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Mulholland MM, Meguerditchian A, Hopkins WD. Age- and sex-related differences in baboon (Papio anubis) gray matter covariation. Neurobiol Aging 2023; 125:41-48. [PMID: 36827943 PMCID: PMC10308318 DOI: 10.1016/j.neurobiolaging.2023.01.005] [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: 10/29/2021] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 01/30/2023]
Abstract
Age-related changes in cognition, brain morphology, and behavior are exhibited in several primate species. Baboons, like humans, naturally develop Alzheimer's disease-like pathology and cognitive declines with age and are an underutilized model for studies of aging. To determine age-related differences in gray matter covariation of 89 olive baboons (Papio anubis), we used source-based morphometry (SBM) to analyze data from magnetic resonance images. We hypothesized that we would find significant age effects in one or more SBM components, particularly those which include regions influenced by age in humans and other nonhuman primates (NHPs). A multivariate analysis of variance revealed that individual weighted gray matter covariation scores differed across the age classes. Elderly baboons contributed significantly less to gray matter covariation components including the brainstem, superior parietal cortex, thalamus, and pallidum compared to juveniles, and middle and superior frontal cortex compared to juveniles and young adults (p < 0.05). Future studies should examine the relationship between the changes in gray matter covariation reported here and age-related cognitive decline.
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Affiliation(s)
- M M Mulholland
- The University of Texas MD Anderson Cancer Center, Bastrop, TX.
| | - A Meguerditchian
- Laboratoire de Psychologie Cognitive UMR7290, LPC, CNRS, Aix-Marseille University, Institute of Language, Communication and the Brain, Marseille, France; Station de Primatologie-Celphedia, UAR846, Rousset, France
| | - W D Hopkins
- The University of Texas MD Anderson Cancer Center, Bastrop, TX
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2
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A within-subject voxel-wise constant-block partial least squares correlation method to explore MRI-based brain structure–function relationship. Cogn Neurodyn 2023. [DOI: 10.1007/s11571-023-09941-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023] Open
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3
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Rim D, Henderson LA, Macefield VG. Brain and cardiovascular-related changes are associated with aging, hypertension, and atrial fibrillation. Clin Auton Res 2022; 32:409-422. [PMID: 36409380 DOI: 10.1007/s10286-022-00907-9] [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: 08/30/2022] [Accepted: 10/31/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE The neural pathways in which the brain regulates the cardiovascular system is via sympathetic and parasympathetic control of the heart and sympathetic control of the systemic vasculature. Various cortical and sub-cortical sites are involved, but how these critical brain regions for cardiovascular control are altered in healthy aging and other risk conditions that may contribute to cardiovascular disease is uncertain. METHODS Here we review the functional and structural brain changes in healthy aging, hypertension, and atrial fibrillation - noting their potential influence on the autonomic nervous system and hence on cardiovascular control. RESULTS Evidence suggests that aging, hypertension, and atrial fibrillation are each associated with functional and structural changes in specific areas of the central nervous system involved in autonomic control. Increased muscle sympathetic nerve activity (MSNA) and significant alterations in the brain regions involved in the default mode network are commonly reported in aging, hypertension, and atrial fibrillation. CONCLUSIONS Further studies using functional and structural magnetic resonance imaging (MRI) coupled with autonomic nerve activity in healthy aging, hypertension, and atrial fibrillation promise to reveal the underlying brain circuitry modulating the abnormal sympathetic nerve activity in these conditions. This understanding will guide future therapies to rectify dysregulation of autonomic and cardiovascular control by the brain.
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Affiliation(s)
- Donggyu Rim
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia.,Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, VIC, 3004, Australia
| | - Luke A Henderson
- School of Medical Sciences (Neuroscience), Brain and Mind Centre, University of Sydney, Camperdown, NSW, 2050, Australia
| | - Vaughan G Macefield
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, 3004, Australia. .,Baker Heart and Diabetes Institute, 75 Commercial Rd, Melbourne, VIC, 3004, Australia. .,Department of Anatomy and Physiology, University of Melbourne, Melbourne, VIC, 3010, Australia.
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4
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Liu K, Li Q, Yao L, Guo X. The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification. Front Neurosci 2022; 16:902528. [PMID: 35720713 PMCID: PMC9205193 DOI: 10.3389/fnins.2022.902528] [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] [Received: 03/23/2022] [Accepted: 04/25/2022] [Indexed: 11/15/2022] Open
Abstract
Structural magnetic resonance imaging (MRI) features have played an increasingly crucial role in discriminating patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal controls (NC). However, the large number of structural MRI studies only extracted low-level neuroimaging features or simply concatenated multitudinous features while ignoring the interregional covariate information. The appropriate representation and integration of multilevel features will be preferable for the precise discrimination in the progression of AD. In this study, we proposed a novel inter-coupled feature representation method and built an integration model considering the two-level (the regions of interest (ROI) level and the network level) coupled features based on structural MRI data. For the intra-coupled interactions about the network-level features, we performed the ROI-level (intra- and inter-) coupled interaction within each network by feature expansion and coupling learning. For the inter-coupled interaction of the network-level features, we measured the coupled relationships among different networks via Canonical correlation analysis. We evaluated the classification performance using coupled feature representations on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results showed that the coupled integration model with hierarchical features achieved the optimal classification performance with an accuracy of 90.44% for AD and NC groups, with an accuracy of 87.72% for the MCI converter (MCI-c) and MCI non-converter (MCI-nc) groups. These findings suggested that our two-level coupled interaction representation of hierarchical features has been the effective means for the precise discrimination of MCI-c from MCI-nc groups and, therefore, helpful in the characterization of different AD courses.
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Affiliation(s)
- Ke Liu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Engineering Research Center of Intelligent Technology and Educational Application, Beijing Normal University, Beijing, China
| | - Qing Li
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Engineering Research Center of Intelligent Technology and Educational Application, Beijing Normal University, Beijing, China
| | - Xiaojuan Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Engineering Research Center of Intelligent Technology and Educational Application, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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Treder MS, Codrai R, Tsvetanov KA. Quality assessment of anatomical MRI images from generative adversarial networks: Human assessment and image quality metrics. J Neurosci Methods 2022; 374:109579. [PMID: 35364110 DOI: 10.1016/j.jneumeth.2022.109579] [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: 10/30/2021] [Revised: 03/01/2022] [Accepted: 03/20/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Generative Adversarial Networks (GANs) can synthesize brain images from image or noise input. So far, the gold standard for assessing the quality of the generated images has been human expert ratings. However, due to limitations of human assessment in terms of cost, scalability, and the limited sensitivity of the human eye to more subtle statistical relationships, a more automated approach towards evaluating GANs is required. NEW METHOD We investigated to what extent visual quality can be assessed using image quality metrics and we used group analysis and spatial independent components analysis to verify that the GAN reproduces multivariate statistical relationships found in real data. Reference human data was obtained by recruiting neuroimaging experts to assess real Magnetic Resonance (MR) images and images generated by a GAN. Image quality was manipulated by exporting images at different stages of GAN training. RESULTS Experts were sensitive to changes in image quality as evidenced by ratings and reaction times, and the generated images reproduced group effects (age, gender) and spatial correlations moderately well. We also surveyed a number of image quality metrics. Overall, Fréchet Inception Distance (FID), Maximum Mean Discrepancy (MMD) and Naturalness Image Quality Evaluator (NIQE) showed sensitivity to image quality and good correspondence with the human data, especially for lower-quality images (i.e., images from early stages of GAN training). However, only a Deep Quality Assessment (QA) model trained on human ratings was able to reproduce the subtle differences between higher-quality images. CONCLUSIONS We recommend a combination of group analyses, spatial correlation analyses, and both distortion metrics (FID, MMD, NIQE) and perceptual models (Deep QA) for a comprehensive evaluation and comparison of brain images produced by GANs.
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Affiliation(s)
- Matthias S Treder
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK.
| | - Ryan Codrai
- School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, CB2 0SZ, UK; Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
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Mutti C, Misirocchi F, Zilioli A, Rausa F, Pizzarotti S, Spallazzi M, Parrino L. Sleep and brain evolution across the human lifespan: A mutual embrace. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:938012. [PMID: 36926070 PMCID: PMC10013002 DOI: 10.3389/fnetp.2022.938012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022]
Abstract
Sleep can be considered a window to ascertain brain wellness: it dynamically changes with brain maturation and can even indicate the occurrence of concealed pathological processes. Starting from prenatal life, brain and sleep undergo an impressive developmental journey that accompanies human life throughout all its steps. A complex mutual influence rules this fascinating course and cannot be ignored while analysing its evolution. Basic knowledge on the significance and evolution of brain and sleep ontogenesis can improve the clinical understanding of patient's wellbeing in a more holistic perspective. In this review we summarized the main notions on the intermingled relationship between sleep and brain evolutionary processes across human lifespan, with a focus on sleep microstructure dynamics.
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Affiliation(s)
- Carlotta Mutti
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Francesco Misirocchi
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Alessandro Zilioli
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Francesco Rausa
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Silvia Pizzarotti
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Marco Spallazzi
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Liborio Parrino
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
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Physical activity, brain tissue microstructure, and cognition in older adults. PLoS One 2021; 16:e0253484. [PMID: 34232955 PMCID: PMC8262790 DOI: 10.1371/journal.pone.0253484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 06/06/2021] [Indexed: 01/28/2023] Open
Abstract
Objective To test whether postmortem MRI captures brain tissue characteristics that mediate the association between physical activity and cognition in older adults. Methods Participants (N = 318) were older adults from the Rush Memory and Aging Project who wore a device to quantify physical activity and also underwent detailed cognitive and motor testing. Following death, cerebral hemispheres underwent MRI to quantify the transverse relaxation rate R2, a metric related to tissue microstructure. For analyses, we reduced the dimensionality of the R2 maps from approximately 500,000 voxels to 30 components using spatial independent component analysis (ICA). Via path analysis, we examined whether these R2 components attenuated the association between physical activity and cognition, controlling for motor abilities and indices of common brain pathologies. Results Two of the 30 R2 components were associated with both total daily physical activity and global cognition assessed proximate to death. We visualized these components by highlighting the clusters of voxels whose R2 values contributed most strongly to each. One of these spatial signatures spanned periventricular white matter and hippocampus, while the other encompassed white matter of the occipital lobe. These two R2 components partially mediated the association between physical activity and cognition, accounting for 12.7% of the relationship (p = .01). This mediation remained evident after controlling for motor abilities and neurodegenerative and vascular brain pathologies. Conclusion The association between physically activity and cognition in older adults is partially accounted for by MRI-based signatures of brain tissue microstructure. Further studies are needed to elucidate the molecular mechanisms underlying this pathway.
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Tsvetanov KA, Henson RNA, Jones PS, Mutsaerts H, Fuhrmann D, Tyler LK, Rowe JB. The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors. Psychophysiology 2021; 58:e13714. [PMID: 33210312 PMCID: PMC8244027 DOI: 10.1111/psyp.13714] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 07/27/2020] [Accepted: 09/28/2020] [Indexed: 12/18/2022]
Abstract
Accurate identification of brain function is necessary to understand neurocognitive aging, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is, therefore, essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable, and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health (CVH) and cerebrovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and cerebrovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and cerebrovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and cerebrovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age differences in RSFA, after controlling for CVH. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and cerebrovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterize neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging.
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Affiliation(s)
- Kamen A. Tsvetanov
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - Richard N. A. Henson
- Medical Research Council Cognition and Brain Sciences UnitCambridgeUK
- Department of PsychiatryUniversity of CambridgeCambridgeUK
| | - P. Simon Jones
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - Henk Mutsaerts
- Department of Radiology and Nuclear MedicineAmsterdam University Medical CenterAmsterdamthe Netherlands
| | - Delia Fuhrmann
- Medical Research Council Cognition and Brain Sciences UnitCambridgeUK
| | - Lorraine K. Tyler
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - Cam‐CAN
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- Department of PsychologyCentre for Speech, Language and the BrainUniversity of CambridgeCambridgeUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- Medical Research Council Cognition and Brain Sciences UnitCambridgeUK
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Kim M, Yan C, Yang D, Liang P, Kaufer DI, Wu G. Constructing Connectome Atlas by Graph Laplacian Learning. Neuroinformatics 2021; 19:233-249. [PMID: 32712763 PMCID: PMC7855351 DOI: 10.1007/s12021-020-09482-8] [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: 10/23/2022]
Abstract
The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a "common" brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods.
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Affiliation(s)
- Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC, 27402, USA
| | - Chenggang Yan
- Intelligent Information Processing Laboratory and School of Automation, Hangzhou Dianzi University, Zhejiang, 310018, Hangzhou, China
| | - Defu Yang
- Intelligent Information Processing Laboratory and School of Automation, Hangzhou Dianzi University, Zhejiang, 310018, Hangzhou, China
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Peipeng Liang
- Department of Psychology, Capital Normal University, Beijing, 100073, China
| | - Daniel I Kaufer
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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Nuzzi D, Pellicoro M, Angelini L, Marinazzo D, Stramaglia S. Synergistic information in a dynamical model implemented on the human structural connectome reveals spatially distinct associations with age. Netw Neurosci 2020; 4:910-924. [PMID: 33615096 PMCID: PMC7888489 DOI: 10.1162/netn_a_00146] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 05/08/2020] [Indexed: 11/24/2022] Open
Abstract
We implement the dynamical Ising model on the large-scale architecture of white matter connections of healthy subjects in the age range 4-85 years, and analyze the dynamics in terms of the synergy, a quantity measuring the extent to which the joint state of pairs of variables is projected onto the dynamics of a target one. We find that the amount of synergy in explaining the dynamics of the hubs of the structural connectivity (in terms of degree strength) peaks before the critical temperature, and can thus be considered as a precursor of a critical transition. Conversely, the greatest amount of synergy goes into explaining the dynamics of more central nodes. We also find that the aging of structural connectivity is associated with significant changes in the simulated dynamics: There are brain regions whose synergy decreases with age, in particular the frontal pole, the subcallosal area, and the supplementary motor area; these areas could then be more likely to show a decline in terms of the capability to perform higher order computation (if structural connectivity was the sole variable). On the other hand, several regions in the temporal cortex show a positive correlation with age in the first 30 years of life, that is, during brain maturation.
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Affiliation(s)
- Davide Nuzzi
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
| | - Mario Pellicoro
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
| | - Leonardo Angelini
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Universitá degli Studi Aldo Moro, Bari and INFN, Bari, Italy
- Center of Innovative Technologies for Signal Detection and Processing (TIRES), Universitá degli Studi Aldo Moro, Bari, Italy
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11
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Alteration of the Intra- and Inter-Lobe Connectivity of the Brain Structural Network in Normal Aging. ENTROPY 2020; 22:e22080826. [PMID: 33286597 PMCID: PMC7517412 DOI: 10.3390/e22080826] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/23/2020] [Accepted: 07/24/2020] [Indexed: 01/18/2023]
Abstract
The morphological changes in cortical parcellated regions during aging and whether these atrophies may cause brain structural network intra- and inter-lobe connectivity alterations are subjects that have been minimally explored. In this study, a novel fractal dimension-based structural network was proposed to measure atrophy of 68 parcellated cortical regions. Alterations of structural network parameters, including intra- and inter-lobe connectivity, were detected in a middle-aged group (30–45 years old) and an elderly group (50–65 years old). The elderly group exhibited significant lateralized atrophy in the left hemisphere, and most of these fractal dimension atrophied regions were included in the regions of the “last-in, first-out” model. Globally, the elderly group had lower modularity values, smaller component size modules, and fewer bilateral association fibers. They had lower intra-lobe connectivity in the frontal and parietal lobes, but higher intra-lobe connectivity in the temporal and occipital lobes. Both groups exhibited similar inter-lobe connecting pattern. The elderly group revealed separations, sparser long association fibers, commissural fibers, and lateral inter-lobe connectivity lost effect, mainly in the right hemisphere. New wiring and reconfiguring modules may have occurred within the brain structural network to compensate for connectivity, decreasing and preventing functional loss in cerebral intra- and inter-lobe connectivity.
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12
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Wen X, He H, Dong L, Chen J, Yang J, Guo H, Luo C, Yao D. Alterations of local functional connectivity in lifespan: A resting-state fMRI study. Brain Behav 2020; 10:e01652. [PMID: 32462815 PMCID: PMC7375100 DOI: 10.1002/brb3.1652] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 04/08/2020] [Accepted: 04/13/2020] [Indexed: 12/24/2022] Open
Abstract
INTRODUCTION As aging attracted attention globally, revealing changes in brain function across the lifespan was largely concerned. In this study, we aimed to reveal the changes of functional networks of the brain (via local functional connectivity, local FC) in lifespan and explore the mechanism underlying them. MATERIALS AND METHODS A total of 523 healthy participants (258 males and 265 females) aged 18-88 years from part of the Cambridge Center for Ageing and Neuroscience (CamCAN) were involved in this study. Next, two data-driven measures of local FC, local functional connectivity density (lFCD) and four-dimensional spatial-temporal consistency of local neural activity (FOCA), were calculated, and then, general linear models were used to assess the changes of them in lifespan. RESULTS Local functional connectivity (lFCD and FOCA) within visual networks (VN), sensorimotor network (SMN), and default mode network (DMN) decreased across the lifespan, while within basal ganglia network (BGN), local connectivity was increased across the lifespan. And, the fluid intelligence decreased within BGN while increased within VN, SMN, and DMN. CONCLUSION These results might suggest that the decline of executive control and intrinsic cognitive ability in the aging population was related to the decline of functional connectivity in VN, SMN, and DMN. Meanwhile, BGN might play a regulatory role in the aging process to compensate for the dysfunction of other functional systems. Our findings may provide important neuroimaging evidence for exploring the brain functional mechanism in lifespan.
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Affiliation(s)
- Xin Wen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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13
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Kuo CY, Lee PL, Hung SC, Liu LK, Lee WJ, Chung CP, Yang AC, Tsai SJ, Wang PN, Chen LK, Chou KH, Lin CP. Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker. Cereb Cortex 2020; 30:5844-5862. [PMID: 32572452 DOI: 10.1093/cercor/bhaa161] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 05/05/2020] [Accepted: 05/21/2020] [Indexed: 12/31/2022] Open
Abstract
The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.
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Affiliation(s)
- Chen-Yuan Kuo
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 11221, Taiwan
| | - Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan
| | - Sheng-Che Hung
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Li-Kuo Liu
- Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Wei-Ju Lee
- Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Department of Family Medicine, Yuanshan Branch, Taipei Veterans General Hospital, Yi-Lan 264, Taiwan
| | - Chih-Ping Chung
- Department of Neurology, School of Medicine, National Yang Ming University, Taipei 11221, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Pei-Ning Wang
- Department of Neurology, School of Medicine, National Yang Ming University, Taipei 11221, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 11217, Taiwan.,Brain Research Center, National Yang Ming University, Taipei 11221, Taiwan
| | - Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang Ming University, Taipei 11221, Taiwan
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 11221, Taiwan.,Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan.,Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang Ming University, Taipei 11221, Taiwan
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14
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Luo N, Sui J, Abrol A, Lin D, Chen J, Vergara VM, Fu Z, Du Y, Damaraju E, Xu Y, Turner JA, Calhoun VD. Age-related structural and functional variations in 5,967 individuals across the adult lifespan. Hum Brain Mapp 2020; 41:1725-1737. [PMID: 31876339 PMCID: PMC7267948 DOI: 10.1002/hbm.24905] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/24/2019] [Accepted: 12/10/2019] [Indexed: 12/13/2022] Open
Abstract
Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting-state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.
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Affiliation(s)
- Na Luo
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Anees Abrol
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Dongdong Lin
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Jiayu Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Victor M. Vergara
- CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of Automation, Chinese Academy of SciencesBeijingChina
| | - Zening Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yuhui Du
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - Eswar Damaraju
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
| | - Yong Xu
- Department of PsychiatryFirst Clinical Medical College/ First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Jessica A. Turner
- Department of PsychologyNeuroscience Institute, Georgia State UniversityAtlantaGeorgia
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgia
- Department of PsychiatryYale University, School of MedicineNew HavenConnecticut
- Department of Psychology, Computer ScienceNeuroscience Institute, and Physics, Georgia State UniversityAtlantaGeorgia
- Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgia
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15
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Quevenco FC, van Bergen JM, Treyer V, Studer ST, Kagerer SM, Meyer R, Gietl AF, Kaufmann PA, Nitsch RM, Hock C, Unschuld PG. Functional Brain Network Connectivity Patterns Associated With Normal Cognition at Old-Age, Local β-amyloid, Tau, and APOE4. Front Aging Neurosci 2020; 12:46. [PMID: 32210782 PMCID: PMC7075450 DOI: 10.3389/fnagi.2020.00046] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 02/10/2020] [Indexed: 12/30/2022] Open
Abstract
Background: Integrity of functional brain networks is closely associated with maintained cognitive performance at old age. Consistently, both carrier status of Apolipoprotein E ε4 allele (APOE4), and age-related aggregation of Alzheimer’s disease (AD) pathology result in altered brain network connectivity. The posterior cingulate and precuneus (PCP) is a node of particular interest due to its role in crucial memory processes. Moreover, the PCP is subject to the early aggregation of AD pathology. The current study aimed at characterizing brain network properties associated with unimpaired cognition in old aged adults. To determine the effects of age-related brain change and genetic risk for AD, pathological proteins β-amyloid and tau were measured by Positron-emission tomography (PET), PCP connectivity as a proxy of cognitive network integrity, and genetic risk by APOE4 carrier status. Methods: Fifty-seven cognitively unimpaired old-aged adults (MMSE = 29.20 ± 1.11; 73 ± 8.32 years) were administered 11C Pittsburgh Compound B and 18F Flutemetamol PET for assessing β-amyloid, and 18F AV-1451 PET for tau. Individual functional connectivity seed maps of the PCP were obtained by resting-state multiband BOLD functional MRI at 3-Tesla for increased temporal resolution. Voxelwise correlations between functional connectivity, β-amyloid- and tau-PET were explored by Biological Parametric Mapping (BPM). Results: Local β-amyloid was associated with increased connectivity in frontal and parietal regions of the brain. Tau was linked to increased connectivity in more spatially distributed clusters in frontal, parietal, occipital, temporal, and cerebellar regions. A positive interaction was observable for APOE4 carrier status and functional connectivity with brain regions characterized by increased local β-amyloid and tau tracer retention. Conclusions: Our data suggest an association between spatially differing connectivity systems and local β-amyloid, and tau aggregates in cognitively normal, old-aged adults, which is moderated by APOE4. Additional longitudinal studies may determine protective connectivity patterns associated with healthy aging trajectories of AD-pathology aggregation.
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Affiliation(s)
- Frances C Quevenco
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Jiri M van Bergen
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Valerie Treyer
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Department of Nuclear Medicine, University of Zurich, Zurich, Switzerland
| | - Sandro T Studer
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Sonja M Kagerer
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Neurimmune, Schlieren, Switzerland
| | - Rafael Meyer
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Anton F Gietl
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, University of Zurich, Zurich, Switzerland
| | - Roger M Nitsch
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Neurimmune, Schlieren, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Neurimmune, Schlieren, Switzerland
| | - Paul G Unschuld
- Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland.,Department of Psychogeriatric Medicine, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland.,Zurich Neuroscience Center (ZNZ), Zurich, Switzerland
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16
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Febo M, Rani A, Yegla B, Barter J, Kumar A, Wolff CA, Esser K, Foster TC. Longitudinal Characterization and Biomarkers of Age and Sex Differences in the Decline of Spatial Memory. Front Aging Neurosci 2020; 12:34. [PMID: 32153384 PMCID: PMC7044155 DOI: 10.3389/fnagi.2020.00034] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 02/04/2020] [Indexed: 01/10/2023] Open
Abstract
The current longitudinal study examined factors (sex, physical function, response to novelty, ability to adapt to a shift in light/dark cycle, brain connectivity), which might predict the emergence of impaired memory during aging. Male and female Fisher 344 rats were tested at 6, 12, and 18 months of age. Impaired spatial memory developed in middle-age (12 months), particularly in males, and the propensity for impairment increased with advanced age. A reduced response to novelty was observed over the course of aging, which is inconsistent with cross-sectional studies. This divergence likely resulted from differences in the history of environmental enrichment/impoverishment for cross-sectional and longitudinal studies. Animals that exhibited lower level exploration of the inner region on the open field test exhibited better memory at 12 months. Furthermore, males that exhibited a longer latency to enter a novel environment at 6 months, exhibited better memory at 12 months. For females, memory at 12 months was correlated with the ability to behaviorally adapt to a shift in light/dark cycle. Functional magnetic resonance imaging of the brain, conducted at 12 months, indicated that the decline in memory was associated with altered functional connectivity within different memory systems, most notably between the hippocampus and multiple regions such as the retrosplenial cortex, thalamus, striatum, and amygdala. Overall, some factors, specifically response to novelty at an early age and the capacity to adapt to shifts in light cycle, predicted spatial memory in middle-age, and spatial memory is associated with corresponding changes in brain connectivity. We discuss similarities and differences related to previous longitudinal and cross-sectional studies, as well as the role of sex differences in providing a theoretical framework to guide future longitudinal research on the trajectory of cognitive decline. In addition to demonstrating the power of longitudinal studies, these data highlight the importance of middle-age for identifying potential predictive indicators of sexual dimorphism in the trajectory in brain and cognitive aging.
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Affiliation(s)
- Marcelo Febo
- Department of Psychiatry, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Asha Rani
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Brittney Yegla
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Jolie Barter
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Ashok Kumar
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Christopher A Wolff
- Department of Physiology and Functional Genomics, Myology Institute, University of Florida, Gainesville, FL, United States
| | - Karyn Esser
- Department of Physiology and Functional Genomics, Myology Institute, University of Florida, Gainesville, FL, United States
| | - Thomas C Foster
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.,Genetics and Genomics Program, University of Florida, Gainesville, FL, United States
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17
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Jiang H, Lu N, Chen K, Yao L, Li K, Zhang J, Guo X. Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks. Front Neurol 2020; 10:1346. [PMID: 31969858 PMCID: PMC6960113 DOI: 10.3389/fneur.2019.01346] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 12/06/2019] [Indexed: 12/21/2022] Open
Abstract
Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain undergoes age-related neuroanatomical changes not only regionally but also on the network level during the normal development and aging process. In recent years, many studies have focused on estimating age using structural MRI measurements. However, the age prediction effects on different structural networks remain unclear. In this study, we established age prediction models based on common structural networks using convolutional neural networks (CNN) with data from 1,454 healthy subjects aged 18–90 years. First, based on the reference map of CorticalParcellation_Yeo2011, we obtained structural network images for each subject, including images of the following: the frontoparietal network (FPN), the dorsal attention network (DAN), the default mode network (DMN), the somatomotor network (SMN), the ventral attention network (VAN), the visual network (VN), and the limbic network (LN). Then, we built a 3D CNN model for each structural network using a large training dataset (n = 1,303) and the predicted ages of the subjects in the test dataset (n = 151). Finally, we estimated the age prediction performance of CNN compared with Gaussian process regression (GPR) and relevance vector regression (RVR). The results of CNN showed that the FPN, DAN, and DMN exhibited the optimal age prediction accuracies with mean absolute errors (MAEs) of 5.55 years, 5.77 years, and 6.07 years, respectively, and the other four networks, i.e., the SMN, VAN, VN, and LN, tended to have larger MAEs of more than 8 years. With respect to GPR and RVR, the top three prediction accuracies were still from the FPN, DAN, and DMN; moreover, CNN made more precise predictions than GPR and RVR for these three networks. Our findings suggested that CNN has the optimal age prediction performance, and our age prediction model can be potentially used for brain disorder diagnosis according to age prediction differences.
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Affiliation(s)
- Huiting Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Na Lu
- College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Li Yao
- College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Ke Li
- Laboratory of Magnetic Resonance Imaging, The 306th Hospital of PLA, Beijing, China
| | - Jiacai Zhang
- College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Xiaojuan Guo
- College of Information Science and Technology, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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18
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Zuo N, Salami A, Liu H, Yang Z, Jiang T. Functional maintenance in the multiple demand network characterizes superior fluid intelligence in aging. Neurobiol Aging 2020; 85:145-153. [DOI: 10.1016/j.neurobiolaging.2019.09.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 08/20/2019] [Accepted: 09/14/2019] [Indexed: 12/13/2022]
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19
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Eich TS, Tsapanou A, Stern Y. When time's arrow doesn't bend: APOE-ε4 influences episodic memory before old age. Neuropsychologia 2019; 133:107180. [PMID: 31473197 PMCID: PMC6817416 DOI: 10.1016/j.neuropsychologia.2019.107180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 08/23/2019] [Accepted: 08/28/2019] [Indexed: 01/28/2023]
Abstract
Episodic memory impairment is the hallmark symptom of Alzheimer's Disease (AD). However, episodic memory has also been shown to decline across the lifespan. Here, we investigated whether episodic memory is differentially affected relative to other cognitive abilities before old age, and whether being an Apolipoprotein E (APOE) ε4 carrier -a genetic risk factor for developing AD-exacerbates any such impairments. We used general linear models to test for performance differences within 4 composite measures of cognition - episodic memory, semantic memory, speed of processing, and fluid reasoning-- as a function of age group (young, Mage = 30.21 vs. middle-aged, Mage = 50.84) and APOE-ε4 genotype status (ε4+ vs. ε4-). We replicated findings of age-related reductions in episodic memory, speed of processing, and fluid reasoning, and age-related increases in semantic memory. However, we also found that APOE genotype status moderated the age-related declines in episodic memory: APOE-ε4+ middle-aged adults exhibited impairments relative to both APOE-ε4- middle-aged participants, and APOE-ε4+ younger adults. These results suggest specific and dynamic alterations to episodic memory as a function of APOE allelic variation and age.
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Affiliation(s)
- Teal S Eich
- Leonard Davis School of Gerontology, University of Southern California, USA; Cognitive Neuroscience Division, Department of Neurology and the Taub Institute, Columbia University, USA.
| | - Angeliki Tsapanou
- Cognitive Neuroscience Division, Department of Neurology and the Taub Institute, Columbia University, USA
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology and the Taub Institute, Columbia University, USA
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20
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Stojan R, Voelcker-Rehage C. A Systematic Review on the Cognitive Benefits and Neurophysiological Correlates of Exergaming in Healthy Older Adults. J Clin Med 2019; 8:jcm8050734. [PMID: 31126052 PMCID: PMC6571688 DOI: 10.3390/jcm8050734] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 05/19/2019] [Accepted: 05/20/2019] [Indexed: 12/16/2022] Open
Abstract
Human aging is associated with structural and functional brain deteriorations and a corresponding cognitive decline. Exergaming (i.e., physically active video-gaming) has been supposed to attenuate age-related brain deteriorations and may even improve cognitive functions in healthy older adults. Effects of exergaming, however, vary largely across studies. Moreover, the underlying neurophysiological mechanisms by which exergaming may affect cognitive and brain function are still poorly understood. Therefore, we systematically reviewed the effects of exergame interventions on cognitive outcomes and neurophysiological correlates in healthy older adults (>60 years). After screening 2709 studies (Cochrane Library, PsycINFO, Pubmed, Scopus), we found 15 eligible studies, four of which comprised neurophysiological measures. Most studies reported within group improvements in exergamers and favorable interaction effects compared to passive controls. Fewer studies found superior effects of exergaming over physically active control groups and, if so, solely for executive functions. Regarding individual cognitive domains, results showed no consistence. Positive effects on neurophysiological outcomes were present in all respective studies. In summary, exergaming seems to be equally or slightly more effective than other physical interventions on cognitive functions in healthy older adults. Tailored interventions using well-considered exergames and intervention designs, however, may result in more distinct effects on cognitive functions.
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Affiliation(s)
- Robert Stojan
- Department of Human Movement Science and Health, Chemnitz University of Technology, Thueringer Weg 11, DE-09126 Chemnitz, Germany.
| | - Claudia Voelcker-Rehage
- Department of Human Movement Science and Health, Chemnitz University of Technology, Thueringer Weg 11, DE-09126 Chemnitz, Germany.
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21
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Richard G, Kolskår K, Sanders AM, Kaufmann T, Petersen A, Doan NT, Monereo Sánchez J, Alnæs D, Ulrichsen KM, Dørum ES, Andreassen OA, Nordvik JE, Westlye LT. Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry. PeerJ 2018; 6:e5908. [PMID: 30533290 PMCID: PMC6276592 DOI: 10.7717/peerj.5908] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 10/10/2018] [Indexed: 01/26/2023] Open
Abstract
Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequately characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18-87 years. To compare the tissue-specific brain ages and their cognitive sensitivity, we applied each of the 11 models in an independent and cognitively well-characterized sample (n = 265, 20-88 years). Correlations between true and estimated age and mean absolute error (MAE) in our test sample were highest for the most comprehensive brain morphometry (r = 0.83, CI:0.78-0.86, MAE = 6.76 years) and white matter microstructure (r = 0.79, CI:0.74-0.83, MAE = 7.28 years) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.
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Affiliation(s)
- Geneviève Richard
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Knut Kolskår
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Anne-Marthe Sanders
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anders Petersen
- Center for Visual Cognition, Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Nhat Trung Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jennifer Monereo Sánchez
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kristine M. Ulrichsen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Erlend S. Dørum
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Ole A. Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Lars T. Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
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22
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Gonzalez-Escamilla G, Muthuraman M, Chirumamilla VC, Vogt J, Groppa S. Brain Networks Reorganization During Maturation and Healthy Aging-Emphases for Resilience. Front Psychiatry 2018; 9:601. [PMID: 30519196 PMCID: PMC6258799 DOI: 10.3389/fpsyt.2018.00601] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 10/29/2018] [Indexed: 12/31/2022] Open
Abstract
Maturation and aging are important life periods that are linked to drastic brain reorganization processes which are essential for mental health. However, the development of generalized theories for delimiting physiological and pathological brain remodeling through life periods linked to healthy states and resilience on one side or mental dysfunction on the other remains a challenge. Furthermore, important processes of preservation and compensation of brain function occur continuously in the cerebral brain networks and drive physiological responses to life events. Here, we review research on brain reorganization processes across the lifespan, demonstrating brain circuits remodeling at the structural and functional level that support mental health and are parallelized by physiological trajectories during maturation and healthy aging. We show evidence that aberrations leading to mental disorders result from the specific alterations of cerebral networks and their pathological dynamics leading to distinct excitability patterns. We discuss how these series of large-scale responses of brain circuits can be viewed as protective or malfunctioning mechanisms for the maintenance of mental health and resilience.
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Affiliation(s)
| | - Muthuraman Muthuraman
- Department of Neurology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Venkata C. Chirumamilla
- Department of Neurology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Johannes Vogt
- Institute for Microscopic Anatomy and Neurobiology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
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23
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Girgis F, Lee DJ, Goodarzi A, Ditterich J. Toward a Neuroscience of Adult Cognitive Developmental Theory. Front Neurosci 2018; 12:4. [PMID: 29410608 PMCID: PMC5787085 DOI: 10.3389/fnins.2018.00004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 01/04/2018] [Indexed: 11/13/2022] Open
Abstract
Piaget's genetic epistemology has provided the constructivist approach upon which child developmental theories were founded, in that infants are thought to progress through distinct cognitive stages until they reach maturity in their early 20's. However, it is now well established that cognition continues to develop after early adulthood, and several “neo-Piagetian” theories have emerged in an attempt to better characterize adult cognitive development. For example, Kegan's Constructive Developmental Theory (CDT) argues that the thought processes used by adults to construct their reality change over time, and reaching higher stages of cognitive development entails becoming objectively aware of emotions and beliefs that were previously in the realm of the subconscious. In recent years, neuroscience has shown a growing interest in the biological substrates and neural mechanisms encompassing adult cognitive development, because psychological and psychiatric disorders can arise from deficiencies therein. In this article, we will use Kegan's CDT as a framework to discuss adult cognitive development in relation to closely correlated existing constructs underlying social processing, such as the perception of self and others. We will review the functional imaging and electrophysiologic evidence behind two key concepts relating to these posited developmental changes. These include self-related processing, a field that distinguishes between having conscious experiences (“being a self”) and being aware of oneself having conscious experiences (“being aware of being a self”); and theory of mind, which is the objective awareness of possessing mental states such as beliefs and desires (i.e., having a “mind”) and the understanding that others possess mental states that can be different from one's own. We shall see that cortical midline structures, including the medial prefrontal cortex and cingulate gyrus, as well as the temporal lobe, are associated with psychological tasks that test these models. In addition, we will review computational modeling approaches to cognitive development, and show how mathematical modeling can provide insights into how sometimes continuous changes in the neural processing substrate can give rise to relatively discrete developmental stages. Because deficiencies in adult cognitive development can result in disorders such as autism and depression, bridging the gaps between developmental psychology, neuroscience, and modeling has potential implications for clinical practice. As neuromodulation techniques such as deep brain and transcranial stimulation continue to advance, interfacing with these systems may lead to the emergence of novel investigational methods and therapeutic strategies in adults suffering from developmental disorders.
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Affiliation(s)
- Fady Girgis
- Department of Neurosurgery, University of California, Davis, Davis, CA, United States
| | - Darrin J Lee
- Department of Neurosurgery, University of Toronto, Toronto, ON, Canada
| | - Amir Goodarzi
- Department of Neurosurgery, University of California, Davis, Davis, CA, United States
| | - Jochen Ditterich
- Center for Neuroscience, University of California, Davis, Davis, CA, United States.,Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, United States
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Manto M, Huisman TAGM. The cerebellum from the fetus to the elderly: history, advances, and future challenges. HANDBOOK OF CLINICAL NEUROLOGY 2018; 155:407-413. [PMID: 29891075 DOI: 10.1016/b978-0-444-64189-2.00027-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
The cerebellum is now at the forefront of research in neuroscience. This is not just a coincidence, occurring about 250 years after the first description of the human cerebellum. The cerebellum contains the majority of neurons in the central nervous system and it is heavily connected with almost all cortical and subcortical areas of the supratentorial region as well as with the brainstem and the spinal cord. Cerebellar circuits are embedded in large-scale networks contributing to motor control and neurocognition. From a phenotypic standpoint, damage to cerebellar lobules interconnected with the sensorimotor cortices leads to a cerebellar motor syndrome, whereas lesions of the posterolateral cerebellum cause cognitive and neuropsychiatric impairments which may or may not be subtle. This topographic rule is valid in children and adults. Midline posterior vermal lesions cause behavioral/affective dysregulation, especially in kids. The extent of the spectrum of human cerebellar disorders is increasingly recognized from the fetus to the elderly, with recognition of consequences for the quality of life and socioeconomic costs due to lifelong morbidity of many cerebellar ataxias/pathologies. The prolonged duration of human cerebellar development makes the cerebellum especially susceptible to developmental disruption, both genetic and nongenetic. This explains the current emphasis on the clarification of the developmental course and impact of the cerebellum. The understanding of how germinal matrix zones and migration of neurons and glial cells end in a highly organized and foliated human cerebellum is essential. This is greatly accelerated by inputs from rodent developmental studies, in particular because cerebellar anatomy is conserved across species. Still, numerous questions on human fetal development remain unanswered. Although both advanced neuroimaging and genetic studies are currently leading to a better definition and understanding of the multitude of cerebellar symptoms, there is a gap, with a great need to develop therapies aiming at first, protection of the cerebellum during development, and second, restoration of cerebellar function in children and in adults. Dynamic profiles of the compensatory processes from newborns to elderly require specific studies.
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Affiliation(s)
- Mario Manto
- Neurology Service, CHU-Charleroi, Charleroi, Belgium; Neuroscience Service, Université de Mons, Mons, Belgium.
| | - Thierry A G M Huisman
- Section of Pediatric Neuroradiology, Division of Pediatric Radiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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