| Autor: |
Pillai, Sreekrishna R., Carmichael, Owen T., Bazzano, Lydia |
| Zdroj: |
Alzheimer's & Dementia: The Journal of the Alzheimer's Association; Dec2023 Supplement 16, Vol. 19, p1-2, 2p |
| Abstrakt: |
Background: Dictionary learning techniques have the potential to efficiently detect functional connectivity impairments in distributed brain networks associated with Alzheimer's Disease (AD) when applied to task functional magnetic resonance imaging (tfMRI) data. However, traditional methods are constrained in what brain networks they can identify due to the statistical independence assumptions they make, and they do not leverage the structure of the tfMRI task when identifying networks. We used a new sparse dictionary learning technique to identify brain functional network correlates of cardiometabolic risk variables in a community‐based cohort. Method: The Bogalusa Heart Study (BHS) is community‐based cohort study of atherosclerosis and risk factors for cardiovascular disease. fMRI data was collected from 100 participants of BHS during performance of a Stroop task. Spatially concatenated tfMRI data was factorized into a group‐wise temporal dictionary matrix and a set of scan‐level spatial patterns while biasing a subset of learned temporal patterns to be paradigm related. Scan‐level spatial patterns obtained as a result were aggregated and factorized to derive a group‐level spatial dictionary matrix and sparse loading coefficients. The sparse loading coefficients obtained from this Constrained Decoupled Dictionary Learning (CDDL) method were used as descriptive biomarkers, the relevance of which was established by using them to predict cardiometabolic variables including fasting glucose level, homeostatic model assessment for insulin resistance (HOMA‐IR) index and fasting insulin level using support vector regression and a leave‐one out cross‐validation. The performance of the algorithm was calculated in terms of Mean Absolute Percentage Error (MAPE) between actual and predicted cardiometabolic variable values Result: The SVM regressor was able to predict the cardiometabolic variables with low MAPE. The prediction of glucose values achieved the least MAPE for all kernel types (Table 1). Conclusion: Combining prior knowledge, decoupling of spatial and temporal patterns, and high‐dimensional regression, we generated brain functional network patterns from real‐world tfMRI data that associated with known cardiometabolic indicators of brain health. These brain functional network patterns thus have the potential to serve as indicators of brain health. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
|
