Poster No:
1373
Submission Type:
Abstract Submission
Authors:
Isabella Young1, Nicholas Dadario2, Peter Nicholas1, Michael Sughrue1, Stephane Doyen1
Institutions:
1Omniscient Neurotechnology, Sydney, New South Wales, 2Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ
First Author:
Co-Author(s):
Introduction:
The RDoC framework, developed by NIMH, shifts mental health research from symptom-based classification to a dimensional, transdiagnostic approach. Constructs span six domains-Negative Valence, Positive Valence, Cognitive Systems, Social Processes, Arousal/Regulatory Systems, and Sensorimotor Systems-linked to neurobiological mechanisms. RDoC aims to uncover biological, behavioral, and environmental contributors to mental health, improving diagnosis and treatment, though limited mapping of constructs to brain regions hinders clinical translation.
fMRI advances link constructs to neural networks, enhancing understanding of transdiagnostic processes and supporting biomarker development for diagnosis, prognosis, and treatment. Such mappings connect dysfunctions to neural circuits, guiding interventions like neurostimulation and cognitive therapies, but variability in methods and construct complexity remain challenges.
This study mapped RDoC constructs to brain regions by reviewing high-quality fMRI literature and converting MNI coordinates into Human Connectome Project (HCP) parcels. Constructs lacking sufficient evidence were excluded, and validation ensured reliable mappings, advancing RDoC integration into neuroscience and clinical research.
Methods:
Construct and Subconstruct Identification
RDoC constructs and subconstructs defined by the NIMH were grouped by domain (e.g., Negative Valence, Positive Valence). This list of 48 constructs and subconstructs formed the foundation of our literature review, detailed in Table 1.
Literature Search and Selection
A systematic literature search in March 2023 identified fMRI studies linking RDoC constructs to neural activity. Studies were included if they used fMRI, reported MNI brain activation coordinates, demonstrated robust methods (e.g., large samples, controls), and linked brain activity to constructs or subconstructs. Of the 48 constructs, sufficient literature was found for 34, while those lacking evidence were excluded (see Table 1).
MNI to HCP Parcel Mapping
MNI coordinates from studies were mapped to HCP parcels using a custom Python script. The script performed spatial comparisons between spherical clusters (based on MNI coordinates and volume) and the HCP-MMP-01 parcellation. It calculated overlap percentages between clusters and parcel regions, identifying the "primary parcel" for each cluster. Steps included creating spherical masks for MNI coordinates, resampling masks to align with the HCP atlas, calculating overlap, and assigning the highest-overlap parcel. Outputs included overlap statistics and visualizations for quality assurance. Mappings were reviewed for alignment with NIH-defined regions, and gaps like cerebellum representation were noted for refinement.
This automated mapping ensures consistent, reproducible links between constructs and HCP parcels, enabling exploration of RDoC constructs' neural substrates.
Results:
Construct Inclusion
Of the 48 constructs, 34 were mapped to HCP parcels based on high-quality fMRI literature. Constructs lacking sufficient evidence or MNI coordinates (14) were excluded.
Mapping to Brain Regions
MNI coordinates were converted to HCP parcels using a validated pipeline. Acute Threat mapped to the amygdala, hippocampus, and prefrontal cortex, while Reward Anticipation aligned with the central executive network and subcortical regions.
Neuroanatomical Coverage
Discrepancies were noted, such as the underrepresentation of the cerebellum in constructs like Agency and Ownership, requiring methodological refinement.
Validation and Refinement
Statistical analysis validated 90% of construct-parcel mappings, with significant group differentiation. Poor mappings were excluded or marked for improvement.
Conclusions:
This study systematically maps RDoC constructs to brain regions using neuroimaging data, offering a robust framework for understanding mental health. Despite limitations, it lays the groundwork for refining mappings and advancing brain-based interventions.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Neuroinformatics and Data Sharing:
Brain Atlases 2
Keywords:
Atlasing
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Was this research conducted in the United States?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Computational modeling
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