scholarly journals Effects of healthy ageing on activation pattern within the brain during movement and motor imagery: an fMRI study

2019 ◽  
Author(s):  
Sanjay Budhdeo ◽  
Jean-Claude Baron ◽  
Nikhil Sharma

AbstractIntroductionMotor imagery (MI) has potential as an intervention to improve performance in neurological disease affecting the motor system and to modulate brain computer interfaces (BCI). We hypothesized that the shared networks of MI and executed movement (EM) would be affected by age. Understanding these changes is important in application of MI in neurological disorders.MethodsUsing tensor-independent component analysis (TICA), we mapped the neural networks involved during MI and EM in 31 healthy volunteers (ages 20-72), who were recruited and screened for their ability to perform imagery. We used an fMRI block-design with MI & rest and EM & rest.ResultsTICA defined 37 independent components (ICs). Eight remained after excluding ICs representing artifacts. These ICs accounted for 35% of variance. While all ICs had greater activation in EM than MI. Two ICs increased with greater age for EM only. These ICs contained a bilateral network of brain areas, including primary motor cortex and cerebellum.ConclusionThis study demonstrates the prominence of shared cerebral networks between MI and EM. There are age-dependent changes to EM activation, while MI activation appeared age independent. This strengthens the rationale for using MI to access the motor networks independent of age.

Author(s):  
Caique de Medeiros Mendes ◽  
Gabriela Castellano ◽  
Carlos Alberto Stefano Filho

Motor imagery (MI) is a commonly used strategy in brain-computer interfaces (BCIs) to modify neuronal activity, in which the user, by imagining motor movements, generates signals that can be recorded and interpreted to control a device. In this study, we sought to investigate how the brain response of users during MI happens, by analyzing a database of EEG signals in which healthy subjects were asked to imagine the movement of their right and left hands. Our goal was to recognize patterns associated with this task, through a spectral evaluation of different segments of the signal. Therefore, we estimated the power spectral density (PSD) for each evaluated segment and then used it for classification, via k-nearest neighbors (k-NN). We found that the accuracy rates obtained with k-NN classification were very similar to random, suggesting, mainly, high inter-subjects variability and choice of a low complexity classifier.


2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Jinyi Long ◽  
Jue Wang ◽  
Tianyou Yu

The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.


2018 ◽  
Vol 44 (3) ◽  
pp. 280-288 ◽  
Author(s):  
M. V. Lukoyanov ◽  
S. Yu. Gordleeva ◽  
A. S. Pimashkin ◽  
N. A. Grigor’ev ◽  
A. V. Savosenkov ◽  
...  

2021 ◽  
Vol 11 (15) ◽  
pp. 6689
Author(s):  
Iván De La Pava Panche ◽  
Andrés Álvarez-Meza ◽  
Paula Marcela Herrera Gómez ◽  
David Cárdenas-Peña ◽  
Jorge Iván Ríos Patiño ◽  
...  

Neural oscillations are present in the brain at different spatial and temporal scales, and they are linked to several cognitive functions. Furthermore, the information carried by their phases is fundamental for the coordination of anatomically distributed processing in the brain. The concept of phase transfer entropy refers to an information theory-based measure of directed connectivity among neural oscillations that allows studying such distributed processes. Phase TE is commonly obtained from probability estimations carried out over data from multiple trials, which bars its use as a characterization strategy in brain–computer interfaces. In this work, we propose a novel methodology to estimate TE between single pairs of instantaneous phase time series. Our approach combines a kernel-based TE estimator defined in terms of Renyi’s α entropy, which sidesteps the need for probability distribution computation with phase time series obtained by complex filtering the neural signals. Besides, a kernel-alignment-based relevance analysis is added to highlight relevant features from effective connectivity-based representation supporting further classification stages in EEG-based brain–computer interface systems. Our proposal is tested on simulated coupled data and two publicly available databases containing EEG signals recorded under motor imagery and visual working memory paradigms. Attained results demonstrate how the introduced effective connectivity succeeds in detecting the interactions present in the data for the former, with statistically significant results around the frequencies of interest. It also reflects differences in coupling strength, is robust to realistic noise and signal mixing levels, and captures bidirectional interactions of localized frequency content. Obtained results for the motor imagery and working memory databases show that our approach, combined with the relevance analysis strategy, codes discriminant spatial and frequency-dependent patterns for the different conditions in each experimental paradigm, with classification performances that do well in comparison with those of alternative methods of similar nature.


NeuroImage ◽  
1996 ◽  
Vol 3 (3) ◽  
pp. S214
Author(s):  
C.A. Porro ◽  
M.P. Francescato ◽  
V. Cettolo ◽  
P. Baraldi ◽  
M.E. Diamond

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2020
Author(s):  
Vivianne Flávia Cardoso ◽  
Denis Delisle-Rodriguez ◽  
Maria Alejandra Romero-Laiseca ◽  
Flávia A. Loterio ◽  
Dharmendra Gurve ◽  
...  

Recently, studies on cycling-based brain–computer interfaces (BCIs) have been standing out due to their potential for lower-limb recovery. In this scenario, the behaviors of the sensory motor rhythms and the brain connectivity present themselves as sources of information that can contribute to interpreting the cortical effect of these technologies. This study aims to analyze how sensory motor rhythms and cortical connectivity behave when volunteers command reactive motor imagery (MI) BCI that provides passive pedaling feedback. We studied 8 healthy subjects who performed pedaling MI to command an electroencephalography (EEG)-based BCI with a motorized pedal to receive passive movements as feedback. The EEG data were analyzed under the following four conditions: resting, MI calibration, MI online, and receiving passive pedaling (on-line phase). Most subjects produced, over the foot area, significant event-related desynchronization (ERD) patterns around Cz when performing MI and receiving passive pedaling. The sharpest decrease was found for the low beta band. The connectivity results revealed an exchange of information between the supplementary motor area (SMA) and parietal regions during MI and passive pedaling. Our findings point to the primary motor cortex activation for most participants and the connectivity between SMA and parietal regions during pedaling MI and passive pedaling.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simone Rossi ◽  
Gionata Salvietti ◽  
Francesco Neri ◽  
Sara M. Romanella ◽  
Alessandra Cinti ◽  
...  

AbstractIt is likely that when using an artificially augmented hand with six fingers, the natural five plus a robotic one, corticospinal motor synergies controlling grasping actions might be different. However, no direct neurophysiological evidence for this reasonable assumption is available yet. We used transcranial magnetic stimulation of the primary motor cortex to directly address this issue during motor imagery of objects’ grasping actions performed with or without the Soft Sixth Finger (SSF). The SSF is a wearable robotic additional thumb patented for helping patients with hand paresis and inherent loss of thumb opposition abilities. To this aim, we capitalized from the solid notion that neural circuits and mechanisms underlying motor imagery overlap those of physiological voluntary actions. After a few minutes of training, healthy humans wearing the SSF rapidly reshaped the pattern of corticospinal outputs towards forearm and hand muscles governing imagined grasping actions of different objects, suggesting the possibility that the extra finger might rapidly be encoded into the user’s body schema, which is integral part of the frontal-parietal grasping network. Such neural signatures might explain how the motor system of human beings is open to very quickly welcoming emerging augmentative bioartificial corticospinal grasping strategies. Such an ability might represent the functional substrate of a final common pathway the brain might count on towards new interactions with the surrounding objects within the peripersonal space. Findings provide a neurophysiological framework for implementing augmentative robotic tools in humans and for the exploitation of the SSF in conceptually new rehabilitation settings.


2011 ◽  
Vol 29 (supplement) ◽  
pp. 352-377 ◽  
Author(s):  
Seon Hee Jang ◽  
Frank E Pollick

The study of dance has been helpful to advance our understanding of how human brain networks of action observation are influenced by experience. However previous studies have not examined the effect of extensive visual experience alone: for example, an art critic or dance fan who has a rich experience of watching dance but negligible experience performing dance. To explore the effect of pure visual experience we performed a single experiment using functional Magnetic Resonance Imaging (fMRI) to compare the neural processing of dance actions in 3 groups: a) 14 ballet dancers, b) 10 experienced viewers, c) 12 novices without any extensive dance or viewing experience. Each of the 36 participants viewed short 2-second displays of ballet derived from motion capture of a professional ballerina. These displays represented the ballerina as only points of light at the major joints. We wished to study the action observation network broadly and thus included two different types of display and two different tasks for participants to perform. The two different displays were: a) brief movies of a ballet action and b) frames from the ballet movies with the points of lights connected by lines to show a ballet posture. The two different tasks were: a) passively observe the display and b) imagine performing the action depicted in the display. The two levels of display and task were combined factorially to produce four experimental conditions (observe movie, observe posture, motor imagery of movie, motor imagery of posture). The set of stimuli used in the experiment are available for download after this paper. A random effects ANOVA was performed on brain activity and an effect of experience was obtained in seven different brain areas including: right Temporoparietal Junction (TPJ), left Retrosplenial Cortex (RSC), right Primary Somatosensory Cortex (S1), bilateral Primary Motor Cortex (M1), right Orbitofrontal Cortex (OFC), right Temporal Pole (TP). The patterns of activation were plotted in each of these areas (TPJ, RSC, S1, M1, OFC, TP) to investigate more closely how the effect of experience changed across these areas. For this analysis, novices were treated as baseline and the relative effect of experience examined in the dancer and experienced viewer groups. Interpretation of these results suggests that both visual and motor experience appear equivalent in producing more extensive early processing of dance actions in early stages of representation (TPJ and RSC) and we hypothesise that this could be due to the involvement of autobiographical memory processes. The pattern of results found for dancers in S1 and M1 suggest that their perception of dance actions are enhanced by embodied processes. For example, the S1 results are consistent with claims that this brain area shows mirror properties. The pattern of results found for the experienced viewers in OFC and TP suggests that their perception of dance actions are enhanced by cognitive processes. For example, involving aspects of social cognition and hedonic processing – the experienced viewers find the motor imagery task more pleasant and have richer connections of dance to social memory. While aspects of our interpretation are speculative the core results clearly show common and distinct aspects of how viewing experience and physical experience shape brain responses to watching dance.


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