Utilizing the Visual Working Memory Paradigm Based on the "Global-First" Topological Approach to Optimize Psychiatric Disorders Diagnosis and Differentiation.

Abstract

Current diagnostic methods present major challenges in accurately diagnosing and differentiating psychiatric disorders. Due to the key role of visual working memory (VWM) for cross-diagnosis research on information processing deficits, we developed a new VWM paradigm based on the "global-first" topological visual perception theory to detect different psychiatric disorders among age groups. In young groups, significant differences in accuracy were observed between topological change and no-shape-change (all p < .05) in bipolar disorder (BD) and schizophrenia (SCZ) at different sequence values, and non-topological change was noted between the other two conditions when the size of the test graphic set in 3 in chronic-SCZ (p_3-same = .000/p_3-non-topo = .003). The accuracy of the no-shape-change in depression differed from that of topological change when the size of the test graphic was set at 4 (p = .002). In older adults, the accuracy of non-topological change is different from the other two conditions in depression when the size of the test graphic set is 3 (p_same = .0314/p_topo = .0390). Our new VWM paradigm demonstrates potential as a complementary diagnostic tool for psychiatric disorders, with preliminary evidence of sensitivity to disease-specific cognitive deficits. While classification accuracy [Area Under Curve (AUC) > 0.70-0.98] suggests clinical utility, further validation against traditional diagnostic instruments (e.g., DSM-5 criteria, symptom scales) is required to establish its role in clinical practice.