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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.

Cortical slow waves are hallmark oscillations of deep sleep and certain anesthetic conditions, yet the neurobiological mechanisms controlling their dynamics remain incompletely understood. Here, we investigated the effects of local near-infrared (NIR) stimulation on slow-wave activity in ketamine/xylazine-anesthetized rats. Using a silicon-based multimodal optrode, we simultaneously delivered NIR light and recorded local field potentials (LFPs) and multi-unit activity (MUA) across cortical layers in the primary somatosensory (S1Tr) and parietal association (PtA) cortices. NIR stimulation induces local tissue heating, resulting in reproducible and reversible changes in the properties of slow waves. Specifically, up-state durations were shortened, down-states prolonged, and MUA amplitudes during up-states increased, with steeper slopes at state transitions, indicative of enhanced neuronal synchronization. LFP amplitude and spectral changes varied across cortical regions: PtA exhibited increased slow wave (0.5–2 Hz) and high delta (2–4 Hz) band activity, while S1Tr showed a trend toward reduction. Our findings demonstrate that local infrared stimulation can reliably modulate cortical slow-wave dynamics, likely through temperature-mediated changes in neuronal excitability. This approach provides a minimally invasive method for precise, local manipulation of cortical network activity and offers new insights into the biophysical regulation of slow oscillations.

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.

Blood oxygenation level dependent (BOLD) responses in fMRI have previously been shown to be nonlinear with regard to changes in stimulus parameters, and as a result they may be asymmetric when comparing stimulus increases with decreases from an initial condition. We measured BOLD responses to varying vibrotactile stimuli of the hand digits in a monkey, including both increases and decreases in intensity and duration, relative to different levels of initial activation. Across variations in stimulus duration and intensity, positive and negative BOLD responses were asymmetric and nonlinear. Moreover, the asymmetry between positive and negative responses was manifest at different levels of baseline activation. The results confirm that the use of a common hemodynamic response function for increases and decreases in activity may underestimate the magnitude of decreases in activation. Electrophysiological recordings from multi‐electrode arrays also revealed nonlinear and asymmetric features in multi‐unit activities, linking neural firing properties to the nonlinear BOLD profiles. BOLD responses to increments and decrements in intensity of vibrotactile stimulation were asymmetric. Negative BOLD effects (decrements in signal) were smaller in amplitude and slower to peak compared with positive BOLD responses to increases of intensity. BOLD responses were nonlinear for changes in stimulus duration and intensity.

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.

Spreading depolarizations (SDs) are major pathophysiological events in several brain diseases, including migraine, brain ischemia, trauma, and epilepsy. However, the electrophysiological detection of SDs remains challenging. In this study, we examined changes in spikes (action potentials (APs) and action currents (ACs)) in layer 5 neurons of the somatosensory cortex of anesthetized rats during transient excitation at the onset of high-potassium-induced SDs. During whole-cell recordings, spike amplitude progressively decreased while spike duration increased during gradual neuronal depolarization at SD onset, culminating in depolarization block. A similar decrease in spike amplitude and increase in spike duration were observed during the pre-SD excitation phase in loose cell-attached recordings from single neurons and in cluster analysis of extracellular spikes. Multiple (non-clustered) unit activity also showed decrease in spike amplitude and spike broadening during pre-SD excitation. These findings suggest that dynamic changes in spike amplitude and duration at SD onset could serve as markers for SD detection.