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Journal articles on the topic 'Aging, Motor Imagery, fMRI'

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Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

Burianová, Hana, Lars Marstaller, Anina N. Rich, Mark A. Williams, Greg Savage, Margaret Ryan, and Paul F. Sowman. "Motor neuroplasticity: A MEG-fMRI study of motor imagery and execution in healthy ageing." Neuropsychologia 146 (September 2020): 107539. http://dx.doi.org/10.1016/j.neuropsychologia.2020.107539.

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2

Allali, Gilles, Marian van der Meulen, Olivier Beauchet, Sebastian W. Rieger, Patrik Vuilleumier, and Frédéric Assal. "The Neural Basis of Age-Related Changes in Motor Imagery of Gait: An fMRI Study." Journals of Gerontology: Series A 69, no. 11 (December 24, 2013): 1389–98. http://dx.doi.org/10.1093/gerona/glt207.

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3

Sharma, Nikhil, and Jean-Claude Baron. "Effects of Healthy Ageing on Activation Pattern within the Primary Motor Cortex during Movement and Motor Imagery: An fMRI Study." PLoS ONE 9, no. 6 (June 2, 2014): e88443. http://dx.doi.org/10.1371/journal.pone.0088443.

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4

Hamada, Hiroyuki, Daisuke Matsuzawa, Yoshiyuki Hirano, Chihiro Sutoh, Eiji Shimizu, and Takayuki Obata. "7 Motor Imagery and fMRI." Journal of the Institute of Image Information and Television Engineers 67, no. 11 (2013): 944–48. http://dx.doi.org/10.3169/itej.67.944.

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5

Saimpont, Arnaud, Francine Malouin, Béatrice Tousignant, and Philip L. Jackson. "Motor Imagery and Aging." Journal of Motor Behavior 45, no. 1 (January 2013): 21–28. http://dx.doi.org/10.1080/00222895.2012.740098.

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6

de Lange, Floris P., Rick C. Helmich, and Ivan Toni. "Posture influences motor imagery: An fMRI study." NeuroImage 33, no. 2 (November 2006): 609–17. http://dx.doi.org/10.1016/j.neuroimage.2006.07.017.

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7

Vingerhoets, Guy, Floris P. de Lange, Pieter Vandemaele, Karel Deblaere, and Erik Achten. "Motor Imagery in Mental Rotation: An fMRI Study." NeuroImage 17, no. 3 (November 2002): 1623–33. http://dx.doi.org/10.1006/nimg.2002.1290.

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8

Cohen, Ori, Sébastien Druon, Sébastien Lengagne, Avi Mendelsohn, Rafael Malach, Abderrahmane Kheddar, and Doron Friedman. "fMRI-Based Robotic Embodiment: Controlling a Humanoid Robot by Thought Using Real-Time fMRI." Presence: Teleoperators and Virtual Environments 23, no. 3 (October 1, 2014): 229–41. http://dx.doi.org/10.1162/pres_a_00191.

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We present a robotic embodiment experiment based on real-time functional magnetic resonance imaging (rt-fMRI). In this study, fMRI is used as an input device to identify a subject's intentions and convert them into actions performed by a humanoid robot. The process, based on motor imagery, has allowed four subjects located in Israel to control a HOAP3 humanoid robot in France, in a relatively natural manner, experiencing the whole experiment through the eyes of the robot. Motor imagery or movement of the left hand, the right hand, or the legs were used to control the robotic motions of left, right, or walk forward, respectively.

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9

Confalonieri, Linda, Giuseppe Pagnoni, Lawrence W. Barsalou, Justin Rajendra, Simon B. Eickhoff, and Andrew J. Butler. "Brain Activation in Primary Motor and Somatosensory Cortices during Motor Imagery Correlates with Motor Imagery Ability in Stroke Patients." ISRN Neurology 2012 (December 29, 2012): 1–17. http://dx.doi.org/10.5402/2012/613595.

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Aims. While studies on healthy subjects have shown a partial overlap between the motor execution and motor imagery neural circuits, few have investigated brain activity during motor imagery in stroke patients with hemiparesis. This work is aimed at examining similarities between motor imagery and execution in a group of stroke patients. Materials and Methods. Eleven patients were asked to perform a visuomotor tracking task by either physically or mentally tracking a sine wave force target using their thumb and index finger during fMRI scanning. MIQ-RS questionnaire has been administered. Results and Conclusion. Whole-brain analyses confirmed shared neural substrates between motor imagery and motor execution in bilateral premotor cortex, SMA, and in the contralesional inferior parietal lobule. Additional region of interest-based analyses revealed a negative correlation between kinaesthetic imagery ability and percentage BOLD change in areas 4p and 3a; higher imagery ability was associated with negative and lower percentage BOLD change in primary sensorimotor areas during motor imagery.

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10

Porro, C. A., M. P. Francescato, V. Cettolo, P. Baraldi, and M. E. Diamond. "Primary motor cortex activity during motor performance and motor imagery: a fMRI study." NeuroImage 3, no. 3 (June 1996): S214. http://dx.doi.org/10.1016/s1053-8119(96)80216-3.

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11

Simon, Joe J., Anouk Welfringer, Gundhild Leifert-Fiebach, and Tobias Brandt. "Motor imagery in chronic neglect: An fMRI pilot study." Journal of Clinical and Experimental Neuropsychology 41, no. 1 (August 6, 2018): 58–68. http://dx.doi.org/10.1080/13803395.2018.1500527.

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12

Szameitat, André J., Shan Shen, and Annette Sterr. "Motor imagery of complex everyday movements. An fMRI study." NeuroImage 34, no. 2 (January 2007): 702–13. http://dx.doi.org/10.1016/j.neuroimage.2006.09.033.

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13

Stefano Filho, Carlos Alberto, Romis Attux, and Gabriela Castellano. "Motor imagery practice and feedback effects on functional connectivity." Journal of Neural Engineering 18, no. 6 (December 1, 2021): 066048. http://dx.doi.org/10.1088/1741-2552/ac456d.

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Abstract Objective. The use of motor imagery (MI) in motor rehabilitation protocols has been increasingly investigated as a potential technique for enhancing traditional treatments, yielding better clinical outcomes. However, since MI performance can be challenging, practice is usually required. This demands appropriate training, actively engaging the MI-related brain areas, consequently enabling the user to properly benefit from it. The role of feedback is central for MI practice. Yet, assessing which underlying neural changes are feedback-specific or purely due to MI practice is still a challenging effort, mainly due to the difficulty in isolating their contributions. In this work, we aimed to assess functional connectivity (FC) changes following MI practice that are either extrinsic or specific to feedback. Approach. To achieve this, we investigated FC, using graph theory, in electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, during MI performance and at resting-state (rs), respectively. Thirty healthy subjects were divided into three groups, receiving no feedback (control), ‘false’ feedback (sham) or actual neurofeedback (active). Participants underwent 12–13 hands-MI EEG sessions and pre- and post-MI training fMRI exams. Main results. Following MI practice, control participants presented significant increases in degree and in eigenvector centrality for occipital nodes at rs-fMRI scans, whereas sham-feedback produced similar effects, but to a lesser extent. Therefore, MI practice, by itself, seems to stimulate visual information processing mechanisms that become apparent during basal brain activity. Additionally, only the active group displayed decreases in inter-subject FC patterns, both during MI performance and at rs-fMRI. Significance. Hence, actual neurofeedback impacted FC by disrupting common inter-subject patterns, suggesting that subject-specific neural plasticity mechanisms become important. Future studies should consider this when designing experimental NFBT protocols and analyses.

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14

Formaggio, Emanuela, Silvia Francesca Storti, Roberto Cerini, Antonio Fiaschi, and Paolo Manganotti. "Brain oscillatory activity during motor imagery in EEG-fMRI coregistration." Magnetic Resonance Imaging 28, no. 10 (December 2010): 1403–12. http://dx.doi.org/10.1016/j.mri.2010.06.030.

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15

Chen, Xiaowei, CheukYing Tang, Hongwei Zhou, and Zhenlan Li. "Effect of amantadine on vegetative state after traumatic brain injury: a functional magnetic resonance imaging study." Journal of International Medical Research 47, no. 2 (December 5, 2018): 1015–24. http://dx.doi.org/10.1177/0300060518814127.

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Objective We assessed the use of functional magnetic resonance imaging (fMRI) to observe residual brain function and responsiveness to amantadine in a patient in a vegetative state (VS) following traumatic brain injury. Method We observed cerebral cortex activation in a 52-year-old man in a VS, and in a healthy individual using fMRI during passive listening and motor-imagery tasks. The patient received oral amantadine for 3 months. fMRI was repeated after treatment. Results Activation around the left insular regions occurred during stimulation by a familiar voice, and activity in the left temporal and bi-occipital cortices occurred during stimulation by a familiar/unfamiliar voice. Activity in the bilateral frontal and parietal cortices occurred during the motor-imagination task. Brain cortex activation was reduced in the VS patient compared with the healthy volunteer. However, the patient responded to certain auditory stimuli and motor imagery, suggesting that he retained some intact auditory and motor cortical functions. fMRI scans after 3 months of treatment showed increased activation of brain areas corresponding to task instructions. Conclusion fMRI could be used to observe the effects of amantadine on brain function, and to aid the diagnosis and prognostic prediction in VS patients in terms of recovery and rehabilitation planning.

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Paizis, C., P. Personnier, T. Pozzo, and C. Papaxanthis. "P2.036 The effects of aging in motor inhibition during motor imagery." Parkinsonism & Related Disorders 14 (February 2008): S52. http://dx.doi.org/10.1016/s1353-8020(08)70265-7.

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17

McGregor, Keith M., Haley Carpenter, Erin Kleim, Atchar Sudhyadhom, Keith D. White, Andrew J. Butler, Jeffrey Kleim, and Bruce Crosson. "Motor map reliability and aging: a TMS/fMRI study." Experimental Brain Research 219, no. 1 (March 31, 2012): 97–106. http://dx.doi.org/10.1007/s00221-012-3070-3.

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18

Willems, Roel M., Ivan Toni, Peter Hagoort, and Daniel Casasanto. "Neural Dissociations between Action Verb Understanding and Motor Imagery." Journal of Cognitive Neuroscience 22, no. 10 (October 2010): 2387–400. http://dx.doi.org/10.1162/jocn.2009.21386.

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According to embodied theories of language, people understand a verb like throw, at least in part, by mentally simulating throwing. This implicit simulation is often assumed to be similar or identical to motor imagery. Here we used fMRI to test whether implicit simulations of actions during language understanding involve the same cortical motor regions as explicit motor imagery. Healthy participants were presented with verbs related to hand actions (e.g., to throw) and nonmanual actions (e.g., to kneel). They either read these verbs (lexical decision task) or actively imagined performing the actions named by the verbs (imagery task). Primary motor cortex showed effector-specific activation during imagery, but not during lexical decision. Parts of premotor cortex distinguished manual from nonmanual actions during both lexical decision and imagery, but there was no overlap or correlation between regions activated during the two tasks. These dissociations suggest that implicit simulation and explicit imagery cued by action verbs may involve different types of motor representations and that the construct of “mental simulation” should be distinguished from “mental imagery” in embodied theories of language.

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19

Chiew, Mark, Stephen M. LaConte, and Simon J. Graham. "Investigation of fMRI neurofeedback of differential primary motor cortex activity using kinesthetic motor imagery." NeuroImage 61, no. 1 (May 2012): 21–31. http://dx.doi.org/10.1016/j.neuroimage.2012.02.053.

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20

Kashuk, S. R., J. Williams, G. Thorpe, P. H. Wilson, and G. F. Egan. "Diminished motor imagery capability in adults with motor impairment: An fMRI mental rotation study." Behavioural Brain Research 334 (September 2017): 86–96. http://dx.doi.org/10.1016/j.bbr.2017.06.042.

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21

Bezmaternykh, Dmitriy, Mikhail Mel'nikov, Andrey Savelov, Evgeny Petrovskiy, and Mark Schtark. "EEG Spectral Correlates of the RT-fMRI Neurofeedback-Assisted Motor Imagery." International Journal of Psychophysiology 168 (October 2021): S159. http://dx.doi.org/10.1016/j.ijpsycho.2021.07.445.

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22

Auer, Tibor, and Jens Frahm. "Confounding factors in neurofeedback training based on fMRI of motor imagery." Neuroscience Letters 500 (July 2011): e32. http://dx.doi.org/10.1016/j.neulet.2011.05.160.

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23

Madkhali, Yahia, Salim Al-Wasity, Norah Aldehmi, and Frank Pollick. "Using Real-Time fMRI Neurofeedback to Modulate M1-Cerebellum Connectivity." Computational Intelligence and Neuroscience 2022 (August 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/8744982.

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Objective. The potential of neurofeedback to alter the M1-cerebellum connectivity was explored using motor imagery-based rt-fMRI. These regions were chosen due to their importance in motor performance and motor rehabilitation. Methods. Four right-handed individuals were recruited to examine the potential to change the M1-cerebellum neurofeedback link. The University of Glasgow Cognitive Neuroimaging Centre used a 3T MRI scanner from January 2019 to January 2020 to conduct this prospective study. Everyone participated in each fMRI session, which included six NF training runs. Participants were instructed to imagine complicated hand motions during the NF training to raise a thermometer bar’s height. To contrast the correlation coefficients between the initial and last NF runs, a t-test was performed post hoc. Results. The neurofeedback connection between M1 and the cerebellum was strengthened in each participant. Motor imagery strategy was a significant task in training M1-cerebellum connectivity as participants used it successfully to enhance the activation level between these regions during M1-cerebellum modulation using real-time fMRI. The t-test and linear regression, on the other hand, showed this increase to be insignificant. Conclusion. A novel technique to manipulate M1-cerebellum connectivity was discovered using real-time fMRI NF. This study showed that each participant’s neurofeedback connectivity between M1 and cerebellum was enhanced. This increase, on the other hand, was insignificant statistically. The results showed that the connectivity between both areas increased positively. Through the integration of fMRI and neurofeedback, M1-cerebellum connectivity can be positively affected.

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24

Beauchet, O., and G. Allali. "MOTOR IMAGERY OF GAIT WITH AGING: MENTAL IMAGERY STRATEGY AND BODY POSITION MATTER." Innovation in Aging 1, suppl_1 (June 30, 2017): 469. http://dx.doi.org/10.1093/geroni/igx004.1671.

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25

Abidi, Malek, Pierre-Francois Pradat, Nicolas Termoz, Annabelle Couillandre, Peter Bede, and Giovanni de Marco. "Motor imagery in amyotrophic lateral Sclerosis: An fMRI study of postural control." NeuroImage: Clinical 35 (2022): 103051. http://dx.doi.org/10.1016/j.nicl.2022.103051.

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26

Chinier, E., S. N’guyen, G. Lignon, A. Ter Minassian, I. Richard, and M. Dinomais. "Effect of motor imagery in children with unilateral cerebral palsy: fMRI study." Annals of Physical and Rehabilitation Medicine 57 (May 2014): e339. http://dx.doi.org/10.1016/j.rehab.2014.03.1242.

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27

Müller, Katharina, Katrin Bacht, Stephanie Schramm, and Rüdiger J. Seitz. "The facilitating effect of clinical hypnosis on motor imagery: An fMRI study." Behavioural Brain Research 231, no. 1 (May 2012): 164–69. http://dx.doi.org/10.1016/j.bbr.2012.03.013.

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28

Chinier, Eva, Sylvie N’Guyen, Grégoire Lignon, Aram Ter Minassian, Isabelle Richard, and Mickaël Dinomais. "Effect of Motor Imagery in Children with Unilateral Cerebral Palsy: fMRI Study." PLoS ONE 9, no. 4 (April 9, 2014): e93378. http://dx.doi.org/10.1371/journal.pone.0093378.

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29

Schulz, Laura, Anja Ischebeck, Selina C. Wriessnegger, David Steyrl, and Gernot R. Müller-Putz. "Action affordances and visuo-spatial complexity in motor imagery: An fMRI study." Brain and Cognition 124 (July 2018): 37–46. http://dx.doi.org/10.1016/j.bandc.2018.03.012.

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30

Wang, Li, Mingguo Qiu, Chen Liu, Rubing Yan, Jun Yang, Jingna Zhang, Ye Zhang, Linqiong Sang, and Xiaolin Zheng. "Age-specific activation of cerebral areas in motor imagery—a fMRI study." Neuroradiology 56, no. 4 (February 5, 2014): 339–48. http://dx.doi.org/10.1007/s00234-014-1331-5.

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31

Kim, Teri, Jingu Kim, and Sechang Kwon. "Neuropsychological Evidence Underlying Counterclockwise Bias in Running: Electroencephalography and Functional Magnetic Resonance Imaging Studies of Motor Imagery." Behavioral Sciences 13, no. 2 (February 15, 2023): 173. http://dx.doi.org/10.3390/bs13020173.

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We aimed to answer the question “why do people run the track counterclockwise (CCW)?” by investigating the neurophysiological differences in clockwise (CW) versus CCW direction using motor imagery. Three experiments were conducted with healthy adults. Electroencephalography (EEG) was used to examine hemispheric asymmetries in the prefrontal, frontal, and central regions during CW and CCW running imagery (n = 40). We also evaluated event-related potential (ERP) N200 and P300 amplitudes and latencies (n = 66) and conducted another experiment using functional magnetic resonance imaging (fMRI) (n = 30). EEG data indicated greater left frontal cortical activation during CCW imagery, whereas right frontal activation was more dominant during CW imagery. The prefrontal and central asymmetries demonstrated greater left prefrontal activation during both CW and CCW imagery, with CCW rotation exhibiting higher, though statistically insignificant, asymmetry scores than CW rotation. As a result of the fMRI experiment, greater activation was found during CW than during CCW running imagery in the brain regions of the left insula, Brodmann area 18, right caudate nucleus, left dorsolateral prefrontal cortex, left superior parietal cortex, and supplementary motor area. In the ERP experiment, no significant differences were found depending on direction. These findings suggest that CCW rotation might be associated with the motivational approach system, behavioral activation, or positive affect. However, CW rotation reflects withdrawal motivation, behavioral inhibition, or negative affect. Furthermore, CW rotation is understood to be associated with neural inefficiency, increased task difficulty, or unfamiliarity.

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Aizenstein, Howard J., Kristi A. Clark, Meryl A. Butters, Jennifer Cochran, V. Andrew Stenger, Carolyn C. Meltzer, Charles F. Reynolds, and Cameron S. Carter. "The BOLD Hemodynamic Response in Healthy Aging." Journal of Cognitive Neuroscience 16, no. 5 (June 2004): 786–93. http://dx.doi.org/10.1162/089892904970681.

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Several previous studies have compared the blood oxygen level-dependent (BOLD) hemodynamic response (HDR) in healthy elderly subjects to the HDR in young subjects. Some studies have found a relative decreased amplitude in the elderly in the visual cortex, whereas other studies have found the elderly HDR amplitude in the visual cortex to be nearly identical to that in young subjects. A possible explanation for the different findings is that the peak voxel HDR is similar between the groups, but that the HDR in the group-averaged region-of-interest (ROI) is “washed out” by the inclusion of less significant voxels (due to a smaller extent of activation in the elderly) or by the inclusion of negative-peaking voxels. We tested this hypothesis using event-related functional magnetic resonance imaging (fMRI). While undergoing fMRI, subjects performed a simple visual and motor task, pressing with their index fingers in response to visual presentation of the word tap. Data from 18 subjects, 8 young and 10 elderly, were analyzed. For each subject, a visual and a motor ROI was selected by choosing the most significant positive voxels within the anatomically defined ROI. This individual subject approach excluded both low-significance and negative-peaking voxels. Similar peaks were found for the elderly and the young subjects in both motor and visual regions and a more sustained BOLD response was found for the elderly in both regions. Additionally, as predicted, a greater percentage of voxels with a negative HDR was found for the elderly in the visual region; this finding was also replicated in our reanalysis of an independent fMRI and aging study from the fMRI Data Center. Functional neuroimaging observations of negative HDRs in visual areas have been interpreted as the effect of unconstrained processing during rest. Our results suggest that the elderly may have more unconstrained visual processing during the rest condition in the scanner. The observation that the group differences in the BOLD response are sensitive to voxel selection (e.g., inclusion of low-significance and/or negative voxels) underscores the importance of ROI selection criteria in the interpretation of fMRI studies using elderly populations.

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Richter, Wolfgang, Ray Somorjai, Randy Summers, Mark Jarmasz, Ravi S. Menon, Joseph S. Gati, Apostolos P. Georgopoulos, Carola Tegeler, Kamil Ugurbil, and Seong-Gi Kim. "Motor Area Activity During Mental Rotation Studied by Time-Resolved Single-Trial fMRI." Journal of Cognitive Neuroscience 12, no. 2 (March 2000): 310–20. http://dx.doi.org/10.1162/089892900562129.

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The functional equivalence of overt movements and dynamic imagery is of fundamental importance in neuroscience. Here, we investigated the participation of the neocortical motor areas in a classic task of dynamic imagery, Shepard and Metzler's mental rotation task, by time-resolved single-trial functional Magnetic Resonance Imaging (fMRI). The subjects performed the mental-rotation task 16 times, each time with different object pairs. Functional images were acquired for each pair separately, and the onset times and widths of the activation peaks in each area of interest were compared to the response times. We found a bilateral involvement of the superior parietal lobule, lateral premotor area, and supplementary motor area in all subjects; we found, furthermore, that those areas likely participate in the very act of mental rotation. We also found an activation in the left primary motor cortex, which seemed to be associated with the right-hand button press at the end of the task period.

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Lange, Floris P. de, Peter Hagoort, and Ivan Toni. "Neural Topography and Content of Movement Representations." Journal of Cognitive Neuroscience 17, no. 1 (January 2005): 97–112. http://dx.doi.org/10.1162/0898929052880039.

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We have used implicit motor imagery to investigate the neural correlates of motor planning independently from actual movements. Subjects were presented with drawings of left or right hands and asked to judge the hand laterality, regardless of the stimulus rotation from its upright orientation. We paired this task with a visual imagery control task, in which subjects were presented with typographical characters and asked to report whether they saw a canonical letter or its mirror image, regardless of its rotation. We measured neurovascular activity with fast event-related fMRI, distinguishing responses parametrically related to motor imagery from responses evoked by visual imagery and other task-related phenomena. By quantifying behavioral and neurovascular correlates of imagery on a trial-by-trial basis, we could discriminate between stimulus-related, mental rotation-related, and response-related neural activity. We found that specific portions of the posterior parietal and precentral cortex increased their activity as a function of mental rotation only during the motor imagery task. Within these regions, the parietal cortex was visually responsive, whereas the dorsal precentral cortex was not. Response- but not rotation-related activity was found around the left central sulcus (putative primary motor cortex) during both imagery tasks. Our study provides novel evidence on the topography and content of movement representations in the human brain. During intended action, the posterior parietal cortex combines somatosensory and visuomotor information, whereas the dorsal premotor cortex generates the actual motor plan, and the primary motor cortex deals with movement execution. We discuss the relevance of these results in the context of current models of action planning.

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Madkhali, Yahia, Norah Aldehmi, and Frank Pollick. "Functional Localizers for Motor Areas of the Brain Using fMRI." Computational Intelligence and Neuroscience 2022 (May 28, 2022): 1–8. http://dx.doi.org/10.1155/2022/7589493.

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Neuroimaging researchers increasingly take advantage of the known functional properties of brain regions to localize motor regions in the brain and investigate changes in their activity under various conditions. Using this noninvasive functional MRI (fMRI) method makes it possible to identify and localize brain activation. There are many localizers that can be used to identify brain areas, namely, motor areas such as functional localizer, anatomical localizer, or Atlas mask. Eighteen right-handed participants were recruited for this research to test the reliability of five localizers for primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), motor cerebellum, and motor thalamus. Motor execution task, namely, hand clenching was used to activate M1, SMA, and motor cerebellum. A combined action observation and motor imagery (AOMI) task was used to functionally activate PMC. Finally, a mask based on Talairach coordinates Atlas was created and used to identify the motor thalamus. Our results show that all localizers were successfully activated in the desired regions of interest. Motor execution successfully activated M1, SMA, and motor cerebellum. A novel localizer based on AOMI was successfully activated in PMC, and the motor thalamus mask obtained from the thalamus mask was successfully implemented on each participant. In conclusion, all five localizers tested in this research were reliable and can be used for rt-fMRI neurofeedback research to define the regions of interest.

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Mehler, David M. A., Angharad N. Williams, Florian Krause, Michael Lührs, Richard G. Wise, Duncan L. Turner, David E. J. Linden, and Joseph R. Whittaker. "The BOLD response in primary motor cortex and supplementary motor area during kinesthetic motor imagery based graded fMRI neurofeedback." NeuroImage 184 (January 2019): 36–44. http://dx.doi.org/10.1016/j.neuroimage.2018.09.007.

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37

Berman, Brian D., Silvina G. Horovitz, Gaurav Venkataraman, and Mark Hallett. "Self-modulation of primary motor cortex activity with motor and motor imagery tasks using real-time fMRI-based neurofeedback." NeuroImage 59, no. 2 (January 2012): 917–25. http://dx.doi.org/10.1016/j.neuroimage.2011.07.035.

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38

Castanho, Gabriela, Eduardo Fontes, Helio Yoshida, Brunno Campos, Elvis Silva, Simone Appenzeller, and Paula Fernandes. "Carbohydrate vs. Placebo: a fMRI BOLD response during different intensities of motor imagery." Revista Neurociências 23, no. 03 (September 30, 2015): 390–96. http://dx.doi.org/10.4181/rnc.2015.23.03.1048.07p.

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39

Yoo, Seung-Schik, Jong-Hwan Lee, Heather O'Leary, Lawrence P. Panych, and Ferenc A. Jolesz. "Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery." International Journal of Imaging Systems and Technology 18, no. 1 (2008): 69–78. http://dx.doi.org/10.1002/ima.20139.

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40

Tian, Qu, Roger Mullins, Abby Corkum, David Reiter, Daniel Pupo, Eleanor M. Simonsick, Dimitrios Kapogiannis, and Stephanie Studenski. "THE AGING BRAIN AND MOTOR LEARNING." Innovation in Aging 3, Supplement_1 (November 2019): S655. http://dx.doi.org/10.1093/geroni/igz038.2430.

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Abstract The effect of aging on motor learning is poorly understood. This study investigated response time and patterns of brain activation induced over the course of a bimanual motor learning task in three age groups. Twenty-two cognitively unimpaired participants (32%women) were grouped into Young (<35,n=6), Middle-Age (36-59,n=10), and Old (60+,n=6). A self-paced bimanual motor learning task was performed during fMRI. The task consisted of using 2 capital and 2 lower case letters in strings of 16 cues with 6 novel alternating with 6 repeated sequence blocks. To assess learning, a repeated measures ANOVA tested whether average time per slide differed over time between novel and sequence conditions. Voxel-wise changes in brain activation between novel and sequence conditions over time were examined using a within-subject repeated measures model. Faster initial time per slide was associated with younger age (p0.05). Old had increased brain activation in repeated sequence than novel conditions in right postcentral and superior parietal regions during the early half of the task compared to the second half (p0.05). We found behavioral evidence of motor learning in Middle-Age and Old, but not Young, perhaps because younger individuals performed quickly and learned sequence almost immediately. Among older individuals, sequence-specific learning in parietal regions challenges the view that it is mediated by only motor areas.

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Leclerc, Marcel P., Thilo Kellermann, Jessica Freiherr, Benjamin Clemens, Ute Habel, and Christina Regenbogen. "Externalization Errors of Olfactory Source Monitoring in Healthy Controls—An fMRI Study." Chemical Senses 44, no. 8 (August 15, 2019): 593–606. http://dx.doi.org/10.1093/chemse/bjz055.

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Abstract Using a combined approach of functional magnetic resonance imaging (fMRI) and noninvasive brain stimulation (transcranial direct current stimulation [tDCS]), the present study investigated source memory and its link to mental imagery in the olfactory domain, as well as in the auditory domain. Source memory refers to the knowledge of the origin of mental experiences, differentiating events that have occurred and memories of imagined events. Because of a confusion between internally generated and externally perceived information, patients that are prone to hallucinations show decreased source memory accuracy; also, vivid mental imagery can lead to similar results in healthy controls. We tested source memory following cathodal tDCS stimulation using a mental imagery task, which required participants to perceive or imagine a set of the same olfactory and auditory stimuli during fMRI. The supplementary motor area (SMA) is involved in mental imagery across different modalities and potentially linked to source memory. Therefore, we attempted to modulate participants’ SMA activation before entering the scanner using tDCS to influence source memory accuracy in healthy participants. Our results showed the same source memory accuracy between the olfactory and auditory modalities with no effects of stimulation. Finally, we found SMA’s subregions differentially involved in olfactory and auditory imagery, with activation of dorsal SMA correlated with auditory source memory.

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Zapparoli, L., P. Invernizzi, M. Gandola, M. Verardi, M. Berlingeri, M. Sberna, A. De Santis, et al. "Mental images across the adult lifespan: a behavioural and fMRI investigation of motor execution and motor imagery." Experimental Brain Research 224, no. 4 (November 25, 2012): 519–40. http://dx.doi.org/10.1007/s00221-012-3331-1.

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Sridhar, Saraswati, and Vidya Manian. "EEG and Deep Learning Based Brain Cognitive Function Classification." Computers 9, no. 4 (December 21, 2020): 104. http://dx.doi.org/10.3390/computers9040104.

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Electroencephalogram signals are used to assess neurodegenerative diseases and develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the applications use only motor imagery or evoked potentials. Here, a deep learning network based on a sensory motor paradigm (auditory, olfactory, movement, and motor-imagery) that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network is developed to assess cognitive functions and identify its relationship with brain signal features, which is hypothesized to consistently indicate cognitive decline. Testing occurred with healthy subjects of age 20–40, 40–60, and >60, and mildly cognitive impaired subjects. Auditory and olfactory stimuli were presented to the subjects and the subjects imagined and conducted movement of each arm during which Electroencephalogram (EEG)/Electromyogram (EMG) signals were recorded. A deep BLSTM Neural Network is trained with Principal Component features from evoked signals and assesses their corresponding pathways. Wavelet analysis is used to decompose evoked signals and calculate the band power of component frequency bands. This deep learning system performs better than conventional deep neural networks in detecting MCI. Most features studied peaked at the age range 40–60 and were lower for the MCI group than for any other group tested. Detection accuracy of left-hand motor imagery signals best indicated cognitive aging (p = 0.0012); here, the mean classification accuracy per age group declined from 91.93% to 81.64%, and is 69.53% for MCI subjects. Motor-imagery-evoked band power, particularly in gamma bands, best indicated (p = 0.007) cognitive aging. Although the classification accuracy of the potentials effectively distinguished cognitive aging from MCI (p < 0.05), followed by gamma-band power.

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Hilt, Pauline M., Mathilde F. Bertrand, Léonard Féasson, Florent Lebon, France Mourey, Célia Ruffino, and Vianney Rozand. "Motor Imagery Training Is Beneficial for Motor Memory of Upper and Lower Limb Tasks in Very Old Adults." International Journal of Environmental Research and Public Health 20, no. 4 (February 17, 2023): 3541. http://dx.doi.org/10.3390/ijerph20043541.

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Human aging is associated with a decline in the capacity to memorize recently acquired motor skills. Motor imagery training is a beneficial method to compensate for this deterioration in old adults. It is not yet known whether these beneficial effects are maintained in very old adults (>80 years), who are more affected by the degeneration processes. The aim of this study was to evaluate the effectiveness of a mental training session of motor imagery on the memorization of new motor skills acquired through physical practice in very old adults. Thus, 30 very old adults performed 3 actual trials of a manual dexterity task (session 1) or a sequential footstep task (session 2) as fast as they could before and after a 20 min motor imagery training (mental-training group) or watching a documentary for 20 min (control group). Performance was improved after three actual trials for both tasks and both groups. For the control group, performance decreased in the manual dexterity task after the 20 min break and remained stable in the sequential footstep task. For the mental-training group, performance was maintained in the manual dexterity task after the 20 min motor imagery training and increased in the sequential footstep task. These results extended the benefits of motor imagery training to the very old population, showing that even a short motor imagery training session improved their performance and favored the motor memory process. These results confirmed that motor imagery training is an effective method to complement traditional rehabilitation protocols.

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Crotti, Monica, Karl Koschutnig, and Selina Christin Wriessnegger. "Handedness impacts the neural correlates of kinesthetic motor imagery and execution: A FMRI study." Journal of Neuroscience Research 100, no. 3 (January 3, 2022): 798–826. http://dx.doi.org/10.1002/jnr.25003.

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Oosterhof, Nikolaas N., Steven P. Tipper, and Paul E. Downing. "Visuo-motor imagery of specific manual actions: A multi-variate pattern analysis fMRI study." NeuroImage 63, no. 1 (October 2012): 262–71. http://dx.doi.org/10.1016/j.neuroimage.2012.06.045.

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Boldyreva, G. N., L. A. Zhavoronkova, E. V. Sharova, O. A. Simonova, L. P. Titova, and D. V. Pyashina. "EEG–fMRI reactions during actual hand movement performance and motor imagery in healthy subjects." International Journal of Psychophysiology 85, no. 3 (September 2012): 418. http://dx.doi.org/10.1016/j.ijpsycho.2012.07.142.

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Taube, Wolfgang, Michael Mouthon, Christian Leukel, Henri-Marcel Hoogewoud, Jean-Marie Annoni, and Martin Keller. "Brain activity during observation and motor imagery of different balance tasks: An fMRI study." Cortex 64 (March 2015): 102–14. http://dx.doi.org/10.1016/j.cortex.2014.09.022.

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Sepulveda, Pradyumna, Ranganatha Sitaram, Mohit Rana, Cristian Montalba, Cristian Tejos, and Sergio Ruiz. "How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI." Human Brain Mapping 37, no. 9 (June 6, 2016): 3153–71. http://dx.doi.org/10.1002/hbm.23228.

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Sun, Limin, Dazhi Yin, Yulian Zhu, Mingxia Fan, Lili Zang, Yi Wu, Jie Jia, Yulong Bai, Bing Zhu, and Yongshan Hu. "Cortical reorganization after motor imagery training in chronic stroke patients with severe motor impairment: a longitudinal fMRI study." Neuroradiology 55, no. 7 (April 26, 2013): 913–25. http://dx.doi.org/10.1007/s00234-013-1188-z.

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