Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

Zhou, Zhen; Li, Hongming; Srinivasan, Dhivya; Abdulkadir, Ahmed; Nasrallah, Ilya M.; Wen, Junhao; Doshi, Jimit; Erus, Güray; Mamourian, Elizabeth; Bryan, R. Nick; Wolk, David A.; Beason‐Held, Lori L.; Resnick, Susan M.; Satterthwaite, Theodore D.; Davatzikos, Christos; Shou, Haochang; Fan, Yong

doi:10.1016/j.neuroimage.2023.119911

Cited by 9 publications

(16 citation statements)

References 98 publications

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“…Interestingly, we also identified increased WN‐FC of the VIS network with age. While it has been previously reported that this network is among the least influenced by aging (Doucet et al, 2021), other studies have also reported a positive association with aging (Seidler et al, 2015; Zhou et al, 2023; Zonneveld et al, 2019). We believe that such differences may be related to a variety of factors such as different brain atlas parcellations (Doucet et al, 2019) and sample size and diversity (Marek et al, 2022), or different preprocessing steps (Power et al, 2012).…”

Section: Discussionmentioning

confidence: 93%

Resting state network connectivity alterations in HIV: Parallels with aging

Lew

McCusker

Bares³

et al. 2023

Human Brain Mapping

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The increasing incidence of age‐related comorbidities in people with HIV (PWH) has led to accelerated aging theories. Functional neuroimaging research, including functional connectivity (FC) using resting‐state functional magnetic resonance imaging (rs‐fMRI), has identified neural aberrations related to HIV infection. Yet little is known about the relationship between aging and resting‐state FC in PWH. This study included 86 virally suppressed PWH and 99 demographically matched controls spanning 22–72 years old who underwent rs‐fMRI. The independent and interactive effects of HIV and aging on FC were investigated both within‐ and between‐network using a 7‐network atlas. The relationship between HIV‐related cognitive deficits and FC was also examined. We also conducted network‐based statistical analyses using a brain anatomical atlas (n = 512 regions) to ensure similar results across independent approaches. We found independent effects of age and HIV in between‐network FC. The age‐related increases in FC were widespread, while PWH displayed further increases above and beyond aging, particularly between‐network FC of the default‐mode and executive control networks. The results were overall similar using the regional approach. Since both HIV infection and aging are associated with independent increases in between‐network FC, HIV infection may be associated with a reorganization of the major brain networks and their functional interactions in a manner similar to aging.

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Section: Discussionmentioning

confidence: 93%

Resting state network connectivity alterations in HIV: Parallels with aging

Lew

McCusker

Bares³

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…Moreover, computational log files are generated to store information of the fMRI dataset and parameters used for the computation, as well as intermediate results in folders named as “Data_Input”, “FN_Computation”, “Group_FN”, and “Personalized_FN”, respectively. FNs and their corresponding time courses are stored in separate files, and based on the time courses, functional connectivity (FC) measures can be computed as Pearson correlation values (Zhou et al, 2023). QC results are stored in a folder named “Quality_Control”.…”

Section: Methodsmentioning

confidence: 99%

“…In the first application, we tested pNet on volumetric fMRI data from the UK Biobank dataset (Littlejohns et al, 2020). We loaded two sets of precomputed group-level FNs (k=17 and 21) from our existing multi-scale FN study (Zhou et al, 2023) and another study (Miller et al, 2016) for testing the MATLAB version. For this dataset, we chose a small subset (10 subjects) to calculate personalized FNs, thereby demonstrating the ease-of-use of the GUI-based pNet and convenience for visual examinations and comparisons.…”

Section: Methodsmentioning

confidence: 99%

“…We adopted a subset of the UK Biobank (UKBB) dataset, which passed quality control in our previous study (1571 subjects, 695 males, ages 45-79, 420 volumes per scan) (Zhou et al, 2023). A standardized preprocessing pipeline was used (Alfaro-Almagro et al, 2018).…”

Section: Application 1: Utilizing Precomputed Group-level Fns To Obta...mentioning

confidence: 99%

“…The toolbox comes with two sets of precomputed group-level FNs (k=17), computed with SR-NMF based on the HCP young adult dataset and a subset of UKBB from the iSTAGING study (Zhou et al, 2023), respectively. Specifically, the HCP derived group-level FNs are in surface format, and the UKBB derived ones are in volumetric format.…”

Section: Toolbox Validationmentioning

confidence: 99%

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pNet: A toolbox for personalized functional networks modeling

Ma,

Li,

Zhou

et al. 2024

Preprint

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Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet.

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Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Yu,

Cui,

Haas

et al. 2024

Human Brain Mapping

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Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics.

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Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

Cited by 9 publications

References 98 publications

Resting state network connectivity alterations in HIV: Parallels with aging

Resting state network connectivity alterations in HIV: Parallels with aging

pNet: A toolbox for personalized functional networks modeling

Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

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