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Zamarreño P, Mateos PM, Valentín A. Working memory training improves episodic memory in older people: transfer based on controlled retrieval processes. Front Psychol 2024; 15:1314483. [PMID: 38572199 PMCID: PMC10987720 DOI: 10.3389/fpsyg.2024.1314483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/06/2024] [Indexed: 04/05/2024] Open
Abstract
Introduction The results of working memory (WM) training to improve episodic memory in older people are inconsistent. This inconsistency could be due to the fact that the episodic memory tests used do not share the same cognitive resources as the trained WM task. The aim of this study was to assess whether performance on an episodic memory test will improve only when this test requires controlled processes of retrieval of information from secondary memory or recollection, similar to the processes exercised during WM training. Method Fifty-five people over 60 years of age participated in the study: 27 were randomly assigned to the experimental group (EG) and the rest to the control group (CG). The EG was trained in complex span tasks. Before and after training, both groups were tested on episodic memory tests (a verbal and a visuospatial recognition test) and WM span tasks (reading, digit and spatial location). Results ANOVAs revealed a greater improvement of recollection estimates in the EG than in the CG for both verbal recognition (p = 0.023) and visuospatial recognition (p = 0.014). Discussion Our results provide support for a cognitive mechanism whose shared presence favored transfer from training on a WM task to a test of episodic memory. Consistent with our predictions, training on complex span tasks improved performance on recognition tests only when recall required a controlled search process in secondary memory, or recollection. We therefore stress the importance of identifying other cognitive resources that are susceptible to transfer from a training task to other untrained tasks. A better understanding of the phenomenon of transfer is crucial for the design of increasingly effective intervention programs for older people.
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Shao X, Li A, Wang Z, Xue G, Zhu B. False recall is associated with larger caudate in males but not in females. Memory 2024:1-8. [PMID: 38416016 DOI: 10.1080/09658211.2024.2319314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/05/2024] [Indexed: 02/29/2024]
Abstract
After learning semantically related words, some people are more likely than others to incorrectly recall unstudied but semantically related lures (i.e., Deese-Roediger-McDermott [DRM] false recall). Previous studies have suggested that neural activity in subcortical regions (e.g., the caudate) is involved in false memory, and that there may be sex differences in the neural basis of false memory. However, sex-specific associations between subcortical volumes and false memory are not well understood. This study investigated whether sex modulates the associations between subcortical volumes and DRM false recall in 400 healthy college students. Volumes of subcortical regions including the caudate, accumbens, amygdala, hippocampus, pallidum, putamen and thalamus were obtained from structural magnetic resonance images and measured using FreeSurfer. The results showed that males had lower true and false recall but larger subcortical volumes than females. Interestingly, higher false recall was associated with a larger caudate in males, but not in females. This association was significant after controlling for age and intracranial volume. This study provides new evidence on the neural basis of false recall. It suggests that the caudate plays a role in false recall in young men, and that future studies of the neural correlates of false memory should consider sex differences.
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Affiliation(s)
- Xuhao Shao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
- Institute of Developmental Psychology, Beijing Normal University, Beijing, People's Republic of China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, People's Republic of China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People's Republic of China
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, People's Republic of China
| | - Ao Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Zehua Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
| | - Bi Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China
- Institute of Developmental Psychology, Beijing Normal University, Beijing, People's Republic of China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, People's Republic of China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, People's Republic of China
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Yang H, Zhang J, Jin Z, Bashivan P, Li L. Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory. Brain Struct Funct 2023; 228:1479-1492. [PMID: 37349540 DOI: 10.1007/s00429-023-02666-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/11/2023] [Indexed: 06/24/2023]
Abstract
Working memory plays a crucial role in our daily lives, and brain imaging has been used to predict working memory performance. Here, we present an improved connectome-based predictive modeling approach for building a predictive model of individual working memory performance from whole-brain functional connectivity. The model was built using n-back task-based fMRI and resting-state fMRI data from the Human Connectome Project. Compared to prior models, our model was more interpretable, demonstrated a closer connection to the known anatomical and functional network. The model also demonstrates strong generalization on nine other cognitive behaviors from the HCP database and can well predict the working memory performance of healthy individuals in external datasets. By comparing the differences in prediction effects of different brain networks and anatomical feature analysis on n-back tasks, we found the essential role of some networks in differentiating between high and low working memory loads conditions.
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Affiliation(s)
- Huayi Yang
- MOE Key Lab for NeuroInformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
- Department of Physiology, McGill University, Montréal, QC, H3G 1Y6, Canada
| | - Junjun Zhang
- MOE Key Lab for NeuroInformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zhenlan Jin
- MOE Key Lab for NeuroInformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Pouya Bashivan
- Department of Physiology, McGill University, Montréal, QC, H3G 1Y6, Canada
- Mila, University of Montreal, Montréal, QC, H2S 3H1, Canada
| | - Ling Li
- MOE Key Lab for NeuroInformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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Far transfer effects of executive working memory training on cognitive flexibility. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-03363-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Hol HR, Flak MM, Chang L, Løhaugen GCC, Bjuland KJ, Rimol LM, Engvig A, Skranes J, Ernst T, Madsen BO, Hernes SS. Cortical Thickness Changes After Computerized Working Memory Training in Patients With Mild Cognitive Impairment. Front Aging Neurosci 2022; 14:796110. [PMID: 35444526 PMCID: PMC9014119 DOI: 10.3389/fnagi.2022.796110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
Background Adaptive computerized working memory (WM) training has shown favorable effects on cerebral cortical thickness as compared to non-adaptive training in healthy individuals. However, knowledge of WM training-related morphological changes in mild cognitive impairment (MCI) is limited. Objective The primary objective of this double-blind randomized study was to investigate differences in longitudinal cortical thickness trajectories after adaptive and non-adaptive WM training in patients with MCI. We also investigated the genotype effects on cortical thickness trajectories after WM training combining these two training groups using longitudinal structural magnetic resonance imaging (MRI) analysis in Freesurfer. Method Magnetic resonance imaging acquisition at 1.5 T were performed at baseline, and after four- and 16-weeks post training. A total of 81 individuals with MCI accepted invitations to undergo 25 training sessions over 5 weeks. Longitudinal Linear Mixed effect models investigated the effect of adaptive vs. non-adaptive WM training. The LME model was fitted for each location (vertex). On all statistical analyzes, a threshold was applied to yield an expected false discovery rate (FDR) of 5%. A secondary LME model investigated the effects of LMX1A and APOE-ε4 on cortical thickness trajectories after WM training. Results A total of 62 participants/patients completed the 25 training sessions. Structural MRI showed no group difference between the two training regimes in our MCI patients, contrary to previous reports in cognitively healthy adults. No significant structural cortical changes were found after training, regardless of training type, across all participants. However, LMX1A-AA carriers displayed increased cortical thickness trajectories or lack of decrease in two regions post-training compared to those with LMX1A-GG/GA. No training or training type effects were found in relation to the APOE-ε4 gene variants. Conclusion The MCI patients in our study, did not have improved cortical thickness after WM training with either adaptive or non-adaptive training. These results were derived from a heterogeneous population of MCI participants. The lack of changes in the cortical thickness trajectory after WM training may also suggest the lack of atrophy during this follow-up period. Our promising results of increased cortical thickness trajectory, suggesting greater neuroplasticity, in those with LMX1A-AA genotype need to be validated in future trials.
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Affiliation(s)
- Haakon R. Hol
- Department of Radiology, Sørlandet Hospital, Arendal, Norway
- Department of Radiology, Oslo University Hospital, Oslo, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
- *Correspondence: Haakon R. Hol,
| | | | - Linda Chang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | | | - Knut Jørgen Bjuland
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lars M. Rimol
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Andreas Engvig
- Department of Medicine, Diakonhjemmet Hospital, Oslo, Norway
| | - Jon Skranes
- Department of Pediatrics, Sørlandet Hospital, Arendal, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas Ernst
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Bengt-Ove Madsen
- Department of Geriatric and Internal Medicine, Sørlandet Hospital, Arendal, Norway
| | - Susanne S. Hernes
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Geriatric and Internal Medicine, Sørlandet Hospital, Arendal, Norway
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Panikratova Y, Vlasova R, Lebedeva I, Sinitsyn V, Pechenkova E. Scope and Perspectives of Neuroimaging and Neurostimulation to Develop the Theory of Systemic and Dynamic Localization of Higher Mental Functions. CULTURAL-HISTORICAL PSYCHOLOGY 2022. [DOI: 10.17759/chp.2022180310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The theory of systemic and dynamic localization of higher mental functions by Lev Vygotsky and Alexander Luria was based on the data obtained via an original method, syndrome analysis of deficits of higher mental functions in patients with local brain injury. When this theory was being constructed, technical methods for brain investigation were only in their early stages. Although in later years Luria and his disciples pointed out that such methods were prominent for further development of Soviet/Russian neuropsychology, they are still rarely used by the followers of these scientists. In this article, we focus on neuroimaging and neurostimulation methods that are both noninvasive and the most accessible in Russia: structural, diffusion-weighted, and functional magnetic resonance imaging, as well as transcranial magnetic stimulation. We discuss their scope and perspectives for addressing research questions in neuropsychology and describe possible designs for neuropsychological studies in patients with local brain injury and healthy individuals.
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Affiliation(s)
| | | | | | - V.E. Sinitsyn
- Lomonosov Moscow State University Medical Research and Educational Center
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Wu Q, Ripp I, Emch M, Koch K. Cortical and subcortical responsiveness to intensive adaptive working memory training: An MRI surface-based analysis. Hum Brain Mapp 2021; 42:2907-2920. [PMID: 33724600 PMCID: PMC8127158 DOI: 10.1002/hbm.25412] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 12/31/2022] Open
Abstract
Working memory training (WMT) has been shown to have effects on cognitive performance, the precise effects and the underlying neurobiological mechanisms are, however, still a matter of debate. In particular, the impact of WMT on gray matter morphology is still rather unclear. In the present study, 59 healthy middle‐aged participants (age range 50–65 years) were pseudo‐randomly single‐blinded allocated to an 8‐week adaptive WMT or an 8‐week nonadaptive intervention. Before and after the intervention, high resolution magnetic resonance imaging (MRI) was performed and cognitive test performance was assessed in all participants. Vertex‐wise cortical volume, thickness, surface area, and cortical folding was calculated. Seven subcortical volumes of interest and global mean cortical thickness were also measured. Comparisons of symmetrized percent change (SPC) between groups were conducted to identify group by time interactions. Greater increases in cortical gyrification in bilateral parietal regions, including superior parietal cortex and inferior parietal lobule as well as precuneus, greater increases in cortical volume and thickness in bilateral primary motor cortex, and changes in surface area in bilateral occipital cortex (medial and lateral occipital cortex) were detected in WMT group after training compared to active controls. Structural training‐induced changes in WM‐related regions, especially parietal regions, might provide a better brain processing environment for higher WM load.
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Affiliation(s)
- Qiong Wu
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of MedicineTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center (TUM‐NIC)Technical University of MunichMunichGermany
- Institute of Medical PsychologyLudwig‐Maximilians‐UniversitätMunichGermany
| | - Isabelle Ripp
- TUM‐Neuroimaging Center (TUM‐NIC)Technical University of MunichMunichGermany
- Department of Nuclear Medicine, School of Medicine, Klinikum Rechts der IsarTechnical University of MunichMunichGermany
- Graduate School of Systemic NeurosciencesLudwig‐Maximilians‐UniversitätMartinsriedGermany
| | - Mónica Emch
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of MedicineTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center (TUM‐NIC)Technical University of MunichMunichGermany
- Graduate School of Systemic NeurosciencesLudwig‐Maximilians‐UniversitätMartinsriedGermany
| | - Kathrin Koch
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of MedicineTechnical University of MunichMunichGermany
- TUM‐Neuroimaging Center (TUM‐NIC)Technical University of MunichMunichGermany
- Graduate School of Systemic NeurosciencesLudwig‐Maximilians‐UniversitätMartinsriedGermany
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