ORCID Profile
0000-0002-8494-0635
Current Organisations
Emory Healthcare
,
University of California Davis
,
Emory University
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Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 12-2021
Publisher: Wiley
Date: 05-08-2021
DOI: 10.1111/EPI.17030
Publisher: Springer Science and Business Media LLC
Date: 14-02-2015
Publisher: Cold Spring Harbor Laboratory
Date: 23-02-2021
DOI: 10.1101/2021.02.22.432356
Abstract: Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. To understand how multiple parameters contribute synergistically to circuit behavior, neuronal computational models are regularly employed. However, traditional models containing anatomically and biophysically realistic neurons are computationally demanding even when scaled to model local circuits. To overcome this limitation, we trained several artificial neural net (ANN) architectures to model the activity of realistic, multicompartmental neurons. We identified a single ANN that accurately predicted both subthreshold and action potential firing and correctly generalized its responses to previously unobserved synaptic input. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach that allows for rapid, detailed network experiments using inexpensive, readily available computational resources.
Publisher: American Physiological Society
Date: 08-2011
Abstract: The rostral ventromedial medulla (RVM) is an important site of opioid actions and forms part of an analgesic pathway that projects to the spinal cord. The neuronal mechanisms by which opioids act within this brain region remain unclear, particularly in relation to the neurotransmitters GABA and serotonin. In the present study, we examined serotonergic and GABAergic immunoreactivity, identified using immunohistochemistry for tryptophan hydroxylase (TPH) and glutamate decarboxylase (GAD), in combination with in vitro whole cell patch cl ing to investigate the role of opioids on the mouse RVM with identified projections to the spinal cord. Tyr-d-Ala-Gly- N-Me-Phe-Gly-ol enkephalin (DAMGO) produced μ-opioid receptor-mediated outward currents in virtually all TPH-immunoreactive projecting neurons and GAD-immunoreactive nonprojecting neurons (87% and 86%). The other groups of RVM neurons displayed mixed responsiveness to DAMGO (40–68%). Deltorphin II and U-69593 produced δ- and κ-opioid receptor-mediated outward currents in smaller subpopulations of RVM neurons, with many of the δ-opioid responders forming a subpopulation of μ-opioid-sensitive GABAergic nonprojecting neurons. These findings are consistent with prior electrophysiological and anatomic studies in the rat RVM and indicate that both serotonergic and GABAergic RVM neurons mediate the actions of μ-opioids. Specifically, μ-opioids have a direct postsynaptic inhibitory influence over both GABAergic and serotonergic neurons, including those that project to the dorsal spinal cord.
Publisher: Wiley
Date: 28-01-2011
DOI: 10.1002/CNE.22559
Publisher: Public Library of Science (PLoS)
Date: 20-01-2010
Publisher: Elsevier BV
Date: 12-2010
Publisher: Cold Spring Harbor Laboratory
Date: 08-04-2023
DOI: 10.1101/2023.04.07.536063
Abstract: Independent automated scoring of sleep-wake and seizures have recently been achieved however, the combined scoring of both states has yet to be reported. Mouse models of epilepsy typically demonstrate an abnormal electroencephalographic (EEG) background with significant variability between mice, making combined scoring a more difficult classification problem for manual and automated scoring. Given the extensive EEG variability between epileptic mice, large group sizes are needed for most studies. As large datasets are unwieldy and impractical to score manually, automatic seizure and sleep-wake classification are warranted. To this end, we developed an accurate automated classifier of sleep-wake states, seizures, and the post-ictal state. Our benchmark was a classification accuracy at or above the 93% level of human inter-rater agreement. Given the failure of parametric scoring in the setting of altered baseline EEGs, we adopted a machine-learning approach. We created several multi-layer neural network architectures that were trained on human-scored training data from an extensive repository of continuous recordings of electrocorticogram (ECoG), left and right hippoc al local field potential (HPC-L and HPC-R), and electromyogram (EMG) in the murine intra-amygdala kainic acid model of medial temporal lobe epilepsy. We then compared different network models, finding a bidirectional long short-term memory (BiLSTM) design to show the best performance with validation and test portions of the dataset. The SWISC (sleep-wake and the ictal state classifier) achieved % scoring accuracy in all categories for epileptic and non-epileptic mice. Classification performance was principally dependent on hippoc al signals and performed well without EMG. Additionally, performance is within desirable limits for recording montages featuring only ECoG channels, expanding its potential scope. This accurate classifier will allow for rapid combined sleep-wake and seizure scoring in mouse models of epilepsy and other neurologic diseases with varying EEG abnormalities, thereby facilitating rigorous experiments with larger numbers of mice.
Publisher: Oxford University Press (OUP)
Date: 06-11-2019
DOI: 10.1093/SLEEP/ZSZ272
Abstract: Despite commercial availability of software to facilitate sleep–wake scoring of electroencephalography (EEG) and electromyography (EMG) in animals, automated scoring of rodent models of abnormal sleep, such as narcolepsy with cataplexy, has remained elusive. We optimize two machine-learning approaches, supervised and unsupervised, for automated scoring of behavioral states in orexin/ataxin-3 transgenic mice, a validated model of narcolepsy type 1, and additionally test them on wild-type mice. The supervised learning approach uses previously labeled data to facilitate training of a classifier for sleep states, whereas the unsupervised approach aims to discover latent structure and similarities in unlabeled data from which sleep stages are inferred. For the supervised approach, we employ a deep convolutional neural network architecture that is trained on expert-labeled segments of wake, non-REM sleep, and REM sleep in EEG/EMG time series data. The resulting trained classifier is then used to infer on the labels of previously unseen data. For the unsupervised approach, we leverage data dimensionality reduction and clustering techniques. Both approaches successfully score EEG/EMG data, achieving mean accuracies of 95% and 91%, respectively, in narcoleptic mice, and accuracies of 93% and 89%, respectively, in wild-type mice. Notably, the supervised approach generalized well on previously unseen data from the same animals on which it was trained but exhibited lower performance on animals not present in the training data due to inter-subject variability. Cataplexy is scored with a sensitivity of 85% and 57% using the supervised and unsupervised approaches, respectively, when compared to manual scoring, and the specificity exceeds 99% in both cases.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 11-01-2022
DOI: 10.1212/WNL.0000000000013033
Abstract: To determine the association between surgical lesions of distinct gray and white structures and connections with favorable postoperative seizure outcomes. Patients with drug-resistant temporal lobe epilepsy (TLE) from 3 epilepsy centers were included. We employed a voxel-based and connectome-based mapping approach to determine the association between favorable outcomes and surgery-induced temporal lesions. Analyses were conducted controlling for multiple confounders, including total surgical resection/ablation volume, hippoc al volumes, side of surgery, and site where the patient was treated. The cohort included 113 patients with TLE (54 women 86 right-handed mean age at seizure onset 16.5 years [SD 11.9] 54.9% left) who were 61.1% free of disabling seizures (Engel Class 1) at follow-up. Postoperative seizure freedom in TLE was associated with (1) surgical lesions that targeted the hippoc us as well as the amygdala–piriform cortex complex and entorhinal cortices (2) disconnection of temporal, frontal, and limbic regions through loss of white matter tracts within the uncinate fasciculus, anterior commissure, and fornix and (3) functional disconnection of the frontal (superior and middle frontal gyri, orbitofrontal region) and temporal (superior and middle pole) lobes. Better postoperative seizure freedom is associated with surgical lesions of specific structures and connections throughout the temporal lobes. These findings shed light on the key components of epileptogenic networks in TLE and constitute a promising source of new evidence for future improvements in surgical interventions. This study provides Class II evidence that for patients with TLE, postoperative seizure freedom is associated with surgical lesions of specific temporal lobe structures and connections.
Publisher: Wiley
Date: 17-10-2011
DOI: 10.1002/CNE.22781
Publisher: Elsevier BV
Date: 04-2008
Publisher: Elsevier BV
Date: 2001
DOI: 10.1016/S0165-0270(00)00347-2
Abstract: We describe a surgical procedure for chronically implanting a Doppler ultrasonic probe around the tail artery of the rat to measure phasic flow velocity in the tail artery of the unrestrained conscious rat. The phasic tail flow signal is highly correlated with the simultaneously recorded superior mesenteric flow signal (range 0.70-0.89 in seven rats) during vasoconstriction induced by exposure to formaldehyde vapour. In response to two quick alerting taps on the cage, tail flow velocity fell from 20+/-2 to 7+/-1 cm/s (P<0.01) and mesenteric flow fell from 30+/-5 to 25+/-4 cm/s (P<0.05), with the fall in tail flow being significantly greater than the fall in mesenteric flow (P<0.05, n=7 rats). In anesthetized rats, the phasic tail flow signal was highly correlated with phasic arterial pressure (range 0.71-0.83 in seven rats). The ability to reliably measure phasic arterial tail flow in the conscious unrestrained rat should facilitate experimental studies of brain pathways regulating flow to this principally cutaneous vascular bed in different physiological situations.
Publisher: Wiley
Date: 14-07-2008
Publisher: Springer Science and Business Media LLC
Date: 10-11-2017
DOI: 10.1038/S41467-017-01004-6
Abstract: Basic and clinical observations suggest that the caudal hypothalamus comprises a key node of the ascending arousal system, but the cell types underlying this are not fully understood. Here we report that glutamate-releasing neurons of the supramammillary region (SuM vglut2 ) produce sustained behavioral and EEG arousal when chemogenetically activated. This effect is nearly abolished following selective genetic disruption of glutamate release from SuM vglut2 neurons. Inhibition of SuM vglut2 neurons decreases and fragments wake, also suppressing theta and gamma frequency EEG activity. SuM vglut2 neurons include a subpopulation containing both glutamate and GABA (SuM vgat/vglut2 ) and another also expressing nitric oxide synthase (SuM Nos1/Vglut2 ). Activation of SuM vgat/vglut2 neurons produces minimal wake and optogenetic stimulation of SuM vgat/vglut2 terminals elicits monosynaptic release of both glutamate and GABA onto dentate granule cells. Activation of SuM Nos1/Vglut2 neurons potently drives wakefulness, whereas inhibition reduces REM sleep theta activity. These results identify SuM vglut2 neurons as a key node of the wake−sleep regulatory system.
Publisher: Elsevier BV
Date: 10-2020
Publisher: Cold Spring Harbor Laboratory
Date: 04-10-2021
DOI: 10.1101/2021.10.01.21264435
Abstract: Studies of epilepsy surgery outcomes are often small and thus underpowered to reach statistically valid conclusions. We hypothesized that ordinal logistic regression would have greater statistical power than binary logistic regression when analyzing epilepsy surgery outcomes. We reviewed 10 manuscripts included in a recent meta-analysis which found that mesial temporal sclerosis (MTS) predicted better surgical outcome after a stereotactic laser amygdalohippoc ectomy (SLAH). We extracted data from 239 patients from eight studies which reported four discrete Engel surgical outcomes after SLAH, stratified by the presence or absence of MTS. The rate of freedom from disabling seizures (Engel I) was 64.3% (110/171) for patients with MTS compared to 44.1% (30/68) without MTS. The statistical power to detect MTS as a predictor for better surgical outcome after a SLAH was 29% using ordinal regression, which was significantly more than the 13% power using binary logistic regression (paired t-test, p .001). Only 120 patients are needed to achieve 80% power to detect MTS as a predictor using ordinal regression, compared to 210 patients that are needed to achieve 80% power using binary logistic regression. Ordinal regression should be considered when analyzing ordinal outcomes (such as Engel surgical outcome), especially for datasets with small s le sizes.
Publisher: Springer Science and Business Media LLC
Date: 03-11-2015
DOI: 10.1038/NCOMMS9744
Abstract: Wakefulness, along with fast cortical rhythms and associated cognition, depend on the basal forebrain (BF). BF cholinergic cell loss in dementia and the sedative effect of anti-cholinergic drugs have long implicated these neurons as important for cognition and wakefulness. The BF also contains intermingled inhibitory GABAergic and excitatory glutamatergic cell groups whose exact neurobiological roles are unclear. Here we show that genetically targeted chemogenetic activation of BF cholinergic or glutamatergic neurons in behaving mice produced significant effects on state consolidation and/or the electroencephalogram but had no effect on total wake. Similar activation of BF GABAergic neurons produced sustained wakefulness and high-frequency cortical rhythms, whereas chemogenetic inhibition increased sleep. Our findings reveal a major contribution of BF GABAergic neurons to wakefulness and the fast cortical rhythms associated with cognition. These findings may be clinically applicable to manipulations aimed at increasing forebrain activation in dementia and the minimally conscious state.
Publisher: Oxford University Press (OUP)
Date: 08-12-2021
DOI: 10.1093/BRAINCOMMS/FCAB284
Abstract: Temporal lobe epilepsy is associated with MRI findings reflecting underlying mesial temporal sclerosis. Identifying these MRI features is critical for the diagnosis and management of temporal lobe epilepsy. To date, this process relies on visual assessment by highly trained human experts (e.g. neuroradiologists, epileptologists). Artificial intelligence is increasingly recognized as a promising aid in the radiological evaluation of neurological diseases, yet its applications in temporal lobe epilepsy have been limited. Here, we applied a convolutional neural network to assess the classification accuracy of temporal lobe epilepsy based on structural MRI. We demonstrate that convoluted neural networks can achieve high accuracy in the identification of unilateral temporal lobe epilepsy cases even when the MRI had been originally interpreted as normal by experts. We show that accuracy can be potentiated by employing smoothed grey matter maps and a direct acyclic graphs approach. We further discuss the foundations for the development of computer-aided tools to assist with the diagnosis of epilepsy.
Publisher: Springer Science and Business Media LLC
Date: 08-01-2015
Publisher: Cold Spring Harbor Laboratory
Date: 16-05-2022
DOI: 10.1101/2022.05.11.22274965
Abstract: Low statistical power is a recognized problem in many fields. We performed a systematic review to determine the median statistical power of studies of epilepsy surgery outcomes. We performed a PubMed search for studies reporting epilepsy surgery outcomes for the years 1980-2020, focusing on studies using stereoelectroencephalography (SEEG). We extracted patient count data for comparisons of surgical outcome between two groups, based on a reported prognostic factor. We defined a clinically meaningful difference as the difference in seizure freedom for MRI positive (66.9%) versus negative (45.5%) from the largest study found. Based on 69 studies of surgery outcomes in patients undergoing SEEG, the median s le size was 38 patients, and the median statistical power was 24%. This implies at least a 17% chance a study with a significant result is false, assuming 1:1 pre-test odds. Results from simulation studies suggest that, if a typical SEEG study finds a significant effect, then the median observed effect size will be more than double the true effect size. We conclude that studies of epilepsy surgery outcomes using SEEG are often statistically underpowered, which limits the reproducibility and reliability of the literature. We discuss how statistical power could be improved. We performed a systematic review to determine the median statistical power of studies of epilepsy surgery outcomes, focused on stereoelectroencephalography. We extracted patient count data for comparisons of outcomes between two groups. We defined a clinically meaningful difference as the prognostic value of a normal versus abnormal MRI. Based on 69 studies, the median s le size was 38 patients, and the median statistical power was 24%. Underpowered studies will overestimate the size of true effects and are more likely to report false positive results. We discuss how statistical power, and thus reproducibility and reliability of results, can be improved.
Publisher: Society for Neuroscience
Date: 27-10-2010
DOI: 10.1523/JNEUROSCI.3037-10.2010
Abstract: Locus ceruleus (LC) neuronal activity is correlated with the waking state, yet LC lesions produce only minor alterations in daily wakefulness. Here, we report that sustained elevations in neurobehavioral and EEG arousal in response to exposure to an environment with novel stimuli, including social interaction, are prevented by selective chemical lesions of the LC in rats. Similar results are seen when the anterior cingulate cortex (ACC), which receives especially dense LC innervation, is selectively denervated of LC input or is ablated by the cell-specific neurotoxin ibotenic acid. Anterograde tracing combined with tyrosine hydroxylase immunohistochemistry demonstrates ACC terminals in apposition with the distal dendrites of LC neurons. Our data implicate the ACC as both a source of input to the LC as well as one of its targets and suggests that the two structures engage in a dialog that may provide a critical neurobiological substrate for sustained attention.
Publisher: SAGE Publications
Date: 05-2019
Abstract: [Box: see text]
Publisher: Elsevier BV
Date: 2006
DOI: 10.1016/J.NEUROSCIENCE.2005.10.052
Abstract: Previous studies using c-Fos immunohistochemistry suggest that a sub-population of neurons in the midbrain periaqueductal gray region is activated during opioid withdrawal. The neurochemical identity of these cells is unknown but cellular physiological studies have implicated GABAergic neurons. The present study investigated whether GABAergic neurons are activated in the mouse periaqueductal gray during opioid withdrawal using dual-antibody immunohistochemistry for Fos and glutamic acid decarboxylase. Both chronic opioid treatment and naloxone-precipitated opioid withdrawal increased Fos expression in the periaqueductal gray, with the greatest increase being four-fold in the caudal ventrolateral sub ision following withdrawal. Neurons stained for both Fos and glutamic acid decarboxylase were greatly enhanced in all sub isions of the periaqueductal gray following withdrawal, particularly in the lateral and ventrolateral isions where the increase was up to 70-fold. These results suggest that activation of a subpopulation of GABAergic interneurons in the periaqueductal gray plays a role in opioid withdrawal.
Publisher: American Society for Clinical Investigation
Date: 11-02-2019
DOI: 10.1172/JCI120110
Publisher: eLife Sciences Publications, Ltd
Date: 07-11-2022
DOI: 10.7554/ELIFE.79535
Abstract: Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.
Publisher: Elsevier BV
Date: 2022
Publisher: American Association for the Advancement of Science (AAAS)
Date: 22-02-2019
Abstract: Two main proteins accumulate in the brain in Alzheimer's disease (AD), β-amyloid (Aβ) and tau. Aβ appears to instigate AD, but tau appears to drive brain damage and cognitive decline. Sleep deprivation is known to increase Aβ acutely and chronically. Now, Holth et al. show that chronic sleep deprivation strongly increases tau acutely over hours and also drives tau pathology spreading in the brains of mice and humans (see the Perspective by Noble and Spires-Jones). Thus, sleep appears to have a direct protective effect on a key protein that drives AD pathology. Science , this issue p. 880 see also p. 813
Publisher: eLife Sciences Publications, Ltd
Date: 21-09-2022
Publisher: Elsevier BV
Date: 03-2020
Location: United States of America
Start Date: 2003
End Date: 2006
Funder: National Health and Medical Research Council
View Funded ActivityStart Date: 2015
End Date: 2017
Funder: American Academy of Neurology and the American Brain Foundation
View Funded ActivityStart Date: 2018
End Date: 2023
Funder: National Institutes of Health
View Funded ActivityStart Date: 2012
End Date: 2014
Funder: NIH, NINDS
View Funded Activity