Progressive disgregation of brain networking from normal aging to Alzheimer’s Disease. Independent Component Analysis on FDG-PET data

Abstract

Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between pre-determined regions or nodes. The selective breakdown of brain networks during the progression from the normal aging (NA) to Alzheimer’s Disease (AD) has also been observed. We implemented Independent Component Analysis (ICA) on 18F-FDG-PET data in five groups of subjects with cognitive state ranging from NA to AD dementia, including mild cognitive impairment patients not converting (ncMCI) and converting (MCI) to mild AD dementia, in order to disclose the spatial distribution of the independent components (ICs) in each cognitive state, and their accuracy in discriminating the groups. We could identify spatially distinct ICs in each group with an increasing generation of new local circuits proportional to the severity of the disease. AD-specific ICs appeared from the late MCI stage and could discriminate MCI and AD dementia from ncMCI with an accuracy of 83.5%. There was a progressive disgregation of the intrinsic networks from NA to MCI and AD dementia, in an inversely proportional fashion to conversion time. Functional brain connectivity on FDG-PET has been shown by ICA to be gradually disrupted across progressive states of cognitive severity in AD, which might be implemented at individual level, especially for prognostic purposes, and as a surrogate biomarker in intervention trials.