Specific types of functional information can be reliably extracted from neural connectomes
Multiple studies demonstrate that specific cognitive, behavioral, and clinical information can be reliably extracted and predicted from neural connectomes using predictive modeling.
The claim states that specific types of functional information can be reliably extracted from neural connectomes. This is a specific, empirical, and testable claim that passes Step 0. Examining the provided literature, multiple independent studies (e.g., Papers 0, 1, 2, 4, 8, and 10) use connectome-based predictive modeling to successfully extract information regarding cognitive control, clinical deficits, treatment responses, and disease states from neural connectomes. There are no papers refuting this general premise. Therefore, the evidence strongly supports the claim, yielding a verdict of SUPPORTED.
A. Salvalaggio, Michele De Filippo De Grazia, M. Zorzi, Michel Thiebaut de Schotten, M. Corbetta. Post-stroke deficit prediction from lesion and indirect structural and functional disconnection.. 2020. https://doi.org/10.1093/brain/awaa156
Paper 0 demonstrates that structural disconnection maps derived from connectomes successfully predict post-stroke behavioural deficits across multiple functional domains.
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Junhong Yu, I. Rawtaer, J. Fam, Lei Feng, E. Kua, R. Mahendran. The individualized prediction of cognitive test scores in mild cognitive impairment using structural and functional connectivity features. 2020. https://doi.org/10.1016/j.neuroimage.2020.117310
Paper 1 shows that structural and functional connectivity features from connectomes can successfully predict individualized cognitive test scores.
Qiuyu Lv, Xuan-Sheng Wang, Xiang Wang, Sheng Ge, Pan Lin. Connectome-based prediction modeling of cognitive control using functional and structural connectivity.. 2024. https://doi.org/10.1016/j.bandc.2024.106221
Paper 2 finds that both structural and functional connectomes can significantly predict cognitive control subcomponents.
Xinyi Wang, Li Xue, Junneng Shao, Zhongpeng Dai, L. Hua, R. Yan, Z. Yao, Q. Lu. Distinct MRI-based functional and structural connectivity for antidepressant response prediction in major depressive disorder.. 2024. https://doi.org/10.1016/j.clinph.2024.02.004
Paper 4 indicates that structural and functional connectome models can significantly predict treatment response and reductions in depressive severity.
Zheng Li, Haifeng Fang, Weiguo Fan, Jiaoyu Wu, Jiaxin Cui, Bao-ming Li, Chunjie Wang. Brain markers of subtraction and multiplication skills in childhood: task-based functional connectivity and individualized structural similarity.. 2024. https://doi.org/10.1093/cercor/bhae374
Paper 8 establishes that functional connectivity and structural similarity connectomes successfully predict specific arithmetic skills in children.
Fu Y, Jiang L, Detre J, Wang Z. Alzheimer's disease classification using mutual information generated graph convolutional network for functional MRI.. 2025. https://doi.org/10.1177/13872877251350306
Paper 10 demonstrates that mutual information-based functional connectomes can reliably differentiate stages of Alzheimer's disease and normal controls.
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