Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex.

Decreased Bilateral FDG-PET Uptake and Inter-Hemispheric Connectivity in Multi-Domain Amnestic Mild Cognitive Impairment Patients: A Preliminary Study

“…Second, in accord with the inter-hemispheric functional connectivity results, correspondingly, MD-aMCI patients demonstrated selective mid-posterior CC degeneration. However, CC volume only identified the main inter-hemispheric connective tracts, rather than illustrating the precise definition of “inter-hemispheric structural connection.” Therefore, future DTI studies by using tractography or network analysis are needed to provide more detailed information about inter-hemispheric white matter pathways in aMCI patients ( Cavanna and Trimble, 2006 ; Rasero et al, 2017 ; Tucholka et al, 2018 ). In the current study, based on the ADNI 2 database, we noticed that subjects do not have both the DTI and the rsfMRI data.…”

“…Anatomically, CC acts as the most robust commissural white matter bundle to maintain the functional connectivity between the hemispheres ( Roland et al, 2017 ). Specifically, some studies demonstrated that the AD and MCI patients exhibit the CC shape change, atrophy, and impaired diffusivity indices impairment, especially in the posterior part ( Janowsky et al, 1996 ; Di Paola et al, 2010 ; Ardekani et al, 2014 ; Rasero et al, 2017 ). Subsequently, some studies further demonstrated patients with MD-aMCI, but not SD-aMCI, have reduced mean diffusivity (MD) in the whole CC compared to controls; moreover, decrease of MD in the CC body is associated with decreased general cognition and executive ability ( Li et al, 2013 ).…”

Connectome-wide network analysis of white matter connectivity in Alzheimer's disease

“…Multivariate distance matrix regression (MDMR) proposed by (Shehzad et al, 2014) allows exploring connectivity-phenotype relationships without any a priori information or parameter selection. This data-driven statistical approach has been recently employed to examine the overall pattern of functional or grey matter structural connectivity associated with various clinical phenotypes, such as anhedonia (Sharma et al, 2017), psychosis-spectrum symptoms (Satterthwaite et al, 2015), and AD (Rasero et al, 2017b). Inspired by this statistical framework, we applied MDMR to compare WM structural connectivity patterns among cognitively normal (CN) subjects, stable MCI (sMCI), MCI converting to AD (cMCI) patients, and AD patients.…”

Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease

“…Many prospective studies have focused on the progression from MCI to AD (Aguilar et al, 2013; Cui et al, 2018, 2011; Rasero et al, 2017). For instance, in a recent MEG study of our group found that the increase in phase synchronization between the right anterior cingulate and temporo-occipital areas together with the immediate recall score in MCI patients predicted the conversion to AD with an accuracy of 89.9% (López et al, 2014a).…”