Search results for "RESTING-STATE"
showing 7 items of 17 documents
EEG Resting-State Functional Networks in Amnestic Mild Cognitive Impairment.
2022
Background. Alzheimer’s cognitive-behavioral syndrome is the result of impaired connectivity between nerve cells, due to misfolded proteins, which accumulate and disrupt specific brain networks. Electroencephalography, because of its excellent temporal resolution, is an optimal approach for assessing the communication between functionally related brain regions. O bjective. To detect and compare EEG resting-state networks (RSNs) in patients with amnesic mild cognitive impairment (aMCI), and healthy elderly (HE). Methods. We recruited 125 aMCI patients and 70 healthy elderly subjects. One hundred and twenty seconds of artifact-free EEG data were selected and compared between patients with aM…
Dissociable Functional Brain Networks Associated With Apathy in Subcortical Ischemic Vascular Disease and Alzheimer’s Disease
2021
Few studies have investigated differences in functional connectivity (FC) between patients with subcortical ischemic vascular disease (SIVD) and Alzheimer’s disease (AD), especially in relation to apathy. Therefore, the aim of this study was to compare apathy-related FC changes among patients with SIVD, AD, and cognitively normal subjects. The SIVD group had the highest level of apathy as measured using the Apathy Evaluation Scale-clinician version (AES). Dementia staging, volume of white matter hyperintensities (WMH), and the Beck Depression Inventory were the most significant clinical predictors for apathy. Group-wise comparisons revealed that the SIVD patients had the worst level of “Ini…
Feasibility in routine clinical setting of combined resting-state fMRI and DTI-tractography for surgical planning of brain tumors
2020
Purpose Methods and materials Results Conclusion Personal information and conflict of interest References
Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach
2022
ObjectiveType 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities.MethodsA total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were ana…
Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression
2021
To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscil…
Unsupervised representation learning of spontaneous MEG data with nonlinear ICA
2023
Funding Information: We wish to thank the reviewers and editors for the useful comments to improve the paper a lot. We thank Dr. Hiroshi Morioka for the useful discussion at the beginning of the project. L.P. was funded in part by the European Research Council (No. 678578 ). A.H. was supported by a Fellowship from CIFAR, and the Academy of Finland. The authors acknowledge the computational resources provided by the Aalto Science-IT project, and also wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources. | openaire: EC/H2020/678578/EU//HRMEG Resting-state magnetoencephalography (MEG) data show complex but stru…
Spatial source phase : A new feature for identifying spatial differences based on complex-valued resting-state fMRI data
2019
Spatial source phase, the phase information of spatial maps extracted from functional magnetic resonance imaging (fMRI) data by data‐driven methods such as independent component analysis (ICA), has rarely been studied. While the observed phase has been shown to convey unique brain information, the role of spatial source phase in representing the intrinsic activity of the brain is yet not clear. This study explores the spatial source phase for identifying spatial differences between patients with schizophrenia (SZs) and healthy controls (HCs) using complex‐valued resting‐state fMRI data from 82 individuals. ICA is first applied to preprocess fMRI data, and post‐ICA phase de‐ambiguity and den…