However, the abnormalities of intra- and inter-network functional connectivity (FC) in schizophrenia and its relationship to symptoms remain unknown. The aim of the present study is to compare the intra- and inter-connectivity of the intrinsic networks between a large sample of patients with schizophrenia and healthy controls. Using the Region of interest (ROI) to ROI FC analyses, the intra- and inter-network FC of the eight resting state networks [default mode network (DMN); salience network (SN); frontoparietal network (FPN); dorsal attention network (DAN); language network (LN); visual network (VN); sensorimotor network (SMN); and cerebellar network (CN)] were investigated in 196 schizophrenia and 169-healthy controls. Compared to the healthy control group, the schizophrenia group exhibited increased intra-network FC in the DMN and decreased intra-network FC in the CN. Additionally, the schizophrenia group showed the decreased inter-network FC mainly involved the SN-DMN, SN-LN and SN-CN while increased inter-network FC in the SN-SMN and SN-DAN (p < 0.05, FDR-corrected). Our study suggests widespread intra- and inter-network dysconnectivity among large-scale RSNs in schizophrenia, mainly involving the DMN, SN and SMN, which may further contribute to the dysconnectivity hypothesis of schizophrenia.

Altered intrinsic cerebellar-cerebral functional connectivity is related to negative symptoms in patients with first-episode psychosis

BACKGROUND

Negative symptoms in schizophrenia include cognitive and affective dysfunction, such as diminished expression and amotivation. Although the cerebellar posterior hemisphere and vermis are involved in cognitive and affective functioning, previous studies on the neural mechanism of negative symptoms have mostly been confined to the cerebral cortex. This study aimed to investigate whether resting-state cerebellar-cerebral functional connectivity (FC) is altered in first-episode psychosis (FEP) patients and whether this connectivity is related to negative symptoms.

METHODS

Resting-state functional magnetic resonance images were obtained from 38 FEP patients and 100 healthy controls (HCs). Using the posterior hemisphere and vermis of the cerebellum as seeds, whole-brain FC was compared between FEP patients and HCs. As cerebellar-parietal cortex connectivity is associated with negative symptoms and sociocognitive dysfunctions in schizophrenia patients, its correlation with negative symptoms was explored in FEP patients.

RESULTS

FEP patients showed hyperconnectivity between the cerebellum and bilateral frontal pole (FP), occipital pole, fusiform gyrus, right lingual gyrus, central opercular cortex, anterior middle temporal gyrus, precuneus, and subcallosal cortex. Hypoconnectivity was found between the cerebellum and left FP, right anterior supramarginal gyrus (aSMG), and cerebellum crus I. FC between the left crus II and right aSMG was negatively correlated with the severity of negative symptoms and diminished expression.

CONCLUSIONS

Altered FC between the cerebellum and cerebral regions related to cognitive, affective, and sensory processing was found in FEP patients and was connected to negative symptoms. These results suggest that the cerebellum plays a role in the pathophysiology of negative symptoms in schizophrenia.

Combined HTR1A/1B methylation and human functional connectome to recognize patients with MDD

OBJECTIVES

This study aimed to use a machine-learning method to identify HTR1A/1B methylation and resting-state functional connectivity (rsFC) related to the diagnosis of MDD, then try to build classification models for MDD diagnosis based on the identified features.

METHODS

Peripheral blood samples were collected from all recruited participants, and part of the participants underwent the resting-state fMRI scan. Features including HTR1A/1B methylation and rsFC were calculated. Then, the initial feature sets of epigenetics and neuroimaging were separately input into an all-relevant feature selection to generate significant discriminative power for MDD diagnosis. Random forest classifiers were constructed and evaluated based on identified features. In addition, the SHapley Additive exPlanations (SHAP) method was adapted to interpret the diagnostic model.

RESULTS

A combination of selected HTR1A/1B methylation and rsFC feature sets achieved better performance than using either one alone - a distinction between MDD and healthy control groups was achieved at 81.78% classification accuracy and 0.8948 AUC.

CONCLUSION

A high classification accuracy can be achieved by combining multidimensional information from epigenetics and cerebral radiomic features in MDD. Our approach can be helpful for accurate clinical diagnosis of MDD and further exploring the pathogenesis of MDD.

Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification

With the development of resting-state functional magnetic resonance imaging (rs-fMRI) technology, the functional connectivity network (FCN) which reflects the statistical similarity of temporal activity between brain regions has shown promising results for the identification of neuropsychiatric disorders. Alteration in FCN is believed to have the potential to locate biomarkers for classifying or predicting schizophrenia (SZ) from healthy control. However, the traditional FCN analysis with stationary assumption, i.e., static functional connectivity network (SFCN) at the time only measures the simple functional connectivity among brain regions, ignoring the dynamic changes of functional connectivity and the high-order dynamic interactions. In this article, the dynamic functional connectivity network (DFCN) is constructed to delineate the characteristic of connectivity variation across time. A high-order functional connectivity network (HFCN) designed based on DFCN, could characterize more complex spatial interactions across multiple brain regions with the potential to reflect complex functional segregation and integration. Specifically, the temporal variability and the high-order network topology features, which characterize the brain FCNs from region and connectivity aspects, are extracted from DFCN and HFCN, respectively. Experiment results on SZ identification prove that our method is more effective (i.e., obtaining a significantly higher classification accuracy, 81.82%) than other competing methods. Post hoc inspection of the informative features in the individualized classification task further could serve as the potential biomarkers for identifying associated aberrant connectivity in SZ.

Positive symptoms of schizophrenia and their relationship with cognitive and emotional executive functions

BACKGROUND

Positive symptoms of schizophrenia are associated with significant difficulties in daily functioning, and these difficulties have been associated with impaired executive functions (EEFF). However, specific cognitive and socio-emotional executive deficits have not been fully established.

OBJECTIVE

The present study has several objectives. First, we aimed to examine the specific deficits in cognitive and socio-emotional EEFF in a group of patients with schizophrenia with a predominance of positive symptoms, as well as to determine if these patients present clinically significant scores in any of the three fronto-subcortical behavioral syndromes: Dorsolateral, Orbitofrontal, or Anterior Cingulate.

METHOD

The sample consisted of 54 patients, 27 with a predominance of positive symptoms, and 27 healthy controls matched for gender, age, and education. The two groups completed four cognitive and three socio-emotional EEFF tasks. In the group of patients, positive symptoms were evaluated using the scale for the Evaluation of Positive Symptoms (SANS), while the behavioral alterations associated with the three fronto-subcortical syndromes were evaluated using the Frontal System Behavior Scale (FrSBe).

RESULTS

The patients, in comparison with a control group, presented specific deficits in cognitive and socio-emotional EEFF. In addition, a high percentage of patients presented clinically significant scores on the three fronto-subcortical syndromes.

CONCLUSION

The affectation that these patients present, in terms of both cognitive and emotional components, highlights the importance of developing a neuropsychological EEFF intervention that promotes the recovery of the affected cognitive capacities and improves the social and emotional functioning of the affected patients.

Lower cortical volume is associated with poor sleep quality after traumatic brain injury

Traumatic brain injury (TBI) is known to be associated with poor sleep. In this report, we aimed to identify associations between differences in cortical volume and sleep quality post-TBI. MRI anatomical scans from 88 cases with TBI were analyzed in this report. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Voxel Based Morphometry (VBM), was used to obtain statistical maps of the association between PSQI and cortical volume in gray matter and white matter voxels. Higher PSQI total scores (poor sleep quality) were strongly associated with smaller gray matter volume in the cerebellum. White matter volume was not associated with total PSQI. The sleep disturbance subcomponent showed a significant association with gray and white matter volumes in the cerebellum. Although not significant, cortical areas such as the cingulate and medial frontal regions were associated with sleep quality. The cerebellum with higher contribution to motor and autonomic systems was associated strongly with poor sleep quality. Additionally, regions that play critical roles in inhibitory brain function and suppress mind wandering (i.e., default mode network including medial frontal and cingulate regions) were associated (although to a lesser extent) with sleep. Our findings suggest that poor sleep quality following TBI is significantly associated with lower cerebellar volume, with trending relationships in regions associated with inhibitory function.

Involvement of cerebellar and subcortical connector hubs in schizophrenia

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Hubs with altered connectivity to multiple networks were identified in patients.

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Identified hubs were located in the cerebellum, midbrain, thalamus, and insula.

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In controls, these hubs were strongly connected with the basal ganglia network.

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Hubs’ connections to large-scale networks were associated with clinical data.

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Their connections were also highly predictive of patients from controls.

Background

Schizophrenia is considered a brain connectivity disorder in which functional integration within the brain fails. Central to the brain’s integrative function are connector hubs, brain regions characterized by strong connections with multiple networks. Given their critical role in functional integration, we hypothesized that connector hubs, including those located in the cerebellum and subcortical regions, are severely impaired in patients with schizophrenia.

Methods

We identified brain voxels with significant connectivity alterations in patients with schizophrenia (n = 76; men = 43) compared to healthy controls (n = 80; men = 43) across multiple large-scale resting state networks (RSNs) using a network metric called functional connectivity overlap ratio (FCOR). From these voxels, candidate connector hubs were identified and verified using seed-based connectivity analysis.

Results

We found that most networks exhibited connectivity alterations in the patient group. Specifically, connectivity with the basal ganglia and high visual networks was severely affected over widespread brain areas in patients, affecting subcortical and cerebellar regions and the regions involved in visual and sensorimotor processing. Furthermore, we identified critical connector hubs in the cerebellum, midbrain, thalamus, insula, and calcarine with connectivity to multiple RSNs affected in the patients. FCOR values of these regions were also associated with clinical data and could classify patient and control groups with > 80 % accuracy.

Conclusions

These findings highlight the critical role of connector hubs, particularly those in the cerebellum and subcortical regions, in the pathophysiology of schizophrenia and the potential role of FCOR as a clinical biomarker for the disorder.

Psychosis symptoms associated with Niemann-Pick disease type C

Niemann-Pick disease type C (NP-C) is a severe neurovisceral lipid storage disease that results in the accumulation of unesterified cholesterol in lysosomes or endosomes. The clinical presentations of NP-C are variable which include visceral symptoms, neurologic symptoms and psychiatric symptoms. Psychosis is the most common psychiatric manifestation of NP-C and is indistinguishable from a typical psychosis presentation of schizophrenia. The common psychotic presentations in NP-C include visual hallucinations, delusions, auditory hallucinations and thought disorders. Psychosis symptoms are more common in adult or adolescent-onset forms compared with pediatric-onset forms. The underlying pathophysiology of psychosis in NP-C is most probably due to dysconnectivity particularly between frontotemporal connectivity and subcortical structures. NP-C sometimes is mistaken for schizophrenia which causes delay in treatment due to lack of awareness and literature review. This review aims to summarize the relevant case reports on psychosis symptoms in NP-C and discuss the genetics and pathophysiology underlying the condition.

Resting state functional connectivity in the default mode network: Relationships between cannabis use, gender, and cognition in adolescents and young adults

INTRODUCTION

Cannabis is the most commonly used illicit substance in the United States, and nearly 1 in 4 young adults are current cannabis users. Chronic cannabis use is associated with changes in resting state functional connectivity (RSFC) in the default mode network (DMN) in adolescents and young adults; results are somewhat inconsistent across studies, potentially due to methodological differences. The aims of the present study were to examine potential differences in DMN RSFC between cannabis users and controls, and to examine, as an exploratory analysis, if gender moderated any findings. We further examined whether differences in RSFC related to differences in performance on selected neuropsychological measures.

MATERIALS AND METHODS

Seventy-seven 16-26-year-old participants underwent an MRI scan (including resting state scan), neuropsychological battery, toxicology screening, and drug use interview. Differences in DMN connectivity were examined between groups (cannabis vs. control) and with an exploratory group by gender interaction, using a left posterior cingulate cortex (PCC) seed-based analysis conducted in AFNI.

RESULTS

Cannabis users demonstrated weaker connectivity than controls between the left PCC and various DMN nodes, and the right Rolandic operculum/Heschl's gyrus. Cannabis users demonstrated stronger connectivity between the left PCC and the cerebellum and left supramarginal gyrus. The group by gender interaction was not significantly associated with connectivity differences. Stronger left PCC-cerebellum connectivity was associated with poorer performance on cognitive measures in cannabis users. In controls, intra-DMN connectivity was positively correlated with performance on a speeded selective/sustained attention measure.

DISCUSSION

Consistent with our hypotheses and other studies, cannabis users demonstrated weaker connectivity between the left PCC and DMN nodes. Chronic THC exposure may alter GABA and glutamate concentrations, which may alter brain communication. Future studies should be conducted with a larger sample size and examine gender differences and the mechanism by which these differences may arise.

Anhedonia Relates to the Altered Global and Local Grey Matter Network Properties in Schizophrenia

Anhedonia is one of the major negative symptoms in schizophrenia and defined as the loss of hedonic experience to various stimuli in real life. Although structural magnetic resonance imaging has provided a deeper understanding of anhedonia-related abnormalities in schizophrenia, network analysis of the grey matter focusing on this symptom is lacking. In this study, single-subject grey matter networks were constructed in 123 patients with schizophrenia and 160 healthy controls. The small-world property of the grey matter network and its correlations with the level of physical and social anhedonia were evaluated using graph theory analysis. In the global scale whole-brain analysis, the patients showed reduced small-world property of the grey matter network. The local-scale analysis further revealed reduced small-world property in the default mode network, salience/ventral attention network, and visual network. The regional-level analysis showed an altered relationship between the small-world properties and the social anhedonia scale scores in the cerebellar lobule in patients with schizophrenia. These results indicate that anhedonia in schizophrenia may be related to abnormalities in the grey matter network at both the global whole-brain scale and local-regional scale.

Cerebellar Activation Deficits in Schizophrenia During an Eyeblink Conditioning Task

The cognitive dysmetria theory of psychotic disorders posits that cerebellar circuit abnormalities give rise to difficulties coordinating motor and cognitive functions. However, brain activation during cerebellar-mediated tasks is understudied in schizophrenia. Accordingly, this study examined whether individuals with schizophrenia have diminished neural activation compared to controls in key regions of the delay eyeblink conditioning (dEBC) cerebellar circuit (eg, lobule VI) and cerebellar regions associated with cognition (eg, Crus I). Participants with schizophrenia-spectrum disorders (n = 31) and healthy controls (n = 43) underwent dEBC during functional magnetic resonance imaging (fMRI). Images were normalized using the Spatially Unbiased Infratentorial Template (SUIT) of the cerebellum and brainstem. Activation contrasts of interest were "early" and "late" stages of paired tone and air puff trials minus unpaired trials. Preliminary whole brain analyses were conducted, followed by cerebellar-specific SUIT and region of interest (ROI) analyses of lobule VI and Crus I. Correlation analyses were conducted between cerebellar activation, neuropsychological test scores, and psychotic symptom scores. In controls, the largest clusters of cerebellar activation peaked in lobule VI during early dEBC and Crus I during late dEBC. The schizophrenia group showed robust cortical activation to unpaired trials but no significant conditioning-related cerebellar activation. Crus I ROI activation during late dEBC was greater in the control than schizophrenia group. Greater Crus I activation correlated with higher working memory scores in the full sample and lower positive psychotic symptom severity in schizophrenia. Findings indicate functional cerebellar abnormalities in schizophrenia which relate to psychotic symptoms, lending direct support to the cognitive dysmetria framework.

EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity-A Machine Learning Approach

A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.

Increased cerebellar-default-mode network connectivity at rest in obsessive-compulsive disorder

Abnormalities of the cerebellum and default-mode network (DMN) in patients with obsessive-compulsive disorder (OCD) have been widely reported. However, alterations of reciprocal functional connections between the cerebellum and DMN at rest in OCD remain unclear. Forty patients with OCD and 38 gender-, age-, and education-matched healthy controls (HCs) underwent resting-state functional magnetic resonance imaging scan. Seed-based functional connectivity (FC) and support vector machine (SVM) were applied to analyze the imaging data. Compared with HCs, patients with OCD exhibited increased FCs between the left Crus I-left superior medial prefrontal cortex (MPFC) and between the right Crus I-left superior MPFC, left middle MPFC, and left middle temporal gyrus (MTG). A significantly negative correlation was observed between the right Crus I-left MTG connectivity and the Yale-Brown Obsessive-Compulsive Scale compulsion subscale scores in the OCD group (r = - 0.476, p = 0.002, Bonferroni corrected). SVM classification analysis indicated that a combination of the left Crus I-left superior MPFC connectivity and the right Crus I-left middle MPFC connectivity can be used to discriminate patients with OCD from HCs with a sensitivity of 85.00%, specificity of 68.42%, and accuracy of 76.92%. Our study highlights the contribution of the cerebellar-DMN connectivity in OCD pathophysiology and provides new findings to OCD research.

Weaker Cerebellocortical Connectivity Within Sensorimotor and Executive Networks in Schizophrenia Compared to Healthy Controls: Relationships with Processing Speed

Background: The cognitive dysmetria theory of schizophrenia proposes that communication between the cerebellum and cerebral cortex is disrupted by structural and functional abnormalities, resulting in psychotic symptoms and cognitive deficits. Methods: Using publicly available data, resting-state functional connectivity (rsFC) was calculated from 20 hemispheric cerebellar lobules as seed regions of interest to the rest of the brain. Group differences in rsFC between individuals with schizophrenia (SZ) and healthy controls (HCs) were computed, and relationships between rsFC and symptom severity and cognitive functioning were explored. Results: HCs demonstrated stronger connectivity than SZ between several cerebellar lobules and cortical regions, most robustly between motor-related cerebellar lobules (V and VIIIa/b) and temporal and parietal cortices. In addition, seven of nine lobules in which reduced cerebellocortical connectivity was observed showed diagnosis × processing speed interactions; HC showed a positive relationship between connectivity and processing speed, whereas SZ did not show this relationship. Other cognitive domains and symptom severity did not show relationships with connectivity. Conclusions: These findings partially support the cognitive dysmetria theory, and suggest that disrupted cerebellocortical connectivity is associated with slowed processing speed in schizophrenia. Impact statement We show in this work that in chronic schizophrenia, there is weaker functional connectivity between previously unstudied inferior posterior cerebellar lobules and cortical association areas. These findings align and extend previous work showing abnormal connectivity of anterior cerebellar lobules. Further, we present a novel finding that these connectivity deficits are differentially associated with processing speed in the schizophrenia versus healthy control groups. Findings provide further evidence for cerebellocortical dysconnectivity and processing speed deficits as biomarkers of schizophrenia, which may have implications for downstream effects on higher order cognitive functions, in line with the cognitive dysmetria theory.

Total Sleep Deprivation Impairs Lateralization of Spatial Working Memory in Young Men

Total sleep deprivation (TSD) negatively affects cognitive function. Previous research has focused on individual variation in cognitive function following TSD, but we know less about how TSD influences the lateralization of spatial working memory. This study used event-related-potential techniques to explore asymmetry in spatial-working-memory impairment. Fourteen healthy male participants performed a two-back task with electroencephalogram (EEG) recordings conducted at baseline and after 36 h of TSD. We selected 12 EEG points corresponding to left and right sides of the brain and then observed changes in N2 and P3 components related to spatial working memory. Before TSD, P3 amplitude differed significantly between the left and right sides of the brain. This difference disappeared after TSD. Compared with baseline, P3 amplitude decreased for a duration as extended as the prolonged latency of N2 components. After 36 h of TSD, P3 amplitude decreased more in the right hemisphere than the left. We therefore conclude that TSD negatively affected spatial working memory, possibly through removing the right hemisphere advantage.

Compression of Cerebellar Functional Gradients in Schizophrenia

Our understanding of cerebellar involvement in brain disorders has evolved from motor processing to high-level cognitive and affective processing. Recent neuroscience progress has highlighted hierarchy as a fundamental principle for the brain organization. Despite substantial research on cerebellar dysfunction in schizophrenia, there is a need to establish a neurobiological framework to better understand the co-occurrence and interaction of low- and high-level functional abnormalities of cerebellum in schizophrenia. To help to establish such a framework, we investigated the abnormalities in the distribution of sensorimotor-supramodal hierarchical processing topography in the cerebellum and cerebellar-cerebral circuits in schizophrenia using a novel gradient-based resting-state functional connectivity (FC) analysis (96 patients with schizophrenia vs 120 healthy controls). We found schizophrenia patients showed a compression of the principal motor-to-supramodal gradient. Specifically, there were increased gradient values in sensorimotor regions and decreased gradient values in supramodal regions, resulting in a shorter distance (compression) between the sensorimotor and supramodal poles of this gradient. This pattern was observed in intra-cerebellar, cerebellar-cerebral, and cerebral-cerebellar FC. Further investigation revealed hyper-connectivity between sensorimotor and cognition areas within cerebellum, between cerebellar sensorimotor and cerebral cognition areas, and between cerebellar cognition and cerebral sensorimotor areas, possibly contributing to the observed compressed pattern. These findings present a novel mechanism that may underlie the co-occurrence and interaction of low- and high-level functional abnormalities of cerebellar and cerebro-cerebellar circuits in schizophrenia. Within this framework of abnormal motor-to-supramodal organization, a cascade of impairments stemming from disrupted low-level sensorimotor system may in part account for high-level cognitive cerebellar dysfunction in schizophrenia.

Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

Effects of risperidone monotherapy on the default-mode network in antipsychotic-naïve first-episode schizophrenia: Posteromedial cortex heterogeneity and relationship with the symptom improvements

The default mode network (DMN) has been consistently detected abnormally in schizophrenia. However, the effects of antipsychotics on this network are still under debate, and inconsistent findings may be due to the functional heterogeneity within the DMN, especially in the component regions of the posteromedial cortex (PMC). Here, we conducted a longitudinal research on the resting-state functional connectivity of the PMC subdivisions on 33 treatment-naive first-episode patients with schizophrenia at baseline and after 8 weeks of risperidone treatment through resting-state functional magnetic resonance imaging. At baseline, the patients demonstrated decreased connectivity of the three PMC seeds with several brain regions (target regions) compared with healthy controls. We then tested the effect of antipsychotic treatment on the functional connectivity between the three seeds and the target regions. We found that, one of the three seeds encompassed in PMC, namely, posterior cingulate cortex (PCC), was observed to have increased functional connectivity with the bilateral thalamus and the left lingual gyrus (LG). On the contrary, the functional connectivity between the target regions and the two remaining seeds, namely, the retrosplenial cortex and precuneus, was unaffected by risperidone treatment. Correlation analysis revealed a positive correlation between longitudinal change of PCC-LG connectivity and symptom improvement. These findings indicated the heterogeneity of the PMC in response to antipsychotic treatment and suggested the role of PCC as a treatment biomarker for schizophrenia.

Abnormal cerebellar volume in somatic vs. non-somatic delusional disorders

Background

There is abundant evidence for cerebellar involvement in schizophrenia, where the cerebellum has been suggested to contribute to cognitive, affective and motor dysfunction. More recently, specific cerebellar regions have also been associated with psychotic symptoms, particularly with auditory verbal hallucinations. In contrast, little is known about cerebellar contributions to delusions, and even less is known about whether cerebellar involvement differs by delusional content.

Methods

Using structural magnetic resonance imaging at 1.0 T together with cerebellum-optimized segmentation techniques, we investigated gray matter volume (GMV) in 14 patients with somatic-type delusional disorder (S-DD), 18 patients with non-somatic delusional disorder (NS-DD) and 18 patients with schizophrenia (SZ) with persistent non-somatic delusions. A total of 32 healthy controls (HC) were included. Between-group comparisons were adjusted for age, gender, chlorpromazine equivalents and illness duration.

Results

Compared to HC, S-DD patients showed decreased GMV in left lobule VIIIa. In addition, S-DD patients showed decreased GMV in lobule V and increased GMV in bilateral lobule VIIa/crus II compared to NS-DD. Patients with SZ showed increased GMV in right lobule VI and VIIa/crus I compared to HC. Significant differences between HC and NS-DD were not found.

Conclusions

The data support the notion of cerebellar dysfunction in psychotic disorders. Distinct cerebellar deficits, predominantly linked to sensorimotor processing, may be detected in delusional disorders presenting with predominantly somatic content.

Schizophrenia Exhibits Bi-directional Brain-Wide Alterations in Cortico-Striato-Cerebellar Circuits

Distributed neural dysconnectivity is considered a hallmark feature of schizophrenia (SCZ), yet a tension exists between studies pinpointing focal disruptions versus those implicating brain-wide disturbances. The cerebellum and the striatum communicate reciprocally with the thalamus and cortex through monosynaptic and polysynaptic connections, forming cortico-striatal-thalamic-cerebellar (CSTC) functional pathways that may be sensitive to brain-wide dysconnectivity in SCZ. It remains unknown if the same pattern of alterations persists across CSTC systems, or if specific alterations exist along key functional elements of these networks. We characterized connectivity along major functional CSTC subdivisions using resting-state functional magnetic resonance imaging in 159 chronic patients and 162 matched controls. Associative CSTC subdivisions revealed consistent brain-wide bi-directional alterations in patients, marked by hyper-connectivity with sensory-motor cortices and hypo-connectivity with association cortex. Focusing on the cerebellar and striatal components, we validate the effects using data-driven k-means clustering of voxel-wise dysconnectivity and support vector machine classifiers. We replicate these results in an independent sample of 202 controls and 145 patients, additionally demonstrating that these neural effects relate to cognitive performance across subjects. Taken together, these results from complementary approaches implicate a consistent motif of brain-wide alterations in CSTC systems in SCZ, calling into question accounts of exclusively focal functional disturbances.

Cerebellar alterations in cannabis users: A systematic review

Cannabis is the most used illicit substance in the world. As many countries are moving towards decriminalization, it is crucial to determine whether and how cannabis use affects human brain and behavior. The role of the cerebellum in cognition, emotion, learning, and addiction is increasingly recognized. Because of its high density in CB1 receptors, it is expected to be highly affected by cannabis use. The aim of this systematic review is to investigate how cannabis use affects cerebellar structure and function, as well as cerebellar-dependent behavioral tasks. Three databases were searched for peer-reviewed literature published until March 2018. We included studies that focused on cannabis effects on cerebellar structure, function, or cerebellar-dependent behavioral tasks. A total of 348 unique records were screened, and 40 studies were included in the qualitative synthesis. The most consistent findings include (1) increases in cerebellar gray matter volume after chronic cannabis use, (2) alteration of cerebellar resting state activity after acute or chronic use, and (3) deficits in memory, decision making, and associative learning. Age of onset and higher exposure to cannabis use were frequently associated with increased cannabis-induced alterations. Chronic cannabis use is associated with alterations in cerebellar structure and function, as well as with deficits in behavioral paradigms that involve the cerebellum (eg, eyeblink conditioning, memory, and decision making). Future studies should consider tobacco as confounding factor and use standardized methods for assessing cannabis use. Paradigms exploring the functional activity of the cerebellum may prove useful as monitoring tools of cannabis-induced impairment.

Topological Disruption of Structural Brain Networks in Patients With Cognitive Impairment Following Cerebellar Infarction

Cerebellar lesions can lead to a series of cognitive and emotional disorders by influencing cerebral activity via cerebro-cerebellar loops. To explore changes in cognitive function and structural brain networks in patients with posterior cerebellar infarction, we conducted the current study using diffusion-weighted MRI (32 cerebellar infarction patients, 29 controls). Moreover, a series of neuropsychological tests were used to assess the subject's cognitive performance. We found cognitive impairment following cerebellar infarction involving multiple cognitive domains, including memory, executive functions, visuospatial abilities, processing speed and language functions, and brain topological abnormalities, including changes in clustering coefficients, shortest path lengths, global efficiency, local efficiencies, betweenness centrality and nodal efficiencies. Our results indicated that measures of local efficiency, mainly in the precuneus, cingulate gyrus and frontal-temporal cortex, were significantly reduced with posterior cerebellar infarction. At the same time, The correlation analysis suggested thatthe abnormal alterations in the right PCG, bilateral DCG, right PCUN may play a core role in the cognitive impairment following cerebellar infarctions. The differences in topological features of the structural brain networks within the cerebro-cerebellar circuits may provide a new approach to explore the pathophysiological mechanisms of cognitive impairment following cerebellar infarction.

Disruption and lateralization of cerebellar-cerebral functional networks in right temporal lobe epilepsy: A resting-state fMRI study

Numerous studies have highlighted important roles for the cerebellum in cognition and movement, based on numerous fiber connections between the cerebrum and cerebellum. Abnormal cerebellar activity caused by epileptic discharges has been reported in previous studies, but researchers have not clearly determined whether aberrant cerebellar activity contributes to the disruption of the cerebellar-cerebral networks in right temporal lobe epilepsy (rTLE). Here, thirty patients with rTLE and 30 age- and sex-matched healthy controls (HCs) were recruited. All participants underwent the Attention Network Test (ANT) and resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Cerebellar functional networks were extracted and analyzed by defining seeds in the cerebellum. A correlation analysis was performed between attentional performance and voxels that showed differences in functional connectivity (FC) in patients compared with HCs. Relative to HCs, patients exhibited significantly decreased FC in the dentate nucleus (DN) network (right DN with the left postcentral gyrus, left precentral gyrus, left cuneus, and left calcarine gyrus) and motor network (right cerebellar lobule V with the right putamen) and increased FC in the executive control network (right cerebellar crus I with the right inferior parietal lobule). Alerting, orienting, and executive control performances were impaired in patients with rTLE. Furthermore, the executive control effect was significantly correlated with aberrant FC strength between the right DN and the left precentral/postcentral gyrus. Our findings highlight that the disrupted cerebellar-cerebral functional network ipsilateral to the epileptogenic focus causes both impairments in and compensatory effects on attentional deficits in patients with rTLE. These findings contribute to our understanding of the cerebellar damage caused by epileptic discharges and the corresponding effect on attentional performance.

Prediction, Psychosis, and the Cerebellum

An increasingly influential hypothesis posits that many of the diverse symptoms of psychosis can be viewed as reflecting dysfunctional predictive mechanisms. Indeed, to perceive something is to take a sensory input and make a prediction of the external source of that signal; thus, prediction is perhaps the most fundamental neural computation. Given the ubiquity of prediction, a more challenging problem is to specify the unique predictive role or capability of a particular brain structure. This question is relevant when considering recent claims that one aspect of the predictive deficits observed in psychotic disorders might be related to cerebellar dysfunction, a subcortical structure known to play a critical role in predictive sensorimotor control and perhaps higher-level cognitive function. Here, we review evidence bearing on this question. We first focus on clinical, behavioral, and neuroimaging findings suggesting cerebellar involvement in psychosis and, specifically, schizophrenia. We then review a relatively novel line of research exploring whether computational models of cerebellar motor function can also account for cerebellar involvement in higher-order human cognition, and in particular, language function. We end the review by highlighting some key gaps in these literatures, limitations that currently preclude strong conclusions regarding cerebellar involvement in psychosis.

Altered resting-state functional connectivity of the cerebellum in schizophrenia

Structural and functional abnormalities of the cerebellum in schizophrenia have been reported. Most previous studies investigating resting-state functional connectivity (rsFC) have relied on a priori restrictions on seed regions or specific networks, which may bias observations. In this study, we aimed to elicit the connectivity alterations of the cerebellum in schizophrenia in a hypothesis-free approach. Ninety-five schizophrenia patients and 93 sex- and age-matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI). A voxel-wise data-driven method, resting-state functional connectivity density (rsFCD), was used to investigate cerebellar connectivity changes in schizophrenia patients. Regions with altered rsFCD were chosen as seeds to perform seed-based resting-state functional connectivity (rsFC) analyses. We found that schizophrenia patients exhibited decreased rsFCD in the right hemispheric VI; moreover, this cerebellar region showed increased rsFC with the prefrontal cortex and subcortical nuclei and decreased rsFC with the visual cortex and sensorimotor cortex. In addition, some rsFC changes were associated with positive symptoms. These findings suggest that abnormalities of the cerebellar hub and cerebellar-subcortical-cortical loop may be the underlying mechanisms of schizophrenia.

Association between Thalamocortical Functional Connectivity Abnormalities and Cognitive Deficits in Schizophrenia

Cognitive deficits are considered a core component of schizophrenia and may predict functional outcome. However, the neural underpinnings of neuropsychological impairment remain to be fully elucidated. Data of 59 schizophrenia patients and 72 healthy controls from a public resting-state fMRI database was employed in our study. Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Battery was used to measure deficits of cognitive abilities in schizophrenia. Neural correlates of cognitive deficits in schizophrenia were examined by linear regression analysis of the thalamocortical network activity with scores of seven cognitive domains. We confirmed the combination of reduced prefrontal-thalamic connectivity and increased sensorimotor-thalamic connectivity in patients with schizophrenia. Correlation analysis with cognition revealed that in schizophrenia (1) the thalamic functional connectivity in the bilateral pre- and postcentral gyri was negatively correlated with attention/vigilance and speed of processing (Pearson’s r ≤ −0.443, p ≤ 0.042, FWE corrected), and positively correlated with patients’ negative symptoms (Pearson’s r ≥ 0.375, p ≤ 0.003, FWE corrected); (2) the thalamic functional connectivity in the right cerebellum was positively correlated with speed of processing (Pearson’s r = 0.388, p = 0.01, FWE corrected). Our study demonstrates that thalamic hyperconnectivity with sensorimotor areas is related to the severity of cognitive deficits and clinical symptoms, and extends our understanding of the neural underpinnings of “cognitive dysmetria” in schizophrenia.

Alterations in Cerebellar Functional Connectivity Are Correlated With Decreased Psychomotor Vigilance Following Total Sleep Deprivation

Previous studies have reported significant changes in functional connectivity among various brain networks following sleep restriction. The cerebellum plays an important role in information processing for motor control and provides this information to higher-order networks. However, little is known regarding how sleep deprivation influences functional connectivity between the cerebellum and the cerebral cortex in humans. The present study aimed to investigate the changes in cerebellar functional connectivity induced by sleep deprivation, and their relationship with psychomotor vigilance. A total of 52 healthy men underwent resting-state functional magnetic resonance imaging before and after 36 h of total sleep deprivation. Functional connectivity was evaluated using region of interest (ROI)-to-ROI analyses, using 26 cerebellar ROIs as seed regions. Psychomotor vigilance was assessed using the psychomotor vigilance test (PVT). Decreased functional connectivity was observed between cerebellar seed regions and the bilateral postcentral, left inferior frontal, left superior medial frontal, and right middle temporal gyri. In contrast, increased functional connectivity was observed between the cerebellum and the bilateral caudate. Furthermore, decrease in functional connectivity between the cerebellum and the postcentral gyrus was negatively correlated with increase in PVT reaction times, while increase in functional connectivity between the cerebellum and the bilateral caudate was positively correlated with increase in PVT reaction times. These results imply that altered cerebellar functional connectivity is associated with impairment in psychomotor vigilance induced by sleep deprivation.

Psychiatric and Cognitive Symptoms Associated with Niemann-Pick Type C Disease: Neurobiology and Management

Niemann-Pick disease type C (NPC) is a lysosomal storage disorder that presents with a spectrum of clinical manifestations from infancy and childhood or in early or mid-adulthood. Progressive neurological symptoms including ataxia, dystonia and vertical gaze palsy are a hallmark of the disease, and psychiatric symptoms such as psychosis and mood disorders are common. These latter symptoms often present early in the course of NPC and thus these patients are often diagnosed with a major psychotic or affective disorder before neurological and cognitive signs present and the diagnosis is revised. The commonalities and characteristics of psychotic symptoms in both NPC and schizophrenia may share neuronal pathways and mechanisms and provide potential targets for research in both disorders. The neurobiology of NPC and its relationship to the pattern of neuropsychiatric and cognitive symptoms is described in this review. A number of neurobiological models are proposed as mechanisms by which NPC causes psychiatric and cognitive symptoms, informed from models proposed in schizophrenia and other metabolic disorders. There are a number of symptomatic and illness-modifying treatments for NPC currently available. The current evidence is discussed; focussing on two medications which have shown promise, miglustat and hydroxypropyl-β-cyclodextrin.

Abnormal cerebellum-DMN regions connectivity in unmedicated bipolar II disorder

OBJECTIVE

Bipolar disorder (BD) is a common psychiatric disease. Previous studies have found abnormalities in structural and functional brain connectivity in BD patients. However, few studies have focused on the functional connectivity (FC) of the cerebellum and its sub-regions in patients with BD. The present study aimed to examine the FC of cerebellum-default mode network (DMN) regions in patients with BD II.

METHOD

Ninety patients with unmedicated BD II depression and 100 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. We selected three pairs of subregions of the cerebellum that are DMN-related (the bilateral Crus I, Crus II, and lobule IX) as seed regions and calculated the whole brain FC for each subregion.

RESULTS

Compared with the HCs, the patients with BD II depression showed increased connectivity between the right Crus I and bilateral precuneus and decreased connectivity between the left Crus II and bilateral medial prefrontal cortex (mPFC) and between the left Crus II and right medial frontal gyrus (MFG). There was no significant difference in the whole FC of the left Crus I and bilateral lobule IX between the BD II depression group and the HCs group.

LIMITATIONS

This study was cross-sectional and did not examine data from euthymic BD patients.

CONCLUSIONS

The findings showed impaired FC of cerebellum-DMN regions in BD; partial FC between the Crus I and precuneus and the Crus II and prefrontal cortex suggests the importance of abnormal cerebellum-DMN regions FC in the pathophysiology of BD.

Cerebello-thalamo-cortical hyperconnectivity as a state-independent functional neural signature for psychosis prediction and characterization

Understanding the fundamental alterations in brain functioning that lead to psychotic disorders remains a major challenge in clinical neuroscience. In particular, it is unknown whether any state-independent biomarkers can potentially predict the onset of psychosis and distinguish patients from healthy controls, regardless of paradigm. Here, using multi-paradigm fMRI data from the North American Prodrome Longitudinal Study consortium, we show that individuals at clinical high risk for psychosis display an intrinsic “trait-like” abnormality in brain architecture characterized as increased connectivity in the cerebello–thalamo–cortical circuitry, a pattern that is significantly more pronounced among converters compared with non-converters. This alteration is significantly correlated with disorganization symptoms and predictive of time to conversion to psychosis. Moreover, using an independent clinical sample, we demonstrate that this hyperconnectivity pattern is reliably detected and specifically present in patients with schizophrenia. These findings implicate cerebello–thalamo–cortical hyperconnectivity as a robust state-independent neural signature for psychosis prediction and characterization.

Brain function alterations in schizophrenia and other psychotic disorders remain poorly understood. Here, the authors discover that increased neural connectivity in the cerebello-thalamo-cortical circuitry predicts psychosis in those at high risk, and is present in people with schizophrenia.

Activation of brainstem and midbrain nuclei during cognitive control in medicated patients with schizophrenia

Evidence suggests that cognitive control functions as well as the underlying brain network, anchored by the prefrontal cortex (PFC) and the dorsal anterior cingulate cortex (dACC), are dysfunctional in schizophrenia. Catecholamine producing midbrain and brainstem nuclei are densely connected with the PFC and dACC and exert profound contributions to cognitive control processes. Dysfunctions within the underlying neurotransmitter systems are considered to play a central role in the occurrence of various symptoms of schizophrenia. We sought to investigate the putatively abnormal activation pattern of the dopaminergic midbrain nuclei, that is, ventral tegmental area (VTA) and substantia nigra as well as that of the noradrenergic locus coeruleus (LC) in patients with schizophrenia during cognitive control. A total of 28 medicated patients and 27 healthy controls were investigated with the manual version of the Stroop task using event-related fMRI. The main finding was a reduced BOLD activation in the VTA during both Stroop task conditions in patients in comparison to controls, which correlated significantly with the degree of negative symptoms. We further detected a comparable LC activation in in patients and healthy controls. However, in controls LC activation was significantly correlated with the Stroop interference time, which was not observed in patients. The finding of reduced VTA activation in schizophrenia patients lends further support to the assumed dysfunction of the DA system in schizophrenia. In addition, despite comparable LC activation, the nonsignificant correlation with the Stroop interference time might indicate altered LC functioning in schizophrenia and, thus, needs further investigations.

Cerebellar abnormalities in first-episode, drug-naive schizophrenia at rest

The cerebellum plays a crucial role in higher cortical functions through a cerebellar-cerebral circuit. However, the specific mechanisms through which the cerebellum contributes to the neurobiology of schizophrenia remain unclear. Forty-nine first-episode, drug-naive patients with schizophrenia and 50 healthy controls underwent structural and resting-state functional magnetic resonance imaging (rs-fMRI). The MRI data were analyzed using voxel-based morphometry, amplitude of low-frequency fluctuations (ALFF), cerebellum homogeneity (CH), and seed-based functional connectivity (FC). Patients with schizophrenia did not have anatomical and CH alterations in the cerebellum compared with healthy controls. However, they exhibited decreased ALFF in the right Crus I and abnormal cerebellar FC with brain regions within the dorsal attention network, default-mode network, and ventral attention network. The findings indicate that cerebellar abnormalities in first-episode schizophrenia are mainly in the cerebellar-cerebral connectivities, which may contribute to the neurobiology of schizophrenia.

Differential Resting-State Connectivity Patterns of the Right Anterior and Posterior Dorsolateral Prefrontal Cortices (DLPFC) in Schizophrenia

In schizophrenia (SCZ), dysfunction of the dorsolateral prefrontal cortex (DLPFC) has been linked to the deficits in executive functions and attention. It has been suggested that, instead of considering the right DLPFC as a cohesive functional entity, it can be divided into two parts (anterior and posterior) based on its whole-brain connectivity patterns. Given these two subregions' differential association with cognitive processes, we investigated the functional connectivity (FC) profile of both subregions through resting-state data to determine whether they are differentially affected in SCZ. Resting-state magnetic resonance imaging (MRI) scans were obtained from 120 patients and 172 healthy controls (HC) at 6 different MRI sites. The results showed differential FC patterns for the anterior and posterior parts of the right executive control-related DLPFC in SCZ with the parietal, the temporal and the cerebellar regions, along with a convergent reduction of connectivity with the striatum and the occipital cortex. An increased psychopathology level was linked to a higher difference in posterior vs. anterior FC for the left IFG/anterior insula, regions involved in higher-order cognitive processes. In sum, the current analysis demonstrated that even between two neighboring clusters connectivity could be differentially disrupted in SCZ. Lacking the necessary anatomical specificity, such notions may in fact be detrimental to a proper understanding of SCZ pathophysiology.

Altered cerebro-cerebellum resting-state functional connectivity in HIV-infected male patients

In addition to the role of planning and executing movement, the cerebellum greatly contributes to cognitive process. Numerous studies have reported structural and functional abnormalities in the cerebellum for HIV-infected patients, but little is known about the altered functional connectivity of particular cerebellar subregions and the cerebrum. Therefore, this study aimed to explore the resting-state functional connectivity (rsFC) changes of the cerebellum and further analyze the relationship between the rsFC changes and the neuropsychological evaluation. The experiment involved 26 HIV-infected men with asymptomatic neurocognitive impairment (ANI) and 28 healthy controls (HC). We selected bilateral hemispheric lobule VI and lobule IX as seed regions and mapped the whole-brain rsFC for each subregion. Results revealed that right lobule VI showed significant increased rsFC with the anterior cingulate cortex (ACC) in HIV-infected subjects. In addition, the correlation analysis on HIV-infected subjects illustrated the increased rsFC was negatively correlated with the attention/working memory score. Moreover, significantly increased cerebellar rsFCs were also observed in HIV-infected patients related to right inferior frontal gyrus (IFG) and right superior medial gyrus (SMG) while decreased rsFC was just found between right lobule VI and the left hippocampus (HIP). These findings suggested that, abnormalities of cerebro-cerebellar functional connectivity might be associated with cognitive dysfunction in HIV-infected men, particularly working memory impairment. It could also be the underlying mechanism of ANI, providing further evidence for early injury in the neural substrate of HIV-infected patients.

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI

Background

A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia.

Methods

A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls.

Findings

Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks.

Interpretation

The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the “disconnectivity” model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites.

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A deep discriminant autoencoder network is developed for cross-site classification of schizophrenia.

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The deep learning method can learn site-shared brain connectivity features and achieve accurate prediction of schizophrenia.

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The learned features reveal dysfunctional integration of the cortical-striatal-cerebellar circuit in schizophrenia.

Altered Structural and Functional Connectivity of Juvenile Myoclonic Epilepsy: An fMRI Study

The aim of this study was to investigate the structural and functional connectivity (FC) of juvenile myoclonic epilepsy (JME) using resting state functional magnetic resonance imaging (rs-fMRI). High-resolution T1-weighted magnetic resonance imaging (MRI) and rs-fMRI data were collected in 25 patients with JME and in 24 control subjects. A FC analysis was subsequently performed, with seeding at the regions that demonstrated between-group differences in gray matter volume (GMV). Then, the observed structural and FCs were associated with the clinical manifestations. The decreased GMV regions were found in the bilateral anterior cerebellum, the right orbital superior frontal gyrus, the left middle temporal gyrus, the left putamen, the right hippocampus, the bilateral caudate, and the right thalamus. The changed FCs were mainly observed in the motor-related areas and the cognitive-related areas. The significant findings of this study revealed an important role for the cerebellum in motor control and cognitive regulation in JME patients, which also have an effect on the activity of the occipital lobe. In addition, the changed FCs were related to the clinical features of JME patients. The current observations may contribute to the understanding of the pathogenesis of JME.

Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity

Schizophrenia is a complex mental disorder with disorganized communication among large-scale brain networks, as demonstrated by impaired resting-state functional connectivity (rsFC). Individual rsFC studies, however, vary greatly in their methods and findings. We searched for consistent patterns of network dysfunction in schizophrenia by using a coordinate-based meta-analysis. Fifty-six seed-based voxel-wise rsFC datasets from 52 publications (2115 patients and 2297 healthy controls) were included in this meta-analysis. Then, coordinates of seed regions of interest (ROI) and between-group effects were extracted and coded. Seed ROIs were categorized into seed networks by their location within an a priori template. Multilevel kernel density analysis was used to identify brain networks in which schizophrenia was linked to hyper-connectivity or hypo-connectivity with each a priori network. Our results showed that schizophrenia was characterized by hypo-connectivity within the default network (DN, self-related thought), affective network (AN, emotion processing), ventral attention network (VAN, processing of salience), thalamus network (TN, gating information) and somatosensory network (SS, involved in sensory and auditory perception). Additionally, hypo-connectivity between the VAN and TN, VAN and DN, VAN and frontoparietal network (FN, external goal-directed regulation), FN and TN, and FN and DN were found in schizophrenia. Finally, the only instance of hyper-connectivity in schizophrenia was observed between the AN and VAN. Our meta-analysis motivates an empirical foundation for a disconnected large-scale brain networks model of schizophrenia in which the salience processing network (VAN) plays the core role, and its imbalanced communication with other functional networks may underlie the core difficulty of patients to differentiate self-representation (inner world) and environmental salience processing (outside world).

Frequency-specific alteration of functional connectivity density in antipsychotic-naive adolescents with early-onset schizophrenia

Early-onset schizophrenia (EOS) is a severe mental illness associated with dysconnectivity that widespread in the brain. However, the functional dysconnectivity in EOS are still mixed. Recently, studies have identified that functional connectivity (FC) arises from a band-limited slow rhythmic mechanism and suggested that the dysconnectivity at specific frequency bands may provide more robust biomarkers for schizophrenia. The frequency-specific changes of FC pattern in EOS remain unclear. To address this issue, resting-state functional magnetic resonance imaging data scans from 39 EOS patients (drug-naive) and 31 healthy controls (HCs) were used to assess the FC density (FCD) across slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz). Results revealed that a remarkable difference between the FCD of the two bands existed mainly in the default mode network (DMN) and subcortical areas. Compared with the HCs, EOS patients showed significantly altered FCD involved in audiovisual information processing, sensorimotor system, and social cognition. Importantly, a significant frequency-by-group interaction was observed in the left precuneus with significantly lower FCD in the slow-4 frequency band, but no significant effect in the slow-5 frequency band. In addition, decreased FC was found between the precuneus and other DMN regions in the slow-4 band. Furthermore, the change in FCD in precuneus was inversely proportional to the clinical symptom in slow-4 band, indicating the key role of precuneus in schizophrenia progress. Our findings demonstrated that the dysconnectivity pattern in EOS could be frequency-dependent.

Using short-range and long-range functional connectivity to identify schizophrenia with a family-based case-control design

Abnormal short-range and long-range functional connectivities (FCs) have been implicated in the neurophysiology of schizophrenia. This study was conducted to examine the potential of short-range and long-range FCs for differentiating the patients from the controls with a family-based case-control design. Twenty-eight first-episode, drug-naive patients with schizophrenia, 28 unaffected siblings of the patients (family-based controls, FBCs), and 40 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI) scans. The data were analyzed by short-range and long-range FC analyses, receiver operating characteristic curve (ROC) and support vector machine (SVM). Compared with the FBCs/HCs, the patients exhibit increased short-range positive FC strength (spFCS) and/or long-range positive FC strength (lpFCS) in the default-mode network (DMN) and decreased spFCS and lpFCS in the sensorimotor circuits. Furthermore, a combination of the spFCS values in the right superior parietal lobule and the lpFCS values in the left fusiform gyrus/cerebellum VI can differentiate the patients from the FBCs with high sensitivity and specificity. The findings highlight the importance of the DMN and sensorimotor circuits in the pathogenesis of schizophrenia. Combining with family-based case-control design may be a viable option to limit the confounding effects of environmental risk factors in neuroimaging studies of schizophrenia.

Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities

In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities.

Frontal lobe alterations in schizophrenia: a review

Abstract Objective: To highlight the changes in the frontal lobe of the human brain in people with schizophrenia. Methods: This was a qualitative review of the literature. Results: Many schizophrenic patients exhibit functional, structural, and metabolic abnormalities in the frontal lobe. Some patients have few or no alterations, while some have more functional and structural changes than others. Magnetic resonance imaging (MRI) shows structural and functional changes in volume, gray matter, white matter, and functional activity in the frontal lobe, but the mechanisms underlying these changes are not yet fully understood. Conclusion: When schizophrenia is studied as an essential topic in the field of neuropsychiatry, neuroscientists find that the frontal lobe is the most commonly involved area of the human brain. A clear picture of how this lobe is affected in schizophrenia is still lacking. We therefore recommend that further research be conducted to improve understanding of the pathophysiology of this psychiatric dilemma.

Aberrant intra-salience network dynamic functional connectivity impairs large-scale network interactions in schizophrenia

Aberrant functional interactions between several large-scale networks, especially the central executive network (CEN), the default mode network (DMN) and the salience network (SN), have been postulated as core pathophysiologic features of schizophrenia; however, the attributing factors of which remain unclear. The study employed resting-state fMRI with 77 participants (42 patients and 35 controls). We performed dynamic functional connectivity (DFC) and functional connectivity (FC) analyses to explore the connectivity patterns of these networks. Furthermore, we performed a structural equation model (SEM) analysis to explore the possible role of the SN in modulating network interactions. The results were as follows: (1) The inter-network connectivity showed decreased connectivity strength and increased time-varying instability in schizophrenia; (2) The SN manifested schizophrenic intra-network dysfunctions in both the FC and DFC patterns; (3) The connectivity properties of the SN were effective in discriminating controls from patients; (4) In patients, the dynamic intra-SN connectivity negatively predicted the inter-network FC, and this effect was mediated by intra-SN connectivity strength. These findings suggest that schizophrenia show systematic deficits in temporal stability of large-scale network connectivity. Furthermore, aberrant network interactions in schizophrenia could be attributed to instable intra-SN connectivity and the dysfunction of the SN may be an intrinsic biomarker of the disease.

Clinical significance of increased cerebellar default-mode network connectivity in resting-state patients with drug-naive somatization disorder

The cerebellum has been proven to be connected to the brain network, as in the default-mode network (DMN), among healthy subjects and patients with psychiatric disorders. However, whether or not abnormal cerebellar DMN connectivity exists and what its clinical significance is among drug-naive patients with somatization disorder (SD) at rest remain unclear.A total of 25 drug-naive patients with SD and 28 healthy controls were enrolled for a resting-state scan. The imaging data were analyzed using the seed-based functional connectivity (FC) method.Compared with the controls, patients with SD showed increased left/right Crus I-left/right angular gyrus (AG) connectivity and Lobule IX-left superior medial prefrontal cortex (MPFC) connectivity. The FC values of the left/right Crus I-right AG connectivity of the patients were positively correlated with their scores in the somatization subscale of the symptom checklist-90 (Scl-90). A trend level of correlations was observed between the FC values of the left Crus I-left AG connectivity of the patients and their scores for the somatization subscale of Scl-90, as well as between the FC values of their Lobule IX-left superior MPFC connectivity and their scores for the Eysenck personality questionnaire (EPQ) extraversion.Our findings show the increased cerebellar DMN connectivity in patients with SD and therefore highlight the importance of the DMN in the neurobiology of SD. Increased cerebellar DMN connectivities are also correlated with their somatization severity and personality, both of which bear clinical significance.

Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance

Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) patterns. This study aimed to investigate effects of clinical and sociodemographic variables on the classification by applying multivariate pattern analysis (MVPA) to both gray matter (GM) volume and FC measures in patients with SZ and healthy controls (HC). RS and structural magnetic resonance imaging data (sMRI) from 74 HC and 71 SZ patients were obtained from a Mind Research Network COBRE dataset available via COINS (http://coins.mrn.org/dx). We used a MVPA framework using support-vector machines embedded in a repeated, nested cross-validation to generate a multi-modal diagnostic system and evaluate its generalizability. The dependence of neurodiagnostic performance on clinical and sociodemographic variables was evaluated. The RS classifier showed a slightly higher accuracy (70.5%) compared to the structural classifier (69.7%). The combination of sMRI and RS outperformed single MRI modalities classification by reaching 75% accuracy. The RS based moderator analysis revealed that the neurodiagnostic performance was driven by older SZ patients with an earlier illness onset and more pronounced negative symptoms. In contrast, there was no linear relationship between the clinical variables and neuroanatomically derived group membership measures. This study achieved higher accuracy distinguishing HC from SZ patients by fusing 2 imaging modalities. In addition the results of RS based moderator analysis showed that age of patients, as well as their age at the illness onset were the most important clinical features.