Disrupted modular architecture of cerebellum in schizophrenia: a graph theoretic analysis

Dysconnectivity of the cerebellum and somatomotor network correlates with the severity of alogia in chronic schizophrenia

Recent fMRI resting-state findings show aberrant functional connectivity within somatomotor network (SMN) in schizophrenia. Moreover, functional connectivity aberrations of the motor system are often reported to be related to the severity of psychotic symptoms. Thus, it is important to validate those findings and confirm their relationship with psychopathology. Therefore, we decided to take an entirely data-driven approach in our fMRI resting-state study of 30 chronic schizophrenia outpatients and 30 matched control subjects. We used independent component analysis (ICA), dual regression, and seed-based connectivity analysis. We found reduced functional connectivity within SMN in schizophrenia patients compared to controls and SMN hypoconnectivity with the cerebellum in schizophrenia patients. The latter was strongly correlated with the severity of alogia, one of the main psychotic symptoms, i.e. poverty of speech and reduction in spontaneous speech,. Our results are consistent with the recent knowledge about the role of the cerebellum in cognitive functioning and its abnormalities in psychiatric disorders, e.g. schizophrenia. In conclusion, the presented results, for the first time clearly showed the involvement of the cerebellum hypoconnectivity with SMN in the persistence and severity of alogia symptoms in schizophrenia.

Cerebellar Neurostimulation for Boosting Social and Affective Functions: Implications for the Rehabilitation of Hereditary Ataxia Patients

Beyond motor deficits, spinocerebellar ataxia (SCA) patients also suffer cognitive decline and show socio-affective difficulties, negatively impacting on their social functioning. The possibility to modulate cerebello-cerebral networks involved in social cognition through cerebellar neurostimulation has opened up potential therapeutic applications for ameliorating social and affective difficulties. The present review offers an overview of the research on cerebellar neurostimulation for the modulation of socio-affective functions in both healthy individuals and different clinical populations, published in the time period 2000-2022. A total of 25 records reporting either transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS) studies were found. The investigated clinical populations comprised different pathological conditions, including but not limited to SCA syndromes. The reviewed evidence supports that cerebellar neurostimulation is effective in improving social abilities in healthy individuals and reducing social and affective symptoms in different neurological and psychiatric populations associated with cerebellar damage or with impairments in functions that involve the cerebellum. These findings encourage to further explore the rehabilitative effects of cerebellar neurostimulation on socio-affective deficits experienced by patients with cerebellar abnormalities, as SCA patients. Nevertheless, conclusions remain tentative at this stage due to the heterogeneity characterizing stimulation protocols, study methodologies and patients' samples.

Variability in white matter structure relates to hallucination proneness

Hallucinations are a prominent transdiagnostic psychiatric symptom but are also prevalent in individuals who do not require clinical care. Moreover, persistent psychosis-like experience in otherwise healthy individuals may be related to an increased risk to transition to a psychotic disorder. This suggests a common etiology across clinical and non-clinical individuals along a multidimensional psychosis continuum that may be detectable in structural variations of the brain. The current diffusion tensor imaging study assessed 50 healthy individuals (35 females) to identify possible differences in white matter associated with hallucination proneness (HP). This approach circumvents potential confounds related to medication, hospitalization, and disease progression common in clinical individuals. We determined how HP relates to white matter structure in selected association, commissural, and projection fiber pathways putatively linked to psychosis. Increased HP was associated with enhanced fractional anisotropy (FA) in the right uncinate fasciculus, the right anterior and posterior arcuate fasciculus, and the corpus callosum. These findings support the notion of a psychosis continuum, providing first evidence of structural white matter variability associated with HP in healthy individuals. Furthermore, alterations in the targeted pathways likely indicate an association between HP-related structural variations and the putative salience and attention mechanisms that these pathways subserve.

Gestational Diabetes Mellitus and its Effects on the Developing Cerebellum: A Narrative Review on Experimental Studies

Diabetes mellitus during pregnancy is a common complication of gestation, but its effects on the offspring's development are poorly understood. Recently, some studies reported that gestational diabetes mellitus (GDM) impairs cerebellar development, and some genetic alterations have been described as consequences. Cerebellum, one of the hindbrain derived structures in the posterior cranial fossa, plays a crucial role in cognition and behavioral functions. In recent years, some surveys stated that gestational diabetes has adverse effects on the fetus's cerebellum. Disruption of cerebellar cortex morphogenesis, reduce the volume of the cerebellum, reduce the thickness of cerebellar cortex layers, and its neuronal cells and effects on the expression of synaptophysin, insulin, and insulin-like growth factor -1 receptors are some of the maternal diabetes effects on developing cerebellum. On other hand, GDM, as a neurotoxic agent, impaired cerebellar development and could be a cause for the behavioral, functional, and structural anomalies observed in pups of diabetic mothers. Based on the literature review, most studies have pointed out that administering insulin in patients with GDM decreased the cellular and molecular alterations that induced by GDM in the developing cerebellum. Undoubtedly, screening strategies for all pregnant women are necessary.

Cerebro-cerebellar gray matter abnormalities associated with cognitive impairment in patients with recent-onset and chronic schizophrenia

Although the role of the cerebellum in schizophrenia has gained attention, its contribution to cognitive impairment remains unclear. We aimed to investigate volumetric alterations in the cerebro-cerebellar gray matter (GM) in patients with recent-onset schizophrenia (ROS) and chronic schizophrenia (CS) compared with healthy controls (HCs). Seventy-two ROS, 43 CS, and 127 HC participants were recruited, and high-resolution T1-weighted structural magnetic resonance images of the brain were acquired. We compared cerebellar GM volumes among the groups using voxel-based morphometry and examined the cerebro-cerebellar GM volumetric correlations in participants with schizophrenia. Exploratory correlation analysis investigated the functional relevance of cerebro-cerebellar GM volume alterations to cognitive function in the schizophrenia group. The ROS and CS participants demonstrated smaller cerebellar GM volumes, particularly in Crus I and II, than HCs. Extracted cerebellar GM volumes demonstrated significant positive correlations with the cerebral GM volume in the fronto-temporo-parietal association areas engaged in higher-order association. The exploratory analysis showed that smaller cerebellar GM in the posterior lobe regions was associated with poorer cognitive performance in participants with schizophrenia. Our study suggests that cerebellar pathogenesis is present in the early stages of schizophrenia and interconnected with structural abnormalities in the cerebral cortex. Integrating the cerebellum into the pathogenesis of schizophrenia will help advance our understanding of the disease and identify novel treatment targets concerning dysfunctional cerebro-cerebellar interactions.

Cerebellar transcranial magnetic stimulation in psychotic disorders: intermittent, continuous, and sham theta-burst stimulation on time perception and symptom severity

Background

The cerebellum contributes to the precise timing of non-motor and motor functions, and cerebellum abnormalities have been implicated in psychosis pathophysiology. In this study, we explored the effects of cerebellar theta burst stimulation (TBS), an efficient transcranial magnetic stimulation protocol, on temporal discrimination and self-reported mood and psychotic symptoms.

Methods

We conducted a case-crossover study in which patients with psychosis (schizophrenias, schizoaffective disorders, or bipolar disorders with psychotic features) were assigned to three sessions of TBS to the cerebellar vermis: one session each of intermittent (iTBS), continuous (cTBS), and sham TBS. Of 28 enrolled patients, 26 underwent at least one TBS session, and 20 completed all three. Before and immediately following TBS, participants rated their mood and psychotic symptoms and performed a time interval discrimination task (IDT). We hypothesized that cerebellar iTBS and cTBS would modulate these measures in opposing directions, with iTBS being adaptive and cTBS maladaptive.

Results

Reaction time (RT) in the IDT decreased significantly after iTBS vs. Sham (LS-mean difference = -73.3, p = 0.0001, Cohen's d = 1.62), after iTBS vs. cTBS (LS-mean difference = -137.6, p < 0.0001, d = 2.03), and after Sham vs. cTBS (LS-mean difference = -64.4, p < 0.0001, d = 1.33). We found no effect on IDT accuracy. We did not observe any effects on symptom severity after correcting for multiple comparisons.

Conclusion

We observed a frequency-dependent dissociation between the effects of iTBS vs. cTBS to the cerebellar midline on the reaction time of interval discrimination in patients with psychosis. iTBS showed improved (adaptive) while cTBS led to worsening (maladaptive) speed of response. These results demonstrate behavioral target engagement in a cognitive dimension of relevance to patients with psychosis and generate testable hypotheses about the potential therapeutic role of cerebellar iTBS in this clinical population.

Clinical Trial Registration

clinicaltrials.gov, identifier NCT02642029.

The Cerebellum Gets Social: Evidence from an Exploratory Study of Cerebellar, Neurodevelopmental, and Psychiatric Disorders

Social prediction is a key feature of social cognition (SC), a function in which the modulating role of the cerebellum is recognized. Accordingly, cerebellar alterations are reported in cerebellar pathologies, neurodevelopmental disorders, and psychiatric conditions that show SC deficits. Nevertheless, to date, no study has directly compared populations representative of these three conditions with respect to SC and cerebellar alterations. Therefore, the present exploratory study aimed to compare the SC profiles of individuals with cerebellar neurodegenerative disorders (CB), autism (ASD), bipolar disorder type 2 (BD2), or healthy subjects (HS) using a battery of social tests requiring different degrees of prediction processing. The patterns of cerebellar gray matter (GM) alterations were compared among the groups using voxel-based morphometry. Compared to HS, the clinical groups showed common SC deficits in tasks involving a moderate to high level of prediction. The behavioral results of the clinical groups are consistent with the presence of overlapping GM reduction in cerebellar right Crus II, an area notably involved in complex social processing and prediction. Although exploratory and preliminary, these results deepen the cerebellar role in social prediction and highlight the transdiagnostic value of the cerebellum in social functioning and prediction in pathologies of different aetiologies, forecasting novel possibilities for shared interventions.

A Comprehensive Analysis of Cerebellar Volumes in the 22q11.2 Deletion Syndrome

BACKGROUND

The presence of a 22q11.2 microdeletion (22q11.2 deletion syndrome [22q11DS]) ranks among the greatest known genetic risk factors for the development of psychotic disorders. There is emerging evidence that the cerebellum is important in the pathophysiology of psychosis. However, there is currently limited information on cerebellar neuroanatomy in 22q11DS specifically.

METHODS

High-resolution 3T magnetic resonance imaging was acquired in 79 individuals with 22q11DS and 70 typically developing control subjects (N = 149). Lobar and lobule-level cerebellar volumes were estimated using validated automated segmentation algorithms, and subsequently group differences were compared. Hierarchical clustering, principal component analysis, and graph theoretical models were used to explore intercerebellar relationships. Cerebrocerebellar structural connectivity with cortical thickness was examined via linear regression models.

RESULTS

Individuals with 22q11DS had, on average, 17.3% smaller total cerebellar volumes relative to typically developing subjects (p < .0001). The lobules of the superior posterior cerebellum (e.g., VII and VIII) were particularly affected in 22q11DS. However, all cerebellar lobules were significantly smaller, even after adjusting for total brain volumes (all cerebellar lobules p < .0002). The superior posterior lobule was disproportionately associated with cortical thickness in the frontal lobes and cingulate cortex, brain regions known be affected in 22q11DS. Exploratory analyses suggested that the superior posterior lobule, particularly Crus I, may be associated with psychotic symptoms in 22q11DS.

CONCLUSIONS

The cerebellum is a critical but understudied component of the 22q11DS neuroendophenotype.

Altered functional connectivity of cerebellar networks in first-episode schizophrenia

Introduction

Abnormalities of the cerebellum have been displayed to be a manifestation of schizophrenia (SCH) which is a detrimental psychiatric disorder. It has been recognized that the cerebellum contributes to motor function, sensorimotor function, cognition, and other brain functions in association with cerebral functions. Multiple studies have observed that abnormal alterations in cerebro-cerebellar functional connectivity (FC) were shown in patients with SCH. However, the FC of cerebellar networks in SCH remains unclear.

Methods

In this study, we explored the FC of cerebellar networks of 45 patients with first-episode SCH and 45 healthy control (HC) subjects by using a defined Yeo 17 network parcellation system. Furthermore, we performed a correlation analysis between cerebellar networks' FC and positive and negative symptoms in patients with first-episode SCH. Finally, we established the classification model to provide relatively suitable features for patients with first-episode SCH concerning the cerebellar networks.

Results

We found lower between-network FCs between 14 distinct cerebellar network pairs in patients with first-episode SCH, compared to the HCs. Significantly, the between-network FC in N2-N15 was positively associated with positive symptom severity; meanwhile, N4-N15 was negatively associated with negative symptom severity. Besides, our results revealed a satisfactory classification accuracy (79%) of these decreased between-network FCs of cerebellar networks for correctly identifying patients with first-episode SCH.

Conclusion

Conclusively, between-network abnormalities in the cerebellum are closely related to positive and negative symptoms of patients with first-episode SCH. In addition, the classification results suggest that the cerebellar networks can be a potential target for further elucidating the underlying mechanisms in first-episode SCH.

Alterations of cerebellar white matter integrity and associations with cognitive impairments in schizophrenia

"Cognitive dysmetria" theory of schizophrenia (SZ) has highlighted that the cerebellum plays a critical role in understanding the pathogenesis and cognitive impairment in SZ. Despite some studies have reported the structural disruption of the cerebellum in SZ using whole brain approach, specific focus on the voxel-wise changes of cerebellar WM microstructure and its associations with cognition impairments in SZ were less investigated. To further explore the voxel-wise structural disruption of the cerebellum in SZ, the present study comprehensively examined volume and diffusion features of cerebellar white matter in SZ at the voxel level (42 SZ vs. 52 controls) and correlated the observed alterations with the cognitive impairments measured by MATRICS Consensus Cognitive Battery. Combing voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) methods, we found, compared to healthy controls (HCs), SZ patients did not show significant alteration in voxel-level cerebellar white matter (WM) volume and tract-wise and skeletonized DTI features. In voxel-wise DTI features of cerebellar peduncles, compared to HCs, SZ patients showed decreased fractional anisotropy and increased radial diffusivity mainly located in left middle cerebellar peduncles (MCP) and inferior cerebellar peduncles (ICP). Interestingly, these alterations were correlated with overall composite and different cognitive domain (including processing speed, working memory, and attention vigilance) in HCs but not in SZ patients. The present findings suggested that the voxel-wise WM integrity analysis might be a more sensitive way to investigate the cerebellar structural abnormalities in SZ patients. Correlation results suggested that inferior and MCP may be a crucial neurobiological substrate of cognition impairments in SZ, thus adding the evidence for taking the cerebellum as a novel therapeutic target for cognitive impairments in SZ patients.

Cerebellar and basal ganglia motor network predicts trait depression and hyperactivity

In the human brain, the cerebellum (CB) and basal ganglia (BG) are implicated in cognition-, emotion-, and motor-related cortical processes and are highly interconnected, both to cortical regions via separate, trans-thalamic pathways and to each other via subcortical disynaptic pathways. We previously demonstrated a distinction between cognitive and motor CB-BG networks (CCBN, MCBN, respectively) as it relates to cortical network integration in healthy young adults, suggesting the subcortical networks separately support cortical networks. The CB and BG are also implicated in the pathophysiology of schizophrenia, Parkinson's, and compulsive behavior; thus, integration within subcortical CB-BG networks may be related to transdiagnostic symptomology. Here, we asked whether CCBN or MCBN integration predicted Achenbach Self-Report scores for anxiety, depression, intrusive thoughts, hyperactivity and inactivity, and cognitive performance in a community sample of young adults. We computed global efficiency for each CB-BG network and 7 canonical resting-state networks for all right-handed participants in the Human Connectome Project 1200 release with a complete set of preprocessed resting-state functional MRI data (N = 783). We used multivariate regression to control for substance abuse and age, and permutation testing with exchangeability blocks to control for family relationships. MCBN integration negatively predicted depression and hyperactivity, and positively predicted cortical network integration. CCBN integration predicted cortical network integration (except for the emotional network) and marginally predicted a positive relationship with hyperactivity, indicating a potential dichotomy between cognitive and motor CB-BG networks and hyperactivity. These results highlight the importance of CB-BG interactions as they relate to motivation and symptoms of depression.

Cerebellum and Emotion in Social Behavior

Accumulating evidence suggests that the cerebellum plays a crucial role not only in the motor and cognitive domains but also in emotions and social behavior. In the present chapter, after a general introduction on the significance of the emotional components of social behavior, we describe recent efforts to understand the contributions of the cerebellum in social cognition focusing on the emotional and affective aspects. Specifically, starting from the description of the cerebello-cortical networks subtending the social-affective domains, we illustrate the most recent findings on the social cerebellum and the possible functional mechanisms by which the cerebellum modulate social-affective behavior. Finally, we discuss the possible consequences of cerebellar dysfunction in the social-affective domain, focusing on those neurological and psychopathological conditions in which emotional and social behavior difficulties have been described as being associated with cerebellar structural or functional alterations.

Multi-spatial-scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia

We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. Multiscale ICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and sex-common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (a) within the subcortical domain, (b) between the somatomotor and cerebellum domains, and (c) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial-scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial-scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.

An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data

Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNNAM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging biomarkers.

Impaired cerebro-cerebellar white matter connectivity and its associations with cognitive function in patients with schizophrenia

Schizophrenia is a complex brain disorder of unknown etiology. Based on the notion of “cognitive dysmetria,” we aimed to investigate aberrations in structural white matter (WM) connectivity that links the cerebellum to cognitive dysfunction in patients with schizophrenia. A total of 112 participants (65 patients with schizophrenia and 47 healthy controls [HCs]) were enrolled and underwent diffusion tensor imaging. Between-group voxel-wise comparisons of cerebellar WM regions (superior/middle [MCP]/inferior cerebellar peduncle and pontine crossing fibers) were performed using Tract-Based Spatial Statistics. Cognitive function was assessed using the Trail Making Test Part A/B (TMT-A/B), Wisconsin Card Sorting Test (WCST), and Rey-Kim Memory Test in 46 participants with schizophrenia. WM connectivity, measured as fractional anisotropy (FA), was significantly lower in the MCP in participants with schizophrenia than in HCs. The mean FAs extracted from the significant MCP cluster were inversely correlated with poorer cognitive performance, particularly longer time to complete the TMB-B (r = 0.559, p < 0.001) and more total errors in the WCST (r = 0.442, p = 0.003). Our findings suggest that aberrant cerebro-cerebellar communication due to disrupted WM connectivity may contribute to cognitive impairments, a core characteristic of schizophrenia. Our results may expand our understanding of the neurobiology of schizophrenia based on the cerebro-cerebellar interconnectivity of the brain.

Genome-wide association study followed by trans-ancestry meta-analysis identify 17 new risk loci for schizophrenia

BACKGROUND

Over 200 schizophrenia risk loci have been identified by genome-wide association studies (GWASs). However, the majority of risk loci were identified in populations of European ancestry (EUR), potentially missing important biological insights. It is important to perform 5 GWASs in non-European populations.

METHODS

To identify novel schizophrenia risk loci, we conducted a GWAS in Han Chinese population (3493 cases and 4709 controls). We then performed a large-scale meta-analysis (a total of 143,438 subjects) through combining our results with previous GWASs conducted in EAS and EUR. In addition, we also carried out comprehensive post-GWAS analysis, including heritability partitioning, enrichment of schizophrenia associations in tissues and cell types, trancscriptome-wide association study (TWAS), expression quantitative trait loci (eQTL) and differential expression analysis.

RESULTS

We identified two new schizophrenia risk loci, including associations in SHISA9 (rs7192086, P = 4.92 × 10-08) and PES1 (rs57016637, P = 2.33 × 10-11) in Han Chinese population. A fixed-effect meta-analysis (a total of 143,438 subjects) with summary statistics from EAS and EUR identifies 15 novel genome-wide significant risk loci. Heritability partitioning with linkage disequilibrium score regression (LDSC) reveals a significant enrichment of schizophrenia heritability in conserved genomic regions, promoters, and enhancers. Tissue and cell-type enrichment analyses show that schizophrenia associations are significantly enriched in human brain tissues and several types of neurons, including cerebellum neurons, telencephalon inhibitory, and excitatory neurons. Polygenic risk score profiling reveals that GWAS summary statistics from trans-ancestry meta-analysis (EAS + EUR) improves prediction performance in predicting the case/control status of our sample. Finally, transcriptome-wide association study (TWAS) identifies risk genes whose cis-regulated expression change may have a role in schizophrenia.

CONCLUSIONS

Our study identifies 17 novel schizophrenia risk loci and highlights the importance and necessity of conducting genetic study in different populations. These findings not only provide new insights into genetic etiology of schizophrenia, but also facilitate to delineate the pathophysiology of schizophrenia and develop new therapeutic targets.

Aberrant cerebellar neural activity and cerebro-cerebellar functional connectivity involving executive dysfunction in schizophrenia with primary negative symptoms

Deficit schizophrenia (DS) is a distinct subtype of schizophrenia characterized by primary and enduring negative symptoms. More severe executive dysfunctions were observed in DS patients, however, the associated neuroimaging characteristics, especially cerebellar functional anomalies in DS, remain largely unknown. We employed resting-state functional and structural MRI data of 106 male participants, including data from 29 DS patients, 39 non-deficit schizophrenia (NDS) patients and 38 healthy controls (HCs). Z-standardized fractional amplitude of low-frequency fluctuation (zfALFF) values were calculated in order to examine spontaneous regional brain activity. Cerebro-cerebellar functional connectivity and changes in the volume of gray matter in the cerebellum were also examined. Relative to the HCs, both DS and NDS patients exhibited decreased zfALFF in the bilateral cerebellar lobules VIII and IX. The zfALFF in the left Crus II was lower in DS patients compared to NDS patients. No significant difference was observed in the volume of cerebellar gray matter among the three groups. Compared with NDS patients, cerebro-cerebellar functional connectivity analysis revealed increased connectivity in the left orbital medial frontal cortex and right putamen regions in DS patients. Reduced zfALFF in the left Crus II in the DS group was significantly positively correlated with Stroop Color and Word scores, while negatively correlated with Trail-Making Test part B scores. The increased functional connectivity in the right putamen in DS patients was significantly positively correlated with Animal Naming Test and semantic Verbal Fluency Test scores. These results highlight cerebellar functional abnormality in DS patients and provide insight into the pathophysiological mechanism of executive dysfunction.

Disrupted brain functional network topology in unilateral acute brainstem ischemic stroke

This study aimed to investigate the topological properties of brain functional connectome in unilateral acute brainstem ischemic stroke using graph theory. Fifty-three acute brainstem ischemic stroke patients, consisted of 27 left-sided and 26 right-sided brainstem stroke patients, and 20 age, gender, and education-matched healthy controls (HCs) were recruited to undergo a resting-state functional magnetic resonance imaging (rs-fMRI) scan in this study. Graph theory analyses were then used to examine the group-specific topological properties of the functional connectomes seperately. The unilateral acute brainstem stroke patients and HCs all exhibited "small-world" brain network topology. The functional connectome of the left brainstem stroke patients showed significant differences in all topological properties while the right brainstem stroke patients showed a significant increase in clustering coefficient Cp (p < 0.001) and local efficiency E_local (p < 0.001), and a significantly decrease in normalized clustering coefficient γ (p < 0.001) and global efficiency E_global (p < 0.001), suggesting both a shift toward regular networks. At the nodal level, abnormal nodal centralities were mainly observed in the defaut mode network, subcortical network, frontal and occipital lobe. The findings of disrupted topological properties of functional brain networks may help better understanding the disease characterization and innovation in management for acute brainstem ischemic stroke patients.

Variants in regulatory elements of PDE4D associate with major mental illness in the Finnish population

We have previously reported a replicable association between variants at the PDE4D gene and familial schizophrenia in a Finnish cohort. In order to identify the potential functional mutations underlying these previous findings, we sequenced 1.5 Mb of the PDE4D genomic locus in 20 families (consisting of 96 individuals and 79 independent chromosomes), followed by two stages of genotyping across 6668 individuals from multiple Finnish cohorts for major mental illnesses. We identified 4570 SNPs across the PDE4D gene, with 380 associated to schizophrenia (p ≤ 0.05). Importantly, two of these variants, rs35278 and rs165940, are located at transcription factor-binding sites, and displayed replicable association in the two-stage enlargement of the familial schizophrenia cohort (combined statistics for rs35278 p = 0.0012; OR = 1.18, 95% CI: 1.06-1.32; and rs165940 p = 0.0016; OR = 1.27, 95% CI: 1.13-1.41). Further analysis using additional cohorts and endophenotypes revealed that rs165940 principally associates within the psychosis (p = 0.025, OR = 1.18, 95% CI: 1.07-1.30) and cognitive domains of major mental illnesses (g-score p = 0.044, β = -0.033). Specifically, the cognitive domains represented verbal learning and memory (p = 0.0091, β = -0.044) and verbal working memory (p = 0.0062, β = -0.036). Moreover, expression data from the GTEx database demonstrated that rs165940 significantly correlates with the mRNA expression levels of PDE4D in the cerebellum (p-value = 0.04; m-value = 0.9), demonstrating a potential functional consequence for this variant. Thus, rs165940 represents the most likely functional variant for major mental illness at the PDE4D locus in the Finnish population, increasing risk broadly to psychotic disorders.

The neurobiological underpinning of the social cognition impairments in patients with spinocerebellar ataxia type 2

Clinical studies described emotional and social behaviour alterations in patients with cerebellar diseases, proposing a role of specific cerebello-cerebral circuits in social cognition. However, for a long time these difficulties were underestimated, and no studies have addressed the correlation between social cognition deficits and topography of the cerebellar damage. The present study aims to investigate the social cognition impairment and the neuroanatomical alterations in patients with spinocerebellar ataxia type 2 (SCA2) and to analyze their relationship. To this purpose a social cognition battery composed by three tests, and a MRI protocol were administered to 13 SCA2 patients and 26 healthy subjects. The pattern of gray matter (GM) atrophy was analyzed by voxel-based morphometry, and the GM volumes of each altered area were correlated with the behavioral scores to investigate anatomo-functional relationships. In addition, we investigated the relationship between social deficits and damage to the cerebellar peduncles using DTI diffusivity indices. Our patients showed impairment of the immediate perceptual component of the mental state recognition (i.e., to recognize feelings and thoughts from the eyes expression), and difficulties in anger attribution, and in the understanding of false or mistaken beliefs. They showed a pattern of GM reduction in cerebellar regions, including lobules IX and VIIIb and Crus II, all of which are involved in specific components of the mentalizing process. Interestingly, the behavioral performance, in which SCA2 patients showed impairments compared to controls, correlated with the degree of cerebellar GM reduction and with the presence of microstructural abnormalities in the cerebellar peduncles. The present study provides the first characterization of the social cognition deficits in a homogenous cohort SCA2 patients and demonstrates that alterations in specific cerebellar regions should represent the neurobiological underpinning of their social behavior difficulties. Our results offer a new point of view in considering these aspects in the clinical practice.

Diffusion Tensor Tractography Characteristics of White Matter Tracts are Associated with Post-Stroke Depression

PURPOSE

To analyze the changes in white matter tracts in patients with post-stroke depression (PSD), and the correlation between these changes and the depressive state.

PATIENTS AND METHODS

The numbers of white matter tracts and corresponding fractional anisotropy (FA) in the acute phase (the onset time <72 hours) were examined in each subject by diffusion tensor tractography (DTT). Diffusion tensor imaging (DTT), a new development of diffusion tensor imaging (DTI), enables visualization of white matter fiber tracts, which are thought to be closely related to the occurrence of PSD, According to the scores of Hamilton Depression Scale (HAMD) recorded at the 2nd week, 1st month, 3rd month, 6th month, and 12th month, forty patients were randomly selected and were classified into PSD group (n=20), non-depression post-stroke group (N-PSD, n=20), and control normal group (NORM, n=20), respectively. Correlations between the number of bundles (lines) in the white matter tract and corresponding FA, and HAMD score were finally assessed.

RESULTS

1) FAs of the ipsilesional crossed corticocerebellar tract, the corticospinal tract, and the anterior thalamic radiation in PSD group were significantly lower than those in N-PSD and NORM groups (P<0.01); 2) Lines in the three areas in the PSD group were significantly lower than those in the N-PSD and NORM groups (P<0.01); and 3) FA and lines in the three areas of PSD patients were negatively correlated to HAMD scores (correlation coefficient=-0.586, -0.793, -0.626, -0.533, -0.642, and -0.524, respectively, all P<0.05).

CONCLUSION

FA and lines of the ipsilesional crossed corticocerebellar tract, the corticospinal tract, and the anterior thalamic radiation in PSD patients are significantly correlated to the depressive state. The crossed corticocerebellar tract, the corticospinal tract and the anterior thalamic radiation are involved in the development of PSD.

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.

Consensus Paper: Cerebellum and Social Cognition

The traditional view on the cerebellum is that it controls motor behavior. Although recent work has revealed that the cerebellum supports also nonmotor functions such as cognition and affect, only during the last 5 years it has become evident that the cerebellum also plays an important social role. This role is evident in social cognition based on interpreting goal-directed actions through the movements of individuals (social "mirroring") which is very close to its original role in motor learning, as well as in social understanding of other individuals' mental state, such as their intentions, beliefs, past behaviors, future aspirations, and personality traits (social "mentalizing"). Most of this mentalizing role is supported by the posterior cerebellum (e.g., Crus I and II). The most dominant hypothesis is that the cerebellum assists in learning and understanding social action sequences, and so facilitates social cognition by supporting optimal predictions about imminent or future social interaction and cooperation. This consensus paper brings together experts from different fields to discuss recent efforts in understanding the role of the cerebellum in social cognition, and the understanding of social behaviors and mental states by others, its effect on clinical impairments such as cerebellar ataxia and autism spectrum disorder, and how the cerebellum can become a potential target for noninvasive brain stimulation as a therapeutic intervention. We report on the most recent empirical findings and techniques for understanding and manipulating cerebellar circuits in humans. Cerebellar circuitry appears now as a key structure to elucidate social interactions.

Association of cigarette smoking with cognitive impairment in male patients with chronic schizophrenia

BACKGROUND

Previous studies have shown that patients with schizophrenia have higher smoking rates and worse cognitive function than healthy controls. However, there is no consistent conclusion about the relationship between smoking and cognitive impairment.

OBJECTIVES

The main purpose of this study was to explore the effects of smoking on cognitive function by using MATRICS Cognitive Consensus Battery (MCCB) in Chinese male patients with schizophrenia.

METHODS

There were 164 patients with chronic schizophrenia and 82 healthy controls. All subjects were interviewed about smoking status. The cognitive function was assessed by MCCB and Stroop tests. The Positive and Negative Syndrome Scale (PANSS) was used to assess the clinical symptoms of the patients.

RESULTS

Compared with healthy controls, patients had lower MCCB scores in all of its domain scores (all p < 0.05). In the patients, the scores of spatial span test (42.3 ± 11.6), digital sequence test (42.9 ± 10.6), and Hopkins Verbal Learning Test (42.2 ± 10.1) were lower in smokers than those in nonsmokers (all p < 0.05, effect size: 0.28-0.45). Logistic regression analysis showed that the smoking status of the patients was correlated with digital sequence score (p < 0.05, OR = 1.072, 95%CI: 1.013-1.134). Multivariate regression analysis showed that the spatial span total score (β = - 0.26, t = - 2.74, p < 0.001) was associated with the duration of smoking in patients with schizophrenia.

CONCLUSIONS

Our findings show that smoking patients with chronic schizophrenia exhibit more severe cognitive impairment than nonsmoking patients, especially in working memory and executive function.

Disrupted Patterns of Rich-Club and Diverse-Club Organizations in Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment

Background

Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) were considered to be a continuum of Alzheimer’s disease (AD) spectrum. The abnormal topological architecture and rich-club organization in the brain functional network can reveal the pathology of the AD spectrum. However, few studies have explored the disrupted patterns of diverse club organizations and the combination of rich- and diverse-club organizations in SCD and aMCI.

Methods

We collected resting-state functional magnetic resonance imaging data of 19 SCDs, 29 aMCIs, and 28 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative. Graph theory analysis was used to analyze the network metrics and rich- and diverse-club organizations simultaneously.

Results

Compared with HC, the aMCI group showed altered small-world and network efficiency, whereas the SCD group remained relatively stable. The aMCI group showed reduced rich-club connectivity compared with the HC. In addition, the aMCI group showed significantly increased feeder connectivity and decreased local connectivity of the diverse club compared with the SCD group. The overlapping nodes of the rich club and diverse club showed a significant difference in nodal efficiency and shortest path length (L_p) between groups. Notably, the L_p values of overlapping nodes in the SCD and aMCI groups were significantly associated with episodic memory.

Conclusion

The present study demonstrates that the network properties of SCD and aMCI have varying degrees of damage. The combination of the rich club and the diverse club can provide a novel insight into the pathological mechanism of the AD spectrum. The altered patterns in overlapping nodes might be potential biomarkers in the diagnosis of the AD spectrum.

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.

Iterative consensus spectral clustering improves detection of subject and group level brain functional modules

Specialized processing in the brain is performed by multiple groups of brain regions organized as functional modules. Although, in vivo studies of brain functional modules involve multiple functional Magnetic Resonance Imaging (fMRI) scans, the methods used to derive functional modules from functional networks of the brain ignore individual differences in the functional architecture and use incomplete functional connectivity information. To correct this, we propose an Iterative Consensus Spectral Clustering (ICSC) algorithm that detects the most representative modules from individual dense weighted connectivity matrices derived from multiple scans. The ICSC algorithm derives group-level modules from modules of multiple individuals by iteratively minimizing the consensus-cost between the two. We demonstrate that the ICSC algorithm can be used to derive biologically plausible group-level (for multiple subjects) and subject-level (for multiple subject scans) brain modules, using resting-state fMRI scans of 589 subjects from the Human Connectome Project. We employed a multipronged strategy to show the validity of the modularizations obtained from the ICSC algorithm. We show a heterogeneous variability in the modular structure across subjects where modules involved in visual and motor processing were highly stable across subjects. Conversely, we found a lower variability across scans of the same subject. The performance of our algorithm was compared with existing functional brain modularization methods and we show that our method detects group-level modules that are more representative of the modules of multiple individuals. Finally, the experiments on synthetic images quantitatively demonstrate that the ICSC algorithm detects group-level and subject-level modules accurately under varied conditions. Therefore, besides identifying functional modules for a population of subjects, the proposed method can be used for applications in personalized neuroscience. The ICSC implementation is available at https://github.com/SCSE-Biomedical-Computing-Group/ICSC.

Cerebellar-cortical dysconnectivity in resting-state associated with sensorimotor tasks in schizophrenia

Abnormalities of cerebellar function have been implicated in the pathophysiology of schizophrenia. Since the cerebellum has afferent and efferent projections to diverse brain regions, abnormalities in cerebellar lobules could affect functional connectivity with multiple functional systems in the brain. Prior studies, however, have not examined the relationship of individual cerebellar lobules with motor and nonmotor resting‐state functional networks. We evaluated these relationships using resting‐state fMRI in 30 patients with a schizophrenia‐spectrum disorder and 37 healthy comparison participants. For connectivity analyses, the cerebellum was parcellated into 18 lobular and vermal regions, and functional connectivity of each lobule to 10 major functional networks in the cerebrum was evaluated. The relationship between functional connectivity measures and behavioral performance on sensorimotor tasks (i.e., finger‐tapping and postural sway) was also examined. We found cerebellar–cortical hyperconnectivity in schizophrenia, which was predominantly associated with Crus I, Crus II, lobule IX, and lobule X. Specifically, abnormal cerebellar connectivity was found to the cerebral ventral attention, motor, and auditory networks. This cerebellar–cortical connectivity in the resting‐state was differentially associated with sensorimotor task‐based behavioral measures in schizophrenia and healthy comparison participants—that is, dissociation with motor network and association with nonmotor network in schizophrenia. These findings suggest that functional association between individual cerebellar lobules and the ventral attentional, motor, and auditory networks is particularly affected in schizophrenia. They are also consistent with dysconnectivity models of schizophrenia suggesting cerebellar contributions to a broad range of sensorimotor and cognitive operations.

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011, and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data

BACKGROUND

Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information.

METHODS

Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs.

FINDINGS

Accuracies of 83·2% and 80·2% were obtained respectively for the multi-site pooling and leave-one-site-out transfer classification. Subsequently, dorsal striatum and cerebellum components contribute the top two group-discriminative time courses, which is true even when adopting different brain atlases to extract time series.

INTERPRETATION

This is the first attempt to apply a multi-scale RNN model directly on fMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications. FUND: Natural Science Foundation of China, the Strategic Priority Research Program of the Chinese Academy of Sciences, National Institutes of Health Grants, National Science Foundation.

Impaired Effective Connectivity During a Cerebellar-Mediated Sensorimotor Synchronization Task in Schizophrenia

Prominent conceptual models characterize schizophrenia as a dysconnectivity syndrome, with recent research focusing on the contributions of the cerebellum in this framework. The present study examined the role of the cerebellum and its effective connectivity to the cerebrum during sensorimotor synchronization in schizophrenia. Specifically, the role of the cerebellum in temporally coordinating cerebral motor activity was examined through path analysis. Thirty-one individuals diagnosed with schizophrenia and 40 healthy controls completed a finger-tapping fMRI task including tone-paced synchronization and self-paced continuation tapping at a 500 ms intertap interval (ITI). Behavioral data revealed shorter and more variable ITIs during self-paced continuation, greater clock (vs motor) variance, and greater force of tapping in the schizophrenia group. In a whole-brain analysis, groups showed robust activation of the cerebellum during self-paced continuation but not during tone-paced synchronization. However, effective connectivity analysis revealed decreased connectivity in individuals with schizophrenia between the cerebellum and primary motor cortex but increased connectivity between cerebellum and thalamus during self-paced continuation compared with healthy controls. These findings in schizophrenia indicate diminished temporal coordination of cerebral motor activity by cerebellum during the continuation tapping portion of sensorimotor synchronization. Taken together with the behavioral finding of greater temporal variability in schizophrenia, these effective connectivity results are consistent with structural and temporal models of dysconnectivity in the disorder.

Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders

Background

Brain connectome research based on graph theoretical analysis shows that small-world topological properties play an important role in the structural and functional alterations observed in patients with psychiatric disorders. However, the reported global topological alterations in small-world properties are controversial, are not consistently conceptualized according to agreed-upon criteria, and are not critically examined for consistent alterations in patients with each major psychiatric disorder.

Methods

Based on a comprehensive PubMed search, we systematically reviewed studies using noninvasive neuroimaging data and graph theoretical approaches for 6 major psychiatric disorders: schizophrenia, major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), obsessive–compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). Here, we describe the main patterns of altered small-world properties and then systematically review the evidence for these alterations in the structural and functional connectome in patients with these disorders.

Results

We selected 40 studies of schizophrenia, 33 studies of MDD, 5 studies of ADHD, 5 studies of BD, 7 studies of OCD and 5 studies of PTSD. The following 4 patterns of altered small-world properties are defined from theperspectives of segregation and integration: "regularization," "randomization," "stronger small-worldization" and "weaker small-worldization." Although more differences than similarities are noted in patients with these disorders, a prominent trend is the structural regularization versus functional randomization in patients with schizophrenia.

Limitations

Differences in demographic and clinical characteristics, preprocessing steps and analytical methods can produce contradictory results, increasing the difficulty of integrating results across different studies.

Conclusion

Four psychoradiological patterns of altered small-world properties are proposed. The analysis of altered smallworld properties may provide novel insights into the pathophysiological mechanisms underlying psychiatric disorders from a connectomic perspective. In future connectome studies, the global network measures of both segregation and integration should be calculated to fully evaluate altered small-world properties in patients with a particular disease.

Reorganization of cerebro-cerebellar circuit in patients with left hemispheric gliomas involving language network: A combined structural and resting-state functional MRI study

The role of cerebellum and cerebro-cerebellar system in neural plasticity induced by cerebral gliomas involving language network has long been ignored. Moreover, whether or not the process of reorganization is different in glioma patients with different growth kinetics remains largely unknown. To address this issue, we utilized preoperative structural and resting-state functional MRI data of 78 patients with left cerebral gliomas involving language network areas, including 46 patients with low-grade glioma (LGG, WHO grade II), 32 with high-grade glioma (HGG, WHO grade III/IV), and 44 healthy controls. Spontaneous brain activity, resting-state functional connectivity and gray matter volume alterations of the cerebellum were examined. We found that both LGG and HGG patients exhibited bidirectional alteration of brain activity in language-related cerebellar areas. Brain activity in areas with increased alteration was significantly correlated with the language and MMSE scores. Structurally, LGG patients exhibited greater gray matter volume in regions with increased brain activity, suggesting a structure-function coupled alteration in cerebellum. Furthermore, we observed that cerebellar regions with decreased brain activity exhibited increased functional connectivity with contralesional cerebro-cerebellar system in LGG patients. Together, our findings provide empirical evidence for a vital role of cerebellum and cerebro-cerebellar circuit in neural plasticity following lesional damage to cerebral language network. Moreover, we highlight the possible different reorganizational mechanisms of brain functional connectivity underlying different levels of behavioral impairments in LGG and HGG patients.

Classification of multi-site MR images in the presence of heterogeneity using multi-task learning

With the advent of Big Data Imaging Analytics applied to neuroimaging, datasets from multiple sites need to be pooled into larger samples. However, heterogeneity across different scanners, protocols and populations, renders the task of finding underlying disease signatures challenging. The current work investigates the value of multi-task learning in finding disease signatures that generalize across studies and populations. Herein, we present a multi-task learning type of formulation, in which different tasks are from different studies and populations being pooled together. We test this approach in an MRI study of the neuroanatomy of schizophrenia (SCZ) by pooling data from 3 different sites and populations: Philadelphia, Sao Paulo and Tianjin (50 controls and 50 patients from each site), which posed integration challenges due to variability in disease chronicity, treatment exposure, and data collection. Some existing methods are also tested for comparison purposes. Experiments show that classification accuracy of multi-site data outperformed that of single-site data and pooled data using multi-task feature learning, and also outperformed other comparison methods. Several anatomical regions were identified to be common discriminant features across sites. These included prefrontal, superior temporal, insular, anterior cingulate cortex, temporo-limbic and striatal regions consistently implicated in the pathophysiology of schizophrenia, as well as the cerebellum, precuneus, and fusiform, middle temporal, inferior parietal, postcentral, angular, lingual and middle occipital gyri. These results indicate that the proposed multi-task learning method is robust in finding consistent and reliable structural brain abnormalities associated with SCZ across different sites, in the presence of multiple sources of heterogeneity.

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

The cerebellum and learning of non-motor associations in individuals at clinical-high risk for psychosis

The cerebello-thalamo-cortical circuit (CTCC) has been implicated in schizophrenia. However, this work has been limited to structural and functional networks, or behavior, and to date, has not been evaluated in clinical high-risk (CHR) youth, a group at elevated risk for psychosis. Here, we used an innovative learning paradigm known to activate the CTCC (while limiting potential motor confounds) to evaluate CHR and healthy control individuals during functional magnetic resonance imaging (fMRI). 20 CHR and 21 healthy control individuals performed a second-order rule learning task while undergoing fMRI. This was preceded and followed by the paradigm under dual-task conditions. In addition, all participants underwent structured clinical interviews to confirm a prodromal syndrome and assess symptom severity. The rate of learning did not differ between groups. However, the CHR group consistently performed more poorly under dual-task conditions, and exhibited a higher dual-task cost after learning. Further, learning rate in the CHR group was significantly associated with symptom severity. Both groups showed activation in regions of the CTCC. During early learning, the CHR group exhibited greater engagement of regions of the default mode network, suggesting that they were less able to engage the appropriate task positive networks. During late learning, there were qualitative differences wherein controls showed more prefrontal cortical activation. Higher order cognitive rule learning is related to symptom severity in CHR individuals. fMRI revealed that CHR individuals may not reliably disengage the default mode network, and during late learning high-risk youth may not engage the prefrontal cortex as extensively as controls.

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There are few fMRI studies in youth at clinical high-risk (CHR) of psychosis.

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This is the first task-based fMRI study of the cerebellum in a CHR population.

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CHR learn at a similar rate, but not as well, as healthy controls.

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Learning rate is related to disorganized symptom severity in the CHR group.

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The groups engage cerebello-thalamo-cortical circuits differently during learning.

Disrupted implicit motor sequence learning in schizophrenia and bipolar disorder revealed with ambidextrous Serial Reaction Time Task

BACKGROUND

Impairment of implicit motor sequence learning was shown in schizophrenia (SZ) and, most recently, in bipolar disorder (BD), and was connected to cerebellar abnormalities. The goal of this study was to compare implicit motor sequence learning in BD and SZ.

METHODS

We examined 33 patients with BD, 33 patients with SZ and 31 healthy controls with a use of ambidextrous Serial Reaction Time Task (SRTT), which allows exploring asymmetries in performance depending on the hand used.

RESULTS

BD and SZ patients presented impaired implicit motor sequence learning, although the pattern of their impairments was different. While BD patients showed no signs of implicit motor sequence learning for both hands, the SZ group presented some features of motor learning when performing with the right, but not with the left hand.

CONCLUSIONS

To our best knowledge this is the first study comparing implicit motor sequence learning in BD and SZ. We show that both diseases share impairments in this domain, however in the case of SZ this impairment differs dependently on the hand performing SRTT. We propose that implicit motor sequence learning impairments constitute an overlapping symptom in BD and SZ and suggest further neuroimaging studies to verify cerebellar underpinnings as its cause.

Disrupted topological organization of structural brain networks in childhood absence epilepsy

Childhood absence epilepsy (CAE) is the most common paediatric epilepsy syndrome and is characterized by frequent and transient impairment of consciousness. In this study, we explored structural brain network alterations in CAE and their association with clinical characteristics. A whole-brain structural network was constructed for each participant based on diffusion-weighted MRI and probabilistic tractography. The topological metrics were then evaluated. For the first time, we uncovered modular topology in CAE patients that was similar to healthy controls. However, the strength, efficiency and small-world properties of the structural network in CAE were seriously damaged. At the whole brain level, decreased strength, global efficiency, local efficiency, clustering coefficient, normalized clustering coefficient and small-worldness values of the network were detected in CAE, while the values of characteristic path length and normalized characteristic path length were abnormally increased. At the regional level, especially the prominent regions of the bilateral precuneus showed reduced nodal efficiency, and the reduction of efficiency was significantly correlated with disease duration. The current results demonstrate significant alterations of structural networks in CAE patients, and the impairments tend to grow worse over time. Our findings may provide a new way to understand the pathophysiological mechanism of CAE.

A case for motor network contributions to schizophrenia symptoms: Evidence from resting-state connectivity

Though schizophrenia (SCZ) is classically defined based on positive symptoms and the negative symptoms of the disease prove to be debilitating for many patients, motor deficits are often present as well. A growing literature highlights the importance of motor systems and networks in the disease, and it may be the case that dysfunction in motor networks relates to the pathophysiology and etiology of SCZ. To test this and build upon recent work in SCZ and in at-risk populations, we investigated cortical and cerebellar motor functional networks at rest in SCZ and controls using publically available data. We analyzed data from 82 patients and 88 controls. We found key group differences in resting-state connectivity patterns that highlight dysfunction in motor circuits and also implicate the thalamus. Furthermore, we demonstrated that in SCZ, these resting-state networks are related to both positive and negative symptom severity. Though the ventral prefrontal cortex and corticostriatal pathways more broadly have been implicated in negative symptom severity, here we extend these findings to include motor-striatal connections, as increased connectivity between the primary motor cortex and basal ganglia was associated with more severe negative symptoms. Together, these findings implicate motor networks in the symptomatology of psychosis, and we speculate that these networks may be contributing to the etiology of the disease. Overt motor deficits in SCZ may signal underlying network dysfunction that contributes to the overall disease state. Hum Brain Mapp 38:4535-4545, 2017. © 2017 Wiley Periodicals, Inc.

Postural changes in different stages of schizophrenia is associated with inflammation and pain: a cross-sectional observational study

OBJECTIVES

To assess the relationship between posture and inflammatory response markers (C-reactive protein [CRP] and von Willebrand factor [vWF]) in schizophrenics.

METHODS

Forty patients with stable schizophrenia were divided into early-stage (<10 years since first episode, n = 15) and late-stage (≥10 years since first episode, n = 25) groups. Both groups were compared to controls (n = 26). All participants underwent postural assessment by biophotogrammetry. Cases alone underwent blood collection. The significance level was set at 5%, and analyses were carried out in SPSS 18.0.

RESULTS

In the early-stage group, 15 postural angles were significantly different from their reference ranges, whereas in the late-stage group, only seven angles were significantly different. In comparison with the control group, only six angles were significantly different. There was no difference in inflammation markers between the early- and late-stage groups. However, CRP levels were higher in cases with greater disease severity, and vWF was associated with forward head posture. Pain correlated with five postural angles, and late-stage patients reported more pain than early-stage cases.

CONCLUSIONS

CRP was associated with disease severity, while vWF and pain were associated with forward head posture, hyperlordosis and scoliosis, suggesting an association between vascular inflammation and pain, with an influence on posture.

Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study

Previous studies suggested that electroconvulsive therapy can influence regional metabolism and dopamine signaling, thereby alleviating symptoms of schizophrenia. It remains unclear what patients may benefit more from the treatment. The present study sought to identify biomarkers that predict the electroconvulsive therapy response in individual patients. Thirty-four schizophrenia patients and 34 controls were included in this study. Patients were scanned prior to treatment and after 6 weeks of treatment with antipsychotics only (n = 16) or a combination of antipsychotics and electroconvulsive therapy (n = 13). Subject-specific intrinsic connectivity networks were computed for each subject using a group information-guided independent component analysis technique. Classifiers were built to distinguish patients from controls and quantify brain states based on intrinsic connectivity networks. A general linear model was built on the classification scores of first scan (referred to as baseline classification scores) to predict treatment response. Classifiers built on the default mode network, the temporal lobe network, the language network, the corticostriatal network, the frontal-parietal network, and the cerebellum achieved a cross-validated classification accuracy of 83.82%, with specificity of 91.18% and sensitivity of 76.47%. After the electroconvulsive therapy, psychosis symptoms of the patients were relieved and classification scores of the patients were decreased. Moreover, the baseline classification scores were predictive for the treatment outcome. Schizophrenia patients exhibited functional deviations in multiple intrinsic connectivity networks which were able to distinguish patients from healthy controls at an individual level. Patients with lower classification scores prior to treatment had better treatment outcome, indicating that the baseline classification scores before treatment is a good predictor for treatment outcome.

The Cerebellum: Adaptive Prediction for Movement and Cognition

Over the past 30 years, cumulative evidence has indicated that cerebellar function extends beyond sensorimotor control. This view has emerged from studies of neuroanatomy, neuroimaging, neuropsychology, and brain stimulation, with the results implicating the cerebellum in domains as diverse as attention, language, executive function, and social cognition. Although the literature provides sophisticated models of how the cerebellum helps refine movements, it remains unclear how the core mechanisms of these models can be applied when considering a broader conceptualization of cerebellar function. In light of recent multidisciplinary findings, we examine how two key concepts that have been suggested as general computational principles of cerebellar function- prediction and error-based learning- might be relevant in the operation of cognitive cerebro-cerebellar loops.

Modular-level alterations of structure-function coupling in schizophrenia connectome

Convergent evidences have revealed that schizophrenia is associated with brain dysconnectivity, which leads to abnormal network organization. However, discrepancies were apparent between the structural connectivity (SC) and functional connectivity (FC) studies, and the relationship between structural and functional deficits in schizophrenia remains largely unknown. In this study, resting-state functional magnetic resonance imaging and structural diffusion tensor imaging were performed in 20 patients with schizophrenia and 20 matched healthy volunteers (patients/controls = 19/17 after head motion rejection). Functional and structural brain networks were obtained for each participant. Graph theoretical approaches were employed to parcellate the FC networks into functional modules. The relationships between the entries of SC and FC were estimated within each module to identify group differences and their correlations with clinical symptoms. Although five common functional modules (including the default mode, occipital, subcortical, frontoparietal, and central modules) were identified in both groups, the patients showed a significantly reduced modularity in comparison with healthy participants. Furthermore, we found that schizophrenia-related aberrations of SC-FC coupling exhibited complex patterns among modules. Compared with controls, patients showed an increased SC-FC coupling in the default mode and the central modules. Moreover, significant SC-FC decoupling was demonstrated in the occipital and the subcortical modules, which was associated with longer duration of illness and more severe clinical manifestations of schizophrenia. Taken together, these findings demonstrated that altered module-dependent SC-FC coupling may underlie abnormal brain function and clinical symptoms observed in schizophrenia and highlighted the potential for using new multimodal neuroimaging biomarkers for diagnosis and severity evaluation of schizophrenia. Hum Brain Mapp 38:2008-2025, 2017. © 2017 Wiley Periodicals, Inc.

Examining Functional Resting-State Connectivity in Psychosis and Its Subgroups in the Bipolar-Schizophrenia Network on Intermediate Phenotypes Cohort

BACKGROUND

We sought to examine resting-state functional magnetic resonance imaging connectivity measures in psychotic patients to both identify cumulative differences across psychosis and subsequently probe deficits across conventional DSM-IV diagnoses and a newly identified classification using cognitive/neurophysiological data (Biotypes).

METHODS

We assessed 1125 subjects, including healthy control subjects, probands (schizophrenia, schizoaffective disorder, psychotic bipolar disorder), and relatives of probands. Probands and relatives were also segregated into Biotype groups (B1-B3, B1R-B3R using a method reported previously). Empirical resting-state functional magnetic resonance imaging networks were derived using independent component analysis. Global psychosis-related abnormalities were first identified. Subsequent post hoc t tests were performed across various diagnostic categories. Follow-up linear mixed model compared significance of within-proband differences across categories. Secondary analyses assessed correlations with biological profile scores.

RESULTS

Voxelwise tests between proband and control subjects revealed nine abnormal networks. Post hoc analysis revealed lower connectivity in most networks for all proband subgroups (DSM and Biotypes). Within-proband effect sizes of discrimination were marginally better for Biotypes over DSM. Reduced connectivity was noted in relatives of patients with schizophrenia in two networks and relatives of patients with psychotic bipolar disorder in one network. Biotype relatives showed similar deficits in one network. Connectivity deficits across four networks were significantly associated with cognitive control profile scores.

CONCLUSIONS

Overall, we found psychosis-related connectivity deficits in nine large-scale networks. Deficits in these networks tracked more closely with cognitive control factors, suggesting potential implications for disease profiling and therapeutic intervention. Biotypes performed marginally better in terms of separating out psychosis subgroups compared with conventional DSM or psychiatric diagnoses.

Cognition and resting-state functional connectivity in schizophrenia

Individuals with schizophrenia consistently display deficits in a multitude of cognitive domains, but the neurobiological source of these cognitive impairments remains unclear. By analyzing the functional connectivity of resting-state functional magnetic resonance imaging (rs-fcMRI) data in clinical populations like schizophrenia, research groups have begun elucidating abnormalities in the intrinsic communication between specific brain regions, and assessing relationships between these abnormalities and cognitive performance in schizophrenia. Here we review studies that have reported analysis of these brain-behavior relationships. Through this systematic review we found that patients with schizophrenia display abnormalities within and between regions comprising (1) the cortico-cerebellar-striatal-thalamic loop and (2) task-positive and task-negative cortical networks. Importantly, we did not observe unique relationships between specific functional connectivity abnormalities and distinct cognitive domains, suggesting that the observed functional systems may underlie mechanisms that are shared across cognitive abilities, the disturbance of which could contribute to the "generalized" cognitive deficit found in schizophrenia. We also note several areas of methodological change that we believe will strengthen this literature.

An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome

Chromosome 22q11.2 deletion syndrome (22q11DS) is a genetic disease known to lead to cerebral structural alterations, which we study using the framework of the macroscopic white-matter connectome. We create weighted connectomes of 44 patients with 22q11DS and 44 healthy controls using diffusion tensor magnetic resonance imaging, and perform a weighted graph theoretical analysis. After confirming global network integration deficits in 22q11DS (previously identified using binary connectomes), we identify the spatial distribution of regions responsible for global deficits. Next, we further characterize the dysconnectivity of the deficient regions in terms of sub-network properties, and investigate their relevance with respect to clinical profiles. We define the subset of regions with decreased nodal integration (evaluated using the closeness centrality measure) as the affected core (A-core) of the 22q11DS structural connectome. A-core regions are broadly bilaterally symmetric and consist of numerous network hubs — chiefly parietal and frontal cortical, as well as subcortical regions. Using a simulated lesion approach, we demonstrate that these core regions and their connections are particularly important to efficient network communication. Moreover, these regions are generally densely connected, but less so in 22q11DS. These specific disturbances are associated to a rerouting of shortest network paths that circumvent the A-core in 22q11DS, “de-centralizing” the network. Finally, the efficiency and mean connectivity strength of an orbito-frontal/cingulate circuit, included in the affected regions, correlate negatively with the extent of negative symptoms in 22q11DS patients, revealing the clinical relevance of present findings. The identified A-core overlaps numerous regions previously identified as affected in 22q11DS as well as in schizophrenia, which approximately 30–40% of 22q11DS patients develop.

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Graph theory confirms reduced integration in 22q11.2 deletion syndrome (22q11DS).

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An “affected core” (A-core) of hub regions drives global integration deficits.

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The A-core is generally densely connected, but less so in 22q11DS.

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Shortest network paths are rerouted around the A-core in 22q11DS.

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Connectivity of a subset of A-core regions correlates with negative symptoms.

Aberrant cerebellar connectivity in motor and association networks in schizophrenia

Schizophrenia is a devastating illness characterized by disturbances in multiple domains. The cerebellum is involved in both motor and non-motor functions, and the "cognitive dysmetria" and "dysmetria of thought" models propose that abnormalities of the cerebellum may contribute to schizophrenia signs and symptoms. The cerebellum and cerebral cortex are reciprocally connected via a modular, closed-loop network architecture, but few schizophrenia neuroimaging studies have taken into account the topographical and functional heterogeneity of the cerebellum. In this study, using a previously defined 17-network cerebral cortical parcellation system as the basis for our functional connectivity seeds, we systematically investigated connectivity abnormalities within the cerebellum of 44 schizophrenia patients and 28 healthy control participants. We found selective alterations in cerebro-cerebellar functional connectivity. Specifically, schizophrenia patients showed decreased cerebro-cerebellar functional connectivity in higher level association networks (ventral attention, salience, control, and default mode networks) relative to healthy control participants. Schizophrenia patients also showed increased cerebro-cerebellar connectivity in somatomotor and default mode networks, with the latter showing no overlap with the regions found to be hypoconnected within the same default mode network. Finally, we found evidence to suggest that somatomotor and default mode networks may be inappropriately linked in schizophrenia. The relationship of these dysconnectivities to schizophrenia symptoms, such as neurological soft signs and altered sense of agency, is discussed. We conclude that the cerebellum ought to be considered for analysis in all future studies of network abnormalities in SZ, and further suggest the cerebellum as a potential target for further elucidation, and possibly treatment, of the underlying mechanisms and network abnormalities producing symptoms of schizophrenia.