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Madden DJ, Merenstein JL, Mullin HA, Jain S, Rudolph MD, Cohen JR. Age-related differences in resting-state, task-related, and structural brain connectivity: graph theoretical analyses and visual search performance. Brain Struct Funct 2024:10.1007/s00429-024-02807-2. [PMID: 38856933 DOI: 10.1007/s00429-024-02807-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 05/13/2024] [Indexed: 06/11/2024]
Abstract
Previous magnetic resonance imaging (MRI) research suggests that aging is associated with a decrease in the functional interconnections within and between groups of locally organized brain regions (modules). Further, this age-related decrease in the segregation of modules appears to be more pronounced for a task, relative to a resting state, reflecting the integration of functional modules and attentional allocation necessary to support task performance. Here, using graph-theoretical analyses, we investigated age-related differences in a whole-brain measure of module connectivity, system segregation, for 68 healthy, community-dwelling individuals 18-78 years of age. We obtained resting-state, task-related (visual search), and structural (diffusion-weighted) MRI data. Using a parcellation of modules derived from the participants' resting-state functional MRI data, we demonstrated that the decrease in system segregation from rest to task (i.e., reconfiguration) increased with age, suggesting an age-related increase in the integration of modules required by the attentional demands of visual search. Structural system segregation increased with age, reflecting weaker connectivity both within and between modules. Functional and structural system segregation had qualitatively different influences on age-related decline in visual search performance. Functional system segregation (and reconfiguration) influenced age-related decline in the rate of visual evidence accumulation (drift rate), whereas structural system segregation contributed to age-related slowing of encoding and response processes (nondecision time). The age-related differences in the functional system segregation measures, however, were relatively independent of those associated with structural connectivity.
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Affiliation(s)
- David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA.
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, 27710, USA.
- Center for Cognitive Neuroscience, Duke University, Durham, NC, 27708, USA.
| | - Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
| | - Hollie A Mullin
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
- Department of Psychology, Pennsylvania State University, University Park, PA, 16802, USA
| | - Shivangi Jain
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC, 27710, USA
- AdventHealth Research Institute, Neuroscience Institute, Orlando, FL, 32804, USA
| | - Marc D Rudolph
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA
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Tao X, Zhu Z, Wang L, Li C, Sun L, Wang W, Gong W. Biomarkers of Aging and Relevant Evaluation Techniques: A Comprehensive Review. Aging Dis 2024; 15:977-1005. [PMID: 37611906 PMCID: PMC11081160 DOI: 10.14336/ad.2023.00808-1] [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: 06/03/2023] [Accepted: 08/08/2023] [Indexed: 08/25/2023] Open
Abstract
The risk of developing chronic illnesses and disabilities is increasing with age. To predict and prevent aging, biomarkers relevant to the aging process must be identified. This paper reviews the known molecular, cellular, and physiological biomarkers of aging. Moreover, we discuss the currently available technologies for identifying these biomarkers, and their applications and potential in aging research. We hope that this review will stimulate further research and innovation in this emerging and fast-growing field.
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Affiliation(s)
- Xue Tao
- Department of Research, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
| | - Ziman Zhu
- Beijing Rehabilitation Medicine Academy, Capital Medical University, Beijing, China.
| | - Liguo Wang
- Key Laboratory of Protein Sciences, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China.
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
| | - Liwei Sun
- School of Biomedical Engineering, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
| | - Wei Wang
- Department of Rehabilitation Radiology, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
| | - Weijun Gong
- Department of Neurological Rehabilitation, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
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Petersen B, Negri S, Milan M, Shi H, Reyff Z, Ballard C, Ihuoma J, Di Francesco A, Tarantini S. Editorial: Effects of vascular function and aging on brain circulation and neurodegeneration. FRONTIERS IN AGING 2024; 5:1385066. [PMID: 38505646 PMCID: PMC10948611 DOI: 10.3389/fragi.2024.1385066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 02/16/2024] [Indexed: 03/21/2024]
Affiliation(s)
- Benjamin Petersen
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Sharon Negri
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Madison Milan
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Helen Shi
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Zeke Reyff
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Cade Ballard
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Jennifer Ihuoma
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | | | - Stefano Tarantini
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Peggy and Charles Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
- International Training Program in Geroscience, Doctoral School of Basic and Translational Medicine/Department of Public Health, Semmelweis University, Budapest, Hungary
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Li M, Schilling KG, Gao F, Xu L, Choi S, Gao Y, Zu Z, Anderson AW, Ding Z, Landman BA, Gore JC. Quantification of mediation effects of white matter functional characteristics on cognitive decline in aging. Cereb Cortex 2024; 34:bhae114. [PMID: 38517178 PMCID: PMC10958767 DOI: 10.1093/cercor/bhae114] [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: 12/12/2023] [Revised: 02/29/2024] [Accepted: 03/03/2024] [Indexed: 03/23/2024] Open
Abstract
Cognitive decline with aging involves multifactorial processes, including changes in brain structure and function. This study focuses on the role of white matter functional characteristics, as reflected in blood oxygenation level-dependent signals, in age-related cognitive deterioration. Building on previous research confirming the reproducibility and age-dependence of blood oxygenation level-dependent signals acquired via functional magnetic resonance imaging, we here employ mediation analysis to test if aging affects cognition through white matter blood oxygenation level-dependent signal changes, impacting various cognitive domains and specific white matter regions. We used independent component analysis of resting-state blood oxygenation level-dependent signals to segment white matter into coherent hubs, offering a data-driven view of white matter's functional architecture. Through correlation analysis, we constructed a graph network and derived metrics to quantitatively assess regional functional properties based on resting-state blood oxygenation level-dependent fluctuations. Our analysis identified significant mediators in the age-cognition relationship, indicating that aging differentially influences cognitive functions by altering the functional characteristics of distinct white matter regions. These findings enhance our understanding of the neurobiological basis of cognitive aging, highlighting the critical role of white matter in maintaining cognitive integrity and proposing new approaches to assess interventions targeting cognitive decline in older populations.
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Affiliation(s)
- Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Fei Gao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
| | - Soyoung Choi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37240, United States
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37240, United States
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
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Schulz M, Petersen M, Cheng B, Thomalla G. Association of structural connectivity with functional brain network segregation in a middle-aged to elderly population. Front Aging Neurosci 2024; 16:1291162. [PMID: 38371399 PMCID: PMC10870644 DOI: 10.3389/fnagi.2024.1291162] [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: 09/08/2023] [Accepted: 01/03/2024] [Indexed: 02/20/2024] Open
Abstract
Introduction The deterioration of white matter pathways is one of the hallmarks of the ageing brain. In theory, this decrease in structural integrity leads to disconnection between regions of brain networks and thus to altered functional connectivity and a decrease in cognitive abilities. However, in many studies, associations between structural and functional connectivity are rather weak or not observed at all. System segregation, defined as the extent of partitioning between different resting state networks has increasingly gained attention in recent years as a new metric for functional changes in the aging brain. Yet there is a shortage of previous reports describing the association of structural integrity and functional segregation. Methods Therefore, we used a large a large sample of 2,657 participants from the Hamburg City Health Study, a prospective population-based study including participants aged 46-78 years from the metropolitan region Hamburg, Germany. We reconstructed structural and functional connectomes to analyze whether there is an association between age-related differences in structural connectivity and functional segregation, and whether this association is stronger than between structural connectivity and functional connectivity. In a second step, we investigated the relationship between functional segregation and executive cognitive function and tested whether this association is stronger than that between functional connectivity and executive cognitive function. Results We found a significant age-independent association between decreasing structural connectivity and decreasing functional segregation across the brain. In addition, decreasing functional segregation showed an association with decreasing executive cognitive function. On the contrary, no such association was observed between functional connectivity and structural connectivity or executive function. Discussion These results indicate that the segregation metric is a more sensitive biomarker of cognitive ageing than functional connectivity at the global level and offers a unique and more complementary network-based explanation.
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Affiliation(s)
- Maximilian Schulz
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
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Gajardo-Vidal A, Montembeault M, Lorca-Puls DL, Licata AE, Bogley R, Erlhoff S, Ratnasiri B, Ezzes Z, Battistella G, Tsoy E, Pereira CW, DeLeon J, Tee BL, Henry ML, Miller ZA, Rankin KP, Mandelli ML, Possin KL, Gorno-Tempini ML. Assessing processing speed and its neural correlates in the three variants of primary progressive aphasia with a non-verbal tablet-based task. Cortex 2024; 171:165-177. [PMID: 38000139 PMCID: PMC10922977 DOI: 10.1016/j.cortex.2023.10.011] [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: 05/26/2023] [Revised: 09/29/2023] [Accepted: 10/18/2023] [Indexed: 11/26/2023]
Abstract
Prior research has revealed distinctive patterns of impaired language abilities across the three variants of Primary Progressive Aphasia (PPA): nonfluent/agrammatic (nfvPPA), logopenic (lvPPA) and semantic (svPPA). However, little is known about whether, and to what extent, non-verbal cognitive abilities, such as processing speed, are impacted in PPA patients. This is because neuropsychological tests typically contain linguistic stimuli and require spoken output, being therefore sensitive to verbal deficits in aphasic patients. The aim of this study is to investigate potential differences in processing speed between PPA patients and healthy controls, and among the three PPA variants, using a brief non-verbal tablet-based task (Match) modeled after the WAIS-III digit symbol coding test, and to determine its neural correlates. Here, we compared performance on the Match task between PPA patients (n = 61) and healthy controls (n = 59) and across the three PPA variants. We correlated performance on Match with voxelwise gray and white matter volumes. We found that lvPPA and nfvPPA patients performed significantly worse on Match than healthy controls and svPPA patients. Worse performance on Match across PPA patients was associated with reduced gray matter volume in specific parts of the left middle frontal gyrus, superior parietal lobule, and precuneus, and reduced white matter volume in the left parietal lobe. To conclude, our behavioral findings reveal that processing speed is differentially impacted across the three PPA variants and provide support for the potential clinical utility of a tabled-based task (Match) to assess non-verbal cognition. In addition, our neuroimaging findings confirm the importance of a set of fronto-parietal regions that previous research has associated with processing speed and executive control. Finally, our behavioral and neuroimaging findings combined indicate that differences in processing speed are largely explained by the unequal distribution of atrophy in these fronto-parietal regions across the three PPA variants.
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Affiliation(s)
- Andrea Gajardo-Vidal
- Centro de Investigación en Complejidad Social (CICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile.
| | - Maxime Montembeault
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA; Douglas Mental Health University Institute, Montréal, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, Montréal, QC H3A 1A1, Canada
| | - Diego L Lorca-Puls
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA; Sección de Neurología, Departamento de Especialidades, Facultad de Medicina, Universidad de Concepción, Concepción, Chile
| | - Abigail E Licata
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Rian Bogley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Sabrina Erlhoff
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Buddhika Ratnasiri
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Zoe Ezzes
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Giovanni Battistella
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Elena Tsoy
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Christa Watson Pereira
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jessica DeLeon
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Boon Lead Tee
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Maya L Henry
- Department of Speech, Language, and Hearing Sciences, University of Texas, Austin, TX, USA
| | - Zachary A Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Katherine P Rankin
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Maria Luisa Mandelli
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Katherine L Possin
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
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Coetzee E, Absalom AR. Pharmacokinetic and Pharmacodynamic Changes in the Elderly: Impact on Anesthetics. Anesthesiol Clin 2023; 41:549-565. [PMID: 37516494 DOI: 10.1016/j.anclin.2023.02.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/31/2023]
Abstract
Anesthesiologists are increasingly required to care for frail elderly patients. A detailed knowledge of the influence of age on the pharmacokinetics and dynamics of the anesthetic drugs is essential for optimal safety and care. For most of the anesthetic drugs, the elderly need lower doses to achieve the same plasma concentrations, and at any given plasma and effect-site concentration, they will have more profound clinical effects than younger patients. Caution is required, with close monitoring of clinical effects and active titration of dose administration to achieve the desired level of effect, ideally following the "start low, go slow" principle.
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Affiliation(s)
- Ettienne Coetzee
- Department of Anaesthesia and Perioperative Medicine, Groote Schuur Hospital, D23, Observatory, Cape Town 7925, Republic of South Africa
| | - Anthony Ray Absalom
- Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Post Box 30.001, Groningen 9700 RB, the Netherlands.
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Brain network architecture constrains age-related cortical thinning. Neuroimage 2022; 264:119721. [PMID: 36341953 DOI: 10.1016/j.neuroimage.2022.119721] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/23/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Age-related cortical atrophy, approximated by cortical thickness measurements from magnetic resonance imaging, follows a characteristic pattern over the lifespan. Although its determinants remain unknown, mounting evidence demonstrates correspondence between the connectivity profiles of structural and functional brain networks and cortical atrophy in health and neurological disease. Here, we performed a cross-sectional multimodal neuroimaging analysis of 2633 individuals from a large population-based cohort to characterize the association between age-related differences in cortical thickness and functional as well as structural brain network topology. We identified a widespread pattern of age-related cortical thickness differences including "hotspots" of pronounced age effects in sensorimotor areas. Regional age-related differences were strongly correlated within the structurally defined node neighborhood. The overall pattern of thickness differences was found to be anchored in the functional network hierarchy as encoded by macroscale functional connectivity gradients. Lastly, the identified difference pattern covaried significantly with cognitive and motor performance. Our findings indicate that connectivity profiles of functional and structural brain networks act as organizing principles behind age-related cortical thinning as an imaging surrogate of cortical atrophy.
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Asci F, Scardapane S, Zampogna A, D’Onofrio V, Testa L, Patera M, Falletti M, Marsili L, Suppa A. Handwriting Declines With Human Aging: A Machine Learning Study. Front Aging Neurosci 2022; 14:889930. [PMID: 35601625 PMCID: PMC9120912 DOI: 10.3389/fnagi.2022.889930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundHandwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders.Materials and MethodsOne-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm.ResultsStroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83).DiscussionHandwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms.
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Affiliation(s)
| | - Simone Scardapane
- Department of Information, Electronic and Communication Engineering (DIET), Sapienza University of Rome, Rome, Italy
| | | | | | - Lucia Testa
- Department of Informatic, Automatic and Gestional Engineering (DIAG), Sapienza University of Rome, Rome, Italy
| | - Martina Patera
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Marco Falletti
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Luca Marsili
- Department of Neurology, Gardner Family Center for Parkinson’s Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, United States
| | - Antonio Suppa
- IRCCS Neuromed Institute, Pozzilli, Italy
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- *Correspondence: Antonio Suppa,
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