KCNQ2/3 Gain-of-Function Variants and Cell Excitability: Differential Effects in CA1 versus L2/3 Pyramidal Neurons.

Gain-of-function (GOF) pathogenic variants in the potassium channels KCNQ2 and KCNQ3 lead to hyperexcitability disorders such as epilepsy and autism spectrum disorders. However, the underlying cellular mechanisms of how these variants impair forebrain function are unclear. Here, we show that the R201C variant in KCNQ2 has opposite effects on the excitability of two types of mouse pyramidal neurons of either sex, causing hyperexcitability in layer 2/3 (L2/3) pyramidal neurons and hypoexcitability in CA1 pyramidal neurons. Similarly, the homologous R231C variant in KCNQ3 leads to hyperexcitability in L2/3 pyramidal neurons and hypoexcitability in CA1 pyramidal neurons. However, the effects of KCNQ3 gain-of-function on excitability are specific to superficial CA1 pyramidal neurons. These findings reveal a new level of complexity in the function of KCNQ2 and KCNQ3 channels in the forebrain and provide a framework for understanding the effects of gain-of-function variants and potassium channels in the brain.SIGNIFICANCE STATEMENT KCNQ2/3 gain-of-function (GOF) variants lead to severe forms of neurodevelopmental disorders, but the mechanisms by which these channels affect neuronal activity are poorly understood. In this study, using a series of transgenic mice we demonstrate that the same KCNQ2/3 GOF variants can lead to either hyperexcitability or hypoexcitability in different types of pyramidal neurons [CA1 vs layer (L)2/3]. Additionally, we show that expression of the recurrent KCNQ2 GOF variant R201C in forebrain pyramidal neurons could lead to seizures and SUDEP. Our data suggest that the effects of KCNQ2/3 GOF variants depend on specific cell types and brain regions, possibly accounting for the diverse range of phenotypes observed in individuals with KCNQ2/3 GOF variants.

COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy.

We present an automated method for COVID-19 screening based on reconstructed phase profiles of red blood cells (RBCs) and a highly comparative time-series analysis (HCTSA). Video digital holographic data -was obtained using a compact, field-portable shearing microscope to capture the temporal fluctuations and spatio-temporal dynamics of live RBCs. After numerical reconstruction of the digital holographic data, the optical volume is calculated at each timeframe of the reconstructed data to produce a time-series signal for each cell in our dataset. Over 6000 features are extracted on the time-varying optical volume sequences using the HCTSA to quantify the spatio-temporal behavior of the RBCs, then a linear support vector machine is used for classification of individual RBCs. Human subjects are then classified for COVID-19 based on the consensus of their cells' classifications. The proposed method is tested on a dataset of 1472 RBCs from 24 human subjects (10 COVID-19 positive, 14 healthy) collected at UConn Health Center. Following a cross-validation procedure, our system achieves 82.13% accuracy, with 92.72% sensitivity, and 73.21% specificity (area under the receiver operating characteristic curve: 0.8357). Furthermore, the proposed system resulted in 21 out of 24 human subjects correctly labeled. To the best of our knowledge this is the first report of a highly comparative time-series analysis using digital holographic microscopy data.

Transcranial direct current stimulation of cerebellum alters spiking precision in cerebellar cortex: A modeling study of cellular responses.

Transcranial direct current stimulation (tDCS) of the cerebellum has rapidly raised interest but the effects of tDCS on cerebellar neurons remain unclear. Assessing the cellular response to tDCS is challenging because of the uneven, highly stratified cytoarchitecture of the cerebellum, within which cellular morphologies, physiological properties, and function vary largely across several types of neurons. In this study, we combine MRI-based segmentation of the cerebellum and a finite element model of the tDCS-induced electric field (EF) inside the cerebellum to determine the field imposed on the cerebellar neurons throughout the region. We then pair the EF with multicompartment models of the Purkinje cell (PC), deep cerebellar neuron (DCN), and granule cell (GrC) and quantify the acute response of these neurons under various orientations, physiological conditions, and sequences of presynaptic stimuli. We show that cerebellar tDCS significantly modulates the postsynaptic spiking precision of the PC, which is expressed as a change in the spike count and timing in response to presynaptic stimuli. tDCS has modest effects, instead, on the PC tonic firing at rest and on the postsynaptic activity of DCN and GrC. In Purkinje cells, anodal tDCS shortens the repolarization phase following complex spikes (-14.7 ± 6.5% of baseline value, mean ± S.D.; max: -22.7%) and promotes burstiness with longer bursts compared to resting conditions. Cathodal tDCS, instead, promotes irregular spiking by enhancing somatic excitability and significantly prolongs the repolarization after complex spikes compared to baseline (+37.0 ± 28.9%, mean ± S.D.; max: +84.3%). tDCS-induced changes to the repolarization phase and firing pattern exceed 10% of the baseline values in Purkinje cells covering up to 20% of the cerebellar cortex, with the effects being distributed along the EF direction and concentrated in the area under the electrode over the cerebellum. Altogether, the acute effects of tDCS on cerebellum mainly focus on Purkinje cells and modulate the precision of the response to synaptic stimuli, thus having the largest impact when the cerebellar cortex is active. Since the spatiotemporal precision of the PC spiking is critical to learning and coordination, our results suggest cerebellar tDCS as a viable therapeutic option for disorders involving cerebellar hyperactivity such as ataxia.

Role of cerebellar GABAergic dysfunctions in the origins of essential tremor.

Essential tremor (ET) is among the most prevalent movement disorders, but its origins are elusive. The inferior olivary nucleus (ION) has been hypothesized as the prime generator of tremor because of the pacemaker properties of ION neurons, but structural and functional changes in ION are unlikely under ET. Abnormalities have instead been reported in the cerebello-thalamo-cortical network, including dysfunctions of the GABAergic projections from the cerebellar cortex to the dentate nucleus. It remains unclear, though, how tremor would relate to a dysfunction of cerebellar connectivity. To address this question, we built a computational model of the cortico-cerebello-thalamo-cortical loop. We simulated the effects of a progressive loss of GABAA α1-receptor subunits and up-regulation of α2/3-receptor subunits in the dentate nucleus, and correspondingly, we studied the evolution of the firing patterns along the loop. The model closely reproduced experimental evidence for each structure in the loop. It showed that an alteration of amplitudes and decay times of the GABAergic currents to the dentate nucleus can facilitate sustained oscillatory activity at tremor frequency throughout the network as well as a robust bursting activity in the thalamus, which is consistent with observations of thalamic tremor cells in ET patients. Tremor-related oscillations initiated in small neural populations and spread to a larger network as the synaptic dysfunction increased, while thalamic high-frequency stimulation suppressed tremor-related activity in thalamus but increased the oscillation frequency in the olivocerebellar loop. These results suggest a mechanism for tremor generation under cerebellar dysfunction, which may explain the origin of ET.

Therapeutic mechanisms of high-frequency stimulation in Parkinson's disease and neural restoration via loop-based reinforcement.

High-frequency deep brain stimulation (HFS) is clinically recognized to treat parkinsonian movement disorders, but its mechanisms remain elusive. Current hypotheses suggest that the therapeutic merit of HFS stems from increasing the regularity of the firing patterns in the basal ganglia (BG). Although this is consistent with experiments in humans and animal models of Parkinsonism, it is unclear how the pattern regularization would originate from HFS. To address this question, we built a computational model of the cortico-BG-thalamo-cortical loop in normal and parkinsonian conditions. We simulated the effects of subthalamic deep brain stimulation both proximally to the stimulation site and distally through orthodromic and antidromic mechanisms for several stimulation frequencies (20-180 Hz) and, correspondingly, we studied the evolution of the firing patterns in the loop. The model closely reproduced experimental evidence for each structure in the loop and showed that neither the proximal effects nor the distal effects individually account for the observed pattern changes, whereas the combined impact of these effects increases with the stimulation frequency and becomes significant for HFS. Perturbations evoked proximally and distally propagate along the loop, rendezvous in the striatum, and, for HFS, positively overlap (reinforcement), thus causing larger poststimulus activation and more regular patterns in striatum. Reinforcement is maximal for the clinically relevant 130-Hz stimulation and restores a more normal activity in the nuclei downstream. These results suggest that reinforcement may be pivotal to achieve pattern regularization and restore the neural activity in the nuclei downstream and may stem from frequency-selective resonant properties of the loop.

Network dynamics of the brain and influence of the epileptic seizure onset zone.

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.

KCNQ2 channels regulate the population activity of neonatal GABAergic neurons ex vivo.

Over the last decade KCNQ2 channels have arisen as fundamental and indispensable regulators of neonatal brain excitability, with KCNQ2 loss-of-function pathogenic variants being increasingly identified in patients with developmental and epileptic encephalopathy. However, the mechanisms by which KCNQ2 loss-of-function variants lead to network dysfunction are not fully known. An important remaining knowledge gap is whether loss of KCNQ2 function alters GABAergic interneuron activity early in development. To address this question, we applied mesoscale calcium imaging ex vivo in postnatal day 4-7 mice lacking KCNQ2 channels in interneurons (Vgat-ires-cre;Kcnq2f/f;GCamp5). In the presence of elevated extracellular potassium concentrations, ablation of KCNQ2 channels from GABAergic cells increased the interneuron population activity in the hippocampal formation and regions of the neocortex. We found that this increased population activity depends on fast synaptic transmission, with excitatory transmission promoting the activity and GABAergic transmission curtailing it. Together, our data show that loss of function of KCNQ2 channels from interneurons increases the network excitability of the immature GABAergic circuits, revealing a new function of KCNQ2 channels in interneuron physiology in the developing brain.

Long-Term In Vivo Molecular Monitoring Using Aptamer-Graphene Microtransistors.

Long-term, real-time molecular monitoring in complex biological environments is critical for our ability to understand, prevent, diagnose, and manage human diseases. Aptamer-based electrochemical biosensors possess the promise due to their generalizability and a high degree of selectivity. Nevertheless, the operation of existing aptamer-based biosensors in vivo is limited to a few hours. Here, we report a first-generation long-term in vivo molecular monitoring platform, named aptamer-graphene microtransistors (AGMs). The AGM incorporates a layer of pyrene-(polyethylene glycol)5-alcohol and DNase inhibitor-doped polyacrylamide hydrogel coating to reduce biofouling and aptamer degradation. As a demonstration of function and generalizability, the AGM achieves the detection of biomolecules such as dopamine and serotonin in undiluted whole blood at 37 °C for 11 days. Furthermore, the AGM successfully captures optically evoked dopamine release in vivo in mice for over one week and demonstrates the capability to monitor behaviorally-induced endogenous dopamine release even after eight days of implantation in freely moving mice. The results reported in this work establish the potential for chronic aptamer-based molecular monitoring platforms, and thus serve as a new benchmark for molecular monitoring using aptamer-based technology.

Quickest detection of drug-resistant seizures: an optimal control approach.

Epilepsy affects 50 million people worldwide, and seizures in 30% of the cases remain drug resistant. This has increased interest in responsive neurostimulation, which is most effective when administered during seizure onset. We propose a novel framework for seizure onset detection that involves (i) constructing statistics from multichannel intracranial EEG (iEEG) to distinguish nonictal versus ictal states; (ii) modeling the dynamics of these statistics in each state and the state transitions; you can remove this word if there is no room. (iii) developing an optimal control-based "quickest detection" (QD) strategy to estimate the transition times from nonictal to ictal states from sequential iEEG measurements. The QD strategy minimizes a cost function of detection delay and false positive probability. The solution is a threshold that non-monotonically decreases over time and avoids responding to rare events that normally trigger false positives. We applied QD to four drug resistant epileptic patients (168 hour continuous recordings, 26-44 electrodes, 33 seizures) and achieved 100% sensitivity with low false positive rates (0.16 false positive/hour). This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Loss of KCNQ2 or KCNQ3 Leads to Multifocal Time-Varying Activity in the Neonatal Forebrain Ex Vivo.

Epileptic encephalopathies represent a group of disorders often characterized by refractory seizures, regression in cognitive development, and typically poor prognosis. Dysfunction of KCNQ2 and KCNQ3 channels has emerged as a major cause of neonatal epilepsy. However, our understanding of the cellular mechanisms that may both explain the origins of epilepsy and inform treatment strategies for KCNQ2 and KCNQ3 dysfunction is still lacking. Here, using mesoscale calcium imaging and pharmacology, we demonstrate that in mouse neonatal brain slices, conditional loss of Kcnq2 from forebrain excitatory neurons (Pyr:Kcnq2 mice) or constitutive deletion of Kcnq3 leads to sprawling hyperactivity across the neocortex. Surprisingly, the generation of time-varying hypersynchrony in slices from Pyr:Kcnq2 mice does not require fast synaptic transmission. This is in contrast to control littermates and constitutive Kcnq3 knock-out mice where activity is primarily driven by fast synaptic transmission in the neocortex. Unlike in the neocortex, hypersynchronous activity in the hippocampal formation from Kcnq2 conditional and Kcnq3 constitutive knock-out mice persists in the presence of synaptic transmission blockers. Thus, we propose that loss of KCNQ2 or KCNQ3 function differentially leads to network hyperactivity across the forebrain in a region-specific and macro-circuit-specific manner.

Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence.

Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be transiently suppressed via transcranial electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained shortly after the end of the stimulation and can be phenomenologically predicted. Finally, we use feature-based statistical-learning and neurophysiological-modelling to show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the aberrant oscillations in the olivocerebellar loop, thus establishing its causal role. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may in the future represent a powerful neuromodulatory strategy to treat brain disorders.

The critical role of persistent sodium current in hippocampal gamma oscillations.

Gamma network oscillations in the brain are fast rhythmic network oscillations in the gamma frequency range (~30-100 Hz), playing key roles in the hippocampus for learning, memory, and spatial processing. There is evidence indicating that GABAergic interneurons, including parvalbumin-expressing basket cells (PVBCs), contribute to cortical gamma oscillations through synaptic interactions with excitatory cells. However, the molecular, cellular, and circuit underpinnings underlying generation and maintenance of cortical gamma oscillations are largely elusive. Recent studies demonstrated that intrinsic and synaptic properties of GABAergic interneurons and excitatory cells are regulated by a slowly inactivating or non-inactivating sodium current (i.e., persistent sodium current, INaP), suggesting that INaP is involved in gamma oscillations. Here, we tested whether INaP plays a role in hippocampal gamma oscillations using pharmacological, optogenetic, and electrophysiological approaches. We found that INaP blockers, phenytoin (40 µM and 100 µM) and riluzole (10 µM), reduced gamma oscillations induced by optogenetic stimulation of CaMKII-expressing cells in CA1 networks. Whole-cell patch-clamp recordings further demonstrated that phenytoin (100 µM) reduced INaP and firing frequencies in both PVBCs and pyramidal cells without altering threshold and amplitude of action potentials, but increased rheobase in both cell types. These results suggest that INaP in pyramidal cells and PVBCs is required for hippocampal gamma oscillations, supporting a pyramidal-interneuron network gamma model. Phenytoin-mediated modulation of hippocampal gamma oscillations may be a mechanism underlying its anticonvulsant efficacy, as well as its contribution to cognitive impairments in epilepsy patients.

Decision Support System for Seizure Onset Zone Localization Based on Channel Ranking and High-Frequency EEG Activity.

Interictal high-frequency oscillations (HFO) are a promising biomarker that can help define the seizure onset zone (SOZ) and predict the surgical outcome after the epilepsy surgery. The utility of HFO in planning the surgery, though, is unclear. Reasons include the variability of the HFO across patients and brain regions and the influence of the sleep-wake cycle, which causes large fluctuations in the ratio between the HFO observed in SOZ and non-SOZ regions. To cope with these limitations, a rank-based solution is proposed to identify the SOZ by using the HFO in multichannel intracranial EEG. A time-varying index of the epileptic susceptibility of the different brain areas is derived from the HFO rate and a support vector machine is applied on this index to identify the SOZ. The solution is trained and tested on separate groups of patients to avoid the use of patient-specific information and provides optimal SOZ prediction using as little as 30 min of recordings per channel (window). Tested on 14 patients with various combinations of seizure type, epilepsy etiology, and SOZ arrangement (172.7 ± 90.1 h/channel per patient and 75.6 ± 23.5 channels/patient, mean ± S.D.), our solution identified the SOZ with 0.92 ± 0.03 accuracy and 0.91 ± 0.03 area under the ROC curve (mean ± S.D.) across patients. For each patient, the window onset time was varied over 72 continuous hours and the prediction of the SOZ remained insensitive to the onset time, thus showing potential for surgery planning.

Modulations in Oscillatory Activity of Globus Pallidus Internus Neurons During a Directed Hand Movement Task-A Primary Mechanism for Motor Planning.

Globus pallidus internus (GPi) neurons in the basal ganglia are traditionally thought to play a significant role in the promotion and suppression of movement via a change in firing rates. Here, we hypothesize that a primary mechanism of movement control by GPi neurons is through specific modulations in their oscillatory patterns. We analyzed neuronal spiking activity of 83 GPi neurons recorded from two healthy nonhuman primates executing a radial center-out motor task. We found that, in directionally tuned neurons, the power in the gamma band is significantly (p < 0.05) greater than that in the beta band (a "cross-over" effect), during the planning stages of movements in their preferred direction. This cross-over effect is not observed in the non-directionally tuned neurons. These data suggest that, during movement planning, information encoding by GPi neurons may be governed by a sudden emergence and suppression of oscillatory activities, rather than simply by a change in average firing rates.

Functional maturation of human neural stem cells in a 3D bioengineered brain model enriched with fetal brain-derived matrix.

Brain extracellular matrix (ECM) is often overlooked in vitro brain tissue models, despite its instructive roles during development. Using developmental stage-sourced brain ECM in reproducible 3D bioengineered culture systems, we demonstrate enhanced functional differentiation of human induced neural stem cells (hiNSCs) into healthy neurons and astrocytes. Particularly, fetal brain tissue-derived ECM supported long-term maintenance of differentiated neurons, demonstrated by morphology, gene expression and secretome profiling. Astrocytes were evident within the second month of differentiation, and reactive astrogliosis was inhibited in brain ECM-enriched cultures when compared to unsupplemented cultures. Functional maturation of the differentiated hiNSCs within fetal ECM-enriched cultures was confirmed by calcium signaling and spectral/cluster analysis. Additionally, the study identified native biochemical cues in decellularized ECM with notable comparisons between fetal and adult brain-derived ECMs. The development of novel brain-specific biomaterials for generating mature in vitro brain models provides an important path forward for interrogation of neuron-glia interactions.

Temporal Pattern of Ripple Events in Temporal Lobe Epilepsy: Towards a Pattern-based Localization of the Seizure Onset Zone.

Ripples (80-250 Hz) are brief high-frequency oscillations that are often detected in intracranial EEG (iEEG) and are currently investigated as a potential biomarker to facilitate the Iocalization of the seizure onset zone (SOZ) in patients with drug-resistant epilepsy. While the rate and shape of these oscillations have been positively correlated with the SOZ, the temporal pattern of these oscillations in the epileptic brain still requires investigation. In this study, we investigate the temporal pattern of ripple events in five patients with temporal lobe epilepsy (TLE), which is one of the most common forms of epilepsy. The rate of ripple events is positively correlated with the SOZ in TLE but its diagnostic utility in localizing the SOZ remains unclear, which suggests that additional ripple-related features should be investigated. By combining point process modeling and cluster analysis, we show that a recurrent, non-stationary bursting pattern characterizes the SOZ channels consistently across patients, while the non-SOZ channels have poor between-channel similarity and no consistent pattern over time nor across patients. Furthermore, the degree of separation between SOZ and non-SOZ model parameter vectors is significantly higher (ANOVA test, ${P}$-value $P\lt 0.01$) than the degree of separation between the ripple rates, which suggests that the temporal pattern more than the rate may contribute to the pre- surgical Iocalization of the SOZ.

Cortical Network Synchrony Under Applied Electrical Field in vitro.

Synchronous network activity plays a crucial role in complex brain functions. Stimulating the nervous system with applied electric field (EF) is a common tool for probing network responses. We used a gold wire-embedded silk protein film-based interface culture to investigate the effects of applied EFs on random cortical networks of in vitro cultures. Two-week-old cultures were exposed to EF of 27 mV/mm for <1 h and monitored by time-lapse calcium imaging. Network activity was represented by calcium signal time series mapped to source neurons and analyzed by using a community detection algorithm. Cortical cultures exhibited large scale, synchronized oscillations under alternating EF of changing frequencies. Field polarity and frequency change were both found to be necessary for network synchrony, as monophasic pulses of similar frequency changes or EF of a constant frequency failed to induce correlated activities of neurons. Group-specific oscillatory patterns were entrained by network-level synchronous oscillations when the alternating EF frequency was increased from 0.2 Hz to 200 kHz. Binary responses of either activity increase or decrease contributed to the opposite phase patterns of different sub-populations. Conversely, when the EF frequency decreased over the same range span, more complex behavior emerged showing group-specific amplitude and phase patterns. These findings formed the basis of a hypothesized network control mechanism for temporal coordination of distributed neuronal activity, involving coordinated stimulation by alternating polarity, and time delay by change of frequency. These novel EF effects on random neural networks have important implications for brain functional studies and neuromodulation applications.

Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease.

Over the last 30 years, deep brain stimulation (DBS) has been used to treat chronic neurological diseases like dystonia, obsessive-compulsive disorders, essential tremor, Parkinson's disease, and more recently, dementias, depression, cognitive disorders, and epilepsy. Despite its wide use, DBS presents numerous challenges for both clinicians and engineers. One challenge is the design of novel, more efficient DBS therapies, which are hampered by the lack of complete understanding about the cellular mechanisms of therapeutic DBS. Another challenge is the existence of redundancy in clinical outcomes, that is, different DBS programs can result in similar clinical benefits but very little information (e.g., predictive models, longitudinal data, metrics, etc.) is available to select one program over another. Finally, there is high variability in patients' responses to DBS, which forces clinicians to carefully adjust the stimulation settings to each patient via lengthy programming sessions. Researchers in neural engineering and systems biology have been tackling these challenges over the past few years with the specific goal of developing novel DBS therapies, design methodologies, and computational tools that optimize the therapeutic effects of DBS in each patient. Furthermore, efforts are being made to automatically adapt the DBS treatment to the fluctuations of disease symptoms. A review of the quantitative approaches currently available for the treatment of Parkinson's disease is presented here with an emphasis on the contributions that systems theoretical approaches have provided to understand the global dynamics of complex neuronal circuits in the brain under DBS. This article is categorized under: Translational, Genomic, and Systems Medicine > Therapeutic Methods Analytical and Computational Methods > Computational Methods Analytical and Computational Methods > Dynamical Methods Physiology > Mammalian Physiology in Health and Disease.

Closed-loop low-frequency DBS restores thalamocortical relay fidelity in a computational model of the motor loop.

Closed-loop modulation of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) is investigated to automatically adjust the stimulation to the patients' conditions, optimize the clinical outcomes, and reduce the energy requirements. This study proposes a closed-loop control system for real-time adaptation of the STN-DBS amplitude based on the neural activity in the motor thalamus. Population-averaged post-stimulus time histograms are used to measure the average effects of STN-DBS on the thalamocortical neurons and a L2-norm minimization problem is solved to design the control algorithm, while the frequency of stimulation is kept constant. Applied on a large-scale, biophysically-based, anatomically-compliant model of the cortico-basal ganglia-thalamo-cortical motor loop under PD conditions, our adaptive DBS significantly (P-value P<;0.05) improved the relay fidelity of the thalamocortical neurons and restored the average power of the thalamocortical spike trains in the band [3, 100] Hz, two indicators of restored thalamocortical activity. Furthermore, adaptive-DBS significantly decreased the energy requirements when compared with non-adaptive-DBS at the same frequency. Finally, 30- and 60-Hz-adaptive-DBS determined the maximal restoration of thalamocortical activity and outperformed high-frequency, non-adaptive-DBS. Overall, results suggest that a feedback-controlled, low-frequency DBS pattern may result in significant restoration of the thalamocortical encoding while lowering the energy requirements.

A novel HFO-based method for unsupervised localization of the seizure onset zone in drug-resistant epilepsy.

High frequency oscillations (HFOs) are potential biomarkers of epileptic areas. In patients with drug-resistant epilepsy, HFO rates tend to be higher in the seizure onset zone (SOZ) than in other brain regions and the resection of HFO-generating areas positively correlates with seizure-free surgery outcome. Nonetheless, the development of robust unsupervised HFO-based tools for SOZ localization remains challenging. Current approaches predict the SOZ by processing small samples of intracranial EEG (iEEG) data and applying patient-specific thresholds on the HFO rate. The HFO rate, though, varies largely over time with the patient's conditions (e.g., sleep versus wakefulness) and across patients. We propose a novel localization method for SOZ that uses a time-varying, HFO-based index to estimate the epileptic susceptibility of the iEEG channels. The method is insensitive to the average HFO rate across channels (which is both patient- and condition-specific), tracks the channel susceptibility over time, and predicts the SOZ based on the temporal evolution of the HFO rate. Tested on a preliminary dataset of continuous multi-day multichannel interictal iEEG recordings from two epileptic patients (117±97.6 h/per patient, mean ± S.D.), the reported SOZ prediction had an average 0.70±0.18 accuracy and 0.67±0.07 area under the ROC curve (mean ± S.D.) across patients.