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Li T, Chen J, Zhao B, Chen H, Yuan C, Garden GA, Giovanello KS, Wu G, Zhu H. The Interaction Effects of Age, Sex, APOE and Common Health Risk Factors on Human Brain Functions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.05.24311482. [PMID: 39148839 PMCID: PMC11326347 DOI: 10.1101/2024.08.05.24311482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Recent studies have shed light on the complex nonlinear changes in brain functions across the lifespan, demonstrating the variability in the individual cognitive and neural development during aging. This variability is influenced by factors such as sex, age, genetics, and modifiable health risk factors (MHRFs), which collectively shape unique patterns of brain functional connectivities (FCs) across different regions. However, their joint effects and underlying mechanisms remain unclear. We conduct a comprehensive analysis to jointly examine the association of common risk factors with brain functional measures, using data from 36,630 UK Biobank participants aged 44-81. Participants were assessed for age, sex, Apolipoprotein E ( APOE ) genotypes, ten common MHRFs, and brain FCs measured via resting-state functional magnetic resonance imaging. Using the fine-grained HCP-MMP parcellation and Ji-12 network atlases, we identified 91 associations with network functional connectivity (NFC) and 102 associations with network edge strength (NES) measures. Hypertension, BMI, and education emerged as the top three influential factors across networks. Notably, a negative interaction between sex and APOE-ε4 ( APOE4 ) genotype was observed, with male APOE4 carriers showing greater reductions in NFC between the cingulo- opercular (CON) and posterior multimodal (PMN) networks. Additionally, a negative age-BMI interaction on NES between the visual and dorsal attention (DAN) networks suggested that higher BMI accelerates the decline in visual-DAN connectivity. A positive age-hypertension interaction between the frontoparietal (FPN) and default mode (DMN) networks indicated a more rapid decrease in functional segregation associated with hypertension. We also identified sex- education interactions, showing more pronounced positive effects on CON-FPN networks in females and PMN-DMN networks in males. Further interactions involving sex and other MHRFs, such as smoking, alcohol consumption, diabetes, and BMI, revealed that smoking, alcohol, and BMI had more detrimental effects in males, while diabetes had a more pronounced negative impact in females within specific networks. These findings underscore the necessity of jointly considering sex, age, genetic factors, and MHRFs to accurately delineate the multifactorial alterations in the FCs during brain aging.
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Criado-Marrero M, Ravi S, Bhaskar E, Barroso D, Pizzi MA, Williams L, Wellington CL, Febo M, Abisambra JF. Age dictates brain functional connectivity and axonal integrity following repetitive mild traumatic brain injuries in mice. Neuroimage 2024; 298:120764. [PMID: 39089604 DOI: 10.1016/j.neuroimage.2024.120764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/08/2024] [Accepted: 07/29/2024] [Indexed: 08/04/2024] Open
Abstract
Traumatic brain injuries (TBI) present a major public health challenge, demanding an in-depth understanding of age-specific symptoms and risk factors. Aging not only significantly influences brain function and plasticity but also elevates the risk of hospitalizations and death following TBIs. Repetitive mild TBIs (rmTBI) compound these issues, resulting in cumulative and long-term brain damage in the brain. In this study, we investigate the impact of age on brain network changes and white matter properties following rmTBI by employing a multi-modal approach that integrates resting-state functional magnetic resonance imaging (rsfMRI), graph theory analysis, diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI). Our hypothesis is that the effects of rmTBI are worsened in aged animals, with this group showing more pronounced alterations in brain connectivity and white matter structure. Utilizing the closed-head impact model of engineered rotational acceleration (CHIMERA) model, we conducted rmTBIs or sham (control) procedures on young (2.5-3-months-old) and aged (22-months-old) male and female mice to model high-risk groups. Functional and structural imaging unveiled age-related reductions in communication efficiency between brain regions, while injuries induced opposhigh-risking effects on the small-world index across age groups, influencing network segregation. Functional connectivity analysis also identified alterations in 79 out of 148 brain regions by age, treatment (sham vs. rmTBI), or their interaction. Injuries exerted pronounced effects on sensory integration areas, including insular and motor cortices. Age-related disruptions in white matter integrity were observed, indicating alterations in various diffusion directions (mean diffusivity, radial diffusivity, axial diffusivity, and fractional anisotropy) and density neurite properties (dispersion index, intracellular and isotropic volume fraction). Neuroinflammation, assessed through Iba-1 and GFAP markers, correlated with higher dispersion in the optic tract, suggesting a neuroinflammatory response in injured aged animals compared to sham aged. These findings offer insight into the interplay between age, injuries, and brain connectivity, shedding light on the long-term consequences of rmTBI.
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Affiliation(s)
- Marangelie Criado-Marrero
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, University of Florida, Gainesville, FL 32610, USA; McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Sakthivel Ravi
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, University of Florida, Gainesville, FL 32610, USA; McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Ekta Bhaskar
- Department of Neuroscience, University of Florida, Gainesville, FL 32610, USA; Department of Computer of Information Science and Engineering (CISE), University of Florida, Gainesville, FL 32610, USA
| | - Daylin Barroso
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, University of Florida, Gainesville, FL 32610, USA; McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Michael A Pizzi
- Department of Neuroscience, University of Florida, Gainesville, FL 32610, USA; McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA; Brain Injury Rehabilitation and Neuroresilience (BRAIN) Center University of Florida, Gainesville, FL 32610, USA; Department of Neurology, University of Florida, Gainesville, FL 32610, USA
| | - Lakiesha Williams
- J. Crayton Pruitt Family Department of Biomedical Engineering, Gainesville, FL 32610, USA
| | - Cheryl L Wellington
- Department of Pathology and Laboratory Medicine, Djavad Mowafaghian Center for Brain Health, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Marcelo Febo
- McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA; Department of Psychiatry, University of Florida, Gainesville, FL 32610, USA; Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL 32610, USA
| | - Jose Francisco Abisambra
- Center for Translational Research in Neurodegenerative Disease (CTRND), University of Florida, Gainesville, FL 32610, USA; Department of Neuroscience, University of Florida, Gainesville, FL 32610, USA; McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA; Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL 32610, USA; Brain Injury Rehabilitation and Neuroresilience (BRAIN) Center University of Florida, Gainesville, FL 32610, USA.
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Shankar A, Tanner JC, Mao T, Betzel RF, Prakash RS. Edge-Community Entropy Is a Novel Neural Correlate of Aging and Moderator of Fluid Cognition. J Neurosci 2024; 44:e1701232024. [PMID: 38719449 PMCID: PMC11209649 DOI: 10.1523/jneurosci.1701-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 02/28/2024] [Accepted: 03/27/2024] [Indexed: 06/21/2024] Open
Abstract
Decreased neuronal specificity of the brain in response to cognitive demands (i.e., neural dedifferentiation) has been implicated in age-related cognitive decline. Investigations into functional connectivity analogs of these processes have focused primarily on measuring segregation of nonoverlapping networks at rest. Here, we used an edge-centric network approach to derive entropy, a measure of specialization, from spatially overlapping communities during cognitive task fMRI. Using Human Connectome Project Lifespan data (713 participants, 36-100 years old, 55.7% female), we characterized a pattern of nodal despecialization differentially affecting the medial temporal lobe and limbic, visual, and subcortical systems. At the whole-brain level, global entropy moderated declines in fluid cognition across the lifespan and uniquely covaried with age when controlling for the network segregation metric modularity. Importantly, relationships between both metrics (entropy and modularity) and fluid cognition were age dependent, although entropy's relationship with cognition was specific to older adults. These results suggest entropy is a potentially important metric for examining how neurological processes in aging affect functional specialization at the nodal, network, and whole-brain level.
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Affiliation(s)
- Anita Shankar
- Department of Psychology, The Ohio State University, Columbus, Ohio 43210
| | - Jacob C Tanner
- Cognitive Science Program, Indiana University, Bloomington, Indiana 47401
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47401
| | - Tianrui Mao
- Department of Psychology, The Ohio State University, Columbus, Ohio 43210
| | - Richard F Betzel
- Cognitive Science Program, Indiana University, Bloomington, Indiana 47401
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47401
- Program in Neuroscience, Indiana University, Bloomington, Indiana 47401
- Network Science Institute, Indiana University, Bloomington, Indiana 47401
| | - Ruchika S Prakash
- Department of Psychology, The Ohio State University, Columbus, Ohio 43210
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio 43210
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Pan Y, Bi C, Kochunov P, Shardell M, Smith JC, McCoy RG, Ye Z, Yu J, Lu T, Yang Y, Lee H, Liu S, Gao S, Ma Y, Li Y, Chen C, Ma T, Wang Z, Nichols T, Hong LE, Chen S. Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594743. [PMID: 38798606 PMCID: PMC11118564 DOI: 10.1101/2024.05.17.594743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.
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Affiliation(s)
- Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Chuan Bi
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Peter Kochunov
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America
| | - Michelle Shardell
- Department of Epidemiology and Public Health and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - J. Carson Smith
- Department of Kinesiology, University of Maryland, College Park, Maryland, United States of America
| | - Rozalina G. McCoy
- Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Jiaao Yu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Tong Lu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Yifan Yang
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Hwiyoung Lee
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yiran Li
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Ze Wang
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Thomas Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - L. Elliot Hong
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
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Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
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Golestani AM, Chen JJ. Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging. Front Neurosci 2024; 18:1223230. [PMID: 38379761 PMCID: PMC10876882 DOI: 10.3389/fnins.2024.1223230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/17/2024] [Indexed: 02/22/2024] Open
Abstract
Introduction Physiological nuisance contributions by cardiac and respiratory signals have a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution. Methods In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on the consequence of changes in low-frequency, cardiac and respiratory signal power, as well as age-related differences in terms of functional connectivity (fcMRI). Results Our results confirm that the ICA-AROMA and GSR removed the most physiological noise but also more low-frequency signals. These methods are also associated with substantially lower age-related fcMRI differences. On the other hand, aCompCor and tCompCor appear to be better at removing high-frequency physiological signals but not low-frequency signal power. These methods are also associated with relatively higher age-related fcMRI differences, whether driven by neuronal signal or residual artifact. These results were reproduced in data downsampled to represent conventional fMRI sampling frequency. Lastly, methods differ in performance depending on the age group. Discussion While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also enhances the understanding of different denoising methods in broader fcMRI applications.
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Affiliation(s)
- Ali M. Golestani
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - J. Jean Chen
- Rotman Research Institute at Baycrest, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Park CH, Durand-Ruel M, Moyne M, Morishita T, Hummel FC. Brain connectome correlates of short-term motor learning in healthy older subjects. Cortex 2024; 171:247-256. [PMID: 38043242 DOI: 10.1016/j.cortex.2023.09.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 03/28/2023] [Accepted: 09/25/2023] [Indexed: 12/05/2023]
Abstract
The motor learning process entails plastic changes in the brain, especially in brain network reconfigurations. In the current study, we sought to characterize motor learning by determining changes in the coupling behaviour between the brain functional and structural connectomes on a short timescale. 39 older subjects (age: mean (SD) = 69.7 (4.7) years, men:women = 15:24) were trained on a visually guided sequential hand grip learning task. The brain structural and functional connectomes were constructed from diffusion-weighted MRI and resting-state functional MRI, respectively. The association of motor learning ability with changes in network topology of the brain functional connectome and changes in the correspondence between the brain structural and functional connectomes were assessed. Motor learning ability was related to decreased efficiency and increased modularity in the visual, somatomotor, and frontoparietal networks of the brain functional connectome. Between the brain structural and functional connectomes, reduced correspondence in the visual, ventral attention, and frontoparietal networks as well as the whole-brain network was related to motor learning ability. In addition, structure-function correspondence in the dorsal attention, ventral attention, and frontoparietal networks before motor learning was predictive of motor learning ability. These findings indicate that, in the view of brain connectome changes, short-term motor learning is represented by a detachment of the brain functional from the brain structural connectome. The structure-function uncoupling accompanied by the enhanced segregation into modular structures over the core functional networks involved in the learning process may suggest that facilitation of functional flexibility is associated with successful motor learning.
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Affiliation(s)
- Chang-Hyun Park
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland
| | - Manon Durand-Ruel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland
| | - Maëva Moyne
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland; Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland
| | - Takuya Morishita
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne EPFL Valais, Clinique Romande de Réadaptation Sion, Switzerland; Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland.
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Criado-Marrero M, Ravi S, Bhaskar E, Barroso D, Pizzi MA, Williams L, Wellington CL, Febo M, Abisambra JF. Age dictates brain functional connectivity and axonal integrity following repetitive mild traumatic brain injuries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.25.577316. [PMID: 38328104 PMCID: PMC10849649 DOI: 10.1101/2024.01.25.577316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Traumatic brain injuries (TBI) present a major public health challenge, demanding an in-depth understanding of age-specific signs and vulnerabilities. Aging not only significantly influences brain function and plasticity but also elevates the risk of hospitalizations and death following repetitive mild traumatic brain injuries (rmTBIs). In this study, we investigate the impact of age on brain network changes and white matter properties following rmTBI employing a multi-modal approach that integrates resting-state functional magnetic resonance imaging (rsfMRI), graph theory analysis, diffusion tensor imaging (DTI), and Neurite Orientation Dispersion and Density Imaging (NODDI). Utilizing the CHIMERA model, we conducted rmTBIs or sham (control) procedures on young (2.5-3 months old) and aged (22-month-old) male and female mice to model high risk groups. Functional and structural imaging unveiled age-related reductions in communication efficiency between brain regions, while injuries induced opposing effects on the small-world index across age groups, influencing network segregation. Functional connectivity analysis also identified alterations in 79 out of 148 brain regions by age, treatment (sham vs. rmTBI), or their interaction. Injuries exerted pronounced effects on sensory integration areas, including insular and motor cortices. Age-related disruptions in white matter integrity were observed, indicating alterations in various diffusion directions (mean, radial, axial diffusivity, fractional anisotropy) and density neurite properties (dispersion index, intracellular and isotropic volume fraction). Inflammation, assessed through Iba-1 and GFAP markers, correlated with higher dispersion in the optic tract, suggesting a neuroinflammatory response in aged animals. These findings provide a comprehensive understanding of the intricate interplay between age, injuries, and brain connectivity, shedding light on the long-term consequences of rmTBIs.
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9
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Chaudhary S, Hu S, Hu K, Dominguez JC, Chao HH, Li CSR. Sex differences in the effects of trait anxiety and age on resting-state functional connectivities of the amygdala. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2023; 14:100646. [PMID: 38105798 PMCID: PMC10723810 DOI: 10.1016/j.jadr.2023.100646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023] Open
Abstract
Background Numerous studies characterized how resting-state functional connectivities (rsFCs) of the amygdala were disrupted in emotional disorders and varied with emotional traits, including anxiety. With trait anxiety known to diminish with age, a critical issue concerns disambiguating the effects of age and anxiety on amygdala rsFCs in studying the neural bases of individual differences in anxiety. Methods Two-hundred adults (83 women) 19-85 years of age underwent fMRI and assessment for trait anxiety. Amygdala rsFC correlates were identified using multiple regression with age and anxiety in the same model for all and separately in men and women. The rsFC correlates were examined for age-anxiety interaction. Results Anxiety was negatively correlated with amygdala-temporooccipital gyri rsFC in all and in men alone. In women, amgydala rsFC with the thalamus/pallidum, angular/supramarginal gyri, inferior temporal gyrus, and posterior insula correlated positively and rsFC with calcarine cortex and caudate correlated negatively with anxiety. We also observed sex differences in age correlation of amgydala-posterior cingulate cortex/precuneus and -insula/temporoparietal rsFCs, with stronger associations in women. In women alone, anxiety and age interacted to determine amygdala rsFC with the thalamus/pallidum, calcarine cortex, and caudate, with older age associated with stronger correlation between anxiety and the rsFCs. Limitations The findings need to be validated in an independent sample and further explored using task-based data. Conclusion Highlighting anxiety- and age- specific as well as interacting correlates of amygdala rsFCs and sex differences in the correlates, the findings may shed light on the neural markers of anxiety.
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Affiliation(s)
- Shefali Chaudhary
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Sien Hu
- Department of Psychology, State University of New York at Oswego, Oswego, NY 13126, USA
| | - Kesong Hu
- Department of Psychology, University of Arkansas, Little Rock, AR 72204, USA
| | | | - Herta H. Chao
- VA Connecticut Healthcare System, West Haven, CT 06516, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Chiang-Shan R. Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
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Jünemann K, Engels A, Marie D, Worschech F, Scholz DS, Grouiller F, Kliegel M, Van De Ville D, Altenmüller E, Krüger THC, James CE, Sinke C. Increased functional connectivity in the right dorsal auditory stream after a full year of piano training in healthy older adults. Sci Rep 2023; 13:19993. [PMID: 37968500 PMCID: PMC10652022 DOI: 10.1038/s41598-023-46513-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 11/02/2023] [Indexed: 11/17/2023] Open
Abstract
Learning to play an instrument at an advanced age may help to counteract or slow down age-related cognitive decline. However, studies investigating the neural underpinnings of these effects are still scarce. One way to investigate the effects of brain plasticity is using resting-state functional connectivity (FC). The current study compared the effects of learning to play the piano (PP) against participating in music listening/musical culture (MC) lessons on FC in 109 healthy older adults. Participants underwent resting-state functional magnetic resonance imaging at three time points: at baseline, and after 6 and 12 months of interventions. Analyses revealed piano training-specific FC changes after 12 months of training. These include FC increase between right Heschl's gyrus (HG), and other right dorsal auditory stream regions. In addition, PP showed an increased anticorrelation between right HG and dorsal posterior cingulate cortex and FC increase between the right motor hand area and a bilateral network of predominantly motor-related brain regions, which positively correlated with fine motor dexterity improvements. We suggest to interpret those results as increased network efficiency for auditory-motor integration. The fact that functional neuroplasticity can be induced by piano training in healthy older adults opens new pathways to countervail age related decline.
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Affiliation(s)
- Kristin Jünemann
- Division of Clinical Psychology & Sexual Medicine, Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
- Center for Systems Neuroscience, Hannover, Germany
| | - Anna Engels
- Division of Clinical Psychology & Sexual Medicine, Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Damien Marie
- Geneva Musical Minds Lab, Geneva School of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO), Geneva, Switzerland
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, MRI UNIGE, University of Geneva, Geneva, Switzerland
| | - Florian Worschech
- Center for Systems Neuroscience, Hannover, Germany
- Institute of Music Physiology and Musicians' Medicine, Hannover University of Music, Drama and Media, Hannover, Germany
| | - Daniel S Scholz
- Institute of Medical Psychology, University of Lübeck, Lübeck, Germany
- Department of Musicians' Health, University of Music Lübeck, Lübeck, Germany
| | - Frédéric Grouiller
- CIBM Center for Biomedical Imaging, MRI UNIGE, University of Geneva, Geneva, Switzerland
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
| | - Matthias Kliegel
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Eckart Altenmüller
- Center for Systems Neuroscience, Hannover, Germany
- Institute of Music Physiology and Musicians' Medicine, Hannover University of Music, Drama and Media, Hannover, Germany
| | - Tillmann H C Krüger
- Division of Clinical Psychology & Sexual Medicine, Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
- Center for Systems Neuroscience, Hannover, Germany
| | - Clara E James
- Geneva Musical Minds Lab, Geneva School of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO), Geneva, Switzerland
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Christopher Sinke
- Division of Clinical Psychology & Sexual Medicine, Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany.
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11
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Gao Y, Zhao Y, Li M, Lawless RD, Schilling KG, Xu L, Shafer AT, Beason-Held LL, Resnick SM, Rogers BP, Ding Z, Anderson AW, Landman BA, Gore JC. Functional alterations in bipartite network of white and grey matters during aging. Neuroimage 2023; 278:120277. [PMID: 37473978 PMCID: PMC10529380 DOI: 10.1016/j.neuroimage.2023.120277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/23/2023] [Accepted: 07/11/2023] [Indexed: 07/22/2023] Open
Abstract
The effects of normal aging on functional connectivity (FC) within various brain networks of gray matter (GM) have been well-documented. However, the age effects on the networks of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.
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Affiliation(s)
- Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
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12
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Verovnik B, Hajduk S, Hulle MV. Predicting phenotypes of elderly from resting state fMRI. RESEARCH SQUARE 2023:rs.3.rs-3201603. [PMID: 37609310 PMCID: PMC10441519 DOI: 10.21203/rs.3.rs-3201603/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Machine learning techniques are increasingly embraced in neuroimaging studies of healthy and diseased human brains. They have been used successfully in predicting phenotypes, or even clinical outcomes, and in turning functional connectome metrics into phenotype biomarkers of both healthy individuals and patients. In this study, we used functional connectivity characteristics based on resting state functional magnetic resonance imaging data to accurately classify healthy elderly in terms of their phenotype status. Additionally, as the functional connections that contribute to the classification can be identified, we can draw inferences about the network that is predictive of the investigated phenotypes. Our proposed pipeline for phenotype classification can be expanded to other phenotypes (cognitive, psychological, clinical) and possibly be used to shed light on the modifiable risk and protective factors in normative and pathological brain aging.
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13
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Sharma R, Dillon K, Williams SEE, McIntosh R. Does emotion regulation network mediate the effect of social network on psychological distress among older adults? Soc Neurosci 2023; 18:142-154. [PMID: 37267049 DOI: 10.1080/17470919.2023.2218619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 05/12/2023] [Indexed: 06/04/2023]
Abstract
Socio-emotional interactions are integral for regulating emotions and buffering psychological distress. Social neuroscience perspectives on aging suggest that empathetic interpersonal interactions are supported by the activation of brain regions involved in regulating negative affect. The current study tested whether resting state functional connectivity of a network of brain regions activated during cognitive emotion regulation, i.e., emotion regulation network (ERN), statistically mediates the frequency of social contact with friends or family on psychological distress. Here, a 10-min resting-state functional MRI scan was collected along with self-reported anxiety/depressive, somatic, and thought problems and social networking from 90 community-dwelling older adults (aged 65-85 years). The frequency of social interactions with family, but not friends and neighbors, was associated with lower psychological distress. The magnitude of this effect was reduced by 33.34% to non-significant upon adding resting state ERN connectivity as a mediator. Follow-up whole-brain graph network analyses revealed that efficiency and centrality of the left inferior frontal gyrus and the right middle temporal gyrus relate to greater family interactions and lower distress. These hubs may help to buffer psychological problems in older adults through interactions involving empathetic and cognitive emotion regulation with close family.
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Affiliation(s)
| | - Kaitlyn Dillon
- Department of Psychology, University of Miami, Miami, Florida, USA
| | | | - Roger McIntosh
- Department of Psychology, University of Miami, Miami, Florida, USA
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14
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Du Y, Guo Y, Calhoun VD. How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083384 DOI: 10.1109/embc40787.2023.10340189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Many studies have shown that changes in the functional connectivity are diverse along with aging. However, few studies have addressed how aging affects connectivity among large-scale brain networks, and it is challenging to examine gradual aging trajectories from middle adulthood to old age. In this work, based on large-sample fMRI data from 6300 subjects aged between 49 to 73 years, we apply an independent component analysis-based method called NeuroMark to extract brain functional networks and their connectivity (i.e., functional network connectivity (FNC)), and then propose a two-level statistical analysis method to explore robust aging-related changes in functional network connectivity. We found that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network and cerebellum. Our results emphasize the diversity of brain aging and provide new evidence for non-pathological aging of the whole brain.Clinical Relevance-This provides new evidence for non-pathological aging of functional network connectivity in the whole brain.
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15
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Montemurro S, Filippini N, Ferrazzi G, Mantini D, Arcara G, Marino M. Education differentiates cognitive performance and resting state fMRI connectivity in healthy aging. Front Aging Neurosci 2023; 15:1168576. [PMID: 37293663 PMCID: PMC10244540 DOI: 10.3389/fnagi.2023.1168576] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/05/2023] [Indexed: 06/10/2023] Open
Abstract
Objectives In healthy aging, the way people cope differently with cognitive and neural decline is influenced by exposure to cognitively enriching life-experiences. Education is one of them, so that in general, the higher the education, the better the expected cognitive performance in aging. At the neural level, it is not clear yet how education can differentiate resting state functional connectivity profiles and their cognitive underpinnings. Thus, with this study, we aimed to investigate whether the variable education allowed for a finer description of age-related differences in cognition and resting state FC. Methods We analyzed in 197 healthy individuals (137 young adults aged 20-35 and 60 older adults aged 55-80 from the publicly available LEMON database), a pool of cognitive and neural variables, derived from magnetic resonance imaging, in relation to education. Firstly, we assessed age-related differences, by comparing young and older adults. Then, we investigated the possible role of education in outlining such differences, by splitting the group of older adults based on their education. Results In terms of cognitive performance, older adults with higher education and young adults were comparable in language and executive functions. Interestingly, they had a wider vocabulary compared to young adults and older adults with lower education. Concerning functional connectivity, the results showed significant age- and education-related differences within three networks: the Visual-Medial, the Dorsal Attentional, and the Default Mode network (DMN). For the DMN, we also found a relationship with memory performance, which strengthen the evidence that this network has a specific role in linking cognitive maintenance and FC at rest in healthy aging. Discussion Our study revealed that education contributes to differentiating cognitive and neural profiles in healthy older adults. Also, the DMN could be a key network in this context, as it may reflect some compensatory mechanisms relative to memory capacities in older adults with higher education.
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Affiliation(s)
| | | | | | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, Leuven, Belgium
| | | | - Marco Marino
- Movement Control and Neuroplasticity Research Group, Leuven, Belgium
- Department of General Psychology, University of Padua, Padua, Italy
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16
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Du Y, Guo Y, Calhoun VD. Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study ( N > 6,000). Front Aging Neurosci 2023; 15:1159054. [PMID: 37273655 PMCID: PMC10233064 DOI: 10.3389/fnagi.2023.1159054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/21/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Numerous studies have shown that aging has important effects on specific functional networks of the brain and leads to brain functional connectivity decline. However, no studies have addressed the effect of aging at the whole-brain level by studying both brain functional networks (i.e., within-network connectivity) and their interaction (i.e., between-network connectivity) as well as their joint changes. Methods In this work, based on a large sample size of neuroimaging data including 6300 healthy adults aged between 49 and 73 years from the UK Biobank project, we first use our previously proposed priori-driven independent component analysis (ICA) method, called NeuroMark, to extract the whole-brain functional networks (FNs) and the functional network connectivity (FNC) matrix. Next, we perform a two-level statistical analysis method to identify robust aging-related changes in FNs and FNCs, respectively. Finally, we propose a combined approach to explore the synergistic and paradoxical changes between FNs and FNCs. Results Results showed that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network, and cerebellum. Aging also greatly affects the connectivity within FNs, and the increased within-network connectivity along with aging are mainly within the sensorimotor network, while the decreased within-network connectivity significantly involves the default mode network. More importantly, many significant joint changes between FNs and FNCs involve default mode and sub-cortical networks. Furthermore, most synergistic changes are present between the FNCs with reduced amplitude and their linked FNs, and most paradoxical changes are present in the FNCs with enhanced amplitude and their linked FNs. Discussion In summary, our study emphasizes the diversity of brain aging and provides new evidence via novel exploratory perspectives for non-pathological aging of the whole brain.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Yating Guo
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
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17
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Szymkowicz SM, Gerlach AR, Homiack D, Taylor WD. Biological factors influencing depression in later life: role of aging processes and treatment implications. Transl Psychiatry 2023; 13:160. [PMID: 37160884 PMCID: PMC10169845 DOI: 10.1038/s41398-023-02464-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 04/23/2023] [Accepted: 04/27/2023] [Indexed: 05/11/2023] Open
Abstract
Late-life depression occurring in older adults is common, recurrent, and malignant. It is characterized by affective symptoms, but also cognitive decline, medical comorbidity, and physical disability. This behavioral and cognitive presentation results from altered function of discrete functional brain networks and circuits. A wide range of factors across the lifespan contributes to fragility and vulnerability of those networks to dysfunction. In many cases, these factors occur earlier in life and contribute to adolescent or earlier adulthood depressive episodes, where the onset was related to adverse childhood events, maladaptive personality traits, reproductive events, or other factors. Other individuals exhibit a later-life onset characterized by medical comorbidity, pro-inflammatory processes, cerebrovascular disease, or developing neurodegenerative processes. These later-life processes may not only lead to vulnerability to the affective symptoms, but also contribute to the comorbid cognitive and physical symptoms. Importantly, repeated depressive episodes themselves may accelerate the aging process by shifting allostatic processes to dysfunctional states and increasing allostatic load through the hypothalamic-pituitary-adrenal axis and inflammatory processes. Over time, this may accelerate the path of biological aging, leading to greater brain atrophy, cognitive decline, and the development of physical decline and frailty. It is unclear whether successful treatment of depression and avoidance of recurrent episodes would shift biological aging processes back towards a more normative trajectory. However, current antidepressant treatments exhibit good efficacy for older adults, including pharmacotherapy, neuromodulation, and psychotherapy, with recent work in these areas providing new guidance on optimal treatment approaches. Moreover, there is a host of nonpharmacological treatment approaches being examined that take advantage of resiliency factors and decrease vulnerability to depression. Thus, while late-life depression is a recurrent yet highly heterogeneous disorder, better phenotypic characterization provides opportunities to better utilize a range of nonspecific and targeted interventions that can promote recovery, resilience, and maintenance of remission.
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Affiliation(s)
- Sarah M Szymkowicz
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Andrew R Gerlach
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Damek Homiack
- Department of Psychiatry, University of Illinois-Chicago, Chicago, IL, USA
| | - Warren D Taylor
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, TN, USA.
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, TN, USA.
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18
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Abrol A, Fu Z, Du Y, Wilson TW, Wang Y, Stephen JM, Calhoun VD. Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study. Hum Brain Mapp 2023; 44:2158-2175. [PMID: 36629328 PMCID: PMC10028673 DOI: 10.1002/hbm.26200] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/02/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
The brain's functional architecture and organization undergo continual development and modification throughout adolescence. While it is well known that multiple factors govern brain maturation, the constantly evolving patterns of time-resolved functional connectivity are still unclear and understudied. We systematically evaluated over 47,000 youth and adult brains to bridge this gap, highlighting replicable time-resolved developmental and aging functional brain patterns. The largest difference between the two life stages was captured in a brain state that indicated coherent strengthening and modularization of functional coupling within the auditory, visual, and motor subdomains, supplemented by anticorrelation with other subdomains in adults. This distinctive pattern, which we replicated in independent data, was consistently less modular or absent in children and presented a negative association with age in adults, thus indicating an overall inverted U-shaped trajectory. This indicates greater synchrony, strengthening, modularization, and integration of the brain's functional connections beyond adolescence, and gradual decline of this pattern during the healthy aging process. We also found evidence that the developmental changes may also bring along a departure from the canonical static functional connectivity pattern in favor of more efficient and modularized utilization of the vast brain interconnections. State-based statistical summary measures presented robust and significant group differences that also showed significant age-related associations. The findings reported in this article support the idea of gradual developmental and aging brain state adaptation processes in different phases of life and warrant future research via lifespan studies to further authenticate the projected time-resolved brain state trajectories.
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Affiliation(s)
- Anees Abrol
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Yuhui Du
- School of Computer & Information TechnologyShanxi UniversityTaiyuanChina
| | - Tony W. Wilson
- Boys Town National Research HospitalInstitute for Human NeuroscienceBoys TownNebraskaUSA
| | - Yu‐Ping Wang
- Department of Biomedical EngineeringTulane UniversityNew OrleansLouisianaUSA
- Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLouisianaUSA
| | | | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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Sastry NC, Roy D, Banerjee A. Stability of sensorimotor network sculpts the dynamic repertoire of resting state over lifespan. Cereb Cortex 2023; 33:1246-1262. [PMID: 35368068 PMCID: PMC9930636 DOI: 10.1093/cercor/bhac133] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/08/2022] [Accepted: 03/07/2022] [Indexed: 11/14/2022] Open
Abstract
Temporally stable patterns of neural coordination among distributed brain regions are crucial for survival. Recently, many studies highlight association between healthy aging and modifications in organization of functional brain networks, across various time-scales. Nonetheless, quantitative characterization of temporal stability of functional brain networks across healthy aging remains unexplored. This study introduces a data-driven unsupervised approach to capture high-dimensional dynamic functional connectivity (dFC) via low-dimensional patterns and subsequent estimation of temporal stability using quantitative metrics. Healthy aging related changes in temporal stability of dFC were characterized across resting-state, movie-viewing, and sensorimotor tasks (SMT) on a large (n = 645) healthy aging dataset (18-88 years). Prominent results reveal that (1) whole-brain temporal dynamics of dFC movie-watching task is closer to resting-state than to SMT with an overall trend of highest temporal stability observed during SMT followed by movie-watching and resting-state, invariant across lifespan aging, (2) in both tasks conditions stability of neurocognitive networks in young adults is higher than older adults, and (3) temporal stability of whole brain resting-state follows a U-shaped curve along lifespan-a pattern shared by sensorimotor network stability indicating their deeper relationship. Overall, the results can be applied generally for studying cohorts of neurological disorders using neuroimaging tools.
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Affiliation(s)
- Nisha Chetana Sastry
- Cognitive Brain Dynamics Laboratory, National Brain Research Centre, NH 8, Manesar, Gurgaon 122052, India
| | - Dipanjan Roy
- School of Artificial Intelligence & Data Science, Centre for Brain Science & Applications, Indian Institute of Technology, Jodhpur NH 62, Surpura Bypass Rd, Karwar, Rajasthan 342030, India
| | - Arpan Banerjee
- Cognitive Brain Dynamics Laboratory, National Brain Research Centre, NH 8, Manesar, Gurgaon 122052, India
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20
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Liu Y, Lim K, Sundman MH, Ugonna C, Ton That V, Cowen S, Chou YH. Association Between Responsiveness to Transcranial Magnetic Stimulation and Interhemispheric Functional Connectivity of Sensorimotor Cortex in Older Adults. Brain Connect 2023; 13:39-50. [PMID: 35620910 PMCID: PMC9942174 DOI: 10.1089/brain.2021.0180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: Repetitive transcranial magnetic stimulation (rTMS) is a promising therapeutic technique, and is believed to accomplish its effect by influencing the stimulated and remotely connected areas. However, responsiveness to rTMS shows high interindividual variability, and this intersubject variability is particularly high in older adults. It remains unclear whether baseline resting-state functional connectivity (rsFC) contributes to this variability in older adults. The aims of this study are to (1) examine rTMS effects over the primary motor cortex (M1) in older adults, and (2) identify baseline network properties that may contribute to the interindividual variability. Methods: We tested response to intermittent theta burst stimulation (iTBS), an effective rTMS protocol, over M1 by using both electromyography and resting-state functional magnetic resonance imaging in older adults. Outcome measures included motor-evoked potential (MEP) elicited by single-pulse transcranial magnetic stimulation and rsFC before and after an iTBS session. Results: iTBS significantly increased MEP amplitudes and rsFC between the stimulation site, sensorimotor cortex, and supplementary motor area (SMA) in older adults. iTBS-induced changes in MEP amplitude were positively correlated with increases in interhemispheric rsFC after iTBS. Furthermore, older adults with lower baseline interhemispheric rsFC between sensorimotor cortex and SMA exhibited stronger MEP response after iTBS. Discussion: Findings of the study suggest that different levels of interhemispheric communication during resting state might contribute to the response heterogeneity to iTBS in older adults. Interhemispheric rsFC may have great potential serving as a useful marker for predicting iTBS responsiveness in older adults. ClinicalTrials.gov ID: 1707654427 Impact statement Factors contributing to interindividual variability of the responsive to repetitive transcranial magnetic stimulation (rTMS) in older adults remain poorly understood. In this study, we examined the effects of rTMS over the primary motor cortex in older adults, and found that response to rTMS is associated with prestimulation interhemispheric connectivity in the sensorimotor and premotor areas. Findings of the study have great potential to be translated into a connectivity-based strategy for identification of responders for rTMS in older adults.
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Affiliation(s)
- Yilin Liu
- Department of Psychology and University of Arizona, Tucson, Arizona, USA
| | - Koeun Lim
- Department of Psychology and University of Arizona, Tucson, Arizona, USA
| | - Mark H. Sundman
- Department of Psychology and University of Arizona, Tucson, Arizona, USA
| | - Chidi Ugonna
- Department of Psychology and University of Arizona, Tucson, Arizona, USA
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA
| | - Viet Ton That
- Department of Psychology and University of Arizona, Tucson, Arizona, USA
| | - Stephen Cowen
- Department of Psychology and University of Arizona, Tucson, Arizona, USA
- Evelyn F McKnight Brain Institute, Arizona Center on Aging, and BIO5 Institute, University of Arizona, Tucson, Arizona, USA
| | - Ying-hui Chou
- Department of Psychology and University of Arizona, Tucson, Arizona, USA
- Evelyn F McKnight Brain Institute, Arizona Center on Aging, and BIO5 Institute, University of Arizona, Tucson, Arizona, USA
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21
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Perus L, Mangin JF, Deverdun J, Gutierrez LA, Gourieux E, Fischer C, Van Dokkum LEH, Manesco C, Busto G, Guyonnet S, Vellas B, Gabelle A, Le Bars E. Impact of multidomain preventive strategies on functional brain connectivity in older adults with cognitive complaint: Subset from the Montpellier center of the ancillary MAPT-MRI study. Front Aging Neurosci 2023; 14:971220. [PMID: 36705622 PMCID: PMC9871772 DOI: 10.3389/fnagi.2022.971220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/15/2022] [Indexed: 01/12/2023] Open
Abstract
Introduction The impact of multi-domain preventive interventions on older adults, in particular on those with higher risk to develop Alzheimer's disease (AD), could be beneficial, as it may delay cognitive decline. However, the precise mechanism of such positive impact is not fully understood and may involve brain reserve and adaptability of brain functional connectivity (FC). Methods To determine the effect of multidomain interventions (involving physical activity, cognitive training, nutritional counseling alone or in combination with omega-3 fatty acid supplementation and vs. a placebo) on the brain, longitudinal FC changes were assessed after 36 months of intervention on 100 older adults (above 70 year-old) with subjective cognitive complaints. Results No global change in FC was detected after uni or multidomain preventive interventions. However, an effect of omega-3 fatty acid supplementation dependent on cognitive decline status was underlined for frontoparietal, salience, visual and sensorimotor networks FC. These findings were independent of the cortical thickness and vascular burden. Discussion These results emphasize the importance of patient stratification, based on risk factors, for preventive interventions.
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Affiliation(s)
- Lisa Perus
- Memory Resources and Research Center, Department of Neurology, Gui de Chauliac Hospital, Montpellier, France,INM, Univ Montpellier, INSERM, CHU Montpellier, Montpellier, France,Institut d'Imagerie Fonctionnelle Humaine, I2FH, Department of Neuroradiology, Gui de Chauliac Hospital and University of Montpellier, Montpellier, France,CATI, US52-UAR2031, CEA, ICM, SU, CNRS, INSERM, APHP, Ile de France, France
| | - Jean-François Mangin
- CATI, US52-UAR2031, CEA, ICM, SU, CNRS, INSERM, APHP, Ile de France, France,Université Paris-Saclay, CEA, CNRS, Neurospin, UMR9027 Baobab, Gif-sur-Yvette, France
| | - Jérémy Deverdun
- Institut d'Imagerie Fonctionnelle Humaine, I2FH, Department of Neuroradiology, Gui de Chauliac Hospital and University of Montpellier, Montpellier, France
| | | | | | - Clara Fischer
- CATI, US52-UAR2031, CEA, ICM, SU, CNRS, INSERM, APHP, Ile de France, France
| | - Liesjet E. H. Van Dokkum
- Institut d'Imagerie Fonctionnelle Humaine, I2FH, Department of Neuroradiology, Gui de Chauliac Hospital and University of Montpellier, Montpellier, France
| | - Clara Manesco
- Laboratoire Charles Coulomb (L2C), University of Montpellier, CNRS, Montpellier, France
| | - Germain Busto
- Memory Resources and Research Center, Department of Neurology, Gui de Chauliac Hospital, Montpellier, France,INM, Univ Montpellier, INSERM, CHU Montpellier, Montpellier, France
| | - Sophie Guyonnet
- Inserm UMR 1295, University of Toulouse III, Toulouse, France,Gérontopôle, Department of Geriatrics, CHU Toulouse, Toulouse, France
| | - Bruno Vellas
- Inserm UMR 1295, University of Toulouse III, Toulouse, France,Gérontopôle, Department of Geriatrics, CHU Toulouse, Toulouse, France
| | - Audrey Gabelle
- Memory Resources and Research Center, Department of Neurology, Gui de Chauliac Hospital, Montpellier, France,INM, Univ Montpellier, INSERM, CHU Montpellier, Montpellier, France
| | - Emmanuelle Le Bars
- Institut d'Imagerie Fonctionnelle Humaine, I2FH, Department of Neuroradiology, Gui de Chauliac Hospital and University of Montpellier, Montpellier, France,*Correspondence: Emmanuelle Le Bars ✉
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22
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Drenth N, Foster-Dingley JC, Bertens AS, Rius Ottenheim N, van der Mast RC, Rombouts SARB, van Rooden S, van der Grond J. Functional connectivity in older adults-the effect of cerebral small vessel disease. Brain Commun 2023; 5:fcad126. [PMID: 37168731 PMCID: PMC10165246 DOI: 10.1093/braincomms/fcad126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 02/08/2023] [Accepted: 04/17/2023] [Indexed: 05/13/2023] Open
Abstract
Ageing is associated with functional reorganization that is mainly characterized by declining functional connectivity due to general neurodegeneration and increasing incidence of disease. Functional connectivity has been studied across the lifespan; however, there is a paucity of research within the older groups (≥75 years) where neurodegeneration and disease prevalence are at its highest. In this cross-sectional study, we investigated associations between age and functional connectivity and the influence of cerebral small vessel disease (CSVD)-a common age-related morbidity-in 167 community-dwelling older adults aged 75-91 years (mean = 80.3 ± 3.8). Resting-state functional MRI was used to determine functional connectivity within ten standard networks and calculate the whole-brain graph theoretical measures global efficiency and clustering coefficient. CSVD features included white matter hyperintensities, lacunar infarcts, cerebral microbleeds, and atrophy that were assessed in each individual and a composite score was calculated. Both main and interaction effects (age*CSVD features) on functional connectivity were studied. We found stable levels of functional connectivity across the age range. CSVD was not associated with functional connectivity measures. To conclude, our data show that the functional architecture of the brain is relatively unchanged after 75 years of age and not differentially affected by individual levels of vascular pathology.
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Affiliation(s)
- Nadieh Drenth
- Correspondence to: Nadieh Drenth Department of Radiology Leiden University Medical Center, Albinusdreef 2, 2300 RC Leiden, The Netherlands. E-mail:
| | - Jessica C Foster-Dingley
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Anne Suzanne Bertens
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Nathaly Rius Ottenheim
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Roos C van der Mast
- Department of Psychiatry, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Collaborative Antwerp Psychiatric Research Institute (CAPRI)–University of Antwerp, Antwerp, Belgium
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
- Institute of Psychology, Leiden University, P.O. Box 9555, 2300 RB Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Sanneke van Rooden
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
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23
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Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability. Neuroimage 2022; 263:119672. [PMID: 36209795 DOI: 10.1016/j.neuroimage.2022.119672] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 11/23/2022] Open
Abstract
Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mainly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults (InLang [Interactive networks of Language] database), providing the opportunity to assess language features across a wide range of linguistic processes. Using this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG atlas). The organization of this data-driven neurocognitive atlas of language was examined at multiple levels, uncovering its major components (or crucial subnetworks), and its anatomical and functional correlates. In addition, we estimated its reconfiguration as a function of linguistic demand (flexibility) or several factors such as age or gender (variability). We observed that several discrete networks could be specifically shaped to promote key functional features of language: coding-decoding (Net1), control-executive (Net2), abstract-knowledge (Net3), and sensorimotor (Net4) functions. The architecture of these systems and the functional connectivity of the pivotal brain regions varied according to the nature of the linguistic process, gender, or age. By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enriching and refining existing neurocognitive models of language. The LANG atlas can also be considered a reference for comparative or clinical studies involving various patients and conditions.
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24
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Zareba MR, Furman W, Binder M. Influence of age and cognitive performance on resting-state functional connectivity of dopaminergic and noradrenergic centers. Brain Res 2022; 1796:148082. [PMID: 36115586 DOI: 10.1016/j.brainres.2022.148082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 08/24/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022]
Abstract
Aging is associated with structural and functional changes in the brain, with a decline in cognitive functions observed as its inevitable concomitant. The body of literature suggests dopamine and noradrenaline as prominent candidate neuromodulators to mediate these effects; however, knowledge regarding the underlying mechanisms is scarce. To fill this gap, we compared resting-state functional connectivity (FC) patterns of ventral tegmental area (VTA), substantia nigra pars compacta (SNc) and locus coeruleus (LC) in healthy young (20-35 years; N = 37) and older adults (55-80 years; N = 27). Additionally, we sought FC patterns of these structures associated with performance in tasks probing executive, attentional and reward functioning, and we compared the functional coupling of the bilateral SNc. The results showed that individual SNc had stronger coupling with ipsilateral cortical and subcortical areas along with the contralateral cerebellum in the whole sample, and that the strength of connections of this structure with angular gyrus and lateral orbitofrontal cortex predicted visuomotor search abilities. In turn, older age was associated with greater local synchronization within VTA, its lower FC with caudate, mediodorsal thalamus, and SNc, as well as higher FC of both midbrain dopaminergic seeds with red nuclei. LC functional coupling showed no differences between the groups and was not associated with any of the behavioral functions. To the best of our knowledge, this work is the first to report the age-related effects on VTA local synchronization and its connectivity with key recipients of dopaminergic innervation, such as striatum and mediodorsal thalamus.
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Affiliation(s)
- Michal Rafal Zareba
- Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Krakow, Poland; Institute of Psychology, Faculty of Philosophy, Jagiellonian University, Krakow, Poland
| | - Wiktoria Furman
- Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Krakow, Poland; Institute of Psychology, Faculty of Philosophy, Jagiellonian University, Krakow, Poland.
| | - Marek Binder
- Institute of Psychology, Faculty of Philosophy, Jagiellonian University, Krakow, Poland
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25
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Kim E, Kim S, Kim Y, Cha H, Lee HJ, Lee T, Chang Y. Connectome-based predictive models using resting-state fMRI for studying brain aging. Exp Brain Res 2022; 240:2389-2400. [PMID: 35922524 DOI: 10.1007/s00221-022-06430-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022]
Abstract
Changes in the brain with age can provide useful information regarding an individual's chronological age. studies have suggested that functional connectomes identified via resting-state functional magnetic resonance imaging (fMRI) could be a powerful feature for predicting an individual's age. We applied connectome-based predictive modeling (CPM) to investigate individual chronological age predictions via resting-state fMRI using open-source datasets. The significant feature for age prediction was confirmed in 168 subjects from the Southwest University Adult Lifespan Dataset. The higher contributing nodes for age production included a positive connection from the left inferior parietal sulcus and a negative connection from the right middle temporal sulcus. On the network scale, the subcortical-cerebellum network was the dominant network for age prediction. The generalizability of CPM, which was constructed using the identified features, was verified by applying this model to independent datasets that were randomly selected from the Autism Brain Imaging Data Exchange I and the Open Access Series of Imaging Studies 3. CPM via resting-state fMRI is a potential robust predictor for determining an individual's chronological age from changes in the brain.
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Affiliation(s)
- Eunji Kim
- Department of Korea Radioisotope Center for Pharmaceuticals, Korea Institute of Radiological and Medical Sciences, Seoul, Korea
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Seungho Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Yunheung Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Hyunsil Cha
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Hui Joong Lee
- Department of Radiology, Kyungpook National University School of Medicine, Daegu, Korea
- Department of Radiology, Kyungpook National University Hospital, Daegu, Korea
| | - Taekwan Lee
- Korea Brain Research Institute, Chumdanro 61, Dong-gu, Daegu, 41021, Republic of Korea.
| | - Yongmin Chang
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea.
- Department of Radiology, Kyungpook National University Hospital, Daegu, Korea.
- The Department of Molecular Medicine and Radiology, Kyungpook National University School of Medicine, 200 Dongduk-Ro Jung-Gu, Daegu, Korea.
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26
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Stumme J, Krämer C, Miller T, Schreiber J, Caspers S, Jockwitz C. Interrelating differences in structural and functional connectivity in the older adult's brain. Hum Brain Mapp 2022; 43:5543-5561. [PMID: 35916531 PMCID: PMC9704795 DOI: 10.1002/hbm.26030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/11/2022] [Accepted: 07/15/2022] [Indexed: 01/15/2023] Open
Abstract
In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.
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Affiliation(s)
- Johanna Stumme
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Camilla Krämer
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Tatiana Miller
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Jan Schreiber
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM‐1), Research Centre JülichJülichGermany,Institute for Anatomy I, Medical Faculty & University Hospital DüsseldorfHeinrich Heine University DüsseldorfDüsseldorfGermany
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27
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Jiang R, Scheinost D, Zuo N, Wu J, Qi S, Liang Q, Zhi D, Luo N, Chung Y, Liu S, Xu Y, Sui J, Calhoun V. A Neuroimaging Signature of Cognitive Aging from Whole-Brain Functional Connectivity. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2201621. [PMID: 35811304 PMCID: PMC9403648 DOI: 10.1002/advs.202201621] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/02/2022] [Indexed: 05/14/2023]
Abstract
Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19-89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within-network connections (especially default mode and ventral attention networks) and increase between-network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenCT06520USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenCT06520USA
- Interdepartmental Neuroscience ProgramYale UniversityNew HavenCT06520USA
- Department of Statistics and Data ScienceYale UniversityNew HavenCT06520USA
- Child Study CenterYale School of MedicineNew HavenCT06510USA
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190P. R. China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049P. R. China
| | - Jing Wu
- Department of Medical OncologyBeijing You‐An HospitalCapital Medical UniversityBeijing100069P. R. China
| | - Shile Qi
- College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing211106P. R. China
| | - Qinghao Liang
- Department of Biomedical EngineeringYale UniversityNew HavenCT06520USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100088P. R. China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190P. R. China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing100049P. R. China
| | - Young‐Chul Chung
- Department of PsychiatryJeonbuk National University Medical SchoolJeonju54907Republic of Korea
- Department of PsychiatryChonbuk National University HospitalJeonju54907Republic of Korea
| | - Sha Liu
- Department of Psychiatry and MDT Center for Cognitive Impairment and Sleep DisordersFirst HospitalFirst Clinical Medical College of Shanxi Medical UniversityTaiyuan030001P. R. China
| | - Yong Xu
- Department of Psychiatry and MDT Center for Cognitive Impairment and Sleep DisordersFirst HospitalFirst Clinical Medical College of Shanxi Medical UniversityTaiyuan030001P. R. China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijing100088P. R. China
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA30303USA
| | - Vince Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA30303USA
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28
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Malagurski B, Deschwanden PF, Jäncke L, Mérillat S. Longitudinal functional connectivity patterns of the default mode network in healthy older adults. Neuroimage 2022; 259:119414. [PMID: 35760292 DOI: 10.1016/j.neuroimage.2022.119414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 06/18/2022] [Accepted: 06/23/2022] [Indexed: 11/29/2022] Open
Abstract
Cross-sectional studies have consistently identified age-associated alterations in default mode network (DMN) functional connectivity (FC). Yet, research on longitudinal trajectories of FC changes of the DMN in healthy aging is less conclusive. For the present study, we used a resting state functional MRI dataset drawn from the Longitudinal Healthy Aging Brain Database Project (LHAB) collected in 5 occasions over a course of 7 years (baseline N = 232, age range: 64-87 y, mean age = 70.85 y). FC strength changes within the DMN and its regions were investigated using a network-based statistical method suitable for the analysis of longitudinal data. The average DMN FC strength remained stable, however, various DMN components showed differential age- and time-related effects. Our results revealed a complex pattern of longitudinal change seen as decreases and increases of FC strength encompassing the majority of DMN regions, while age-related effects were negative and present in select brain areas. These findings testify to the growing importance of longitudinal studies using more sophisticated fine-grained tools needed to highlight the complexity of the functional reorganization of DMN with healthy aging.
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Affiliation(s)
- Brigitta Malagurski
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland
| | | | - Lutz Jäncke
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland; Division of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Susan Mérillat
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland.
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29
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Eising CM, Maercker A, Malagurski B, Jäncke L, Mérillat S. A longitudinal resting-state functional connectivity analysis on trauma exposure and post-traumatic stress symptoms in older individuals. Neuroimage Clin 2022; 35:103052. [PMID: 35644110 PMCID: PMC9144015 DOI: 10.1016/j.nicl.2022.103052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 05/09/2022] [Accepted: 05/15/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Given the present demographic shift towards an aging society, there is an increased need to investigate the brain's functional connectivity in the context of aging. Trauma exposure and post-traumatic stress disorder (PTSD) symptoms are factors known to impact healthy aging and have been reported to be associated with functional connectivity differences. In the present study, we examined and compared differences in within-default mode network (DMN), within-salience network (SN) and between-DMN-SN functional connectivity, between trauma-exposed individuals with and without PTSD symptoms as well as non-traumatized individuals in a non-clininical older adult sample. METHODS Resting state functional MRI and behavioral data is taken from the Longitudinal Healthy Aging Brain Database Project (LHAB). For the present analysis, participants who completed the questionnaires on trauma exposure and PTSD symptoms (N = 110 individuals of which n = 50 individuals reported previous trauma exposure and n = 25 individuals reported PTSD symptoms; mean age = 70.55 years, SD = 4.82) were included. RESULTS The reporting of PTSD symptoms relative to no symptoms was associated with lower within-DMN connectivity, while on a trend level trauma-exposed individuals showed higher within-SN connectivity compared to non-trauma exposed individuals. Consistent with existing models of healthy aging, between-DMN-SN functional connectivity showed an increase across time in older age. CONCLUSION Present results suggest that alterations in within-DMN and within-SN functional connectivity also occur in non-treatment seeking older adult populations with trauma exposure and in association with PTSD symptoms. These changes manifest, alongside altered between-DMN-SN functional connectivity, in older age supposedly independent of aging-related functional desegregation.
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Affiliation(s)
- Carla M Eising
- University of Zürich, Psychopathology and Clinical Intervention, Institute of Psychology, Binzmuehlestrasse 14 / 17, 8050 Zürich, Switzerland; University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland.
| | - Andreas Maercker
- University of Zürich, Psychopathology and Clinical Intervention, Institute of Psychology, Binzmuehlestrasse 14 / 17, 8050 Zürich, Switzerland; University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland
| | - Brigitta Malagurski
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland
| | - Lutz Jäncke
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland; Division Neuropsychology, Department of Psychology, University of Zurich, Binzmuehlestrasse 14, 8050 Zurich, Switzerland
| | - Susan Mérillat
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Switzerland
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30
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Foo H, Thalamuthu A, Jiang J, Koch F, Mather KA, Wen W, Sachdev PS. Age- and Sex-Related Topological Organization of Human Brain Functional Networks and Their Relationship to Cognition. Front Aging Neurosci 2022; 13:758817. [PMID: 34975453 PMCID: PMC8718995 DOI: 10.3389/fnagi.2021.758817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 11/25/2021] [Indexed: 11/23/2022] Open
Abstract
Age and sex associated with changes in the functional brain network topology and cognition in large population of older adults have been poorly understood. We explored this question further by examining differences in 11 resting-state graph theory measures with respect to age, sex, and their relationships with cognitive performance in 17,127 United Kingdom Biobank participants (mean = 62.83 ± 7.41 years). Age was associated with an overall decrease in the effectiveness of network communication (i.e., integration) and loss of functional specialization (i.e., segregation) of specific brain regions. Sex differences were also observed, with women showing more efficient networks, which were less segregated than in men (FDR adjusted p < 0.05). The age-related changes were also more apparent in men than in women, which suggests that men may be more vulnerable to cognitive decline with age. Interestingly, while network segregation and strength of limbic network were only nominally associated with cognitive performance, the network measures collectively were significantly associated with cognition (FDR adjusted p ≤ 0.002). This may imply that individual measures may be inadequate to capture much of the variance in the neural activity or its output and need further refinement. The complexity of the organization of the functional brain may be shaped by the age and sex of an individual, which ultimately may influence the cognitive performance of older adults. Age and sex stratification may be used to inform clinical neuroscience research to identify older adults at risk of cognitive dysfunction.
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Affiliation(s)
- Heidi Foo
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Forrest Koch
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Karen A Mather
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia.,Neuroscience Research Australia, Randwick, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Aging, CHeBA, School of Psychiatry, University of New South Wales Medicine, Kensington, NSW, Australia.,Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Randwick, NSW, Australia
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Kocher M, Jockwitz C, Lerche C, Sabel M, Lohmann P, Stoffels G, Filss C, Mottaghy FM, Ruge MI, Fink GR, Shah NJ, Galldiks N, Caspers S, Langen KJ. Case Report: Disruption of Resting-State Networks and Cognitive Deficits After Whole Brain Irradiation for Singular Brain Metastasis. Front Neurosci 2021; 15:738708. [PMID: 34776847 PMCID: PMC8578854 DOI: 10.3389/fnins.2021.738708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 09/30/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Long-term survivors of whole brain radiation (WBRT) are at significant risk for developing cognitive deficits, but knowledge about the underlying pathophysiological mechanisms is limited. Therefore, we here report a rare case with a singular brain metastasis treated by resection and WBRT that survived for more than 10 years where we investigated the integrity of brain networks using resting-state functional MRI. Methods: A female patient with a left frontal non-small cell lung cancer (NSCLC) brain metastasis had resection and postoperative WBRT (30.0 in 3.0 Gy fractions) and stayed free from brain metastasis recurrence for a follow-up period of 11 years. Structural magnetic resonance imaging (MRI) and amino acid [O-(2-[18F]fluoroethyl)-L-tyrosine] positron emission tomography (FET PET) were repeatedly acquired. At the last follow up, neurocognitive functions and resting-state functional connectivity (RSFC) using resting-state fMRI were assessed. Within-network and inter-network connectivity of seven resting-state networks were computed from a connectivity matrix. All measures were compared to a matched group of 10 female healthy subjects. Results: At the 11-year follow-up, T2/FLAIR MR images of the patient showed extended regions of hyper-intensities covering mainly the white mater of the bilateral dorsal frontal and parietal lobes while sparing most of the temporal lobes. Compared to the healthy subjects, the patient performed significantly worse in all cognitive domains that included executive functions, attention and processing speed, while verbal working memory, verbal episodic memory, and visual working memory were left mostly unaffected. The connectivity matrix showed a heavily disturbed pattern with a widely distributed, scattered loss of RSFC. The within-network RSFC revealed a significant loss of connectivity within all seven networks where the dorsal attention and fronto-parietal control networks were affected most severely. The inter-network RSFC was significantly reduced for the visual, somato-motor, and dorsal and ventral attention networks. Conclusion: As demonstrated here in a patient with a metastatic NSCLC and long-term survival, WBRT may lead to extended white matter damage and cause severe disruption of the RSFC in multiple resting state networks. In consequence, executive functioning which is assumed to depend on the interaction of several networks may be severely impaired following WBRT apart from the well-recognized deficits in memory function.
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Affiliation(s)
- Martin Kocher
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.,Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne and Duesseldorf, Cologne, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany.,Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
| | - Christoph Lerche
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany
| | - Michael Sabel
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne and Duesseldorf, Cologne, Germany.,Department of Neurosurgery, Medical Faculty, Center of Neuro-Oncology, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.,Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne and Duesseldorf, Cologne, Germany
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany
| | - Christian Filss
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany.,Department of Nuclear Medicine, University Hospital Aachen, Rheinisch-Westfaelische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Felix M Mottaghy
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne and Duesseldorf, Cologne, Germany.,Department of Nuclear Medicine, University Hospital Aachen, Rheinisch-Westfaelische Technische Hochschule (RWTH) Aachen University, Aachen, Germany.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Maximilian I Ruge
- Department of Stereotaxy and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.,Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne and Duesseldorf, Cologne, Germany
| | - Gereon R Fink
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne and Duesseldorf, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany.,Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nadim J Shah
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany.,Department of Neurology, University Hospital Aachen, Rheinisch-Westfaelische Technische Hochschule (RWTH) Aachen University, Aachen, Germany.,Juelich-Aachen Research Alliance-Section JARA-Brain, Juelich, Germany
| | - Norbert Galldiks
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne and Duesseldorf, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany.,Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany.,Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany.,Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne and Duesseldorf, Cologne, Germany.,Department of Nuclear Medicine, University Hospital Aachen, Rheinisch-Westfaelische Technische Hochschule (RWTH) Aachen University, Aachen, Germany.,Juelich-Aachen Research Alliance-Section JARA-Brain, Juelich, Germany
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Garaschuk O. Understanding normal brain aging. Pflugers Arch 2021; 473:711-712. [PMID: 33885976 PMCID: PMC8076117 DOI: 10.1007/s00424-021-02567-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Olga Garaschuk
- Institute of Physiology, Department of Neurophysiology, Eberhard Karls University of Tübingen, Tübingen, Germany.
- Department of Neurophysiology, University of Tübingen, Keplerstr. 15, 72074, Tübingen, Germany.
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