Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia

Κορδά, Αλεξάνδρα; Ventouras, E.; Asvestas, Pantelis A.; Toumaian, Maida; Matsopoulos, George K.; Smyrnis, Nikolaos

doi:10.1016/j.clinph.2022.04.010

Cited by 12 publications

(6 citation statements)

References 64 publications

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“…Statistically significant differences were revealed in the occipital and temporal lobe for CHR against HC, and could serve as biomarkers of psychotic disorders. Many studies present the involvement of the occipital lobe in FEP and CHR (42)(43)(44): Subjects with predominant attenuated psychotic symptoms are characterized by a reduction of GMintensity values in the occipital cortex (44). In previous studies in at-risk individuals progressive gray matter reductions in temporal regions were reported (45)(46)(47).…”

Section: Discussionmentioning

confidence: 99%

“…Wavelet transform (WT) employs a fully scalable modulated window, which provides an extensively tested solution to the windowing function selection problem in frequency-related (scale-related) signal processing methodologies ( 43 ). The window slides across the signal, and for every position a spectrum is calculated.…”

Section: Methodsmentioning

confidence: 99%

“…A positive lambda expresses sensitive dependence on initial conditions for a dynamical system. A positive lambda presents the average rate over the whole attractor, at which two nearby trajectories become exponentially separate with time evolution (38). A practical numerical technique for calculating lambda is the method developed by Rosenstein et al (39), which works well with small datasets and is robust to changes in the embedding dimension, reconstruction delay, and noise level (40).…”

Section: Largest-lyapunov-exponentmentioning

confidence: 99%

“…where λ 1 is the lambda value, d (t) is the average divergence at the voxel t, and d (0) is a constant that normalizes the initial separation. Lambda can be estimated using the matrix X of the reconstructed state space as in (38). A spatial-dependent value of lambda, λ 1 k , where k the target voxel and T the distance between voxels in the state space, can be estimated as:…”

Section: Largest-lyapunov-exponentmentioning

confidence: 99%

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

Κορδά

Andreou²,

Avram³

et al. 2022

Front. Psychiatry

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Largest-lyapunov-exponentmentioning

confidence: 99%

Section: Largest-lyapunov-exponentmentioning

confidence: 99%

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

Κορδά

Andreou²,

Avram³

et al. 2022

Front. Psychiatry

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“…The numerical technique for the calculation of the λ and the wavelet transformation is presented in Appendix 1 and has been used in previous studies. 21 , 22 The mean of the λ values was calculated for each slice, concluding in 81 values per participant in axial view, 97 values in coronal view and 101 values in sagittal view. Figure 1 shows examples of the extracted distances in axial view, and the mean value of the corresponding generated λ values of 1 CHR, 1 FEP and 1 healthy control participant.…”

Section: Methodsmentioning

confidence: 99%

Chaos analysis of the cortical boundary for the recognition of psychosis

Κορδά

Andreou

Avram

et al. 2023

jpn

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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.

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