Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Investigation of the effects of balance exercises on visuospatial skills using EEG brain oscillations.

Cognitive neurodynamics·2026
Same author

Glymphatic clearance as revealed by diffusion tensor imaging along the perivascular space (DTI-ALPS) is associated with Alzheimer's disease neuropathology and periodic rsEEG alpha rhythms in mild cognitive impairment participants.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

Abnormal periodic and aperiodic resting-state electroencephalographic markers in Lewy body and Alzheimer's diseases with cognitive decline.

GeroScience·2026
Same author

Source-space EEG alpha activity reveals brain age gaps due to neurodegeneration and disparity.

Communications biology·2026
Same author

Impaired event-related theta spectral coherence in emotional facial expression processing in neurodegenerative disorders.

Frontiers in human neuroscience·2026
Same author

Feasibility and preliminary effects of a wearable technology-supported multicomponent intervention on self-efficacy and quality of life in family caregivers of people with dementia.

Disability and rehabilitation. Assistive technology·2026
Same journal

Cortical similarity networks in the rat brain: Postnatal development and sensitivity to early life stress.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Restoring oscillatory dynamics in Alzheimer's disease: A laminar whole-brain model of serotonergic psychedelic effects.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Distributed cortical network dynamics of binocular convergent eye movements in humans.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

High-resolution Bayesian Virtual Epileptic Patient using neural field models.

Network neuroscience (Cambridge, Mass.)·2026
See all related articles

Related Experiment Video

Updated: Jun 29, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

Reconstructing brain functional networks through identifiability and deep learning.

Massimiliano Zanin1, Tuba Aktürk2,3, Ebru Yıldırım2

  • 1Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain.

Network Neuroscience (Cambridge, Mass.)
|April 2, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to map brain activity by analyzing how brain region signals change together. The approach reveals distinct functional network differences in Alzheimer's and Parkinson's patients compared to healthy individuals.

Keywords:
Alzheimer’s diseaseDeep learningEEGFunctional networksParkinson’s disease

More Related Videos

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.2K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K

Related Experiment Videos

Last Updated: Jun 29, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.2K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Medical Imaging

Background:

  • Understanding brain dynamics is crucial for diagnosing neurological disorders.
  • Existing methods for functional network reconstruction often rely on assumptions about neural interactions.
  • Identifying unique brain signal patterns can help differentiate between healthy and diseased states.

Purpose of the Study:

  • To propose a novel, assumption-free method for reconstructing brain functional networks.
  • To investigate brain dynamics in Alzheimer's and Parkinson's disease patients using electroencephalography (EEG).
  • To analyze topological differences in functional networks between patients and healthy controls.

Main Methods:

  • Developed a deep learning-based approach to estimate brain region identifiability, inferring functional network connections from signal coparticipation.
  • Applied the method to EEG recordings from Alzheimer's patients, Parkinson's disease patients, and healthy volunteers under resting-state conditions (eyes-open and eyes-closed).
  • Analyzed reconstructed functional networks using standard topological metrics (e.g., clustering coefficient, assortativity) across different frequency bands.

Main Results:

  • Both Alzheimer's and Parkinson's patients exhibited reduced EEG signal identifiability compared to controls.
  • Distinct patterns supporting signal identifiability were observed in patient groups.
  • Functional networks derived from the novel method showed similarities and differences compared to correlation-based networks.
  • Network metrics revealed significant differences between patients and controls, particularly in specific frequency bands and under different resting states (eyes-open/closed).

Conclusions:

  • The proposed deep learning method effectively reconstructs functional brain networks without prior assumptions.
  • The study highlights altered brain dynamics and network topology in Alzheimer's and Parkinson's disease.
  • The findings suggest the potential of this novel approach for neurodegenerative disease research and diagnosis.