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Kraft JN, O'Shea A, Albizu A, Evangelista ND, Hausman HK, Boutzoukas E, Nissim NR, Van Etten EJ, Bharadwaj PK, Song H, Smith SG, Porges E, DeKosky S, Hishaw GA, Wu S, Marsiske M, Cohen R, Alexander GE, Woods AJ. Structural Neural Correlates of Double Decision Performance in Older Adults. Front Aging Neurosci 2020; 12:278. [PMID: 33117145 PMCID: PMC7493680 DOI: 10.3389/fnagi.2020.00278] [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: 11/22/2019] [Accepted: 08/11/2020] [Indexed: 11/13/2022] Open
Abstract
Speed of processing is a cognitive domain that encompasses the speed at which an individual can perceive a given stimulus, interpret the information, and produce a correct response. Speed of processing has been shown to decline more rapidly than other cognitive domains in an aging population, suggesting that this domain is particularly vulnerable to cognitive aging (Chee et al., 2009). However, given the heterogeneity of neuropsychological measures used to assess the domains underpinning speed of processing, a diffuse pattern of brain regions has been implicated. The current study aims to investigate the structural neural correlates of speed of processing by assessing cortical volume and speed of processing scores on the POSIT Double Decision task within a healthy older adult population (N = 186; mean age = 71.70 ± 5.32 years). T1-weighted structural images were collected via a 3T Siemens scanner. The current study shows that less cortical thickness in right temporal, posterior frontal, parietal and occipital lobe structures were significantly associated with poorer Double Decision scores. Notably, these include the lateral orbitofrontal gyrus, precentral gyrus, superior, transverse, and inferior temporal gyrus, temporal pole, insula, parahippocampal gyrus, fusiform gyrus, lingual gyrus, superior and inferior parietal gyrus and lateral occipital gyrus. Such findings suggest that speed of processing performance is associated with a wide array of cortical regions that provide unique contributions to performance on the Double Decision task.
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Affiliation(s)
- Jessica N Kraft
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.,Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Andrew O'Shea
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, 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
| | - Alejandro Albizu
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.,Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Nicole D Evangelista
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, 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
| | - Hanna K Hausman
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, 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
| | - Emanuel Boutzoukas
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Nicole R Nissim
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Emily J Van Etten
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
| | - Pradyumna K Bharadwaj
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
| | - Hyun Song
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
| | - Samantha G Smith
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States
| | - Eric Porges
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, 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
| | - Steven DeKosky
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.,Department of Neurology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Georg A Hishaw
- Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs, and BIO5 Institute, University of Arizona and Arizona Alzheimer's Consortium, Tucson, AZ, United States
| | - Samuel Wu
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Michael Marsiske
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, 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 Cohen
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, 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
| | - Gene E Alexander
- Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States.,Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs, and BIO5 Institute, University of Arizona and Arizona Alzheimer's Consortium, Tucson, AZ, United States
| | - Adam J Woods
- Center for Cognitive Aging and Memory Clinical Translational Research, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.,Department of Neuroscience, 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
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3
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Wu Q, Yue Z, Ge Y, Ma D, Yin H, Zhao H, Liu G, Wang J, Dou W, Pan Y. Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training. Front Neurol 2020; 10:1419. [PMID: 32082238 PMCID: PMC7000923 DOI: 10.3389/fneur.2019.01419] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 12/30/2019] [Indexed: 12/21/2022] Open
Abstract
Brain computer interface (BCI)-based training is promising for the treatment of stroke patients with upper limb (UL) paralysis. However, most stroke patients receive comprehensive treatment that not only includes BCI, but also routine training. The purpose of this study was to investigate the topological alterations in brain functional networks following comprehensive treatment, including BCI training, in the subacute stage of stroke. Twenty-five hospitalized subacute stroke patients with moderate to severe UL paralysis were assigned to one of two groups: 4-week comprehensive treatment, including routine and BCI training (BCI group, BG, n = 14) and 4-week routine training without BCI support (control group, CG, n = 11). Functional UL assessments were performed before and after training, including, Fugl-Meyer Assessment-UL (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT). Neuroimaging assessment of functional connectivity (FC) in the BG was performed by resting state functional magnetic resonance imaging. After training, as compared with baseline, all clinical assessments (FMA-UL, ARAT, and WMFT) improved significantly (p < 0.05) in both groups. Meanwhile, better functional improvements were observed in FMA-UL (p < 0.05), ARAT (p < 0.05), and WMFT (p < 0.05) in the BG. Meanwhile, FC of the BG increased across the whole brain, including the temporal, parietal, and occipital lobes and subcortical regions. More importantly, increased inter-hemispheric FC between the somatosensory association cortex and putamen was strongly positively associated with UL motor function after training. Our findings demonstrate that comprehensive rehabilitation, including BCI training, can enhance UL motor function better than routine training for subacute stroke patients. The reorganization of brain functional networks topology in subacute stroke patients allows for increased coordination between the multi-sensory and motor-related cortex and the extrapyramidal system. Future long-term, longitudinal, controlled neuroimaging studies are needed to assess the effectiveness of BCI training as an approach to promote brain plasticity during the subacute stage of stroke.
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Affiliation(s)
- Qiong Wu
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Zan Yue
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Yunxiang Ge
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Di Ma
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Hang Yin
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Hongliang Zhao
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Gang Liu
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jing Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Weibei Dou
- Department of Electronic Engineering, Tsinghua University, Beijing, China.,Beijing National Research Center for Information Science and Technology, Beijing, China
| | - Yu Pan
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
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