Back

Adaptation And Feasibility Of A Brief, Integrated Cognitive Control Training Intervention For Depression: A Proof-Of-Concept Trial

Kodancha, P.; Kashyap, H.; Desai, G.

2026-08-13 psychiatry and clinical psychology
10.64898/2026.08.12.26360264 medRxiv
Show abstract

Cognitive deficits in depression often persist despite pharmacological and psychotherapeutic treatment. Existing cognitive retraining programs are typically time- and resource-intensive, and place limited emphasis on addressing subjectively perceived cognitive difficulties or generalization of gains. This proof-of-concept study aimed to adapt the Integrated Cognitive Control Training (ICCT) into a brief format for patients with depression and to generate preliminary evidence of feasibility and effectiveness. The intervention was adapted into a manualized five-session program through a literature review, expert surveys involving clinicians and individuals with lived experience of depression, and a trial run. The study followed a single-group, open-label pre-post design (N = 16). Significant improvements were observed in cognitive flexibility (Color Trails Test-2: t = 3.52, p = 0.003, d = 0.88), depression severity (Montgomery-[A]sberg Depression Rating Scale: t = 6.66, p < 0.001, d = 1.67), and subjective cognition (Perceived Deficits Questionnaire: t = 5.06, p < 0.001, d = 1.3). The intervention demonstrated high acceptability and demand. These findings suggest that the Brief ICCT is a feasible and potentially effective approach for addressing cognitive deficits, with improvements extending to depressive symptom severity and socio-occupational functioning. These proof-of-concept findings justify further evaluation of Brief ICCT in adequately powered randomized controlled trials.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.