Influence of individual's age on the characteristics of brain effective connectivity.

Nakisa Nourzadegan, Sepideh Baghernezhad, Mohammad Reza Daliri

Abstract read

In one paragraph

Article in GeroScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 · What the graph read from it

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1 citing paper in PubMed.

Sensory modality shapes post-learning functional connectivity.iScience · 2026
Article

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5 · Who and what money

Authors and funding

3 authors.

Nakisa NourzadeganNeuroscience & Neuroengineering Research Laboratory, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.

Sepideh BaghernezhadNeuroscience & Neuroengineering Research Laboratory, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.

Mohammad Reza DaliriNeuroscience & Neuroengineering Research Laboratory, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran. daliri@iust.ac.ir.ORCID 0000-0001-9241-8751

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014

$34.7M

NIMH NIH HHS U54 MH091657

6 · The paper itself

Abstract

Given the increasing number of older adults in society, there is a growing need for studies on changes in the aging brain. The aim of this research is to investigate the effective connectivity of different age groups using resting-state functional magnetic resonance imaging (fMRI) and graph theory. By examining connectivity in different age groups, a better understanding of age-related changes can be achieved. Lifespan pilot data from the Human Connectome Project (HCP) were used to examine dynamic effective connectivity (dEC) changes across different age groups. The Granger causality method with time windowing was employed to calculate dEC. After extracting graph measures, statistical analyses were performed to compare the age groups. Support vector machine and decision tree classifiers were used to classify the different age groups based on the extracted graph measures. Based on the obtained results, it can be concluded that there are significant differences in the effective connectivity among the three age groups. Statistical analyses revealed disassortativity. The global efficiency exhibited a decreasing trend, and the transitivity measure showed an increasing trend with the advancing age. The decision tree classifier showed an accuracy of

Indexed as

AgingBrainConnectomeAdolescentAdultAgedAged, 80 and overAge FactorsFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedPilot ProjectsSupport Vector MachineYoung AdultAge-related changeDynamic effective connectivityFMRI dataGranger causalityGraph theoryHuman Connectome ProjectMachine learning techniques

Identifiers

PMID39549197

DOI10.3389/fninf.2018.00007

PMCPMC11978603

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.