Evidence for Network-Based Cortical Thickness Reductions in Schizophrenia

Wannan, Cassandra; Cropley, Vanessa; Chakravarty, M. Mallar; Bousman, Chad; Ganella, Eleni P.; Bruggemann, Jason; Weickert, Thomas; Weickert, Cynthia Shannon; Everall, Ian; McGorry, Patrick D.; Velakoulis, Dennis; Wood, Stephen; Bartholomeusz, Cali F.; Pantelis, Christos; Zalesky, Andrew

doi:10.1176/appi.ajp.2019.18040380

Cited by 124 publications

(126 citation statements)

References 64 publications

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“…Our control analyses on a subset of patients indicate that the network effects reported here are unlikely to be correlated with chlorpromazine equivalent antipsychotic medication dose. Other groups have recently reported similar relationships between anatomical covariance networks and changes in cortical thickness in firstepisode psychosis, chronic schizophrenia and treatmentresistant schizophrenia [73], suggesting that the observed effect is likely to be present earlier in the disease and in drug-naive patients. The authors thank Dr. Alessandra Griffa for data collection and processing of healthy structural and functional networks and providing insightful comments on the manuscript.…”

Section: Methodological Considerationsmentioning

confidence: 60%

“…Interestingly, several recent studies found complementary results. Wannan and colleagues found that cortical thinning in schizophrenia was more likely among areas with greater anatomical covariance [73]. Di Biase and colleagues found that cortical thinning and reduction in local white matter anisotropy are anticorrelated in patients with schizophrenia [23].…”

Section: Network Patterning Of Deformationmentioning

confidence: 99%

“…For example, cortical thinning and infracortical white matter breakdown (as measured by fractional anisotropy) appear to be concomitant in patients with schizophrenia [23] and in patients with de novo psychotic illness [42]. In addition, cortical thinning is more pronounced among regions with stronger grey matter covariance [73]. Altogether, there is considerable -though circumstantial -evidence that connectivity influences deformation patterns.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodological Considerationsmentioning

confidence: 60%

Section: Network Patterning Of Deformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Shafiei

Markello

Talpalaru

et al. 2019

Preprint

Self Cite

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“…Schizophrenia is associated with profound changes in brain structure, including altered cortical morphology (1)(2)(3)(4), subcortical volume loss (5) and white matter alterations (6)(7)(8)(9) leading to structural connectopathies (10)(11)(12)(13)(14)(15). Despite numerous cross-sectional and longitudinal investigations, the heterogeneity of schizophrenia has defied attempts to derive reproducible and definitive brain maps of the precise anatomical loci of these structural changes.…”

Section: Introductionmentioning

confidence: 99%

Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort

Biase

Cash

et al. 2020

Preprint

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Word count: 4000 Number of figures: 4 (supplementary: 7)Number of tables: 0 (supplementary: 1) Abstract Background: The heterogeneity of schizophrenia has defied efforts to derive reproducible and definitive anatomical maps of structural brain changes associated with the disorder. We aimed to map deviations from normative ranges of brain structure for individual patients and evaluate whether the loci of individual deviations recapitulated group-average brain maps of schizophrenia pathology. Methods:For each of 48 white matter tracts and 68 cortical regions, normative percentiles of variation in fractional anisotropy (FA) and cortical thickness (CT) were established using diffusionweighted and structural MRI from healthy adults (n=195). Individuals with schizophrenia (n=322) were classified as either within the normative range for healthy individuals of the same age and sex (5-95% percentiles), infra-normal (<5% percentile) or supra-normal (>95% percentile).Repeating this classification for each tract and region yielded a deviation map for each individual.Results: Compared to the healthy comparison group, the schizophrenia group showed widespread reductions in FA and CT, involving virtually all white matter tracts and cortical regions.Paradoxically, however, no more than 15-20% of patients deviated from the normative range for any single tract or region, whereas 79% of patients showed infra-normal deviations for at least one locus (healthy individuals: 59±2%, p<0.001). Higher polygenic risk for schizophrenia associated with a greater number of regions with infra-normal deviations in CT (r=-0.17, p=0.006). Conclusions: Anatomical loci of schizophrenia-related changes are highly heterogeneous across individuals to the extent that group-consensus pathological maps are not representative of most individual patients. Normative modeling can aid in parsing schizophrenia heterogeneity and guiding personalized interventions.

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“…20 It is important to further our understanding of the possible neurobiological mechanisms the underlie MR-visible cortical morphology as alterations in cortical thickness could act as a potential phenotypic marker of several common neurodevelopmental and psychiatric disorders including autism spectrum disorders and schizophrenia. [21][22][23][24] One promising approach to this problem pairs non-invasive MRI with genetic databases that detail the transcription of thousands of genes across the brain at high spatial resolution. This allows a precise comparison between patterns of gene expression and in vivo neuroanatomy.…”

Section: Introductionmentioning

confidence: 99%

Cortical remodelling in childhood is associated with genes enriched for neurodevelopmental disorders

Ball

Seidlitz

Beare

et al. 2019

Preprint

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AbstractCortical development during childhood and adolescence has been characterised in recent years using metrics derived from Magnetic Resonance Imaging (MRI). Changes in cortical thickness are greatest in the first two decades of life and recapitulate the genetic organisation of the cortex, highlighting the potential early impact of gene expression on differences in cortical architecture over the lifespan. It is important to further our understanding of the possible neurobiological mechanisms that underlie these changes as differences in cortical thickness may act as a potential phenotypic marker of several common neurodevelopmental and psychiatric disorders.In this study, we combine MRI acquired from a large typically-developing childhood population (n=768) with comprehensive human gene expression databases to test the hypothesis that disrupted mechanisms common to neurodevelopmental disorders are encoded by genes expressed early in development and nested within those associated with typical cortical remodelling in childhood.We find that differential rates of thinning across the developing cortex are associated with spatially-varying gradients of gene expression. Genes that are expressed highly in regions of accelerated thinning are expressed predominantly in cortical neurons, involved in synaptic remodeling, and associated with common cognitive and neurodevelopmental disorders. Further, we identify subsets of genes that are highly expressed in the prenatal period and jointly associated with both developmental cortical morphology and neurodevelopmental disorders.

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Evidence for Network-Based Cortical Thickness Reductions in Schizophrenia

Cited by 124 publications

References 64 publications

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

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

Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort

Cortical remodelling in childhood is associated with genes enriched for neurodevelopmental disorders

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