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Schubert AL, Löffler C, Hagemann D, Sadus K. How robust is the relationship between neural processing speed and cognitive abilities? Psychophysiology 2023; 60:e14165. [PMID: 35995756 DOI: 10.1111/psyp.14165] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/08/2022] [Accepted: 07/31/2022] [Indexed: 01/04/2023]
Abstract
Individual differences in processing speed are consistently related to individual differences in cognitive abilities, but the mechanisms through which a higher processing speed facilitates reasoning remain largely unknown. To identify these mechanisms, researchers have been using latencies of the event-related potential (ERP) to study how the speed of cognitive processes associated with specific ERP components is related to cognitive abilities. Although there is some evidence that latencies of ERP components associated with higher-order cognitive processes are related to intelligence, results are overall quite inconsistent. These inconsistencies likely result from variations in analytic procedures and little consideration of the psychometric properties of ERP latencies in relatively small sample studies. Here we used a multiverse approach to evaluate how different analytical choices regarding references, low-pass filter cutoffs, and latency measures affect the psychometric properties of P2, N2, and P3 latencies and their relations with cognitive abilities in a sample of 148 participants. Latent correlations between neural processing speed and cognitive abilities ranged from -.49 to -.78. ERP latency measures contained about equal parts of measurement error variance and systematic variance, and only about half of the systematic variance was related to cognitive abilities, whereas the other half reflected nuisance factors. We recommend addressing these problematic psychometric properties by recording EEG data from multiple tasks and modeling relations between ERP latencies and covariates in latent variable models. All in all, our results indicate that there is a substantial and robust relationship between neural processing speed and cognitive abilities when those issues are addressed.
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Affiliation(s)
| | - Christoph Löffler
- Department of Psychology, University of Mainz, Mainz, Germany.,Institute of Psychology, Heidelberg University, Heidelberg, Germany
| | - Dirk Hagemann
- Institute of Psychology, Heidelberg University, Heidelberg, Germany
| | - Kathrin Sadus
- Institute of Psychology, Heidelberg University, Heidelberg, Germany
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2
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Stammen C, Fraenz C, Grazioplene RG, Schlüter C, Merhof V, Johnson W, Güntürkün O, DeYoung CG, Genç E. Robust associations between white matter microstructure and general intelligence. Cereb Cortex 2023:6994402. [PMID: 36682883 DOI: 10.1093/cercor/bhac538] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/24/2023] Open
Abstract
Few tract-based spatial statistics (TBSS) studies have investigated the relations between intelligence and white matter microstructure in healthy (young) adults, and those have yielded mixed observations, yet white matter is fundamental for efficient and accurate information transfer throughout the human brain. We used a multicenter approach to identify white matter regions that show replicable structure-function associations, employing data from 4 independent samples comprising over 2000 healthy participants. TBSS indicated 188 voxels exhibited significant positive associations between g factor scores and fractional anisotropy (FA) in all 4 data sets. Replicable voxels formed 3 clusters, located around the left-hemispheric forceps minor, superior longitudinal fasciculus, and cingulum-cingulate gyrus with extensions into their surrounding areas (anterior thalamic radiation, inferior fronto-occipital fasciculus). Our results suggested that individual differences in general intelligence are robustly associated with white matter FA in specific fiber bundles distributed across the brain, consistent with the Parieto-Frontal Integration Theory of intelligence. Three possible reasons higher FA values might create links with higher g are faster information processing due to greater myelination, more direct information processing due to parallel, homogenous fiber orientation distributions, or more parallel information processing due to greater axon density.
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Affiliation(s)
- Christina Stammen
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139 Dortmund, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139 Dortmund, Germany
| | | | - Caroline Schlüter
- Department of Biopsychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, 44801 Bochum, Germany
| | - Viola Merhof
- Chair of Research Methods and Psychological Assessment, University of Mannheim, 68161 Mannheim, Germany
| | - Wendy Johnson
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Onur Güntürkün
- Department of Biopsychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, 44801 Bochum, Germany
| | - Colin G DeYoung
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), 44139 Dortmund, Germany
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Firbank MJ, daSilva Morgan K, Collerton D, Elder GJ, Parikh J, Olsen K, Schumacher J, Ffytche D, Taylor JP. Investigation of structural brain changes in Charles Bonnet Syndrome. Neuroimage Clin 2022; 35:103041. [PMID: 35576854 PMCID: PMC9118504 DOI: 10.1016/j.nicl.2022.103041] [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: 12/09/2021] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 11/14/2022]
Abstract
Reduced grey matter in the occipital cortex in eye disease groups. Widespread altered diffusivity in eye disease groups. No cortical or white matter changes associated with presence of visual hallucinations. Negative association between hippocampal volume and Hallucination severity.
Background and objectives In Charles Bonnet Syndrome (CBS), visual hallucinations (VH) are experienced by people with sight loss due to eye disease or lesional damage to early visual pathways. The aim of this cross-sectional study was to investigate structural brain changes using magnetic resonance imaging (MRI) in CBS. Methods Sixteen CBS patients, 17 with eye disease but no VH, and 19 normally sighted people took part. Participants were imaged on a 3T scanner, with 1 mm resolution T1 weighted structural imaging, and diffusion tensor imaging with 64 diffusion directions. Results The three groups were well matched for age, sex and cognitive scores (MMSE). The two eye disease groups were matched on visual acuity. Compared to the sighted controls, we found reduced grey matter in the occipital cortex in both eye disease groups. We also found reductions of fractional anisotropy and increased diffusivity in widespread areas, including occipital tracts, the corpus callosum, and the anterior thalamic radiation. We did not find any significant differences between the eye disease participants with VH versus without VH, but did observe a negative association between hippocampal volume and VH severity in the CBS group. Discussion Our findings suggest that although there are cortical and subcortical effects associated with sight loss, structural changes do not explain the occurrence of VHs. CBS may relate instead to connectivity or excitability changes in brain networks linked to vision.
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Affiliation(s)
- Michael J Firbank
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
| | - Katrina daSilva Morgan
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Daniel Collerton
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Greg J Elder
- Northumbria Sleep Research, Department of Psychology, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Jehill Parikh
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Kirsty Olsen
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Julia Schumacher
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Dominic Ffytche
- Department of Old Age Psychiatry, Institute of Psychiatry, King's College London, UK
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
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Levakov G, Faskowitz J, Avidan G, Sporns O. Mapping individual differences across brain network structure to function and behavior with connectome embedding. Neuroimage 2021; 242:118469. [PMID: 34390875 PMCID: PMC8464439 DOI: 10.1016/j.neuroimage.2021.118469] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 07/29/2021] [Accepted: 08/10/2021] [Indexed: 01/21/2023] Open
Abstract
The connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n = 542; Cam-CAN: n = 601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping, are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.
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Affiliation(s)
- Gidon Levakov
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Israel; Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel.
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, USA; Program in Neuroscience, Indiana University, USA
| | - Galia Avidan
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Israel; Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel; Department of Psychology, Ben-Gurion University of the Negev, Israel
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, USA; Program in Neuroscience, Indiana University, USA
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The Evidence for Geary's Theory on the Role of Mitochondrial Functioning in Human Intelligence Is Not Entirely Convincing. J Intell 2020; 8:jintelligence8030029. [PMID: 32698405 PMCID: PMC7555447 DOI: 10.3390/jintelligence8030029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 07/01/2020] [Accepted: 07/16/2020] [Indexed: 11/24/2022] Open
Abstract
Geary (2018, 2019) suggested that heritable and environmentally caused differences in mitochondrial functioning affect the integrity and efficiency of neurons and supporting glia cells and may thus contribute to individual differences in higher-order cognitive functioning and physical health. In our comment, we want to pose three questions aimed at different aspects of Geary’s theory that critically evaluate his theory in the light of evidence from neurocognitive, cognitive enhancement, and behavioral genetics research. We question (1) if Geary’s theory explains why certain cognitive processes show a stronger age-related decline than others; (2) if intervention studies in healthy younger adults support the claim that variation in mitochondrial functioning underlies variation in human intelligence; and (3) if predictions arising from the matrilineal heredity of mitochondrial DNA are supported by behavioral genetics research. We come to the conclusion that there are likely many more biological and social factors contributing to variation in human intelligence than mitochondrial functioning.
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Holleran L, Kelly S, Alloza C, Agartz I, Andreassen OA, Arango C, Banaj N, Calhoun V, Cannon D, Carr V, Corvin A, Glahn DC, Gur R, Hong E, Hoschl C, Howells FM, James A, Janssen J, Kochunov P, Lawrie SM, Liu J, Martinez C, McDonald C, Morris D, Mothersill D, Pantelis C, Piras F, Potkin S, Rasser PE, Roalf D, Rowland L, Satterthwaite T, Schall U, Spalletta G, Spaniel F, Stein DJ, Uhlmann A, Voineskos A, Zalesky A, van Erp TG, Turner JA, Deary IJ, Thompson PM, Jahanshad N, Donohoe G. The Relationship Between White Matter Microstructure and General Cognitive Ability in Patients With Schizophrenia and Healthy Participants in the ENIGMA Consortium. Am J Psychiatry 2020; 177:537-547. [PMID: 32212855 PMCID: PMC7938666 DOI: 10.1176/appi.ajp.2019.19030225] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVE Schizophrenia has recently been associated with widespread white matter microstructural abnormalities, but the functional effects of these abnormalities remain unclear. Widespread heterogeneity of results from studies published to date preclude any definitive characterization of the relationship between white matter and cognitive performance in schizophrenia. Given the relevance of deficits in cognitive function to predicting social and functional outcomes in schizophrenia, the authors carried out a meta-analysis of available data through the ENIGMA Consortium, using a common analysis pipeline, to elucidate the relationship between white matter microstructure and a measure of general cognitive performance, IQ, in patients with schizophrenia and healthy participants. METHODS The meta-analysis included 760 patients with schizophrenia and 957 healthy participants from 11 participating ENIGMA Consortium sites. For each site, principal component analysis was used to calculate both a global fractional anisotropy component (gFA) and a fractional anisotropy component for six long association tracts (LA-gFA) previously associated with cognition. RESULTS Meta-analyses of regression results indicated that gFA accounted for a significant amount of variation in cognition in the full sample (effect size [Hedges' g]=0.27, CI=0.17-0.36), with similar effects sizes observed for both the patient (effect size=0.20, CI=0.05-0.35) and healthy participant groups (effect size=0.32, CI=0.18-0.45). Comparable patterns of association were also observed between LA-gFA and cognition for the full sample (effect size=0.28, CI=0.18-0.37), the patient group (effect size=0.23, CI=0.09-0.38), and the healthy participant group (effect size=0.31, CI=0.18-0.44). CONCLUSIONS This study provides robust evidence that cognitive ability is associated with global structural connectivity, with higher fractional anisotropy associated with higher IQ. This association was independent of diagnosis; while schizophrenia patients tended to have lower fractional anisotropy and lower IQ than healthy participants, the comparable size of effect in each group suggested a more general, rather than disease-specific, pattern of association.
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Affiliation(s)
- Laurena Holleran
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - Sinead Kelly
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey
| | - Clara Alloza
- Department of Psychiatry, University of Edinburgh, Edinburgh; Department of Child and Adolescent Psychiatry, Instituto de Investigación Sanitaria Gregorio Marañón, IiSGM, Hospital General Universitario Gregorio Marañón, School of Medicine, CIBERSAM, Universidad Complutense, Madrid
| | - Ingrid Agartz
- NORMENT, K.G. Jebsen Center for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo
| | - Ole A. Andreassen
- Department of Psychiatry, Ullevål University Hospital and Institute of Psychiatry, University of Oslo, Oslo
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Instituto de Investigación Sanitaria Gregorio Marañón, IiSGM, Hospital General Universitario Gregorio Marañón, School of Medicine, CIBERSAM, Universidad Complutense, Madrid
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome
| | - Vince Calhoun
- Mind Research Network and Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque
| | - Dara Cannon
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - Vaughan Carr
- Neuroscience Research Australia and School of Psychiatry, University of New South Wales, Sydney
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin
| | - David C. Glahn
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital and Department of Psychiatry, Yale University School of Medicine, New Haven, Conn
| | - Ruben Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia
| | - Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Cyril Hoschl
- National Institute of Mental Health, Klecany, Czech Republic
| | - Fleur M. Howells
- Department of Psychiatry and Mental Health, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | | | - Joost Janssen
- Department of Child and Adolescent Psychiatry, Instituto de Investigación Sanitaria Gregorio Marañón, IiSGM, Hospital General Universitario Gregorio Marañón, School of Medicine, CIBERSAM, Universidad Complutense, Madrid
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | | | - Jingyu Liu
- Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, N.Mex
| | - Covadonga Martinez
- Department of Child and Adolescent Psychiatry, Instituto de Investigación Sanitaria Gregorio Marañón, IiSGM, Hospital General Universitario Gregorio Marañón, School of Medicine, CIBERSAM, Universidad Complutense, Madrid
| | - Colm McDonald
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - Derek Morris
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - David Mothersill
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome
| | - Steven Potkin
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine
| | - Paul E. Rasser
- Priority Centre for Brain and Mental Health Research, Priority Research Centre for Stroke and Brain Injury, University of Newcastle, Newcastle, Australia
| | - David Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia
| | - Laura Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | | | - Ulrich Schall
- Priority Centre for Brain and Mental Health Research, University of Newcastle, Newcastle, Australia
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome; Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston
| | - Filip Spaniel
- National Institute of Mental Health, Klecany, Czech Republic
| | - Dan J. Stein
- Department of Psychiatry and Mental Health, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Anne Uhlmann
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Aristotle Voineskos
- Kimel Family Translational Imaging-Genetics Research Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia; Department of Biomedical Engineering and Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Australia
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, and Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine
| | | | - Ian J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey
| | - Gary Donohoe
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
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Schubert AL, Frischkorn GT. Neurocognitive Psychometrics of Intelligence: How Measurement Advancements Unveiled the Role of Mental Speed in Intelligence Differences. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 2020. [DOI: 10.1177/0963721419896365] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
More intelligent individuals typically show faster reaction times. However, individual differences in reaction times do not represent individual differences in a single cognitive process but in multiple cognitive processes. Thus, it is unclear whether the association between mental speed and intelligence reflects advantages in a specific cognitive process or in general processing speed. In this article, we present a neurocognitive-psychometrics account of mental speed that decomposes the relationship between mental speed and intelligence. We summarize research employing mathematical models of cognition and chronometric analyses of neural processing to identify distinct stages of information processing strongly related to intelligence differences. Evidence from both approaches suggests that the speed of higher-order processing is greater in smarter individuals, which may reflect advantages in the structural and functional organization of brain networks. Adopting a similar neurocognitive-psychometrics approach for other cognitive processes associated with intelligence (e.g., working memory or executive control) may refine our understanding of the basic cognitive processes of intelligence.
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Moura AR, Lee S, Habeck C, Razlighi Q, Stern Y. The relationship between white matter hyperintensities and cognitive reference abilities across the life span. Neurobiol Aging 2019; 83:31-41. [PMID: 31585365 PMCID: PMC6901174 DOI: 10.1016/j.neurobiolaging.2019.08.024] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 08/12/2019] [Accepted: 08/24/2019] [Indexed: 11/29/2022]
Abstract
We examined the relationship between white matter hyperintensities (WMH) burden and performance on 4 reference abilities: episodic memory, perceptual speed, fluid reasoning, and vocabulary. Cross-sectional data of 486 healthy adults from 20 to 80 years old enrolled in an ongoing longitudinal study were analyzed. A piecewise regression across age identified an inflection point at 43 years old, where WMH total volume began to increase with age. Subsequent analyses focused on participants above that age (N = 351). WMH total volume had significant inverse correlations with perceptual speed and memory. Regional measures of WMH showed inverse correlations with all reference abilities. We performed principal component analysis of the regional WMH data to create a model of principal components regression. Parietal WMH regional volume burden mediated the relationship between age and perceptual speed in simple and multiple mediation models. The principal components regression pattern associated with perceptual speed also mediated the relationship between age and perceptual speed performance. These results across the extended adult life span help clarify the influence of WMH on cognitive aging.
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Affiliation(s)
- Ana R Moura
- Cognitive Neuroscience Division, The Taub Institute for Research on Aging and Alzheimer's Disease, Columbia University, New York, NY, USA; Departamento de Psiquiatria e Saúde Mental, Centro Hospitalar Lisboa Ocidental, Lisboa, Portugal
| | - Seonjoo Lee
- Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA; Department of Biostatistics, Columbia University, New York, NY, USA; Department of Biostatistics and Psychiatry, Columbia University, New York, NY, USA
| | - Christian Habeck
- Cognitive Neuroscience Division, The Taub Institute for Research on Aging and Alzheimer's Disease, Columbia University, New York, NY, USA
| | - Qolamreza Razlighi
- Cognitive Neuroscience Division, The Taub Institute for Research on Aging and Alzheimer's Disease, Columbia University, New York, NY, USA
| | - Yaakov Stern
- Cognitive Neuroscience Division, The Taub Institute for Research on Aging and Alzheimer's Disease, Columbia University, New York, NY, USA.
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Kundu S, Ghodadra A, Fakhran S, Alhilali LM, Rohde GK. Assessing Postconcussive Reaction Time Using Transport-Based Morphometry of Diffusion Tensor Images. AJNR Am J Neuroradiol 2019; 40:1117-1123. [PMID: 31196860 DOI: 10.3174/ajnr.a6087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 04/27/2019] [Indexed: 01/05/2023]
Abstract
BACKGROUND AND PURPOSE Cognitive deficits are among the most commonly reported post-concussive symptoms, yet the underlying microstructural injury is poorly understood. Our aim was to discover white matter injury underlying reaction time in mild traumatic brain injury DTI by applying transport-based morphometry. MATERIALS AND METHODS In this retrospective study, we performed DTI on 64 postconcussive patients (10-28 years of age; 69% male, 31% female) between January 2006 and March 2013. We measured the reaction time percentile by using Immediate Post-Concussion Assessment and Cognitive Testing. Using the 3D transport-based morphometry technique we developed, we mined fractional anisotropy maps to extract the common microstructural injury associated with reaction time percentile in an automated manner. Permutation testing established statistical significance of the extracted injuries. We visualized the physical substrate responsible for reaction time through inverse transport-based morphometry transformation. RESULTS The direction in the transport space most correlated with reaction time was significant after correcting for covariates of age, sex, and time from injury (Pearson r = 0.44, P < .01). Inverting the computed direction using transport-based morphometry illustrates physical shifts in fractional anisotropy in the corpus callosum (increase) and within the optic radiations, corticospinal tracts, and anterior thalamic radiations (decrease) with declining reaction time. The observed shifts are consistent with biologic pathways underlying the visual-spatial interpretation and response-selection aspects of reaction time. CONCLUSIONS Transport-based morphometry discovers complex white matter injury underlying postconcussive reaction time in an automated manner. The potential influences of edema and axonal loss are visualized in the visual-spatial interpretation and response-selection pathways. Transport-based morphometry can bridge the gap between brain microstructure and function in diseases in which the structural basis is unknown.
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Affiliation(s)
- S Kundu
- Department of Biomedical Engineering at Carnegie Mellon University and Medical Scientist Training Program (S.K.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - A Ghodadra
- Department of Radiology (A.G.), Banner Health and Hospital Systems, Mesa, Arizona
| | - S Fakhran
- Department of Neuroradiology (S.F.), Barrow Neurological Institute, Phoenix, Arizona
| | - L M Alhilali
- From the Department of Biomedical Engineering, Electrical and Computer Engineering (G.K.R.), University of Virginia, Charlottesville, Virginia
| | - G K Rohde
- From the Department of Biomedical Engineering, Electrical and Computer Engineering (G.K.R.), University of Virginia, Charlottesville, Virginia
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Rabin JS, Perea RD, Buckley RF, Neal TE, Buckner RL, Johnson KA, Sperling RA, Hedden T. Global White Matter Diffusion Characteristics Predict Longitudinal Cognitive Change Independently of Amyloid Status in Clinically Normal Older Adults. Cereb Cortex 2019; 29:1251-1262. [PMID: 29425267 PMCID: PMC6499008 DOI: 10.1093/cercor/bhy031] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 01/08/2018] [Indexed: 02/07/2023] Open
Abstract
White matter degradation has been proposed as one possible explanation for age-related cognitive decline. In the present study, we examined 2 main questions: 1) Do diffusion characteristics predict longitudinal change in cognition independently or synergistically with amyloid status? 2) Are the effects of diffusion characteristics on longitudinal cognitive change tract-specific or global in nature? Cognitive domains of executive function, episodic memory, and processing speed were measured annually (mean follow-up = 3.93 ± 1.25 years). Diffusion tensor imaging and Pittsburgh Compound-B positron emission tomography were performed at baseline in 265 clinically normal older adults (aged 63-90). Tract-specific diffusion was measured as the mean fractional anisotropy (FA) for 9 major white matter tracts. Global diffusion was measured as the mean FA across the 9 white matter tracts. Linear mixed models demonstrated independent, rather than synergistic, effects of global FA and amyloid status on cognitive decline. After controlling for amyloid status, lower global FA was associated with worse longitudinal performance in episodic memory and processing speed, but not executive function. After accounting for global FA, none of the individual tracts predicted a significant change in cognitive performance. These findings suggest that global, rather than tract-specific, diffusion characteristics predict longitudinal cognitive decline independently of amyloid status.
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Affiliation(s)
- Jennifer S Rabin
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rodrigo D Perea
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel F Buckley
- Florey Institutes of Neuroscience and Mental Health, Melbourne and Melbourne School of Psychological Science, University of Melbourne, Melbourne, Australia
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Taylor E Neal
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Randy L Buckner
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Keith A Johnson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Boston, MA, USA
| | - Trey Hedden
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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11
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Deary IJ, Ritchie SJ, Muñoz Maniega S, Cox SR, Valdés Hernández MC, Luciano M, Starr JM, Wardlaw JM, Bastin ME. Brain Peak Width of Skeletonized Mean Diffusivity (PSMD) and Cognitive Function in Later Life. Front Psychiatry 2019; 10:524. [PMID: 31402877 PMCID: PMC6676305 DOI: 10.3389/fpsyt.2019.00524] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/03/2019] [Indexed: 11/13/2022] Open
Abstract
It is suggested that the brain's peak width of skeletonized water mean diffusivity (PSMD) is a neuro-biomarker of processing speed, an important aspect of cognitive aging. We tested whether PSMD is more strongly correlated with processing speed than with other cognitive domains, and more strongly than other structural brain MRI indices. Participants were 731 Lothian Birth Cohort 1936 members, mean age = 73 years (SD = 0.7); analytical sample was 656-680. Cognitive domains tested were as follows: processing speed (5 tests), visuospatial (3), memory (3), and verbal (3). Brain-imaging variables included PSMD, white matter diffusion parameters, hyperintensity volumes, gray and white matter volumes, and perivascular spaces. PSMD was significantly associated with processing speed (-0.27), visuospatial ability (-0.23), memory ability (-0.17), and general cognitive ability (-0.25); comparable correlations were found with other brain-imaging measures. In a multivariable model with the other imaging variables, PSMD provided independent prediction of visuospatial ability and general cognitive ability. This incremental prediction, coupled with its ease to compute and possibly better tractability, might make PSMD a useful brain biomarker in studies of cognitive aging.
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Affiliation(s)
- Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Stuart J Ritchie
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom
| | - Simon R Cox
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom
| | - Maria C Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Dementia Research Centre, Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Michelle Luciano
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Dementia Research Centre, Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Brain Research Imaging Centre, Division of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), University of Edinburgh, Edinburgh, United Kingdom
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12
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Marioni RE, McRae AF, Bressler J, Colicino E, Hannon E, Li S, Prada D, Smith JA, Trevisi L, Tsai PC, Vojinovic D, Simino J, Levy D, Liu C, Mendelson M, Satizabal CL, Yang Q, Jhun MA, Kardia SLR, Zhao W, Bandinelli S, Ferrucci L, Hernandez DG, Singleton AB, Harris SE, Starr JM, Kiel DP, McLean RR, Just AC, Schwartz J, Spiro A, Vokonas P, Amin N, Ikram MA, Uitterlinden AG, van Meurs JBJ, Spector TD, Steves C, Baccarelli AA, Bell JT, van Duijn CM, Fornage M, Hsu YH, Mill J, Mosley TH, Seshadri S, Deary IJ. Meta-analysis of epigenome-wide association studies of cognitive abilities. Mol Psychiatry 2018; 23:2133-2144. [PMID: 29311653 PMCID: PMC6035894 DOI: 10.1038/s41380-017-0008-y] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 10/02/2017] [Accepted: 10/27/2017] [Indexed: 12/21/2022]
Abstract
Cognitive functions are important correlates of health outcomes across the life-course. Individual differences in cognitive functions are partly heritable. Epigenetic modifications, such as DNA methylation, are susceptible to both genetic and environmental factors and may provide insights into individual differences in cognitive functions. Epigenome-wide meta-analyses for blood-based DNA methylation levels at ~420,000 CpG sites were performed for seven measures of cognitive functioning using data from 11 cohorts. CpGs that passed a Bonferroni correction, adjusting for the number of CpGs and cognitive tests, were assessed for: longitudinal change; being under genetic control (methylation QTLs); and associations with brain health (structural MRI), brain methylation and Alzheimer's disease pathology. Across the seven measures of cognitive functioning (meta-analysis n range: 2557-6809), there were epigenome-wide significant (P < 1.7 × 10-8) associations for global cognitive function (cg21450381, P = 1.6 × 10-8), and phonemic verbal fluency (cg12507869, P = 2.5 × 10-9). The CpGs are located in an intergenic region on chromosome 12 and the INPP5A gene on chromosome 10, respectively. Both probes have moderate correlations (~0.4) with brain methylation in Brodmann area 20 (ventral temporal cortex). Neither probe showed evidence of longitudinal change in late-life or associations with white matter brain MRI measures in one cohort with these data. A methylation QTL analysis suggested that rs113565688 was a cis methylation QTL for cg12507869 (P = 5 × 10-5 and 4 × 10-13 in two lookup cohorts). We demonstrate a link between blood-based DNA methylation and measures of phonemic verbal fluency and global cognitive ability. Further research is warranted to understand the mechanisms linking genomic regulatory changes with cognitive function to health and disease.
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Affiliation(s)
- Riccardo E. Marioni
- 0000 0004 1936 7988grid.4305.2Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK ,0000 0004 1936 7988grid.4305.2Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK ,0000 0000 9320 7537grid.1003.2Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD Australia
| | - Allan F. McRae
- 0000 0000 9320 7537grid.1003.2Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD Australia ,0000 0000 9320 7537grid.1003.2Queensland Brain Institute, University of Queensland, Brisbane, QLD Australia
| | - Jan Bressler
- 0000 0000 9206 2401grid.267308.8Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Elena Colicino
- 0000000419368729grid.21729.3fColumbia University Mailman School of Public Health, New York, NY USA ,0000 0001 0670 2351grid.59734.3cIcahn School of Medicine at Mount Sinai, New York, NY USA
| | - Eilis Hannon
- 0000 0004 1936 8024grid.8391.3University of Exeter Medical School, Exeter, UK
| | - Shuo Li
- 0000 0004 1936 7558grid.189504.1Department of Biostatistics, Boston University School of Public Health, Boston, MA USA
| | - Diddier Prada
- 0000 0004 1777 1207grid.419167.cInstituto Nacional de Cancerologia, Mexico City, Mexico
| | - Jennifer A Smith
- 0000000086837370grid.214458.eDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA ,0000000086837370grid.214458.eSurvey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI USA
| | - Letizia Trevisi
- 000000041936754Xgrid.38142.3cHarvard Medical School, Boston, MA USA
| | - Pei-Chien Tsai
- 0000 0001 2322 6764grid.13097.3cDepartment of Twin Research and Genetic Epidemiology, King’s College London, London, UK ,grid.145695.aDepartment of Biomedical Sciences, Chang Gung University, Taoyuan City, Taiwan ,Division of Allergy, Asthma, and Rheumatology, Department of Pediatrics, Chang Gung Memorial Hospital, Linkou, Taiwan
| | - Dina Vojinovic
- 000000040459992Xgrid.5645.2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jeannette Simino
- 0000 0004 1937 0407grid.410721.1Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS USA ,0000 0004 1937 0407grid.410721.1MIND Center, University of Mississippi Medical Center, Jackson, MS USA
| | - Daniel Levy
- Framingham Heart Study, Framingham, MA USA ,0000 0001 2293 4638grid.279885.9Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD USA
| | - Chunyu Liu
- 0000 0004 1936 7558grid.189504.1Department of Biostatistics, Boston University School of Public Health, Boston, MA USA ,Framingham Heart Study, Framingham, MA USA ,0000 0001 2293 4638grid.279885.9Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD USA
| | - Michael Mendelson
- Framingham Heart Study, Framingham, MA USA ,0000 0001 2293 4638grid.279885.9Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD USA ,0000 0004 0367 5222grid.475010.7Boston University School of Medicine, Boston, MA USA ,0000 0004 0378 8438grid.2515.3Department of Cardiology, Boston Children’s Hospital, Boston, MA USA
| | - Claudia L. Satizabal
- Framingham Heart Study, Framingham, MA USA ,0000 0004 0367 5222grid.475010.7Department of Neurology, Boston University School of Medicine, Boston, MA USA
| | - Qiong Yang
- 0000 0004 1936 7558grid.189504.1Department of Biostatistics, Boston University School of Public Health, Boston, MA USA
| | - Min A. Jhun
- 0000000086837370grid.214458.eDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA ,0000 0001 2180 1622grid.270240.3Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - Sharon L. R. Kardia
- 0000000086837370grid.214458.eDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA
| | - Wei Zhao
- 0000000086837370grid.214458.eDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA
| | - Stefania Bandinelli
- 0000 0004 1756 9121grid.423864.fGeriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy
| | - Luigi Ferrucci
- 0000 0000 9372 4913grid.419475.aClinical Research Branch, National Institute on Aging, Baltimore, MD USA
| | - Dena G. Hernandez
- 0000 0001 2297 5165grid.94365.3dLaboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD USA
| | - Andrew B. Singleton
- 0000 0001 2297 5165grid.94365.3dLaboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD USA
| | - Sarah E. Harris
- 0000 0004 1936 7988grid.4305.2Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK ,0000 0004 1936 7988grid.4305.2Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - John M. Starr
- 0000 0004 1936 7988grid.4305.2Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK ,0000 0004 1936 7988grid.4305.2Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Douglas P. Kiel
- 000000041936754Xgrid.38142.3cHarvard Medical School, Boston, MA USA ,000000041936754Xgrid.38142.3cHebrew SeniorLife Institute for Aging Research, Boston, MA USA
| | - Robert R. McLean
- 000000041936754Xgrid.38142.3cHarvard Medical School, Boston, MA USA ,000000041936754Xgrid.38142.3cHebrew SeniorLife Institute for Aging Research, Boston, MA USA
| | - Allan C. Just
- 0000 0001 0670 2351grid.59734.3cIcahn School of Medicine at Mount Sinai, New York, NY USA
| | - Joel Schwartz
- 000000041936754Xgrid.38142.3cHarvard T.H. Chan School of Public Health, Boston, MA USA
| | - Avron Spiro
- 0000 0004 1936 7558grid.189504.1Boston University Schools of Public Health and Medicine, Boston, MA USA ,0000 0004 4657 1992grid.410370.1VA Boston Healthcare System, Boston, MA USA
| | - Pantel Vokonas
- 0000 0004 1936 7558grid.189504.1Boston University Schools of Public Health and Medicine, Boston, MA USA ,0000 0004 4657 1992grid.410370.1VA Boston Healthcare System, Boston, MA USA
| | - Najaf Amin
- 000000040459992Xgrid.5645.2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M. Arfan Ikram
- 000000040459992Xgrid.5645.2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands ,000000040459992Xgrid.5645.2Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands ,000000040459992Xgrid.5645.2Departments of Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Andre G. Uitterlinden
- 000000040459992Xgrid.5645.2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands ,000000040459992Xgrid.5645.2Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Joyce B. J. van Meurs
- 000000040459992Xgrid.5645.2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands ,000000040459992Xgrid.5645.2Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Tim D. Spector
- 0000 0001 2322 6764grid.13097.3cDepartment of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Claire Steves
- 0000 0001 2322 6764grid.13097.3cDepartment of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Andrea A. Baccarelli
- 0000000419368729grid.21729.3fColumbia University Mailman School of Public Health, New York, NY USA
| | - Jordana T. Bell
- 0000 0001 2322 6764grid.13097.3cDepartment of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Cornelia M. van Duijn
- 000000040459992Xgrid.5645.2Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Myriam Fornage
- 0000 0000 9206 2401grid.267308.8Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX USA ,0000 0000 9206 2401grid.267308.8Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX USA
| | - Yi-Hsiang Hsu
- 000000041936754Xgrid.38142.3cHarvard Medical School, Boston, MA USA ,000000041936754Xgrid.38142.3cHebrew SeniorLife Institute for Aging Research, Boston, MA USA ,grid.66859.34Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Jonathan Mill
- 0000 0001 0670 2351grid.59734.3cIcahn School of Medicine at Mount Sinai, New York, NY USA
| | - Thomas H. Mosley
- 0000 0004 1937 0407grid.410721.1MIND Center, University of Mississippi Medical Center, Jackson, MS USA ,0000 0004 1937 0407grid.410721.1Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, MS USA
| | - Sudha Seshadri
- 0000 0004 0367 5222grid.475010.7Department of Neurology, Boston University School of Medicine, Boston, MA USA ,0000 0001 0629 5880grid.267309.9Glenn Biggs Institute of Alzheimer and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX USA
| | - Ian J. Deary
- 0000 0004 1936 7988grid.4305.2Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK ,0000 0004 1936 7988grid.4305.2Department of Psychology, University of Edinburgh, Edinburgh, UK
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13
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Buyukturkoglu K, Fleyser L, Byrd D, Morgello S, Inglese M. Diffusion Kurtosis Imaging Shows Similar Cerebral Axonal Damage in Patients with HIV Infection and Multiple Sclerosis. J Neuroimaging 2018; 28:320-327. [PMID: 29380545 DOI: 10.1111/jon.12497] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Accepted: 12/21/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND AND PURPOSE In this pilot study, we sought to investigate the pathological changes in the white matter (WM) of medically complex, combination antiretroviral therapy (cART)-treated patients with human immunodeficiency virus (HIV), comparing them to patients with long-standing, secondary progressive multiple sclerosis (SPMS). METHODS Using diffusion kurtosis imaging (DKI)-derived WM tract integrity (WMTI) metrics, 15 HIV and 15 age- and sex-matched SPMS patients with similar disease duration underwent magnetic resonance imaging analysis. Maps of WMTI metrics were created. Tract-based spatial statistics analysis of the whole brain and regions of interest analysis of the corpus callosum (CC) and the anterior thalamic radiations (ATRs) were performed and the derived WMTI metrics were compared between the groups of patients. RESULTS Axonal water fraction, an index of chronic axonal loss, showed similarities between HIV and the chronic MS patients in all regions; in contrast, tortuosity, a measure more sensitive to myelin loss, was regionally variable. In addition, in HIV patients, WMTI metrics of the CC and left ATR were associated with cognitive test scores, suggesting clinical relevance for these measures of WM damage. CONCLUSIONS We conclude that DKI-derived WMTI metrics may be a valuable tool in assessing the WM changes of medically complex HIV-infected individuals. While not powered to examine potential etiologies of WM changes in this pilot sample, regional variations in WMTI metrics were seen. When contrasted with changes consequent to chronic MS of similar duration, HIV and its comorbidities appear to result in similar degrees of axonal damage, but regionally variable amounts of myelin loss and extraxonal abnormality.
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Affiliation(s)
| | - Lazar Fleyser
- Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Desiree Byrd
- Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Susan Morgello
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY.,Pathology, Icahn School of Medicine at Mount Sinai, New York, NY.,Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Matilde Inglese
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY.,Radiology, Icahn School of Medicine at Mount Sinai, New York, NY.,Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
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14
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Early life characteristics and late life burden of cerebral small vessel disease in the Lothian Birth Cohort 1936. Aging (Albany NY) 2017; 8:2039-2061. [PMID: 27652981 PMCID: PMC5076451 DOI: 10.18632/aging.101043] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 09/04/2016] [Indexed: 11/25/2022]
Abstract
It is unknown whether relations between early-life factors and overall health in later life apply to burden of cerebral small vessel disease (cSVD), a major cause of stroke and dementia. We explored relations between early-life factors and cSVD in the Lothian Birth Cohort, a healthy aging cohort. Participants were recruited at age 70 (N = 1091); most had completed a test of cognitive ability at age 11 as part of the Scottish Mental Survey of 1947. Of those, 700 participants had brain MRI that could be rated for cSVD conducted at age 73. Presence of lacunes, white matter hyperintensities, microbleeds, and perivascular spaces were summed in a score of 0-4 representing all MRI cSVD features. We tested associations with early-life factors using multivariate logistic regression. Greater SVD score was significantly associated with lower age-11 IQ (OR higher SVD score per SD age-11 IQ = .78, 95%CI 0.65-.95, p=.01). The associations between SVD score and own job class (OR higher job class, .64 95%CI .43-.95, p=.03), age-11 deprivation index (OR per point deprivation score, 1.08, 95%CI 1.00-1.17, p=.04), and education (OR some qualifying education, .60 95%CI .37-.98, p=.04) trended towards significance (p<.05 for all) but did not meet thresholds for multiple testing. No early-life factor was significantly associated with any one individual score component. Early-life factors may contribute to age-73 burden of cSVD. These relations, and the potential for early social interventions to improve brain health, deserve further study.
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15
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Correlating quantitative tractography at 3T MRI and cognitive tests in healthy older adults. Brain Imaging Behav 2017; 10:1223-1230. [PMID: 26650629 DOI: 10.1007/s11682-015-9495-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
This study used diffusion tensor imaging tractography at 3 T MRI to relate cognitive function to white matter tracts in the brain. Brain T2 fluid attenuated inversion recovery-weighted and diffusion tensor 3 T MRI scans were acquired in thirty-three healthy participants without mild cognitive impairment or dementia. They completed a battery of neuropsychological tests including the Montreal Cognitive Assessment, Stroop test, Trail Making Test B, Wechsler Memory Scale-III Longest span forward, Wechsler Memory Scale-III Longest span backward, Mattis Dementia Rating Scale, California Verbal Learning Test Version II Long Delay Free Recall, and Letter Number Sequencing. Tractography was generated by the Fiber Assignment by Continuous Tracking method. The corpus callosum, cingulum, long association fibers, corticospinal/bulbar tracts, thalamic projection fibers, superior cerebellar peduncle, middle cerebellar peduncle and inferior cerebellar peduncle were manually segmented. The fractional anisotropy (FA) and mean diffusivity (MD) of these tracts were quantified. We studied the association between cognitive test scores and the MD and FA of tracts while controlling for age and total white matter hyperintensities volume. Worse scores on the Stroop test was associated with decreased FA of the corpus callosum, corticospinal/bulbar tract, and thalamic projection tracts. Scores on the other cognitive tests were not associated with either the FA or MD of measured tracts. In healthy persons the Stroop test appears to be a better predictor of the microstructural integrity of white matter tracts measured by DTI tractography than other cognitive tests.
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16
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Baker LM, Laidlaw DH, Cabeen R, Akbudak E, Conturo TE, Correia S, Tate DF, Heaps-Woodruff JM, Brier MR, Bolzenius J, Salminen LE, Lane EM, McMichael AR, Paul RH. Cognitive reserve moderates the relationship between neuropsychological performance and white matter fiber bundle length in healthy older adults. Brain Imaging Behav 2017; 11:632-639. [PMID: 26961092 PMCID: PMC7083104 DOI: 10.1007/s11682-016-9540-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Recent work using novel neuroimaging methods has revealed shorter white matter fiber bundle length (FBL) in older compared to younger adults. Shorter FBL also corresponds to poorer performance on cognitive measures sensitive to advanced age. However, it is unclear if individual factors such as cognitive reserve (CR) effectively moderate the relationship between FBL and cognitive performance. This study examined CR as a potential moderator of cognitive performance and brain integrity as defined by FBL. Sixty-three healthy adults underwent neuropsychological evaluation and 3T brain magnetic resonance imaging. Cognitive performance was measured using the Repeatable Battery of Assessment of Neuropsychological Status (RBANS). FBL was quantified from tractography tracings of white matter fiber bundles, derived from the diffusion tensor imaging. CR was determined by estimated premorbid IQ. Analyses revealed that lower scores on the RBANS were associated with shorter whole brain FBL (p = 0.04) and lower CR (p = 0.01) CR moderated the relationship between whole brain FBL and RBANS score (p < 0.01). Tract-specific analyses revealed that CR also moderated the association between FBL in the hippocampal segment of the cingulum and RBANS performance (p = 0.03). These results demonstrate that lower cognitive performance on the RBANS is more common with low CR and short FBL. On the contrary, when individuals have high CR, the relationship between FBL and cognitive performance is attenuated. Overall, CR protects older adults against lower cognitive performance despite age-associated reductions in FBL.
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Affiliation(s)
- Laurie M Baker
- Department of Psychological Sciences, University of Missouri - Saint Louis, One University Boulevard, Stadler Hall 327, Saint Louis, MO, 63121, USA.
| | - David H Laidlaw
- Computer Science Department, Brown University, Providence, RI, 02912, USA
| | - Ryan Cabeen
- Computer Science Department, Brown University, Providence, RI, 02912, USA
| | - Erbil Akbudak
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Thomas E Conturo
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Stephen Correia
- Division of Biology and Medicine, Brown Medical School, Providence, RI, 02912, USA
| | - David F Tate
- Missouri Institute of Mental Health, St. Louis, MO, 63134, USA
| | | | - Matthew R Brier
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jacob Bolzenius
- Missouri Institute of Mental Health, St. Louis, MO, 63134, USA
| | - Lauren E Salminen
- Department of Psychological Sciences, University of Missouri - Saint Louis, One University Boulevard, Stadler Hall 327, Saint Louis, MO, 63121, USA
| | - Elizabeth M Lane
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Amanda R McMichael
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Robert H Paul
- Department of Psychological Sciences, University of Missouri - Saint Louis, One University Boulevard, Stadler Hall 327, Saint Louis, MO, 63121, USA
- Missouri Institute of Mental Health, St. Louis, MO, 63134, USA
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17
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Damle NR, Ikuta T, John M, Peters BD, DeRosse P, Malhotra AK, Szeszko PR. Relationship among interthalamic adhesion size, thalamic anatomy and neuropsychological functions in healthy volunteers. Brain Struct Funct 2016; 222:2183-2192. [PMID: 27866270 DOI: 10.1007/s00429-016-1334-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 11/02/2016] [Indexed: 11/30/2022]
Abstract
The interthalamic adhesion (ITA) is an understudied neuroanatomical structure that forms a bridge of tissue connecting the thalamus of each hemisphere across the midline whose functional significance remains largely unknown. The likelihood of ITA absence has been reported in some studies to be increased in males, but findings have been inconsistent. We used magnetic resonance imaging to investigate the size and absence of the ITA and their relationship to thalamic volume, putative indices of white matter integrity (fractional anisotropy and mean diffusivity) within the anterior thalamic radiation and neuropsychological functions in 233 (129 M/104 F) healthy volunteers (age range 8-68). To ensure high reliability in this study two operators independently rated the absence of the ITA and measured its size for all individuals. The ITA was absent in 4% of all individuals with no sex differences in its absence. Females had greater ITA size compared to males overall with both groups demonstrating nonlinear age-associated changes across the age range examined. ITA size among females correlated significantly with thalamus volume and lower mean diffusivity in the anterior thalamic radiation. Path modeling indicated that ITA size statistically mediated the relationship between age and attention among females. Our findings provide evidence for sex differences in ITA size across the lifespan, which are associated with the surrounding thalamic anatomy and neuropsychological functions.
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Affiliation(s)
| | - Toshikazu Ikuta
- Department of Communication Sciences and Disorders, School of Applied Sciences, University of Mississippi, Oxford, MS, USA
| | - Majnu John
- Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA.,Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA.,Department of Mathematics, Hofstra University, Hempstead, NY, USA
| | - Bart D Peters
- Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA.,Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Pamela DeRosse
- Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA.,Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA.,Departments of Psychiatry and Molecular Medicine, Hofstra Northwell School of Medicine, Hempstead, NY, USA
| | - Anil K Malhotra
- Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA.,Center for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA.,Departments of Psychiatry and Molecular Medicine, Hofstra Northwell School of Medicine, Hempstead, NY, USA
| | - Philip R Szeszko
- James J. Peters Veterans Affairs Medical Center, Mental Health Care Center, 130 W Kingsbridge Rd, Bronx, NY, 10468, USA. .,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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18
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Salthouse TA. Little relation of adult age with cognition after controlling general influences. Dev Psychol 2016; 52:1545-1554. [PMID: 27505697 DOI: 10.1037/dev0000162] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Both general (i.e., shared across different cognitive measures) and specific (i.e., unique to particular cognitive measures) influences can be postulated to contribute to the relations between adult age and measures of cognitive functioning. Estimates of general and specific influences on measures of memory, speed, reasoning, and spatial visualization were derived in cross-sectional (N = 5,014) and 3-occasion longitudinal (N = 1,353) data in adults between 18 and 99 years of age. Increased age was negatively associated with estimates of general influences on cognitive functioning in both the cross-sectional differences and the longitudinal changes. Furthermore, after statistically controlling general influences, the relations of age on the cognitive measures were much smaller than were those in the original measures. Results from these and other analytical procedures converge on the conclusion that adult age appears to have weak relations with specific measures of cognitive functioning, defined as independent of influences shared across different types of cognitive measures, and that this is true in both cross-sectional and longitudinal comparisons. An implication of these findings is that general, as well as domain-specific, influences should be considered when attempting to explain the relations of age on cognitive functioning. (PsycINFO Database Record
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19
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Kim WH, Kim HJ, Adluru N, Singh V. Latent Variable Graphical Model Selection using Harmonic Analysis: Applications to the Human Connectome Project (HCP). PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2016; 2016:2443-2451. [PMID: 28255221 PMCID: PMC5330303 DOI: 10.1109/cvpr.2016.268] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
A major goal of imaging studies such as the (ongoing) Human Connectome Project (HCP) is to characterize the structural network map of the human brain and identify its associations with covariates such as genotype, risk factors, and so on that correspond to an individual. But the set of image derived measures and the set of covariates are both large, so we must first estimate a 'parsimonious' set of relations between the measurements. For instance, a Gaussian graphical model will show conditional independences between the random variables, which can then be used to setup specific downstream analyses. But most such data involve a large list of 'latent' variables that remain unobserved, yet affect the 'observed' variables sustantially. Accounting for such latent variables is not directly addressed by standard precision matrix estimation, and is tackled via highly specialized optimization methods. This paper offers a unique harmonic analysis view of this problem. By casting the estimation of the precision matrix in terms of a composition of low-frequency latent variables and high-frequency sparse terms, we show how the problem can be formulated using a new wavelet-type expansion in non-Euclidean spaces. Our formulation poses the estimation problem in the frequency space and shows how it can be solved by a simple sub-gradient scheme. We provide a set of scientific results on ~500 scans from the recently released HCP data where our algorithm recovers highly interpretable and sparse conditional dependencies between brain connectivity pathways and well-known covariates.
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Affiliation(s)
- Won Hwa Kim
- Dept. of Computer Sciences, University of Wisconsin, Madison, WI, U.S.A
| | - Hyunwoo J Kim
- Dept. of Computer Sciences, University of Wisconsin, Madison, WI, U.S.A
| | | | - Vikas Singh
- Dept. of Computer Sciences, University of Wisconsin, Madison, WI, U.S.A; Dept. of Biostatistics & Med. Informatics, University of Wisconsin, Madison, WI, U.S.A
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20
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Reginold W, Luedke AC, Tam A, Itorralba J, Fernandez-Ruiz J, Reginold J, Islam O, Garcia A. Cognitive Function and 3-Tesla Magnetic Resonance Imaging Tractography of White Matter Hyperintensities in Elderly Persons. Dement Geriatr Cogn Dis Extra 2015; 5:387-94. [PMID: 26628897 PMCID: PMC4662291 DOI: 10.1159/000439045] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Background/Aims This study used 3-Tesla magnetic resonance imaging (MRI) tractography to determine if there was an association between tracts crossing white matter hyperintensities (WMH) and cognitive function in elderly persons. Methods Brain T2-weighted fluid-attenuated inversion recovery (FLAIR) and diffusion tensor MRI scans were acquired in participants above the age of 60 years. Twenty-six persons had WMH identified on T2 FLAIR scans. They completed a battery of neuropsychological tests and were classified as normal controls (n = 15) or with Alzheimer's dementia (n = 11). Tractography was generated by the Fiber Assignment by Continuous Tracking method. All tracts that crossed WMH were segmented. The average fractional anisotropy and average mean diffusivity of these tracts were quantified. We studied the association between cognitive test scores with the average mean diffusivity and average fractional anisotropy of tracts while controlling for age, total WMH volume and diagnosis. Results An increased mean diffusivity of tracts crossing WMH was associated with worse performance on the Wechsler Memory Scale-III Longest Span Forward (p = 0.02). There was no association between the fractional anisotropy of tracts and performance on cognitive testing. Conclusion The mean diffusivity of tracts crossing WMH measured by tractography is a novel correlate of performance on the Wechsler Memory Scale-III Longest Span Forward in elderly persons.
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Affiliation(s)
- William Reginold
- Memory Clinics, Division of Geriatric Medicine, Department of Medicine, Ont., Canada
| | - Angela C Luedke
- Centre for Neuroscience Studies, Kingston General Hospital, Queen's University, Kingston, Ont., Canada
| | - Angela Tam
- Centre for Neuroscience Studies, Kingston General Hospital, Queen's University, Kingston, Ont., Canada
| | - Justine Itorralba
- Centre for Neuroscience Studies, Kingston General Hospital, Queen's University, Kingston, Ont., Canada
| | - Juan Fernandez-Ruiz
- Centre for Neuroscience Studies, Kingston General Hospital, Queen's University, Kingston, Ont., Canada
| | | | - Omar Islam
- Department of Diagnostic Radiology, Kingston General Hospital, Queen's University, Kingston, Ont., Canada
| | - Angeles Garcia
- Memory Clinics, Division of Geriatric Medicine, Department of Medicine, Ont., Canada ; Centre for Neuroscience Studies, Kingston General Hospital, Queen's University, Kingston, Ont., Canada
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21
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Abstract
Understanding aging-related cognitive decline is of growing importance in aging societies, but relatively little is known about its neural substrates. Measures of white matter microstructure are known to correlate cross-sectionally with cognitive ability measures, but only a few small studies have tested for longitudinal relations among these variables. We tested whether there were coupled changes in brain white matter microstructure indexed by fractional anisotropy (FA) and three broad cognitive domains (fluid intelligence, processing speed, and memory) in a large cohort of human participants with longitudinal diffusion tensor MRI and detailed cognitive data taken at ages 73 years (n = 731) and 76 years (n = 488). Longitudinal changes in white matter microstructure were coupled with changes in fluid intelligence, but not with processing speed or memory. Individuals with higher baseline white matter FA showed less subsequent decline in processing speed. Our results provide evidence for a longitudinal link between changes in white matter microstructure and aging-related cognitive decline during the eighth decade of life. They are consistent with theoretical perspectives positing that a corticocortical "disconnection" partly explains cognitive aging.
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22
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Salthouse TA, Habeck C, Razlighi Q, Barulli D, Gazes Y, Stern Y. Breadth and age-dependency of relations between cortical thickness and cognition. Neurobiol Aging 2015; 36:3020-3028. [PMID: 26356042 DOI: 10.1016/j.neurobiolaging.2015.08.011] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Revised: 08/03/2015] [Accepted: 08/10/2015] [Indexed: 10/23/2022]
Abstract
Recent advances in neuroimaging have identified a large number of neural measures that could be involved in age-related declines in cognitive functioning. A popular method of investigating neural-cognition relations has been to determine the brain regions in which a particular neural measure is associated with the level of specific cognitive measures. Although this procedure has been informative, it ignores the strong interrelations that typically exist among the measures in each modality. An alternative approach involves investigating the number and identity of distinct dimensions within the set of neural measures and within the set of cognitive measures before examining relations between the 2 types of measures. The procedure is illustrated with data from 297 adults between 20 and 79 years of age with cortical thickness in different brain regions as the neural measures and performance on 12 cognitive tests as the cognitive measures. The results revealed that most of the relations between cortical thickness and cognition occurred at a general level corresponding to variance shared among different brain regions and among different cognitive measures. In addition, the strength of the thickness-cognition relation was substantially reduced after controlling the variation in age, which suggests that at least some of the thickness-cognition relations in age-heterogeneous samples may be attributable to the influence of age on each type of measure.
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Affiliation(s)
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Taub Institute for Research on Alzheimer's Disease and The Aging Brain, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Qolamreza Razlighi
- Cognitive Neuroscience Division, Department of Neurology, Taub Institute for Research on Alzheimer's Disease and The Aging Brain, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Daniel Barulli
- Cognitive Neuroscience Division, Department of Neurology, Taub Institute for Research on Alzheimer's Disease and The Aging Brain, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Yunglin Gazes
- Cognitive Neuroscience Division, Department of Neurology, Taub Institute for Research on Alzheimer's Disease and The Aging Brain, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Taub Institute for Research on Alzheimer's Disease and The Aging Brain, Columbia University College of Physicians and Surgeons, New York, NY, USA
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23
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Brain white matter structure and information processing speed in healthy older age. Brain Struct Funct 2015; 221:3223-35. [PMID: 26254904 PMCID: PMC4920858 DOI: 10.1007/s00429-015-1097-5] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 07/29/2015] [Indexed: 11/09/2022]
Abstract
Cognitive decline, especially the slowing of information processing speed, is associated with normal ageing. This decline may be due to brain cortico-cortical disconnection caused by age-related white matter deterioration. We present results from a large, narrow age range cohort of generally healthy, community-dwelling subjects in their seventies who also had their cognitive ability tested in youth (age 11 years). We investigate associations between older age brain white matter structure, several measures of information processing speed and childhood cognitive ability in 581 subjects. Analysis of diffusion tensor MRI data using Tract-based Spatial Statistics (TBSS) showed that all measures of information processing speed, as well as a general speed factor composed from these tests (gspeed), were significantly associated with fractional anisotropy (FA) across the white matter skeleton rather than in specific tracts. Cognitive ability measured at age 11 years was not associated with older age white matter FA, except for the gspeed-independent components of several individual processing speed tests. These results indicate that quicker and more efficient information processing requires global connectivity in older age, and that associations between white matter FA and information processing speed (both individual test scores and gspeed), unlike some other aspects of later life brain structure, are generally not accounted for by cognitive ability measured in youth.
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24
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Staals J, Booth T, Morris Z, Bastin ME, Gow AJ, Corley J, Redmond P, Starr JM, Deary IJ, Wardlaw JM. Total MRI load of cerebral small vessel disease and cognitive ability in older people. Neurobiol Aging 2015; 36:2806-11. [PMID: 26189091 PMCID: PMC4706154 DOI: 10.1016/j.neurobiolaging.2015.06.024] [Citation(s) in RCA: 191] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2014] [Revised: 06/08/2015] [Accepted: 06/23/2015] [Indexed: 01/06/2023]
Abstract
Cerebral small vessel disease (SVD) may cause cognitive dysfunction. We tested the association between the combined presence of magnetic resonance imaging (MRI) features of SVD and cognitive ability in older age. Cognitive testing and brain MRI were performed in 680 older participants. MRI presence of lacunes, white matter hyperintensities, microbleeds, and perivascular spaces were summed in a score of 0-4 representing all SVD features combined. We also applied latent variable modeling to test whether the 4 MRI features form a unitary SVD construct. The SVD score showed significant associations with general cognitive ability. Latent variable modeling indicated that the 4 MRI markers formed a unitary construct, which showed consistent associations with cognitive ability compared with the SVD score. Total MRI load of SVD is associated with lower general cognitive ability in older age. The total SVD score performed consistently with the more complex latent variable model, suggesting validity and potential utility in future research for determining total SVD load.
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Affiliation(s)
- Julie Staals
- Department of Neurology and Cardiovascular Research Institute Maastricht, Maastricht University Medical Centre, Maastricht, The Netherlands; Brain Research Imaging Centre, Department of Clinical Neurosciences, University of Edinburgh, Edinburgh, UK
| | - Tom Booth
- Department of Psychology, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Zoe Morris
- Brain Research Imaging Centre, Department of Clinical Neurosciences, University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Brain Research Imaging Centre, Department of Clinical Neurosciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Alan J Gow
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, School of Life Sciences, Heriot-Watt University, Edinburgh, UK
| | - Janie Corley
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - John M Starr
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Brain Research Imaging Centre, Department of Clinical Neurosciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
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25
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Yoong M. Quantifying the deficit-imaging neurobehavioural impairment in childhood epilepsy. Quant Imaging Med Surg 2015; 5:225-37. [PMID: 25853081 DOI: 10.3978/j.issn.2223-4292.2015.01.06] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Accepted: 11/14/2014] [Indexed: 11/14/2022]
Abstract
BACKGROUND Neurobehavioral impairments such as learning difficulty, autism, attention deficit hyperactivity disorder (ADHD) and mood or behavioural problems are known to be increased in children with epilepsy; however, they remain under-recognised and often cause considerable morbidity. Quantitative neuroimaging techniques offer a potential avenue to improving our understanding of the underlying pathological basis for these disorders, aiding with diagnosis and risk stratification. METHODS A systematic review was undertaken for original research articles involving magnetic resonance imaging in children with epilepsy and one or more neurobehavioural impairments. Studies were reviewed with respect to patient population, methodology and magnetic resonance imaging (MRI) findings. RESULTS A total of 25 studies were identified and included in this review. The majority of studies looked at single impairments, commonly cognitive impairment or ADHD, with few studies reporting on other impairments. Reductions in cortical grey matter and disruptions of functional and structural brain networks were associated with poorer cognitive performance and disruptions of grey and white matter within a fronto-striatal-cerebellar network associated with ADHD. Insufficient studies were available to report on other impairments. CONCLUSIONS Relatively few studies exist in this field and those that do are methodologically diverse. Further investigation is required to determine if the changes reported to date are epilepsy syndrome specific or have broader applicability.
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Affiliation(s)
- Michael Yoong
- Muir Maxwell Epilepsy Centre, University of Edinburgh, Edinburgh, UK
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26
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Booth T, Royle NA, Corley J, Gow AJ, Valdés Hernández MDC, Muñoz Maniega S, Ritchie SJ, Bastin ME, Starr JM, Wardlaw JM, Deary IJ. Association of allostatic load with brain structure and cognitive ability in later life. Neurobiol Aging 2015; 36:1390-9. [PMID: 25659881 PMCID: PMC4353502 DOI: 10.1016/j.neurobiolaging.2014.12.020] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2013] [Revised: 12/09/2014] [Accepted: 12/15/2014] [Indexed: 11/23/2022]
Abstract
Allostatic load (AL) has been proposed as a general framework for understanding the cumulative effects of life stress on individuals. Despite growing interest in AL, limited research has been conducted on aging samples. We consider the association of AL (operationalized by a range of inflammatory, cardiovascular, and metabolic measures) with a range of brain volume measurements and cognitive ability in a large cohort sample of older adults (n = 658, mean age = 72.5 years, standard deviation = 0.7) using structural equation modeling. AL was significantly inversely associated with total brain volume (range of standardized β = -0.16 to -0.20) and white-matter volume (-0.35 to -0.36) and positively with hippocampal volume (0.10-0.15) but not gray-matter volume (0.04). AL was also significantly inversely associated with general cognitive ability (range β = -0.13 to -0.20), processing speed (-0.20 to -0.22), and knowledge (-0.18 to -0.20) but not memory or nonverbal reasoning. The associations of AL with cognitive abilities were not mediated by these brain volume measures. AL did not predict cognitive change from age 11 to approximately age 73. The findings suggest a link between AL and later life brain health and cognitive functioning.
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Affiliation(s)
- Tom Booth
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK.
| | - Natalie A Royle
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Division of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Janie Corley
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Alan J Gow
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Department of Psychology, School of Life Sciences, Heriot-Watt University, Edinburgh, UK
| | - Maria del C Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Division of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Division of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Stuart J Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Division of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Geriatric Medicine Unit, The University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Division of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK
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27
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Kievit RA, Davis SW, Mitchell DJ, Taylor JR, Duncan J, Henson RNA. Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking. Nat Commun 2014; 5:5658. [PMID: 25519467 PMCID: PMC4284640 DOI: 10.1038/ncomms6658] [Citation(s) in RCA: 114] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Accepted: 10/24/2014] [Indexed: 12/25/2022] Open
Abstract
Ageing is characterized by declines on a variety of cognitive measures. These declines are often attributed to a general, unitary underlying cause, such as a reduction in executive function owing to atrophy of the prefrontal cortex. However, age-related changes are likely multifactorial, and the relationship between neural changes and cognitive measures is not well-understood. Here we address this in a large (N=567), population-based sample drawn from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data. We relate fluid intelligence and multitasking to multiple brain measures, including grey matter in various prefrontal regions and white matter integrity connecting those regions. We show that multitasking and fluid intelligence are separable cognitive abilities, with differential sensitivities to age, which are mediated by distinct neural subsystems that show different prediction in older versus younger individuals. These results suggest that prefrontal ageing is a manifold process demanding multifaceted models of neurocognitive ageing.
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Affiliation(s)
- Rogier A Kievit
- MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK
| | - Simon W Davis
- Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK
| | - Daniel J Mitchell
- MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK
| | - Jason R Taylor
- 1] MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK [2] School of Psychological Sciences, The University of Manchester, Brunswick Street, Manchester M13 9PL, UK
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK
| | | | - Richard N A Henson
- MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK
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