Dataset Information

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

ABSTRACT: The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying components between hemispheres across subjects. SBL is successfully implemented with simulated data with inherent differences in laterality. SBL is then compared with a voxel-wise analysis utilizing structural data from a sample of patients with schizophrenia and controls without schizophrenia. SBL group comparisons identified three distinct temporal regions and one cerebellar region with significantly altered laterality in patients with schizophrenia relative to controls. Previous work highlights reductions in laterality (ie, reduced left gray matter volume) in patients with schizophrenia compared with controls without schizophrenia. Results from this pilot SBL project are the first, to our knowledge, to identify covarying laterality differences within discrete temporal brain regions. The authors argue SBL provides a unique focus to detect covarying laterality differences in patients with schizophrenia, facilitating the discovery of laterality aspects undetected in previous work.

SUBMITTER: DeRamus TP

PROVIDER: S-EPMC8311554 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

DeRamus Thomas P TP Silva Rogers F RF Iraji Armin A Damaraju Eswar E Belger Aysenil A Ford Judith M JM McEwen Sarah C SC Mathalon Daniel H DH Mueller Bryon A BA Pearlson Godfrey D GD Potkin Steven G SG Preda Adrian A Turner Jessica A JA Vaidya Jatin G JG van Erp Theo G M TGM Calhoun Vince D VD

NMR in biomedicine 20200323 6

The human brain is asymmetrically lateralized for certain functions (such as language processing) to regions in one hemisphere relative to the other. Asymmetries are measured with a laterality index (LI). However, traditional LI measures are limited by a lack of consensus on metrics used for its calculation. To address this limitation, source-based laterality (SBL) leverages an independent component analysis for the identification of laterality-specific alterations, identifying covarying compone ...[more]

PMID: 32207187

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Project description:Objective: The objective of the study was to investigate whether radiomics features of extrahippocampal regions differ between patients with epilepsy and healthy controls, and whether any differences can identify patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE). Methods: Data from 36 patients with hippocampal sclerosis (HS) and 50 healthy controls were used to construct a radiomics model. A total of 1,618 radiomics features from the affected hippocampal and extrahippocampal regions were compared with features from healthy controls and the unaffected side of patients. Using a stepwise selection method with a univariate t-test and elastic net penalization, significant predictors for identifying TLE were separately selected for the hippocampus (H+) and extrahippocampal region (H-). Each model was independently validated with an internal set of MRI-negative adult TLE patients (n = 22) and pediatric validation cohort with MRI-negative TLE (n = 20) from another tertiary center; diagnostic performance was calculated using area under the curve (AUC) of the receiver-operating-characteristic curve analysis. Results: Forty-eight significant H+ radiomic features and 99 significant H- radiomic features were selected from the affected side of patients and used to create a hippocampus model and an extrahippocampal model, respectively. Texture features were the most frequently selected feature. Training set showed slightly higher accuracy between hippocampal (AUC = 0.99) and extrahippocampal model (AUC = 0.97). In the internal validation and external validation sets, the extrahippocampal model (AUC = 0.80 and 0.92, respectively) showed higher diagnostic performance for identifying the affected side of patients than the hippocampus model (AUC = 0.67 and 0.69). Significance: Radiomics revealed extrahippocampal abnormality in the affected side of patients with TLE and could potentially help to identify MRI-negative TLE. Classification of Evidence: Class IV Criteria for Rating Diagnostic Accuracy Studies.

Dataset Information

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

Publications

Covarying structural alterations in laterality of the temporal lobe in schizophrenia: A case for source-based laterality.

Similar Datasets

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