Neural Responses to Affective Sentences Reveal Signatures of Depression
Kommineni, Aditya, Jeong, Woojae, Avramidis, Kleanthis, McDaniel, Colin, Hughes, Myzelle, McGee, Thomas, Kaiser, Elsi, Lerman, Kristina, Blank, Idan A., Byrd, Dani, Habibi, Assal, Cahn, B. Rael, Kadiri, Sudarsana, Medani, Takfarinas, Leahy, Richard M., Narayanan, Shrikanth
–arXiv.org Artificial Intelligence
Depression is one of the most prevalent mental health disorders worldwide, with estimates indicating that around 5% of the worlds' adult population [1, 2] suffers from this condition. The primary methods for screening and monitoring depression rely on self-reported questionnaires, such as the Patient Health Questionnaire (PHQ-9) [3], Beck's Depression Inventory (BDI) [4] and Hamilton Depression Ratings Scale (HDRS) [5]. While these questionnaires are effective to varying degrees at screening patients for depression, they provide only limited information about the affected underlying neuro-cognitive processes in individuals, limiting the ability to personalize treatments. Given the heterogeneity of depressive symptomatology across patient populations [6, 7], it is crucial to elucidate the underlying neurophysiological mechanisms to support the development of more effective and individualized procedures for screening, monitoring, and treatment. Prior functional imaging studies have identified increased activity in anterior cin-gulate cortex (especially the subgenual anterior cingulate) during presentation of emotional stimuli, altered connectivity in prefrontal cortical areas, and default mode network as potential differentiating markers in depressed participants [8-13].
arXiv.org Artificial Intelligence
Jun-9-2025
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