Anatomical connectivity changes in bipolar disorder and schizophrenia investigated using whole-brain tract-based spatial statistics and machine learning approaches
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Schizophrenia and bipolar disorder have similar clinical features. Their differential diagnosis is crucial because each has different prognostic and therapeutic characteristics. Earlier studies have used numerous methods, including magnetic resonance investigation, in an effort to differentiate these two disorders. Research has consistently shown that there is reduced white matter density in the fronto-temporal and fronto-thalamic pathways in both patients with bipolar disorder and schizophrenia; however, the sensitivity of the methods used is limited. Tract-based spatial statistics is a method of whole-brain analysis that relies on voxel-based comparison, and uses nonlinear image transformation and permutation tests with correction for multiple comparisons. The primary aim of the present study was to investigate anatomical connectivity changes in patients with bipolar disorder and schizophrenia using tract-based spatial statistics, to classify the patients according to white matter integrity patterns using machine learning, and to identify features that represent the key differences between the disorders. Whole-brain images of 41 bipolar disorder patients, 39 schizophrenia patients, and 23 controls were acquired using a 1.5 T magnetic resonance investigation scanner. As compared to the controls, the schizophrenia and bipolar disorder patients had reduced fractional anisotropy in similar white matter tracts. In addition, the imaging method employed differentiated the schizophrenia and bipolar disorder patients with 81.25% accuracy. Although the bipolar disorder and schizophrenia patients exhibited similar anatomical connectivity changes, as compared to the controls, the connectivity reductions in the right hemisphere in the bipolar disorder patients differentiated them from the schizophrenia patients. The present findings improve our understanding of the etiology and pathogenesis of bipolar disorder and schizophrenia, and can potentially be used as a biomarker for the diagnosis and treatment of both disorders.
KeywordsBipolar disorder Schizophrenia Tract-based spatial statistics Machine learning
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Conflict of interest
The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.
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