Task-based functional magnetic resonance imaging (tb-fMRI) has advanced our understanding of brain-behavior relationships. Standard tb-fMRI analyses suffer from limited reliability and low effect sizes, and machine learning (ML) approaches often require thousands of subjects, restricting their ability to inform how brain function may arise from and contribute to individual differences. Using data from 9,024 early adolescents, we derived a classifier ('neural signature') distinguishing between high and low working memory loads in an emotional n-back fMRI task, which captures individual differences in the separability of activation to the two task conditions. Signature predictions were more reliable and had stronger associations with task performance, cognition, and psychopathology than standard estimates of regional brain activation. Further, the signature was more sensitive to psychopathology associations and required a smaller training sample (N=320) than standard ML approaches. Neural signatures hold tremendous promise for enhancing the informativeness of tb-fMRI individual differences research and revitalizing its use.
Competing Interest StatementThe authors have declared no competing interest.
Funding StatementThis study was supported by K99AA030808 (DAAB) and R01DA54750 (RB). Additional funding included: AJG (DGE-213989), SEP (F31AA029934), ASH (K01AA030083), RB (R21AA027827, U01DA055367). Data for this study were provided by the Adolescent Brain Cognitive Development (ABCD) study which was funded by awards U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147 from the NIH and additional federal partners (https://abcdstudy.org/federal-partners.html). A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.
Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
IRB of Washington University in St. Louis gave ethical approval for this work.
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Data AvailabilityData used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9-10 and follow them over 10 years into early adulthood. The ABCD data repository grows and changes over time. The ABCD data used in this report came from the 5.1 release, available at https://nda.nih.gov/study.html?id=2313. Analysis code is available at https://github.com/WashU-BG/NeuralSignature.
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