Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

Abstract

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

“…HBOA follows the honey badger's behavior to catch the prey, and this optimization a rithm has two main steps (digging and honey) for resolving the optimization probl In the honey step, the honey badgers follow honey birds for determining the beehiv the digging step, the prey is determined based on the smelling ability of honey bad [38,39]. First, we initialized the agents, as shown in Equation (8).…”

“…Currently, several medical image acquisition modalities are invented on the basis of physical principles. Specifically, several non-invasive and invasive medical imaging methods are utilized for diagnosing AD-like fMRI, Magnetic Resonance Imaging (MRI), structural Magnetic Resonance Imaging (sMRI), cerebrospinal fluids, computerized tomography, and Positron Emission Tomography (PET) [ 7 , 8 , 9 ]. The medical imaging methods assist clinicians and physicians in improving the healthcare systems for AD.…”

Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

“…As a potential biomarker, the functional connectivity (FC) matrix has attracted much attention in many studies [6]. In recent years, strategies for dementia diagnosis based on deep learning methods have achieved good results over traditional machine learning methods because deep learning models can extract the differential feature representation hierarchically and can naturally combine features of different levels together [7]. Most domestic and foreign research is based on deep learning for horizontal classification diagnosis, such as normal control groups (NC) and AD dichotomies or NC, MCI, and AD tri-classification studies [8].…”

Entropy in Brain Networks

“…They propose a new methodology based on analyzing the distribution of the weights of the brain network from EEG after source reconstruction to improve understanding of the neural mechanisms associated with the progression of Alzheimer disease (AD). Wang et al [ 8 ] also want to delve into the AD progression from the functional connectivity point of view. Making use of functional resonance imaging (fMRI) and network entropy, microcanonical and canonical ensembles are applied to describe the altered macroscopic properties of the brain network due to AD.…”