Beyond Focal Lesions: Dynamical Network Effects of White Matter Hyperintensities
Bibliographic record
Abstract
White matter (WM) tracts shape the brain's dynamical activity and their damage (e.g., white matter hyperintensities, WMH) yields relevant functional alterations, ultimately leading to cognitive symptoms. The mechanisms linking the structural damage caused by WMH to the arising alterations of brain dynamics is currently unknown. To estimate the impact of WMH on brain dynamics, we combine neural-mass whole-brain modeling with a virtual-lesioning (disconnectome) approach informed by empirical data. We account for the heterogeneous effects of WMH either on inter-regional communication (i.e., edges) or on dynamics (i.e., nodes) and create models of their local versus global, and edge versus nodal effects using a large fMRI dataset comprising 188 non-demented individuals (120 cognitively normal, 68 with mild cognitive impairment) with varying degrees of WMH. We show that, although WMH mainly determine local damage to specific WM tracts, these lesions yield relevant global dynamical effects by reducing the overall synchronization of the brain through a reduction of global coupling. Alterations of local nodal dynamics through disconnections are less relevant and present only at later stages of WMH damage. Exploratory analyses suggest that education might play a beneficial role in counteracting the reduction in global coupling associated with WMH. This study provides generative models linking the structural damage caused by WMH to alterations in brain dynamics. These models might be used to evaluate the detrimental effects of WMH on brain dynamics in a subject-specific manner. Furthermore, it validates the use of whole-brain modeling for hypothesis-testing of structure-function relationships in diseased states characterized by empirical disconnections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".