Edmond and Lily Safra International Institute of Neuroscience of Natal


Edmond and Lily Safra International Institute of Neuroscience of Natal

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Dzirasa K.,Duke University | Kumar S.,Duke University | Sachs B.D.,Duke University | Caron M.G.,Duke University | And 2 more authors.
Journal of Neuroscience | Year: 2013

Although the majority of first-line antidepressants increase brain serotonin and rare polymorphisms in tryptophan hydroxlase-2 (Tph2), the rate-limiting enzyme in the brain serotonin synthesis pathway, have been identified in cohorts of subjects with major depressive disorder, the circuit level alterations that results from serotonergic hypofunction remain poorly understood. Here we use chronic multicircuit neurophysiological recordings to characterize functional interactions across cortical and limbic circuits in mice engineered to express a human loss-of-function depression allele Tph2-(R441H) [Tph2 knockin (Tph2KI)]. Our results show that Tph2KI mice exhibit increased intra-network synchrony within medial prefrontal cortex (mPFC) and basal amygdala (AMY) and increased internetwork synchrony between these two brain networks. Moreover, we demonstrate that chronic treatment with fluoxetine reverses several of the circuit alterations observed within Tph2KI mice. Together, our findings establish a functional link between functional hyposerotonergia and altered mPFC-AMY network dynamics. © 2013 the authors.

Li Z.,Duke University | O'doherty J.E.,Duke University | Lebedev M.A.,Duke University | Nicolelis M.A.L.,Duke University | Nicolelis M.A.L.,Edmond and Lily Safra International Institute of Neuroscience of Natal
Neural Computation | Year: 2011

Brain-machine interfaces (BMIs) transform the activity of neurons recorded in motor areas of the brain into movements of external actuators. Representation ofmovements by neuronal populations varies over time, during both voluntary limb movements and movements controlled through BMIs, due to motor learning, neuronal plasticity, and instability in recordings. To ensure accurate BMI performance over long time spans, BMI decoders must adapt to these changes. We propose the Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder. This novel paradigm uses the decoder's output to periodically update its neuronal tuning model in a Bayesian linear regression. We use two previously known statistical formulations of Bayesian linear regression: a joint formulation, which allows fast and exact inference, and a factorized formulation, which allows the addition and temporary omission of neurons from updates but requires approximate variational inference. To evaluate these methods, we performed offline reconstructions and closed-loop experiments with rhesus monkeys implanted cortically with microwire electrodes. Offline reconstructions used data recorded in areas M1, S1, PMd, SMA, and PP of three monkeys while they controlled a cursor using a handheld joystick. The Bayesian regression self-training updates significantly improved the accuracy of offline reconstructions compared to the same decoder without updates.We performed 11 sessions of real-time, closed-loop experiments with amonkey implanted in areas M1 and S1. These sessions spanned 29 days. The monkey controlled the cursor using the decoderwith andwithout updates. The updatesmaintained control accuracy and did not require information about monkey handmovements, assumptions about desired movements, or knowledge of the intended movement goals as training signals. These results indicate that Bayesian regression self-training can maintain BMI control accuracy over long periods,making clinical neuroprosthetics more viable. © 2011 Massachusetts Institute of Technology.

Hanson T.L.,Duke University | Fuller A.M.,Duke University | Lebedev M.A.,Duke University | Turner D.A.,Duke University | And 2 more authors.
Journal of Neuroscience | Year: 2012

Deep brain stimulation (DBS) has expanded as an effective treatment for motor disorders, providing a valuable opportunity for intraoperative recording of the spiking activity of subcortical neurons. The properties of these neurons and their potential utility in neuroprosthetic applications are not completely understood. DuringDBSsurgeries in 25humanpatients with either essential tremor or Parkinson's disease, we acutely recorded the single-unit activity of 274 ventral intermediate/ventral oralis posterior motor thalamus (Vim/Vop) neurons and 123 subthalamic nucleus (STN) neurons. These subcortical neuronal ensembles (up to 23 neurons sampled simultaneously) were recorded while the patients performed a target-tracking motor task using a cursor controlled by a haptic glove. We observed that modulations in firing rate of a substantial number of neurons in both Vim/Vop and STN represented target onset, movement onset/direction, and hand tremor. Neurons in both areas exhibited rhythmic oscillations and pairwise synchrony. Notably, all tremorassociated neurons exhibited synchrony within the ensemble. The data further indicate that oscillatory (likely pathological) neurons and behaviorally tuned neurons are not distinct but rather form overlapping sets. Whereas previous studies have reported a linear relationship between power spectra of neuronal oscillations and hand tremor, we report a nonlinear relationship suggestive of complex encoding schemes. Even in the presence of this pathological activity, linear models were able to extract motor parameters from ensemble discharges. Based on these findings, we propose that chronic multielectrode recordings from Vim/Vop and STN could prove useful for further studying, monitoring, and even treating motor disorders. ©2012 the authors.

Medina L.E.,Duke University | Lebedev M.A.,Duke University | O'Doherty J.E.,Duke University | O'Doherty J.E.,University of California at San Francisco | And 2 more authors.
Journal of Neuroscience | Year: 2012

Artificial sensation via electrical or optical stimulation of brain sensory areas offers a promising treatment for sensory deficits. For a brain-machine-brain interface, such artificial sensation conveys feedback signals from a sensorized prosthetic limb. The ways neural tissue can be stimulated to evoke artificial sensation and the parameter space of such stimulation, however, remain largely unexplored. Here we investigated whether stochastic facilitation (SF) could enhance an artificial tactile sensation produced by intracortical microstimulation (ICMS). Two rhesus monkeys learned to use a virtual hand, which they moved with a joystick, to explore virtual objects on a computer screen. They sought an object associated with a particular artificial texture (AT) signaled by a periodic ICMS pattern delivered to the primary somatosensory cortex (S1) through a pair of implanted electrodes. During each behavioral trial, aperiodic ICMS (i.e., noise) of randomly chosen amplitude was delivered to S1 through another electrode pair implanted 1 mm away from the site of AT delivery. Whereas high-amplitude noise worsened AT detection, moderate noise clearly improved the detection of weak signals, significantly raising the proportion of correct trials. These findings suggest that SF could be used to enhance prosthetic sensation. © 2012 the authors.

Ifft P.J.,Duke University | Lebedev M.A.,Duke University | Nicolelis M.A.L.,Duke University | Nicolelis M.A.L.,Edmond and Lily Safra International Institute of Neuroscience of Natal
Frontiers in Integrative Neuroscience | Year: 2011

Fitts' law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts' law has been extensively studied in ergonomics and has guided the design of human-computer interfaces, there have been few studies on its neuronal cor-relates.To elucidate sensorimotor cortical activity underlying Fitts' law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory(S1) cortices.The monkeys performed reaches with a joystick-controlled cursor toward tar-gets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifica-tions of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was corre-lated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control. © 2011 Ifft, Lebedev and Nicolelis.

Zhang H.,Duke University | Lin S.-C.,Duke University | Lin S.-C.,U.S. National Institute on Aging | Nicolelis M.A.L.,Duke University | Nicolelis M.A.L.,Edmond and Lily Safra International Institute of Neuroscience of Natal
Journal of Neuroscience | Year: 2010

Both acetylcholine (ACh) and theta oscillations are important for learning and memory, but the dynamic interaction between these two processes remains unclear. Recent advances in amperometry techniques have revealed phasic ACh releases in vivo. However, it is unknown whether phasic ACh release co-occurs with theta oscillations. We investigated this issue in the CA1 region of urethane-anesthetized male rats using amperometric and electrophysiological recordings. We found that ACh release was highly correlated with the appearance of both spontaneous and induced theta oscillations. Moreover, the maximal ACh release was observed around or slightly above the pyramidal layer. Interestingly, such release lagged behind theta initiation by 25-60 s. The slow ACh release profile was matched by the slow firing rate increase of a subset of medial-septal low-firing-rate neurons. Together, these results establish, for the first time, the in vivo coupling between phasic ACh release and theta oscillations on spatiotemporal scales much finer than previously known. These findings also suggest that phasic ACh is not required for theta initiation and may instead operate synergistically with theta oscillations to promote neural plasticity in the service of learning and memory. Copyright © 2010 the authors.

Costa M.R.,Edmond and Lily Safra International Institute of Neuroscience of Natal | Costa M.R.,Federal University of Rio Grande do Norte | Hedin-Pereira C.,Federal University of Rio de Janeiro
Frontiers in Neuroanatomy | Year: 2010

Since the pioneer work of Lorente de Nó, Ramón y Cajal, Brodmann, Mountcastle, Hubel and Wiesel and others, the cerebral cortex has been seen as a jigsaw of anatomic and functional modules involved in the processing of different sets of information. In fact, a columnar distribution of neurons displaying similar functional properties throughout the cerebral cortex has been observed by many researchers. Although it has been suggested that much of the anatomical substrate for such organization would be already specified at early developmental stages, before activity-dependent mechanisms could take place, it is still unclear whether gene expression in the ventricular zone (VZ) could play a role in the development of discrete functional units, such as minicolumns or columns. Cell lineage experiments using replication-incompetent retroviral vectors have shown that the progeny of a single neuroepithelial/radial glial cell in the dorsal telencephalon is organized into discrete radial clusters of sibling excitatory neurons, which have a higher propensity for developing chemical synapses with each other rather than with neighboring non-siblings. Here, we will discuss the possibility that the cell lineage of single neuroepithelial/ radial glia cells could contribute for the columnar organization of the neocortex by generating radial columns of sibling, interconnected neurons. Borrowing some concepts from the studies on cell-cell recognition and transcription factor networks, we will also touch upon the potential molecular mechanisms involved in the establishment of sibling-neuron circuits. © 2010 Costa and Hedin-Pereira.

Gutierrez R.,Duke University | Simon S.A.,Duke University | Nicolelis M.A.L.,Duke University | Nicolelis M.A.L.,Edmond and Lily Safra International Institute of Neuroscience of Natal
Journal of Neuroscience | Year: 2010

Animals learn which foods to ingest and which to avoid. Despite many studies, the electrophysiological correlates underlying this behavior at the gustatory-reward circuit level remain poorly understood. For this reason, we measured the simultaneous electrical activity of neuronal ensembles in the orbitofrontal cortex, insular cortex, amygdala, and nucleus accumbens while rats licked for taste cues and learned to perform a taste discrimination go/no-go task. This study revealed that rhythmic licking entrains the activity in all these brain regions, suggesting that the animal's licking acts as an "internal clock signal" against which single spikes can be synchronized. That is, as animals learned a go/no-go task, there were increases in the number of licking coherent neurons as well as synchronous spiking between neuron pairs from different brain regions. Moreover, a subpopulation of gustatory cue-selective neurons that fired in synchrony with licking exhibited a greater ability to discriminate among tastants than nonsynchronized neurons. This effect was seen in all four recorded areas and increased markedly after learning, particularly after the cue was delivered and before the animals made a movement to obtain an appetitive or aversive tastant. Overall, these results show that, throughout a large segment of the taste-reward circuit, appetitive and aversive associative learning improves spike-timing precision, suggesting that proficiency in solving a taste discrimination go/no-go task requires licking-induced neural ensemble synchronous activity. Copyright © 2010 the authors.

Osan R.,Boston University | Tort A.B.L.,Federal University of Rio Grande do Norte | Tort A.B.L.,Edmond and Lily Safra International Institute of Neuroscience of Natal | Amaral O.B.,Federal University of Rio de Janeiro
PLoS ONE | Year: 2011

The processes of memory reconsolidation and extinction have received increasing attention in recent experimental research, as their potential clinical applications begin to be uncovered. A number of studies suggest that amnestic drugs injected after reexposure to a learning context can disrupt either of the two processes, depending on the behavioral protocol employed. Hypothesizing that reconsolidation represents updating of a memory trace in the hippocampus, while extinction represents formation of a new trace, we have built a neural network model in which either simple retrieval, reconsolidation or extinction of a stored attractor can occur upon contextual reexposure, depending on the similarity between the representations of the original learning and reexposure sessions. This is achieved by assuming that independent mechanisms mediate Hebbian-like synaptic strengthening and mismatch-driven labilization of synaptic changes, with protein synthesis inhibition preferentially affecting the former. Our framework provides a unified mechanistic explanation for experimental data showing (a) the effect of reexposure duration on the occurrence of reconsolidation or extinction and (b) the requirement of memory updating during reexposure to drive reconsolidation. © 2011 Osan et al.

Ifft P.J.,Duke University | Shokur S.,Duke University | Shokur S.,Ecole Polytechnique Federale de Lausanne | Li Z.,Duke University | And 3 more authors.
Science Translational Medicine | Year: 2013

Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374 to 497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a fifth-order unscented Kalman filter (UKF). The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals' performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. These findings should help in the design of more sophisticated BMIs capable of enabling bimanual motor control in human patients.

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