Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory

Khazaee, Ali; Ebrahimzadeh, Ataollah; Babajani‐Feremi, Abbas

doi:10.1016/j.clinph.2015.02.060

Cited by 196 publications

(173 citation statements)

References 70 publications

Supporting

Mentioning

152

Contrasting

Order By: Relevance

“…Toward applied clinical utility, rsfMRI graph statistics analyzed can be combined with machine learning to build models that identify autism [91], Alzheimer’s disease [92, 93], schizophrenia [94] depression [95], relative to controls, in some cases with very high sensitivity and specificity. In general, the opportunities to apply machine learning to any type of neuroimaging data or derived measure are enormous and can provide novel observations [96].…”

Section: Optimism For Clinical Identification Prediction and Translmentioning

confidence: 99%

Graph Theoretic Analysis of Resting State Functional MR Imaging

Medaglia

2017

Neuroimaging Clinics of North America

View full text Add to dashboard Cite

Graph theoretic analyses applied to examine the brain at rest have played a critical role in clarifying the foundations of the brain’s intrinsic and task-related activity. There are many opportunities for clinical scientists to describe and predict dysfunction using a network perspective. This primer describes the theoretical basis and practical application of graph theoretic analysis to resting state functional magnetic resonance imaging data. Major practices, concepts, and findings are concisely reviewed. The theoretical and practical frontiers are highlighted with observations about major avenues for opportunity in theoretical development and clinical translation.

show abstract

Section: Optimism For Clinical Identification Prediction and Translmentioning

confidence: 99%

Graph Theoretic Analysis of Resting State Functional MR Imaging

Medaglia

2017

Neuroimaging Clinics of North America

View full text Add to dashboard Cite

show abstract

“…Brier et al (2013) also explored a decrease in clustering with increment of CDR (clinical dementia rating) and reduced modularity (Brier et al, 2013) Even though many studies presented automatic techniques for the diagnosis of brain disease employing rs-fMRI (Chen et al, 2011;Brier et al, 2012;Zeng et al, 2014), they didn't achieve a good performance because they employed features based on local dynamics ignoring how the different brain areas are functionally connected. Recently, a study appeared that first presented an automatic classification scheme of AD from normal subjects using rs-fcfMRI (Khazaee et al, 2015). Adopting graph theory with 90 distinct regions using the automated anatomical labeling atlas, nodal network features, feature extraction strategies and a classifier, they succeeded to classify patients with AD from healthy control ones with accuracy of 100%.…”

Section: See Article Pages Xxx-xxxmentioning

confidence: 99%

Quantifying the predictive power of resting-state functional connectivity (rs-fc) fMRI for identifying patients with Alzheimer’s disease (AD)

Dimitriadis

2015

Clinical Neurophysiology

View full text Add to dashboard Cite

“…1c). The extent of SW organization in brain networks is heritable 115,116 , and predictive of AD status 117,118 and progression 119 . Moreover, changes to the SW balance can be detected prior to neurodegeneration or cognitive decline in people with elevated brain amyloid levels 120 .…”

Section: Insights From Ad Network Neuroimagingmentioning

confidence: 99%

Genetic variants in Alzheimer disease — molecular and brain network approaches

et al. 2016

View full text Add to dashboard Cite

Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care for AD. However, due to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extracting actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effect of LOAD-associated genetic variants. We then discuss emerging combinations of omic data types in multiscale models, which provide a more comprehensive representation of the effect of LOAD-associated genetic variants at multiple biophysical scales. Further, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.

show abstract

Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory

Cited by 196 publications

References 70 publications

Graph Theoretic Analysis of Resting State Functional MR Imaging

Graph Theoretic Analysis of Resting State Functional MR Imaging

Quantifying the predictive power of resting-state functional connectivity (rs-fc) fMRI for identifying patients with Alzheimer’s disease (AD)

Genetic variants in Alzheimer disease — molecular and brain network approaches

Contact Info

Product

Resources

About