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Zwilling CE, Wu J, Barbey AK. Investigating nutrient biomarkers of healthy brain aging: a multimodal brain imaging study. NPJ AGING 2024; 10:27. [PMID: 38773079 PMCID: PMC11109270 DOI: 10.1038/s41514-024-00150-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 04/15/2024] [Indexed: 05/23/2024]
Abstract
The emerging field of Nutritional Cognitive Neuroscience aims to uncover specific foods and nutrients that promote healthy brain aging. Central to this effort is the discovery of nutrient profiles that can be targeted in nutritional interventions designed to promote brain health with respect to multimodal neuroimaging measures of brain structure, function, and metabolism. The present study therefore conducted one of the largest and most comprehensive nutrient biomarker studies examining multimodal neuroimaging measures of brain health within a sample of 100 older adults. To assess brain health, a comprehensive battery of well-established cognitive and brain imaging measures was administered, along with 13 blood-based biomarkers of diet and nutrition. The findings of this study revealed distinct patterns of aging, categorized into two phenotypes of brain health based on hierarchical clustering. One phenotype demonstrated an accelerated rate of aging, while the other exhibited slower-than-expected aging. A t-test analysis of dietary biomarkers that distinguished these phenotypes revealed a nutrient profile with higher concentrations of specific fatty acids, antioxidants, and vitamins. Study participants with this nutrient profile demonstrated better cognitive scores and delayed brain aging, as determined by a t-test of the means. Notably, participant characteristics such as demographics, fitness levels, and anthropometrics did not account for the observed differences in brain aging. Therefore, the nutrient pattern identified by the present study motivates the design of neuroscience-guided dietary interventions to promote healthy brain aging.
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Affiliation(s)
- Christopher E Zwilling
- Department of Psychology, University of Illinois, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, IL, USA
| | - Jisheng Wu
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Aron K Barbey
- Department of Psychology, University of Illinois, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, IL, USA.
- Decision Neuroscience Laboratory, University of Nebraska-Lincoln, Lincoln, NE, USA.
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA.
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA.
- Department of Bioengineering, University of Illinois, Urbana, IL, USA.
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Li Y, Ma X, Sunderraman R, Ji S, Kundu S. Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence. Hum Brain Mapp 2023; 44:4772-4791. [PMID: 37466292 PMCID: PMC10400788 DOI: 10.1002/hbm.26415] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/08/2023] [Accepted: 06/20/2023] [Indexed: 07/20/2023] Open
Abstract
Neuroimaging-based prediction methods for intelligence have seen a rapid development. Among different neuroimaging modalities, prediction using functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, with limited investigations on the merits of such analysis compared to prediction using dynamic FC or region-level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI, we propose a bi-directional long short-term memory (bi-LSTM) approach that incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient algorithm and applied for predicting intelligence using region-level time series and dynamic FC. We compare the prediction performance using different fMRI features acquired from the Adolescent Brain Cognitive Development (ABCD) study involving nearly 7000 individuals. Our detailed analysis illustrates the consistently inferior performance of static FC compared to region-level time series or dynamic FC for single and combined rest and task fMRI experiments. The joint analysis of task and rest fMRI leads to improved intelligence prediction under all models compared to using fMRI from only one experiment. In addition, the proposed bi-LSTM pipeline based on region-level time series identifies several shared and differential important brain regions across fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected regions shows strong reliability across cross-validation folds. Given the large sample size of ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.
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Affiliation(s)
- Yang Li
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Xin Ma
- Department of BiostatisticsColumbia UniversityNew YorkNew YorkUSA
| | - Raj Sunderraman
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Shihao Ji
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Suprateek Kundu
- Department of BiostatisticsThe University of Texas at MD Anderson Cancer CenterHoustonTexasUSA
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Lopes S, Shi L, Pan X, Gu Y, Dengler-Crish C, Li Y, Tiwari B, Zhang D. Meditation and Cognitive Outcomes: A Longitudinal Analysis Using Data From the Health and Retirement Study 2000-2016. Mindfulness (N Y) 2023; 14:1705-1717. [PMID: 37808263 PMCID: PMC10557979 DOI: 10.1007/s12671-023-02165-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/03/2023] [Indexed: 10/10/2023]
Abstract
Objective We aimed to assess the association between meditation practice and cognitive function over time among middle-aged and older adults. Method We included Health and Retirement Study (HRS) participants assessed for meditation practice in the year 2000 as part of the HRS alternative medicine module (n = 1,160) and were followed up for outcomes over 2000-2016 period. We examined the association between meditation ≥ twice a week vs none/less frequent practice and changes in the outcomes of recall, global cognitive function, and quantitative reasoning using generalized linear regression models. Stratified analyses among persons with/without self-reported baseline depressive symptoms were conducted to assess the link between meditation and cognitive outcomes. Results Among our full study sample, meditation ≥ twice a week was not significantly associated with total recall [β ; 95% CI: -0.97, 0.57; p = 0.61], global cognitive function [β ; 95% CI: -1.01, 1.12; p = 0.92], and quantitative reasoning [β ; 95% CI: -31.27, 8.32; p = 0.26]. However, among those who did not have self-reported depressive symptoms at baseline, meditation ≥ twice a week was associated with improvement in cognitive outcomes such as total recall [β ; 95% CI: 0.03, 0.18; p = 0.01] and global cognitive function [β ; 95% CI: 0.05, 0.40; p = 0.01] over time. Conclusions Frequent meditation practice might have a protective effect on cognitive outcomes over time, but this protection could be limited to those without self-reported baseline depressive symptoms. Future studies could incorporate more precise meditation practice assessment, investigate the effect of meditation on cognitive outcomes over time, and include more rigorous study designs with randomized group assignment. Pre-registration This study is not preregistered.
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Affiliation(s)
- Snehal Lopes
- Department of Public Health Sciences, Clemson University, Clemson, SC, USA
| | - Lu Shi
- Department of Public Health Sciences, Clemson University, Clemson, SC, USA
| | - Xi Pan
- Department of Sociology, Texas State University, San Marcos, Texas 78666, USA
| | - Yian Gu
- The Department of Neurology, The Department of Epidemiology, The Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, and The Gertrude H. Sergievsky Center, Columbia University, New York, NY 10032, USA
| | - Christine Dengler-Crish
- Department of Pharmaceutical Sciences, Northeast Ohio Medical University, Rootstown, OH 44272, USA
| | - Yan Li
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Biplav Tiwari
- Department of Health Policy and Management, College of Public Health, University of Georgia, Athens, GA 30602, USA
| | - Donglan Zhang
- New York University Long Island School of Medicine, Mineola, NY 11501, USA
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Yang YS, Smucny J, Zhang H, Maddock RJ. Meta-analytic evidence of elevated choline, reduced N-acetylaspartate, and normal creatine in schizophrenia and their moderation by measurement quality, echo time, and medication status. Neuroimage Clin 2023; 39:103461. [PMID: 37406595 PMCID: PMC10509531 DOI: 10.1016/j.nicl.2023.103461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Brain metabolite abnormalities measured with magnetic resonance spectroscopy (MRS) provide insight into pathological processes in schizophrenia. Prior meta-analyses have not yet answered important questions about the influence of clinical and technical factors on neurometabolite abnormalities and brain region differences. To address these gaps, we performed an updated meta-analysis of N-acetylaspartate (NAA), choline, and creatine levels in patients with schizophrenia and assessed the moderating effects of medication status, echo time, measurement quality, and other factors. METHODS We searched citations from three earlier meta-analyses and the PubMed database after the most recent meta-analysis to identify studies for screening. In total, 113 publications reporting 366 regional metabolite datasets met our inclusion criteria and reported findings in medial prefrontal cortex (MPFC), dorsolateral prefrontal cortex, frontal white matter, hippocampus, thalamus, and basal ganglia from a total of 4445 patient and 3944 control observations. RESULTS Patients with schizophrenia had reduced NAA in five of the six brain regions, with a statistically significant sparing of the basal ganglia. Patients had elevated choline in the basal ganglia and both prefrontal cortical regions. Patient creatine levels were normal in all six regions. In some regions, the NAA and choline differences were greater in studies enrolling predominantly medicated patients compared to studies enrolling predominantly unmedicated patients. Patient NAA levels were more reduced in hippocampus and frontal white matter in studies using longer echo times than those using shorter echo times. MPFC choline and NAA abnormalities were greater in studies reporting better metabolite measurement quality. CONCLUSIONS Choline is elevated in the basal ganglia and prefrontal cortical regions, suggesting regionally increased membrane turnover or glial activation in schizophrenia. The basal ganglia are significantly spared from the well-established widespread reduction of NAA in schizophrenia suggesting a regional difference in disease-associated factors affecting NAA. The echo time findings agree with prior reports and suggest microstructural changes cause faster NAA T2 relaxation in hippocampus and frontal white matter in schizophrenia. Separating the effects of medication status and illness chronicity on NAA and choline abnormalities will require further patient-level studies. Metabolite measurement quality was shown to be a critical factor in MRS studies of schizophrenia.
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Affiliation(s)
- Yvonne S Yang
- VISN22 Mental Illness Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, 11301 Wilshire Blvd, Los Angeles, CA 90073, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, USA.
| | - Jason Smucny
- Imaging Research Center, University of California, Davis, 4701 X Street, Sacramento, CA 95817, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Davis, 2230 Stockton Blvd, Sacramento, CA 95817, USA
| | - Huailin Zhang
- Department of Internal Medicine, Adventist Health White Memorial, 1720 E Cesar E Chavez Ave, Los Angeles, CA 90033, USA
| | - Richard J Maddock
- Imaging Research Center, University of California, Davis, 4701 X Street, Sacramento, CA 95817, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Davis, 2230 Stockton Blvd, Sacramento, CA 95817, USA.
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Pietschnig J, Gerdesmann D, Zeiler M, Voracek M. Of differing methods, disputed estimates and discordant interpretations: the meta-analytical multiverse of brain volume and IQ associations. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211621. [PMID: 35573038 PMCID: PMC9096623 DOI: 10.1098/rsos.211621] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/19/2022] [Indexed: 05/03/2023]
Abstract
Brain size and IQ are positively correlated. However, multiple meta-analyses have led to considerable differences in summary effect estimations, thus failing to provide a plausible effect estimate. Here we aim at resolving this issue by providing the largest meta-analysis and systematic review so far of the brain volume and IQ association (86 studies; 454 effect sizes from k = 194 independent samples; N = 26 000+) in three cognitive ability domains (full-scale, verbal, performance IQ). By means of competing meta-analytical approaches as well as combinatorial and specification curve analyses, we show that most reasonable estimates for the brain size and IQ link yield r-values in the mid-0.20s, with the most extreme specifications yielding rs of 0.10 and 0.37. Summary effects appeared to be somewhat inflated due to selective reporting, and cross-temporally decreasing effect sizes indicated a confounding decline effect, with three quarters of the summary effect estimations according to any reasonable specification not exceeding r = 0.26, thus contrasting effect sizes were observed in some prior related, but individual, meta-analytical specifications. Brain size and IQ associations yielded r = 0.24, with the strongest effects observed for more g-loaded tests and in healthy samples that generalize across participant sex and age bands.
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Affiliation(s)
- Jakob Pietschnig
- Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Austria
| | - Daniel Gerdesmann
- Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Austria
- Department of Physics Education, Faculty of Mathematics, Natural Sciences and Technology, University of Education Freiburg, Germany
| | - Michael Zeiler
- Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Martin Voracek
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Austria
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Sofologi M, Pliogou V, Bonti E, Efstratopoulou M, Kougioumtzis GA, Papatzikis E, Ntritsos G, Moraitou D, Papantoniou G. An Investigation of Working Memory Profile and Fluid Intelligence in Children With Neurodevelopmental Difficulties. Front Psychol 2022; 12:773732. [PMID: 35370868 PMCID: PMC8973915 DOI: 10.3389/fpsyg.2021.773732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 12/29/2021] [Indexed: 11/13/2022] Open
Abstract
The present study aims to evaluate the distinct patterns of working memory (WM) capacity of children with Developmental Language Disorder (DLD), High-functioning children with Autism Spectrum Disorder (ASD) and children with Down syndrome (DS). More specifically, the current study investigates the complex relationship of fluid intelligence and WM between 39 children with DLD, 20 H igh-functioning children with ASD, and 15 children with DS. All children were evaluated in different measures of Phonological Working Memory, Visual-spatial Working Memory whereas Fluid Intelligence was measured with Raven Progressive Matrices. The result analysis revealed a significant difference among the three groups, both among each function separately and the correlations among them, as well. The results revealed that the DLD groups and High-functioning ASD group exhibited a common picture or an overlap of performances in all Phonological and Visuo-spatial working memory measures, except Backward Digit Recall task. As for the DS group research findings revealed different and unique working memory patterns in comparison to DLD group and High-functioning ASD. Their differences have been studied and further conclusions have been drawn about the different patterns of working memory among the three clinical groups. The implications of these findings are discussed in light of support for learning. The common profile that characterize the two developmental conditions and the distinct pattern of working memory performance in DS group underlies the need for further research in the field.
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Affiliation(s)
- Maria Sofologi
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, Ioannina, Greece
- Institute of Humanities and Social Sciences, University Research Centre of Ioannina (URCI), Ioannina, Greece
| | - Vassiliki Pliogou
- Department of Early Childhood Education, School of Humanities and Social Sciences, University of Western Macedonia, Florina, Greece
| | - Eleni Bonti
- Department of Psychiatry, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Department of Education, School of Education, University of Nicosia, Nicosia, Cyprus
- Department of Special Education (CEDU), United Arab Emirates University (UAEU), Al Ain, United Arab Emirates
| | - Maria Efstratopoulou
- Department of Special Education (CEDU), United Arab Emirates University (UAEU), Al Ain, United Arab Emirates
| | - Georgios A. Kougioumtzis
- Department of Turkish and Modern Asian Studies, National and Kapodistrian University of Athens, Athens, Greece
| | - Efthymios Papatzikis
- Department of Early Childhood Education and Care, Oslo Metropolitan University, Oslo, Norway
| | - Georgios Ntritsos
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta, Greece
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
| | - Despina Moraitou
- Laboratory of Psychology, Section of Experimental and Cognitive Psychology, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgia Papantoniou
- Laboratory of Psychology, Department of Early Childhood Education, School of Education, University of Ioannina, Ioannina, Greece
- Institute of Humanities and Social Sciences, University Research Centre of Ioannina (URCI), Ioannina, Greece
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Balkan Center, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Saha S, Pagnozzi A, Bradford D, Fripp J. Predicting fluid intelligence in adolescence from structural MRI with deep learning methods. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Larsen RJ, Gagoski B, Morton SU, Ou Y, Vyas R, Litt J, Grant PE, Sutton BP. Quantification of magnetic resonance spectroscopy data using a combined reference: Application in typically developing infants. NMR IN BIOMEDICINE 2021; 34:e4520. [PMID: 33913194 DOI: 10.1002/nbm.4520] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 03/18/2021] [Accepted: 03/19/2021] [Indexed: 06/12/2023]
Abstract
Quantification of proton magnetic resonance spectroscopy (1 H-MRS) data is commonly performed by referencing the ratio of the signal from one metabolite, or metabolite group, to that of another, or to the water signal. Both approaches have drawbacks: ratios of two metabolites can be difficult to interpret because study effects may be driven by either metabolite, and water-referenced data must be corrected for partial volume and relaxation effects in the water signal. Here, we introduce combined reference (CRef) analysis, which compensates for both limitations. In this approach, metabolites are referenced to the combined signal of several reference metabolites or metabolite groups. The approach does not require the corrections necessary for water scaling and produces results that are less sensitive to the variation of any single reference signal, thereby aiding the interpretation of results. We demonstrate CRef analysis using 202 1 H-MRS acquisitions from the brains of 140 infants, scanned at approximately 1 and 3 months of age. We show that the combined signal of seven reference metabolites or metabolite groups is highly correlated with the water signal, corrected for partial volume and relaxation effects associated with cerebral spinal fluid. We also show that the combined reference signal is equally or more uniform across subjects than the reference signals from single metabolites or metabolite groups. We use CRef analysis to quantify metabolite concentration changes during the first several months of life in typically developing infants.
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Affiliation(s)
- Ryan J Larsen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Borjan Gagoski
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Sarah U Morton
- Harvard Medical School, Boston, Massachusetts, USA
- Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Rutvi Vyas
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Jonathan Litt
- Harvard Medical School, Boston, Massachusetts, USA
- Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Bradley P Sutton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Williamson JB, Lamb DG, Porges EC, Bottari S, Woods AJ, Datta S, Langer K, Cohen RA. Cerebral Metabolite Concentrations Are Associated With Cortical and Subcortical Volumes and Cognition in Older Adults. Front Aging Neurosci 2021; 12:587104. [PMID: 33613261 PMCID: PMC7886995 DOI: 10.3389/fnagi.2020.587104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 12/03/2020] [Indexed: 01/05/2023] Open
Abstract
Background Cerebral metabolites are associated with different physiological processes in brain aging. Cortical and limbic structures play important roles in cognitive aging; however, the relationship between these structures and age remains unclear with respect to physiological underpinnings. Regional differences in metabolite levels may be related to different structural and cognitive changes in aging. Methods Magnetic resonance imaging and spectroscopy were obtained from 117 cognitively healthy older adults. Limbic and other key structural volumes were measured. Concentrations of N-acetylaspartate (NAA) and choline-containing compounds (Cho) were measured in frontal and parietal regions. Neuropsychological testing was performed including measures of crystallized and fluid intelligence and memory. Results NAA in the frontal voxel was associated with limbic and cortical volumes, whereas Cho in parietal cortex was negatively associated with hippocampal and other regional volumes. Hippocampal volume was associated with forgetting, independent of age. Further, parietal Cho and hippocampal volume contributed independent variance to age corrected discrepancy between fluid and crystallized abilities. Conclusion These findings suggest that physiological changes with age in the frontal and parietal cortices may be linked to structural changes in other connected brain regions. These changes are differentially associated with cognitive performance, suggesting potentially divergent mechanisms.
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Affiliation(s)
- John B Williamson
- Center for Cognitive Aging and Memory, Clinical Translational Research Program, College of Medicine, University of Florida, Gainesville, FL, United States.,Center for OCD and Anxiety Related Disorders, Department of Psychiatry, McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL, United States.,Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States.,Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, FL, United States
| | - Damon G Lamb
- Center for Cognitive Aging and Memory, Clinical Translational Research Program, College of Medicine, University of Florida, Gainesville, FL, United States.,Center for OCD and Anxiety Related Disorders, Department of Psychiatry, McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL, United States.,Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States.,Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, FL, United States
| | - Eric C Porges
- Center for Cognitive Aging and Memory, Clinical Translational Research Program, College of Medicine, University of Florida, Gainesville, FL, United States.,Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Sarah Bottari
- Center for OCD and Anxiety Related Disorders, Department of Psychiatry, McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL, United States.,Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Adam J Woods
- Center for Cognitive Aging and Memory, Clinical Translational Research Program, College of Medicine, University of Florida, Gainesville, FL, United States.,Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Somnath Datta
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Kailey Langer
- Center for Cognitive Aging and Memory, Clinical Translational Research Program, College of Medicine, University of Florida, Gainesville, FL, United States.,Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Ronald A Cohen
- Center for Cognitive Aging and Memory, Clinical Translational Research Program, College of Medicine, University of Florida, Gainesville, FL, United States.,Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States.,Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Gainesville, FL, United States
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10
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White TL, Gonsalves MA, Cohen RA, Harris AD, Monnig MA, Walsh EG, Nitenson AZ, Porges EC, Lamb DG, Woods AJ, Borja CB. The neurobiology of wellness: 1H-MRS correlates of agency, flexibility and neuroaffective reserves in healthy young adults. Neuroimage 2020; 225:117509. [PMID: 33127477 PMCID: PMC7869459 DOI: 10.1016/j.neuroimage.2020.117509] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/08/2020] [Accepted: 10/21/2020] [Indexed: 02/06/2023] Open
Abstract
Proton magnetic resonance spectroscopy (1H-MRS) is a noninvasive imaging technique that measures the concentration of metabolites in defined areas of the human brain in vivo. The underlying structure of natural metabolism-emotion relationships is unknown. Further, there is a wide range of between-person differences in metabolite concentration in healthy individuals, but the significance of this variation for understanding emotion in healthy humans is unclear. Here we investigated the relationship of two emotional constructs, agency and flexibility, with the metabolites glutamate and glutamine (Glx), N-acetylaspartate (tNAA), choline (Cho), creatine (tCr), and myo-inositol (Ins) in the right dorsal anterior cingulate cortex (dACC) in medically and psychiatrically healthy volunteers (N = 20, 9 female; mean age = 22.8 years, SD = 3.40). The dACC was selected because this region is an integrative hub involved in multiple brain networks of emotion, cognition and behavior. Emotional traits were assessed using the Multidimensional Personality Questionnaire Brief Form (MPQ-BF), an empirically derived self-report instrument with an orthogonal factor structure. Phenotypes evaluated were positive and negative agency (MPQ-BF Social Potency, Aggression), emotional and behavioral flexibility (MPQ-BF Absorption, Control-reversed), and positive and negative affect (MPQ-BF Social Closeness; Stress Reaction, Alienation). The resting concentration of tNAA in the dACC was robustly positively correlated with Absorption (r = +0.56, unadjusted p = .005), moderately positively correlated with Social Potency (r = +0.42, unadjusted p = .03), and robustly negatively correlated with Aggression (r = −0.59, unadjusted p = .003). Absorption and Aggression accounted for substantial variance in tNAA (R2 = 0.31, 0.35; combined R2 = 0.50), and survived correction for multiple comparisons (Holm-Bonferroni adjusted p = .032, 0.021, respectively). dACC Glx and Cho had modest relationships with behavioral flexibility and social affiliation that did not survive this multiple correction, providing effect sizes for future work. Principal Component Analysis (PCA) revealed a three-factor orthogonal solution indicating specific relationships between: 1) Glx and behavioral engagement; 2) Cho and affiliative bonding; and 3) tNAA and a novel dimension that we term neuroaffective reserves. Our results inform the neurobiology of agency and flexibility and lay the groundwork for understanding mechanisms of natural emotion using 1H-MRS.
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Affiliation(s)
- Tara L White
- Center for Alcohol and Addiction Studies, Brown University, Box G-S121-4, 121 South Main St., Providence, RI 02912, USA; Department of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, RI, USA; Carney Institute for Brain Science, Brown University, Providence, RI, USA.
| | | | - Ronald A Cohen
- Department of Clinical and Health Psychology, Center for Cognitive Aging and Memory, and McKnight Brain Research Foundation, University of Florida, Gainesville, FL, USA
| | - Ashley D Harris
- Department of Radiology, CAIR Program, Alberta Children's Hospital Research Institute, and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Mollie A Monnig
- Center for Alcohol and Addiction Studies, Brown University, Box G-S121-4, 121 South Main St., Providence, RI 02912, USA
| | - Edward G Walsh
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Adam Z Nitenson
- Neuroscience Graduate Program, Brown University, Providence, RI, USA
| | - Eric C Porges
- Department of Clinical and Health Psychology, Center for Cognitive Aging and Memory, and McKnight Brain Research Foundation, University of Florida, Gainesville, FL, USA
| | - Damon G Lamb
- Department of Psychiatry, and Center for Cognitive Aging and Memory, McKnight Brain Research Foundation, University of Florida, Gainesville, FL, USA; Center for Neuropsychological Studies, Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA; Brain Rehabilitation Research Center, Malcom Randall Veterans Affairs Medical Center, Gainesville, FL, USA
| | - Adam J Woods
- Department of Clinical and Health Psychology, Center for Cognitive Aging and Memory, and McKnight Brain Research Foundation, University of Florida, Gainesville, FL, USA
| | - Cara B Borja
- Neuroscience Graduate Program, Brown University, Providence, RI, USA
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11
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Larsen RJ, Raine LB, Hillman CH, Kramer AF, Cohen NJ, Barbey AK. Body mass and cardiorespiratory fitness are associated with altered brain metabolism. Metab Brain Dis 2020; 35:999-1007. [PMID: 32350752 DOI: 10.1007/s11011-020-00560-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 03/05/2020] [Indexed: 12/23/2022]
Abstract
Magnetic Resonance Spectroscopy provides measures of brain chemistry that are sensitive to cardiorespiratory fitness and body composition. The concentration of N-acetyl aspartic acid (NAA) is of interest because it is a marker of neuronal integrity. The ratio of NAA to creatine, a standard reference metabolite, has been shown to correlate with measures of both cardiorespiratory fitness and body composition. However, previous studies have explored these effects in isolation, making it impossible to know which of these highly correlated measures drive the correlations with NAA/Cr. As a result, the mechanisms underlying their association remain to be established. We therefore conducted a comprehensive study to investigate the relative contributions of cardiorespiratory fitness and percent body fat in predicting NAA/Cr. We demonstrate that NAA/Cr in white matter is correlated with percent body fat, and that this relationship largely subsumes the correlation of NAA/Cr with cardiorespiratory fitness. These results underscore the association of body composition with axonal integrity and suggests that this relationship drives the association of NAA/Cr with physical fitness in young adults.
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Affiliation(s)
- Ryan J Larsen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana, Champaign, IL, USA.
| | - Lauren B Raine
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Charles H Hillman
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana, Champaign, IL, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
- Department of Physical Therapy, Movement, & Rehabilitation Sciences, Northeastern University, Boston, MA, USA
| | - Arthur F Kramer
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana, Champaign, IL, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Neal J Cohen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana, Champaign, IL, USA
- Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA
| | - Aron K Barbey
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana, Champaign, IL, USA
- Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA
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12
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Ujma PP, Bódizs R, Dresler M. Sleep and intelligence: critical review and future directions. Curr Opin Behav Sci 2020. [DOI: 10.1016/j.cobeha.2020.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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13
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Liang C, Liu YC, Chang Y, Liang CT. Differences in numeric, verbal, and spatial reasoning between engineering and literature students through a neurocognitive lens. COGN SYST RES 2020. [DOI: 10.1016/j.cogsys.2019.11.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Santarnecchi E, Emmendorfer A, Pascual-Leone A. Dissecting the parieto-frontal correlates of fluid intelligence: A comprehensive ALE meta-analysis study. INTELLIGENCE 2017. [DOI: 10.1016/j.intell.2017.04.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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15
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Pezoulas VC, Zervakis M, Michelogiannis S, Klados MA. Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to [corrected] IQ and Gender. Front Hum Neurosci 2017; 11:189. [PMID: 28491028 PMCID: PMC5405083 DOI: 10.3389/fnhum.2017.00189] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 03/31/2017] [Indexed: 11/17/2022] Open
Abstract
During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high fluid Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.
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Affiliation(s)
- Vasileios C Pezoulas
- School of Electrical and Computer Engineering, Technical University of CreteChania, Greece
| | - Michalis Zervakis
- School of Electrical and Computer Engineering, Technical University of CreteChania, Greece
| | - Sifis Michelogiannis
- Neurophysiological Research Laboratory (L. Widén), School of Medicine, University of CreteHeraklion, Greece
| | - Manousos A Klados
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain SciencesLeipzig, Germany
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16
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Larsen RJ, Newman M, Nikolaidis A. Reduction of variance in measurements of average metabolite concentration in anatomically-defined brain regions. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2016; 272:73-81. [PMID: 27662403 DOI: 10.1016/j.jmr.2016.09.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 09/09/2016] [Accepted: 09/10/2016] [Indexed: 06/06/2023]
Abstract
Multiple methods have been proposed for using Magnetic Resonance Spectroscopy Imaging (MRSI) to measure representative metabolite concentrations of anatomically-defined brain regions. Generally these methods require spectral analysis, quantitation of the signal, and reconciliation with anatomical brain regions. However, to simplify processing pipelines, it is practical to only include those corrections that significantly improve data quality. Of particular importance for cross-sectional studies is knowledge about how much each correction lowers the inter-subject variance of the measurement, thereby increasing statistical power. Here we use a data set of 72 subjects to calculate the reduction in inter-subject variance produced by several corrections that are commonly used to process MRSI data. Our results demonstrate that significant reductions of variance can be achieved by performing water scaling, accounting for tissue type, and integrating MRSI data over anatomical regions rather than simply assigning MRSI voxels with anatomical region labels.
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Affiliation(s)
- Ryan J Larsen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, United States.
| | - Michael Newman
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, United States
| | - Aki Nikolaidis
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, United States
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