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Direct brain recordings have provided important insights into how high-frequency activity captured through intracranial EEG (iEEG) supports human memory retrieval. The extent to which such activity is comprised of transient fluctuations that reflect the dynamic coordination of underlying neurons, however, remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that fast oscillations within the previously identified 80–120 Hz ripple band contribute to broadband high-frequency activity in the human cortex. These ripple oscillations exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human temporal lobe is organized into transient bouts of ripple oscillations that reflect underlying bursts of spiking activity.

Human neuronal activity, recorded in vivo from microelectrodes, may offer valuable insights into physiological mechanisms underlying human cognition and pathophysiological mechanisms of brain diseases, in particular epilepsy. Continuous and long-term recordings are necessary to monitor non predictable pathological and physiological activities like seizures or sleep. Because of their high impedance, microelectrodes are more sensitive to noise than macroelectrodes. Low noise levels are crucial to detect action potentials from background noise, and to further isolate single neuron activities. Therefore, long-term recordings of multi-unit activity remains a challenge. We shared here our experience with microelectrode recordings and our efforts to reduce noise levels in order to improve signal quality. We also provided detailed technical guidelines for the connection, recording, imaging and signal analysis of microelectrode recordings. During the last 10 years, we implanted 122 bundles of Behnke-Fried hybrid macro-microelectrodes, in 56 patients with pharmacoresistant focal epilepsy. Microbundles were implanted in the temporal lobe (74%), as well as frontal (15%), parietal (6%) and occipital (5%) lobes. Low noise levels depended on our technical setup. The noise reduction was mainly obtained after electrical insulation of the patient's recording room and the use of a reinforced microelectrode model, reaching median root mean square values of 5.8 µV. Seventy percent of the bundles could record multi-units activities (MUA), on around 3 out of 8 wires per bundle and for an average of 12 days. Seizures were recorded by microelectrodes in 91% of patients, when recorded continuously, and MUA were recorded during seizures for 75 % of the patients after the insulation of the room. Technical guidelines are proposed for (i) electrode tails manipulation and protection during surgical bandage and connection to both clinical and research amplifiers, (ii) electrical insulation of the patient's recording room and shielding, (iii) data acquisition and storage, and (iv) single-units activities analysis. We progressively improved our recording setup and are now able to record (i) microelectrode signals with low noise level up to 3 weeks duration, and (ii) MUA from an increased number of wires . We built a step by step procedure from electrode trajectory planning to recordings. All these delicate steps are essential for continuous long-term recording of units in order to advance in our understanding of both the pathophysiology of ictogenesis and the neuronal coding of cognitive and physiological functions.

Prior studies of the neural representation of episodic memory in the human hippocampus have identified generic memory signals representing the categorical status of test items (novel vs. repeated), whereas other studies have identified item specific memory signals representing individual test items. Here, we report that both kinds of memory signals can be detected in hippocampal neurons in the same experiment. We recorded single-unit activity from four brain regions (hippocampus, amygdala, anterior cingulate, and prefrontal cortex) of epilepsy patients as they completed a continuous recognition task. The generic signal was found in all four brain regions, whereas the item-specific memory signal was detected only in the hippocampus and reflected sparse coding. That is, for the item-specific signal, each hippocampal neuron responded strongly to a small fraction of repeated words, and each repeated word elicited strong responding in a small fraction of neurons. The neural code was sparse, pattern-separated, and limited to the hippocampus, consistent with longstanding computational models.We suggest that the item-specific episodic memory signal in the hippocampus is fundamental, whereas the more widespread generic memory signal is derivative and is likely used by different areas of the brain to perform memory-related functions that do not require itemspecific information.

Understanding the neural code has been one of the central aims of neuroscience research for decades. Spikes are commonly referred to as the units of information transfer, but multi-unit activity (MUA) recordings are routinely analyzed in aggregate forms such as binned spike counts, peri-stimulus time histograms, firing rates, or population codes. Various forms of averaging also occur in the brain, from the spatial averaging of spikes within dendritic trees to their temporal averaging through synaptic dynamics. However, how these forms of averaging are related to each other or to the spatial and temporal units of information representation within the neural code has remained poorly understood. In this work we developed NeuroPixelHD, a symbolic hyperdimensional model of MUA, and used it to decode the spatial location and identity of static images shown to = 9 mice in the Allen Institute Visual Coding-NeuroPixels dataset from large-scale MUA recordings. We parametrically varied the spatial and temporal resolutions of the MUA data provided to the model, and compared its resulting decoding accuracy. For almost all subjects, we found 125ms temporal resolution to maximize decoding accuracy for both the spatial location of Gabor patches (81 classes for patches presented over a 9×9 grid) as well as the identity of natural images (118 classes corresponding to 118 images) across the whole brain. This optimal temporal resolution nevertheless varied greatly between different regions, followed a sensory-associate hierarchy, and was significantly modulated by the central frequency of theta-band oscillations across different regions. Spatially, the optimal resolution was at either of two mesoscale levels for almost all mice: the area level, where the spiking activity of all neurons within each brain area are combined, and the population level, where neuronal spikes within each area are combined across fast spiking (putatively inhibitory) and regular spiking (putatively excitatory) neurons, respectively. We also observed an expected interplay between optimal spatial and temporal resolutions, whereby increasing the amount of averaging across one dimension (space or time) decreases the amount of averaging that is optimal across the other dimension, and vice versa. Our findings corroborate existing empirical practices of spatiotemporal binning and averaging in MUA data analysis, and provide a rigorous computational framework for optimizing the level of such aggregations. Our findings can also synthesize these empirical practices with existing knowledge of the various sources of biological averaging in the brain into a new theory of neural information processing in which the varies dynamically based on neuronal signal and noise correlations across space and time.

The efficacy of deep brain stimulation (DBS) therapy in Parkinson's disease (PD) patients is highly dependent on the precise localization of the target structures such as subthalamic nucleus (STN). Most commonly, microelectrode single unit activity (SUA) recordings are performed to refine the target. This process is heavily experience based and can be technically challenging. Local field potentials (LFPs), representing the activity of a population of neurons, can be obtained from the same microelectrodes used for SUA recordings and allow flexible online processing with less computational complexity due to lower sampling rate requirements. Although LFPs have been shown to contain biomarkers capable of predicting patients' symptoms and differentiating various structures, their use in the localization of the STN in the clinical practice is not prevalent. Here we present, for the first time, a randomized and double-blinded pilot study with intraoperative online LFP processing in which we compare the clinical benefit from SUA- versus LFP-based implantation. Ten PD patients referred for bilateral STN-DBS were randomly implanted using either SUA or LFP guided targeting in each hemisphere. Although both SUA and LFP were recorded for each STN, the electrophysiologist was blinded to one at a time. Three months postoperatively, the patients were evaluated by a neurologist blinded to the intraoperative recordings to assess the performance of each modality. While SUA-based decisions relied on the visual and auditory inspection of the raw traces, LFP-based decisions were given through an online signal processing and machine learning pipeline. We found a dramatic agreement between LFP- and SUA-based localization (16/20 STNs) providing adequate clinical improvement (51.8% decrease in 3-month contralateral motor assessment scores), with LFP-guided implantation resulting in greater average improvement in the discordant cases (74.9%, = 3 STNs). The selected tracks were characterized by higher activity in beta (11-32 Hz) and high-frequency (200-400 Hz) bands ( < 0.01) of LFPs and stronger non-linear coupling between these bands ( < 0.05). Our pilot study shows equal or better clinical benefit with LFP-based targeting. Given the robustness of the electrode interface and lower computational cost, more centers can utilize LFP as a strategic feedback modality intraoperatively, in conjunction to the SUA-guided targeting.

Cervical dystonia (CD) is a movement disorder characterized by a stereotyped pattern of involuntary turning or tilting of the head, often combined with jerky or tremulous movements. Hypotheses for the origin of CD have traditionally focused on the basal ganglia, but the contemporary discussion has considered the potential role of altered cerebellar function. As basal ganglia and the cerebellum largely project to the different thalamic nuclei, alterations in pallidal versus cerebellar output could be reflected in the activity of these thalamic regions. In this study, we analyzed a unique historic database where the single-unit activity of pallidal and cerebellar receiving thalamic nuclei was measured en route to the mesencephalon. We compared the single-unit activity of pallidal and cerebellar receiving thalamic neurons in three groups of CD patients manifesting as pure dystonia, pure jerky head oscillations, and dystonia plus jerky head oscillations. We found that among different CD manifestations, the characteristics of neuronal firing, such as burst versus a single-spike pattern, vary in cerebellar thalamic receiving nuclei. The cerebellar receiving region in patients with jerky oscillations had single-spikes neurons primarily. Wherein the manifestation of CD did not influence pattern distribution in the pallidal receiving thalamic area. We also found increased neuronal firing rate correlated with strength of theta-band neuronal oscillations during muscle contractions associated with dystonia. These results demonstrate that the manifestations of CD, such as pure dystonia, pure jerky head oscillations, or dystonia and jerky head oscillations, determine the thalamic neuronal properties.

We used a novel microendoscope system to record simultaneously optical activity (fluorescence of a calcium indicator dye) and electrical activity (multi-unit activity and local field potentials) from the dorsal inferior colliculus of the echolocating bat, Carollia perspicillata. Optically recorded calcium responses to wide-band noise and to frequency-modulated bursts were recorded at probe depths down to 1300 µm, with the majority of active sites encountered at more shallow depths down to 800 µm. Calcium activity exhibited long latencies, within the time span of 50–100 ms after stimulus onset, significantly longer than onset latencies of either multi-unit activity or local field potentials. Latencies and amplitude/latency trading of these electrical responses were consistent with those seen in standard electrophysiological recordings, confirming that the microendoscope was able to record both neural and optical activity successfully. Optically recorded calcium responses rose and decayed slowly and were correlated in time with long-latency negative deflections in local field potentials. These data suggest that calcium-evoked responses may reflect known, sustained inhibitory interactions in the inferior colliculus.

Human brain imaging has revealed that stimulus-induced activity does generally not simply add to the pre-stimulus activity, but rather builds in a non-additive way on this activity. Here we investigate this subject at the single neuron level and address the question whether and to what extent a strong form of non-additivity where activity drops post-cue is present in different areas of monkey cortex, including prefrontal and agranular frontal areas, during a perceptual decision making task involving action and tactic selection. Specifically we analyze spike train data recorded in vivo from the posterior dorsomedial prefrontal cortex (pmPFC), the supplementary motor area (SMA) and the presupplementary motor area (pre-SMA). For each neuron, we compute the ratio of the trial-averaged pre-stimulus spike count to the trial-averaged post-stimulus count. We also perform the ratio and averaging procedures in reverse order. We find that the statistics of these quantities behave differently across areas. pmPFC involved in tactic selection shows stronger non-additivity compared to the two other areas which more generically just increase their firing rate pos-stimulus. pmPFC behaved more similarly to pre-SMA, a likely consequence of the reciprocal connections between these areas. The trial-averaged ratio statistic was reproduced by a surrogate inhomogeneous Poisson process in which the measured trial-averaged firing rate for a given neuron is used as its time-dependent rate. Principal component analysis (PCA) of the trial-averaged firing rates of neuronal ensembles further reveals area-specific time courses of response to the stimulus, including latency to peak neural response, for the typical population activity. Our work demonstrates subtle forms of area-specific non-additivity based on the fine variability structure of pre- and post-stimulus spiking activity on the single neuron level. It also reveals significant differences between areas for PCA and surrogate analysis, complementing previous observations of regional differences based solely on post-stimulus responses. Moreover, we observe regional differences in non-additivity which are related to the monkey’s successful tactic selection and decision making.

Anatomical studies have demonstrated that distant cortical points are interconnected through long range axon collaterals of pyramidal cells. However, the functional properties of these intrinsic synaptic connections, especially their relationship with the cortical representations of body movements, have not been systematically investigated. To address this issue, we used multielectrode arrays chronically implanted in the motor cortex of two rhesus monkeys to analyze the effects of single-pulse intracortical microstimulation (sICMS) applied at one electrode on the neuronal activities recorded at all other electrodes. The temporal and spatial distribution of the evoked responses of single and multiunit activities was quantified to determine the properties of horizontal propagation. The typical responses were characterized by a brief excitatory peak followed by inhibition of longer duration. Significant excitatory responses to sICMS could be evoked up to 4 mm away from the stimulation site, but the strength of the response decreased exponentially and its latency increased linearly with the distance. We then quantified the direction and strength of the propagation in relation to the somatotopic organization of the motor cortex. We observed that following sICMS the propagation of neural activity is mainly directed rostro-caudally near the central sulcus but follows medio-lateral direction at the most anterior electrodes. The fact that these interactions are not entirely symmetrical may characterize a critical functional property of the motor cortex for the control of upper limb movements. Overall, these results support the assumption that the motor cortex is not functionally homogeneous but forms a complex network of interacting subregions.

Stimulus repetition suppresses the neural activity in different sensory areas of the brain. This mechanism of so-called stimulus-specific adaptation (SSA) has been observed in both spiking activity and local field potential (LFP) responses. However, much remains to be known about the effect of SSA on the spike-LFP relation. In this study, we approached this issue by investigating the spike-phase coupling (SPC) in control and adapting paradigms. For the control paradigm, pure tones were presented in a random unbiased sequence. In the adapting paradigm, the same stimuli were presented in a random pattern but it was biased to an adapter stimulus. In fact, the adapter occupied 80% of the adapting sequence. During the tasks, LFP and multi-unit activity were recorded simultaneously from the primary auditory cortex of 15 anesthetized rats. To clarify the effect of adaptation on the relation between spike and LFP responses, the SPC of the adapter stimulus in these two paradigms was evaluated. Here, we employed phase locking value method for calculating the SPC. Our data show a strong coupling of spikes to LFP phase most prominently in beta band. This coupling was observed to decrease in the adapting condition compared to the control one. Importantly, we found that adaptation reduces spikes dominantly from the preferred phase of LFP in which spikes are more likely to be present there. As a result, the preferred phase of LFP may play a key role in coordinating neuronal spiking activity in neural adaptation mechanism. This finding is important for interpretation of the underlying neural mechanism of adaptation and also can be used in the context of the network and related connectivity models.