Measures of Morphological Complexity of Gray Matter on Magnetic Resonance Imaging for Control Age Grouping

Andreou

Avram

et al. 2023

jpn

Background: Structural MRI studies in people with first-episode psychosis (FEP) and those in the clinical high-risk (CHR) state have consistently shown volumetric abnormalities that depict changes in the structural complexity of the cortical boundary. The aim of the present study was to employ chaos analysis in the identification of people with psychosis based on the structural complexity of the cortical boundary and subcortical areas. Methods: We performed chaos analysis of the grey matter distribution on structural MRIs. First, the outer boundary points for each slice in the axial, coronal and sagittal view were calculated for grey matter maps. Next, the distance of each boundary point from the centre of mass in the grey matter was calculated and stored as spatial series, which was further analyzed by extracting the Largest Lyapunov Exponent (lambda [λ]), a feature depicting the structural complexity of the cortical boundary. Results: Structural MRIs were acquired from 77 FEP, 73 CHR and 44 healthy controls. We compared λ brain maps between groups, which resulted in statistically significant differences in all comparisons. By matching the λ values extracted in axial view with the Morlet wavelet, differences on the surface relief are observed between groups. Limitations: Parameters were selected after experimentation on the examined sample. Investigation of the effectiveness of the method in a larger data set is needed. Conclusion: The proposed framework using spatial series verifies diagnosis-relevant features and may contribute to the identification of structural biomarkers for psychosis.

Section: Introductionmentioning

confidence: 99%

Chaos analysis of the cortical boundary for the recognition of psychosis

Andreou

Avram

et al. 2023

jpn

“…As the GM morphology is inherently complex, chaos and nonlinear dynamics analyses of these spatial data are suitable mathematical techniques for extracting their informative statistical properties. Chaos and nonlinear dynamics have been increasingly reported as effective computational methods for analyzing complex data in medicine and biology ( 22 ). Wahman et al ( 23 ), suggested that psychiatric disorders are better accounted for by the nonlinear dynamics of chaos theory than by a unidirectional vector model of cause to effect.…”

Section: Introductionmentioning

confidence: 99%

“…The term spatial-series refers to the distribution of the weighted distance by voxel intensity from the GM center of the mass; this approach, i.e., the conversion of images into sequences for application of time-series analysis has been utilized for solving several problems in image data mining ( 27 ), including investigation of abnormal brain folding in Alzheimer’s disease and aging ( 28 ). In addition, a previous study reported differences on the complexity of brain folding in aging by transforming the sMRI scans into spatial series and comparing the Largest-Lyapunov-Exponent values between patients and controls ( 22 ).…”

Section: Introductionmentioning

confidence: 99%

Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients

Andreou²,

Avram³

et al. 2022

Front. Psychiatry

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis for the identification of brain topology differences in people with psychosis. Structural MRI were acquired from 77 FEP, 73 CHR and 44 healthy controls (HC). Chaos analysis of the gray matter distribution was performed: First, the distances of each voxel from the center of mass in the gray matter image was calculated. Next, the distances multiplied by the voxel intensity were represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts thus how the gray matter topology changes. Between-group differences were identified by (a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and (b) matching the lambda series with the Morlet wavelet, which resulted in statistically significant differences in the scalograms of FEP against CHR and HC. The proposed framework using spatial-series extraction enhances the between-group differences of FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

“…We introduce the term of GM topology for analysis of GM changes combining two features, each voxel's distance from the center of mass and each voxel's intensity. Chaos and nonlinear dynamics have been increasingly reported as effective computational methods for analyzing complex data in medicine and biology [15]. A previous study reported differences in complexity of brain folding in Alzheimer's disease and aging [16] by transforming sMRI images into spatial-series and comparing the Largest-Lyapunov-Exponent values in two groups.…”

Section: Introductionmentioning

confidence: 99%

Chaos Analysis of the Brain Topology in Grey Matter Images for the Recognition of Psychosis

Avram

Andreou

et al. 2021

Preprint

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis in the identification of people with psychosis. Structural MRI were acquired from 73 CHR, 77 FEP and 44 healthy controls (HC). Chaos analysis of the grey matter distribution was performed: first, the distances of each voxel from the center of mass in the grey matter image was calculated. Next, the distances multiplied by the voxel intensity was represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts how the grey matter topology changes. The classification of a subject’s clinical status was finally predicted by a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and b) matching the lambda series with the Morlet wavelet, which resulted in 100% accuracy in distinguishing between FEP and CHR. The proposed framework using spatial-series extraction enhances the classification decision for FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.