Dataset Information

Multivariate patterns of brain-cognition associations relating to vulnerability and clinical outcome in the at-risk mental states for psychosis.

ABSTRACT:

Background

Neuropsychological deficits are a core feature of established psychosis and have been previously linked to fronto-temporo-limbic brain alterations. Both neurocognitive and neuroanatomical abnormalities characterize clinical at-risk mental states (ARMS) for psychosis. However, structure-cognition relationships in the ARMS have not been directly explored using multivariate neuroimaging techniques.

Methods

Voxel-based morphometry and partial least squares were employed to study system-level covariance patterns between whole-brain morphological data and processing speed, working memory, verbal learning/IQ, and executive functions in 40 ARMS subjects and 30 healthy controls (HC). The detected structure-cognition covariance patterns were tested for significance and reliability using non-parametric permutation and bootstrap resampling.

Results

We identified ARMS-specific covariance patterns that described a generalized association of neurocognitive measures with predominantly prefronto-temporo-limbic and subcortical structures as well as the interconnecting white matter. In the conversion group, this generalized profile particularly involved working memory and verbal IQ and was positively correlated with limbic, insular and subcortical volumes as well as negatively related to prefrontal, temporal, parietal, and occipital cortices. Conversely, the neurocognitive profiles in the HC group were confined to working memory, learning and IQ, which were diffusely associated with cortical and subcortical brain regions.

Conclusions

These findings suggest that the ARMS and prodromal phase of psychosis are characterized by a convergent mapping from multi-domain neurocognitive measures to a set of prefronto-temporo-limbic and subcortical structures. Furthermore, a neuroanatomical separation between positive and negative brain-cognition correlations may not only point to a biological process determining the clinical risk for disease transition, but also to possible compensatory or dysmaturational neural processes.

SUBMITTER: Koutsouleris N

PROVIDER: S-EPMC6870479 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Publications

Multivariate patterns of brain-cognition associations relating to vulnerability and clinical outcome in the at-risk mental states for psychosis.

Koutsouleris Nikolaos N Gaser Christian C Patschurek-Kliche Katja K Scheuerecker Johanna J Bottlender Ronald R Decker Petra P Schmitt Gisela G Reiser Maximilian M Möller Hans-Jürgen HJ Meisenzahl Eva M EM

Human brain mapping 20110830 9

<h4>Background</h4>Neuropsychological deficits are a core feature of established psychosis and have been previously linked to fronto-temporo-limbic brain alterations. Both neurocognitive and neuroanatomical abnormalities characterize clinical at-risk mental states (ARMS) for psychosis. However, structure-cognition relationships in the ARMS have not been directly explored using multivariate neuroimaging techniques.<h4>Methods</h4>Voxel-based morphometry and partial least squares were employed to ...[more]

PMID: 22887825

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Project description:BackgroundWhite matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer's disease (AD) risk factors, and cognition.MethodsIn this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant's white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance.ResultsPrincipal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed.ConclusionOur study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present.

Dataset Information

Multivariate patterns of brain-cognition associations relating to vulnerability and clinical outcome in the at-risk mental states for psychosis.

Background

Methods

Results

Conclusions

Publications

Multivariate patterns of brain-cognition associations relating to vulnerability and clinical outcome in the at-risk mental states for psychosis.

Similar Datasets

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