Seemingly unrelated regression empowers detection of network failure in dementia

Abstract

Brain connectivity is progressively disrupted in Alzheimer’s disease (AD). Here we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain’s fiber networks from diffusion-weighted MRI scans of 200 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of DTI scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of MCI or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model--combining genotype, educational level, and clinical diagnosis.

Whole-genome analyses of whole-brain data: working within an expanded search space

“…For example, worldwide, there are many more structural brain MRI scans from people who have been genotyped than functional imaging scans or scans of pathology (such as amyloid imaging). To empower the search for variants affecting data that is in shorter supply, methods such as seemingly unrelated regression (SUR) can exploit information from all data types, such as clinical and imaging features combined, boosting power to detect associations 42 . Other multivariate methods combine data from multiple scan types; however, they often require a complete data set, where all subjects have all sets of information.…”

Spectral graph theory and graph energy metrics show evidence for the alzheimer's disease disconnection syndrome in APOE-4 risk gene carriers

“…Several studies report alterations in the brain connectome in AD patients [3–5, 11], suggesting disconnection. Here, we were able to directly quantify, using the spectrum of a graph (Fig.…”

“…Most prominently affected regions were the left precuneus ( p -value=5.0×10 −5 ) and left and right parahippocampal regions ( p -values<3.2×10 −5 ). Although nodal strength was previously studied in AD participants [3, 11], here we computed it on thresholded matrices – unlike what was done before, to aid the interpretation of the spectral graph theory metrics that were not previously applied on AD networks in this context. We also used this measure to compute the average strength of connectivity in the frontal, temporal, parietal and occipital lobes in AD, in relation to healthy participants.…”

“…Studies of the human connectome are increasingly popular [3–5, 11], and there is growing interest in analyzing the brain using network theory. Brain networks may be represented as matrices or graphs - these contain nodes, typically defined as anatomical regions segmented from MRI, and edges that connect these regions.…”

“…We analyzed the brain spectrum of 50 healthy elderly participants and 42 patients with Alzheimer’s disease (AD) in relation to the genetic variant – apolipoprotein E epsilon 4 allele ( APOE -4), a known genetic risk factor for developing AD [3, 11]. Individuals who carry one or two copies of the APOE -4 risk gene have an increased chance of developing AD and tend to have a lower mean age at onset than noncarriers [3].…”