Extracting Root-Causal Brain Activity Driving Psychopathology from Resting State fMRI
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917255192772608 |
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| author | Strobl, Eric V. |
| author_facet | Strobl, Eric V. |
| contents | Neuroimaging studies of psychiatric disorders often correlate imaging patterns with diagnostic labels or composite symptom scores, yielding diffuse associations that obscure underlying mechanisms. We instead seek to identify root-causal maps -- localized BOLD disturbances that initiate pathological cascades -- and to link them selectively to symptom dimensions. We introduce a bilevel structural causal model that connects between-subject symptom structure to within-subject resting-state fMRI via independent latent sources with localized direct effects. Based on this model, we develop SOURCE (Symptom-Oriented Uncovering of Root-Causal Elements), a procedure that links interpretable symptom axes to a parsimonious set of localized drivers. Experiments show that SOURCE recovers localized maps consistent with root-causal BOLD drivers and increases interpretability and anatomical specificity relative to existing comparators. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_07233 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Extracting Root-Causal Brain Activity Driving Psychopathology from Resting State fMRI Strobl, Eric V. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Neurons and Cognition Neuroimaging studies of psychiatric disorders often correlate imaging patterns with diagnostic labels or composite symptom scores, yielding diffuse associations that obscure underlying mechanisms. We instead seek to identify root-causal maps -- localized BOLD disturbances that initiate pathological cascades -- and to link them selectively to symptom dimensions. We introduce a bilevel structural causal model that connects between-subject symptom structure to within-subject resting-state fMRI via independent latent sources with localized direct effects. Based on this model, we develop SOURCE (Symptom-Oriented Uncovering of Root-Causal Elements), a procedure that links interpretable symptom axes to a parsimonious set of localized drivers. Experiments show that SOURCE recovers localized maps consistent with root-causal BOLD drivers and increases interpretability and anatomical specificity relative to existing comparators. |
| title | Extracting Root-Causal Brain Activity Driving Psychopathology from Resting State fMRI |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2602.07233 |