Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer’s Disease and Behavioral Variant Fronto-Temporal Dementia

Abstract

Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer’s disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort. The study population comprised 202 healthy elderly subjects, 213 with bvFTD and 213 with AD. We report that both CT and SA network architecture can be described in terms of highly correlated networks whose positive and inverse links map onto the intrinsic modular organization of the four cortical lobes. The topology of the disturbance in structural network is different in the two disease conditions, and both are different from normal aging. The changes from normal are global in character and are not restricted to fronto-temporal and temporo-parietal lobes, respectively, in bvFTD and AD, and indicate an increase in both global correlational strength and in particular nonhomologous inter-lobar connectivity defined by inverse correlations. These inverse correlations appear to be adaptive in character, reflecting coordinated increases in CT and SA that may compensate for corresponding impairment in functionally linked nodes. The effects were more pronounced in the cortical thickness atrophy network in bvFTD and in the surface area network in AD. Although lobar modularity is preserved in the context of neurodegenerative disease, the hub-like organization of networks differs both from normal and between the two forms of dementia. This implies that hubs may be secondary features of the connectivity adaptation to neurodegeneration and may not be an intrinsic property of the brain. However, analysis of the topological differences in hub-like organization CT and SA networks, and their underlying positive and negative correlations, may provide a basis for assisting in the differential diagnosis of bvFTD and AD.

Novel Volumetric and Surface-Based Magnetic Resonance Indices of the Aging Brain – Does Male and Female Brain Age in the Same Way?

“…Fractal dimension has been found to correlate with IQ and the number of years of education; thus, it has a significant relationship with intelligence and education ( 10 ). Cortical indices such as CT, GI, SC, and FS have been reported to change in older people and in neurodegenerative diseases ( 11 , 12 ). In healthy adults, age-related thinning is observed across the majority of the cortex but is generally more robust in bilateral frontal cortices, superior temporal regions, and supramarginal gyri ( 13 ).…”

Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions

“…Accurate and reliable skull stripping is important for cortical thickness estimation, since false positive classification of non-brain tissue (e.g., skull, dura, and pial maters) could result in poor estimation of the GM-WM border, which in turn can result in erroneous patterns of cortical thickness. Skull stripping segmentation accuracy is particularly relevant in aging and neurodegenerative populations, where brain atrophy is accompanied by increased CSF volumes and a decreased separation between GM and WM intensities (Seo et al, 2010;Walhout et al, 2015;Yau et al, 2018;Vuksanović et al, 2019;Wilson et al, 2019).…”

“…Cortical thickness quantification obtained from magnetic resonance imaging (MRI) can be used to examine regional variations of the cerebral cortex that have been associated with normal aging and dementia due to neurodegeneration (Fischl and Dale, 2000;Salat et al, 2004;Lerch and Evans, 2005;Raamana et al, 2015;Redolfi et al, 2015). Cortical thinning in specific topographical regions of the brain has been used to accurately determine patterns of neurodegeneration in mild cognitive impairment (MCI) (Raamana et al, 2014), Alzheimer's disease (AD), frontotemporal dementia (FTD) (Du et al, 2007;Lerch et al, 2008;Bakkour et al, 2009;Richards et al, 2009;Cho et al, 2012;Hartikainen et al, 2012;Paternicó et al, 2016;Vuksanović et al, 2019), Parkinson's disease (Uribe et al, 2016;Gao et al, 2018;Yau et al, 2018;Wilson et al, 2019), amyotrophic lateral sclerosis (ALS) (Verstraete et al, 2012;Mezzapesa et al, 2013;Schuster et al, 2014;Walhout et al, 2015), and vascular cognitive impairment (Seo et al, 2010;Kim et al, 2014;Jung et al, 2018).…”

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

“…Approximately 60-70% of dementia is caused by AD. AD is a progressive brain disorder in which memory and cognitive function cause increasing impairment until death (Vuksanović et al, 2019;Feng et al, 2020). To date, there is no pharmacological cure available, except for treatment to manage symptoms.…”