Spiking Laguerre Volterra networks - predicting neuronal activity from local field potentials.

OBJECTIVE: Understanding the generative mechanism between Local Field Potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations. APPROACH: Here, we fill this gap by proposing novel spiking Laguerre-Volterra Network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics. MAIN RESULTS: The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease (PD) patients during Deep Brain Stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features. SIGNIFICANCE: These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Astrocytes Learn to Detect and Signal Deviations From Critical Brain Dynamics.

Astrocytes are nonneuronal brain cells that were recently shown to actively communicate with neurons and are implicated in memory, learning, and regulation of cognitive states. Interestingly, these information processing functions are also closely linked to the brain's ability to self-organize at a critical phase transition. Investigating the mechanistic link between astrocytes and critical brain dynamics remains beyond the reach of cellular experiments, but it becomes increasingly approachable through computational studies. We developed a biologically plausible computational model of astrocytes to analyze how astrocyte calcium waves can respond to changes in underlying network dynamics. Our results suggest that astrocytes detect synaptic activity and signal directional changes in neuronal network dynamics using the frequency of their calcium waves. We show that this function may be facilitated by receptor scaling plasticity by enabling astrocytes to learn the approximate information content of input synaptic activity. This resulted in a computationally simple, information-theoretic model, which we demonstrate replicating the signaling functionality of the biophysical astrocyte model with receptor scaling. Our findings provide several experimentally testable hypotheses that offer insight into the regulatory role of astrocytes in brain information processing.

The role of astrocytes in place cell formation: A computational modeling study.

Place cells develop spatially-tuned receptive fields during the early stages of novel environment exploration. The generative mechanism underlying these spatially-selective responses remains largely elusive, but has been associated with theta rhythmicity. An important factor implicating the transformation of silent cells to place cells is a spatially-uniform depolarization that is mediated by a persistent sodium current. This neuronal current is modulated by extracellular calcium concentration, which, in turn, is actively controlled by astrocytes. However, there is no established relationship between the neuronal depolarization and astrocytic activity. To consider this link, we designed a bioplausible computational model of a neuronal-astrocytic network, where astrocytes induced the transient emergence of place fields in silent cells, and accelerated the plasticity-induced consolidation of place cells. Interestingly, theta oscillations emerged naturally at the network level, resulting from the astrocytic modulation of subcellular neuronal properties. Our results suggest that astrocytes participate in spatial mapping and exploration, and further highlight the computational roles of these cells in the brain.

A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity.

The effective decoding of movement from non-invasive electroencephalography (EEG) is essential for informing several therapeutic interventions, from neurorehabilitation robots to neural prosthetics. Deep neural networks are most suitable for decoding real-time data but their use in EEG is hindered by the gross classes of motor tasks in the currently available datasets, which are solvable even with network architectures that do not require specialized design considerations. Moreover, the weak association with the underlying neurophysiology limits the generalizability of modern networks for EEG inference. Here, we present a neurophysiologically interpretable 3-dimensional convolutional neural network (3D-CNN) that captured the spatiotemporal dependencies in brain areas that get co-activated during movement. The 3D-CNN received topography-preserving EEG inputs, and predicted complex components of hand movements performed on a plane using a back-drivable rehabilitation robot, namely (a) the reaction time (RT) for responding to stimulus (slow or fast), (b) the mode of movement (active or passive, depending on whether there was an assistive force provided by the apparatus), and (c) the orthogonal directions of the movement (left, right, up, or down). We validated the 3D-CNN on a new dataset that we acquired from an in-house motor experiment, where it achieved average leave-one-subject-out test accuracies of 79.81%, 81.23%, and 82.00% for RT, active vs. passive, and direction classifications, respectively. Our proposed method outperformed the modern 2D-CNN architecture by a range of 1.1% to 6.74% depending on the classification task. Further, we identified the EEG sensors and time segments crucial to the classification decisions of the network, which aligned well with the current neurophysiological knowledge on brain activity in motor planning and execution tasks. Our results demonstrate the importance of biological relevance in networks for an accurate decoding of EEG, suggesting that the real-time classification of other complex brain activities may now be within our reach.

Pediatric robotic rehabilitation: Current knowledge and future trends in treating children with sensorimotor impairments.

BACKGROUND: Robot-aided sensorimotor therapy imposes highly repetitive tasks that can translate to substantial improvement when patients remain cognitively engaged into the clinical procedure, a goal that most children find hard to pursue. Knowing that the child's brain is much more plastic than an adult's, it is reasonable to expect that the clinical gains observed in the adult population during the last two decades would be followed up by even greater gains in children. Nonetheless, and despite the multitude of adult studies, in children we are just getting started: There is scarcity of pediatric robotic rehabilitation devices that are currently available and the number of clinical studies that employ them is also very limited. PURPOSE: We have recently developed the MIT's pedi-Anklebot, an adaptive habilitation robotic device that continuously motivates physically impaired children to do their best by tracking the child's performance and modifying their therapy accordingly. The robot's design is based on a multitude of studies we conducted focusing on the ankle sensorimotor control. In this paper, we briefly describe the device and the adaptive environment we built around the impaired children, present the initial clinical results and discuss how they could steer future trends in pediatric robotic therapy. CONCLUSIONS: The results support the potential for future interventions to account for the differences in the sensorimotor control of the targeted limbs and their functional use (rhythmic vs. discrete movements and mechanical impedance training) and explore how the new technological advancements such as the augmented reality would employ new knowledge from neuroscience.

Classification and Prediction of Clinical Improvement in Deep Brain Stimulation From Intraoperative Microelectrode Recordings.

We present a random forest (RF) classification and regression technique to predict, intraoperatively, the unified Parkinson's disease rating scale (UPDRS) improvement after deep brain stimulation (DBS). We hypothesized that a data-informed combination of features extracted from intraoperative microelectrode recordings (MERs) can predict the motor improvement of Parkinson's disease patients undergoing DBS surgery. We modified the employed RFs to account for unbalanced datasets and multiple observations per patient, and showed, for the first time, that only five neurophysiologically interpretable MER signal features are sufficient for predicting UPDRS improvement. This finding suggests that subthalamic nucleus (STN) electrophysiological signal characteristics are strongly correlated to the extent of motor behavior improvement observed in STN-DBS.

Altered Onset Response Dynamics in Somatosensory Processing in Autism Spectrum Disorder.

Abnormalities in cortical connectivity and evoked responses have been extensively documented in autism spectrum disorder (ASD). However, specific signatures of these cortical abnormalities remain elusive, with data pointing toward abnormal patterns of both increased and reduced response amplitudes and functional connectivity. We have previously proposed, using magnetoencephalography (MEG) data, that apparent inconsistencies in prior studies could be reconciled if functional connectivity in ASD was reduced in the feedback (top-down) direction, but increased in the feedforward (bottom-up) direction. Here, we continue this line of investigation by assessing abnormalities restricted to the onset, feedforward inputs driven, component of the response to vibrotactile stimuli in somatosensory cortex in ASD. Using a novel method that measures the spatio-temporal divergence of cortical activation, we found that relative to typically developing participants, the ASD group was characterized by an increase in the initial onset component of the cortical response, and a faster spread of local activity. Given the early time window, the results could be interpreted as increased thalamocortical feedforward connectivity in ASD, and offer a plausible mechanism for the previously observed increased response variability in ASD, as well as for the commonly observed behaviorally measured tactile processing abnormalities associated with the disorder.

A biologically inspired image classifier: adaptive feature detection.

Today's artificial neural networks use computational models and algorithms inspired by the knowledge of the brain in the '90s. Powerful as they are, artificial networks are impressive but their domain specificity and reliance on vast numbers of labeled examples are obvious limitations. About a decade ago, spiking neural networks (SNNs) emerged as a new formalism that takes advantage of the spike timing and are particularly versatile when depicting spatio-temporal representations. The challenge now is to design rules for SNNs that can help them interact with their environment just like humans do. Specifically for visual classification tasks, we need to design a set of simple features that can describe any input, seen and unseen, by adapting to the environment. Herein, we propose an adaptive mechanism for deducing feature detectors from input data. Our proposed method adapts online to new instances of existing categories pooled from the MNIST database of handwritten numbers. The extracted features are comparable to those found in biological neural networks for certain classes of inputs. We anticipate that our proposed model will be embedded in our ongoing effort to design an SNN for image classification.

Virtual reality for pediatric neuro-rehabilitation: adaptive visual feedback of movement to engage the mirror neuron system.

Sensorimotor therapy gives optimal results when patients are cognitively engaged into highly repetitive tasks, a goal that most children find hard to pursue. This paper presents the key developments of our ongoing effort to design an interactive rehabilitation environment that motivates physically impaired children throughout their therapy. The continuous motivation is achieved by the system adapting fundamental therapeutic components to the performance of each child. The relevant movement is mirrored to an animated character projected in front of the child. We speculate that the visual observation of one's own movements will activate the "mirror neuron system", a brain system underlying the human capacity to learn by imitation. Our rehabilitation algorithm personalizes the difficulty of the tasks by adapting the difficulty of reaching virtual targets on the animated environment through changing the visual gain between real and animated movements. To track the sensorimotor performance, we estimated the time required to reach a target. To give a proof of concept for the adaptation of the visual gain, we developed a serious game driven by a Leap Motion device. In addition to becoming a testbed for studying sensorimotor integration and neuroplasticity, the proposed notion of visual gain can be integrated into a highly engaging environment in which physically impaired children will play their way to recovery.

Robot-Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation.

This paper presents the pediAnklebot, an impedance-controlled low-friction, backdriveable robotic device developed at the Massachusetts Institute of Technology that trains the ankle of neurologically impaired children of ages 6-10 years old. The design attempts to overcome the known limitations of the lower extremity robotics and the unknown difficulties of what constitutes an appropriate therapeutic interaction with children. The robot's pilot clinical evaluation is on-going and it incorporates our recent findings on the ankle sensorimotor control in neurologically intact subjects, namely the speed-accuracy tradeoff, the deviation from an ideally smooth ankle trajectory, and the reaction time. We used these concepts to develop the kinematic and kinetic performance metrics that guided the ankle therapy in a similar fashion that we have done for our upper extremity devices. Here we report on the use of the device in at least nine training sessions for three neurologically impaired children. Results demonstrated a statistically significant improvement in the performance metrics assessing explicit and implicit motor learning. Based on these initial results, we are confident that the device will become an effective tool that harnesses plasticity to guide habilitation during childhood.

Beta-band frequency peaks inside the subthalamic nucleus as a biomarker for motor improvement after deep brain stimulation in Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) remains an empirical, yet highly effective, surgical treatment for advanced Parkinson's disease (PD). DBS outcome depends on accurate stimulation of the STN sensorimotor area which is a trial-and-error procedure taking place during and after surgery. Pathologically enhanced beta-band (13-35 Hz) oscillatory activity across the cortico-basal ganglia pathways is a prominent neurophysiological phenomenon associated with PD. We hypothesized that weighing together beta-band frequency peaks from simultaneous microelectrode recordings in "off-state" PD patients could map the individual neuroanatomical variability and serve as a biomarker for the location of the STN sensorimotor neurons. We validated our hypothesis with 9 and 11 patients that, respectively, responded well and poorly to bilateral DBS, after at least two years of follow up. We categorized "good" and "poor" DBS responders based on their clinical assessment alongside a > 40% and <30% change, respectively, in "off" unified PD rating scale motor scores. Good (poor) DBS responders had, in average, 1 mm (3.5 mm) vertical distance between the maximum beta-peak weighted across the parallel microelectrodes and the center of the stimulation area. The distances were statistically different in the two groups ( p = 0.0025 ). Our biomarker could provide personalized intra- and postoperative support in stimulating the STN sensorimotor area associated with optimal long-term clinical benefits.

A Comparative Analysis of Speed Profile Models for Ankle Pointing Movements: Evidence that Lower and Upper Extremity Discrete Movements are Controlled by a Single Invariant Strategy.

Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cerebral palsy highlighted the importance of analyzing and modeling the kinematics of the lower limbs, in general, and those of the ankle joints, in particular. We recruited 15 young healthy adults that performed in total 1,386 visually evoked, visually guided, and target-directed discrete pointing movements with their ankle in dorsal-plantar and inversion-eversion directions. Using a non-linear, least-squares error-minimization procedure, we estimated the parameters for 19 models, which were initially designed to capture the dynamics of upper limb movements of various complexity. We validated our models based on their ability to reconstruct the experimental data. Our results suggest a remarkable similarity between the top-performing models that described the speed profiles of ankle pointing movements and the ones previously found for the UE both during arm reaching and wrist pointing movements. Among the top performers were the support-bounded lognormal and the beta models that have a neurophysiological basis and have been successfully used in upper extremity studies with normal subjects and patients. Our findings suggest that the same model can be applied to different "human" hardware, perhaps revealing a key invariant in human motor control. These findings have a great potential to enhance our rehabilitation efforts in any population with lower extremity deficits by, for example, assessing the level of motor impairment and improvement as well as informing the design of control algorithms for therapeutic ankle robots.

Reaction time in ankle movements: a diffusion model analysis.

Reaction time (RT) is one of the most commonly used measures of neurological function and dysfunction. Despite the extensive studies on it, no study has ever examined the RT in the ankle. Twenty-two subjects were recruited to perform simple, 2- and 4-choice RT tasks by visually guiding a cursor inside a rectangular target with their ankle. RT did not change with spatial accuracy constraints imposed by different target widths in the direction of the movement. RT increased as a linear function of potential target stimuli, as would be predicted by Hick-Hyman law. Although the slopes of the regressions were similar, the intercept in dorsal-plantar (DP) direction was significantly smaller than the intercept in inversion-eversion (IE) direction. To explain this difference, we used a hierarchical Bayesian estimation of the Ratcliff's (Psychol Rev 85:59, 1978) diffusion model parameters and divided processing time into cognitive components. The model gave a good account of RTs, their distribution and accuracy values, and hence provided a testimony that the non-decision processing time (overlap of posterior distributions between DP and IE < 0.045), the boundary separation (overlap of the posterior distributions < 0.1) and the evidence accumulation rate (overlap of the posterior distributions < 0.01) components of the RT accounted for the intercept difference between DP and IE. The model also proposed that there was no systematic change in non-decision processing time or drift rate when spatial accuracy constraints were altered. The results were in agreement with the memory drum hypothesis and could be further justified neurophysiologically by the larger innervation of the muscles controlling DP movements. This study might contribute to assessing deficits in sensorimotor control of the ankle and enlighten a possible target for correction in the framework of our on-going effort to develop robotic therapeutic interventions to the ankle of children with cerebral palsy.

Encoding cortical dynamics in sparse features.

Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz-Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical MEG/EEG source imaging data.

Pointing with the ankle: the speed-accuracy trade-off.

This study investigated the trade-off between speed and accuracy in pointing movements with the ankle during goal-directed movements in dorsal-plantar (DP) and inversion-eversion (IE). Nine subjects completed a series of discrete pointing movements with the ankle between spatial targets of varying difficulty. Six different target sets were presented, with a range of task difficulty between 2.2 and 3.8 bits of information. Our results demonstrated that for visually evoked, visually guided discrete DP and IE ankle pointing movements, performance can be described by a linear function, as predicted by Fitts' law. These results support our ongoing effort to develop an adaptive algorithm employing the speed-accuracy trade-off concept to control our pediatric anklebot while delivering therapy for children with cerebral palsy.