Dynamic and static functional network connectivity distinguish symptomatic and non‐symptomatic individuals with CADASIL
Bibliographic record
Abstract
Abstract Background This study focuses on Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), a key model for studying arterial degradation and its impact on brain network communication. We explore functional network connectivity in CADASIL patients, shedding light on how arterial changes affect brain network interactions. Method Overcoming COVID‐19 challenges, we've enrolled over 200 participants for longitudinal assessments. The study compares Symptomatic (SYM) individuals (Rankin Scale scores 1‐3) with Non‐Symptomatic (NSYM) counterparts. The SYM group, mainly females (63) and Caucasians (101), shows lower cognitive performance (average MOCA score 25.2) and processing speed (SDMT average 43.7) compared to NSYM's higher scores (MOCA 26.8, SDMT 51.4). Additionally, the SYM group, older on average (53 years), exhibits more functional impairment (average WHODAS 9.1) than NSYM (47.7 years, WHODAS 3.1). Advanced neuroimaging (3T Siemens Prisma Fit or 3T Signa Premier scanners) and Spatially Constrained Independent Component Analysis are used for dynamic functional network connectivity (dFNC) analysis. Results The SYM and NSYM groups display significant cognitive and functional differences. Connectivity analyses reveal moderate to substantial differences in both static and dynamic states, particularly in Visual and Cognitive Control domains. Static analysis identifies disparities in 8 intrinsic connectivity networks across 5 domains, while dynamic analysis shows 3 of 4 states with significant differences in cognitive control networks. Conclusion This research underlines the profound impact of CADASIL on brain network connectivity, notably in cognitive control and visual processing. The findings, integral to the USA CADASIL Consortium, aim to characterize CADASIL's clinical and biological markers. These results are pivotal for global efforts to understand and treat vascular contributions to cognitive impairment and dementia, enhancing the landscape of CADASIL research and therapy development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".