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Cash, S. S.

Publications and source records attributed to Cash, S. S..

At least 37 records · Page 2Linked to original sources

Robust online multiband drift estimation in electrophysiology data

High-density electrophysiology probes have opened new possibilities for systems neuroscience in human and non-human animals, but probe motion (or drift) while recording poses a challenge for downstream analyses, particularly in human recordings. Here, we improve on the state of the art for tracking this drift with an algorithm termed DREDge (Decentralized Registration of Electrophysiology Data) with four major contributions. First, we extend previous decentralized methods to exploit multiband information, leveraging the local field potential (LFP), in addition to spikes detected from the action potentials (AP). Second, we show that the LFP-based approach enables registration at sub-second temporal resolution. Third, we introduce an efficient online motion tracking algorithm, allowing the method to scale up to longer and higher spatial resolution recordings, which could facilitate real-time applications. Finally, we improve the robustness of the approach by accounting for the nonstationarities that occur in real data and by automating parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from both humans and mice.

neuroscience↗

Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain

Over the past decade, stereotactically placed electrodes have become the gold standard for deep brain recording and stimulation for a wide variety of neurological and psychiatric diseases. Current electrodes, however, are limited in their spatial resolution and ability to record from small populations of neurons, let alone individual neurons. Here, we report on a novel, reconfigurable, monolithically integrated human-grade flexible depth electrode capable of recording from up to 128 channels and able to record at a depth of 10 cm in brain tissue. This thin, stylet-guided depth electrode is capable of recording local field potentials and single unit neuronal activity (action potentials), validated across species. This device represents a major new advance in manufacturing and design approaches which extends the capabilities of a mainstay technology in clinical neurology. One-Sentence SummaryA human-grade thin-film depth electrode offers new opportunities in spatial and temporal resolution for recording brain activity.

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Characterizing ketamine-induced dissociation using human intracranial neurophysiology: brain dynamics, network activity, and interactions with propofol

Subanesthetic doses of ketamine produce rapid and sustained anti-depressant effects in patients with treatment-resistant depression. Unfortunately, the usefulness of ketamine as a treatment is limited by its potential for abuse because of psychotropic side effects such as dissociation. Understanding the brain dynamics and the neural circuits involved in ketamines effects could lend insight into improved therapies for depression with fewer adverse effects. It is believed that ketamine acts via NMDA receptor and hyperpolarization-activated cyclic nucleotide-gated 1 (HCN1) channels to produce changes in oscillatory brain dynamics. Here we show, in humans, a detailed description of the principal oscillatory changes in cortical and subcortical structures by administration of a subanesthetic dose of ketamine. Using recordings from intracranial electrodes, we found that ketamine increased gamma oscillations within prefrontal cortical areas and the hippocampus--structures previously implicated in ketamines antidepressant effects. Furthermore, our studies provide direct evidence of a ketamine-induced 3 Hz oscillation in posteromedial cortex that has been proposed as a mechanism for its dissociative effects. By analyzing changes in neural oscillations after the addition of propofol, whose GABAergic activity antagonizes ketamines NMDA-mediated disinhibition alongside a shared HCN1 inhibitory effect, we identified brain dynamics that could be attributed to NMDA-mediated disinhibition versus HCN1 inhibition. Overall, our results imply that ketamine engages different neural circuits in distinct frequency-dependent patterns of activity to produce its antidepressant and dissociative sensory effects. These insights may help guide the development of novel brain dynamic biomarkers and therapeutics for depression.

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Propofol disrupts alpha dynamics in distinct thalamocortical networks underlying sensory and cognitive function during loss of consciousness

During propofol-induced general anesthesia, alpha rhythms undergo a striking shift from posterior to anterior, termed anteriorization. We combined human intracranial recordings with diffusion imaging to show that anteriorization occurs with opposing dynamics in two distinct thalamocortical subnetworks. The cortical and thalamic anatomy involved, as well as their known functional roles, suggest multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.

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Magnetic Temporal Interference for Noninvasive Focal Brain Stimulation

Non-invasive stimulation of deep brain regions has been a major goal for neuroscience and neuromodulation in the past three decades. Transcranial magnetic stimulation (TMS), for instance, cannot target deep regions in the brain without activating the overlying tissues and has a poor spatial resolution. In this manuscript, we propose a new concept that relies on the temporal interference of two high-frequency magnetic fields generated by two electromagnetic solenoids. To illustrate the concept, custom solenoids were fabricated and optimized to generate temporal interfering electric fields for rodent brain stimulation. C-Fos expression was used to track neuronal activation. C-Fos expression was not present in regions impacted by only one high-frequency magnetic field indicating ineffective recruitment of neural activity in non-target regions. In contrast, regions impacted by two fields that interfere to create a low-frequency envelope display a strong increase in c-Fos expression. Therefore, this magnetic temporal interference solenoid-based system provides a framework to perform further stimulation studies that would investigate the advantages it could bring over conventional TMS systems.

bioengineering↗

Impact of stimulation location relative to grey and white matter on single pulse electrical stimulation responses in the human brain

BackgroundElectrical neuromodulation is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, possibly a result of our limited understanding of the mechanisms and the massive stimulation parameter space. Objective/HypothesisTo better understand the role different parameters play in inducing a response, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration and location in the brain and relative to grey and white matter. MethodsWe measured voltage peak amplitudes and area under the curve of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N=52, n=719 stimulation sites). ResultsIncreasing stimulation pulse width increased response values near the stimulation location. Increasing stimulation amplitude (current) increased responses nonlinearly. Locally (<15 mm from the stimulation site), stimulation closer to the grey matter-white matter boundary induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. These relationships were different between cingulate, lateral prefrontal, and lateral temporal cortical stimulation. ConclusionThese results demonstrate the importance of location and stimulation parameters in inducing a specific output and indicate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter may be needed for network activation, suggesting that stimulation location can be tailored for a specific outcome, key to informed neuromodulatory therapy. HighlightsO_LIIntracranial single pulse electrical stimulation (SPES) response increases with increased pulse duration mostly near the stimulation site C_LIO_LISPES response varies nonlinearly with injected current with an effect of distance from the stimulation site. C_LIO_LISPES near the grey-white boundary and 90{degrees} to the nearest cortical axis induces larger local responses, but white matter stimulation produces larger distant responses. C_LIO_LIThe relationship between SPES location and responses depends on brain region stimulated C_LI

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The timescale and magnitude of aperiodic activity decreases with cortical depth in humans, macaques and mice

Cortical dynamics obey a 1/f power law, exhibiting an exponential decay of spectral power with increasing frequency. The slope and offset of this 1/f decay reflect the timescale and magnitude of aperiodic neural activity, respectively. These properties are tightly linked to cellular and circuit mechanisms (e.g. excitation:inhibition balance and firing rates) as well as cognitive processes (e.g. perception, memory, and state). However, the physiology underlying the 1/f power law in cortical dynamics is not well understood. Here, we compared laminar recordings from human, macaque and mouse cortex to evaluate how 1/f aperiodic dynamics vary across cortical layers and species. We report that 1/f slope is steepest in superficial layers and flattest in deep layers in each species. Additionally, the magnitude of this 1/f decay is greatest in superficial cortex and decreases with depth. We could account for both of these findings with a simple model in which superficial cortical transmembrane currents had longer time constants and greater densities than those in deeper layers. Together, our results provide novel insight into the organization of cortical dynamics, suggesting that the amplitude and time constant of local currents control circuit processing as a function of laminar depth. This may represent a general mechanism to facilitate appropriate integration of fast sensory inputs (infragranular) with slow feedback-type inputs (supragranular) across cortical areas and species.

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Large-scale neural recordings with single-cell resolution in human cortex using high-density Neuropixels probes

Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution. In humans, however, current approaches either restrict recordings to only a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here, we describe a new probe variant and set of techniques which enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single unit classes, with differing firing rates, positions along the length of the linear electrode array, spatial spread of the waveform, and modulation by LFP events such as inter-ictal discharges and burst suppression. While some additional challenges remain in creating a turn-key recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution.

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Brain network dynamics codify heterogeneity in seizure propagation

Dynamic functional brain connectivity facilitates adaptive cognition and behavior. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures. A central but unresolved question concerns the mechanisms by which extraordinarily diverse dynamics of seizures emerge. Here, we apply a graph-theoretical approach to assess dynamic reconfigurations in the functional brain connectivity before, during, and after seizures that display heterogeneous propagation patterns despite sharing similar origins. We demonstrate unique reconfigurations in globally-defined network properties preceding seizure onset that predict propagation patterns of impending seizures, and in locally-defined network properties that differentiate post-onset dynamics. These results characterize quantitative network features underlying the heterogeneity of seizure dynamics and the accompanying clinical manifestations. Decoding these network properties could improve personalized preventative treatment strategies for epilepsy as well as other neurological disorders.

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Cortical ripples provide the conditions for consolidation during NREM sleep in humans

Hippocampal ripples index the reconstruction of spatiotemporal neuronal firing patterns essential for the consolidation of memories in the cortex during non-rapid eye movement sleep (NREM). Recently, cortical ripples in humans have been shown to enfold the replay of neuron firing patterns during cued recall. Here, using intracranial recordings from 18 patients (12 female), we show that cortical ripples also occur during NREM in humans, with similar density, oscillation frequency ([~]90 Hz), duration, and amplitude to waking. Ripples occurred in all cortical regions with similar characteristics, unrelated to putative hippocampal connectivity, and were less dense and robust in higher association areas. Putative pyramidal and interneuron spiking phase-locked to cortical ripples during NREM, with phase delays consistent with ripple generation through pyramidal-interneuron feedback. Cortical ripples were smaller in amplitude than hippocampal ripples, but were similar in density, frequency, and duration. Cortical ripples during NREM typically occurred just prior to the upstate peak, often during spindles. Upstates and spindles have previously been associated with memory consolidation, and we found that cortical ripples grouped co-firing between units within the window of spike-timing-dependent plasticity. Thus, human NREM cortical ripples are: ubiquitous and stereotyped with a tightly focused oscillation frequency; similar to hippocampal ripples; associated with upstates and spindles; and associated with unit co-firing. These properties are consistent with cortical ripples possibly contributing to memory consolidation and other functions during NREM in humans. Significance StatementIn rodents, hippocampal ripples organize replay during sleep to promote memory consolidation in the cortex, where ripples also occur. However, evidence for cortical ripples in human sleep is limited, and their anatomical distribution and physiological properties are unexplored. Here, using human intracranial recordings, we demonstrate that ripples occur throughout the cortex during waking and sleep with highly stereotyped characteristics. During sleep, cortical ripples tend to occur during spindles on the down-to-upstate transition, and thus participate in a sequence of sleep waves that is important for consolidation. Furthermore, cortical ripples organize single unit spiking with timing optimal to facilitate plasticity. Therefore, cortical ripples in humans possess essential physiological properties to support memory and other cognitive functions.

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Widespread ripples synchronize human cortical activity

Declarative memory encoding, consolidation, and retrieval require the integration of elements encoded in widespread cortical locations. The mechanism whereby such binding of different components of mental events into unified representations occurs is unknown. The binding-bysynchrony theory proposes that distributed encoding areas are bound by synchronous oscillations enabling enhanced communication. However, evidence for such oscillations is sparse. Brief high-frequency oscillations ( ripples) occur in the hippocampus and cortex, and help organize memory recall and consolidation. Here, using intracranial recordings in humans, we report that these ~70ms duration 90Hz ripples often couple (within {+/-}500ms), co-occur ([&ge;]25ms overlap), and crucially, phase-lock (have consistent phase-lags) between widely distributed focal cortical locations during both sleep and waking, even between hemispheres. Cortical ripple co-occurrence is facilitated through activation across multiple sites, and phaselocking increases with more cortical sites co-rippling. Ripples in all cortical areas co-occur with hippocampal ripples but do not phase-lock with them, further suggesting that cortico-cortical synchrony is mediated by cortico-cortical connections. Ripple phase-lags vary across sleep nights, consistent with participation in different networks. During waking, we show that hippocampo-cortical and cortico-cortical co-ripples increase preceding successful delayed memory recall, when binding between the cue and response is essential. Ripples increase and phase-modulate unit firing, and co-ripples increase high-frequency correlations between areas, suggesting synchronized unit-spiking facilitating information exchange. Co-occurrence, phasesynchrony, and high-frequency correlation are maintained with little decrement over very long distances (25cm). Hippocampo-cortico-cortical co-ripples appear to possess the essential properties necessary to support binding-by-synchrony during memory retrieval, and perhaps generally in cognition. Significance StatementDifferent elements of a memory, or any mental event, are encoded in locations distributed across the cortex. A prominent hypothesis proposes that widespread networks are integrated with bursts of synchronized high-frequency oscillations called ripples, but evidence is limited. Here, using recordings inside the human brain, we show that ripples occur simultaneously in multiple lobes in both cortical hemispheres, and the hippocampus, generally during sleep and waking, and especially during memory recall. Ripples phase-lock local cell firing, and phase-synchronize with little decay between locations separated by up to 25cm, enabling long-distance integration. Indeed, co-rippling sites have increased correlation of very high-frequency activity which reflects cell firing. Thus, ripples may help bind information across the cortex in memory and other mental events.

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Active probing to highlight approaching transitions to ictal states in coupled neural mass models

The extraction of electrophysiological features that reliably forecast the occurrence of seizures is one of the most challenging goals in epilepsy research. Among possible approaches to tackle this problem is the use of active probing paradigms in which responses to stimuli are used to detect underlying system changes leading up to seizures. This work evaluates the theoretical and mechanistic underpinnings of this strategy using a well-studied seizure-generating neural mass model with two coupled populations. Different simulations are done by shifting parameters (excitability, slow inhibition, and coupling gains) towards ictal states while low-frequency probing stimuli are applied to either one or both populations. The correlation between the extracted features and the shifted parameter indicates if the impending transition to the ictal state may be identified in advance. Results show that not only can the response to the probing stimuli forecast seizures but this is true regardless of the altered ictogenic parameter. That is, similar feature changes are highlighted by probing stimuli responses in advance of the seizure including: increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of both model subsets. These changes were restricted to the stimulated population, showing a local effect of this perturbational approach. The transition latencies from normal activity to sustained discharges of spikes were not affected, suggesting that stimuli had no pro-ictal effects. However, stimuli were found to elicit interictal-like spikes just before the transition to the ictal state. Furthermore, the observed feature changes highlighted by probing the neuronal populations may reflect the phenomenon of critical slowing down, where increased recovery times from perturbations may signal the loss of a systems resilience and are common hallmarks of an impending critical transition. These results provide more evidence that active probing approaches highlight information about underlying system changes involved in ictogenesis and may be able to play a role in assisting seizure forecasting methods which can be incorporated into early-warning systems that ultimately enable closing the loop for targeted seizure-controlling interventions. Author summaryEpilepsy is characterized by spontaneous and recurrent seizures. Developing a method to forecast the occurrence of these events would ease part of the burden to patients by providing warning systems and identifying critical periods for closed-loop seizure suppressing methods. However, this task is far from trivial and doing this in a clinical setting has been a challenge that is not yet solved. A possible way to achieve this is by using active probing paradigms, in which stimuli are used to highlight otherwise hidden changes in brain dynamics that may lead to seizures. In this work, this approach is tested in a computational model with two coupled neuronal populations. Active probing responses highlighted underlying model parameter changes that led neuronal activity closer to seizures, something that was not detected with the passive observation in most evaluated model settings. Observed feature changes revealed higher excitability, longer recovery times from stimuli, and increased synchrony between populations as the threshold to seizure-like activity approached. This indicates that low frequency stimuli may be used to reveal early-warning signs and assist in seizure forecasting methods.

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Travelling spindles create necessary conditions for spike-timing-dependent plasticity in humans

Sleep spindles facilitate memory consolidation in the cortex during mammalian non-rapid eye movement (NREM) sleep. In rodents, phase-locked firing during spindles may facilitate spike-timing-dependent plasticity (STDP) by grouping pre- and post-synaptic cell firing within [~]25ms. Currently, microphysiological evidence in humans for conditions conducive for STDP during spindles is absent. We analyzed local field potentials and supragranular unit spiking during spindles from 10x10 arrays of microelectrodes at 400{micro}m pitch in humans. We found strong tonic and phase-locked increases in firing and co-firing within 25ms during spindles. Co-firing, spindle co-occurrence, and spindle coherence were greatest between sites within [~]2mm, and high co-firing of units on different electrodes was largely restricted to moments of high spindle coherence between those electrodes. Spindles propagated at [~]0.23m/s in distinct patterns, with correlated cell co-firing sequences. These results suggest that spindles may organize spatiotemporal patterns of neuronal co-firing which promote memory consolidation during NREM sleep.

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Seizure onset location shapes dynamics of initiation

ObjectiveIctal electrographic patterns are widely thought to reflect underlying neural mechanisms of seizures. Here we studied the degree to which seizure patterns are consistent in a given patient, relate to particular brain regions and if two candidate biomarkers (high-frequency oscillations, HFOs; infraslow activity, ISA) and network activity, as assessed with cross-frequency interactions, can discriminate between seizure types. MethodsWe analyzed temporal changes in low and high frequency oscillations recorded during seizures, as well as phase-amplitude coupling (PAC) to monitor the interactions between delta/theta and ripple/fast ripple frequency bands at seizure onset. ResultsSeizures of multiple pattern types were observed in a given patient and brain region. While there was an increase in HFO rate across different electrographic patterns, there are specific relationships between types of HFO activity and onset region. Similarly, changes in PAC dynamics were more closely related to seizure onset region than they were to electrographic patterns while ISA was a poor indicator for seizure onset. ConclusionsOur findings suggest that the onset region sculpts neurodynamics at seizure initiation and that unique features of the cytoarchitecture and/or connectivity of that region play a significant role in determining seizure mechanism. SignificanceClinicians should consider more than just overt electrographic patterns when considering seizure mechanisms and regions of onset. Examination of onset pattern in conjunction with the interactions between different oscillatory frequencies in the context of different brain regions might be more informative and lead to more reliable clinical inference as well as novel therapeutic approaches.

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Bidirectional modulation of human emotional conflict resolution using intracranial stimulation

The ability to regulate emotions in the service of meeting ongoing goals and task demands is a key aspect of adaptive human behavior in our volatile social world. Consequently, difficulties in processing and responding to emotional stimuli underlie many psychiatric diseases ranging from depression to anxiety, the common thread being effects on behavior. Behavior, which is made up of shifting, difficult to measure hidden states such as attention and emotion reactivity, is a product of integrating external input and latent mental processes. Directly measuring, and differentiating, separable hidden cognitive, emotional, and attentional states contributing to emotion conflict resolution, however, is challenging, particularly when only using task-relevant behavioral measures such as reaction time. State-space representations are a powerful method for investigating hidden states underlying complex systems. Using state-space modeling of behavior, we identified relevant hidden cognitive states and predicted behavior in a standardized emotion regulation task. After identifying and validating models which best fit the behavior and narrowing our focus to one model, we used targeted intracranial stimulation of the emotion regulation-relevant neurocircuitry, including prefrontal structures and the amygdala, to causally modulate separable states. Finally, we focused on this one validated state-space model to perform real-time, bidirectional closed-loop adaptive stimulation in a subset of participants. These approaches enable an improved understanding of how to sample and understand emotional processing in a way which could be leveraged in neuromodulatory therapy for disorders of emotional regulation.

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Microscale physiological events on the human cortical surface detected with PEDOT:PSS Electrodes

Despite ongoing advancements in our understanding of the local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their intermediate microscale local circuit dynamics. Here, we utilized ultrahigh density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper layers of the grey matter. They were also observed in rodents, non-human primates, and semi-chronic recordings in humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that with the proper combination of high resolution microelectrodes and analytic techniques it is possible to capture neuronal dynamics that lay between somatic action potentials and aggregate population activity and that understanding these intermediate microscale dynamics may reveal important details of the full circuit behavior in human cognition.

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Intracortical neural activity distal to seizure-onset-areas predicts human focal seizures

The apparent unpredictability of epileptic seizures has a major impact in the quality of life of people with pharmacologically resistant seizures. Here, we present initial results and a proof-of-concept of how focal seizures can be predicted early in advance based on intracortical signals recorded from small neocortical patches away from identified seizure onset areas. We show that machine learning algorithms can discriminate between interictal and preictal periods based on multiunit activity (i.e. thresholded action potential counts) and multi-frequency band local field potentials recorded via 4 X 4 mm2 microelectrode arrays. Microelectrode arrays were implanted in 5 patients undergoing neuromonitoring for resective surgery. Post-implant analysis revealed arrays were outside the seizure onset areas. Preictal periods were defined as the 1-hour period leading to a seizure. A 5-minute gap between the preictal period and the putative seizure onset was enforced to account for potential errors in the determination of actual seizure onset times. We used extreme gradient boosting and long short-term memory networks for prediction. Prediction accuracy based on the area under the receiver operating characteristic curves reached 90% for at least one feature type in each patient. Importantly, successful prediction could be achieved based exclusively on multiunit activity. This result indicates that preictal activity in the recorded neocortical patches involved not only subthreshold postsynaptic potentials, perhaps driven by the distal seizure onset areas, but also neuronal spiking in distal recurrent neocortical networks. Beyond the commonly identified seizure onset areas, our findings point to the engagement of large-scale neuronal networks in the neural dynamics building up toward a seizure. Our initial results obtained on currently available human intracortical recordings warrant new studies on larger datasets, and open new perspectives for seizure prediction and control by emphasizing the contribution of multiscale neural signals in large-scale neuronal networks.

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A statistical modeling framework to assess cross-frequency coupling while accounting for confounding effects

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate how CFC evolves during seizures and is affected by electrical stimuli.

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