A computational framework to study neural-structural interactions in human walking

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 91-94).Neuroscientists researching locomotion take a top-down approach by elucidating high- level cortical control circuits. In contrast, biomechanists prefer to focus on structural and mechanical aspects of the legged movement apparatus. We posit that studying interplay between neural co-ordination and legged biomechanics can yield crucial insight into (a) motor control and (b) human leg morphology. Physiological facts indicate that muscle actuator state (activation, length and velocity) is key to this neural-structural interplay. Here we present a novel model-based framework to resolve individual muscle state and describe neural-structural interactions in normal gait. We solve the inverse problem of using kinematic, kinetic and electro-myographic data recorded on healthy humans during level-ground,self-selected speed walking to estimate state of three major ankle muscles. Our approach comprises of two steps. First, we estimate neurally-controlled muscle activity from EMG data by building on statistical and mechanistic methods in the literature. Second, we perform a system ID on a mechanistic (Hill-type) model of the three muscles to nd tendon morpho- logical parameters governing evolution of muscle length and velocity. We implement the parameter identication as an optimization based on the hypothesis that neural control and lower limb morphology have co-evolved for optimal metabolic economy of natural walking.(cont.) We cross-validate our framework against independent datasets, and nd good model-empirical ankle torque agreement (R 2 = 0.96). The resulting muscle length and velocity predictions are consistent with in vivo ultra- sound scan measures. Further, model predictions reveal how leg structure and neural control come together to (a) dene roles of individual plantar exor muscles and (b) boost their joint performance. We nd that the Soleus operates as a steady ecient force source, while the Gastrocnemius functions as a burst mechanical power source. An analysis of the estimated states and optimized parameters reveals that the plantar exors operate jointly at a net mechanical eciency of 0.69 ±0.12. This is roughly three times higher than the maximal eciency of skeletal muscle performing positive work. Our results suggest that neural control may be tuned to exploit the elasticity of tendinous structures in the leg and achieve the high walking economy of humans.by Pavitra Krishnaswamy.S.M

Joint Learning of Word and Label Embeddings for Sequence Labelling in Spoken Language Understanding

We propose an architecture to jointly learn word and label embeddings for slot filling in spoken language understanding. The proposed approach encodes labels using a combination of word embeddings and straightforward word-label association from the training data. Compared to the state-of-the-art methods, our approach does not require label embeddings as part of the input and therefore lends itself nicely to a wide range of model architectures. In addition, our architecture computes contextual distances between words and labels to avoid adding contextual windows, thus reducing memory footprint. We validate the approach on established spoken dialogue datasets and show that it can achieve state-of-the-art performance with much fewer trainable parameters.Comment: Accepted for publication at ASRU 201

Deep Offline Reinforcement Learning for Real-World Treatment Optimization Applications

There is increasing interest in data-driven approaches for dynamically choosing optimal treatment strategies in many chronic disease management and critical care applications. Reinforcement learning methods are well-suited to this sequential decision-making problem, but must be trained and evaluated exclusively on retrospective medical record datasets as direct online exploration is unsafe and infeasible. Despite this requirement, the vast majority of dynamic treatment optimization studies use off-policy RL methods (e.g., Double Deep Q Networks (DDQN) or its variants) that are known to perform poorly in purely offline settings. Recent advances in offline RL, such as Conservative Q-Learning (CQL), offer a suitable alternative. But there remain challenges in adapting these approaches to real-world applications where suboptimal examples dominate the retrospective dataset and strict safety constraints need to be satisfied. In this work, we introduce a practical transition sampling approach to address action imbalance during offline RL training, and an intuitive heuristic to enforce hard constraints during policy execution. We provide theoretical analyses to show that our proposed approach would improve over CQL. We perform extensive experiments on two real-world tasks for diabetes and sepsis treatment optimization to compare performance of the proposed approach against prominent off-policy and offline RL baselines (DDQN and CQL). Across a range of principled and clinically relevant metrics, we show that our proposed approach enables substantial improvements in expected health outcomes and in consistency with relevant practice and safety guidelines

Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression

Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI.National Institutes of Health (U.S.) (Award DP1-OD003646)National Institutes of Health (U.S.) (Award TR01-GM104948)National Institutes of Health (U.S.) (Grant R44NS071988)National Institute of Neurological Diseases and Stroke (U.S.) (Grant Grant R44NS071988

Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG

Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields generated by neuronal activity in subcortical structures can be recorded noninvasively, using magnetoencephalography (MEG) and electroencephalography (EEG). However, these subcortical signals are much weaker than those generated by cortical activity. In addition, we show here that it is difficult to resolve subcortical sources because distributed cortical activity can explain the MEG and EEG patterns generated by deep sources. We then demonstrate that if the cortical activity is spatially sparse, both cortical and subcortical sources can be resolved with M/EEG. Building on this insight, we develop a hierarchical sparse inverse solution for M/EEG. We assess the performance of this algorithm on realistic simulations and auditory evoked response data, and show that thalamic and brainstem sources can be correctly estimated in the presence of cortical activity. Our work provides alternative perspectives and tools for characterizing electrophysiological activity in subcortical structures in the human brain

Human Leg Model Predicts Ankle Muscle-Tendon Morphology, State, Roles and Energetics in Walking

A common feature in biological neuromuscular systems is the redundancy in joint actuation. Understanding how these redundancies are resolved in typical joint movements has been a long-standing problem in biomechanics, neuroscience and prosthetics. Many empirical studies have uncovered neural, mechanical and energetic aspects of how humans resolve these degrees of freedom to actuate leg joints for common tasks like walking. However, a unifying theoretical framework that explains the many independent empirical observations and predicts individual muscle and tendon contributions to joint actuation is yet to be established. Here we develop a computational framework to address how the ankle joint actuation problem is resolved by the neuromuscular system in walking. Our framework is founded upon the proposal that a consideration of both neural control and leg muscle-tendon morphology is critical to obtain predictive, mechanistic insight into individual muscle and tendon contributions to joint actuation. We examine kinetic, kinematic and electromyographic data from healthy walking subjects to find that human leg muscle-tendon morphology and neural activations enable a metabolically optimal realization of biological ankle mechanics in walking. This optimal realization (a) corresponds to independent empirical observations of operation and performance of the soleus and gastrocnemius muscles, (b) gives rise to an efficient load-sharing amongst ankle muscle-tendon units and (c) causes soleus and gastrocnemius muscle fibers to take on distinct mechanical roles of force generation and power production at the end of stance phase in walking. The framework outlined here suggests that the dynamical interplay between leg structure and neural control may be key to the high walking economy of humans, and has implications as a means to obtain insight into empirically inaccessible features of individual muscle and tendons in biomechanical tasks.National Institutes of Health (U.S.) (NIH Pioneer Award DP1 OD003646)Massachusetts Institute of Technology. Media Laboratory (Consortia Account 2736448)Massachusetts Institute of Technology. Media Laboratory (Consortia Account 6895867

Algorithms for enhanced spatiotemporal imaging of human brain function

Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2014.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 123-142).Studies of human brain function require technologies to non-invasively image neuronal dynamics with high spatiotemporal resolution. The electroencephalogram (EEG) and magnetoencephalogram (MEG) measure neuronal activity with high temporal resolution, and provide clinically accessible signatures of brain states. However, they have limited spatial resolution for regional dynamics. Combinations of M/EEG with functional and anatomical magnetic resonance imaging (MRI) can enable jointly high temporal and spatial resolution. In this thesis, we address two critical challenges limiting multimodal imaging studies of spatiotemporal brain dynamics. First, simultaneous EEG-fMRI offers a promising means to relate rapidly evolving EEG signatures with slower regional dynamics measured on fMRI. However, the potential of this technique is undermined by MRI-related ballistocardiogram artifacts that corrupt the EEG. We identify a harmonic basis for these artifacts, develop a local likelihood estimation algorithm to remove them, and demonstrate enhanced recovery of oscillatory and evoked EEG dynamics in the MRI scanner. Second, M/EEG source imaging offers a means to characterize rapidly evolving regional dynamics within an estimation framework informed by anatomical MRI. However, existing approaches are limited to cortical structures. Crucial dynamics in subcortical structures, which generate weaker M/EEG signals, are largely unexplored. We identify robust distinctions in M/EEG field patterns arising from subcortical and cortical structures, and develop a hierarchical subspace pursuit algorithm to estimate neural currents in subcortical structures. We validate efficacy for recovering thalamic and brainstem contributions in simulated and experimental studies. These results establish the feasibility of using non-invasive M/EEG measurements to estimate millisecond-scale dynamics involving subcortical structures. Finally, we illustrate the potential of these techniques for novel studies in cognitive and clinical neuroscience. Within an EEG-fMRI study of auditory stimulus processing under propofol anesthesia, we observed EEG signatures accompanying distinct changes in thalamocortical dynamics at loss of consciousness and subsequently, at deeper levels of anesthesia. These results suggest neurophysiologic correlates to better interpret clinical EEG signatures demarcating brain dynamics under anesthesia. Overall, the algorithms developed in this thesis provide novel opportunities to non-invasively relate fast timescale measures of neuronal activity with their underlying regional brain dynamics, thus paving a way for enhanced spatiotemporal imaging of human brain function.by Pavitra Krishnaswamy.Ph. D