Distribution of White Matter Hyperintensities across Arterial Territories in Neurodegenerative Diseases
Bibliographic record
Abstract
ABSTRACT MRI-detected white matter hyperintensities (WMH) are often recognized as markers of cerebrovascular abnormalities and an index of vascular brain injury. The literature establishes a strong link between WMH burden and cognitive decline, and suggests that the anatomical distribution of WMH mediates cognitive dysfunction. Pathological remodeling of major cerebral arteries (anterior, ACA; middle, MCA; posterior, PCA) may increase WMH burden in an arterial territory (AT)-specific manner. However, this has not been systematically studied across neurodegenerative diseases (NDDs). To address this gap, we aimed to assess WMH distribution (i) across ATs per clinical category, (ii) across clinical categories per AT, and (iii) between men and women. We also investigated the association between AT-specific WMH burden and cognition. Using two cohorts – Canadian CCNA-COMPASS-ND (N=927) and US-based NIFD (N=194) – we examined WMH distribution across ten clinical categories: cognitively unimpaired (CU), subjective cognitive decline (SCD), mild cognitive impairment (MCI), Alzheimer disease (AD), MCI and AD with high vascular injury (+V), Lewy body dementia, frontotemporal dementia, Parkinson’s disease (PD), and PD with cognitive impairment or dementia. WMH masks were segmented from FLAIR MRI and mapped onto an arterial atlas. Cognitive performance was assessed using four psychometric tests evaluating reaction time and overall cognition, namely Simple Reaction Time (SRT), Choice Reaction Time (CRT), Digit Symbol Substitution Test (DSST), and Montreal Cognitive Assessment (MoCA). Statistical analyses involved linear regression models, controlling for demographic factors, with a 5% False Discovery Rate for multiple comparisons. Our transdiagnostic analysis revealed unique AT-specific WMH burden patterns. Comparisons between ACA and PCA territories revealed distinct burden patterns in clinical categories with similar whole-brain WMH burden, while the MCA territory consistently exhibited the highest burden across all categories, despite accounting for AT size. Hemispheric asymmetries were noted in seven diagnostic categories, with most showing higher WMH burden in the left MCA territory. Our results further revealed distinct AT-specific WMH patterns in diagnostic groups that are more vascular than neurodegenerative (i.e., MCI+V, AD+V). Categories often misdiagnosed in clinical practice, such as FTD and AD, displayed contrasting WMH signatures across ATs. SCD showed distinct AT-specific WMH patterns compared to CU and NDD participants. Additionally, sex-specific differences emerged in five NDDs, with varying AT effects. Importantly, AT-specific WMH burden was associated with slower processing speed in MCI (PCA) and AD (ACA, MCA). This study highlights the importance of evaluating WMH distribution through a vascular-based brain parcellation. We identified ATs with increased vulnerability to WMH accumulation across NDDs, revealing distinct WMH signatures for multiple clinical categories. In the AD continuum, these signatures correlated with cognitive impairment, underscoring the potential for vascular considerations in imaging criteria to improve diagnostic precision.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".