Model‐free group analysis shows altered BOLD FMRI networks in dementia

Abstract

FMRI research in Alzheimer's disease (AD) and mild cognitive impairment (MCI) typically is aimed at determining regional changes in brain function, most commonly by creating a model of the expected BOLD-response and estimating its magnitude using a general linear model (GLM) analysis. This crucially depends on the suitability of the temporal assumptions of the model and on assumptions about normality of group distributions. Exploratory data analysis techniques such as independent component analysis (ICA) do not depend on these assumptions and are able to detect unknown, yet structured spatiotemporal processes in neuroimaging data. Tensorial probabilistic ICA (T-PICA) is a model free technique that can be used for analyzing multiple subjects and groups, extracting signals of interest (components) in the spatial, temporal, and also subject domain of FMRI data. We applied T-PICA and model-based GLM to study FMRI signal during face encoding in 18 AD, 28 MCI patients, and 41 healthy elderly controls. T-PICA showed activation in regions associated with motor, visual, and cognitive processing, and deactivation in the default mode network. Six networks showed a significantly decreased response in patients. For two networks the T-PICA technique was significantly more sensitive to detect group differences than the standard model-based technique. We conclude that T-PICA is a promising tool to identify and detect differences in (de)activated brain networks in elderly controls and dementia patients. The technique is more sensitive than the commonly applied model-based method. Consistent with other research, we show that networks of activation and deactivation show decreased reactivity in dementia.

Functional brain network centrality is related to APOE genotype in cognitively normal elderly

“…The main result of this study is that APOE‐ε4 carriers show decreased EC in comparison with noncarriers in visual cortical regions; which have previously been demonstrated to be affected in MCI and AD (Rombouts et al., 2009; Sanz‐Arigita et al., 2010). Furthermore, lower EC values were associated with older age and worse visual task performance in noncarriers.…”

Recent developments of functional magnetic resonance imaging research for drug development in Alzheimer's disease

“…In AD, deficits in a cognitive domain are typically linked to metabolic and structural abnormalities in the corresponding brain regions, and the pattern of these abnormalities in AD predicts the manifestation of cognitive loss in this disease. For example, hippocampal atrophy and functional changes is associated with deterioration in memory in AD (Desgranges et al 1998;Desgranges et al 2002;Chetelat et al 2003;Gilboa et al 2005;Celone et al 2006;Rombouts et al 2009;, while metabolic decline of the parietal cortex and posterior cingulate is evident at the earliest stages of the disease as well as associated with visual-spatial A c c e p t e d M a n u s c r i p t impairments (Duara et al 1986;Meguro et al 2001;Alexander et al 2002;Drzezga et al 2003;Ishii et al 2005). However, this association of cognitive deficits with regional brain pathology is limited because cognitive functions are usually not underpinned by a single cortical region but by a network of regions forming so called 'degenerate systems' which can fully or partially sustain the cognitive function even when one region or part of the network has been damaged (Noppeney et al 2004).…”

Toward discovery science of human brain function

“…Recent R-fMRI studies have identified putative biomarkers of neuropsychiatric illness (12,(15)(16)(17)(18), provided insight into the development of functional networks in the maturing and aging brain (19)(20)(21)(22), demonstrated a shared intrinsic functional architecture (23) between humans and nonhuman primates (24,25), and delineated the effects of sleep (26), anesthesia (27), and pharmacologic agents on R-fMRI measures (28,29). Given the many sources of variability inherent in fMRI, the remaining challenge is to demonstrate the feasibility and utility of adopting a high-throughput model for RfMRI, commensurate with the scale used by human genetics studies to have the power to detect both single gene and combinatorial genetic and environmental effects on complex phenotypes.…”