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Regions consistently activated during Pure motor imagery, motor imagery of motor sequences and the laterality judgment task. A: Maps of consistent activations during Pure motor imagery tasks (red) or the laterality judgment task (LJT) (green). Regions consistently activated by both types of movements are shown in yellow. B: Results of the subtraction analysis: regions with more consistent activity during Pure motor imagery are shown in red and during the LJT in green. C: Maps of consistent activations during Pure motor imagery tasks (red) or tasks using imagery of motor sequences (green). Regions consistently activated by both types of movements are shown in yellow. D: Results of the subtraction analysis: regions with more consistent activity during motor imagery of motor sequences than Pure motor imagery are shown in green. See Fig. 2 for conventions.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Source publication
Motor imagery (MI) or the mental simulation of action is now increasingly being studied using neuroimaging techniques such as positron emission tomography and functional magnetic resonance imaging. The booming interest in capturing the neural underpinning of MI has provided a large amount of data which until now have never been quantitatively summa...
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Context 1
... MI consistently activated bilaterally the SMA, IPL, PcG, and CB (left lobule VII, right lobule VI), left IFG (including pars opercularis and triangularis), SMG, temporal pole, putamen, and anterior insula, and right rolandic operculum, angular gyrus, PreC and pallidum (see Fig. 5; Table 5). Results for visual MI showed consistent activations in bilateral SMA, left PcG, lingual gyrus, and CB (lobule V), and right MfG and PocG (see Fig. 5; Table 5). Conjunction between kinesthetic and visual MI revealed consistent activations in the left PcG, SMA, and anterior insula and bilaterally in the putamen (see Fig. 5; Table 4). Contrasting the two modalities of MI did not reveal any significant difference between ALE maps. Next, we compared the LJT (implicit MI task) to the Pure MI task (explicit MI task) (see Fig. 6; Table 5). As the proportion of studies on lower limb/gait MI was very different for the LJT and the Pure MI (no study on lower-limb movement for the LJT vs. around 1/3 for Pure MI), we decided to only look at studies on MI of the upper limb for both types of tasks. The LJT consistently activated regions in bilateral SPL, PocG, left IPL, and CB (lobule VII), in addition to right MfG and putamen. Pure MI consistently activated regions in bilateral SMA, PcG, IFG (including pars opercularis but only left pars triangularis), IPL, SPL, and PocG, in the left SMG and MfG, and in the right caudate and CB (lobule VI). The conjunction of the LTJ and Pure MI showed consistent activation in bilateral MfG and left IPL. The LTJ consistently activated more right SPL, MfG, and PocG while Pure MI consistently activated more bilateral SMA and the left SMG (see Fig. 6; Table 4). MI of motor sequences (simple or complex) consistently activated bilaterally the SMA, MfG, IFG (including pars opercularis), SMG, SPL, and putamen, in addition to left PcG, IPL, thalamus, and CB (lobules VI and VII), and right MCC, PocG, angular gyrus and anterior insula (see Fig. 6; Table 5). The conjunction of MI of motor sequence and Pure MI showed bilateral activations in the SMA and IFG (including pars opercularis), in the left PcG, IPL, SPL, anterior insula and putamen, in addition to right activations in the MfG and IPL. MI of motor sequence had more consistent activation in bilateral MfG and IPL, SPL, left putamen, PocG and right PcG, PreC, SMG and MCC, while the inverse comparison yielded no significant difference (see Fig. 6; Table ...
Context 2
... MI consistently activated bilaterally the SMA, IPL, PcG, and CB (left lobule VII, right lobule VI), left IFG (including pars opercularis and triangularis), SMG, temporal pole, putamen, and anterior insula, and right rolandic operculum, angular gyrus, PreC and pallidum (see Fig. 5; Table 5). Results for visual MI showed consistent activations in bilateral SMA, left PcG, lingual gyrus, and CB (lobule V), and right MfG and PocG (see Fig. 5; Table 5). Conjunction between kinesthetic and visual MI revealed consistent activations in the left PcG, SMA, and anterior insula and bilaterally in the putamen (see Fig. 5; Table 4). Contrasting the two modalities of MI did not reveal any significant difference between ALE maps. Next, we compared the LJT (implicit MI task) to the Pure MI task (explicit MI task) (see Fig. 6; Table 5). As the proportion of studies on lower limb/gait MI was very different for the LJT and the Pure MI (no study on lower-limb movement for the LJT vs. around 1/3 for Pure MI), we decided to only look at studies on MI of the upper limb for both types of tasks. The LJT consistently activated regions in bilateral SPL, PocG, left IPL, and CB (lobule VII), in addition to right MfG and putamen. Pure MI consistently activated regions in bilateral SMA, PcG, IFG (including pars opercularis but only left pars triangularis), IPL, SPL, and PocG, in the left SMG and MfG, and in the right caudate and CB (lobule VI). The conjunction of the LTJ and Pure MI showed consistent activation in bilateral MfG and left IPL. The LTJ consistently activated more right SPL, MfG, and PocG while Pure MI consistently activated more bilateral SMA and the left SMG (see Fig. 6; Table 4). MI of motor sequences (simple or complex) consistently activated bilaterally the SMA, MfG, IFG (including pars opercularis), SMG, SPL, and putamen, in addition to left PcG, IPL, thalamus, and CB (lobules VI and VII), and right MCC, PocG, angular gyrus and anterior insula (see Fig. 6; Table 5). The conjunction of MI of motor sequence and Pure MI showed bilateral activations in the SMA and IFG (including pars opercularis), in the left PcG, IPL, SPL, anterior insula and putamen, in addition to right activations in the MfG and IPL. MI of motor sequence had more consistent activation in bilateral MfG and IPL, SPL, left putamen, PocG and right PcG, PreC, SMG and MCC, while the inverse comparison yielded no significant difference (see Fig. 6; Table ...
Context 3
... MI consistently activated bilaterally the SMA, IPL, PcG, and CB (left lobule VII, right lobule VI), left IFG (including pars opercularis and triangularis), SMG, temporal pole, putamen, and anterior insula, and right rolandic operculum, angular gyrus, PreC and pallidum (see Fig. 5; Table 5). Results for visual MI showed consistent activations in bilateral SMA, left PcG, lingual gyrus, and CB (lobule V), and right MfG and PocG (see Fig. 5; Table 5). Conjunction between kinesthetic and visual MI revealed consistent activations in the left PcG, SMA, and anterior insula and bilaterally in the putamen (see Fig. 5; Table 4). Contrasting the two modalities of MI did not reveal any significant difference between ALE maps. Next, we compared the LJT (implicit MI task) to the Pure MI task (explicit MI task) (see Fig. 6; Table 5). As the proportion of studies on lower limb/gait MI was very different for the LJT and the Pure MI (no study on lower-limb movement for the LJT vs. around 1/3 for Pure MI), we decided to only look at studies on MI of the upper limb for both types of tasks. The LJT consistently activated regions in bilateral SPL, PocG, left IPL, and CB (lobule VII), in addition to right MfG and putamen. Pure MI consistently activated regions in bilateral SMA, PcG, IFG (including pars opercularis but only left pars triangularis), IPL, SPL, and PocG, in the left SMG and MfG, and in the right caudate and CB (lobule VI). The conjunction of the LTJ and Pure MI showed consistent activation in bilateral MfG and left IPL. The LTJ consistently activated more right SPL, MfG, and PocG while Pure MI consistently activated more bilateral SMA and the left SMG (see Fig. 6; Table 4). MI of motor sequences (simple or complex) consistently activated bilaterally the SMA, MfG, IFG (including pars opercularis), SMG, SPL, and putamen, in addition to left PcG, IPL, thalamus, and CB (lobules VI and VII), and right MCC, PocG, angular gyrus and anterior insula (see Fig. 6; Table 5). The conjunction of MI of motor sequence and Pure MI showed bilateral activations in the SMA and IFG (including pars opercularis), in the left PcG, IPL, SPL, anterior insula and putamen, in addition to right activations in the MfG and IPL. MI of motor sequence had more consistent activation in bilateral MfG and IPL, SPL, left putamen, PocG and right PcG, PreC, SMG and MCC, while the inverse comparison yielded no significant difference (see Fig. 6; Table ...
Context 4
... MI consistently activated bilaterally the SMA, IPL, PcG, and CB (left lobule VII, right lobule VI), left IFG (including pars opercularis and triangularis), SMG, temporal pole, putamen, and anterior insula, and right rolandic operculum, angular gyrus, PreC and pallidum (see Fig. 5; Table 5). Results for visual MI showed consistent activations in bilateral SMA, left PcG, lingual gyrus, and CB (lobule V), and right MfG and PocG (see Fig. 5; Table 5). Conjunction between kinesthetic and visual MI revealed consistent activations in the left PcG, SMA, and anterior insula and bilaterally in the putamen (see Fig. 5; Table 4). Contrasting the two modalities of MI did not reveal any significant difference between ALE maps. Next, we compared the LJT (implicit MI task) to the Pure MI task (explicit MI task) (see Fig. 6; Table 5). As the proportion of studies on lower limb/gait MI was very different for the LJT and the Pure MI (no study on lower-limb movement for the LJT vs. around 1/3 for Pure MI), we decided to only look at studies on MI of the upper limb for both types of tasks. The LJT consistently activated regions in bilateral SPL, PocG, left IPL, and CB (lobule VII), in addition to right MfG and putamen. Pure MI consistently activated regions in bilateral SMA, PcG, IFG (including pars opercularis but only left pars triangularis), IPL, SPL, and PocG, in the left SMG and MfG, and in the right caudate and CB (lobule VI). The conjunction of the LTJ and Pure MI showed consistent activation in bilateral MfG and left IPL. The LTJ consistently activated more right SPL, MfG, and PocG while Pure MI consistently activated more bilateral SMA and the left SMG (see Fig. 6; Table 4). MI of motor sequences (simple or complex) consistently activated bilaterally the SMA, MfG, IFG (including pars opercularis), SMG, SPL, and putamen, in addition to left PcG, IPL, thalamus, and CB (lobules VI and VII), and right MCC, PocG, angular gyrus and anterior insula (see Fig. 6; Table 5). The conjunction of MI of motor sequence and Pure MI showed bilateral activations in the SMA and IFG (including pars opercularis), in the left PcG, IPL, SPL, anterior insula and putamen, in addition to right activations in the MfG and IPL. MI of motor sequence had more consistent activation in bilateral MfG and IPL, SPL, left putamen, PocG and right PcG, PreC, SMG and MCC, while the inverse comparison yielded no significant difference (see Fig. 6; Table ...
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Here we describe the public neuroimaging and behavioral dataset entitled "Cross-Sectional Multidomain Lexical Processing" available on the OpenNeuro project (https://openneuro.org). This dataset explores the neural mechanisms and development of lexical processing through task based functional magnetic resonance imaging (fMRI) of rhyming, spelling,...
Citations
... Inference on model parameters using Bayesian model averaging revealed that physical inference is associated with bidirectional increases in connectivity between SMA and SPL, and between SMA and PMd. The SMA has been closely linked to imagery, particularly motor imagery (Hétu et al. 2013), but also other modalities such as visual imagery (Palmiero et al. 2009). Interestingly, we found the increase in connectivity from the PMd to the SMA to be predictive of the self-rated vividness of the inferred physical scene. ...
Anticipating the behaviour of moving objects in the physical environment is essential for a wide range of daily actions. This ability is thought to rely on mental simulations and has been shown to involve frontoparietal and early visual areas. Yet, the connectivity patterns between these regions during intuitive physical inference remain largely unknown. In this study, participants underwent fMRI while performing a task requiring them to infer the parabolic trajectory of an occluded ball falling under Newtonian physics, and a control task. Building on our previous research showing that when solving the physical inference task, early visual areas encode task-specific and perception-like information about the inferred trajectory, the present study aimed to (i) identify regions that are functionally coupled with early visual areas during the physical inference task, and (ii) investigate changes in effective connectivity within this network of regions. We found that early visual areas are functionally connected to a set of parietal and premotor regions when inferring occluded trajectories. Using dynamic causal modelling, we show that predicting occluded trajectories is associated with changes in effective connectivity within a parieto-premotor network, which may drive internally generated early visual activity in a top-down fashion. These findings offer new insights into the interaction between early visual and frontoparietal regions during physical inference, contributing to our understanding of the neural mechanisms underlying the ability to predict physical outcomes.
... The MVPA revealed that cortical parcels containing task-related information were localized around the bilateral SM1 when the partici- analysis (Hétu et al., 2013). As the MVPA focused on the difference in the activity patterns within the functional parcels between the relaxation and imagery periods, the contralateral SM1, by which the targeted effector was innervated, could be emphasized because of the endogenously induced changes in the activity patterns rather than the task-related potentiation of the BOLD signals within the region. ...
Closed‐loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high‐density whole‐head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain‐computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI‐based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.
... In support of this view, studies suggest that the ability to perform MI in childhood is associated with the development and expression of important motor functions, including motor planning (Fuelscher et al. 2016;Toussaint et al. 2013) and adaptive control (Fuelscher et al. 2015b). Further, where motor skill is impaired in childhood (e.g., in children with including the prefrontal, premotor and parietal cortices, the supplementary motor area, the basal ganglia and the cerebellum (Hardwick et al. 2018;Hétu et al. 2013;Zapparoli et al. 2014). Accordingly, MI paradigms are considered to provide insight into the internal action representations that unconsciously precede and subserve movement (Gabbard 2009;Munzert et al. 2009). ...
... What is currently known about the neurobiological basis of implicit MI is largely derived from task-based functional MRI studies examining patterns of brain activation while participants perform the HRT (Hardwick et al. 2018;Hétu et al. 2013;Zapparoli et al. 2014). These studies have highlighted several brain regions that show an increased blood-oxygen-level-dependent (BOLD) response during MI. ...
... These studies have highlighted several brain regions that show an increased blood-oxygen-level-dependent (BOLD) response during MI. These include frontal motor regions (the inferior, middle, and superior frontal gyri and the supplementary motor area), parietal regions (the inferior and superior parietal lobes and the postcentral gyrus), and the cerebellum (lobules VI and VII; Hardwick et al. 2018;Hétu et al. 2013;Zapparoli et al. 2014). Although this research has provided valuable insight into the brain regions associated with implicit MI performance, these results were based on adult studies. ...
Despite the important role of motor imagery (MI) in motor development, our understanding of the contribution of white matter fibre properties to MI performance in childhood remains limited. To provide novel insight into the white matter correlates of MI performance, this study examined the association between white matter fibre properties and motor imagery performance in a sample of typically developing children. High angular diffusion weighted imaging data were collected from 22 typically developing children aged 6–14 years (12 female, MAge= 10.56). Implicit motor imagery performance was assessed using a mental hand rotation paradigm. The cerebellar peduncles and the superior longitudinal fasciculus were reconstructed using TractSeg, a semi-automated method. For each tract, white matter microstructure (fibre density, FD) and morphology (fibre bundle cross-section, FC) were estimated using Fixel-Based Analysis. Permutation-based inference testing and partial correlation analyses demonstrated that higher FC in the middle cerebellar peduncles was associated with better MI performance. Tract-based region of interest analyses showed that higher FC in the middle and superior cerebellar peduncles were associated with better MI performance. Results suggest that white matter connectivity along the cerebellar peduncles may facilitate MI performance in childhood. These findings advance our understanding of the neurobiological systems that underlie MI performance in childhood and provide early evidence for the relevance of white matter sensorimotor pathways to internal action representations.
... Studies have shown that both action observation and mental imagery augment motor performance for upper limb, lower limb, and whole-body functional tasks [12][13][14][15][16]. Furthermore, research indicates that MI in motor behavior and motor learning paradigms facilitate activity-dependent neuroplastic adaptation which is fundamental to achieving lasting behavioral change [17][18][19]. For instance, functional brain imaging studies have demonstrated overlapping neural networks (e.g., frontoparietal network) and similar neural connectivity patterns in primary motor and motor-associated brain regions when comparing action observation and mental imagery to physical practice [19][20][21][22]. Additionally, corticospinal excitability has shown comparable changes in response to MI or physical practice of complex motor tasks [23][24][25]. ...
Background
Several changes occur in the central nervous system with increasing age that contribute toward declines in mobility. Neurorehabilitation has proven effective in improving motor function though achieving sustained behavioral and neuroplastic adaptations is more challenging. While effective, rehabilitation usually follows adverse health outcomes, such as injurious falls. This reactive intervention approach may be less beneficial than prevention interventions. Therefore, we propose the development of a prehabilitation intervention approach to address mobility problems before they lead to adverse health outcomes. This protocol article describes a pilot study to examine the feasibility and acceptability of a home-based, self-delivered prehabilitation intervention that combines motor imagery (mentally rehearsing motor actions without physical movement) and neuromodulation (transcranial direct current stimulation, tDCS; to the frontal lobes). A secondary objective is to examine preliminary evidence of improved mobility following the intervention.
Methods
This pilot study has a double-blind randomized controlled design. Thirty-four participants aged 70–95 who self-report having experienced a fall within the prior 12 months or have a fear of falling will be recruited. Participants will be randomly assigned to either an active or sham tDCS group for the combined tDCS and motor imagery intervention. The intervention will include six 40-min sessions delivered every other day. Participants will simultaneously practice the motor imagery tasks while receiving tDCS. Those individuals assigned to the active group will receive 20 min of 2.0-mA direct current to frontal lobes, while those in the sham group will receive 30 s of stimulation to the frontal lobes. The motor imagery practice includes six instructional videos presenting different mobility tasks related to activities of daily living. Prior to and following the intervention, participants will undergo laboratory-based mobility and cognitive assessments, questionnaires, and free-living activity monitoring.
Discussion
Previous studies report that home-based, self-delivered tDCS is safe and feasible for various populations, including neurotypical older adults. Additionally, research indicates that motor imagery practice can augment motor learning and performance. By assessing the feasibility (specifically, screening rate (per month), recruitment rate (per month), randomization (screen eligible who enroll), retention rate, and compliance (percent of completed intervention sessions)) and acceptability of the home-based motor imagery and tDCS intervention, this study aims to provide preliminary data for planning larger studies.
Trial registration
This study is registered on ClinicalTrials.gov (NCT05583578). Registered October 13, 2022. https://www.clinicaltrials.gov/study/NCT05583578
... Of particular interest is how these two forms of motor simulation activate the motor system, and the motor cortex in particular (Hétu et al., 2013). The motor cortex has only recently been recognized for its role in strength production, yet the findings are encouraging. ...
Limb immobilization causes rapid declines in muscle strength and mass. Given the role of the nervous system in immobilization‐induced weakness, targeted interventions may be able to preserve muscle strength, but not mass, and vice versa. The purpose of this study was to assess the effects of two distinct interventions during 1 week of knee joint immobilization on muscle strength (isometric and concentric isokinetic peak torque), mass (bioimpedance spectroscopy and ultrasonography), and neuromuscular function (transcranial magnetic stimulation and interpolated twitch technique). Thirty‐nine healthy, college‐aged adults (21 males, 18 females) were randomized into one of four groups: immobilization only ( n = 9), immobilization + action observation/mental imagery (AOMI) ( n = 10), immobilization + neuromuscular electrical stimulation (NMES) ( n = 12), or control group ( n = 8). The AOMI group performed daily video observation and mental imagery of knee extensions. The NMES group performed twice daily stimulation of the quadriceps femoris. Based on observed effect sizes, it appears that AOMI shows promise as a means of preserving voluntary strength, which may be modulated by neural adaptations. Strength increased from PRE to POST in the AOMI group, with +7.2% (Cohen's d = 1.018) increase in concentric isokinetic peak torque at 30°/s. However, NMES did not preserve muscle mass. Though preliminary, our findings highlight the specific nature of clinical interventions and suggest that muscle strength can be independently targeted during rehabilitation. This study was prospectively registered: ClinicalTrials.gov NCT05072652.
... In other words, the internal representation or neural codes used for imagery are also those responsible for execution (see also , Hommel, Müsseler, Aschersleben, & Prinz, 2001). This view has been strongly evidenced by neurophysiological findings including the increased effector-specific corticospinal excitability following movement imagery (Fadiga et al., 1998;Wright et al., 2018), as well as the similar patterns of activation within the inferior frontal/premotor neural regions following movement imagery and execution (Filimon, Nelson, Hagler, & Sereno, 2007;Hétu et al., 2013;Schubotz & von Cramon, 2004). Perhaps the strongest support to-date within the behavioural literature comes from the mental chronometry paradigm, where imagined movement time is captured by short micro-movements or press/release to index the start and end without any overt movement in between. ...
The motor-cognitive model proposes that movement imagery additionally requires conscious monitoring owing to an absence of veridical online sensory feedback. Therefore, it is predicted that there would be a comparatively limited ability for individuals to update or correct movement imagery as they could within execution. To investigate, participants executed and imagined target-directed aiming movements featuring either an unexpected target perturbation (Exp. 1) or removal of visual sensory feedback (Exp. 2). The results of both experiments indicated that the time-course of executed and imagined movements was equally influenced by each of these online visual manipulations. Thus, contrary to some of the tenets of the motor-cognitive model, movement imagery holds the capacity to interpolate online corrections despite the absence of veridical sensory feedback. The further theoretical implications of these findings are discussed.
... In fact, the premotor cortex (PMC), supplementary motor areas (Gerardin et al. 2000;Johnson et al. 2002;Kuhtz-Buschbeck et al. 2003;Oostra et al. 2016;Orlandi et al. 2020), parietal regions (Decety et al. 1994;Sirigu et al. 1996;Pelgrims et al. 2009), cerebellum and basal ganglia (Decety et al. 1994;Grealy and Lee 2011;Heremans et al. 2011;Oostra et al. 2016) have been found active in various motor imagery tasks. The PMC and parietal areas would share a functional neural circuitry in the distributed Fronto-Parietal Network (dFPN) (Hétu et al. 2013;Ptak et al. 2017), enabling emulation. This core process specifically handles dynamic motor representations, regardless of the stimulus or output mechanism (Ptak et al. 2017), and does not seem to include the primary motor cortex (Barhoun et al. 2022). ...
... "Movement" motivational state was also associated with a large activation of the parietal ROI (see the Supplementary file), which aligns with prior literature suggesting that motor imagery is grounded in a distributed Fronto-Parietal Network enabling "emulation". This core process would specifically deals with motor representations that generates a dynamic representation of abstract movement kinematics, supporting the internal manipulation of these representations and ensuring their short-term maintenance (Hétu et al. 2013;Ptak et al. 2017). Consistent with this notion, disruptions in this prefrontal-parietal network could explain impaired motor imagery ability (McInnes et al. 2016;Oostra et al. 2016). ...
The literature has demonstrated the potential for detecting accurate electrical signals that correspond to the will or intention to move, as well as decoding the thoughts of individuals who imagine houses, faces or objects. This investigation examines the presence of precise neural markers of imagined motivational states through the combining of electrophysiological and neuroimaging methods. 20 participants were instructed to vividly imagine the desire to move, listen to music or engage in social activities. Their EEG was recorded from 128 scalp sites and analysed using individual standardized Low-Resolution Brain Electromagnetic Tomographies (LORETAs) in the N400 time window (400–600 ms). The activation of 1056 voxels was examined in relation to the 3 motivational states. The most active dipoles were grouped in eight regions of interest (ROI), including Occipital, Temporal, Fusiform, Premotor, Frontal, OBF/IF, Parietal, and Limbic areas. The statistical analysis revealed that all motivational imaginary states engaged the right hemisphere more than the left hemisphere. Distinct markers were identified for the three motivational states. Specifically, the right temporal area was more relevant for “Social Play”, the orbitofrontal/inferior frontal cortex for listening to music, and the left premotor cortex for the “Movement” desire. This outcome is encouraging in terms of the potential use of neural indicators in the realm of brain-computer interface, for interpreting the thoughts and desires of individuals with locked-in syndrome.
... At the brain level, MI activates similar networks to those involved during executed movement, particularly the parietal, frontal, and primary motor (M 1 ) cortices (Hanakawa et al., 2008;Hétu et al., 2013). Beyond the temporal congruence between actual and imagined movements, MI ability may be assessed through its vividness, namely the clarity of the visual images and intensity of the kinaesthetic information evoked during MI (McAvinue & Robertson, 2008;Saimpont et al., 2015). ...
... The lack of tDCS effects is a priori not due to difficulties in imagining finger movements, as the participants self-reported, on average, moderate to clear/intense perception of movements during MIt, and none rated MI vividness under 2/5. However, the recruitment of M 1 during MIt may not have been sufficient, as its involvement is not systematic (see Hétu et al., 2013) and it was not controlled in the present study. Even with physical training, the role of M 1 during MSL is not fully understood, with some studies reporting either no change or a decrease in M 1 activity during the acquisition phase (Floyer-Lea & Matthews, 2005;Toni et al., 1998). ...
... This activation pattern strongly resembles the network engaged during visuo-spatial motor imagery, encompassing the IFG, SMA, aINS as well as inferior and superior parietal lobules. Also, subcortical regions such as cerebellum and thalamus have been implicated in motor imagery (Hétu et al., 2013). This is consistent with our expectation that a mental coin toss task likely involves simulating motor actions. ...
In some cases, when we are making decisions, the available choices can appear to be equivalent. When this happens, our choices appear not to be constrained by external factors and instead we can believe to be selecting "randomly". Furthermore, randomness is sometimes even explicitly required by task conditions such as in random sequence generation (RSG) tasks. This is a challenging task that involves the coordination of multiple cognitive processes, which can include the inhibition of habitual choice patterns and monitoring of the running choice sequence.
It has been shown that random choices are strongly influenced by the way they are instructed. This raises the question whether the brain mechanisms underlying random selection also differ between different task instructions. To assess this, we measured brain activity while participants were engaging in three different variations of a sequence generation task: Based on previous work, participants were instructed to either (1) "generate a random sequence of choices", (2) "simulate a fair coin toss", or (3) "choose freely".
Our results reveal a consistent frontoparietal activation pattern that is shared across all tasks. Specifically, increased activity was observed in bilateral inferior and right middle frontal gyrus, left pre-supplementary motor area, bilateral inferior parietal lobules and portions of anterior insular cortex in both hemispheres. Activity in the mental coin toss condition was higher in right dorsolateral prefrontal cortex, left (pre-) supplementary motor area, a portion of right inferior frontal gyrus, bilateral superior parietal lobules and bilateral anterior insula. Additionally, our multivariate analysis revealed a distinct region in the right frontal pole to be predictive of the outcome of choices, but only when randomness was explicitly instructed.
These results emphasize that different randomization tasks involve both shared and unique neural mechanisms. Thus, even seemingly similar randomization behavior can be produced by different neural pathways.
... These benefits may be ascribed, at least partially, to the activation of sensorimotor brain regions that overlap those engaged during actual movement (Jeannerod, 2001;Sharma & Baron, 2013). Indeed, numerous studies employing fMRI have posited that MI engages an extensive frontoparietal network and subcortical structures, including the primary motor cortex, supplementary motor area, parietal lobe, and cerebellum (Habas & Manto, 2014;Hardwick et al., 2018;Hétu et al., 2013;Lotze et al., 1999). Nevertheless, although fMRI offers an outstanding spatial resolution, its limited temporal resolution restricts the ability to probe dynamic and subtle cortical changes, such as those underlying MI. ...
While both motor imagery (MI) and low-frequency sound listening have independently demonstrated the ability to modulate brain activity, there remains an unexplored frontier regarding the potential synergistic effects that may arise from their combined application. Any further modulation derived from this combination may be relevant for motor learning and/or rehabilitation. We conducted an experiment probing the electrophysiological activity of brain during these processes. By means of EEG, we recorded alpha and beta band power amplitude, which serve as markers of brain activity. Twenty volunteers were instructed to i) explicitly imagine finger flexion/extension movements in a kinaesthetic modality, ii) listen to low-frequency sounds, iii) imagine finger flexion while listening to low-frequency sounds, and iv) stay at rest. We observed a bimodal distribution, suggesting the presence of variability in brain activity across participants during both MI and low-frequency sound listening. One group of participants (12 individuals) displayed increased alpha power within contralateral sensorimotor and ipsilateral medial parieto-occipital regions during MI. Another group (8 individuals) exhibited a decrease in alpha and beta band power within sensorimotor areas. Interestingly, low-frequency sound listening elicited a similar pattern of brain activity within both groups. Surprisingly, the combination of MI and sound listening did not result in additional changes in alpha and beta power amplitudes compared to these processes in isolation, regardless of group. Altogether, these findings shed significant insight into the brain activity and its variability generated during MI and low-frequency sound listening. Nevertheless, it appears that the simultaneous engagement of MI and low-frequency sound listening could not further modulate alpha power amplitude, possibly due to concurrent cortical activations. This prompts us to inquire whether administering these interventions sequentially could uncover any additional modulation.
