Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI

Abstract

Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group (“AD, MCI-C, and MCI-NC” or “MCI-C, MCI-NC, and HC”) and four-group (“AD, MCI-C, MCI-NC, and HC”) classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.

Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease

“…These facts drive interest in more sophisticated neuroimaging, such as positron emission tomography-based studies, which are able to image the amyloid and tau proteins (4), and connectomicbased approaches, leveraging imaging studies such as functional magnetic resonance imaging (fMRI) and diffusion tractography imaging (DTI) (5). A growing number of researchers work on the development of personalized, reproducible, non-invasive, and neuroscientifically interpretable biomarkers for early diagnosis or prediction of AD even on the subjective cognition decline (SCD) stage (6)(7)(8), yet most of which is focused on the consistent abnormal connection within the multimodal imaging as the combination with DTI and fMRI (9,10). Given the subtle and often diffuse nature of dementing disorders, machine learningbased approaches provide the most realistic method for complex imaging datasets (11,12).…”

“…It is one of today's most rapidly growing technical fields (13), which performs throughout science including health care (14) such as identification and classification for diseases like AD (15)(16)(17), traffic programming (18), and marketing apps designing (19), which allows us to process largescale, multidimensional, complex datasets in this information explosion of an era. Machine learning-based analysis of connectomic data created from neuroimaging studies in patients AD has been extensively studied in the literature (5,9,12,20,21). Most such efforts utilize a method for modeling features of either DTI and/or fMRI studies, which allow a model to differentiate between some combination of healthy controls, patients with mild cognitive impairment, and those with AD.…”

Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

“…A network science approach helps us to understand the brain as a network through local and distributed processes 1 – 4 . Such an approach has been used in functional human neuroimaging (e.g., functional magnetic resonance imaging, fMRI) studies to understand Alzheimer’s diseases 5 – 9 , epilepsy 10 , 11 and schizophrenia 12 – 14 , and to investigate cognitive functions associated with learning 15 , behavior 16 and task performance 17 , 18 . The relational and causal association of distributed brain regions with various cognitive functions have been mapped to reveal the connectome of the human brain 19 , 20 .…”

Cognitive Deficits and Altered Functional Brain Network Organization in Pediatric Brain Tumor Patients

“…Importantly, rsfMRI, in tandem with network analytic tools, can be used to map the functional network architecture of the brain. Atypical network organization has consistently been identified in patients with brain disorders, including, but not limited to, Alzheimer Disease, Temporal Lobe Epilepsy, and Attention Deficit Hyperactivity Disorder (Fair et al, 2012;Hojjati et al, 2019;Shah et al, 2018).…”