TY - JOUR

T1 - Future directions for cognitive neuroscience in psychiatry

T2 - recommendations for biomarker design based on recent test re-test reliability work

AU - Blair, Robert James Richard

AU - Mathur, Avantika

AU - Haines, Nathaniel

AU - Bajaj, Sahil

N1 - Publisher Copyright: © 2022

PY - 2022

Y1 - 2022

N2 - The identification of clinically relevant neuroimaging biomarkers in psychiatry is a research priority. Neuropsychological tasks and functional MRI (fMRI) are used, via FDA-approved assessments, in clinical decision-making in many neurology departments. However, currently, psychiatry lacks neuro-psychological/fMRI biomarkers that could help in diagnosis and treatment planning. In our opinion, this likely reflects task design choices commonly used with psychiatric patients that limit test re-test reliability (TRR). Clinical decision-making can only occur via tests with excellent TRR. Statistical analyses indicate that TRR is particularly compromised if: (1) there are relatively few trials per condition; and (2) contrast-based analyses are adopted. We suggest, on the basis of the simulation work, that machine learning techniques combined with increasing the number of trials (per condition) and limiting the reliance on contrast-based analyses, can increase TRR and thus allow the successful development of cognitive neuroscience-based biomarkers for psychiatry in the near future.

AB - The identification of clinically relevant neuroimaging biomarkers in psychiatry is a research priority. Neuropsychological tasks and functional MRI (fMRI) are used, via FDA-approved assessments, in clinical decision-making in many neurology departments. However, currently, psychiatry lacks neuro-psychological/fMRI biomarkers that could help in diagnosis and treatment planning. In our opinion, this likely reflects task design choices commonly used with psychiatric patients that limit test re-test reliability (TRR). Clinical decision-making can only occur via tests with excellent TRR. Statistical analyses indicate that TRR is particularly compromised if: (1) there are relatively few trials per condition; and (2) contrast-based analyses are adopted. We suggest, on the basis of the simulation work, that machine learning techniques combined with increasing the number of trials (per condition) and limiting the reliance on contrast-based analyses, can increase TRR and thus allow the successful development of cognitive neuroscience-based biomarkers for psychiatry in the near future.

U2 - 10.1016/j.cobeha.2022.101102

DO - 10.1016/j.cobeha.2022.101102

M3 - Review

AN - SCOPUS:85125199227

VL - 44

JO - Current Opinion in Behavioral Sciences

JF - Current Opinion in Behavioral Sciences

SN - 2352-1546

M1 - 101102

ER -