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 - 2022/4
Y1 - 2022/4
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.
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U2 - 10.1016/j.cobeha.2022.101102
DO - 10.1016/j.cobeha.2022.101102
M3 - Review article
AN - SCOPUS:85125199227
SN - 2352-1546
VL - 44
JO - Current Opinion in Behavioral Sciences
JF - Current Opinion in Behavioral Sciences
M1 - 101102
ER -