Gene E Alexander

Abstract:

Analysis of functional neuroimaging data in the studies of human brain has become very critical in understanding neuro-degenerative diseases such as Alzheimer's disease (AD). The most common approach in AD neuroimaging studies has been of univariate nature, where individual brain regions/voxels are analyzed separately. In many cases these techniques prove to be effective. However, they could not shed light on inter brain region connectivity associated with the brain function or disease of interest. Indeed, human brain is a very complex organ anatomically and the functional interactions between its regions are even more. As a result, there is a need to understand this interdependency or inter-connection of brain regions. There are several existing techniques to address this issue. They include principal component analysis (PCA), PCA based scaled subprofile modeling (SSM), Bayesian network approach and independent component analysis (ICA). In this study, we propose a machine learning technique called "Sparse Inverse Covariance Analysis" to learn the brain region interactivity, with minimal computational cost and appropriate degree of sparsity. Under Gaussian assumption, each element of the inverse covariance matrix represents conditional dependence between the constituent pair of variables, given all other variables. By introducing sparsity constraint, unnecessary/noisy functional dependencies are eliminated by setting the constituent element to zero, resulting into conditional independence between the variable pairs. Using functional FDG-PET data acquired from 49 AD and 67 normal subjects from the Alzheimer's disease neuroimaging initiative (ADNI) project, we evaluate this technique in terms of distinct brain region connectivity pattern in patients with AD compared to that in normal control subjects. It was found that the patients with AD had disconnections that are not present in the normal controls. This different connectivity pattern is potentially usable for clinical diagnosis and for establishing sensitive markers for the disease progression and treatment evaluation. ©2009 IEEE.

PMID: 10331111;Abstract:

Background: Prior studies have indicated abnormal frontal lobes in Down syndrome (DS). The Wisconsin Card Sorting Test (WCST) has been used during functional brain imaging studies to activate the prefrontal cortex. Whether this activation is dependent on successful performance remains unclear. To determine frontal lobe regional cerebral blood flow (rCBF) response in DS and to further understand the effect of performance on rCBF during the WCST, we studied DS adults who perform poorly on this task. Methods: Initial slope (IS), an rCBF index, was measured with the ¹³³Xe inhalation technique during a Numbers Matching Control Task and the WCST. Ten healthy DS subjects (mean age 28.3 years) and 20 sex-matched healthy volunteers (mean age 28.7 years) were examined. Results: Performance of DS subjects was markedly impaired compared to controls. Both DS and control subjects significantly increased prefrontal IS indices compared to the control task during the WCST. Conclusions: Prefrontal activation in DS during the WCST was not related to performance of that task, but may reflect engagement of some components involved in the task, such as effort. Further, these results show that failure to activate prefrontal cortex during WCST in schizophrenia is unlikely to be due to poor performance alone.

PMID: 22348880;PMCID: PMC3325642;Abstract:

We previously introduced a voxel-based, multi-modal application of the partial least square algorithm (MMPLS) to characterize the linkage between patterns in a person's complementary complex datasets without the need to correct for multiple regional comparisons. Here we used it to demonstrate a strong correlation between MMPLS scores to characterize the linkage between the covarying patterns of fluorodeoxyglucose positron emission tomography (FDG PET) measurements of regional glucose metabolism and magnetic resonance imaging (MRI) measurements of regional gray matter associated with apolipoprotein E (APOE) ε4 gene dose (i.e., three levels of genetic risk for late-onset Alzheimer's disease (AD)) in cognitively normal, late-middle-aged persons. Coregistered and spatially normalized FDG PET and MRI images from 70% of the subjects (27 ε4 homozygotes, 36 ε4 heterozygotes and 67 ε4 non-carriers) were used in a hypothesis-generating MMPLS analysis to characterize the covarying pattern of regional gray matter volume and cerebral glucose metabolism most strongly correlated with APOE-ε4 gene dose. Coregistered and spatially normalized FDG PET and MRI images from the remaining 30% of the subjects were used in a hypothesis-testing MMPLS analysis to generate FDG PET-MRI gray matter MMPLS scores blind to their APOE genotype and characterize their relationship to APOE-ε4 gene dose. The hypothesis-generating analysis revealed covarying regional gray matter volume and cerebral glucose metabolism patterns that resembled those in traditional univariate analyses of AD and APOE-ε4 gene dose and PET-MRI scores that were strongly correlated with APOE-ε4 gene dose (p1×10 ^-16). The hypothesis-testing analysis results showed strong correlations between FDG PET-MRI gray matter scores and APOE-ε4 gene dose (p=8.7×10 ^-4). Our findings support the possibility of using the MMPLS to analyze complementary datasets from the same person in the presymptomatic detection and tracking of AD. © 2012 Elsevier Inc..