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Li W, Fan L, Shi W, Lu Y, Li J, Luo N, Wang H, Chu C, Ma L, Song M, Li K, Cheng L, Cao L, Jiang T. Brainnetome atlas of preadolescent children based on anatomical connectivity profiles. Cereb Cortex 2022; 33:5264-5275. [PMID: 36255322 PMCID: PMC10151881 DOI: 10.1093/cercor/bhac415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/17/2022] [Accepted: 09/18/2022] [Indexed: 11/13/2022] Open
Abstract
During the preadolescent period, when the cerebral thickness, curvature, and myelin are constantly changing, the brain's regionalization patterns underwent persistent development, contributing to the continuous improvements of various higher cognitive functions. Using a brain atlas to study the development of these functions has attracted much attention. However, the brains of children do not always have the same topological patterns as those of adults. Therefore, age-specific brain mapping is particularly important, serving as a basic and indispensable tool to study the normal development of children. In this study, we took advantage of longitudinal data to create the brain atlas specifically for preadolescent children. The resulting human Child Brainnetome Atlas, with 188 cortical and 36 subcortical subregions, provides a precise period-specific and cross-validated version of the brain atlas that is more appropriate for adoption in the preadolescent period. In addition, we compared and illustrated for regions with different topological patterns in the child and adult atlases, providing a topologically consistent reference for subsequent research studying child and adolescent development.
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Affiliation(s)
- Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China
| | - Luqi Cheng
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, No.1 Jinji Road, Qixing District, Guilin 541004, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou 311100, China
| | - Long Cao
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu 610054, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China.,Research Center for Augmented Intelligence, Zhejiang Lab, Kechuang Avenue, Zhongtai Sub-District, Yuhang District, Hangzhou 311100, China
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Ossmy O, Mansano L, Frenkel-Toledo S, Kagan E, Koren S, Gilron R, Reznik D, Soroker N, Mukamel R. Motor learning in hemi-Parkinson using VR-manipulated sensory feedback. Disabil Rehabil Assist Technol 2022; 17:349-361. [PMID: 32657187 DOI: 10.1080/17483107.2020.1785561] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/07/2020] [Accepted: 06/17/2020] [Indexed: 01/10/2023]
Abstract
AIMS Modalities for rehabilitation of the neurologically affected upper-limb (UL) are generally of limited benefit. The majority of patients seriously affected by UL paresis remain with severe motor disability, despite all rehabilitation efforts. Consequently, extensive clinical research is dedicated to develop novel strategies aimed to improve the functional outcome of the affected UL. We have developed a novel virtual-reality training tool that exploits the voluntary control of one hand and provides real-time movement-based manipulated sensory feedback as if the other hand is the one that moves. The aim of this study was to expand our previous results, obtained in healthy subjects, to examine the utility of this training setup in the context of neuro-rehabilitation. METHODS We tested the training setup in patient LA, a young man with significant unilateral UL dysfunction stemming from hemi-parkinsonism. LA underwent daily intervention in which he intensively trained the non-affected upper limb, while receiving online sensory feedback that created an illusory perception of control over the affected limb. Neural changes were assessed using functional magnetic resonance imaging (fMRI) scans before and after training. RESULTS Training-induced behavioral gains were accompanied by enhanced activation in the pre-frontal cortex and a widespread increase in resting-state functional connectivity. DISCUSSION Our combination of cutting edge technologies, insights gained from basic motor neuroscience in healthy subjects and well-known clinical treatments, hold promise for the pursuit of finding novel and more efficient rehabilitation schemes for patients suffering from hemiplegia.Implications for rehabilitationAssistive devices used in hospitals to support patients with hemiparesis require expensive equipment and trained personnel - constraining the amount of training that a given patient can receive. The setup we describe is simple and can be easily used at home with the assistance of an untrained caregiver/family member. Once installed at the patient's home, the setup is lightweight, mobile, and can be used with minimal maintenance . Building on advances in machine learning, our software can be adapted to personal use at homes. Our findings can be translated into practice with relatively few adjustments, and our experimental design may be used as an important adjuvant to standard clinical care for upper limb hemiparesis.
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Affiliation(s)
- Ori Ossmy
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Lihi Mansano
- Department of Neurological Rehabilitation, Loewenstein Hospital, Ra'anana, Israel
| | - Silvi Frenkel-Toledo
- Department of Physiotherapy, Faculty of Health Sciences, Ariel University, Ariel, Israel
| | - Evgeny Kagan
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Shiri Koren
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Roee Gilron
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Daniel Reznik
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Nachum Soroker
- Department of Neurological Rehabilitation, Loewenstein Hospital, Ra'anana, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Roy Mukamel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
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Zuber P, Gaetano L, Griffa A, Huerbin M, Pedullà L, Bonzano L, Altermatt A, Tsagkas C, Parmar K, Hagmann P, Wuerfel J, Kappos L, Sprenger T, Sporns O, Magon S. Additive and interaction effects of working memory and motor sequence training on brain functional connectivity. Sci Rep 2021; 11:23089. [PMID: 34845312 PMCID: PMC8630199 DOI: 10.1038/s41598-021-02492-9] [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: 05/25/2021] [Accepted: 10/29/2021] [Indexed: 11/08/2022] Open
Abstract
Although shared behavioral and neural mechanisms between working memory (WM) and motor sequence learning (MSL) have been suggested, the additive and interactive effects of training have not been studied. This study aimed at investigating changes in brain functional connectivity (FC) induced by sequential (WM + MSL and MSL + WM) and combined (WM × MSL) training programs. 54 healthy subjects (27 women; mean age: 30.2 ± 8.6 years) allocated to three training groups underwent twenty-four 40-min training sessions over 6 weeks and four cognitive assessments including functional MRI. A double-baseline approach was applied to account for practice effects. Test performances were compared using linear mixed-effects models and t-tests. Resting state fMRI data were analysed using FSL. Processing speed, verbal WM and manual dexterity increased following training in all groups. MSL + WM training led to additive effects in processing speed and verbal WM. Increased FC was found after training in a network including the right angular gyrus, left superior temporal sulcus, right superior parietal gyrus, bilateral middle temporal gyri and left precentral gyrus. No difference in FC was found between double baselines. Results indicate distinct patterns of resting state FC modulation related to sequential and combined WM and MSL training suggesting a relevance of the order of training performance. These observations could provide new insight for the planning of effective training/rehabilitation.
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Affiliation(s)
- Priska Zuber
- Division of Cognitive Neuroscience, Faculty of Psychology, University of Basel, Basel, Switzerland
| | | | - Alessandra Griffa
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Center of Neuroprosthetics, Institute of Bioengineering, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Manuel Huerbin
- Medical Image Analysis Center (MIAC AG), Basel, Switzerland
| | - Ludovico Pedullà
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
- Italian Multiple Sclerosis Foundation, Scientific Research Area, Genoa, Italy
| | - Laura Bonzano
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Anna Altermatt
- Medical Image Analysis Center (MIAC AG), Basel, Switzerland
| | - Charidimos Tsagkas
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Katrin Parmar
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Reha Rheinfelden, Rheinfelden, Switzerland
| | - Patric Hagmann
- Center of Neuroprosthetics, Institute of Bioengineering, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center (MIAC AG), Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Till Sprenger
- Department of Neurology, DKD Helios Klinik, Wiesbaden, Germany
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Stefano Magon
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
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Lim H, Kim WS, Ku J. Transcranial Direct Current Stimulation Effect on Virtual Hand Illusion. CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING 2020; 23:541-549. [PMID: 32478563 DOI: 10.1089/cyber.2019.0741] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Virtual reality (VR) is effectively used to evoke the mirror illusion, and transcranial direct current stimulation (tDCS) synergistically facilitates this illusion. This study investigated whether a mirror virtual hand illusion (MVHI) induced by an immersive, first-person-perspective, virtual mirror system could be modulated by tDCS of the primary motor cortex. Fourteen healthy adults (average age 21.86 years ±0.47, seven men and seven women) participated in this study, and they experienced VR with and without tDCS-the tDCS and sham conditions, each of which takes ∼30 minutes-on separate days to allow the washout of the tDCS effect. While experiencing VR, the movements of the virtual left hand reflected the flexion and extension of the real right hand. Subsequently, electroencephalogram was recorded, the magnitude of the proprioceptive shift was measured, and the participants provided responses to a questionnaire regarding hand ownership. A significant difference in the proprioceptive shift was observed between the tDCS and sham conditions. In addition, there was significant suppression of the mu power in Pz, and augmentation of the beta power in the Pz, P4, O1, and O2 channels. The difference in proprioceptive deviation between the two conditions showed significant negative correlation with mu suppression over the left frontal lobe in the tDCS condition. Finally, the question "I felt that the virtual hand was my own hand" received a significantly higher score under the tDCS condition. In short, applying tDCS over the motor cortex facilitates the MVHI by activating the attentional network over the parietal and frontal lobes such that the MVHI induces more proprioceptive drift, which suggests that the combination of VR and tDCS can enhance the immersive effect in VR. This result provides better support for the use of the MVHI paradigm in combination with tDCS for recovery from illnesses such as stroke.
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Affiliation(s)
- Hyunmi Lim
- Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, Republic of Korea
| | - Won-Seok Kim
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jeonghun Ku
- Department of Biomedical Engineering, School of Medicine, Keimyung University, Daegu, Republic of Korea
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5
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Suppression of EEG mu rhythm during action observation corresponds with subsequent changes in behavior. Brain Res 2018; 1691:55-63. [DOI: 10.1016/j.brainres.2018.04.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 04/11/2018] [Accepted: 04/12/2018] [Indexed: 12/30/2022]
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Ossmy O, Mukamel R. Behavioral and neural effects of congruency of visual feedback during short-term motor learning. Neuroimage 2018; 172:864-873. [PMID: 29253651 DOI: 10.1016/j.neuroimage.2017.12.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 11/23/2017] [Accepted: 12/09/2017] [Indexed: 10/18/2022] Open
Abstract
Visual feedback can facilitate or interfere with movement execution. Here, we describe behavioral and neural mechanisms by which the congruency of visual feedback during physical practice of a motor skill modulates subsequent performance gains. 18 healthy subjects learned to execute rapid sequences of right hand finger movements during fMRI scans either with or without visual feedback. Feedback consisted of a real-time, movement-based display of virtual hands that was either congruent (right virtual hand movement), or incongruent (left virtual hand movement yoked to the executing right hand). At the group level, right hand performance gains following training with congruent visual feedback were significantly higher relative to training without visual feedback. Conversely, performance gains following training with incongruent visual feedback were significantly lower. Interestingly, across individual subjects these opposite effects correlated. Activation in the Supplementary Motor Area (SMA) during training corresponded to individual differences in subsequent performance gains. Furthermore, functional coupling of SMA with visual cortices predicted individual differences in behavior. Our results demonstrate that some individuals are more sensitive than others to congruency of visual feedback during short-term motor learning and that neural activation in SMA correlates with such inter-individual differences.
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Affiliation(s)
- Ori Ossmy
- Sagol School of Neuroscience and School of Psychological Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel
| | - Roy Mukamel
- Sagol School of Neuroscience and School of Psychological Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel.
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Perception as a Route for Motor Skill Learning: Perspectives from Neuroscience. Neuroscience 2018; 382:144-153. [PMID: 29694916 DOI: 10.1016/j.neuroscience.2018.04.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/05/2018] [Accepted: 04/12/2018] [Indexed: 12/11/2022]
Abstract
Learning a motor skill requires physical practice that engages neural networks involved in movement. These networks have also been found to be engaged during perception of sensory signals associated with actions. Nonetheless, despite extensive evidence for the existence of such sensory-evoked neural activity in motor pathways, much less is known about their contribution to learning and actual changes in behavior. Primate studies usually involve an overlearned task while studies in humans have largely focused on characterizing activity of the action observation network (AON) in the context of action understanding, theory of mind, and social interactions. Relatively few studies examined neural plasticity induced by perception and its role in transfer of motor knowledge. Here, we review this body of literature and point to future directions for the development of alternative, physiologically grounded ways in which sensory signals could be harnessed to improve motor skills.
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Ossmy O, Mukamel R. Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another. J Vis Exp 2017. [PMID: 28994768 PMCID: PMC5752261 DOI: 10.3791/55965] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee's hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.
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Affiliation(s)
- Ori Ossmy
- Sagol School of Neuroscience, Tel-Aviv University; School of Psychological Sciences, Tel-Aviv University
| | - Roy Mukamel
- Sagol School of Neuroscience, Tel-Aviv University; School of Psychological Sciences, Tel-Aviv University;
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Short Term Motor-Skill Acquisition Improves with Size of Self-Controlled Virtual Hands. PLoS One 2017; 12:e0168520. [PMID: 28056023 PMCID: PMC5215900 DOI: 10.1371/journal.pone.0168520] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 12/01/2016] [Indexed: 12/03/2022] Open
Abstract
Visual feedback in general, and from the body in particular, is known to influence the performance of motor skills in humans. However, it is unclear how the acquisition of motor skills depends on specific visual feedback parameters such as the size of performing effector. Here, 21 healthy subjects physically trained to perform sequences of finger movements with their right hand. Through the use of 3D Virtual Reality devices, visual feedback during training consisted of virtual hands presented on the screen, tracking subject’s hand movements in real time. Importantly, the setup allowed us to manipulate the size of the displayed virtual hands across experimental conditions. We found that performance gains increase with the size of virtual hands. In contrast, when subjects trained by mere observation (i.e., in the absence of physical movement), manipulating the size of the virtual hand did not significantly affect subsequent performance gains. These results demonstrate that when it comes to short-term motor skill learning, the size of visual feedback matters. Furthermore, these results suggest that highest performance gains in individual subjects are achieved when the size of the virtual hand matches their real hand size. These results may have implications for optimizing motor training schemes.
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Friedman J, Korman M. Offline Optimization of the Relative Timing of Movements in a Sequence Is Blocked by Retroactive Behavioral Interference. Front Hum Neurosci 2016; 10:623. [PMID: 28066205 PMCID: PMC5167724 DOI: 10.3389/fnhum.2016.00623] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 11/23/2016] [Indexed: 01/15/2023] Open
Abstract
Acquisition of motor skills often involves the concatenation of single movements into sequences. Along the course of learning, sequential performance becomes progressively faster and smoother, presumably by optimization of both motor planning and motor execution. Following its encoding during training, "how-to" memory undergoes consolidation, reflecting transformations in performance and its neurobiological underpinnings over time. This offline post-training memory process is characterized by two phenomena: reduced sensitivity to interference and the emergence of delayed, typically overnight, gains in performance. Here, using a training protocol that effectively induces motor sequence memory consolidation, we tested temporal and kinematic parameters of performance within (online) and between (offline) sessions, and their sensitivity to retroactive interference. One group learned a given finger-to-thumb opposition sequence (FOS), and showed robust delayed (consolidation) gains in the number of correct sequences performed at 24 h. A second group learned an additional (interference) FOS shortly after the first and did not show delayed gains. Reduction of touch times and inter-movement intervals significantly contributed to the overall offline improvement of performance overnight. However, only the offline inter-movement interval shortening was selectively blocked by the interference experience. Velocity and amplitude, comprising movement time, also significantly changed across the consolidation period but were interference -insensitive. Moreover, they paradoxically canceled out each other. Current results suggest that shifts in the representation of the trained sequence are subserved by multiple processes: from distinct changes in kinematic characteristics of individual finger movements to high-level, temporal reorganization of the movements as a unit. Each of these processes has a distinct time course and a specific susceptibility to retroactive interference. This multiple-component view may bridge the gap in understanding the link between the behavioral changes, which define online and offline learning, and the biological mechanisms that support those changes.
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Affiliation(s)
- Jason Friedman
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv UniversityTel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv UniversityTel Aviv, Israel
| | - Maria Korman
- Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of HaifaHaifa, Israel
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11
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Neural Network Underlying Intermanual Skill Transfer in Humans. Cell Rep 2016; 17:2891-2900. [DOI: 10.1016/j.celrep.2016.11.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 07/07/2016] [Accepted: 10/28/2016] [Indexed: 11/22/2022] Open
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