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Lee HK, Basak C, Grant SJ, Ray NR, Skolasinska PA, Oehler C, Qin S, Sun A, Smith ET, Hulon Sherard G, Rivera-Dompenciel A, Merzenich M, Voss MW. The effects of computerized cognitive training in older adults' cognitive performance and biomarkers of structural brain aging. J Gerontol B Psychol Sci Soc Sci 2024:gbae075. [PMID: 38686621 DOI: 10.1093/geronb/gbae075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Indexed: 05/02/2024] Open
Abstract
OBJECTIVES Cognitive Training (CT) has been investigated as a means of delaying age-related cognitive decline in older adults. However, its impact on biomarkers of age-related structural brain atrophy has rarely been investigated, leading to a gap in our understanding of the linkage between improvements in cognition and brain plasticity. This study aimed to explore the impact of CT on cognitive performance and brain structure in older adults. METHODS 124 cognitively normal older adults recruited from two study sites were randomly assigned to either an adaptive CT (n=60) or a casual game training (Active Control, AC, n= 64). RESULTS After 10 weeks of training, CT participants showed greater improvements in the overall cognitive composite score (Cohen's d=.66, p<.01) with non-significant benefits after 6 months from the completion of training (Cohen's d=.36, p=.094). The CT group showed significant maintenance of the caudate volume as well as significant maintained fractional anisotropy (FA) in the left Internal Capsule (IC) and in left Superior Longitudinal Fasciculus (SLF) compared to the AC group. The AC group displayed an age-related decrease in these metrics of brain structure. DISCUSSION Results from this multi-site clinical trial demonstrate that the CT intervention improves cognitive performance and helps maintain caudate volume and integrity of white matter regions that are associated with cognitive control, adding to our understanding of the changes in brain structure contributing to changes in cognitive performance from adaptive CT.
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Affiliation(s)
- Hyun Kyu Lee
- Department of Research and Development, Posit Science Corporation, San Francisco, California, USA
| | | | - Sarah-Jane Grant
- Department of Research and Development, Posit Science Corporation, San Francisco, California, USA
| | - Nicholas R Ray
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | | | - Chris Oehler
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa, USA
| | - Shuo Qin
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | - Andrew Sun
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | - Evan T Smith
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | - G Hulon Sherard
- Department of Psychology, University of Texas at Dallas, Dallas, Texas, USA
| | | | - Mike Merzenich
- Department of Research and Development, Posit Science Corporation, San Francisco, California, USA
| | - Michelle W Voss
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa, USA
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Ray NR, Ayodele T, Jean-Francois M, Baez P, Fernandez V, Bradley J, Crane PK, Dalgard CL, Kuzma A, Nicaretta H, Sims R, Williams J, Cuccaro ML, Pericak-Vance MA, Mayeux R, Wang LS, Schellenberg GD, Cruchaga C, Beecham GW, Reitz C. The Early-Onset Alzheimer's Disease Whole-Genome Sequencing Project: Study design and methodology. Alzheimers Dement 2023; 19:4187-4195. [PMID: 37390458 PMCID: PMC10527497 DOI: 10.1002/alz.13370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/02/2023]
Abstract
INTRODUCTION Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations, resulting in a lack of understanding of its molecular etiology. METHODS Whole-genome sequencing and harmonization of clinical, neuropathological, and biomarker data of over 5000 EOAD cases of diverse ancestries. RESULTS A publicly available genomics resource for EOAD with extensive harmonized phenotypes. Primary analysis will (1) identify novel EOAD risk loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits. DISCUSSION This novel resource complements over 50,000 control and late-onset AD samples generated through the Alzheimer's Disease Sequencing Project (ADSP). The harmonized EOAD/ADSP joint call will be available through upcoming ADSP data releases and will allow for additional analyses across the full onset range. HIGHLIGHTS Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations. This results in a significant lack of understanding of the molecular etiology of this devastating form of the disease. The Early-Onset Alzheimer's Disease Whole-genome Sequencing Project is a collaborative initiative to generate a large-scale genomics resource for early-onset Alzheimer's disease with extensive harmonized phenotype data. Primary analyses are designed to (1) identify novel EOAD risk and protective loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits. The harmonized genomic and phenotypic data from this initiative will be available through NIAGADS.
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Affiliation(s)
- Nicholas R. Ray
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Columbia University, New York, NY 10032, USA
| | - Temitope Ayodele
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
| | - Melissa Jean-Francois
- The John P. Hussman Institute for Human Genomics,
University of Miami, Miami, FL 33136, USA
- Dr. John T. MacDonald Foundation Department of Human
Genetics, University of Miami, Coral Gables, FL 33146, USA
| | - Penelope Baez
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
| | - Victoria Fernandez
- Department of Psychiatry, Neurology and Genetics,
Washington University School of Medicine, St. Louis, MO 63130, USA
- Neurogenomics and Informatic (NGI) Center, Washington
University School of Medicine, St. Louis, MO 63130, USA
| | - Joseph Bradley
- Department of Psychiatry, Neurology and Genetics,
Washington University School of Medicine, St. Louis, MO 63130, USA
- Neurogenomics and Informatic (NGI) Center, Washington
University School of Medicine, St. Louis, MO 63130, USA
| | - Paul K. Crane
- Division of General Internal Medicine, University of
Washington, Seattle, WA 98195, USA
| | - Clifton L. Dalgard
- Department of Anatomy, Physiology & Genetics,
Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
- The American Genome Center, Uniformed Services University
of the Health Sciences, Bethesda, MD 20814, USA
| | - Amanda Kuzma
- Penn Neurodegeneration Genomics Center, Department of
Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of
Medicine, Philadelphia, PA 19104, USA
| | - Heather Nicaretta
- Penn Neurodegeneration Genomics Center, Department of
Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of
Medicine, Philadelphia, PA 19104, USA
| | - Rebecca Sims
- Division of Psychological Medicine and Clinical
Neurosciences, School of Medicine, Cardiff University, Cardiff CF10 3AT, UK
| | - Julie Williams
- UK Dementia Research Institute, Cardiff University,
Cardiff CF10 3AT, UK
- Division of Psychological Medicine and Clinical
Neurosciences, School of Medicine, Cardiff University, Cardiff CF10 3AT, UK
| | - Michael L. Cuccaro
- The John P. Hussman Institute for Human Genomics,
University of Miami, Miami, FL 33136, USA
- Dr. John T. MacDonald Foundation Department of Human
Genetics, University of Miami, Coral Gables, FL 33146, USA
| | - Margaret A. Pericak-Vance
- The John P. Hussman Institute for Human Genomics,
University of Miami, Miami, FL 33136, USA
- Dr. John T. MacDonald Foundation Department of Human
Genetics, University of Miami, Coral Gables, FL 33146, USA
| | - Richard Mayeux
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Columbia University, New York, NY 10032, USA
- Department of Neurology, Columbia University, New York, NY
10032, USA
- Department of Epidemiology, Columbia University, New York,
NY 10032, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of
Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of
Medicine, Philadelphia, PA 19104, USA
| | - Gerard D. Schellenberg
- Penn Neurodegeneration Genomics Center, Department of
Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of
Medicine, Philadelphia, PA 19104, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Neurology and Genetics,
Washington University School of Medicine, St. Louis, MO 63130, USA
- Neurogenomics and Informatic (NGI) Center, Washington
University School of Medicine, St. Louis, MO 63130, USA
| | - Gary W. Beecham
- The John P. Hussman Institute for Human Genomics,
University of Miami, Miami, FL 33136, USA
- Dr. John T. MacDonald Foundation Department of Human
Genetics, University of Miami, Coral Gables, FL 33146, USA
| | - Christiane Reitz
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Columbia University, New York, NY 10032, USA
- Department of Neurology, Columbia University, New York, NY
10032, USA
- Department of Epidemiology, Columbia University, New York,
NY 10032, USA
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Ray NR, Kunkle BW, Hamilton-Nelson K, Kurup JT, Rajabli F, Cosacak MI, Kizil C, Jean-Francois M, Cuccaro M, Reyes-Dumeyer D, Cantwell L, Kuzma A, Vance JM, Gao S, Hendrie HC, Baiyewu O, Ogunniyi A, Akinyemi RO, Lee WP, Martin ER, Wang LS, Beecham GW, Bush WS, Farrer LA, Haines JL, Byrd GS, Schellenberg GD, Mayeux R, Pericak-Vance MA, Reitz C. Extended genome-wide association study employing the African Genome Resources Panel identifies novel susceptibility loci for Alzheimer's Disease in individuals of African ancestry. medRxiv 2023:2023.08.29.23294774. [PMID: 37693582 PMCID: PMC10491365 DOI: 10.1101/2023.08.29.23294774] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
INTRODUCTION Despite a two-fold increased risk, individuals of African ancestry have been significantly underrepresented in Alzheimer's Disease (AD) genomics efforts. METHODS GWAS of 2,903 AD cases and 6,265 cognitive controls of African ancestry. Within-dataset results were meta-analyzed, followed by gene-based and pathway analyses, and analysis of RNAseq and whole-genome sequencing data. RESULTS A novel AD risk locus was identified in MPDZ on chromosome 9p23 (rs141610415, MAF=.002, P =3.68×10 -9 ). Two additional novel common and nine novel rare loci approached genome-wide significance at P <9×10 -7 . Comparison of association and LD patterns between datasets with higher and lower degrees of African ancestry showed differential association patterns at chr12q23.2 ( ASCL1 ), suggesting that the association is modulated by regional origin of local African ancestry. DISCUSSION Increased sample sizes and sample sets from Africa covering as much African genetic diversity as possible will be critical to identify additional disease-associated loci and improve deconvolution of local genetic ancestry effects.
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Ray NR, O'Connell MA, Nashiro K, Smith ET, Qin S, Basak C. Evaluating the relationship between white matter integrity, cognition, and varieties of video game learning. Restor Neurol Neurosci 2018; 35:437-456. [PMID: 28968249 DOI: 10.3233/rnn-160716] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Many studies are currently researching the effects of video games, particularly in the domain of cognitive training. Great variability exists among video games however, and few studies have attempted to compare different types of video games. Little is known, for instance, about the cognitive processes or brain structures that underlie learning of different genres of video games. OBJECTIVE To examine the cognitive and neural underpinnings of two different types of game learning in order to evaluate their common and separate correlates, with the hopes of informing future intervention research. METHODS Participants (31 younger adults and 31 older adults) completed an extensive cognitive battery and played two different genres of video games, one action game and one strategy game, for 1.5 hours each. DTI scans were acquired for each participant, and regional fractional anisotropy (FA) values were extracted using the JHU atlas. RESULTS Behavioral results indicated that better performance on tasks of working memory and perceptual discrimination was related to enhanced learning in both games, even after controlling for age, whereas better performance on a perceptual speed task was uniquely related with enhanced learning of the strategy game. DTI results indicated that white matter FA in the right fornix/stria terminalis was correlated with action game learning, whereas white matter FA in the left cingulum/hippocampus was correlated with strategy game learning, even after controlling for age. CONCLUSION Although cognition, to a large extent, was a common predictor of both types of game learning, regional white matter FA could separately predict action and strategy game learning. Given the neural and cognitive correlates of strategy game learning, strategy games may provide a more beneficial training tool for adults suffering from memory-related disorders or declines in processing speed, particularly older adults.
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Affiliation(s)
- Nicholas R Ray
- University of Texas at Dallas, Richardson, TX, USA.,The Center for Vital Longevity, Dallas, TX, USA
| | - Margaret A O'Connell
- University of Texas at Dallas, Richardson, TX, USA.,The Center for Vital Longevity, Dallas, TX, USA
| | - Kaoru Nashiro
- University of Southern California, Los Angeles, CA, USA
| | - Evan T Smith
- University of Texas at Dallas, Richardson, TX, USA.,The Center for Vital Longevity, Dallas, TX, USA
| | - Shuo Qin
- University of Texas at Dallas, Richardson, TX, USA.,The Center for Vital Longevity, Dallas, TX, USA
| | - Chandramallika Basak
- University of Texas at Dallas, Richardson, TX, USA.,The Center for Vital Longevity, Dallas, TX, USA
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Gupta K, Bhattacharya S, Nandi D, Dhar A, Maity A, Mukhopadhyay A, Chattopadhyay DJ, Ray NR, Sen P, Ghosh UC. Arsenic(III) sorption on nanostructured cerium incorporated manganese oxide (NCMO): a physical insight into the mechanistic pathway. J Colloid Interface Sci 2012; 377:269-76. [PMID: 22515993 DOI: 10.1016/j.jcis.2012.01.066] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2011] [Revised: 12/24/2011] [Accepted: 01/09/2012] [Indexed: 11/18/2022]
Abstract
Arsenic(III) sorption was investigated with nanostructured cerium incorporated manganese oxide (NCMO). The pH between 6.0 and 8.0 was optimized for the arsenic(III) sorption. Kinetics and equilibrium data (pH=7.0±0.2, T=303±1.6 K, and I=0.01 M) of arsenic(III) sorption by NCMO described, respectively, the pseudo-second order and the Freundlich isotherm equations well. The sorption process was somewhat complicated in nature and divided into two different segments, initially very fast sorption followed by slow intraparticle diffusion process. Sorption reaction of arsenic(III) on NCMO was endothermic (ΔH°=+13.46 kJ mol(-1)) and spontaneous (ΔG°=-24.75 to -30.15 kJ mol(-1) at T=283-323 K), which took place with increasing entropy (ΔS°=+0.14 kJ mol(-1)K(-1)) at solid-liquid interface. Energy of arsenic(III) sorption estimated by analyzing the equilibrium data using the D-R isotherm model was 15.4 kJ mol(-1), indicating the ion-exchange type mechanism. Raman, FT-IR, pH effect, desorption, etc. studies indicated that arsenic(III) was oxidized to arsenic(V) during the sorption process.
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Affiliation(s)
- K Gupta
- Department of Chemistry, Presidency University, 86/1 College Street, Kolkata 700 073, India
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