Neural Constraints on Cognitive Experience and Mental Health
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913742701199360 |
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| author | Shariatpanahi, Bita Nozari, Erfan Daftarian, Soroush Arab, Fahimeh Kheirkhah, Mina Bernhard, Felix P. Khodadadi, Shiva Giltay, Erik J. Hebbrecht, Kaat Hofmann, Stefan G. Hahn, Tim Jamalabadi, Hamidreza |
| author_facet | Shariatpanahi, Bita Nozari, Erfan Daftarian, Soroush Arab, Fahimeh Kheirkhah, Mina Bernhard, Felix P. Khodadadi, Shiva Giltay, Erik J. Hebbrecht, Kaat Hofmann, Stefan G. Hahn, Tim Jamalabadi, Hamidreza |
| contents | Understanding how neural dynamics shape cognitive experiences remains a central challenge in neuroscience and psychiatry. Here, we present a novel framework leveraging state-to-output controllability from dynamical systems theory to model the interplay between cognitive perturbations, neural activity, and subjective experience. We demonstrate that large-scale fMRI signals are constrained to low-dimensional manifolds, where affective and cognitive states are naturally organized. Furthermore, we provide a theoretically robust method to estimate the controllability Gramian from steady-state neural responses, offering a direct measure of the energy required to steer cognitive outcomes. In five healthy participants viewing 2,185 emotionally evocative short videos, our analyses reveal a strong alignment between neural activations and affective ratings, with an average correlation of $r \approx 0.7$. In a clinical cohort of 255 patients with major depressive disorder, biweekly Hamilton Rating Scale trajectories over 11 weeks significantly mapped onto these manifolds, explaining approximately 20% more variance than chance ($p < 10^{-10}$, numerically better than chance in 93% reaching statistical significance in one-third of subjects). Our work bridges dynamical systems theory and clinical neuroscience, providing a principled approach to optimize mental health treatments by targeting the most efficient neural pathways for cognitive change. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Constraints on Cognitive Experience and Mental Health Shariatpanahi, Bita Nozari, Erfan Daftarian, Soroush Arab, Fahimeh Kheirkhah, Mina Bernhard, Felix P. Khodadadi, Shiva Giltay, Erik J. Hebbrecht, Kaat Hofmann, Stefan G. Hahn, Tim Jamalabadi, Hamidreza Neurons and Cognition Understanding how neural dynamics shape cognitive experiences remains a central challenge in neuroscience and psychiatry. Here, we present a novel framework leveraging state-to-output controllability from dynamical systems theory to model the interplay between cognitive perturbations, neural activity, and subjective experience. We demonstrate that large-scale fMRI signals are constrained to low-dimensional manifolds, where affective and cognitive states are naturally organized. Furthermore, we provide a theoretically robust method to estimate the controllability Gramian from steady-state neural responses, offering a direct measure of the energy required to steer cognitive outcomes. In five healthy participants viewing 2,185 emotionally evocative short videos, our analyses reveal a strong alignment between neural activations and affective ratings, with an average correlation of $r \approx 0.7$. In a clinical cohort of 255 patients with major depressive disorder, biweekly Hamilton Rating Scale trajectories over 11 weeks significantly mapped onto these manifolds, explaining approximately 20% more variance than chance ($p < 10^{-10}$, numerically better than chance in 93% reaching statistical significance in one-third of subjects). Our work bridges dynamical systems theory and clinical neuroscience, providing a principled approach to optimize mental health treatments by targeting the most efficient neural pathways for cognitive change. |
| title | Neural Constraints on Cognitive Experience and Mental Health |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2503.13981 |