Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach

Abstract

ObjectiveIt is well known that altered functional connectivity is a robust neuroimaging marker of schizophrenia. However, there is inconsistency in the direction of alterations, i.e., increased or decreased connectivity. In this study, we aimed to determine the direction of the connectivity alteration associated with schizophrenia using a multivariate, data-driven approach.MethodsResting-state functional magnetic resonance imaging data were acquired from 109 individuals with schizophrenia and 120 controls across two openly available datasets. A whole-brain resting-state functional connectivity (rsFC) matrix was computed for each individual. A modified connectome-based predictive model (CPM) with a support vector machine (SVM) was used to classify patients and controls. We conducted a series of multivariate classification analyses using three different feature sets, increased, decreased, and both increased and decreased rsFC.ResultsFor both datasets, combining information from both increased and decreased rsFC substantially improved prediction accuracy (Dataset 1: accuracy = 70.2%, permutation p = 0.001; Dataset 2: accuracy = 64.4%, permutation p = 0.003). When tested across datasets, the prediction model using decreased rsFC performed best. The identified predictive features of decreased rsFC were distributed mostly in the motor network for both datasets.ConclusionThese findings suggest that bidirectional alterations in rsFC are distributed in schizophrenia patients, with the pattern of decreased rsFC being more similar across different populations.

FLMatchQA: a recursive neural network-based question answering with customized federated learning model

“…They also detailed the systems and their success rate and corrected answers.QA aims to answer user questions correctly using feature engineering in early research. Statistical syntax-based models softly align questions with answers, while WorldNet’s lexical semantic information improves matching ( Kim et al, 2023 ). A machine translation model converts query and answer terms, introducing synonyms ( Kim, 2014 ).…”