Specific Substantial Dysconnectivity in Schizophrenia: A Transdiagnostic Multimodal Meta-analysis of Resting-State Functional and Structural Magnetic Resonance Imaging Studies

Brandl, Felix; Avram, Mihai; Weise, Benedikt; Shang, Jing; Simões, Beatriz; Bertram, Teresa; Ayala, Daniel Hoffmann; Penzel, Nora; Gürsel, Deniz A.; Bäuml, Josef; Wohlschläger, Afra M.; Vukadinovic, Zoran; Koutsouleris, Nikolaos; Leucht, Stefan; Sorg, Christian

doi:10.1016/j.biopsych.2018.12.003

Cited by 107 publications

(94 citation statements)

References 54 publications

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“…Neuroimaging meta‐analysis including CBMA has been widely used in various neuropsychiatric disorders such as depression (Kaiser, Andrews‐Hanna, Wager, & Pizzagalli, ; Lai, ; Muller et al, ; Sacher et al, ), bipolar disorder (Wang et al, ; Wegbreit et al, ), schizophrenia (Brandl et al, ; Jardri, Pouchet, Pins, & Thomas, ; Xiao, Zhang, Lui, Yao, & Gong, ), Alzheimer's disease (Ferreira, Diniz, Forlenza, Busatto, & Zanetti, ; Schroeter, Stein, Maslowski, & Neumann, ; Wang et al, ), Parkinson's disease (Herz, Eickhoff, Lokkegaard, & Siebner, ; Tahmasian et al, ; Wang, Zhang, Zang, & Wu, ), post‐traumatic stress disorder(Kuhn & Gallinat, ; Ramage et al, ; Wang et al, ), sleep disorders (Javaheipour et al, ; Shi et al, ; Tahmasian et al, ; Tahmasian et al, ), as well as neurodevelopmental disorders such as autism (Dickstein et al, ; Nickl‐Jockschat et al, ) and attention‐deficit/hyperactivity disorder (ADHD) (Cortese et al, ; Cortese et al, ; Hart, Radua, Mataix‐Cols, & Rubia, ; Hart, Radua, Nakao, Mataix‐Cols, & Rubia, ; Samea et al, ).…”

Section: Various Approaches For Neuroimaging Meta‐analysesmentioning

confidence: 99%

Practical recommendations to conduct a neuroimaging meta‐analysis for neuropsychiatric disorders

Tahmasian

Sepehry

Samea

et al. 2019

Human Brain Mapping

137

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Over the past decades, neuroimaging has become widely used to investigate structural and functional brain abnormality in neuropsychiatric disorders. The results of individual neuroimaging studies, however, are frequently inconsistent due to small and heterogeneous samples, analytical flexibility, and publication bias toward positive findings. To consolidate the emergent findings toward clinically useful insight, meta‐analyses have been developed to integrate the results of studies and identify areas that are consistently involved in pathophysiology of particular neuropsychiatric disorders. However, it should be considered that the results of meta‐analyses could also be divergent due to heterogeneity in search strategy, selection criteria, imaging modalities, behavioral tasks, number of experiments, data organization methods, and statistical analysis with different multiple comparison thresholds. Following an introduction to the problem and the concepts of quantitative summaries of neuroimaging findings, we propose practical recommendations for clinicians and researchers for conducting transparent and methodologically sound neuroimaging meta‐analyses. This should help to consolidate the search for convergent regional brain abnormality in neuropsychiatric disorders.

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Section: Various Approaches For Neuroimaging Meta‐analysesmentioning

confidence: 99%

Practical recommendations to conduct a neuroimaging meta‐analysis for neuropsychiatric disorders

Tahmasian

Sepehry

Samea

et al. 2019

Human Brain Mapping

137

View full text Add to dashboard Cite

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“…Moreover, given the structural connectome and the observed deformation pattern, we find that the likeliest epicenters are located in the cingulate cortices. These loci-situated at the intersection of the ventral attention/salience and default networks-are implicated in schizophrenia, including changes in tissue volume [25], structural connectivity [62,79], activation [47,48], and functional connectivity [12,61,75]. Interestingly, the same areas are frequently found to be affected in a wide range of mental illnesses, including schizophrenia, bipolar disorder, depression, addiction, obsessive-compulsive disorder and anxiety [32].…”

Section: Network Patterning Of Deformationmentioning

confidence: 99%

Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture

Shafiei

Markello

Talpalaru

et al. 2019

Preprint

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∀These authors contributed equally to this work. AbstractBackground. There is growing recognition that connectome architecture shapes cortical and subcortical grey matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown. Methods. Here we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Grey matter deformation was estimated in a sample of N = 133 individuals with chronic schizophrenia (48 female, 34.7 ± 12.9 years) and N = 113 controls (64 female, 23.5 ± 8.4 years). Deformation-based morphometry (DBM) was used to estimate cortical and subcortical grey matter deformation from T1-weighted MR images. Structural and functional connectivity patterns were derived from an independent sample of N = 70 healthy participants using diffusion spectrum imaging and resting-state functional MRI. Results. We find that regional deformation is correlated with the deformation of structurally-and functionally-connected neighbours. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex. Conclusions. Altogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25-35% of regional variance in deformation. Keywords. connectome | schizophrenia | intrinsic networks | disease epicenter | anterior cingulate | ventral attention network Running title. Network-driven tissue volume loss in schizophrenia

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“…Third, we detected increased OFC and superior frontal GMV in pt-DD patients compared to HC. This may appear surprising at first glance, since lower GMV in OFC could be rather expected given the recent meta-analytic evidence of lower GMV in patients with psychotic disorders [63][64][65]. In psychotic disorders, OFC volume has been previously associated with hallucinations and persecutory delusions [66].…”

Section: Discussionmentioning

confidence: 97%

Aberrant Gray Matter Volume and Cortical Surface in Paranoid-Type Delusional Disorder

et al. 2020

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Introduction: Delusions are core symptoms of schizophrenia-spectrum and related disorders. Despite their clinical relevance, the neural correlates underlying such phenomena are unclear. Recent research suggests that specific delusional content may be associated with distinct neural substrates. Objective: Here, we used structural magnetic resonance imaging to investigate multiple parameters of brain morphology in patients presenting with paranoid type delusional disorder (pt-DD, n = 14) compared to those of healthy controls (HC, n = 25). Methods: Voxel-and surface-based morphometry for structural data was used to investigate gray matter volume (GMV), cortical thickness (CT) and gyrification. Re-sults: Compared to HC, patients with pt-DD showed reduced GMV in bilateral amygdala and right inferior frontal gyrus. Higher GMV in patients was found in bilateral orbitofrontal and in left superior frontal cortices. Patients also had lower CT in frontal and temporal regions. Abnormal gyrification in patients was evident in frontal and temporal areas, as well as in bilateral insula. Conclusions: The data suggest the presence of aberrant GMV in a right prefrontal region associated with belief evaluation, as well as distinct structural abnormalities in areas that essentially subserve processing of fear, anxiety and threat in patients with pt-DD. It is possible that cortical features of distinct evolutionary and genetic origin, i.e. CT and gyrification, contribute differently to the pathogenesis of pt-DD.

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Specific Substantial Dysconnectivity in Schizophrenia: A Transdiagnostic Multimodal Meta-analysis of Resting-State Functional and Structural Magnetic Resonance Imaging Studies

Cited by 107 publications

References 54 publications

Practical recommendations to conduct a neuroimaging meta‐analysis for neuropsychiatric disorders

Practical recommendations to conduct a neuroimaging meta‐analysis for neuropsychiatric disorders

Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture

Aberrant Gray Matter Volume and Cortical Surface in Paranoid-Type Delusional Disorder

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