Application of fMRI for action representation: decoding, aligning and modulating

Functional magnetic resonance imaging (fMRI) is an important tool for understanding neural mechanisms underlying human brain function. Understanding how the human brain responds to stimuli and how different cortical regions represent the information, and if these representational spaces are shared across brains and critical for our understanding of how the brain works. Recently, multivariate pattern analysis (MVPA) has a growing importance to predict mental states from fMRI data and to detect the coarse and fine scale neural responses. However, a major limitation of MVPA is the difficulty of aligning features across brains due to high variability in subjects’ responses and hence MVPA has been generally used as a subject specific analysis. Hyperalignment, solved this problem of feature alignment across brains by mapping neural responses into a common model to facilitate between subject classifications. Another technique of growing importance in understanding brain function is real-time fMRI Neurofeedback, which can be used to enable individuals to alter their brain activity. It facilitates people’s ability to learn control of their cognitive processes like motor control and pain by learning to modulate their brain activation in targeted regions. The aim of this PhD research is to decode and to align the motor representations of multi-joint arm actions based on different modalities of motor simulation, for instance Motor Imagery (MI) and Action Observation (AO) using functional Magnetic Resonance Imaging (fMRI) and to explore the feasibility of using a real-time fMRI neurofeedback to alter these action representations. The first experimental study of this thesis was performed on able-bodied participants to align the neural representation of multi-joint arm actions (lift, knock and throw) during MI tasks in the motor cortex using hyperalignment. Results showed that hyperalignment affords a statistically higher between-subject classification (BSC) performance compared to anatomical alignment. Also, hyperalignment is sensitive to the order in which subjects entered the hyperalignment algorithm to create the common model space. These results demonstrate the effectiveness of hyperalignment to align neural responses in motor cortex across subjects to enable BSC of motor imagery. The second study extended the use of hyperalignment to align fronto-parietal motor regions by addressing the problems of localization and cortical parcellation using cortex based alignment. Also, representational similarity analysis (RSA) was applied to investigate the shared neural code between AO+MI and MI of different actions. Results of MVPA revealed that these actions as well as their modalities can be decoded using the subject’s native or the hyperaligned neural responses. Furthermore, the RSA showed that AO+MI and MI representations formed separate clusters but that the representational organization of action types within these clusters was identical. These findings suggest that the neural representations of AO+MI and MI are neither the same nor totally distinct but exhibit a similar structural geometry with respect to different types of action. Results also showed that MI dominates in the AO+MI condition. The third study was performed on phantom limb pain (PLP) patients to explore the feasibility of using real-time fMRI neurofeedback to down-regulate the activity of premotor (PM) and anterior cingulate (ACC) cortices and whether the successful modulation will reduce the pain intensity. Results demonstrated that PLP patients were able to gain control and decrease the ACC and PM activation. Those patients reported decrease in the ongoing level of pain after training, but it was not statistically significant. The fourth study was conducted on healthy participants to study the effectiveness of fMRI neurofeedback on improving motor function by targeting Supplementary Motor Cortex (SMA). Results showed that participants learnt to up-regulate their SMA activation using MI of complex body actions as a mental strategy. In addition, behavioural changes, i.e. shortening of motor reaction time was found in those participants. These results suggest that fMRI neurofeedback can assist participants to develop greater control over motor regions involved in motor-skill learning and it can be translated into an improvement in motor function. In summary, this PhD thesis extends and validates the usefulness of hyperalignment to align the fronto-parietal motor regions and explores its ability to generalise across different levels of motor representation. Furthermore, it sheds light on the dominant role of MI in the AO+MI condition by examining the neural representational similarity of AO+MI and MI tasks. In addition, the fMRI neurofeedback studies in this thesis provide proof-of-principle of using this technology to reduce pain in clinical applications and to enhance motor functions in a healthy population, with the potential for translation into the clinical environment

Cortical Functional Domains Show Distinctive Oscillatory Dynamic in Bimanual and Mirror Visual Feedback Tasks

It is believed that Mirror Visual Feedback (MVF) increases the interlimb transfer but the exact mechanism is still a matter of debate. The aim of this study was to compare between a bimanual task (BM) and a MVF task, within functionally rather than geometrically defined cortical domains. Measure Projection Analysis (MPA) approach was applied to compare the dynamic oscillatory activity (event-related synchronization/desynchronization ERS/ERD) between and within domains. EEG was recorded in 14 healthy participants performing a BM and an MVF task with the right hand. The MPA was applied on fitted equivalent current dipoles based on independent components to define domains containing functionally similar areas. The measure of intradomain similarity was a “signed mutual information,” a parameter based on the coherence. Domain analysis was performed for joint tasks (BM and MVF) and for each task separately. MVF created 9 functional domains while MB task had only 4 functionally distinctive domains, two over the left hemispheres and two bilateraly. For all domains identified for BM task alone, similar domains could be identified in MVF and joint tasks analysis. In addition MVF had domains related to motor planning on the right hemisphere and to self-recognition of action. For joint tasks analysis, seven domains were identified, with similar functions for the left and the right hand with exception of a domain covering BA32 (self-recognition of action) of the left hand only. In joint task domain analysis, the ERD/ERS showed a larger difference between domains than between tasks. All domains which involved the sensory cortex had a visible beta ERS at the onset of movement, and post movement beta ERS. The frequency of ERD varied between domains. Largest difference between tasks existed in domains responsible for the awareness of action. In conclusion, functionally distinctive domains have different ERD/ERS patterns, similar for both tasks. MVF activates contralateral hemisphere in similar manner to BM movements, while at the same time also activating the ipsilateral hemisphere. Significance: Following stroke cortical activation and interhemispheric inhibition from the contralesional side is reduced. MVF creates stronger ipsilateral activity than BM, which is highly relevant of neurorehabilitation of movements

Hyperalignment of motor cortical areas based on motor imagery during action observation

Multivariate Pattern Analysis (MVPA) has grown in importance due to its capacity to use both coarse and fine scale patterns of brain activity. However, a major limitation of multivariate analysis is the difficulty of aligning features across brains, which makes MVPA a subject specific analysis. Recent work by Haxby et al. (2011) introduced a method called Hyperalignment that explored neural activity in ventral temporal cortex during object recognition and demonstrated the ability to align individual patterns of brain activity into a common high dimensional space to facilitate Between Subject Classification (BSC). Here we examined BSC based on Hyperalignment of motor cortex during a task of motor imagery of three natural actions (lift, knock and throw). To achieve this we collected brain activity during the combined tasks of action observation and motor imagery to a parametric action space containing 25 stick-figure blends of the three natural actions. From these responses we derived Hyperalignment transformation parameters that were used to map subjects’ representational spaces of the motor imagery task in the motor cortex into a common model representational space. Results showed that BSC of the neural response patterns based on Hyperalignment exceeded both BSC based on anatomical alignment as well as a standard Within Subject Classification (WSC) approach. We also found that results were sensitive to the order in which participants entered the Hyperalignment algorithm. These results demonstrate the effectiveness of Hyperalignment to align neural responses across subject in motor cortex to enable BSC of motor imagery

Using real-time fMRI neurofeedback to modulate M1-cerebellum connectivity

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

Characterising the neurobiological mechanisms of action of exercise and cognitive behavioural interventions for rheumatoid arthritis fatigue: an MRI brain study

Objective: Chronic fatigue is a major clinical unmet need among patients with rheumatoid arthritis (RA). Current therapies are limited to nonpharmacological interventions, such as personalized exercise programs (PEPs) and cognitive–behavioral approaches (CBAs); however, most patients still continue to report severe fatigue. To inform more effective therapies, we conducted a magnetic resonance imaging (MRI) brain study of PEPs and CBAs, nested within a randomized controlled trial (RCT), to identify their neurobiological mechanisms of fatigue reduction in RA. Methods: A subgroup of patients with RA (n = 90), participating in an RCT of PEPs and CBAs for fatigue, undertook a multimodal MRI brain scan following randomization to either usual care (UC) alone or in addition to PEPs and CBAs and again after the intervention (six months). Brain regional volumetric, functional, and structural connectivity indices were curated and then computed employing a causal analysis framework. The primary outcome was fatigue improvement (Chalder fatigue scale). Results: Several structural and functional connections were identified as mediators of fatigue improvement in both PEPs and CBAs compared to UC. PEPs had a more pronounced effect on functional connectivity than CBAs; however, structural connectivity between the left isthmus cingulate cortex (L-ICC) and left paracentral lobule (L-PCL) was shared, and the size of mediation effect ranked highly for both PEPs and CBAs (ßAverage = −0.46, SD 0.61; ßAverage = −0.32, SD 0.47, respectively). Conclusion: The structural connection between the L-ICC and L-PCL appears to be a dominant mechanism for how both PEPs and CBAs reduce fatigue among patients with RA. This supports its potential as a substrate of fatigue neurobiology and a putative candidate for future targeting

New E-Learning opportunities based Artificial Neural Networks for Mobility impairments

E-learning is becoming globally widespread and more common. The usefulness of interactive systems in e-learning lies not in performing the processing task itself but in communicating requests and results between the system and its user. Therefor the E-Learning can offer great chances to students with disabilities that can access its application through an alternative channel using Human- Computer interaction (HCI) methods. This paper tries to review the various methods of (HCI) used for mobility disabilities and implement one of these methods using Electrooculography (EOG) signals to write using a virtual keyboard

Upregulation of Supplementary Motor Area Activation with fMRI Neurofeedback during Motor Imagery

Functional magnetic resonance imaging (fMRI) neurofeedback (NF) is a promising tool to study the relationship between behavior and brain activity. It enables people to self-regulate their brain signal. Here, we applied fMRI NF to train healthy participants to increase activity in their supplementary motor area (SMA) during a motor imagery (MI) task of complex body movements while they received a continuous visual feedback signal. This signal represented the activity of participants’ localized SMA regions in the NF group and a prerecorded signal in the control group (sham feedback). In the NF group only, results showed a gradual increase in SMA-related activity across runs. This upregulation was largely restricted to the SMA, while other regions of the motor network showed no, or only marginal NF effects. In addition, we found behavioral changes, i.e., shorter reaction times in a Go/No-go task after the NF training only. These results suggest that NF can assist participants to develop greater control over a specifically targeted motor region involved in motor skill learning. The results contribute to a better understanding of the underlying mechanisms of SMA NF based on MI with a direct implication for rehabilitation of motor dysfunctions

Empathy to emotional voices and the use of real-time fMRI to enhance activation of the anterior insula

The right anterior insula (AI), known to have a key role in the processing and understanding of social emotions, is activated during tasks that involve the act of empathising. Neurofeedback provides individuals with a visualisation of their own brain activity, enabling them to regulate and modify this activity. Following previous research investigating the ability of individuals to up-regulate right AI activity levels through neurofeedback, we investigated whether this could be similarly accomplished during an empathy task involving auditory stimuli of human positive and negative emotional expressions. Twenty participants, ten with feedback from right anterior insula and ten with feedback from a sham brain region, participated in two sessions that included sixteen neurofeedback runs and four transfer runs. Results showed that for the second session participants in the right AI neurofeedback group demonstrated better ability to up-regulate their right AI compared to the control group who received sham feedback. Examination of the relationship between individual participants' empathic traits and their ability to up-regulate right AI activity showed that participants low on empathic traits produced a greater increase in activation of right AI by the end of training. Moreover, the response to positively valenced audio stimuli was greater than for negatively valenced stimuli. These results have implications for therapeutic training of empathy in populations with limited empathic response

Real-time Image-based Motion Correction for 7T Task-based Functional MRI

Introduction: Compared to standard MRI clinical field strengths, 7T has a higher resolution potential but is also more susceptible to motion artefacts. This is pronounced in the longer, high-resolution acquisitions used for functional MRI (fMRI), which are typically corrected with retrospective motion correction [1]. The restricted environment in 7T scanners makes markerless, non-hardware techniques a compelling option. This abstract presents an implementation of the markerless, real-time Multislice Prospective Acquisition Correction (MS-PACE) technique for 7T task-based fMRI. MS-PACE estimates motion by continuously registering a subset of equidistant 2D-EPI slices to a reference volume. This allows for sub-repetition-time motion correction. This method has previously been implemented at 3T [2]. Methods: The study was performed in a MAGNETOM Terra 7T scanner (Siemens Healthineers, Erlangen, Germany) using an in-house-developed GRE-EPI sequence on 10 healthy subjects (age 31±9). The fMRI protocol consisted of 3 scan groups: 2 resting scans; 2 left-hand tapping; 2 right-hand tapping. Motion correction was applied to 1 scan/group. The scan parameters were otherwise identical: voxel size 2×2×2mm3, matrix 96×96, GRAPPA factor 3, 60 slices, 110 volumes, TR 4s, TE 18ms, total acquisition time 7m32s. The tapping stimulus was transmitted by PsychoPy [3]. Fig.1 shows how the motion correction pipeline operates. Estimated motion parameters were subsequently used to update the scanner. The rigid-body motion parameters were calculated in the Image Calculation Environment (Siemens Healthineers, Erlangen, Germany) using ITK open-source image registration libraries. Results: Fig.2 compares the mean voxel displacement from each scan group across all subjects. It demonstrates the consistent ability of the technique to correct for motion in subjects with various levels of movements. Conclusion: This study evaluated an implementation of a real-time motion correction technique for 7T task-based fMRI and showed that it can consistently reduce the effects of long-term motion in a motion-propensity diverse cohort of subjects