Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks

Levodopa-induced dyskinesias are abnormal involuntary movements experienced by the majority of persons with Parkinson's disease (PwP) at some point over the course of the disease. Choreiform as the most common phenomenology of levodopa-induced dyskinesias can be managed by adjusting the dose/frequency of PD medication(s) based on a PwP's motor fluctuations over a typical day. We developed a sensor-based assessment system to provide such information. We used movement data collected from the upper and lower extremities of 15 PwPs along with a deep recurrent model to estimate dyskinesia severity as they perform different activities of daily living (ADL). Subjects performed a variety of ADLs during a 4-h period while their dyskinesia severity was rated by the movement disorder experts. The estimated dyskinesia severity scores from our model correlated highly with the expert-rated scores (r = 0.87 (p < 0.001)), which was higher than the performance of linear regression that is commonly used for dyskinesia estimation (r = 0.81 (p < 0.001)). Our model provided consistent performance at different ADLs with minimum r = 0.70 (during walking) to maximum r = 0.84 (drinking) in comparison to linear regression with r = 0.00 (walking) to r = 0.76 (cutting food). These findings suggest that when our model is applied to at-home sensor data, it can provide an accurate picture of changes of dyskinesia severity facilitating effective medication adjustments.

Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III

BACKGROUND

Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson's disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson's disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications.

METHODS

We developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time-frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time-frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data.

RESULTS

The algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scores had a correlation of [Formula: see text] and a mean absolute error of 5.95 with the clinical examination scores without requiring the patients to perform any specific tasks.

CONCLUSION

Our analysis demonstrates the potential of our algorithm for estimating PD severity scores unobtrusively at home. Such an algorithm could provide the required motor-complication measurements without unnecessary clinical visits and help the treating physician provide effective management of the disease.

Dyskinesia Severity Estimation in Patients with Parkinson's Disease Using Wearable Sensors and A Deep LSTM Network

Dyskinesias are abnormal involuntary movements that patients with mid-stage and advanced Parkinson's disease (PD) may suffer from. These troublesome motor impairments are reduced by adjusting the dose or frequency of medication levodopa. However, to make a successful adjustment, the treating physician needs information about the severity rating of dyskinesia as patients experience in their natural living environment. In this work, we used movement data collected from the upper and lower extremities of PD patients along with a deep model based on Long Short-Term Memory to estimate the severity of dyskinesia. We trained and validated our model on a dataset of 14 PD subjects with dyskinesia. The subjects performed a variety of daily living activities while their dyskinesia severity was rated by a neurologist. The estimated dyskinesia severity ratings from our developed model highly correlated with the neurologist-rated dyskinesia scores (r=0.86 (p<0.001) and 1.77 MAE (6%)) indicating the potential of the developed the approach in providing the information required for effective medication adjustments for dyskinesia management.

Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements

Tremor is one of the main symptoms of Parkinson's Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients' tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients' tremor from continuous monitoring of the subjects' movement in their natural environment.

Utilization of rehabilitation therapy services in Parkinson disease in the United States

OBJECTIVE

To examine rehabilitation therapy utilization for Parkinson disease (PD).

METHODS

We identified 174,643 Medicare beneficiaries with a diagnosis of PD in 2007 and followed them through 2009. The main outcome measures were annual receipt of physical therapy (PT), occupational therapy (OT), or speech therapy (ST).

RESULTS

Outpatient rehabilitation fee-for-service use was low. In 2007, only 14.2% of individuals with PD had claims for PT or OT, and 14.6% for ST. Asian Americans were the highest users of PT/OT (18.4%) and ST (18.4%), followed by Caucasians (PT/OT 14.4%, ST 14.8%). African Americans had the lowest utilization (PT/OT 7.8%, ST 8.2%). Using logistic regression models that accounted for repeated measures, we found that African American patients (adjusted odds ratio [AOR] 0.63 for PT/OT, AOR 0.63 for ST) and Hispanic patients (AOR 0.97 for PT/OT, AOR 0.91 for ST) were less likely to have received therapies compared to Caucasian patients. Patients with PD with at least one neurologist visit per year were 43% more likely to have a claim for PT evaluation as compared to patients without neurologist care (AOR 1.43, 1.30-1.48), and this relationship was similar for OT evaluation, PT/OT treatment, and ST. Geographically, Western states had the greatest use of rehabilitation therapies, but provider supply did not correlate with utilization.

CONCLUSIONS

This claims-based analysis suggests that rehabilitation therapy utilization among older patients with PD in the United States is lower than reported for countries with comparable health care infrastructure. Neurologist care is associated with rehabilitation therapy use; provider supply is not.

Telehealth Management of Parkinson's Disease Using Wearable Sensors: An Exploratory Study

Background

Parkinson's disease (PD) motor symptoms can fluctuate and may not be accurately reflected during a clinical evaluation. In addition, access to movement disorder specialists is limited for many with PD. The objective was to assess the impact of motion sensor-based telehealth diagnostics on PD clinical care and management.

Methods

Eighteen adults with PD were randomized to control or experimental groups. All participants were instructed to use a motion sensor-based monitoring system at home one day per week, for seven months. The system included a finger-worn motion sensor and tablet-based software interface that guided patients through tasks to quantify tremor, bradykinesia, and dyskinesia. Data were processed into motor symptom severity reports, which were reviewed by a movement disorders neurologist for experimental group participants. After three months and six months, control group participants visited the clinic for a routine appointment, while experimental group participants had a videoconference or phone call instead.

Results

Home based assessments were completed with median compliance of 95.7%. For a subset of participants, the neurologist successfully used information in the reports such as quantified response to treatment or progression over time to make therapy adjustments. Changes in clinical characteristics from study start to end were not significantly different between groups.

Discussion

Individuals with PD were able and willing to use remote monitoring technology. Patient management aided by telehealth diagnostics provided comparable outcomes to standard care. Telehealth technologies combined with wearable sensors have the potential to improve care for disparate PD populations or those unable to travel.

Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors

OBJECTIVE

Fluctuations in response to levodopa in Parkinson's disease (PD) are difficult to treat as tools to monitor temporal patterns of symptoms are hampered by several challenges. The objective was to use wearable sensors to quantify the dose response of tremor, bradykinesia, and dyskinesia in individuals with PD.

METHODS

Thirteen individuals with PD and fluctuating motor benefit were instrumented with wrist and ankle motion sensors and recorded by video. Kinematic data were recorded as subjects completed a series of activities in a simulated home environment through transition from off to on medication. Subjects were evaluated using the unified Parkinson disease rating scale motor exam (UPDRS-III) at the start and end of data collection. Algorithms were applied to the kinematic data to score tremor, bradykinesia, and dyskinesia. A blinded clinician rated severity observed on video. Accuracy of algorithms was evaluated by comparing scores with clinician ratings using a receiver operating characteristic (ROC) analysis.

RESULTS

Algorithm scores for tremor, bradykinesia, and dyskinesia agreed with clinician ratings of video recordings (ROC area > 0.8). Summary metrics extracted from time intervals before and after taking medication provided quantitative measures of therapeutic response (p < 0.01). Radar charts provided intuitive visualization, with graphical features correlated with UPDRS-III scores (R = 0.81).

CONCLUSION

A system with wrist and ankle motion sensors can provide accurate measures of tremor, bradykinesia, and dyskinesia as patients complete routine activities.

SIGNIFICANCE

This technology could provide insight on motor fluctuations in the context of daily life to guide clinical management and aid in development of new therapies.

Automatic assessment of medication states of patients with Parkinson's disease using wearable sensors

Motor fluctuations are a major focus of clinical managements in patients with mid-stage and advance Parkinson's disease (PD). In this paper, we develop a new patient-specific algorithm that can classify those fluctuations during a variety of activities. We extract a set of temporal and spectral features from the ambulatory signals and then introduce a semi-supervised classification algorithm based on K-means and self-organizing tree map clustering methods. Two different types of cluster labeling are introduced: hard and fuzzy labeling. The developed algorithm is evaluated on a dataset from triaxial gyroscope sensors for 12 PD patients. The average result of using K-means and fuzzy labeling on the trunk and the more affected leg sensors' readings was 75.96%, 70.57%, and 86.93% for accuracy, sensitivity, and specificity, respectively. The accuracy for individual patients varied from 99.95% to 42.53%, which was correlated with dyskinesia severity and the improvement of the PD symptoms with medication.

Technology in Parkinson's disease: Challenges and opportunities

The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society.

Motion sensor dyskinesia assessment during activities of daily living

BACKGROUND

Dyskinesia throughout the levodopa dose cycle has been previously measured in patients with Parkinson's disease (PD) using a wrist-worn motion sensor during the stationary tasks of arms resting and extended. Quantifying dyskinesia during unconstrained activities poses a unique challenge since these involuntary movements are kinematically similar to voluntary movement.

OBJECTIVE

To determine the feasibility of using motion sensors to measure dyskinesia during activities of daily living.

METHODS

Fifteen PD subjects performed scripted activities of daily living while wearing motion sensors on bilateral hands, thighs, and ankles over the course of a levodopa dose cycle. Videos were scored by clinicians using the modified Abnormal Involuntary Movement Scale to rate dyskinesia severity in separate body regions, with the total score used as an overall measure. Kinematic features were extracted from the motion data and algorithms were generated to output severity scores.

RESULTS

Movements when subjects were experiencing dyskinesia were less smooth than when they were not experiencing dyskinesia. Dyskinesia scores predicted by the model using all sensors were highly correlated with clinician scores, with a correlation coefficient of 0.86 and normalized root-mean-square-error of 7.4%. Accurate predictions were maintained when two sensors on the most affected side of the body (one on the upper extremity and one on the lower extremity) were used.

CONCLUSIONS

A system with motion sensors may provide an accurate measure of overall dyskinesia that can be used to monitor patients as they complete typical activities, and thus provide insight on symptom fluctuation in the context of daily life.

Objective motion sensor assessment highly correlated with scores of global levodopa-induced dyskinesia in Parkinson's disease

BACKGROUND

Chronic use of medication for treating Parkinson's disease (PD) can give rise to peak-dose dyskinesia. Adjustments in medication often sacrifice control of motor symptoms, and thus balancing this trade-off poses a significant challenge for disease management.

OBJECTIVE

To determine whether a wrist-worn motion sensor unit could be used to ascertain global dyskinesia severity over a levodopa dose cycle and to develop a severity scoring algorithm highly correlated with clinician ratings.

METHODS

Fifteen individuals with PD were instrumented with a wrist-worn motion sensor unit, and data were collected with arms in resting and extended positions once every hour for three hours after taking a levodopa dose. Two neurologists blinded to treatment status viewed subject videos and rated global and upper extremity dyskinesia severity based on the modified Abnormal Involuntary Movement Scale (mAIMS). Linear regression models were developed using kinematic features extracted from motion sensor data and extremity, global, or combined (average of extremity and global) mAIMS scores.

RESULTS

Dyskinesia occurring during a levodopa dose cycle was successfully measured using a wrist-worn sensor. The logarithm of the power spectrum area between 0.3-3 Hz and the combined clinician scores resulted in the best model performance, with a correlation coefficient between clinician and model scores of 0.81 and root mean square error of 0.55, both averaged across the arms resting and extended postures.

CONCLUSIONS

One sensor unit worn on either hand can effectively predict global dyskinesia severity during the arms resting or extended positions.

Quantitative assessment of levodopa-induced dyskinesia using automated motion sensing technology

The objective was to capture levodopa-induced dyskinesia (LID) in patients with Parkinson's disease (PD) using body-worn motion sensors. Dopaminergic treatment in PD can induce abnormal involuntary movements, including choreatic dyskinesia (brief, rapid, irregular movements). Adjustments in medication to reduce LID often sacrifice control of motor symptoms, and balancing this tradeoff poses a significant challenge for management of advanced PD. Fifteen PD subjects with known LID were recruited and instructed to perform two stationary motor tasks while wearing a compact wireless motion sensor unit positioned on each hand over the course of a levodopa dose cycle. Videos of subjects performing the motor tasks were later scored by expert clinicians to assess global dyskinesia using the modified Abnormal Involuntary Rating Scale (m-AIMS). Kinematic features were extracted from motion data in different frequency bands (1-3Hz and 3-8Hz) to quantify LID severity and to distinguish between LID and PD tremor. Receiver operator characteristic analysis was used to determine thresholds for individual features to detect the presence of LID. A sensitivity of 0.73 and specificity of 1.00 were achieved. A neural network was also trained to output dyskinesia severity on a 0 to 4 scale, similar to the m-AIMS. The model generalized well to new data (coefficient of determination= 0.85 and mean squared error= 0.3). This study demonstrated that hand-worn motion sensors can be used to assess global dyskinesia severity independent of PD tremor over the levodopa dose cycle.

Amyloid imaging of Lewy body-associated disorders

Clinicopathologic studies of Parkinson disease dementia (PDD) and dementia with Lewy bodies (DLB) commonly reveal abnormal β-amyloid deposition in addition to diffuse Lewy bodies (α-synuclein aggregates), but the relationship among these neuropathologic features and the development of dementia in these disorders remains uncertain. The purpose of this study was to determine whether amyloid-β deposition detected by PET imaging with Pittsburgh Compound B (PIB) distinguishes clinical subtypes of Lewy body-associated disorders. Nine healthy controls, 8 PD with no cognitive impairment, 9 PD with mild cognitive impairment, 6 DLB, and 15 PDD patients underwent [(11)C]-PIB positron emission tomography imaging, clinical examination, and cognitive testing. The binding potential (BP) of PIB for predefined regions and the mean cortical BP (MCBP) were calculated for each participant. Annual longitudinal follow-up and postmortem examinations were performed on a subset of participants. Regional PIB BPs and the proportion of individuals with abnormally elevated MCBP were not significantly different across participant groups. Elevated PIB binding was associated with worse global cognitive impairment in participants with Lewy body disorders but was not associated with any other clinical or neuropsychological features, including earlier onset or faster rate of progression of cognitive impairment. These results suggest that the presence of fibrillar amyloid-β does not distinguish between clinical subtypes of Lewy body-associated disorders, although larger numbers are needed to more definitively rule out this association. Amyloid-β may modify the severity of global cognitive impairment in individuals with Lewy body-associated dementia.

In vivo amyloid imaging in autopsy-confirmed Parkinson disease with dementia

OBJECTIVE

To investigate the specificity of in vivo amyloid imaging with [(11)C]-Pittsburgh Compound B (PIB) in Parkinson disease dementia (PDD).

METHODS

We performed detailed neuropathologic examination for 3 individuals with PDD who had PIB PET imaging within 15 months of death.

RESULTS

We observed elevated cortical uptake of [(11)C]-PIB on in vivo PET imaging in 2 of the 3 cases. At autopsy, all 3 individuals had abundant cortical Lewy bodies (Braak PD stage 6), and were classified as low-probability Alzheimer disease (AD) based on NIA-Reagan criteria. The 2 PIB-positive individuals had abundant diffuse Abeta plaques but only sparse neuritic plaques and intermediate neurofibrillary tangle pathology. The PIB-negative individual had rare diffuse plaques, no neuritic plaques, and low neurofibrillary tangle burden.

CONCLUSIONS

[(11)C]-Pittsburgh Compound B (PIB) PET is specific for fibrillar Abeta molecular pathology but not for pathologic diagnosis of comorbid Alzheimer disease in individuals with Parkinson disease dementia. The ability to specifically identify fibrillar Abeta amyloid in the setting of alpha-synucleinopathy makes [(11)C]-PIB PET a valuable tool for prospectively evaluating how the presence of Abeta amyloid influences the clinical course of dementia in patients with Lewy body disorders.

Sorting and directed transport of membrane proteins during development of hippocampal neurons in culture

Hippocampal neurons in culture develop morphological polarity in a sequential pattern; axons form before dendrites. Molecular differences, particularly those of membrane proteins, underlie the functional polarity of these domains, yet little is known about the temporal relationship between membrane protein polarization and morphological polarization. We took advantage of viral expression systems to determine when during development the polarization of membrane proteins arises. All markers were unpolarized in neurons before axonogenesis. In neurons with a morphologically distinguishable axon, even on the first day in culture, both axonal and dendritic proteins were polarized. The degree of polarization at these early stages was somewhat less than in mature cells and varied from cell to cell. The cellular mechanism responsible for the polarization of the dendritic marker protein transferrin receptor (TfR) in mature cells centers on directed transport to the dendritic domain. To examine the relationship between cell surface polarization and transport, we assessed the selectivity of transport by live cell imaging. TfR-green fluorescent protein-containing vesicles were already preferentially transported into dendrites at 2 days, the earliest time point we could measure. The selectivity of transport also varied somewhat among cells, and the amount of TfR-green fluorescent protein fluorescence on intracellular structures within the axon correlated with the amount of cell surface expression. This observation implies that selective microtubule-based transport is the primary mechanism that underlies the polarization of TfR on the cell surface. By 5 days in culture, the extent of polarization on the cell surface and the selectivity of transport reached mature levels.

The role of selective transport in neuronal protein sorting

To assess whether selective microtubule-based vesicle transport underlies the polarized distribution of neuronal proteins, we expressed green fluorescent protein- (GFP-) tagged chimeras of representative axonal and dendritic membrane proteins in cultured hippocampal neurons and visualized the transport of carrier vesicles containing these proteins in living cells. Vesicles containing a dendritic protein, transferrin receptor (TfR), were preferentially transported into dendrites and excluded from axons. In contrast, vesicles containing the axonal protein NgCAM (neuron-glia cell adhesion molecule) were transported into both dendrites and axons. These data demonstrate that neurons utilize two distinct mechanisms for the targeting of polarized membrane proteins, one (for dendritic proteins) based on selective transport, the other (for axonal proteins) based on a selectivity "filter" that occurs downstream of transport.

Gangliosides GM1 and GD1b are not polarized in mature hippocampal neurons

Analysis of the binding of cholera toxin to ganglioside GM1 in both living and fixed neurons, and comparison with the distribution of defined axonal and dendritic proteins, demonstrates that ganglioside GM1 is distributed in a non-polarized manner over the axonal and dendritic plasma membranes of mature, cultured hippocampal neurons. Likewise, ganglioside GD1b is also distributed in a non-polarized manner. These results suggest that a recent report [Ledesma, M.D. et al. EMBO J. 18 (1999) 1761-1771] proposing that ganglioside GM1 is highly enriched on the axonal versus dendritic membrane of hippocampal neurons may need to be re-evaluated.

Site-specific regulation of Alzheimer-like tau phosphorylation in living neurons

The microtubule-associated protein tau is more highly phosphorylated at certain residues in developing brain and in Alzheimer's disease paired helical filaments than in adult brain. We examined the regulation of tau phosphorylation at some of these sites in rat brain using the phosphorylation state-dependent anti-tau antibodies AT8, Tau1, and PHF1. The AT8 and PHF1 antibodies bind to phosphorylated tau, while Tau1 binds to unphosphorylated tau. Levels of tau reactive for AT8 were high only during the first postnatal week, with levels in adult declining to approximately 5% of the levels in neonates. In neonatal forebrain slices, tau became rapidly dephosphorylated at the AT8 and Tau1 sites during incubation at 34 degrees C, but was incompletely dephosphorylated at the PHF1 site. Dephosphorylation at AT8 sites, but not at Tau1 or PHF1 sites, was completely inhibited by 1 microM okadaic acid. Hence the regulation of tau phosphorylation by okadaic acid-sensitive phosphatase(s) was site-specific. Addition of 1 microM okadaic acid after dephosphorylation at the AT8 locus yielded a partial recovery of AT8 immunoreactivity, and incubation with basic fibroblast growth factor increased phosphorylation at the AT8 site in a dose-dependent manner. These results indicate that endogenously active and basic fibroblast growth factor stimulated tau kinases directed toward an Alzheimer's disease-related site were present in the slices. In adult brain slices, the small pool of AT8-reactive tau was remarkably insensitive to dephosphorylation during incubation, and okadaic acid treatment induced only small increases in AT8 immunoreactivity. These results suggest that tau phosphorylation in adult brain is less dynamic than in neonatal brain. These findings indicate that neonatal tau is not only phosphorylated more highly than adult tau, but also more dynamically regulated by protein phosphatases and protein kinases than adult tau. The inability of okadaic acid to induce large increases in tau phosphorylation in adult rat brain slices suggests that a loss of protein phosphatase activity alone would not be sufficient to produce the hyperphosphorylation observed in Alzheimer's disease paired helical filaments. Stimulation of kinase activity by basic fibroblast growth factor is likely to modulate tau function during development, and may contribute to the genesis of hyperphosphorylated tau in Alzheimer's disease.

Selective suppression of in vitro electrographic seizures by low-dose tetrodotoxin: a novel anticonvulsant effect

Localized injections of 50 microM tetrodotoxin (TTX) in rat hippocampal slices blocked stimulus train-evoked electrographic seizures (EGSs) for several hours. Responses to single stimuli were minimally altered during TTX block of the EGSs. This selective reduction of epileptiform activity could result from general blockade of action potentials in an anatomically distinct group of neurons in the slice. To test this hypothesis, we systematically mapped TTX injection sites in the hippocampal slice, and found that TTX injections that blocked EGSs were nearly always located in or invaded CA2/3 stratum radiatum and/or stratum lacunosum-moleculare. A high degree of recurrent activity in this region contributes to both epileptiform activity and responses to single stimuli; hence our selective inhibition of EGSs suggests a more pharmacologically specific anticonvulsant effect of TTX. Consistent with this hypothesis, we found that low concentrations of TTX (5, 10, or 20 nM) in the perfusion medium blocked EGSs without decreasing the amplitude of extracellular responses to single stimuli. Polysynaptic activity and/or antidromic firing may be particularly vulnerable to TTX action on voltage-gated sodium channels, due to their lower the safety factor for action potential propagation. Selective reduction of this activity may disrupt the abnormal neuronal activity underlying EGSs.