Evidence map›Paper›PMID 35274787›Full record

ArticleHuman brain mapping2022

A novel method for estimating connectivity-based parcellation of the human brain from diffusion MRI: Application to an aging cohort.

Ana Coelho, Ricardo Magalhães, Pedro S Moreira, Liliana Amorim, Carlos Portugal-Nunes, Teresa Castanho, Nadine Correia Santos, Nuno Sousa, Henrique M Fernandes

Abstract read
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Article in Human brain mapping, 2022. 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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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Ana CoelhoLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.ORCID 0000-0001-8489-5750
Ricardo MagalhãesLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
Pedro S MoreiraLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
Liliana AmorimLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
Carlos Portugal-NunesLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
Teresa CastanhoLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
Nadine Correia SantosLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
Nuno SousaLife and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.
Henrique M FernandesCenter for Music in the Brain (MIB), Aarhus University, Aarhus, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Connectivity-based parcellation (CBP) methods are used to define homogenous and biologically meaningful parcels or nodes-the foundations of brain network fingerprinting-by grouping voxels with similar patterns of brain connectivity. However, we still lack a gold standard method and the use of CBPs to study the aging brain remains scarce. Our study proposes a novel CBP method from diffusion MRI data and shows its potential to produce a more accurate characterization of the longitudinal alterations in brain network topology occurring in aging. For this, we constructed whole-brain connectivity maps from diffusion MRI data of two datasets: an aging cohort evaluated at two timepoints (mean interval time: 52.8 ± 7.24 months) and a normative adult cohort-MGH-HCP. State-of-the-art clustering techniques were used to identify the best performing technique. Furthermore, we developed a new metric (connectivity homogeneity fingerprint [CHF]) to evaluate the success of the final CBP in improving regional/global structural connectivity homogeneity. Our results show that our method successfully generates highly homogeneous parcels, as described by the significantly larger CHF score of the resulting parcellation, when compared to the original. Additionally, we demonstrated that the developed parcellation provides a robust anatomical framework to assess longitudinal changes in the aging brain. Our results reveal that aging is characterized by a reorganization of the brain's structural network involving the decrease of intra-hemispheric, increase of inter-hemispheric connectivity, and topological rearrangement. Overall, this study proposes a new methodology to perform accurate and robust evaluations of CBP of the human brain.

Indexed as

BrainDiffusion Magnetic Resonance ImagingAdultAgingBrain MappingCohort StudiesHumansMagnetic Resonance Imagingagingbrain parcellationclusteringdiffusion MRInetwork neurosciencestructural connectivity

Identifiers

PMID35274787
PMCPMC9057102

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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.