Dynamic Functional Connectivity Fading Analysis and Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Normal Control Subjects based on Resting-State fMRI Data

AbstractIt is evident the need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI). MCI patients have a high risk of developing Alzheimer’s disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings about the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys)function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings.The activity of different brain rhythms {δ, θ, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine ninety anatomical regions of interest (ROIs). A dynamic functional connectivity graph (DFCG) was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and also cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 Mild Cognitive Impairment (MCI) patients and 20 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments that further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. Estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (nμstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation.We succeeded a high classification accuracy on the blind dataset (85 %) which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could manipulate properly the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.

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P3-421: DISRUPTED FUNCTIONAL CONNECTIVITY OF CORNU AMMONIS SUBREGIONS IN MILD COGNITIVE IMPAIRMENT: A LONGITUDINAL RESTING-STATE FMRI STUDY

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.1784 ◽

2006 ◽

Vol 14 (7S_Part_24) ◽

pp. P1269-P1269 ◽

Cited By ~ 1

Author(s):

Hui Li ◽

Kuncheng Li

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Fmri Study ◽

Cornu Ammonis

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Dynamic functional connectivity changes associated with dementia in Parkinson’s disease

Brain ◽

10.1093/brain/awz192 ◽

2019 ◽

Vol 142 (9) ◽

pp. 2860-2872 ◽

Cited By ~ 19

Author(s):

Eleonora Fiorenzato ◽

Antonio P Strafella ◽

Jinhee Kim ◽

Roberta Schifano ◽

Luca Weis ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Functional Connectivity ◽

Parkinson’S Disease Dementia ◽

Dynamic Functional Connectivity ◽

Control Subjects ◽

Healthy Control ◽

Normal Cognition

AbstractDynamic functional connectivity captures temporal variations of functional connectivity during MRI acquisition and it may be a suitable method to detect cognitive changes in Parkinson’s disease. In this study, we evaluated 118 patients with Parkinson’s disease matched for age, sex and education with 35 healthy control subjects. Patients with Parkinson’s disease were classified with normal cognition (n = 52), mild cognitive impairment (n = 46), and dementia (n = 20) based on an extensive neuropsychological evaluation. Resting state functional MRI and a sliding-window approach were used to study the dynamic functional connectivity. Dynamic analysis suggested two distinct connectivity ‘States’ across the entire group: a more frequent, segregated brain state characterized by the predominance of within-network connections, State I, and a less frequent, integrated state with strongly connected functional internetwork components, State II. In Parkinson’s disease, State I occurred 13.89% more often than in healthy control subjects, paralleled by a proportional reduction of State II. Parkinson’s disease subgroups analyses showed the segregated state occurred more frequently in Parkinson’s disease dementia than in mild cognitive impairment and normal cognition groups. Further, patients with Parkinson’s disease dementia dwelled significantly longer in the segregated State I, and showed a significant lower number of transitions to the strongly interconnected State II compared to the other subgroups. Our study indicates that dementia in Parkinson’s disease is characterized by altered temporal properties in dynamic connectivity. In addition, our results show that increased dwell time in the segregated state and reduced number of transitions between states are associated with presence of dementia in Parkinson’s disease. Further studies on dynamic functional connectivity changes could help to better understand the progressive dysfunction of networks between Parkinson’s disease cognitive states.

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