Passive exercise of the hind limbs after complete thoracic transection of the spinal cord promotes cortical reorganization.

Physical exercise promotes neural plasticity in the brain of healthy subjects and modulates pathophysiological neural plasticity after sensorimotor loss, but the mechanisms of this action are not fully understood. After spinal cord injury, cortical reorganization can be maximized by exercising the non-affected body or the residual functions of the affected body. However, exercise per se also produces systemic changes - such as increased cardiovascular fitness, improved circulation and neuroendocrine changes - that have a great impact on brain function and plasticity. It is therefore possible that passive exercise therapies typically applied below the level of the lesion in patients with spinal cord injury could put the brain in a more plastic state and promote cortical reorganization. To directly test this hypothesis, we applied passive hindlimb bike exercise after complete thoracic transection of the spinal cord in adult rats. Using western blot analysis, we found that the level of proteins associated with plasticity - specifically ADCY1 and BDNF - increased in the somatosensory cortex of transected animals that received passive bike exercise compared to transected animals that received sham exercise. Using electrophysiological techniques, we then verified that neurons in the deafferented hindlimb cortex increased their responsiveness to tactile stimuli delivered to the forelimb in transected animals that received passive bike exercise compared to transected animals that received sham exercise. Passive exercise below the level of the lesion, therefore, promotes cortical reorganization after spinal cord injury, uncovering a brain-body interaction that does not rely on intact sensorimotor pathways connecting the exercised body parts and the brain

Spinal direct current stimulation modulates the activity of gracile nucleus and primary somatosensory cortex in anaesthetized rats

Afferent somatosensory activity from the spinal cord has a profound impact on the activity of the brain. Here we investigated the effects of spinal stimulation using direct current, delivered at the thoracic level, on the spontaneous activity and on the somatosensory evoked potentials of the gracile nucleus, which is the main entry point for hindpaw somatosensory signals reaching the brain from the dorsal columns, and of the primary somatosensory cortex in anaesthetized rats. Anodal spinal direct current stimulation (sDCS) increased the spontaneous activity and decreased the amplitude of evoked responses in the gracile nucleus, whereas cathodal sDCS produced the opposite effects. At the level of the primary somatosensory cortex, the changes in spontaneous activity induced by sDCS were consistent with the effects observed in the gracile nucleus, but the changes in cortical evoked responses were more variable and state dependent. Therefore, sDCS can modulate in a polarity-specific manner the supraspinal activity of the somatosensory system, offering a versatile bottom-up neuromodulation technique that could potentially be useful in a number of clinical applications

Measuring Instantaneous Frequency of Local Field Potential Oscillations using the Kalman Smoother

Rhythmic local field potentials (LFPs) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalman smoother based dynamic autoregressive model for tracking the instantaneous frequency (iFreq) and frequency modulation (FM) of noisy and non-stationary sinusoids such as those found in LFP data. We verify the performance of our algorithm using simulated data with broad spectral content, and demonstrate its application using real data recorded from behavioral learning experiments. In analyses of ripple oscillations (100–250 Hz) recorded from the rodent hippocampus, our algorithm identified novel repetitive, short timescale frequency dynamics. Our results suggest that iFreq and FM may be useful measures for the quantification of small timescale LFP dynamics.National Institutes of Health (U.S.) (NIH/NIMH R01 MH59733)National Institutes of Health (U.S.) (NIH/NIHLB R01 HL084502)Massachusetts Institute of Technology (Henry E. Singleton Presidential Graduate Fellowship Award

A framework to assess the impact of number of trials on the amplitude of motor evoked potentials

The amplitude of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) is a common yet highly variable measure of corticospinal excitability. The tradeoff between maximizing the number of trials and minimizing experimental time remains a hurdle. It is therefore important to establish how many trials should be used. The aim of this study is not to provide rule-of-thumb answers that may be valid only in specific experimental conditions, but to offer a more general framework to inform the decision about how many trials to use under different experimental conditions. Specifically, we present a set of equations that show how the number of trials affects single-subject MEP amplitude, population MEP amplitude, hypothesis testing and test–retest reliability, depending on the variability within and between subjects. The equations are derived analytically, validated with Monte Carlo simulations, and representatively applied to experimental data. Our findings show that the minimum number of trials for estimating single-subject MEP amplitude largely depends on the experimental conditions and on the error considered acceptable by the experimenter. Conversely, estimating population MEP amplitude and hypothesis testing are markedly more dependent on the number of subjects than on the number of trials. These tools and results help to clarify the impact of the number of trials in the design and reproducibility of past and future experiments

Optogenetics and deep brain stimulation neurotechnologies

Brain neural network is composed of densely packed, intricately wired neurons whose activity patterns ultimately give rise to every behavior, thought, or emotion that we experience. Over the past decade, a novel neurotechnique, optogenetics that combines light and genetic methods to control or monitor neural activity patterns, has proven to be revolutionary in understanding the functional role of specific neural circuits. We here briefly describe recent advance in optogenetics and compare optogenetics with deep brain stimulation technology that holds the promise for treating many neurological and psychiatric disorders

Safety study of transcranial static magnetic field stimulation (tSMS) of the human cortex

Transcranial static magnetic field stimulation (tSMS) in humans reduces cortical excitability. Objective: The objective of this study was to determine if prolonged tSMS (2 h) could be delivered safely in humans. Safety limits for this technique have not been described. Methods: tSMS was applied for 2 h with a cylindric magnet on the occiput of 17 healthy subjects. We assessed tSMS-related safety aspects at tissue level by measuring levels of neuron-specific enolase (NSE,a marker of neuronal damage) and S100 (a marker of glial reactivity and damage). We also included an evaluation of cognitive side effects by using a battery of visuomotor and cognitive tests. Results: tSMS did not induce any significant increase in NSE or S100. No cognitive alteration was detected. Conclusions: Our data indicate that the application of tSMS is safe in healthy human subjects, at least within these parameter

A torque-based method demonstrates increased rigidity in Parkinson’s disease during low-frequency stimulation

Low-frequency oscillations in the basal ganglia are prominent in patients with Parkinson’s disease off medication. Correlative and more recent interventional studies potentially implicate these rhythms in the pathophysiology of Parkinson’s disease. However, effect sizes have generally been small and limited to bradykinesia. In this study, we investigate whether these effects extend to rigidity and are maintained in the on-medication state. We studied 24 sides in 12 patients on levodopa during bilateral stimulation of the STN at 5, 10, 20, 50, 130 Hz and in the off-stimulation state. Passive rigidity at the wrist was assessed clinically and with a torque-based mechanical device. Low-frequency stimulation at ≤20 Hz increased rigidity by 24 % overall (p = 0.035), whereas high-frequency stimulation (130 Hz) reduced rigidity by 18 % (p = 0.033). The effects of low-frequency stimulation (5, 10 and 20 Hz) were well correlated with each other for both flexion and extension (r = 0.725 ± SEM 0.016 and 0.568 ± 0.009, respectively). Clinical assessments were unable to show an effect of low-frequency stimulation but did show a significant effect at 130 Hz (p = 0.002). This study provides evidence consistent with a mechanistic link between oscillatory activity at low frequency and Parkinsonian rigidity and, in addition, validates a new method for rigidity quantification at the wrist

Emergent Oscillations in Networks of Stochastic Spiking Neurons

Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework

Millisecond-Timescale Local Network Coding in the Rat Primary Somatosensory Cortex

Correlation among neocortical neurons is thought to play an indispensable role in mediating sensory processing of external stimuli. The role of temporal precision in this correlation has been hypothesized to enhance information flow along sensory pathways. Its role in mediating the integration of information at the output of these pathways, however, remains poorly understood. Here, we examined spike timing correlation between simultaneously recorded layer V neurons within and across columns of the primary somatosensory cortex of anesthetized rats during unilateral whisker stimulation. We used Bayesian statistics and information theory to quantify the causal influence between the recorded cells with millisecond precision. For each stimulated whisker, we inferred stable, whisker-specific, dynamic Bayesian networks over many repeated trials, with network similarity of 83.3±6% within whisker, compared to only 50.3±18% across whiskers. These networks further provided information about whisker identity that was approximately 6 times higher than what was provided by the latency to first spike and 13 times higher than what was provided by the spike count of individual neurons examined separately. Furthermore, prediction of individual neurons' precise firing conditioned on knowledge of putative pre-synaptic cell firing was 3 times higher than predictions conditioned on stimulus onset alone. Taken together, these results suggest the presence of a temporally precise network coding mechanism that integrates information across neighboring columns within layer V about vibrissa position and whisking kinetics to mediate whisker movement by motor areas innervated by layer V

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role