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Mismatch negativity predicts initial auditory-based targeted cognitive training performance in a heterogeneous
Yash B Joshi1, Christopher E Gonzalez1, Juan L Molina1
1VA San Diego Healthcare System, La Jolla, CA, USA; University of California, San Diego, Department of Psychiatry, La Jolla, CA, USA; Desert Pacific Mental Illness Research Education and Clinical Center, La Jolla, CA, USA.
Psychiatry Research
|July 5, 2023
Summary
Early auditory information processing biomarkers, like mismatch negativity (MMN), can predict cognitive training success in diverse neuropsychiatric conditions. MMN shows potential for guiding auditory-based targeted cognitive training (ATCT) interventions.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Psychiatry
Background:
- Auditory-based targeted cognitive training (ATCT) aims to improve cognitive function by enhancing auditory processing.
- Biomarkers of early auditory information processing (EAIP), such as mismatch negativity (MMN) and P3a, have previously predicted ATCT gains in schizophrenia.
Purpose of the Study:
- To investigate the predictive ability of EAIP biomarkers (MMN and P3a) for ATCT performance across multiple neuropsychiatric diagnoses.
- To assess the utility of MMN and P3a in predicting cognitive training outcomes in individuals with schizophrenia, major depressive disorder, post-traumatic stress disorder, and generalized anxiety disorder.
Main Methods:
- Collected MMN and P3a data from 26 participants diagnosed with SZ, MDD, PTSD, or GAD.
- Assessed cognitive function using the MATRICS Consensus Cognitive Battery (MCCB).
- Participants completed 1 hour of the "Sound Sweeps" ATCT exercise, with performance measured across the first two training levels.
Main Results:
- While groups showed similar MMN responses, the schizophrenia group exhibited attenuated P3a.
- MMN and MCCB cognitive domain t-scores, but not P3a, strongly correlated with ATCT performance, explaining up to 61% of the variance.
- Participant diagnosis was not a significant predictor of ATCT performance.
Conclusions:
- MMN serves as a reliable predictor of ATCT performance in heterogeneous neuropsychiatric populations.
- EAIP biomarkers, particularly MMN, should be considered for guiding ATCT interventions across diverse diagnostic groups.
- These findings support the use of MMN in tailoring ATCT for individuals with various neuropsychiatric conditions.
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