Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients.

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion: Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Vascular Burden Score Impacts Cognition Independent of Amyloid PET and MRI Measures of Alzheimer's Disease and Vascular Brain Injury.

BACKGROUND/OBJECTIVE: To determine the impact of vascular burden on rates of decline in episodic memory and executive function. We hypothesize that greater vascular burden will have an additive negative impact on cognition after accounting for baseline cognitive impairment, positron emission tomography (PET) amyloid burden, and magnetic resonance imaging (MRI) measures. METHODS: Individuals were followed an average of 5 years with serial cognitive assessments. Predictor variables include vascular burden score (VBS), quantitative brain MRI assessment, and amyloid imaging. Subjects consisted of 65 individuals, 53% of whom were male, aged 73.2±7.2 years on average with an average of 15.5±3.3 years of educational achievement. RESULTS: Baseline cognitive impairment was significantly associated poorer episodic memory (p < 0.0001), smaller hippocampal volume (p < 0.0001), smaller brain volume (p = 0.0026), and greater global Pittsburg Imaging Compound B (PiB) index (p = 0.0008). Greater amyloid burden was associated with greater decline in episodic memory over time (ß= -0.20±0.07, p < 0.005). VBS was significantly associated with the level of executive function performance (ß= -0.14±0.05, p < 0.005) and there was a significant negative interaction between VBS, cognitive impairment, and PiB index (ß= -0.065±0.03, p = 0.03). CONCLUSIONS: Our results find a significant influence of VBS independent of standard MRI measures and cerebral amyloid burden on executive function. In addition, VBS reduced the amount of cerebral amyloid burden needed to result in cognitive impairment. We conclude that the systemic effects of vascular disease as reflected by the VBS independently influence cognitive ability.

Most edges in Markov random fields for white matter hyperintensity segmentation are worthless.

The time and space complexities of Markov random field (MRF) algorithms for image segmentation increase with the number of edges that represent statistical dependencies between adjacent pixels. This has made MRFs too computationally complex for cutting-edge applications such as joint segmentation of longitudinal sequences of many high-resolution magnetic resonance images (MRIs). Here, we show that simply removing edges from full MRFs can reduce the computational complexity of MRF parameter estimation and inference with no notable decrease in segmentation performance. In particular, we show that for segmentation of white matter hyperintensities in 88 brain MRI scans of elderly individuals, as many as 66% of MRF edges can be removed without substantially degrading segmentation accuracy. We then show that removing edges from MRFs makes MRF parameter estimation and inference computationally tractable enough to enable modeling statistical dependencies within and across a larger number of brain MRI scans in a longitudinal series; this improves segmentation performance compared to separate segmentations of each individual scan in the series.

Impact of Markov Random Field optimizer on MRI-based tissue segmentation in the aging brain.

Automatically segmenting brain magnetic resonance images into grey matter, white matter, and cerebrospinal fluid compartments is a fundamentally important neuroimaging problem whose difficulty is heightened in the presence of aging and neurodegenerative disease. Current methods overlap greatly in terms of identifiable algorithmic components, and the impact of specific components on performance is generally unclear in important real-world scenarios involving serial scanning, multiple scanners, and neurodegenerative disease. Therefore we evaluated the impact that one such component, the Markov Random Field (MRF) optimizer that encourages spatially-smooth tissue labelings, has on brain tissue segmentation performance. Two challenging elderly data sets were used to test segmentation consistency across scanners and biological plausibility of tissue change estimates; and a simulated young brain data set was used to test accuracy against ground truth. Belief propagation (BP) and graph cuts (GC), used as the MRF optimizer component of a standardized segmentation system, provide high segmentation performance on aggregate that is competitive with end-to-end systems provided by SPM and FSL (FAST) as well as the more traditional MRF optimizer iterated conditional modes (ICM). However, the relative performance of each method varied strongly by performance criterion and differed between young and old brains. The findings emphasize the unique difficulties involved in segmenting the aging brain, and suggest that optimal algorithm components may depend in part on performance criteria.