Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Abstract

Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (p < 0.001 tested with 1000-times permutation). The results demonstrated evidence of hippocampal and amygdaloid structural changes in schizophrenia patients, and also suggested that morphological features from the amygdaloid and hippocampal subregions could be used by machine learning algorithms for the classification of schizophrenia.

Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity

“…Classification was performed using a linear support vector machine classifier with the cost set as 1 [ 45 – 47 ] and the classification accuracy was evaluated using a leave-one-out cross-validation method for each feature set. Linear support vector machine has been widely applied in multivariate pattern analysis with its high accuracy, generalization, and interpretability [ 48 – 50 ]. Many studies have used the leave-one-out cross-validation method claiming that it is more appropriate for small data since more data can be trained for a classification model and that it imitates clinical setting where clinicians can learn from large data and apply the findings to new each case [ 51 – 53 ].…”

Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study

“…Recently, machine learning (ML) methods using neuroimaging data have been increasingly applied in the classification between SZ patients and normal controls (NCs), in which the classification accuracy varies from 0.65 to 0.95 ( Arbabshirani et al, 2013 ; Talpalaru et al, 2019 ; Cao et al, 2020 ; Guo et al, 2020 ; Steardo et al, 2020 ). The majority of previous studies have mainly applied ML methods to a single neuroimaging modality, including structural MRI (sMRI) ( Xiao et al, 2017 ; Oh et al, 2020 ), diffusion tensor imaging (DTI) ( Ingalhalikar et al, 2010 ; Ardekani et al, 2011 ), resting-state functional MRI (rs-fMRI) ( Koch et al, 2015 ; Skåtun et al, 2017 ; Cai et al, 2020 ), and electroencephalogram (EEG) ( Ke et al, 2021 ).…”

A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data

“…where E −q j implies that we calculate the expectation over all random variables except the j-th variable, and p(X, y, Θ) is the joint probability. Therefore, we can apply (9) to the joint probability for each random variable to obtain the model update rules. Firstly, the distribution of the dual weights a is:…”

“…These algorithms are capable of analyzing any data source, either images (structural or functional), genetic information [6] or behavioral information [7], to carry out an automatic diagnosis of the pathology. Recent approaches based on Support Vector Machine algorithm (SVM) have been applied in Magnetic Resonance Imaging (MRI), showing great results in this field and detecting relevant brain areas involved in the pathology, as well as inferring new useful biomarkers for their diagnosis [8][9][10]. However, although these models have provided accurate results for automatic classification, the lack of interpretability in their results prevents the characterization of the pathology.…”