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Xue T, Zhang F, Zekelman LR, Zhang C, Chen Y, Cetin-Karayumak S, Pieper S, Wells WM, Rathi Y, Makris N, Cai W, O'Donnell LJ. TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography. Front Neurosci 2024; 18:1411797. [PMID: 38988766 PMCID: PMC11233814 DOI: 10.3389/fnins.2024.1411797] [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: 04/03/2024] [Accepted: 06/10/2024] [Indexed: 07/12/2024] Open
Abstract
Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.
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Affiliation(s)
- Tengfei Xue
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Leo R. Zekelman
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Chaoyi Zhang
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Yuqian Chen
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Steve Pieper
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - William M. Wells
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Weidong Cai
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Lauren J. O'Donnell
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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Qiu T, Liu Z, Rheault F, Legarreta JH, Valcourt Caron A, St‐Onge F, Strikwerda‐Brown C, Metz A, Dadar M, Soucy J, Pichet Binette A, Spreng RN, Descoteaux M, Villeneuve S. Structural white matter properties and cognitive resilience to tau pathology. Alzheimers Dement 2024; 20:3364-3377. [PMID: 38561254 PMCID: PMC11095478 DOI: 10.1002/alz.13776] [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: 10/03/2023] [Revised: 01/11/2024] [Accepted: 02/07/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION We assessed whether macro- and/or micro-structural white matter properties are associated with cognitive resilience to Alzheimer's disease pathology years prior to clinical onset. METHODS We examined whether global efficiency, an indicator of communication efficiency in brain networks, and diffusion measurements within the limbic network and default mode network moderate the association between amyloid-β/tau pathology and cognitive decline. We also investigated whether demographic and health/risk factors are associated with white matter properties. RESULTS Higher global efficiency of the limbic network, as well as free-water corrected diffusion measures within the tracts of both networks, attenuated the impact of tau pathology on memory decline. Education, age, sex, white matter hyperintensities, and vascular risk factors were associated with white matter properties of both networks. DISCUSSION White matter can influence cognitive resilience against tau pathology, and promoting education and vascular health may enhance optimal white matter properties. HIGHLIGHTS Aβ and tau were associated with longitudinal memory change over ∼7.5 years. White matter properties attenuated the impact of tau pathology on memory change. Health/risk factors were associated with white matter properties.
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Affiliation(s)
- Ting Qiu
- Douglas Mental Health University InstituteMontrealCanada
| | - Zhen‐Qi Liu
- Montreal Neurological InstituteDepartment of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
| | - François Rheault
- Medical Imaging and NeuroInformatics LabUniversité de SherbrookeSherbrookeCanada
| | - Jon Haitz Legarreta
- Department of RadiologyBrigham and Women's HospitalMass General Brigham/Harvard Medical SchoolBostonMassachusettsUSA
| | - Alex Valcourt Caron
- Sherbrooke Connectivity Imaging LaboratoryUniversité de SherbrookeSherbrookeCanada
| | | | - Cherie Strikwerda‐Brown
- Douglas Mental Health University InstituteMontrealCanada
- School of Psychological ScienceThe University of Western AustraliaPerthAustralia
| | - Amelie Metz
- Douglas Mental Health University InstituteMontrealCanada
| | - Mahsa Dadar
- Douglas Mental Health University InstituteMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
| | - Jean‐Paul Soucy
- Montreal Neurological InstituteDepartment of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
| | | | - R. Nathan Spreng
- Douglas Mental Health University InstituteMontrealCanada
- Montreal Neurological InstituteDepartment of Neurology and NeurosurgeryMcGill UniversityMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging LaboratoryUniversité de SherbrookeSherbrookeCanada
| | - Sylvia Villeneuve
- Douglas Mental Health University InstituteMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
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Zeller D, Hiew S, Odorfer T, Nguemeni C. Considering the response in addition to the challenge - a narrative review in appraisal of a motor reserve framework. Aging (Albany NY) 2024; 16:5772-5791. [PMID: 38499388 PMCID: PMC11006496 DOI: 10.18632/aging.205667] [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: 07/12/2023] [Accepted: 01/04/2024] [Indexed: 03/20/2024]
Abstract
The remarkable increase in human life expectancy over the past century has been achieved at the expense of the risk of age-related impairment and disease. Neurodegeneration, be it part of normal aging or due to neurodegenerative disorders, is characterized by loss of specific neuronal populations, leading to increasing clinical impairment. The individual course may be described as balance between aging- or disease-related pathology and intrinsic mechanisms of adaptation. There is plenty of evidence that the human brain is provided with exhaustible resources to maintain function in the face of adverse conditions. While a reserve concept has mainly been coined in cognitive neuroscience, emerging evidence suggests similar mechanisms to underlie individual differences of adaptive capacity within the motor system. In this narrative review, we summarize what has been proposed to date about a motor reserve (mR) framework. We present current evidence from research in aging subjects and people with neurological conditions, followed by a description of what is known about potential neuronal substrates of mR so far. As there is no gold standard of mR quantification, we outline current approaches which describe various indicators of mR. We conclude by sketching out potential future directions of research. Expediting our understanding of differences in individual motor resilience towards aging and disease will eventually contribute to new, individually tailored therapeutic strategies. Provided early diagnosis, enhancing the individual mR may be suited to postpone disease onset by years and may be an efficacious contribution towards healthy aging, with an increased quality of life for the elderly.
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Affiliation(s)
- Daniel Zeller
- Department of Neurology, University Hospital Würzburg, Würzburg 97080, Germany
| | - Shawn Hiew
- Department of Neurology, University Hospital Würzburg, Würzburg 97080, Germany
| | - Thorsten Odorfer
- Department of Neurology, University Hospital Würzburg, Würzburg 97080, Germany
| | - Carine Nguemeni
- Department of Neurology, University Hospital Würzburg, Würzburg 97080, Germany
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González Rodríguez LL, Osorio I, Cofre G. A, Hernandez Larzabal H, Román C, Poupon C, Mangin JF, Hernández C, Guevara P. Phybers: a package for brain tractography analysis. Front Neurosci 2024; 18:1333243. [PMID: 38529266 PMCID: PMC10962387 DOI: 10.3389/fnins.2024.1333243] [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: 11/04/2023] [Accepted: 02/09/2024] [Indexed: 03/27/2024] Open
Abstract
We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.
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Affiliation(s)
| | - Ignacio Osorio
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Alejandro Cofre G.
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Hernan Hernandez Larzabal
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
| | - Claudio Román
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | - Cyril Poupon
- CEA, CNRS, Baobab, Neurospin, Université Paris-Saclay, Gif-sur-Yvette, France
| | | | - Cecilia Hernández
- Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Center for Biotechnology and Bioengineering (CeBiB), Santiago, Chile
| | - Pamela Guevara
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
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Huang W, Dong X, Zhao T, Kucikova L, Fu A, Shu N. DCP: A pipeline toolbox for diffusion connectome. Hum Brain Mapp 2024; 45:e26626. [PMID: 38375916 PMCID: PMC10877999 DOI: 10.1002/hbm.26626] [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: 06/08/2023] [Revised: 12/29/2023] [Accepted: 02/02/2024] [Indexed: 02/21/2024] Open
Abstract
The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.
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Affiliation(s)
- Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
- School of Systems Science, Beijing Normal UniversityBeijingPR China
- Department of NeuroscienceSheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of SheffieldSheffieldUK
| | - Xinyi Dong
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Ludmila Kucikova
- Department of NeuroscienceSheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of SheffieldSheffieldUK
| | - Anguo Fu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
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Byun K, Hyodo K, Suwabe K, Fukuie T, Ha MS, Damrongthai C, Kuwamizu R, Koizumi H, Yassa MA, Soya H. Mild exercise improves executive function with increasing neural efficiency in the prefrontal cortex of older adults. GeroScience 2024; 46:309-325. [PMID: 37318716 PMCID: PMC10828372 DOI: 10.1007/s11357-023-00816-3] [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: 01/05/2023] [Accepted: 04/27/2023] [Indexed: 06/16/2023] Open
Abstract
This study examined whether a 3-month mild-exercise intervention could improve executive function in healthy middle-aged and older adults in a randomized control trial. Ultimately, a total of 81 middle-aged and older adults were randomly assigned to either an exercise group or a control group. The exercise group received 3 months of mild cycle exercise intervention (3 sessions/week, 30-50 min/session). The control group was asked to behave as usual for the intervention period. Before and after the intervention, participants did color-word matching Stroop tasks (CWST), and Stroop interference (SI)-related reaction time (RT) was assessed as an indicator of executive function. During the CWST, prefrontal activation was monitored using functional near-infrared spectroscopy (fNIRS). SI-related oxy-Hb changes and SI-related neural efficiency (NE) scores were assessed to examine the underlying neural mechanism of the exercise intervention. Although the mild-exercise intervention significantly decreased SI-related RT, there were no significant effects of exercise intervention on SI-related oxy-Hb changes or SI-related NE scores in prefrontal subregions. Lastly, changes in the effects of mild exercise on NE with advancing age were examined. The 81 participants were divided into two subgroups (younger-aged subgroup [YA], older-aged subgroup [OA], based on median age [68 years.]). Interestingly, SI-related RT significantly decreased, and SI-related NE scores in all ROIs of the prefrontal cortex significantly increased only in the OA subgroup. These results reveal that a long-term intervention of very light-intensity exercise has a positive effect on executive function especially in older adults, possibly by increasing neural efficiency in the prefrontal cortex.
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Affiliation(s)
- Kyeongho Byun
- Sport Neuroscience Division, Advanced Research Initiative for Human High Performance (ARIHHP), Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Division of Sport Science; Sport Science Institute & Health Promotion Center, College of Arts & Physical Education, Incheon National University, Yeonsu, Incheon, Republic of Korea
| | - Kazuki Hyodo
- Physical Fitness Research Institute, Meiji Yasuda Life Foundation of Health and Welfare, Hachioji, Tokyo, Japan
- Laboratory of Exercise Biochemistry and Neuroendocrinology, Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Kazuya Suwabe
- Sport Neuroscience Division, Advanced Research Initiative for Human High Performance (ARIHHP), Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Faculty of Health and Sport Sciences, Ryutsu Keizai University, Ryugasaki, Ibaraki, Japan
| | - Takemune Fukuie
- Laboratory of Exercise Biochemistry and Neuroendocrinology, Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Min-Seong Ha
- Sport Neuroscience Division, Advanced Research Initiative for Human High Performance (ARIHHP), Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Department of Sports Science, College of the Arts and Sports, University of Seoul, Dongdaemun, Seoul, Republic of Korea
| | - Chorphaka Damrongthai
- Sport Neuroscience Division, Advanced Research Initiative for Human High Performance (ARIHHP), Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
- Laboratory of Exercise Biochemistry and Neuroendocrinology, Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Ryuta Kuwamizu
- Laboratory of Exercise Biochemistry and Neuroendocrinology, Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Hikaru Koizumi
- Laboratory of Exercise Biochemistry and Neuroendocrinology, Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Michael A Yassa
- Sport Neuroscience Division, Advanced Research Initiative for Human High Performance (ARIHHP), Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan.
- Department of Neurobiology and Behavior, Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA.
| | - Hideaki Soya
- Sport Neuroscience Division, Advanced Research Initiative for Human High Performance (ARIHHP), Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan.
- Laboratory of Exercise Biochemistry and Neuroendocrinology, Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan.
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7
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Krämer C, Stumme J, da Costa Campos L, Dellani P, Rubbert C, Caspers J, Caspers S, Jockwitz C. Prediction of cognitive performance differences in older age from multimodal neuroimaging data. GeroScience 2024; 46:283-308. [PMID: 37308769 PMCID: PMC10828156 DOI: 10.1007/s11357-023-00831-4] [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: 03/02/2023] [Accepted: 05/17/2023] [Indexed: 06/14/2023] Open
Abstract
Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.
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Affiliation(s)
- Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lucas da Costa Campos
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Paulo Dellani
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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Zhang Y, Wang X, Li W, Yang Y, Wu Z, Lyu Y, Yue C. Intestinal microbiota: a new perspective on delaying aging? Front Microbiol 2023; 14:1268142. [PMID: 38098677 PMCID: PMC10720643 DOI: 10.3389/fmicb.2023.1268142] [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: 07/27/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023] Open
Abstract
The global aging situation is severe, and the medical pressures associated with aging issues should not be underestimated. The need and feasibility of studying aging and intervening in aging have been confirmed. Aging is a complex natural physiological progression, which involves the irreversible deterioration of body cells, tissues, and organs with age, leading to enhanced risk of disease and ultimately death. The intestinal microbiota has a significant role in sustaining host dynamic balance, and the study of bidirectional communication networks such as the brain-gut axis provides important directions for human disease research. Moreover, the intestinal microbiota is intimately linked to aging. This review describes the intestinal microbiota changes in human aging and analyzes the causal controversy between gut microbiota changes and aging, which are believed to be mutually causal, mutually reinforcing, and inextricably linked. Finally, from an anti-aging perspective, this study summarizes how to achieve delayed aging by targeting the intestinal microbiota. Accordingly, the study aims to provide guidance for further research on the intestinal microbiota and aging.
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Affiliation(s)
- Yuemeng Zhang
- Yan’an Key Laboratory of Microbial Drug Innovation and Transformation, School of Basic Medicine, Yan’an University, Yan’an, Shaanxi, China
| | - Xiaomei Wang
- Yan’an University of Physical Education, Yan’an University, Yan’an, Shaanxi, China
| | - Wujuan Li
- Yan’an Key Laboratory of Microbial Drug Innovation and Transformation, School of Basic Medicine, Yan’an University, Yan’an, Shaanxi, China
| | - Yi Yang
- Yan’an Key Laboratory of Microbial Drug Innovation and Transformation, School of Basic Medicine, Yan’an University, Yan’an, Shaanxi, China
| | - Zhuoxuan Wu
- Yan’an Key Laboratory of Microbial Drug Innovation and Transformation, School of Basic Medicine, Yan’an University, Yan’an, Shaanxi, China
| | - Yuhong Lyu
- Yan’an Key Laboratory of Microbial Drug Innovation and Transformation, School of Basic Medicine, Yan’an University, Yan’an, Shaanxi, China
| | - Changwu Yue
- Yan’an Key Laboratory of Microbial Drug Innovation and Transformation, School of Basic Medicine, Yan’an University, Yan’an, Shaanxi, China
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Richter A, Soch J, Kizilirmak JM, Fischer L, Schütze H, Assmann A, Behnisch G, Feldhoff H, Knopf L, Raschick M, Schult A, Seidenbecher CI, Yakupov R, Düzel E, Schott BH. Single‐value scores of memory‐related brain activity reflect dissociable neuropsychological and anatomical signatures of neurocognitive aging. Hum Brain Mapp 2023; 44:3283-3301. [PMID: 36972323 PMCID: PMC10171506 DOI: 10.1002/hbm.26281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 02/27/2023] [Accepted: 03/08/2023] [Indexed: 03/29/2023] Open
Abstract
Memory-related functional magnetic resonance imaging (fMRI) activations show age-related differences across multiple brain regions that can be captured in summary statistics like single-value scores. Recently, we described two single-value scores reflecting deviations from prototypical whole-brain fMRI activity of young adults during novelty processing and successful encoding. Here, we investigate the brain-behavior associations of these scores with age-related neurocognitive changes in 153 healthy middle-aged and older adults. All scores were associated with episodic recall performance. The memory network scores, but not the novelty network scores, additionally correlated with medial temporal gray matter and other neuropsychological measures including flexibility. Our results thus suggest that novelty-network-based fMRI scores show high brain-behavior associations with episodic memory and that encoding-network-based fMRI scores additionally capture individual differences in other aging-related functions. More generally, our results suggest that single-value scores of memory-related fMRI provide a comprehensive measure of individual differences in network dysfunction that may contribute to age-related cognitive decline.
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10
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Zhao H, Wen W, Cheng J, Jiang J, Kochan N, Niu H, Brodaty H, Sachdev P, Liu T. An accelerated degeneration of white matter microstructure and networks in the nondemented old-old. Cereb Cortex 2022; 33:4688-4698. [PMID: 36178117 DOI: 10.1093/cercor/bhac372] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 11/12/2022] Open
Abstract
The nondemented old-old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young-old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old-old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old-old compared with young-old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old-old but not in the young-old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.
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Affiliation(s)
- Haichao Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia
| | - Nicole Kochan
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA), University of New South Wales, Sydney, NSW, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
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11
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Seer C, Adab HZ, Sidlauskaite J, Dhollander T, Chalavi S, Gooijers J, Sunaert S, Swinnen SP. Bridging cognition and action: executive functioning mediates the relationship between white matter fiber density and complex motor abilities in older adults. Aging (Albany NY) 2022; 14:7263-7281. [PMID: 35997651 PMCID: PMC9550248 DOI: 10.18632/aging.204237] [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: 05/04/2022] [Accepted: 07/11/2022] [Indexed: 11/25/2022]
Abstract
Aging may be associated with motor decline that is attributed to deteriorating white matter microstructure of the corpus callosum (CC), among other brain-related factors. Similar to motor functioning, executive functioning (EF) typically declines during aging, with age-associated changes in EF likewise being linked to altered white matter connectivity in the CC. Given that both motor and executive functions rely on white matter connectivity via the CC, and that bimanual control is thought to rely on EF, the question arises whether EF can at least party account for the proposed link between CC-connectivity and motor control in older adults. To address this, diffusion magnetic resonance imaging data were obtained from 84 older adults. A fiber-specific approach was used to obtain fiber density (FD), fiber cross-section (FC), and a combination of both metrics in eight transcallosal white matter tracts. Motor control was assessed using a bimanual coordination task. EF was determined by a domain-general latent EF-factor extracted from multiple EF tasks, based on a comprehensive test battery. FD of transcallosal prefrontal fibers was associated with cognitive and motor performance. EF partly accounted for the relationship between FD of prefrontal transcallosal pathways and motor control. Our results underscore the multidimensional interrelations between callosal white matter connectivity (especially in prefrontal brain regions), EF across multiple domains, and motor control in the older population. They also highlight the importance of considering EF when investigating brain-motor behavior associations in older adults.
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Affiliation(s)
- Caroline Seer
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Hamed Zivari Adab
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Justina Sidlauskaite
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | | | - Sima Chalavi
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jolien Gooijers
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Stefan Sunaert
- Department of Imaging and Pathology, KU Leuven and University Hospital Leuven (UZ Leuven), Leuven, Belgium
| | - Stephan P Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
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12
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Chang YK, Erickson KI, Aghjayan SL, Chen FT, Li RH, Shih JR, Chang SH, Huang CM, Chu CH. The multi-domain exercise intervention for memory and brain function in late middle-aged and older adults at risk for Alzheimer's disease: A protocol for Western-Eastern Brain Fitness Integration Training trial. Front Aging Neurosci 2022; 14:929789. [PMID: 36062144 PMCID: PMC9435311 DOI: 10.3389/fnagi.2022.929789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background Aging is associated with cognitive decline, increased risk for dementia, and deterioration of brain function. Modifiable lifestyle factors (e.g., exercise, meditation, and social interaction) have been proposed to benefit memory and brain function. However, previous studies have focused on a single exercise modality or a single lifestyle factor. Consequently, the effect of a more comprehensive exercise program that combines multiple exercise modalities and lifestyle factors, as well as examines potential mediators and moderators, on cognitive function and brain health in late middle-aged and older adults remains understudied. This study's primary aim is to examine the effect of a multi-domain exercise intervention on memory and brain function in cognitively healthy late middle-aged and older adults. In addition, we will examine whether apolipoprotein E (ApoE) genotypes, physical fitness (i.e., cardiovascular fitness, body composition, muscular fitness, flexibility, balance, and power), and brain-derived neurotrophic factor (BDNF) moderate and mediate the exercise intervention effects on memory and brain function. Methods The Western-Eastern Brain Fitness Integration Training (WE-BFit) is a single-blinded, double-arm, 6-month randomized controlled trial. One hundred cognitively healthy adults, aged 45-70 years, with different risks for Alzheimer's disease (i.e., ApoE genotype) will be recruited and randomized into either a multi-domain exercise group or an online educational course control group. The exercise intervention consists of one 90-min on-site and several online sessions up to 60 min per week for 6 months. Working memory, episodic memory, physical fitness, and BDNF will be assessed before and after the 6-month intervention. The effects of the WE-BFit on memory and brain function will be described and analyzed. We will further examine how ApoE genotype and changes in physical fitness and BDNF affect the effects of the intervention. Discussion WE-BFit is designed to improve memory and brain function using a multi-domain exercise intervention. The results will provide insight into the implementation of an exercise intervention with multiple domains to preserve memory and brain function in adults with genetic risk levels for Alzheimer's disease. Clinical trial registration ClinicalTrials.gov, identifier: NCT05068271.
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Affiliation(s)
- Yu-Kai Chang
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
- Institute for Research Excellence in Learning Science, National Taiwan Normal University, Taipei, Taiwan
| | - Kirk I. Erickson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, United States
- AdventHealth Research Institute, Neuroscience Institute, Orlando, FL, United States
| | - Sarah L. Aghjayan
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Feng-Tzu Chen
- Department of Sport Medicine, China Medical University, Taichung, Taiwan
| | - Ruei-Hong Li
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
| | - Jia-Ru Shih
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
| | - Shao-Hsi Chang
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chien-Heng Chu
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
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13
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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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14
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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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15
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Feng G, Wang Y, Huang W, Chen H, Dai Z, Ma G, Li X, Zhang Z, Shu N. Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome. Hum Brain Mapp 2022; 43:3775-3791. [PMID: 35475571 PMCID: PMC9294303 DOI: 10.1002/hbm.25883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/22/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022] Open
Abstract
An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Yiwen Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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16
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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17
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Huang W, Li X, Li H, Wang W, Chen K, Xu K, Zhang J, Chen Y, Wei D, Shu N, Zhang Z. Accelerated Brain Aging in Amnestic Mild Cognitive Impairment: Relationships with Individual Cognitive Decline, Risk Factors for Alzheimer Disease, and Clinical Progression. Radiol Artif Intell 2021; 3:e200171. [PMID: 34617021 PMCID: PMC8489444 DOI: 10.1148/ryai.2021200171] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 05/06/2021] [Accepted: 06/07/2021] [Indexed: 06/13/2023]
Abstract
PURPOSE To determine whether a brain age prediction model could quantify individual deviations from a healthy brain-aging trajectory (predicted age difference [PAD]) in patients with amnestic mild cognitive impairment (aMCI) and to determine if PAD was associated with individual cognitive impairment. MATERIALS AND METHODS In this retrospective study, a machine learning approach was trained to determine brain age based on T1-weighted MRI scans. Two datasets were used for model training and testing-the Beijing Aging Brain Rejuvenation Initiative (BABRI) (616 healthy controls and 80 patients with aMCI, 2010-2018) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) (589 healthy controls and 144 patients with aMCI, 2010-2018). A total of 974 healthy controls were used for model training (490 from BABRI and 484 from ADNI; age range, 49-95 years). The trained model was then tested on both healthy controls (126 from BABRI and 105 from ADNI) and patients with aMCI (80 from BABRI and 144 from ADNI) to estimate PAD (predicted age - actual age). Furthermore, the associations between PAD with cognitive impairment, genetic risk factors and pathologic markers of Alzheimer disease (AD), and clinical progression in patients with aMCI were examined using a partial correlation analysis, a two-way analysis of covariance, and a general linear model, respectively. RESULTS Based on the prediction model, patients with aMCI were found to have higher PADs than those of healthy controls (BABRI: 2.65 ± 4.91 [standard deviation] vs 0.18 ± 4.79 [P < .001]; ADNI: 1.68 ± 5.28 vs 0.05 ± 4.41 [P < .001]). Moreover, the PAD was significantly associated with individual cognitive impairment in several cognitive domains in patients with aMCI (P < .05, corrected). When considering different AD-related risk factors, apolipoprotein E ε4 allele carriers were observed to have higher PADs than noncarriers (3.76 ± 4.82 vs 0.10 ± 5.05; P = .017), and patients with amyloid-positive aMCI were observed to have higher PADs than patients with amyloid-negative status (2.40 ± 5.25 vs 0.93 ± 5.20; P = .003). Finally, PAD combined with other markers of AD at baseline for differentiating between progressive and stable aMCI resulted in an area under the curve value of 0.87. CONCLUSION The PAD is a sensitive imaging marker related to individual cognitive differences in patients with aMCI.Keywords: MR Imaging, Brain/Brain Stem, Brain Age, Machine Learning, Mild Cognitive Impairment, Structural MRI Supplemental material is available for this article. © RSNA, 2021.
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18
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Ma L, Tian L, Hu T, Jiang T, Zuo N. Development of Individual Variability in Brain Functional Connectivity and Capability across the Adult Lifespan. Cereb Cortex 2021; 31:3925-3938. [PMID: 33822909 DOI: 10.1093/cercor/bhab059] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 01/26/2021] [Accepted: 02/07/2021] [Indexed: 11/14/2022] Open
Abstract
Individual variability exists in both brain function and behavioral performance. However, changes in individual variability in brain functional connectivity and capability across adult development and aging have not yet been clearly examined. Based on resting-state functional magnetic resonance imaging data from a large cohort of participants (543 adults, aged 18-88 years), brain functional connectivity was analyzed to characterize the spatial distribution and differences in individual variability across the adult lifespan. Results showed high individual variability in the association cortex over the adult lifespan, whereas individual variability in the primary cortex was comparably lower in the initial stage but increased with age. Individual variability was also negatively correlated with the strength/number of short-, medium-, and long-range functional connections in the brain, with long-range connections playing a more critical role in increasing global individual variability in the aging brain. More importantly, in regard to specific brain regions, individual variability in the motor cortex was significantly correlated with differences in motor capability. Overall, we identified specific patterns of individual variability in brain functional structure during the adult lifespan and demonstrated that functional variability in the brain can reflect behavioral performance. These findings advance our understanding of the underlying principles of the aging brain across the adult lifespan and suggest how to characterize degenerating behavioral capability using imaging biomarkers.
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Affiliation(s)
- Liying Ma
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Lixia Tian
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Tianyu Hu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China.,Chinese Institute for Brain Research, Beijing 102206, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Key Laboratory for Neuro-Information of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China.,Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Nianming Zuo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China.,Chinese Institute for Brain Research, Beijing 102206, China
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Madden DJ, Jain S, Monge ZA, Cook AD, Lee A, Huang H, Howard CM, Cohen JR. Influence of structural and functional brain connectivity on age-related differences in fluid cognition. Neurobiol Aging 2020; 96:205-222. [PMID: 33038808 PMCID: PMC7722190 DOI: 10.1016/j.neurobiolaging.2020.09.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 08/08/2020] [Accepted: 09/05/2020] [Indexed: 01/01/2023]
Abstract
We used graph theoretical measures to investigate the hypothesis that structural brain connectivity constrains the influence of functional connectivity on the relation between age and fluid cognition. Across 143 healthy, community-dwelling adults 19-79 years of age, we estimated structural network properties from diffusion-weighted imaging and functional network properties from resting-state functional magnetic resonance imaging. We confirmed previous reports of age-related decline in the strength and efficiency of structural networks, as well as in the connectivity strength within and between structural network modules. Functional networks, in contrast, exhibited age-related decline only in system segregation, a measure of the distinctiveness among network modules. Aging was associated with decline in a composite measure of fluid cognition, particularly tests of executive function. Functional system segregation was a significant mediator of age-related decline in executive function. Structural network properties did not directly influence the age-related decline in functional system segregation. The raw correlational data underlying the graph theoretical measures indicated that structural connectivity exerts a limited constraint on age-related decline in functional connectivity.
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Affiliation(s)
- David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.
| | - Shivangi Jain
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Zachary A Monge
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
| | - Angela D Cook
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Alexander Lee
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Hua Huang
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
| | - Cortney M Howard
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
| | - Jessica R Cohen
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA; Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Relationship between the disrupted topological efficiency of the structural brain connectome and glucose hypometabolism in normal aging. Neuroimage 2020; 226:117591. [PMID: 33248254 DOI: 10.1016/j.neuroimage.2020.117591] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 11/02/2020] [Accepted: 11/19/2020] [Indexed: 12/18/2022] Open
Abstract
Normal aging is accompanied by structural degeneration and glucose hypometabolism in the human brain. However, the relationship between structural network disconnections and hypometabolism in normal aging remains largely unknown. In the present study, by combining MRI and PET techniques, we investigated the metabolic mechanism of the structural brain connectome and its relationship with normal aging in a cross-sectional, community-based cohort of 42 cognitively normal elderly individuals aged 57-84 years. The structural connectome was constructed based on diffusion MRI tractography, and the network efficiency metrics were quantified using graph theory analyses. FDG-PET scanning was performed to evaluate the glucose metabolic level in the cortical regions of the individuals. The results of this study demonstrated that both network efficiency and cortical metabolism decrease with age (both p < 0.05). In the subregions of the bilateral thalamus, significant correlations between nodal efficiency and cortical metabolism could be observed across subjects. Individual-level analyses indicated that brain regions with higher nodal efficiency tend to exhibit higher metabolic levels, implying a tight coupling between nodal efficiency and glucose metabolism (r = 0.56, p = 1.15 × 10-21). Moreover, efficiency-metabolism coupling coefficient significantly increased with age (r = 0.44, p = 0.0046). Finally, the main findings were also reproducible in the ADNI dataset. Together, our results demonstrate a close coupling between structural brain connectivity and cortical metabolism in normal elderly individuals and provide new insight that improve the present understanding of the metabolic mechanisms of structural brain disconnections in normal aging.
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