Cerebellar–cortical dysconnectivity in resting‐state associated with sensorimotor tasks in schizophrenia

Kim, Dae‐Jin; Moussa-Tooks, Alexandra B.; Bolbecker, Amanda R.; Apthorp, Deborah; Newman, Sharlene D.; O’Donnell, Brian F.

doi:10.1002/hbm.25002

Cited by 29 publications

(18 citation statements)

References 80 publications

(119 reference statements)

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“…Connectional topography plays important role in brain functions and behaviors, and can provide insight into neurodevelopment, too (Phillips, Fish, Kambi, Redinbaugh, & Saalmann, 2019;Thompson, Mohammadi-Nejad, Robinson, Andersson, & Sotiropoulos, 2020;Wu, Calhoun, Jung, & Caprihan, 2015). Many neuropsychiatric disorders, such as schizophrenia, have been manifested by disrupted communications for connections between distributed brain networks (Gong et al, 2019;Jiang et al, 2019;Kim et al, 2020;Rolls et al, 2020;Sheffield, Rogers, Blackford, Heckers, & Woodward, 2020). Thus, connectional topography mapping is a matter of utmost importance for revealing the connectional organization of the brain.…”

Section: Introductionmentioning

confidence: 99%

Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

Cheng

Liu

2021

Human Brain Mapping

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Connectional topography mapping has been gaining widespread attention in human brain imaging studies. However, existing methods might not effectively utilize the information from neuroimaging data, thus hindering the understanding of the underlying connectional organization in the brain and uncovering the optimal clustering number from the data. In this study, we propose a novel method for the automated construction of inherent functional connectivity topography in a data-driven manner by leveraging the power of co-clustering-based on resting state fMRI (rs-fMRI) data.We propose the co-clustering-based method not only for concurrently parcellating two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also for estimating the connectivity between these subregions from the two brain ROIs. In particular, we first model the connectional topography mapping as a co-clustering-based bipartite graph partitioning problem for constructing the inherent functional connectivity topography between the two interconnected brain ROIs. We also adopt an objective criterion, that is, silhouette width index measuring clustering quality, for determining the optimal number of clusters. The proposed method has been validated for mapping thalamocortical connectional topography based on rs-fMRI data of 57 subjects. Validation results have demonstrated that our method identified the optimal solution with five pairs of mutually connected subregions of the thalamocortical system from the rs-fMRI data, and could yield more meaningful, interpretable, and homogenous connectional topography than existing methods. The proposed method was further validated by the high symmetry of the mapped connectional topography between two hemispheres.

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Section: Introductionmentioning

confidence: 99%

Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

Cheng

Liu

2021

Human Brain Mapping

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“…In addition, functional connectivity studies provide robust evidence for abnormalities in cortico-cerebellar and thalamo-cerebellar functional connectivity in patients with schizophrenia and high-risk individuals (10,(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). These studies also report both hypo-and hyperconnectivity for different cerebellar regions and distinct cerebro-cerebellar connections, supporting the diverse functional roles and heterogenous connectivity profiles of individual cerebellar regions (27).…”

Section: Introductionmentioning

confidence: 60%

“…Task-activation studies indicate both hypo-and (to a lesser extent) hyper-activation of the cerebellum in a range of tasks (19,20), with review/meta-analytic data suggesting that hypoactivations predominate in medial portions of the anterior lobe and lobules IV and V, while hyperactivations localize more laterally in lobules VI and VII (20) and that abnormalities in task-activation may stem from an altered functional topography of the cerebellum (19). In addition, functional connectivity studies provide robust evidence for abnormalities in cortico-cerebellar and thalamo-cerebellar functional connectivity in patients with schizophrenia and high-risk individuals (10,(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). These studies also report both hypo-and hyperconnectivity for different cerebellar regions and distinct cerebro-cerebellar connections, supporting the diverse functional roles and heterogenous connectivity profiles of individual cerebellar regions (27).…”

Section: Introductionmentioning

confidence: 99%

“…Across studies, there appears to be relatively consistent evidence for cerebro-cerebellar hyperconnectivity in some networks including the somatomotor and default-mode network (DMN) (23,25,26,29), while findings for other functional networks are more mixed. For example, some studies report hypoconnectivity between regions of the ventral attention or salience network and corresponding cerebellar clusters (26,29) while others report hyperconnectivity for similar cerebro-cerebellar connections (27) Neuropathological studies have also found evidence for cerebellar pathology in schizophrenia (32), including findings of a decreased density (33) and size (34) of Purkinje cells. The latter finding is particularly significant as Purkinje cells provide input to deep nuclei including the DN, and thereby play a key role in modulating the output from the cerebellum to the cerebral cortex (17,34).…”

Section: Introductionmentioning

confidence: 99%

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Abnormal function in dentate nuclei precedes the onset of psychosis: a resting-state fMRI study in high-risk individuals

Anteraper

Guell

Collin

et al. 2021

Preprint

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Objective: The cerebellum serves a wide range of functions and is suggested to be composed of discrete regions dedicated to unique functions. We recently developed a new parcellation of the dentate nuclei (DN), the major output nuclei of the cerebellum, which optimally divides the structure into three functional territories that contribute uniquely to default-mode, motor-salience, and visual processing networks as indexed by resting-state functional connectivity (RsFc). Here we test for the first time whether RsFc differences in the DN precede the onset of psychosis in individuals at risk of developing schizophrenia. Methods: We used the MRI dataset from the Shanghai At Risk for Psychosis study that included subjects at high risk to develop schizophrenia (N=144), with longitudinal follow-up to determine which subjects developed a psychotic episode within one year of their fMRI scan (converters N=23). Analysis used the three functional parcels (default-mode, salience-motor, and visual territory) from the DN as seed regions of interest for whole-brain RsFc analysis. Results: RsFc analysis revealed abnormalities at baseline in high-risk individuals who developed psychosis, compared to high-risk individuals who did not develop psychosis. The nature of the observed abnormalities was found to be anatomically specific such that abnormal RsFc was localized predominantly in cerebral cortical networks that matched the three functional territories of the DN that were evaluated. Conclusions: We show for the first time that abnormal RsFc of the DN may precede the onset of psychosis. This new evidence highlights the role of the cerebellum as a potential target for psychosis prediction and prevention.

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“… 37 , 69 Cerebellar (Crus-I, lobule IX and lobule X) overconnectivity has been also reported with the salience and sensorimotor networks. 42 …”

Section: Lessons Learned From Top-down Studiesmentioning

confidence: 99%

Dissecting autism and schizophrenia through neuroimaging genomics

Moreau

Raznahan

Bellec

et al. 2021

Brain

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Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a ‘top-down’ approach, starting with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy starts at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: Why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In the review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioral and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modeling for diagnosis and clinical outcomes.

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Cerebellar–cortical dysconnectivity in resting‐state associated with sensorimotor tasks in schizophrenia

Cited by 29 publications

References 80 publications

Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

Abnormal function in dentate nuclei precedes the onset of psychosis: a resting-state fMRI study in high-risk individuals

Dissecting autism and schizophrenia through neuroimaging genomics

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