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Machine Learning-Identified Connectivity Markers Dissociating Misophonia from Hyperacusis Comorbidity

In a 2026 study, Ajmera attempted to identify neural connectivity-based markers of misophonia using an optimized machine learning framework applied to resting-state fMRI data from 76 university students (ages 18–25) categorized into misophonia-only, misophonia-with-hyperacusis, and control groups based on structured clinical interviews and established questionnaires. They found that both misophonia-only and comorbid groups showed altered connectivity in salience, somatomotor, and frontoparietal control networks, with misophonia-only specifically showing subcortical basal ganglia disruptions and the comorbid group showing unique visual network alterations. From this they concluded that core misophonia-related brain alterations persist despite hyperacusis comorbidity, that subcortical connectivity dissociates the two conditions, and that the findings support psychology-based (CBT) therapy targeting dysfunctional connectivity in salience and control circuits for treating misophonia. (Ajmera et al., 2026)