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Clements GM, Camacho P, Bowie DC, Low KA, Sutton BP, Gratton G, Fabiani M. Effects of Aging, Fitness, and Cerebrovascular Status on White Matter Microstructural Health. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.04.606520. [PMID: 39211213 PMCID: PMC11361032 DOI: 10.1101/2024.08.04.606520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
White matter (WM) microstructural health declines with increasing age, with evidence suggesting that improved cardiorespiratory fitness (CRF) may mitigate this decline. Specifically, higher fit older adults tend to show preserved WM microstructural integrity compared to their lower fit counterparts. However, the extent to which fitness and aging independently impact WM integrity across the adult lifespan is still an open question, as is the extent to which cerebrovascular health mediates these relationships. In a large sample (N = 125, aged 25-72), we assessed the impact of age and fitness on fractional anisotropy (FA, derived using diffusion weighted imaging, DWI) and probed the mediating role of cerebrovascular health (derived using diffuse optical tomography of the cerebral arterial pulse, pulse-DOT) in these relationships. After orthogonalizing age and fitness and computing a PCA on whole brain WM regions, we found several WM regions impacted by age that were independent from the regions impacted by fitness (hindbrain areas, including brainstem and cerebellar tracts), whereas other areas showed interactive effects of age and fitness (midline areas, including fornix and corpus callosum). Critically, cerebrovascular health mediated both relationships suggesting that vascular health plays a linking role between age, fitness, and brain health. Secondarily, we assessed potential sex differences in these relationships and found that, although females and males generally showed the same age-related FA declines, males exhibited somewhat steeper declines than females. Together, these results suggest that age and fitness impact specific WM regions and highlight the mediating role of cerebrovascular health in maintaining WM health across adulthood.
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Ng C, Huang P, Cho Y, Lee P, Liu Y, Chang T. Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth. Hum Brain Mapp 2024; 45:e26777. [PMID: 39046114 PMCID: PMC11267564 DOI: 10.1002/hbm.26777] [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: 08/18/2023] [Revised: 06/03/2024] [Accepted: 06/19/2024] [Indexed: 07/25/2024] Open
Abstract
The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.
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Affiliation(s)
- Chan‐Tat Ng
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Po‐Hsien Huang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Cheng Cho
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
| | - Pei‐Hong Lee
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Yi‐Chang Liu
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
| | - Ting‐Ting Chang
- Department of PsychologyNational Chengchi UniversityTaipeiTaiwan
- Research Center for Mind, Brain & LearningNational Chengchi UniversityTaipeiTaiwan
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Golestani AM, Chen JJ. Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging. Front Neurosci 2024; 18:1223230. [PMID: 38379761 PMCID: PMC10876882 DOI: 10.3389/fnins.2024.1223230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 01/17/2024] [Indexed: 02/22/2024] Open
Abstract
Introduction Physiological nuisance contributions by cardiac and respiratory signals have a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution. Methods In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on the consequence of changes in low-frequency, cardiac and respiratory signal power, as well as age-related differences in terms of functional connectivity (fcMRI). Results Our results confirm that the ICA-AROMA and GSR removed the most physiological noise but also more low-frequency signals. These methods are also associated with substantially lower age-related fcMRI differences. On the other hand, aCompCor and tCompCor appear to be better at removing high-frequency physiological signals but not low-frequency signal power. These methods are also associated with relatively higher age-related fcMRI differences, whether driven by neuronal signal or residual artifact. These results were reproduced in data downsampled to represent conventional fMRI sampling frequency. Lastly, methods differ in performance depending on the age group. Discussion While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also enhances the understanding of different denoising methods in broader fcMRI applications.
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Affiliation(s)
- Ali M. Golestani
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - J. Jean Chen
- Rotman Research Institute at Baycrest, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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4
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Yu J. Age-related decrease in inter-subject similarity of cortical morphology and task and resting-state functional connectivity. GeroScience 2024; 46:697-711. [PMID: 38006514 PMCID: PMC10828367 DOI: 10.1007/s11357-023-01008-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/02/2023] [Indexed: 11/27/2023] Open
Abstract
"All old people are the same" is an unfortunate characterization of the perceived homogeneity in the older age group. This study attempts to debunk this myth in the context of the structural and functional brain. Within older relative to younger age groups, individuals are hypothesized to be more dissimilar to their similar-aged peers-thus demonstrating an age-related divergence. This study analyzed functional connectivity (FC) during multiple fMRI paradigms (2 rest + 5 tasks) and cortical thickness (CT) data from two lifespan datasets (Ntotal = 1161). On average, between-subject FC/CT correlations became weaker in the older age groups. Further analyses ruled out the possibility that more rapid age-related changes in older brains have increased the dissimilarity in these older age groups. Brain-wide analyses revealed significant effects of age-related divergence across most of the brain. Finally, CT similarity between a dyad significantly predicted their FC similarity across multiple fMRI task paradigms-demonstrating a close relationship between brain structure and function even at the between-dyad level. Contrary to the myth that "all old people are the same," these findings suggest young people are more similar to each other. This study presents major implications in the study of neural fingerprinting and brain-behavior associations.
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Affiliation(s)
- Junhong Yu
- Psychology, School of Social Sciences, Nanyang Technological University, 48 Nanyang Avenue, Singapore, 639798, Singapore.
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5
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Klimecki OM, Liebscher M, Gaubert M, Hayek D, Zarucha A, Dyrba M, Bartels C, Buerger K, Butryn M, Dechent P, Dobisch L, Ewers M, Fliessbach K, Freiesleben SD, Glanz W, Hetzer S, Janowitz D, Kilimann I, Kleineidam L, Laske C, Maier F, Munk MH, Perneczky R, Peters O, Priller J, Rauchmann BS, Roy N, Scheffler K, Schneider A, Spruth EJ, Spottke A, Teipel SJ, Wiltfang J, Wolfsgruber S, Yakupov R, Düzel E, Jessen F, Wagner M, Roeske S, Wirth M. Long-term environmental enrichment is associated with better fornix microstructure in older adults. Front Aging Neurosci 2023; 15:1170879. [PMID: 37711996 PMCID: PMC10498282 DOI: 10.3389/fnagi.2023.1170879] [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: 02/21/2023] [Accepted: 08/04/2023] [Indexed: 09/16/2023] Open
Abstract
Background Sustained environmental enrichment (EE) through a variety of leisure activities may decrease the risk of developing Alzheimer's disease. This cross-sectional cohort study investigated the association between long-term EE in young adulthood through middle life and microstructure of fiber tracts associated with the memory system in older adults. Methods N = 201 cognitively unimpaired participants (≥ 60 years of age) from the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) baseline cohort were included. Two groups of participants with higher (n = 104) or lower (n = 97) long-term EE were identified, using the self-reported frequency of diverse physical, intellectual, and social leisure activities between the ages 13 to 65. White matter (WM) microstructure was measured by fractional anisotropy (FA) and mean diffusivity (MD) in the fornix, uncinate fasciculus, and parahippocampal cingulum using diffusion tensor imaging. Long-term EE groups (lower/higher) were compared with adjustment for potential confounders, such as education, crystallized intelligence, and socio-economic status. Results Reported participation in higher long-term EE was associated with greater fornix microstructure, as indicated by higher FA (standardized β = 0.117, p = 0.033) and lower MD (β = -0.147, p = 0.015). Greater fornix microstructure was indirectly associated (FA: unstandardized B = 0.619, p = 0.038; MD: B = -0.035, p = 0.026) with better memory function through higher long-term EE. No significant effects were found for the other WM tracts. Conclusion Our findings suggest that sustained participation in a greater variety of leisure activities relates to preserved WM microstructure in the memory system in older adults. This could be facilitated by the multimodal stimulation associated with the engagement in a physically, intellectually, and socially enriched lifestyle. Longitudinal studies will be needed to support this assumption.
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Affiliation(s)
- Olga M Klimecki
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Maxie Liebscher
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Malo Gaubert
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
- Department of Neuroradiology, Rennes University Hospital Centre Hospitalier Universitaire (CHU), Rennes, France
| | - Dayana Hayek
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Alexis Zarucha
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Göttingen, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Michaela Butryn
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Peter Dechent
- Magnetic Resonance (MR)-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Göttingen, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Silka Dawn Freiesleben
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, United Kingdom
| | - Oliver Peters
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
- University of Edinburgh and United Kingdom Dementia Research Institute (UK DRI), Edinburgh, United Kingdom
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Eike Jakob Spruth
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Neurosciences and Signaling Group, Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Sandra Roeske
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Miranka Wirth
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
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Elyounssi S, Kunitoki K, Clauss JA, Laurent E, Kane K, Hughes DE, Hopkinson CE, Bazer O, Sussman RF, Doyle AE, Lee H, Tervo-Clemmens B, Eryilmaz H, Gollub RL, Barch DM, Satterthwaite TD, Dowling KF, Roffman JL. Uncovering and mitigating bias in large, automated MRI analyses of brain development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530498. [PMID: 36909456 PMCID: PMC10002762 DOI: 10.1101/2023.02.28.530498] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Large, population-based MRI studies of adolescents promise transformational insights into neurodevelopment and mental illness risk 1,2. However, MRI studies of youth are especially susceptible to motion and other artifacts 3,4. These artifacts may go undetected by automated quality control (QC) methods that are preferred in high-throughput imaging studies, 5 and can potentially introduce non-random noise into clinical association analyses. Here we demonstrate bias in structural MRI analyses of children due to inclusion of lower quality images, as identified through rigorous visual quality control of 11,263 T1 MRI scans obtained at age 9-10 through the Adolescent Brain Cognitive Development (ABCD) Study6. Compared to the best-rated images (44.9% of the sample), lower-quality images generally associated with decreased cortical thickness and increased cortical surface area measures (Cohen's d 0.14-2.84). Variable image quality led to counterintuitive patterns in analyses that associated structural MRI and clinical measures, as inclusion of lower-quality scans altered apparent effect sizes in ways that increased risk for both false positives and negatives. Quality-related biases were partially mitigated by controlling for surface hole number, an automated index of topological complexity that differentiated lower-quality scans with good specificity at Baseline (0.81-0.93) and in 1,000 Year 2 scans (0.88-1.00). However, even among the highest-rated images, subtle topological errors occurred during image preprocessing, and their correction through manual edits significantly and reproducibly changed thickness measurements across much of the cortex (d 0.15-0.92). These findings demonstrate that inadequate QC of youth structural MRI scans can undermine advantages of large sample size to detect meaningful associations.
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Affiliation(s)
- Safia Elyounssi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Keiko Kunitoki
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Jacqueline A. Clauss
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Eline Laurent
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Kristina Kane
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Dylan E. Hughes
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Departments of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles
| | - Casey E. Hopkinson
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Oren Bazer
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Rachel Freed Sussman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Alysa E. Doyle
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Center for Genomic Medicine, Massachusetts General Hospital
| | - Hang Lee
- Biostatistics Center, Massachusetts General Hospital and Harvard Medical School
| | | | - Hamdi Eryilmaz
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Randy L. Gollub
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Deanna M. Barch
- Department of Psychological and Brain Sciences, Washington University in St. Louis
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine
- Penn Lifespan and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine
- Penn-CHOP Lifespan Brain Institute
| | - Kevin F. Dowling
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Department of Psychiatry, University of Pittsburgh
| | - Joshua L. Roffman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
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7
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Saccà V, Novellino F, Salsone M, Abou Jaoude M, Quattrone A, Chiriaco C, Madrigal JLM, Quattrone A. Challenging functional connectivity data: machine learning application on essential tremor recognition. Neurol Sci 2023; 44:199-207. [PMID: 36123559 DOI: 10.1007/s10072-022-06400-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND AND AIMS This paper aimed to investigate the usefulness of applying machine learning on resting-state fMRI connectivity data to recognize the pattern of functional changes in essential tremor (ET), a disease characterized by slight brain abnormalities, often difficult to detect using univariate analysis. METHODS We trained a support vector machine with a radial kernel on the mean signals extracted by 14 brain networks obtained from resting-state fMRI scans of 18 ET and 19 healthy control (CTRL) subjects. Classification performance between pathological and control subjects was evaluated using a tenfold cross-validation. Recursive feature elimination was performed to rank the importance of the extracted features. Moreover, univariate analysis using Mann-Whitney U test was also performed. RESULTS The machine learning algorithm achieved an AUC of 0.75, with four networks (language, primary visual, cerebellum, and attention), which have an essential role in ET pathophysiology, being selected as the most important features for classification. By contrast, the univariate analysis was not able to find significant results among these two conditions. CONCLUSION The machine learning approach identifies the changes in functional connectivity of ET patients, representing a promising instrument to discriminate specific pathological conditions and find novel functional biomarkers in resting-state fMRI studies.
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Affiliation(s)
- Valeria Saccà
- Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy.,Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Fabiana Novellino
- Department of Pharmacology and Toxicology, School of Medicine, Universidad Complutense de Madrid (UCM), Av. Complutense s/n, 28040, Madrid, Spain. .,Instituto de Investigación Neuroquímica (IUINQ-UCM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Investigación Sanitaria Hospital, 12 de Octubre (Imas12), Madrid, Spain. .,Institute of Bioimaging and Molecular Physiology (IBFM), National Research Council, Catanzaro, Italy.
| | - Maria Salsone
- Institute of Molecular Bioimaging and Physiology, National Research Council, Milan, Italy.,Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | | | - Andrea Quattrone
- Institute of Neurology, University Magna Graecia, Catanzaro, Italy.,Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - José L M Madrigal
- Department of Pharmacology and Toxicology, School of Medicine, Universidad Complutense de Madrid (UCM), Av. Complutense s/n, 28040, Madrid, Spain.,Instituto de Investigación Neuroquímica (IUINQ-UCM), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Investigación Sanitaria Hospital, 12 de Octubre (Imas12), Madrid, Spain
| | - Aldo Quattrone
- Institute of Bioimaging and Molecular Physiology (IBFM), National Research Council, Catanzaro, Italy. .,Neuroscience Research Center, Magna Graecia University, Catanzaro, Italy.
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8
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Tomasi D, Volkow ND. Brain motion networks predict head motion during rest- and task-fMRI. Front Neurosci 2023; 17:1096232. [PMID: 37113158 PMCID: PMC10126373 DOI: 10.3389/fnins.2023.1096232] [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: 11/11/2022] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
Introduction The capacity to stay still during scanning, which is necessary to avoid motion confounds while imaging, varies markedly between people. Methods Here we investigated the effect of head motion on functional connectivity using connectome-based predictive modeling (CPM) and publicly available brain functional magnetic resonance imaging (fMRI) data from 414 individuals with low frame-to-frame motion (Δd < 0.18 mm). Leave-one-out was used for internal cross-validation of head motion prediction in 207 participants, and twofold cross-validation was used in an independent sample (n = 207). Results and Discussion Parametric testing, as well as CPM-based permutations for null hypothesis testing, revealed strong linear associations between observed and predicted values of head motion. Motion prediction accuracy was higher for task- than for rest-fMRI, and for absolute head motion (d) than for Δd. Denoising attenuated the predictability of head motion, but stricter framewise displacement threshold (FD = 0.2 mm) for motion censoring did not alter the accuracy of the predictions obtained with lenient censoring (FD = 0.5 mm). For rest-fMRI, prediction accuracy was lower for individuals with low motion (mean Δd < 0.02 mm; n = 200) than for those with moderate motion (Δd < 0.04 mm; n = 414). The cerebellum and default-mode network (DMN) regions that forecasted individual differences in d and Δd during six different tasks- and two rest-fMRI sessions were consistently prone to the deleterious effect of head motion. However, these findings generalized to a novel group of 1,422 individuals but not to simulated datasets without neurobiological contributions, suggesting that cerebellar and DMN connectivity could partially reflect functional signals pertaining to inhibitory motor control during fMRI.
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Affiliation(s)
- Dardo Tomasi
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States
- *Correspondence: Dardo Tomasi,
| | - Nora D. Volkow
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States
- National Institute on Drug Abuse, Bethesda, MD, United States
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Soares JF, Abreu R, Lima AC, Sousa L, Batista S, Castelo-Branco M, Duarte JV. Task-based functional MRI challenges in clinical neuroscience: Choice of the best head motion correction approach in multiple sclerosis. Front Neurosci 2022; 16:1017211. [PMID: 36570849 PMCID: PMC9768441 DOI: 10.3389/fnins.2022.1017211] [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: 08/11/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction Functional MRI (fMRI) is commonly used for understanding brain organization and connectivity abnormalities in neurological conditions, and in particular in multiple sclerosis (MS). However, head motion degrades fMRI data quality and influences all image-derived metrics. Persistent controversies regarding the best correction strategy motivates a systematic comparison, including methods such as scrubbing and volume interpolation, to find optimal correction models, particularly in studies with clinical populations prone to characterize by high motion. Moreover, strategies for correction of motion effects gain more relevance in task-based designs, which are less explored compared to resting-state, have usually lower sample sizes, and may have a crucial role in describing the functioning of the brain and highlighting specific connectivity changes. Methods We acquired fMRI data from 17 early MS patients and 14 matched healthy controls (HC) during performance of a visual task, characterized motion in both groups, and quantitatively compared the most used and easy to implement methods for correction of motion effects. We compared task-activation metrics obtained from: (i) models containing 6 or 24 motion parameters (MPs) as nuisance regressors; (ii) models containing nuisance regressors for 6 or 24 MPs and motion outliers (scrubbing) detected with Framewise Displacement or Derivative or root mean square VARiance over voxelS; and (iii) models with 6 or 24 MPs and motion outliers corrected through volume interpolation. To our knowledge, volume interpolation has not been systematically compared with scrubbing, nor investigated in task fMRI clinical studies in MS. Results No differences in motion were found between groups, suggesting that recently diagnosed MS patients may not present problematic motion. In general, models with 6 MPs perform better than models with 24 MPs, suggesting the 6 MPs as the best trade-off between correction of motion effects and preservation of valuable information. Parsimonious models with 6 MPs and volume interpolation were the best combination for correcting motion in both groups, surpassing the scrubbing methods. A joint analysis regardless of the group further highlighted the value of volume interpolation. Discussion Volume interpolation of motion outliers is an easy to implement technique, which may be an alternative to other methods and may improve the accuracy of fMRI analyses, crucially in clinical studies in MS and other neurological populations.
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Affiliation(s)
- Júlia F. Soares
- Coimbra Institute for Biomedical Imaging and Translational Research, Institute for Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal
| | - Rodolfo Abreu
- Coimbra Institute for Biomedical Imaging and Translational Research, Institute for Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal
| | - Ana Cláudia Lima
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Lívia Sousa
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Sónia Batista
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research, Institute for Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal,Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - João Valente Duarte
- Coimbra Institute for Biomedical Imaging and Translational Research, Institute for Nuclear Sciences Applied to Health, University of Coimbra, Coimbra, Portugal,Faculty of Medicine, University of Coimbra, Coimbra, Portugal,*Correspondence: João Valente Duarte,
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10
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Scheel N, Keller JN, Binder EF, Vidoni ED, Burns JM, Thomas BP, Stowe AM, Hynan LS, Kerwin DR, Vongpatanasin W, Rossetti H, Cullum CM, Zhang R, Zhu DC. Evaluation of noise regression techniques in resting-state fMRI studies using data of 434 older adults. Front Neurosci 2022; 16:1006056. [PMID: 36340768 PMCID: PMC9626831 DOI: 10.3389/fnins.2022.1006056] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/27/2022] [Indexed: 01/24/2023] Open
Abstract
Subject motion is a well-known confound in resting-state functional MRI (rs-fMRI) and the analysis of functional connectivity. Consequently, several clean-up strategies have been established to minimize the impact of subject motion. Physiological signals in response to cardiac activity and respiration are also known to alter the apparent rs-fMRI connectivity. Comprehensive comparisons of common noise regression techniques showed that the "Independent Component Analysis based strategy for Automatic Removal of Motion Artifacts" (ICA-AROMA) was a preferred pre-processing technique for teenagers and adults. However, motion and physiological noise characteristics may differ substantially for older adults. Here, we present a comprehensive comparison of noise-regression techniques for older adults from a large multi-site clinical trial of exercise and intensive pharmacological vascular risk factor reduction. The Risk Reduction for Alzheimer's Disease (rrAD) trial included hypertensive older adults (60-84 years old) at elevated risk of developing Alzheimer's Disease (AD). We compared the performance of censoring, censoring combined with global signal regression, non-aggressive and aggressive ICA-AROMA, as well as the Spatially Organized Component Klassifikator (SOCK) on the rs-fMRI baseline scans from 434 rrAD subjects. All techniques were rated based on network reproducibility, network identifiability, edge activity, spatial smoothness, and loss of temporal degrees of freedom (tDOF). We found that non-aggressive ICA-AROMA did not perform as well as the other four techniques, which performed table with marginal differences, demonstrating the validity of these techniques. Considering reproducibility as the most important factor for longitudinal studies, given low false-positive rates and a better preserved, more cohesive temporal structure, currently aggressive ICA-AROMA is likely the most suitable noise regression technique for rs-fMRI studies of older adults.
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Affiliation(s)
- Norman Scheel
- Department of Radiology, Michigan State University, East Lansing, MI, United States
| | - Jeffrey N. Keller
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Ellen F. Binder
- Division of Geriatrics and Nutritional Science, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric D. Vidoni
- Alzheimer’s Disease Center, University of Kansas, Fairway, KS, United States
| | - Jeffrey M. Burns
- Alzheimer’s Disease Center, University of Kansas, Fairway, KS, United States
| | - Binu P. Thomas
- UT Southwestern Medical Center, Dallas, TX, United States
| | - Ann M. Stowe
- Department of Neurology, University of Kentucky, Lexington, KY, United States
| | - Linda S. Hynan
- UT Southwestern Medical Center, Dallas, TX, United States
| | - Diana R. Kerwin
- Texas Health Presbyterian Hospital, Dallas, TX, United States
| | | | - Heidi Rossetti
- UT Southwestern Medical Center, Dallas, TX, United States
| | | | - Rong Zhang
- UT Southwestern Medical Center, Dallas, TX, United States,Texas Health Presbyterian Hospital, Dallas, TX, United States
| | - David C. Zhu
- Department of Radiology, Michigan State University, East Lansing, MI, United States,*Correspondence: David C. Zhu,
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11
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Yu J, Fischer NL. Asymmetric generalizability of multimodal brain-behavior associations across age-groups. Hum Brain Mapp 2022; 43:5593-5604. [PMID: 35906870 PMCID: PMC9704787 DOI: 10.1002/hbm.26035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/20/2022] [Accepted: 07/15/2022] [Indexed: 01/15/2023] Open
Abstract
Machine learning methods have increasingly been used to map out brain-behavior associations (BBA), and to predict out-of-scanner behavior of unseen subjects. Given the brain changes that occur in the context of aging, the accuracy of these predictions is likely to depend on how similar the training and testing data sets are in terms of age. To this end, we examined how well BBAs derived from an age-group generalize to other age-groups. We partitioned the CAM-CAN data set (N = 550) into the young, middle, and old age-groups, then used the young and old age-groups to construct prediction models for 11 behavioral outcomes using multimodal neuroimaging features (i.e., structural and resting-state functional connectivity, and gray matter volume/cortical thickness). These models were then applied to all three age-groups to predict their behavioral scores. When the young-derived models were used, a graded pattern of age-generalization was generally observed across most behavioral outcomes-predictions are the most accurate in the young subjects in the testing data set, followed by the middle and then old-aged subjects. Conversely, when the old-derived models were used, the disparity in the predictive accuracy across age-groups was mostly negligible. These findings hold across different imaging modalities. These results suggest the asymmetric age-generalization of BBAs-old-derived BBAs generalized well to all age-groups, however young-derived BBAs generalized poorly beyond their own age-group.
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Affiliation(s)
- Junhong Yu
- Psychology, School of Social SciencesNational Technological UniversitySingaporeSingapore
| | - Nastassja L. Fischer
- Centre for Research and Development in Learning (CRADLE)Nanyang Technological UniversitySingaporeSingapore
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12
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Plumley A, Watkins L, Treder M, Liebig P, Murphy K, Kopanoglu E. Rigid motion-resolved B1+ prediction using deep learning for real-time parallel-transmission pulse design. Magn Reson Med 2022; 87:2254-2270. [PMID: 34958134 PMCID: PMC7613077 DOI: 10.1002/mrm.29132] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 11/30/2021] [Accepted: 12/03/2021] [Indexed: 11/19/2022]
Abstract
PURPOSE Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B 1 + distributions following within-slice motion, which can then be used for tailored pTx pulse redesign. METHODS Using simulated data, conditional generative adversarial networks were trained to predict B 1 + distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground-truth (simulated, following motion) B1 maps. Tailored pTx pulses were designed using B1 maps at the original position (simulated, no motion) and evaluated using simulated B1 maps at displaced position (ground-truth maps) to quantify motion-related excitation error. A second pulse was designed using predicted maps (also evaluated on ground-truth maps) to investigate improvement offered by the proposed method. RESULTS Predicted B 1 + maps corresponded well with ground-truth maps. Error in predicted maps was lower than motion-related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst-case flip-angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps. CONCLUSION We propose a framework for predicting B 1 + maps online with deep neural networks. Predicted maps can then be used for real-time tailored pulse redesign, helping to overcome head motion-related error in pTx.
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Affiliation(s)
- Alix Plumley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Luke Watkins
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- School of Physics & Astronomy, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
| | - Matthias Treder
- School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | | | - Kevin Murphy
- School of Physics & Astronomy, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
| | - Emre Kopanoglu
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
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13
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McIntosh R, Lobo JD, Carvalho N, Ironson G. Learning to forget: Hippocampal-amygdala connectivity partially mediates the effect of sexual trauma severity on verbal recall in older women undiagnosed with posttraumatic stress disorder. J Trauma Stress 2022; 35:631-643. [PMID: 35156236 PMCID: PMC11021133 DOI: 10.1002/jts.22778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 11/08/2022]
Abstract
Verbal learning deficits are common among sexually traumatized women who have not been formally diagnosed with posttraumatic stress disorder (PTSD). Aberrant resting-state functional connectivity (rsFC) of the amygdala and hippocampus are implicated in PTSD and verbal memory impairment. We tested rsFC between bilateral dentate gyrus (DG) and both centromedial (CM) and basolateral (BL) nuclei of the amygdala as statistical mediators for the effect of sexual trauma-related symptom severity on delayed verbal recall performance in 63 older women (age: 60-85 years) undiagnosed with PTSD. Participant data were drawn from the NKI-Rockland Study. Individuals completed a 10-min resting-state scan, Rey Auditory Verbal Learning Test (RAVLT), and the Sexual Abuse Trauma Index (SATI) from the Trauma Symptom Checklist. Z-scores indicating rsFC of DG with BL and CM amygdala seeds were evaluated in two separate mediation models. Higher SATI scores were associated with lower RAVLT after controlling for age, β = -.23, 95% CI [.48, .03], p = .039. This effect was negated upon adding a negative path from SATI to rsFC of left DG and right CM, β = -.29, 95% CI [-.52, -.02], p = .022, and a positive path from that seed pair to RAVLT List A recall, β = .28, 95% CI [.03, 0.48], p = .015. Chi-square fit indices supported partial mediation by this seed pair, p = .762. In the absence of PTSD sexual trauma symptoms partially relate to verbal learning deficits as a function of aberrant rsFC between left hippocampus DG and right amygdala CM nuclei.
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Affiliation(s)
- Roger McIntosh
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Judith D Lobo
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Nicole Carvalho
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Gail Ironson
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
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14
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Yu J, Fischer NL. Age-specificity and generalization of behavior-associated structural and functional networks and their relevance to behavioral domains. Hum Brain Mapp 2022; 43:2405-2418. [PMID: 35274793 PMCID: PMC9057094 DOI: 10.1002/hbm.25759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/09/2021] [Accepted: 12/13/2021] [Indexed: 12/31/2022] Open
Abstract
Behavior-associated structural connectivity (SC) and resting-state functional connectivity (rsFC) networks undergo various changes in aging. To study these changes, we proposed a continuous dimension where at one end networks generalize well across age groups in terms of behavioral predictions (age-general) and at the other end, they predict behaviors well in a specific age group but fare poorly in another age group (age-specific). We examined how age generalizability/specificity of multimodal behavioral associated brain networks varies across behavioral domains and imaging modalities. Prediction models consisting of SC and/or rsFC networks were trained to predict a diverse range of 75 behavioral outcomes in a young adult sample (N = 92). These models were then used to predict behavioral outcomes in unseen young (N = 60) and old (N = 60) subjects. As expected, behavioral prediction models derived from the young age group, produced more accurate predictions in the unseen young than old subjects. These behavioral predictions also differed significantly across behavioral domains, but not imaging modalities. Networks associated with cognitive functions, except for a few mostly relating to semantic knowledge, fell toward the age-specific end of the spectrum (i.e., poor young-to-old generalizability). These findings suggest behavior-associated brain networks are malleable to different degrees in aging; such malleability is partly determined by the nature of the behavior.
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Affiliation(s)
- Junhong Yu
- Psychology, School of Social Sciences, National Technological University, Singapore, Singapore
| | - Nastassja Lopes Fischer
- Centre for Family and Population Research, Faculty of Arts and Social Sciences, National University of Singapore, Singapore, Singapore.,Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, Singapore, Singapore
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15
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Bernard JA, Ballard HK, Jackson TB. Cerebellar Dentate Connectivity across Adulthood: A Large-Scale Resting State Functional Connectivity Investigation. Cereb Cortex Commun 2021; 2:tgab050. [PMID: 34527949 PMCID: PMC8436571 DOI: 10.1093/texcom/tgab050] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 06/20/2021] [Accepted: 07/19/2021] [Indexed: 12/23/2022] Open
Abstract
Cerebellar contributions to behavior in advanced age are of interest and importance, given its role in motor and cognitive performance. There are differences and declines in cerebellar structure in advanced age and cerebellar resting state connectivity is lower. However, the work on this area to date has focused on the cerebellar cortex. The deep cerebellar nuclei provide the primary cerebellar inputs and outputs to the cortex, as well as the spinal and vestibular systems. Dentate networks can be dissociated such that the dorsal region is associated with the motor cortex, whereas the ventral aspect is associated with the prefrontal cortex. However, whether dentato-thalamo-cortical networks differ across adulthood remains unknown. Here, using a large adult sample (n = 590) from the Cambridge Center for Ageing and Neuroscience, we investigated dentate connectivity across adulthood. We replicated past work showing dissociable resting state networks in the dorsal and ventral aspects of the dentate. In both seeds, we demonstrated that connectivity is lower with advanced age, indicating that connectivity differences extend beyond the cerebellar cortex. Finally, we demonstrated sex differences in dentate connectivity. This expands our understanding of cerebellar circuitry in advanced age and underscores the potential importance of this structure in age-related performance differences.
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Affiliation(s)
- Jessica A Bernard
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Hannah K Ballard
- Texas A&M Institute for Neuroscience, Texas A&M University, College Station, TX 77843, USA
| | - Trevor Bryan Jackson
- Department of Psychological and Brain Sciences, Texas A&M University, College Station, TX 77843, USA
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