| Title: |
Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment |
| Authors: |
Petersen, Marvin; Coenen, Mirthe; DeCarli, Charles; De Luca, Alberto; van der Lelij, Ewoud; Weiner, Michael; Aisen, Paul; Petersen, Ronald; Jack, Clifford R; Jagust, William; Landau, Susan; Rivera-Mindt, Monica; Okonkwo, Ozioma; Shaw, Leslie M; Lee, Edward B; Toga, Arthur W; Beckett, Laurel; Harvey, Danielle; Green, Robert C; Saykin, Andrew J; Nho, Kwangsik; Perrin, Richard J; Tosun, Duygu; Sachdev, Pallavi; Drake, Erin; Montine, Tom; Conti, Cat; Weiner, Michael W; Nosheny, Rachel; Sacrey, Diana Truran; Fockler, Juliet; Miller, Melanie J; Conti, Catherine; Kwang, Winnie; Jin, Chengshi; Diaz, Adam; Ashford, Miriam; Flenniken, Derek; Rafii, Michael; Raman, Rema; Jimenez, Gustavo; Donohue, Michael; Salazar, Jennifer; Fidell, Andrea; Boatwright, Virginia; Robison, Justin; Zimmerman, Caileigh; Cabrera, Yuliana; Walter, Sarah; Clanton, Taylor; Shaffer, Elizabeth; Webb, Caitlin; Hergesheimer, Lindsey; Smith, Stephanie; Ogwang, Sheila; Adegoke, Olusegun; Mahboubi, Payam; Pizzola, Jeremy; Jenkins, Cecily; Saito, Naomi; Hussen, Kedir Adem; Amaza, Hannatu; Thao, Mai Seng; Parkins, Shaniya; Ayo, Omobolanle; Glittenberg, Matt; Hoang, Isabella; Germano, Kaori Kubo; Strong, Joe; Weisensel, Trinity; Magana, Fabiola; Thomas, Lisa; Guzman, Vanessa; Ajayi, Adeyinka; Benedetto, Joseph Di; Talavera, Sandra; Felmlee, Joel; Fox, Nick C; Thompson, Paul; Forghanian-Arani, Arvin; Borowski, Bret; Reyes, Calvin; Hedberg, Caitie; Ward, Chad; Schwarz, Christopher; Reyes, Denise |
| Source: |
Brain, vol 147, iss 12 |
| Publisher Information: |
eScholarship, University of California |
| Publication Year: |
2024 |
| Collection: |
University of California: eScholarship |
| Subject Terms: |
5202 Biological Psychology (for-2020); 52 Psychology (for-2020); Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) (rcdc); Vascular Cognitive Impairment/Dementia (rcdc); Neurodegenerative (rcdc); Aging (rcdc); Neurosciences (rcdc); Clinical Research (rcdc); Cerebrovascular (rcdc); Dementia (rcdc); Alzheimer's Disease Related Dementias (ADRD) (rcdc); Basic Behavioral and Social Science (rcdc); Acquired Cognitive Impairment (rcdc); Brain Disorders (rcdc); Biomedical Imaging (rcdc); Mental Health (rcdc); Behavioral and Social Science (rcdc); 2.1 Biological and endogenous factors (hrcs-rac); Neurological (hrcs-hc); Humans (mesh); Male (mesh); Female (mesh); White Matter (mesh); Aged (mesh); Middle Aged (mesh); Cross-Sectional Studies (mesh); Magnetic Resonance Imaging (mesh); Cognitive Dysfunction (mesh); Cognition (mesh); Neuropsychological Tests (mesh) |
| Time: |
4265 - 4279 |
| Description: |
White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain 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. Lesion network mapping (LNM) enables us 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 LNM to test the following hypotheses: (i) LNM-informed markers surpass WMH volumes in predicting cognitive performance; and (ii) WMH contributing to cognitive impairment map to specific brain networks. We analysed cross-sectional data of 3485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in four 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 to 480 atlas-based grey and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/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 connectivity to WMH, in grey and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. 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 ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
qt90d7x3f3; https://escholarship.org/uc/item/90d7x3f3; https://escholarship.org/content/qt90d7x3f3/qt90d7x3f3.pdf |
| DOI: |
10.1093/brain/awae315 |
| Availability: |
https://escholarship.org/uc/item/90d7x3f3; https://escholarship.org/content/qt90d7x3f3/qt90d7x3f3.pdf; https://doi.org/10.1093/brain/awae315 |
| Rights: |
CC-BY |
| Accession Number: |
edsbas.94A2DCB0 |
| Database: |
BASE |