Recent research aims at developing a biomarker to predict antidepressant treatment outcomes in Major Depressive Disorder (MDD). The Antidepressant Treatment Response index (ATRindex) has been correlated with response to antidepressant medication (Leuchter et al., 2009a, 2009b) but has not been assessed in a placebo-controlled trial. EEGs were used to calculate ATR-index for 23 randomized MDD subjects to eight weeks of fluoxetine treatment (FLX) 20 mg (n=12) or placebo (n=11). The 17-item Hamilton Depression Rating Scale (HamD17) assessed symptom severity, while a percent change in HamD17 score, endpoint response (≥ 50% improvement) and remission (HamD17 score ≤ 7) were used to assess ATR-index as a predictor. ATR-index was significantly associated with improvement on FLX (r = .64, p = .01), with a higher mean ATR-index for FLX responders than non-responders (t(10)= −2.07, p=0.03). Receiver Operating Characteristic analysis found a .83 area under the curve (p = .03), for ATR-index as a predictor for FLX, while an optimized ATR-index cutoff of 47.3 yielded 100% sensitivity, 66.7% specificity, 75% PPV and 100% NPV. Importantly, ATR-index did not correlate significantly with placebo outcomes. Results extend ATR-index findings to include predictive validity with fluoxetine, suggesting that this biomarker has specificity for drug effects.

Purpose

To compare three methods of localizing the source of epileptiform activity recorded with magnetoencephalography (MEG): equivalent current dipole (ECD), minimum current estimate (MCE), and dynamic statistical parametric mapping (dSPM), and to evaluate the solutions by comparison with clinical symptoms and other electrophysiological and neuroradiological findings.

Methods

Fourteen children of 3 to 15 years old were studied. MEG was collected with a whole-head 204-channel helmet-shaped sensor array. We calculated ECDs and made MCE and dSPM movies to estimate the cortical distribution of interictal epileptic discharges (IED) in these patients.

Results

The results for 4 patients with localization related epilepsy (LRE) and 1 patient with Landau-Kleffner Syndrome were consistent among all 3 analysis methods. In the rest of the patients MCE and dSPM suggested multifocal or widespread activity; in these patients the ECD results were so scattered that interpretation of the results was not possible. For 9 patients with LRE and generalized epilepsy, the epileptiform discharges were wide-spread or only slow waves, but dSPM suggested a possible propagation path of the IED.

Conclusion

MCE and dSPM could identify the propagation of epileptiform activity with high temporal resolution. The results of dSPM were more stable because the solutions were less sensitive to background brain activity.

The following are “minimum standards” for the routine clinical recording of magnetic evoked fields (MEFs) in all age-groups.

Practicing at minimum standards should not be the goal of a magnetoencephalography (MEG) center but rather a starting level for continued improvement. Minimum standards meet only the most basic responsibilities to the patient and the referring physician.

These minimum standards have been put forth to improve standardization of procedures, to facilitate interchange of recordings and reports among laboratories in the United States, and to confirm the expectations of referring physicians.

Recommendations regarding Laboratory (Center) Environment and Preparation for MEG Recordings are detailed in the American Clinical Magnetoencephalography Society Clinical Practice Guideline (CPG) 1 : Recording and Analysis of Spontaneous Cerebral Activity, except for its EEG aspect that is not considered necessary (although may be helpful in trained hands) for MEFs (presurgical functional brain mapping).

Purpose

Correct outcome prediction after cardiac arrest in children may improve clinical decision making and family counseling. Various investigators have used EEG to predict outcome with varying success, but one limiting issue is the potential lack of reproducibility of EEG interpretation. Therefore, we aimed to evaluate interobserver agreement using standardized terminology in the interpretation of EEG tracings obtained from critically ill children following cardiac arrest.

Methods

3 pediatric neurophysiologists scored 74 EEG samples using standardized categories, terminology, and interpretation rules. Interobserver agreement was evaluated using kappa and intra-class correlation coefficients.

Results

Agreement was substantial for the categories of continuity, burst suppression, sleep architecture, and overall rating. Agreement was moderate for seizure occurrence and inter-ictal epileptiform discharge type. Agreement was fair for inter-ictal epileptiform discharge presence, beta activity, predominant frequency, and fastest frequency. Agreement was slight for maximum voltage and focal slowing presence.

Conclusions

The variability of inter-rater agreement suggests that some EEG features are superior to others for use in a predictive algorithm. Using only reproducible EEG features is needed to ensure the most accurate and consistent predictions. Since even seizure identification had only moderate agreement, studies of non-convulsive seizures in critically ill patients must be conducted and interpreted cautiously.

Theta Burst Stimulation (TBS) protocols have recently emerged as a method to transiently alter cortical excitability in the human brain through repetitive transcranial magnetic stimulation (rTMS). TBS involves applying short trains of stimuli at high frequency repeated at intervals of 200ms. Because rTMS is known to carry a risk of seizures, safety guidelines have been established. TBS has the theoretical potential of conferring an even higher risk of seizure than other rTMS protocols because it delivers high frequency bursts. In light of the recent report of a seizure induced by TBS, the safety of this new protocol deserves consideration. We performed an English language literature search, and reviewed all studies published from May 2004-December 2009 in which TBS was applied. The adverse events were documented and crude risk was calculated. The majority of adverse events attributed to TBS were mild and occurred in 5% of subjects. Based on this review, TBS appears to be a safe and efficacious technique. However, given its novelty, it should be applied with caution. Additionally, this review highlights the need for rigorous documentation of adverse events associated with TBS, as well as intensity dosing studies to assess the seizure risk associated with various stimulation parameters (e.g. frequency, intensity, location).

Microseizures are highly focal low-frequency epileptiform-appearing events recorded from the neocortex of epilepsy patients. Because of their tiny, often submillimeter distribution they may be regarded as a high resolution window into the epileptic process, providing an excellent opportunity to study the fine temporal structure of their origin and spread. A 16 mm2 96 microelectrode array with 400 micron interelectrode spacing was implanted in seven patients undergoing invasive EEG monitoring for medically refractory epilepsy. Seven microdischarge populations were tested for a substantial contribution by volume conduction to the observed waveform amplitudes. Single unit activity was examined for specific evidence of neural activity at multiple sites within the microdischarge fields. We found that microdischarges appear to originate at a highly focal source location, likely within a single cortical macrocolumn, and spread to local and more distant sites via neural propagation.

In the neocortex, neurons participate in epochs of elevated activity, or Up states, during periods of quiescent wakefulness, slow-wave sleep, and general anesthesia. The regulation of firing during and between Up states is of great interest because it can reflect the underlying connectivity and excitability of neurons within the network. Automated analysis of the onset and characteristics of Up state firing across different experiments and conditions requires a robust and accurate method for Up state detection. Using measurements of membrane potential mean and variance calculated from whole-cell recordings of neurons from control and postseizure tissue, the authors have developed such a method. This quantitative and automated method is independent of cell- or condition-dependent variability in underlying noise or tonic firing activity. Using this approach, the authors show that Up state frequency and firing rates are significantly increased in layer 2/3 neocortical neurons 24 hours after chemo-convulsant-induced seizure. Down states in postseizure tissue show greater membrane-potential variance characterized by increased synaptic activity. Previously, the authors have found that postseizure increase in excitability is linked to a gain-of-function in BK channels, and blocking BK channels in vitro and in vivo can decrease excitability and eliminate seizures. Thus, the authors also assessed the effect of BK-channel antagonists on Up state properties in control and postseizure neurons. These data establish a robust and broadly applicable algorithm for Up state detection and analysis, provide a quantitative description of how prior seizures increase spontaneous firing activity in cortical networks, and show how BK-channel antagonists reduce this abnormal activity.

People aged 90 and older (oldest-old), the fastest growing segment of the United States population, are known to have high rates of spells of all types, including strokes, transient ischemic attacks, and seizures. This study examined the prevalence of EEG abnormalities in 12 physically and cognitively healthy oldest-old (mean age=94) with no history of seizures or spells. Abnormalities were found in 83% of participants: temporal intermittent polymorphic slowing was seen in 67%, background slowing (alpha rhythm <8 Hz) was present in 33%, and temporal intermittent rhythmic delta was found in 17%. The high rates of EEG abnormalities found in these physically and cognitively healthy participants prompt reappraisal of pathological significance in this unique population.

Elderly subjects exhibit declining sleep efficiency parameters with longer time spent awake at night and greater sleep fragmentation. In this paper, we report on the changes in cortical interdependence during sleep stages between 15 middle aged (range: 42-50 years) and 15 elderly (range: 71-86 years) women subjects. Cortical interdependence assessed from EEG signals typically exhibits increasing levels of correlation as human subjects progress from wake to deeper stages of sleep. EEG signals acquired from previously existing polysomnogram data sets were subjected to mutual information (MI) analysis to detect changes in information transmission associated with change in sleep stage and to understand how age affects the interdependence values. We observed a significant reduction in the interdependence between central EEG signals of elderly subjects in NREM and REM stage sleep in comparison to middle-aged subjects (age group effect: elderly vs. middle aged p<0.001, sleep stage effect: p<0.001, interaction effect between age group and sleep stage: p=0.007). A narrow band analysis revealed that the reduction in MI was present in delta, theta and sigma frequencies. These findings suggest that the lowered cortical interdependence in sleep of elderly subjects may indicate independently evolving dynamic neural activities at multiple cortical sites. The loss of synchronization between neural activities during sleep in the elderly may make these women more susceptible to localized disturbances that could lead to frequent arousals.

Objective

Develop a method for automatic detection of seizures prior to or immediately after clinical onset using features derived from scalp EEG.

Methods

This detection method is patient-specific. It uses recurrent neural networks and a variety of input features. For each patient we trained and optimized the detection algorithm for two cases: 1) during the period immediately preceding seizure onset, and 2) during the period immediately following seizure onset. Continuous scalp EEG recordings (duration 15 – 62 h, median 25 h) from 25 patients, including a total of 86 seizures, were used in this study.

Results

Pre-onset detection was successful in 14 of the 25 patients. For these 14 patients, all of the testing seizures were detected prior to seizure onset with a median pre-onset time of 51 sec and false positive rate was 0.06/h. Post-onset detection had 100% sensitivity, 0.023/hr false positive rate and median delay of 4 sec after onset.

Conclusions

The unique results of this study relate to pre-onset detection.

Significance

Our results suggest that reliable pre-onset seizure detection may be achievable for a significant subset of epilepsy patients without use of invasive electrodes.

Summary

Eight right-handed subjects were asked to silently generate a verb to a visual stimulus while the magnetic flux normal to the scalp surface was recorded with a whole-head neuromagnetometer. The spatiotemporal patterns of activation in lateral occipital, inferior parietal, superior temporal, basal temporal, and inferior frontal cortices were estimated using minimum estimation, a distributed source analysis methodology. Although there was significant variability among subjects, averaged data indicated that latencies of peak activation in these regions of interest progressed from posterior to anterior. Peak latencies were earliest in lateral occipital cortex and latest in pars opercularis and pars triangularis in the inferior frontal gyrus. Lateralization of activation was strongest in pars opercularis, which is part of classical Broca’s area, with activation being stronger in this area within the left hemisphere in every subject. Results provide support for the use of magnetoencephalography in conjunction with MNE analysis for the purpose of lateralizing and localizing language-specific activation in frontal areas as well as the study of the spatiotemporal parameters of brain activation associated with cognitive function.

Purpose

EEGs are widely used to detect interictal epileptiform discharges (IEDs) in patients with a known history of seizures. However, prior studies have not found a consistent association between the presence or frequency of IEDs and clinical epilepsy severity, possibly because of differences in subject characteristics and recording techniques. We sought to investigate this relationship in a population and setting reflective of the most common clinical usage.

Methods

We analyzed EEGs and clinical records of all consenting patients with a history of at least two presumed focal-onset seizures who presented for routine EEG recording over one year’s time in an academic neurophysiology laboratory (n = 129).

Results

Despite adequate statistical power, we did not find an association between the presence or absence of IEDs or IED frequency and the most recently determined seizure frequency (median 4 per year). A higher IED incidence was seen in subjects with longer epilepsy duration (p = 0.04). Neither IED incidence nor frequency (median 10.0 per hour) correlated with age or antiepileptic drug use.

Conclusions

Our results differ from those of some prior studies, most of which focused on more narrow subject populations, suggesting that the patient’s clinical circumstances must be taken into account before assuming the utility of IEDs on routine EEG in predicting epilepsy severity.

This study investigated the effects of acute psychosocial stress on trapezius single motor unit discharge behaviors. Twenty-one healthy women performed feedback-controlled isometric contractions under conditions of low and high psychosocial stress in the same experimental session. Psychosocial stress was manipulated using a verbal math task combined with social evaluative threat which significantly increased perceived anxiety, heart rate, and blood pressure (P<0.001). Motor unit discharge behaviors including the threshold and discharge rate at recruitment (7.7 (5.7) %MVC and 7.3 (6.8) pps, P>0.121, N=103) and derecruitment (6.0(4.4) %MVC and 6.5(4.1) pps, P>0.223, N=99), the mean (11.3 (2.3) pps, P=0.309, N=106) and variability (2.5 (0.91) pps, P=0.958, N=106) of discharge rate, and the proportion of motor units exhibiting double discharges (21%, P=0.446) did not change across stress conditions. Discharge rate modulation with changes in contraction intensity was highly variable and similar across stress conditions (P>0.308, N=89). Rate-rate modulation of concurrently active motor units was also highly variable (r=−0.84–1.00, N=75). Estimates of ΔF for motor unit pairs with rate-rate modulation ≥0.7 were positive and similar across stress conditions (4.7(2.0) pps, P=0.405, N=16). Results indicate that acute psychosocial stress does not alter trapezius motor unit discharge behaviors during a precisely controlled motor task in healthy women.

In 9 patients with essential tremor (14 thalami), we varied frequency, voltage, and pulsewidth of thalamic deep brain stimulation (DBS), and quantified postural tremor. Low frequency stimulation aggravated tremor; the effect increased with increasing voltage. High frequency stimulation had a U-shaped relation to voltage, with minimum tremor at an optimal voltage characteristic of the individual thalamus and increases in voltage beyond the optimum reduced tremor suppression.

Based on the hypothesis that tremor response to DBS resulted from two competing processes, we successfully modeled the relationship of tremor to voltage and frequency of stimulation using a mathematical model. The optimum voltage predicted by the model agreed with the empirically measured value. Moreover, the model made accurate predictions at high stimulation frequency based on measurements made at low stimulation frequency.

Our results indicate there is an optimal voltage for tremor suppression by thalamic DBS in most patients with ET. The optimum varies across patients, and this is related to electrode position. A mathematical model based on ‘competing processes’ successfully predicts optimum voltage in individual patients. This supports a competing processes model of DBS effects.

Summary

The authors report the use of dense two-dimensional microelectrode array recordings to characterize fine resolution electrocortical activity (“μEEG”) in epileptogenic human cortex. A 16-mm2 96 microelectrode array with 400-μm interelectrode spacing was implanted in five patients undergoing invasive EEG monitoring for medically refractory epilepsy. High spatial resolution data from the array were analyzed in conjunction with simultaneously acquired data from standard intracranial electrode grids and strips. μEEG recorded from within the epileptogenic zone demonstrates discharges resembling both interictal epileptiform activity (“microdischarges”) and electrographic seizures (“microseizures”) but confined to cortical regions as small as 200 μm2. In two patients, this activity appeared to be involved in the initiation or propagation of electrographic seizures. The authors hypothesize that microdischarges and microseizures are generated by small cortical domains that form the substrate of epileptogenic cortex and play important roles in seizure initiation and propagation.

The regularity of EEG signals was compared between middle-aged (47.2 ± 2.0 yrs) and elderly (78.4 ± 3.8 yrs) female subjects in Wake (W), NREM stages 2 and 3 (S2, S3), and REM. Signals from C3A2 leads of healthy normal subjects, acquired from polysomnograms obtained from the Sleep Heart Health Study, were analyzed using both Sample Entropy (SaEn) and power spectral analysis (delta, theta, alpha, and beta frequency band powers). SaEn changed systematically and significantly (p<0.001) with sleep state in both age groups, following the relationships W > REM > S2 > S3. SaEn was found to be negatively correlated with delta power and positively correlated with beta power. Small changes in SaEn appear to reflect changes in spectral content rather than changes in regularity of the signal. A better predictor of SaEn than the frequency band powers was the logarithm of the power ratio (alpha+beta)/(delta+theta). Thus, SaEn appears to reflect the balance between sleep-promoting and alertness-promoting mechanisms. SaEn of the elderly was larger than that of middle-aged subjects in S2 (p=0.029) and REM (p=0.001), suggesting that cortical state is shifted towards alertness in elderly subjects in these sleep states compared to middle-aged.

This paper describes the design and test results of a 3-stage automated system for neonatal EEG seizure detection. Stage I of the system is the initial detection stage, and identifies overlapping 5-s segments of suspected seizure activity in each EEG channel. In Stage II, the detected segments from Stage I are spatiotemporally clustered to produce multi-channel candidate seizures. In Stage III, the candidate seizures are processed further using measures of quality and context-based rules to eliminate false candidates. False candidates due to artifacts and commonly occurring EEG background patterns such as bifrontal delta activity are also rejected. Seizures at least 10 s in duration are considered for reporting results.

The testing data consisted of recordings of 28 seizure subjects (34 hrs of data) and 48 non-seizure subjects (87 hrs of data) obtained in the neonatal intensive care unit. The data were not edited to remove artifacts and were identical in every way to data normally processed visually. The system was able to detect seizures of widely varying morphology with an average detection sensitivity of almost 80% and a subject sensitivity of 96%, in comparison to a team of clinical neurophysiologists who had scored the same recordings. The average false detection rate obtained in non-seizure subjects was 0.74 per hr.

It is desirable to estimate both location and extent information of epileptogenic zones from noninvasive EEG. In the present study, we use a subspace source localization method, i.e. FINE, combined with a local thresholding technique to achieve such tasks. We have evaluated the performance of this method in interictal spikes from three pediatric patients with medically intractable partial epilepsy. The present results suggest that the thresholded subspace correlation, which is obtained from FINE scanning, is a favorable marker, which implies the extents of current sources associated with epileptic activities. Our findings were validated through comparison to invasive ECoG recordings during interictal spikes. The surgical resections in these three patients are well correlated with the epileptogenic zones identified from both EEG sources and ECoG potential distributions. The value of the proposed noninvasive technique for estimating epileptiform activity was supported by satisfactory surgery outcomes.

Blood pressure (BP) changes in response to the Valsalva maneuver (VM) reflect the integrity of the baroreflex that regulates BP. Performing this maneuver in the standard supine position often prevents adequate venous preload reduction, resulting in a rise rather than a fall in BP, the “flat top” Valsalva response. We determined whether performing the Valsalva Maneuver (VM) at a 20 degree angle of head up tilt (20_deg) improves preload reduction, thereby reducing the frequency of flat top responses, improving reflex vasoconstriction, and increasing the Valsalva ratio (VR). 130 patients were evaluated in a prospective study. Each patient performed the VM in both supine and 20_deg positions.

Flat top responses were present in 18% of subjects when supine. 20_deg position reduced the flat top response by 87%. The components of the response that are dependent on preload reduction (VR and phases II_E, II_L, and IV) also showed significant improvement with 20_degree.

A 20 degree angle of tilt is sufficient to reduce venous preload, decreasing flat top response rate and improving the VR and the morphology of the VM. We recommend this modification for laboratory evaluation of the VM, whenever a “flat-top” response is seen.

Summary

Simultaneous EEG and functional magnetic resonance imaging have been applied to the study of brain states associated with alpha waves using a magnetic field strength of 1.5 Tesla and has been shown in recent years to be feasible up to 3 Tesla for other applications. This study demonstrates this technique’s continued viability at a field strength of 4 Tesla, affording a proportionally greater sensitivity to changes in Blood Oxygen Level Dependent (BOLD) signal. In addition, for the study of alpha correlations, the authors used a larger number of subjects and scanning sessions than in the previous work. Random effects group regression analysis of 35 EEG/functional magnetic resonance imaging sessions against occipital alpha magnitude in a relaxed state detected bilateral widespread activation of dorsal thalamus and portions of the anterior cingulate and cerebellum. In the same group analysis, deactivations arose predominantly in the fusiform and adjacent visual association areas with a small activation cluster also detected in dorsolateral prefrontal cortex. This pattern is consistent with a correspondence between alpha magnitude variations and resting state network dynamics ascertained by recent studies of low frequency spontaneous BOLD fluctuations. The central role of the thalamus in resting state networks correlated with alpha activity is highlighted. Demonstrating the applicability of simultaneous EEG/functional magnetic resonance imaging up to 4 Tesla is particularly important for clinically relevant research involving challenging spontaneous EEG abnormalities, such as those of epilepsy.

Network simulations can help identify underlying mechanisms of epileptic activity which are hard to isolate in biological preparations. To be useful, simulations must be suffciently realistic to make possible biological and clinical prediction. This requirement for large networks of sufficiently detailed neurons raises challenges both with regard to computational load and the difficulty of obtaining insights with large numbers of free parameters and the large amounts of generated data. We have addressed these problems by simulating computationally managable networks of moderate size consisting of 1000-3000 neurons with multiple intrinsic and synaptic properties. Experiments on these simulations demonstrated the presence of epileptiform behavior in the form of repeatitive high-intensity population events (clonic behavior) or latch-up with near maximal activity (tonic behavior). We found that intrinsic neuronal excitability is not always a predictor of network epileptiform activity but may paradoxically produce anti-epileptic effects, depending on the settings of other parameters. We noted in several simulations the importance of random coincident inputs to shift a network from a low-activation to a high-activation epileptiform state. Finally, we explored the effects of a simulated anticonvulsant acting on excitability, noting that this tended to preferentially decrease tonic activity.

In human partial epilepsies, as well as in experimental models of chronic and/or acute epilepsy, the role of inhibition and the relationship between the inhibition and excitation and epileptogenesis has long been questioned. Besides experimental methods carried out either in vitro (human or animal tissue) or in vivo (animals), pathophysiological mechanisms can be approached by direct recording of brain electrical activity in human epilepsy. Indeed, in some clinical pre-surgical investigation methods like stereoelectroencephalopraphy (SEEG), intracerebral electrodes are used in patients suffering from drug resistant epilepsy to directly record paroxysmal activities with excellent temporal resolution (in the order of one millisecond). The study of neurophysiological mechanisms underlying such depth-EEG activities is crucial to progress in the understanding of the interictal to ictal transition.

In this study, we relate electrophysiological patterns typically observed during the transition from interictal to ictal activity in human mesial temporal lobe epilepsy (MTLE) to mechanisms (at a neuronal population level) involved in seizure generation through a computational model of EEG activity. Intracerebral EEG signals recorded from hippocampus in five patients with MTLE during four periods (during interictal activity, just before seizure onset, during seizure onset and during ictal activity) were used to identify the three main parameters of a model of hippocampus EEG activity (related to excitation, slow dendritic inhibition and fast somatic inhibition). The identification procedure used optimization algorithms to minimize a spectral distance between real and simulated signals. Results demonstrated that the model generates very realistic signals for automatically identified parameters. They also showed that the transition from interictal to ictal activity can not be simply explained by an increase in excitation and a decrease in inhibition but rather by time-varying ensemble interactions between pyramidal cells and local interneurons projecting to either their dendritic or perisomatic region (with slow and fast GABAA kinetics). Particularly, during preonset activity, an increasing dendritic GABAergic inhibition compensates a gradually increasing excitation up to a brutal drop at seizure onset when faster oscillations (beta and low gamma band, 15 to 40 Hz) are observed. These faster oscillations are then explained by the model feedback loop between pyramidal cells and interneurons targeting their perisomatic region. These findings obtained from model identification in human TLE are in agreement with some results obtained experimentally, either on animal models of epilepsy or on the human epileptic tissue.

Summary

Narcolepsy is a neurological condition with a prevalence of up to 1 per 1,000 that is characterized by irresistible bouts of sleep. Associated features include the pathological manifestations of rapid-eye-movement (REM) sleep: cataplexy, sleep paralysis, hypnagogic hallucinations, and abnormal sleep-onset REM periods and disturbed nocturnal sleep. The condition is strongly associated with the HLA-DR2 and DQw1 phenotype. The phenomenology of narcolepsy is discussed, and diagnostic procedures are reviewed. Treatment modalities involving central nervous system stimulants for somnolence and tricyclic drugs for REM-sleep abnormalities are discussed. Sleep laboratory studies on the treatment efficacy of methylphenidate, pemoline, dextroamphetamine, protriptyline, and viloxazine are presented. Data suggest that: (1) methylphenidate and dextro-amphetamine objectively improve somnolence; (2) pemoline, at doses up to 112.5 mg, is less effective in controlling somnolence but may improve certain aspects of performance; and (3) protriptyline and viloxazine are effective anticataplectic agents that produce little improvement in somnolence.

Summary

Primary motor cortex (MI), a key region for voluntary motor control, has been considered a first choice as the source of neural signals to control prosthetic devices for humans with paralysis. Less is known about the potential for other areas of frontal cortex as prosthesis signal sources. The frontal cortex is widely engaged in voluntary behavior. Single neuron recordings in monkey frontal cortex beyond MI have readily identified activity related to planning and initiating movement direction, remembering movement instructions over delays, or mixtures of these features (Kurata & Wise, 1988; Boussaoud & Wise, 1993; Crammond & Kalaska, 1994, 2000). Human functional imaging and lesion studies also support this role (Toni et al., 1999; Simon et al., 2002). Intraoperative mapping during deep brain stimulator placement in humans (Benabid et al., 1989) provides a unique opportunity to evaluate potential prosthesis control signals derived from non-primary areas and to expand our understanding of frontal lobe function and its role in movement disorders. Here we show that recordings from small groups of human prefrontal/premotor cortex neurons can provide information about movement planning, production and decision making sufficient to decode the planned direction of movement. Thus, additional frontal areas, beyond M1, may be valuable signal sources for human neuromotor prostheses.