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Background Mechanical waves produced by ultrasound pulses have been shown to activate mechanosensitive ion channels and modulate peripheral nerves. However, while peripheral ultrasound neuromodulation has been demonstrated in vitro and in pre-clinical models, there have been few reports of clinical tests. Aim We modified a diagnostic imaging system for ultrasound neuromodulation in human subjects. We report the first safety and feasibility outcomes in subjects with type 2 diabetes mellitus (T2D) and discuss these outcomes in relation to previous pre-clinical results. Design The study was performed as an open label feasibility study to assess the effects of hepatic ultrasound (targeted to the porta hepatis) on glucometabolic parameters in subjects with T2D. Stimulation (pFUS Treatment) was performed for 3 days (i.e., 15-minutes per day), was preceded by a baseline examination, and followed by a two-week observation period. Methods Multiple metabolic assays were employed including measures of fasting glucose and insulin, insulin resistance, and glucose metabolism. The safety and tolerability were also assessed by monitoring adverse events, changes in vital signs, electrocardiogram parameters, and clinical laboratory measures. Results and Conclusion We report post-pFUS trends in several outcomes that were consistent with previous pre-clinical findings. Fasting insulin was lowered, resulting in a reduction of HOMA-IR scores (p-value 0.01; corrected Wilcoxon Signed-Rank Test). Additional safety and exploratory markers demonstrated no device-related adverse impact of pFUS. Our findings demonstrate that pFUS represents a promising new treatment modality that could be used as a non-pharmaceutical adjunct or even alternative to current drug treatments in diabetes.

Peripheral neurons that sense glucose relay signals of glucose availability to integrative clusters of neurons in the brain. However, the roles of such signalling pathways in the maintenance of glucose homoeostasis and their contribution to disease are unknown. Here we show that the selective activation of the nerve plexus of the hepatic portal system via peripheral focused ultrasound stimulation (pFUS) improves glucose homoeostasis in mice and rats with insulin-resistant diabetes and in swine subject to hyperinsulinemic-euglycaemic clamps. pFUS modulated the activity of sensory projections to the hypothalamus, altered the concentrations of metabolism-regulating neurotransmitters, and enhanced glucose tolerance and utilization in the three species, whereas physical transection or chemical blocking of the liver–brain nerve pathway abolished the effect of pFUS on glucose tolerance. Longitudinal multi-omic profiling of metabolic tissues from the treated animals confirmed pFUS-induced modifications of key metabolic functions in liver, pancreas, muscle, adipose, kidney and intestinal tissues. Non-invasive ultrasound activation of afferent autonomic nerves may represent a non-pharmacologic therapy for the restoration of glucose homoeostasis in type-2 diabetes and other metabolic diseases.

Deep brain stimulation (DBS) is an established treatment for certain movement disorders and has additionally shown promise for various psychiatric, cognitive, and seizure disorders. However, the mechanisms through which stimulation exerts therapeutic effects are incompletely understood. A technique that may help to address this knowledge gap is functional magnetic resonance imaging (fMRI). This is a non-invasive imaging tool which permits the observation of DBS effects in vivo.The objective of this review was to provide a comprehensive overview of studies in which fMRI during active DBS was performed, including studied disorders, stimulated brain regions, experimental designs, and the insights gleaned from stimulation-evoked fMRI responses.We conducted a systematic review of published human studies in which fMRI was performed during active stimulation in DBS patients. The search was conducted using PubMED and MEDLINE.The rate of fMRI DBS studies is increasing over time, with 37 studies identified overall. The median number of DBS patients per study was 10 (range = 1-67, interquartile range = 11). Studies examined fMRI responses in various disease cohorts, including Parkinson's disease (24 studies), essential tremor (3 studies), epilepsy (3 studies), obsessive-compulsive disorder (2 studies), pain (2 studies), Tourette syndrome (1 study), major depressive disorder, anorexia, and bipolar disorder (1 study), and dementia with Lewy bodies (1 study). The most commonly stimulated brain region was the subthalamic nucleus (24 studies). Studies showed that DBS modulates large-scale brain networks, and that stimulation-evoked fMRI responses are related to the site of stimulation, stimulation parameters, patient characteristics, and therapeutic outcomes. Finally, a number of studies proposed fMRI-based biomarkers for DBS treatment, highlighting ways in which fMRI could be used to confirm circuit engagement and refine DBS therapy.A review of the literature reflects an exciting and expanding field, showing that the combination of DBS and fMRI represents a uniquely powerful tool for simultaneously manipulating and observing neural circuitry. Future work should focus on relatively understudied disease cohorts and stimulated regions, while focusing on the prospective validation of putative fMRI-based biomarkers.

Ultrasound scanning is essential in several medical diagnostic and therapeutic applications. It is used to visualize and analyze anatomical features and structures that influence treatment plans. However, it is both labor intensive, and its effectiveness is operator dependent. Real-time accurate and robust automatic detection and tracking of anatomical structures while scanning would significantly impact diagnostic and therapeutic procedures to be consistent and efficient. In this paper, we propose a deep learning framework to automatically detect and track a specific anatomical target structure in ultrasound scans. Our framework is designed to be accurate and robust across subjects and imaging devices, to operate in real-time, and to not require a large training set. It maintains a localization precision and recall higher than 90% when trained on training sets that are as small as 20% in size of the original training set. The framework backbone is a weakly trained segmentation neural network based on U-Net. We tested the framework on two different ultrasound datasets with the aim to detect and track the Vagus nerve, where it outperformed current state-of-the-art real-time object detection networks., 7 pages , 4 figures, submitted to the IEEE EMBC 2021 conference

BACKGROUND: Chronic pain occurs in 83% of Parkinson disease (PD) patients and deep brain stimulation (DBS) has shown to result in pain relief in a subset of patients, though the mechanism is unclear. OBJECTIVE: To compare functional magnetic resonance imaging (MRI) data in PD patients with chronic pain without DBS, those whose pain was relieved (PR) with DBS and those whose pain was not relieved (PNR) with DBS. METHODS: Functional MRI (fMRI) with blood oxygen level-dependent activation data was obtained in 15 patients in control, PR, and PNR patients. fMRI was obtained in the presence and absence of a mechanical stimuli with DBS ON and DBS OFF. Voxel-wise analysis using pain OFF data was used to determine which regions were altered during pain ON periods. RESULTS: At the time of MRI, pain was scored a 5.4 ± 1.2 out of 10 in the control, 4.25 ± 1.18 in PNR, and 0.8 ± 0.67 in PR cohorts. Group analysis of control and PNR groups showed primary somatosensory (SI) deactivation, whereas PR patients showed thalamic deactivation and SI activation. DBS resulted in more decreased activity in PR than PNR (P

Mild traumatic brain injury (mTBI) affects about 42 million people worldwide. It is often associated with headache, cognitive deficits, and balance difficulties but rarely shows any abnormalities on conventional computed tomography (CT) or magnetic resonance imaging (MRI). Although in most mTBI patients the symptoms resolve within 3 months, 10-15% of patients continue to exhibit symptoms beyond a year. Also, it is known that there exists a vulnerable period post-injury, when a second injury may exacerbate clinical prognosis. Identifying this vulnerable period may be critical for patient outcome, but very little is known about the neural underpinnings of mTBI and its recovery. In this work, we used advanced functional neuroimaging to study longitudinal changes in functional organization of the brain during the 3-month recovery period post-mTBI. Fractional amplitude of low frequency fluctuations (fALFF) measured from resting state functional MRI (rs-fMRI) was found to be associated with symptom severity score (SSS, r = -0.28, p = 0.002). Decreased fALFF was observed in specific functional networks for patients with higher SSS, and fALFF returned to higher values when the patient recovered (lower SSS). In addition, functional connectivity of the same networks was found to be associated with concurrent SSS, and connectivity immediately after injury (10 days) was capable of predicting SSS at a later time-point (3 weeks to 3 months, p 0.05). Specific networks including motor, default-mode, and visual networks were found to be associated with SSS (p 0.001), and connectivity between these networks predicted 3-month clinical outcome (motor and visual: p 0.001, default-mode: p 0.006). Our results suggest that functional connectivity in these networks comprise potential biomarkers for predicting mTBI recovery profiles and clinical outcome.

Rapid and flexible learning during behavioral choices is critical to our daily endeavors and constitutes a hallmark of dynamic reasoning. An important paradigm to examine flexible behavior involves learning new arbitrary associations mapping visual inputs to motor outputs. We conjectured that visuomotor rules are instantiated by translating visual signals into actions through dynamic interactions between visual, frontal and motor cortex. We evaluated the neural representation of such visuomotor rules by performing intracranial field potential recordings in epilepsy subjects during a rule-learning delayed match-to-behavior task. Learning new visuomotor mappings led to the emergence of specific responses associating visual signals with motor outputs in 3 anatomical clusters in frontal, anteroventral temporal and posterior parietal cortex. After learning, mapping selective signals during the delay period showed interactions with visual and motor signals. These observations provide initial steps towards elucidating the dynamic circuits underlying flexible behavior and how communication between subregions of frontal, temporal, and parietal cortex leads to rapid learning of task-relevant choices.

Commonly used for Parkinson’s disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming., Deep brain stimulation programming for Parkinson’s disease entails the assessment of a large number of possible simulation settings, requiring numerous clinic visits after surgery. Here, the authors show that patterns of functional MRI can predict the optimal stimulation settings.

Objective Anterior nucleus of thalamus (ANT) deep brain stimulation (DBS) has shown promise as a treatment for medically refractory epilepsy. To better understand the mechanism of this intervention, we used functional magnetic resonance imaging (fMRI) to map the acute blood oxygen level-dependent (BOLD) response pattern to thalamic DBS in fully implanted patients with epilepsy. Methods Two patients with epilepsy implanted with bilateral ANT-DBS devices underwent four fMRI acquisitions each, during which active left-sided monopolar stimulation was delivered in a 30-s DBS-ON/OFF cycling paradigm. Each fMRI acquisition featured left-sided stimulation of a different electrode contact to vary the locus of stimulation within the thalamus and to map the brain regions modulated as a function of different contact selection. To determine the extent of peri-electrode stimulation and the engagement of local structures during each fMRI acquisition, volume of tissue activated (VTA) modeling was also performed. Results Marked changes in the pattern of BOLD response were produced with thalamic stimulation, which varied with the locus of the active contact in each patient. BOLD response patterns to stimulation that directly engaged at least 5% of the anterior nuclear group by volume were characterized by changes in the bilateral putamen, thalamus, and posterior cingulate cortex, ipsilateral middle cingulate cortex and precuneus, and contralateral medial prefrontal and anterior cingulate. Significance The differential BOLD response patterns associated with varying thalamic DBS parameters provide mechanistic insights and highlight the possibilities of fMRI biomarkers of optimizing stimulation in patients with epilepsy.

BACKGROUND Models have been developed for predicting ideal contact and amplitude for subthalamic nucleus (STN) deep brain stimulation (DBS) for Parkinson disease (PD). Pulse-width is generally varied to modulate the size of the energy field produced. Effects of varying frequency in humans have not been systematically evaluated. OBJECTIVE To examine how altered frequencies affect blood oxygen level-dependent activation in PD. METHODS PD subjects with optimized DBS programming underwent functional magnetic resonance imaging (fMRI). Frequency was altered and fMRI scans/Unified Parkinson Disease Rating Scale motor subunit (UPDRS-III) scores were obtained. Analysis using DBS-OFF data was used to determine which regions were activated during DBS-ON. Peak activity utilizing T-values was obtained and compared. RESULTS At clinically optimized settings (n = 14 subjects), thalamic, globus pallidum externa (GPe), and posterior cerebellum activation were present. Activation levels significantly decreased in the thalamus, anterior cerebellum, and the GPe when frequency was decreased (P

Purpose To assess the impact of synchronization errors between the assumed functional MRI paradigm timing and the deep brain stimulation (DBS) on/off cycling using a custom electrocardiogram-based triggering system Methods A detector for measuring and predicting the on/off state of cycling deep brain stimulation was developed and tested in six patients in office visits. Three-electrode electrocardiogram measurements, amplified by a commercial bio-amplifier, were used as input for a custom electronics box (e-box). The e-box transformed the deep brain stimulation waveforms into transistor-transistor logic pulses, recorded their timing, and propagated it in time. The e-box was used to trigger task-based deep brain stimulation functional MRI scans in 5 additional subjects; the impact of timing accuracy on t-test values was investigated in a simulation study using the functional MRI data. Results Following locking to each patient's individual waveform, the e-box was shown to predict stimulation onset with an average absolute error of 112 ± 148 ms, 30 min after disconnecting from the patients. The subsecond accuracy of the e-box in predicting timing onset is more than adequate for our slow varying, 30-/30-s on/off stimulation paradigm. Conversely, the experimental deep brain stimulation onset prediction accuracy in the absence of the e-box, which could be off by as much as 4 to 6 s, could significantly decrease activation strength. Conclusions Using this detector, stimulation can be accurately synchronized to functional MRI acquisitions, without adding any additional hardware in the MRI environment. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

BACKGROUND Deep brain stimulation (DBS) is a well-accepted treatment of Parkinson's disease (PD). Motor phenotypes include tremor-dominant (TD), akinesia-rigidity (AR), and postural instability gait disorder (PIGD). The mechanism of action in how DBS modulates motor symptom relief remains unknown. OBJECTIVE Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) was used to determine whether the functional activity varies in response to DBS depending on PD phenotypes. MATERIALS AND METHODS Subjects underwent an fMRI scan with DBS cycling ON and OFF. The effects of DBS cycling on BOLD activation in each phenotype were documented through voxel-wise analysis. For each region of interest, ANOVAs were performed using T-values and covariate analyses were conducted. Further, a correlation analysis was performed comparing stimulation settings to T-values. Lastly, T-values of subjects with motor improvement were compared to those who worsened. RESULTS As a group, BOLD activation with DBS-ON resulted in activation in the motor thalamus (p

Background The majority of Parkinson's disease patients with deep brain stimulation (DBS) use a monopolar configuration, which presents challenges for EEG and MRI studies. The literature reports algorithms to convert monopolar to bipolar settings. Purpose/hypothesis To assess brain responses of Parkinson's disease patients implanted with DBS during fMRI studies using their clinical and presumed equivalent settings using a published conversion recipe. Study type Prospective. Subjects Thirteen DBS patients. Field strength/sequence 1.5T and 3T, fMRI using gradient echo-planar imaging. Assessment Patients underwent 30/30sec ON/OFF DBS fMRI scans using monopolar and bipolar settings. To convert to a bipolar setting, the negative contact used for the monopolar configuration remained constant and the adjacent dorsal contact was rendered positive, while increasing the voltage by 30%. fMRI activation/deactivation maps and motor Unified Parkinson's Disease Rating Scale (UPDRS-III) scores were compared for patients in both configurations. Statistical tests T-tests were used to compare UPDRS scores and volumes of tissue activated (VTA) diameters in monopolar and bipolar configurations. Results The patterns of fMRI activation in the monopolar and bipolar configurations were generally different. The thalamus, pallidum, and visual cortices exhibited higher activation using the patient's clinical settings than the presumed equivalent settings. VTA diameters were lower (7 mm vs. 6.3 mm, P = 0.047) and UPDRS scores were generally higher in the bipolar (33.2 ± 16) than in the monopolar configuration (28.3 ± 17.4), without reaching statistical significance (P > 0.05). Data conclusion Monopolar and bipolar configurations result in different patterns of brain activation while using a previously published monopolar-bipolar conversion algorithm. Clinical benefits may be achieved with varying patterns of brain responses. Blind conversion from one to the other should be avoided for purposes of understanding the mechanisms of DBS. Level of evidence 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

How do neurons encode and store information for long periods of time? Recurring patterns of activity have been reported in various cortical structures and were suggested to play a role in information processing and memory. To study the potential role of bursts of action potentials in memory mechanisms, we investigated patterns of spontaneous multi-single-unit activity in dissociated rat cortical cultures in vitro. Spontaneous spikes were recorded from networks of approximately 50 000 neurons and glia cultured on a grid of 60 extracellular substrate- embedded electrodes (multi-electrode arrays). These networks expressed spontaneous culture- wide bursting from approximately one week in vitro. During bursts, a large portion of the active electrodes showed elevated levels of firing. Spatiotemporal activity patterns within spontaneous bursts were clustered using a correlation-based clustering algorithm, and the occurrences of these burst clusters were tracked over several hours. This analysis revealed spatiotemporally diverse bursts occurring in well-defined patterns, which remained stable for several hours. Activity evoked by strong local tetanic stimulation resulted in significant changes in the occurrences of spontaneous bursts belonging to different clusters, indicating that the dynamical flow of information in the neuronal network had been altered. The diversity of spatiotemporal structure and long-term stability of spontaneous bursts together with their plastic nature strongly suggests that such network patterns could be used as codes for information transfer and the expression of memories stored in cortical networks.

Learning novel sequences constitutes an example of declarative memory formation, involving conscious recall of temporal events. Performance in sequence learning tasks improves with repetition and involves forming temporal associations over scales of seconds to minutes. To further understand the neural circuits underlying declarative sequence learning over trials, we tracked changes in intracranial field potentials (IFPs) recorded from 1142 electrodes implanted throughout temporal and frontal cortical areas in 14 human subjects, while they learned the temporal-order of multiple sequences of images over trials through repeated recall. We observed an increase in power in the gamma frequency band (30–100 Hz) in the recall phase, particularly in areas within the temporal lobe including the parahippocampal gyrus. The degree of this gamma power enhancement decreased over trials with improved sequence recall. Modulation of gamma power was directly correlated with the improvement in recall performance. When presenting new sequences, gamma power was reset to high values and decreased again after learning. These observations suggest that signals in the gamma frequency band may play a more prominent role during the early steps of the learning process rather than during the maintenance of memory traces.

Summary Natural vision often involves recognizing objects from partial information. Recognition of objects from parts presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowledge. Here we recorded intracranial field potentials of 113 visually selective electrodes from epilepsy patients in response to whole and partial objects. Responses along the ventral visual stream, particularly the inferior occipital and fusiform gyri, remained selective despite showing only 9%–25% of the object areas. However, these visually selective signals emerged ∼100 ms later for partial versus whole objects. These processing delays were particularly pronounced in higher visual areas within the ventral stream. This latency difference persisted when controlling for changes in contrast, signal amplitude, and the strength of selectivity. These results argue against a purely feedforward explanation of recognition from partial information, and provide spatiotemporal constraints on theories of object recognition that involve recurrent processing.

Sensory signals must be interpreted in the context of goals and tasks. To detect a target in an image, the brain compares input signals and goals to elicit the correct behavior. We examined how target detection modulates visual recognition signals by recording intracranial field potential responses from 776 electrodes in 10 epileptic human subjects. We observed reliable differences in the physiological responses to stimuli when a cued target was present versus absent. Goal-related modulation was particularly strong in the inferior temporal and fusiform gyri, two areas important for object recognition. Target modulation started after 250 ms post stimulus, considerably after the onset of visual recognition signals. While broadband signals exhibited increased or decreased power, gamma frequency power showed predominantly increases during target presence. These observations support models where task goals interact with sensory inputs via top-down signals that influence the highest echelons of visual processing after the onset of selective responses.

Neural oscillations are important features in a working central nervous system, facilitating efficient communication across large networks of neurons. To better study the role of these oscillations in various cognitive processes, and to be able to build clinical applications around them, accurate and precise estimations of the instantaneous frequency and phase are required. Here, we present methodology based on autoregressive modeling to accomplish this in real time. This allows the targeting of stimulation to a specific phase of a detected oscillation. Using intracranial EEG recorded from two patients performing a Sternberg memory task, we characterize our algorithm's phase-locking performance on physiologic theta oscillations.

Neural oscillations are important features in a working central nervous system, facilitating efficient communication across large networks of neurons. They are implicated in a diverse range of processes such as synchronization and synaptic plasticity, and can be seen in a variety of cognitive processes. For example, hippocampal theta oscillations are thought to be a crucial component of memory encoding and retrieval. To better study the role of these oscillations in various cognitive processes, and to be able to build clinical applications around them, accurate and precise estimations of the instantaneous frequency and phase are required. Here, we present methodology based on autoregressive modeling to accomplish this in real time. This allows the targeting of stimulation to a specific phase of a detected oscillation. We first assess performance of the algorithm on two signals where the exact phase and frequency are known. Then, using intracranial EEG recorded from two patients performing a Sternberg memory task, we characterize our algorithm's phase-locking performance on physiologic theta oscillations: optimizing algorithm parameters on the first patient using a genetic algorithm, we carried out cross-validation procedures on subsequent trials and electrodes within the same patient, as well as on data recorded from the second patient.

Introduction Neuronal network function is likely to depend on single synapse details, but reconstructing wiring diagrams with single synapse precision is difficult to do on a large scale. We describe a method for circuit reconstruction on a large scale by using high-throughput but low-resolution readouts such as somatic calcium recordings and extracellular electrical recordings. We propose that these techniques may scale very well using new optical recording and stimulus techniques, and enable large-scale yet precise circuit reconstruction.

We culture high-density cortical cultures on multi-electrode arrays (MEAs), which allow us to stimulate and record from thousands of neurons. One of the modes of activity in these high-density cultures is dish-wide synchronized bursting. Unlike in vivo, these synchronized patterns persist for the lifetime of the culture. Such aberrant patterns of activity might be due to the fact that cortical cultures are sensory-deprived and arrested in development. We have devised methods to control this spontaneous activity by multi-electrode electrical stimulation and to study long-term functional neural plasticity, on a background of such burst-quieting stimulation. Here, we investigate whether burst quieting reveals long-term plasticity induced by tetanic stimulation. Spatio-temporal activity patterns (STAPs) that result from probe pulses were clustered and quantified in quieted and non-quieted cultures. Burst-quieted cultures show more tetanus-induced functional change than cultures which are allowed to express spontaneous bursts. The methods developed for this study will help in the understanding of network dynamics and appreciation of their role in long-term plasticity and information processing in the brain.

We culture dissociated mouse cortical neurons in a dense monolayer on multi-electrode arrays, which allow us to stimulate and record from thousands of neurons. Tetanization has been widely used in the study of long-term plasticity. Stimulus-evoked responses constantly change (drift), which makes it difficult to observe the changes caused by the plasticity inducing tetanic stimulation. The most robust pattern of activity in these cultures is spontaneous culture-wide bursting. We studied the effect of tetanization on the properties of spontaneous bursts. We show that the burst-based quantity, spatial extent, does not drift and shows significant change after tetanization. Thus, a burst-based quantity might be a more robust method to study long-term plasticity compared to stimulus-evoked responses

There are two fundamentally different goals for neural interfacing. On the biology side, to interface living neurons to external electronics allows the observation and manipulation of neural circuits to elucidate their fundamental mechanisms. On the engineering side, neural interfaces in animals, people, or in cell culture have the potential to restore missing functionality, or someday, to enhance existing functionality. At the Laboratory for NeuroEngineering at Georgia Tech, we are developing new technologies to help make both goals attainable. We culture dissociated mammalian neurons on multi-electrode arrays, and use them as the brain of a 'Hybrot', or hybrid neural-robotic system. Distributed neural activity patterns are used to control mobile robots. We have created the hardware and software necessary to feed the robots' sensory inputs back to the cultures in real time, as electrical stimuli. By embodying cultured networks, we study learning and memory at the cellular and network level, using 2-photon laser-scanning microscopy to image plasticity while it happens. We have observed a very rich dynamical landscape of activity patterns in networks of only a few thousand cells. We can alter this landscape via electrical stimuli, and use the hybrot system to study the emergent properties of networks in vitro.

Women on the Frontline - Three women in advanced graduate studies talk about their work and how to attract more young women into science.