Machine learning technique reveals intrinsic characteristics of schizophrenia: an alternative method

Brain Imaging and Behavior
Junhua LiRongjun Yu

Abstract

Machine learning technique has long been utilized to assist disease diagnosis, increasing clinical physicians' confidence in their decision and expediting the process of diagnosis. In this case, machine learning technique serves as a tool for distinguishing patients from healthy people. Additionally, it can also serve as an exploratory method to reveal intrinsic characteristics of a disease based on discriminative features, which was demonstrated in this study. Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 148 participants (including patients with schizophrenia and healthy controls). Connective strengths were estimated by Pearson correlation for each pair of brain regions partitioned according to automated anatomical labelling atlas. Subsequently, consensus connections with high discriminative power were extracted under the circumstance of the best classification accuracy. Investigating these consensus connections, we found that schizophrenia group predominately exhibited weaker strengths of inter-regional connectivity compared to healthy group. Aberrant connectivities in both intra- and inter-hemispherical connections were observed. Within intra-hemispherical connections, the number of aber...Continue Reading

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Citations

Jun 14, 2019·Frontiers in Human Neuroscience·Hironobu FujiwaraToshiya Murai
Feb 6, 2020·Frontiers in Bioengineering and Biotechnology·Yizhen XiangJin Liu
Oct 24, 2020·APL Bioengineering·Alice SegatoElena De Momi
Apr 24, 2021·Neuropsychiatric Disease and Treatment·Stéphane PotvinAdrianna Mendrek
Jul 3, 2021·International Journal of Environmental Research and Public Health·Joel Weijia LaiKang Hao Cheong

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Methods Mentioned

BETA
feature extraction

Software Mentioned

REST
MATLAB

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