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Magnetic resonance imaging (MRI) is a non-destructive technique that is capable of localizing pathologies and assessing other anatomical features (e.g., tissue volume, microstructure, and white matter connectivity) in postmortem, ex vivo human brains. However, when brains are removed from the skull and cerebrospinal fluid (i.e., their normal in vivo magnetic environment), air bubbles and air–tissue interfaces typically cause magnetic susceptibility artifacts that severely degrade the quality of ex vivo MRI data. In this report, we describe a relatively simple and cost-effective experimental setup for acquiring artifact-free ex vivo brain images using a clinical MRI system with standard hardware. In particular, we outline the necessary steps, from collecting an ex vivo human brain to the MRI scanner setup, and have also described changing the formalin (as might be necessary in longitudinal postmortem studies). Finally, we share some representative ex vivo MRI images that have been acquired using the proposed setup in order to demonstrate the efficacy of this approach. We hope that this protocol will provide both clinicians and researchers with a straight-forward and cost-effective solution for acquiring ex vivo MRI data from whole postmortem human brains.

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation.

Robust functional connectivity in sensorimotor resting-state network (RSN) has been linked to positive neurodevelopmental outcomes in neonates. In the current study, we aimed to map the developmental trajectory of sensorimotor RSN in awake neonates using functional near-infrared spectroscopy (fNIRS). We acquired fNIRS resting-state data from 41 healthy newborns (17 females, gestational age range=36+0 to 42+1 weeks) within the first 48 hours after birth. At the group level, we observed robust positive connectivity in numerous channel-pairs across the sensorimotor network, especially in the left hemisphere. Next, we examined the relationship between functional connectivity, gestational age and postnatal age, while controlling for sex and subject effects. We found both gestational and postnatal age to be positively associated with an increase in functional connectivity in the posterior portion of sensorimotor RSN. In summary, our findings demonstrate the feasibility of fNIRS for monitoring early developmental changes in functional networks in awake infants.

While different cognitive abilities mature, the conscious experiences of children likely become richer and more elaborate. A challenge in investigating relationships between cognitive development and real-world experiences is having measures that assess naturalistic processing. Movie watching offers a solution, since following the plot of a film requires cognitive processes that are similar to real-world experiences. When different adults watch the same film, their brain activity begins to align (known as neural synchrony). The strength of this alignment has been shown to reflect the degree to which different individuals are having a similar experience of the movie. While this phenomenon has been established in adults, much less is known about the neural mechanisms supporting naturalistic processing in children and adolescents. The current thesis investigated the neural correlates of movie watching across late childhood and early adolescence. In Chapter 2, I found that autistic children showed more variable brain responses in regions associated with social cognition when watching a movie compared to children without autism. In Chapter 3, I found that adolescents (ages 11-15) with higher cognitive scores showed greater neural synchrony during movie watching in brain regions associated with social processing and executive functions compared to those with below average cognitive scores. This pattern was not evident in children (ages 7-11) who differed in their cognitive scores. In Chapter 4, I found that although the spatial topographies of children’s functional brain networks were nearly indistinguishable during movie watching and rest, these two states differed in the degree of neural synchrony that was present within much of the brain. That is, movies led to significantly more neural synchrony compared to rest, except for in parts of the prefrontal cortex. Taken together, these results suggest that 1) autistic children have more distinct experiences when processing naturalistic stimuli compared to those without autism, 2) adolescents with higher cognitive scores have more similar experiences with each other when watching a movie compared to those with lower scores, and 3) although children’s brain networks during movie watching and rest have a similar functional architecture, processing a film leads to neural synchrony, whereas resting state does not.

Musculoskeletal injuries can severely impact the ability to produce and control body motion. In order to regain function, rehabilitation is often required. Wearable smart devices are currently under development to provide therapy and assistance for people with impaired arm function. Electromyography (EMG) signals are used as an input to pattern recognition systems to determine intended movements. However, there is a gap between the accuracy of pattern recognition systems in constrained laboratory settings, and usability when used for detecting dynamic unconstrained movements. Motion factors such as limb position, interaction force, and velocity, are known to have a negative impact on the pattern recognition. A possible solution lies in the use of data from other sensors along with the EMG signals, such as signals from accelerometers (ACC), in the training and use of classifiers in order to improve classification accuracy. The objectives of this study were to quantify the impact of motion factors on ACC signals, and to use these ACC signals along with EMG signals for classifying categories of motion factors. To address these objectives, a dataset containing EMG and ACC signals while individuals performed unconstrained arm motions was studied. Analyses of the EMG and accelerometer signals and their use in training classification models to predict characteristics of intended motion were completed. The results quantify how accelerometer features change with variations in arm position, interaction forces, and motion velocities. The results also show that the combination of EMG and ACC data have relatively increased the accuracy of motion intention detection. Velocity could be distinguished between stationary and moving with less than 10% error using a Decision Tree ensemble classifier. Future work should expand on motion factors and EMG-ACC sensor fusion to identify interactions between a person and the environment, in order to guide tuning of control models working towards controlling wearable mechatronic devices during dynamic movements.