EEG/ECoG/EMG publications

Electrocorticogram (ECoG) recordings of the 6-hydroxydopamine (6-OHDA)–lesioned parkinsonian rat have shown an increase in the power of cortical β-band (15–30 Hz) oscillations ipsilateral to the lesion. The power of these oscillations is decreased with dopamine agonist administration. Here, we demonstrate that stimulation of an electrode implanted in the subthalamic nucleus alters the power of cortical β and γ oscillations in 6-OHDA–lesioned animals. These alterations are dependent on stimulation frequency, charge, and amplitude/pulse width. Oscillations were significantly reduced during 200- and 350-Hz stimulation. A minimum charge of 4 nC was required to elicit a reduction in oscillation power. A number of amplitude and pulse width combinations that reached 4 nC were tested; it was found that only the combinations of 33 μA/120 μs and 65 μA/60 μs significantly reduced cortical oscillations. The reduction in β/γ oscillation power due to deep brain stimulation (DBS) was consistent with a significant reduction in the animals' rotational behavior, a typical symptom of parkinsonism in the rat. A significant shift from high β to low γ was observed in the peak frequencies of ECoG recordings while animals were at rest versus walking on a treadmill. However, DBS exhibited no differential effect on oscillations between these two states. EEG recordings from rodent models of DBS may provide surrogate information about the neural signatures of Parkinson's disease relative to the efficacy of DBS.

A brain-computer interface (BCI) is a communication system that takes recorded brain signals and translates them into real-time actions, in this case movement of a cursor on a computer screen. This work applied Fitts' law to the evaluation of performance on a target acquisition task during sensorimotor rhythm-based BCI training. Fitts' law, which has been used as a predictor of movement time in studies of human movement, was used here to determine the information transfer rate, which was based on target acquisition time and target difficulty. The information transfer rate was used to make comparisons between control modalities and subject groups on the same task. Data were analyzed from eight able-bodied and five motor disabled participants who wore an electrode cap that recorded and translated their electroencephalogram (EEG) signals into computer cursor movements. Direct comparisons were made between able-bodied and disabled subjects, and between EEG and joystick cursor control in able-bodied subjects. Fitts' law aptly described the relationship between movement time and index of difficulty for each task movement direction when evaluated separately and averaged together. This study showed that Fitts' law can be successfully applied to computer cursor movement controlled by neural signals.

The purpose of this study was to determine the effect of signal level and signal-to-noise ratio (SNR) on the latency and amplitude of evoked cortical activity to further our understanding of how the human central auditory system encodes signals in noise. Cortical auditory evoked potentials (CAEPs) were recorded from 15 young normal-hearing adults in response to a 1000 Hz tone presented at two tone levels in quiet and while continuous background noise levels were varied in five equivalent SNR steps. These 12 conditions were used to determine the effects of signal level and SNR level on CAEP components P1, N1, P2, and N2. Based on prior signal-in-noise experiments conducted in animals, we hypothesized that SNR, would be a key contributor to human CAEP characteristics. As hypothesized, amplitude increased and latency decreased with increasing SNR; in addition, there was no main effect of tone level across the two signal levels tested (60 and 75 dB SPL). Morphology of the P1-N1-P2 complex was driven primarily by SNR, highlighting the importance of noise when recording CAEPs. Results are discussed in terms of the current interest in recording CAEPs in hearing aid users.

Changes in oscillatory brain activity have been related to perceptual and cognitive processes such as selective attention and memory matching. Here we examined brain oscillations, measured with electroencephalography (EEG), during a semantic speech processing task that required both lexically mediated memory matching and selective attention. Participants listened to nouns spoken in male and female voices, and detected an animate target (p=20%) in a train of inanimate standards or vice versa. For a control task, subjects listened to the same words and detected a target male voice in standards of a female voice or vice versa. The standard trials of the semantic task showed enhanced upper beta (25-30 Hz) and gamma band (GBA, 30-60 Hz) activity compared to the voice task. Upper beta and GBA enhancement was accompanied by a suppression of alpha (8-12 Hz) and lower to mid beta (13-20 Hz) activity mainly localized to posterior electrodes. Enhancement of phase-locked theta activity peaking near 275 ms also occurred over the midline electrodes. Theta, upper beta, and gamma band enhancement may reflect lexically mediated template matching in auditory memory, whereas the alpha and beta suppression likely indicate increased attentional processes and memory demands.

Object Control signals for brain-machine interfaces may be obtained from a variety of sources, each with their own relative merits. Electrocorticography (ECoG) provides better spatial and spectral resolution than scalp electroencephalography and does not include the risks attendant upon penetration of the brain parenchyma associated with single and multiunit recordings. For these reasons, subdural electrode recordings have been proposed as useful primary or adjunctive control signals for brain-machine interfaces. The goal of the present study was to determine if 2D control signals could be decoded from ECoG. Methods Six patients undergoing invasive monitoring for medically intractable epilepsy using subdural grid electrodes were asked to perform a motor task involving moving a joystick in 1 of 4 cardinal directions (up, down, left, or right) and a fifth condition (“trigger”). Evoked activity was synchronized to joystick movement and analyzed in the theta, alpha, beta, gamma, and high-gamma frequency bands. Results Movement-related cortical potentials could be accurately differentiated from rest with very high accuracy (83–96%). Further distinguishing the movement direction (up, down, left, or right) could also be resolved with high accuracy (58–86%) using information only from the high-gamma range, whereas distinguishing the trigger condition from the remaining directions provided better accuracy. Conclusions Two-dimensional control signals can be derived from ECoG. Local field potentials as measured by ECoG from subdural grids will be useful as control signals for a brain-machine interface.

Intracortical microelectrode arrays record multi-unit extracellular activity for neurophysiology studies and for brain-machine interface applications. The common first step is neural spike-detection; a process complicated by common-noise signals from motion artifacts, electromyographic activity, and electric field pickup, especially in awake/behaving subjects. Often common-noise spikes are very similar to neural spikes in their magnitude, spectral, and temporal features. Provided sufficient spacing exists between electrodes of the array, a local neural spike is rarely recorded on multiple electrodes simultaneously. This is not true for distant common-noise sources. Two new techniques compatible with standard spike-detection schemes are introduced and evaluated. The first method, virtual referencing (VR), takes the average recording from all functional electrodes in the array (represents the signal from a virtual-electrode at the array's center) and subtracts it from the test electrode signal. The second method, inter-electrode correlation (IEC), computes a correlation coefficient between threshold exceeding candidate spike segments on the test electrode and concurrent segments from remaining electrodes. When sufficient correlation is detected, the candidate spike is rejected as originating from a distant common-noise source. The performance of these algorithms was compared with standard thresholding and differential referencing approaches using neural recordings from un-anaesthetized rats. By evaluating characteristics of mean-spike waveforms generated by each method under different levels of common-noise, it was found that IEC consistently offered the most robust means of neural spike-detection. Furthermore, IEC's rejection of supra-threshold events not likely originating from local neurons significantly reduces data handling for downstream spike sorting and processing operations.

In this study, we propose and evaluate a technique known as common average referencing (CAR) to generate a more ideal reference electrode for microelectrode recordings. CAR is a computationally simple technique, and therefore amenable to both on-chip and real-time applications. CAR is commonly used in EEG, where it is necessary to identify small signal sources in very noisy recordings. To study the efficacy of common average referencing, we compared CAR to both referencing with a stainless steel bone-screw and a single microelectrode site. Data consisted of in vivo chronic recordings in anesthetized Sprague-Dawley rats drawn from prior studies, as well as previously unpublished data. By combining the data from multiple studies, we generated and analyzed one of the more comprehensive chronic neural recording datasets to date. Reference types were compared in terms of noise level, signal-to-noise ratio, and number of neurons recorded across days. Common average referencing was found to drastically outperform standard types of electrical referencing, reducing noise by >30%. As a result of the reduced noise floor, arrays referenced to a CAR yielded almost 60% more discernible neural units than traditional methods of electrical referencing. CAR should impart similar benefits to other microelectrode recording technologies—for example, chemical sensing—where similar differential recording concepts apply. In addition, we provide a mathematical justification for CAR using Gauss-Markov theorem and therefore help place the application of CAR into a theoretical context.

Electrical activity in the brain spans a wide range of spatial and temporal scales, requiring simultaneous recording of multiple modalities of neurophysiological signals in order to capture various aspects of brain state dynamics. Here, we present a 16-channel neural interface integrated circuit fabricated in a 0.5 mum 3M2P CMOS process for selective digital acquisition of biopotentials across the spectrum of neural signal modalities in the brain, ranging from single spike action potentials to local field potentials (LFP), electrocorticograms (ECoG), and electroencephalograms (EEG). Each channel is composed of a tunable bandwidth, fixed gain front-end amplifier and a programmable gain/resolution continuous-time incremental DeltaSigma analog-to-digital converter (ADC). A two-stage topology for the front-end voltage amplifier with capacitive feedback offers independent tuning of the amplifier bandpass frequency corners, and attains a noise efficiency factor (NEF) of 2.9 at 8.2 kHz bandwidth for spike recording, and a NEF of 3.2 at 140 Hz bandwidth for EEG recording. The amplifier has a measured midband gain of 39.6 dB, frequency response from 0.2 Hz to 8.2 kHz, and an input-referred noise of 1.94 muV rms while drawing 12.2 muA of current from a 3.3 V supply. The lower and higher cutoff frequencies of the bandpass filter are adjustable from 0.2 to 94 Hz and 140 Hz to 8.2 kHz, respectively. At 10-bit resolution, the ADC has an SNDR of 56 dB while consuming 76 muW power. Time-modulation feedback in the ADC offers programmable digital gain (1-4096) for auto-ranging, further improving the dynamic range and linearity of the ADC. Experimental recordings with the system show spike signals in rat somatosensory cortex as well as alpha EEG activity in a human subject.

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings.

Faces are processed by a neural system with distributed anatomical components, but the roles of these components remain unclear. A dominant theory of face perception postulates independent representations of invariant aspects of faces (e.g., identity) in ventral temporal cortex including the fusiform gyrus, and changeable aspects of faces (e.g., emotion) in lateral temporal cortex including the superior temporal sulcus. Here we recorded neuronal activity directly from the cortical surface in 9 neurosurgical subjects undergoing epilepsy monitoring while they viewed static and dynamic facial expressions. Applying novel decoding analyses to the power spectrogram of electrocorticograms (ECoG) from over 100 contacts in ventral and lateral temporal cortex, we found better representation of both invariant and changeable aspects of faces in ventral than lateral temporal cortex. Critical information for discriminating faces from geometric patterns was carried by power modulations between 50 to 150 Hz. For both static and dynamic face stimuli, we obtained a higher decoding performance in ventral than lateral temporal cortex. For discriminating fearful from happy expressions, critical information was carried by power modulation between 60-150 Hz and below 30 Hz, and again better decoded in ventral than lateral temporal cortex. Task-relevant attention improved decoding accuracy more than 10% across a wide frequency range in ventral but not at all in lateral temporal cortex. Spatial searchlight decoding showed that decoding performance was highest around the middle fusiform gyrus. Finally, we found that the right hemisphere, in general, showed superior decoding to the left hemisphere. Taken together, our results challenge the dominant model for independent face representation of invariant and changeable aspects: information about both face attributes was better decoded from a single region in the middle fusiform gyrus.

As part of an ongoing study of age-related changes in auditory processing, sensitivity to small changes in frequency were assessed using the cortical auditory evoked potential, P1-N1-P2, in younger and older adults with normal hearing. Behavioral measures have shown age-related differences in intensity and frequency discrimination that are larger at lower than higher frequencies. However, substantial individual differences and equivocal results among studies have been reported. This variability may reflect differences in tasks and procedures, as well as subject variables, such as hearing sensitivity and level of attention. To minimize these subject variables, the P1-N1-P2 response was investigated using a passive listening paradigm. Subjects were 10 younger and 10 older adults. The P1-N1-P2 was elicited by a 150-ms change in frequency in otherwise continuous 500-Hz and 3000-Hz pure tones presented at 70 dB SPL. P1-N1-P2 threshold was defined as the smallest change in frequency needed to evoke a P1-N1-P2 response. Furthermore, a frequency-dependent aging effect was observed for P1-N1-P2 thresholds, such that older subjects were significantly less sensitive to the frequency change than younger subjects, with significantly larger age-related differences at 500 Hz than at 3000 Hz. Age-related changes in response latencies and amplitude of the P1-N1-P2 response were also evident at 500 and 3000 Hz. These results are consistent with age-related changes in the central auditory system and suggest that changes in frequency discrimination abilities of older adults may be, in part, related to changes in preattentive levels of auditory processing.

Objectives: The objective of this study was to describe the cortical evoked response to silent gaps in a group of young adults with normal hearing using stimulus conditions identical to those used in psychophysical studies of gap detection. Specifically, we sought to examine the P1-N1-P2 auditory evoked response to the onsets of stimuli (markers) defining a silent gap for within-channel (spectrally identical markers) and across-channel (spectrally different markers) conditions using four perceptually-equated gap durations. It was hypothesized that (1) P1, N1, and P2 would be present and consistent for 1st marker (before the gap) onsets; (2) for within-channel markers, P1, N1, and P2 would be present for 2nd marker (after the gap) onsets only when the gap was of a duration equal to or larger than the behaviorally measured gap detection threshold; and (3) for the across-channel conditions, P1, N1, and P2 would be present for 2nd marker onsets regardless of gap duration. This is expected due to the additional cue of frequency change following the gap. Design: Twelve young adults (mean age 26 years) with normal hearing participated. Within-channel and across-channel gap detection thresholds were determined using an adaptive psychophysical procedure. Next, cortical auditory evoked potentials (P1-N1-P2) were recorded with a 32-channel Neuroscan electroencephalogram system using within-channel and across-channel markers identical to those used for the psychophysical task and four perceptually weighted gap durations: (1) individual listener's gap detection threshold; (2) above gap detection threshold; (3) below gap detection threshold; and (4) a 1-ms gap identical to the gap in the standard interval of the psychophysical task. P1-N1-P2 peak latencies and amplitudes were analyzed using repeated-measures analyses of variance. A temporal-spatial principal component analysis was also conducted. Results: The latency of P2 and the amplitude of P1, N1, and P2 were significantly affected by the acoustic characteristics of the 2nd marker as well as the duration of the gap. Larger amplitudes and shorter latencies were generally found for the conditions in which the acoustic cues were most salient (e.g., across-channel markers, 1st markers, large gap durations). Interestingly, the temporal-spatial principal component analysis revealed activity elicited by gap durations equal to gap detection threshold in the latency regions of 167 and 183 ms for temporal-parietal and right-frontal spatial locations. Conclusions: The cortical response to a silent gap is unique to specific marker characteristics and gap durations among young adults with normal hearing. Specifically, when the onset of the 2nd marker is perceptually salient, the amplitude of the P1-N1-P2 response is relatively larger and the P2 latency is relatively shorter than for nonsalient 2nd marker onsets, providing noninvasive, nonbehavioral indicators of the neural coding of this important temporal cue in the thalamic-cortical region of the central auditory system. Gap duration appears to be most clearly indicated by P1 and T-complex amplitude.

During the second postnatal week in rats, the hippocampus exhibits a transient period of hyperexcitability. To systematically assess the relationship between the onset and end of this period and spontaneous hippocampal activity, we used silicon depth electrodes in unanaesthetized head‐fixed rats from postnatal day (P)2 to P18. At all ages, hippocampal sharp waves (SPWs) were prominent in the EEG. Beginning at P6, however, marked changes in SPWs and associated oscillations were detected. SPW‐related ‘gamma tails’ (60–100 Hz) and ‘ripples’ (140–200 Hz) were first observed at P6 and P7, respectively, and both oscillations persisted up to P18. Transiently, between P6 and P11, SPW duration decreased and the occurrence of SPW doublets increased. In addition, between P8 and P11, a subset of rats exhibited ‘spontaneous potentiated SPWs’ characterized by double polarity reversals, enhanced likelihood of gamma tails, and population spikes. Having identified a suite of transient hippocampal features consistent with a window of increased excitability, we next assessed whether electrographic seizure activity would be most easily induced during this period. To do this, kainic acid (KA; 200 ng/infusion) was infused into the hippocampus contralateral to the recording probe. KA did not induce seizure activity until P7 and reached peak effectiveness at P9. Thereafter, sensitivity to KA declined. All together, these findings provide in vivo neurophysiological support for the notion of a developmental window of heightened seizure susceptibility during the second postnatal week, and also suggest that spontaneous nonpathological hippocampal activity can be used to mark the onset and end of this period.

Deprivation from normal sensory input has been shown to alter tonotopic organization of the human auditory cortex. In this context, cochlear implant subjects provide an interesting model in that profound deafness is made partially reversible by the cochlear implant. In restoring afferent activity, cochlear implantation may also reverse some of the central changes related to deafness. The purpose of the present study was to address whether the auditory cortex of cochlear implant subjects is tonotopically organized. The subjects were thirteen adults with at least 3 months of cochlear implant experience. Auditory event-related potentials were recorded in response to electrical stimulation delivered at different intracochlear electrodes. Topographic analysis of the auditory N1 component (∼85 ms latency) showed that the locations on the scalp and the relative amplitudes of the positive/negative extrema differ according to the stimulated electrode, suggesting that distinct sets of neural sources are activated. Dipole modeling confirmed electrode-dependent orientations of these sources in temporal areas, which can be explained by nearby, but distinct sites of activation in the auditory cortex. Although the cortical organization in cochlear implant users is similar to the tonotopy found in normal-hearing subjects, some differences exist. Nevertheless, a correlation was found between the N1 peak amplitude indexing cortical tonotopy and the values given by the subjects for a pitch scaling task. Hence, the pattern of N1 variation likely reflects how frequencies are coded in the brain.

When measured behaviorally, older adults with normal hearing have poorer intensity discrimination thresholds than younger adults, but only at lower frequencies. Poor intensity discrimination at lower but not higher frequencies for older adults can be associated with an age-related decline in temporal processing. The current study was designed to assess age-related effects on intensity discrimination at 500 and 3000 Hz using the cortical auditory evoked potential, N1--P2. Subjects were 10 younger and 10 older adults with normal hearing. The N1--P2 was elicited by an intensity increase in an otherwise continuous pure tone presented at 70 dB SPL. Intensity increments ranged from 0 dB to 5 dB at 500 Hz and from 0 d B to 8 d B at 3000 Hz in 1-dB steps. Intensity discrimination threshold was defined as the smallest intensity change needed to evoke an N1-P2 response. Consistent with behavioral measures, N1-P2 response thresholds were significantly higher for older subjects than younger subjects at 500 Hz but did not differ significantly at 3000 Hz. In addition, N1 and P2 latencies for older subjects were significantly prolonged at 500 Hz, but not at 3000 Hz. As intensity increments increased above threshold, amplitudes tended to be larger in older than in younger subjects, however, these differences were not statistically significant. In older subjects, response latencies and amplitudes were significantly larger at 500 Hz than at 3000 Hz. In younger subjects, response latencies and amplitudes were similar across frequency. Similar intensity discrimination thresholds and age-related differences for behavioral measures and evoked potentials support the notion that the N1-P2 measures reflect the physiological detection of intensity change which in turn relates to intensity discrimination. A possible explanation for the decreased intensity discrimination at low frequencies, and enhanced amplitudes with prolonged latencies in older subjects is an age-related decline in inhibitory control within the central auditory nervous system.

Object The authors sought to examine the therapeutic efficacy of motor cortex stimulation (MCS) in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)–treated macaques and to characterize therapeutic differences with varying modes, frequencies, and durations of stimulation. Methods Motor cortex stimulation was delivered at currents below motor threshold and at frequencies between 5 and 150 Hz through epidural electrodes over the primary motor cortex. The animals were studied during and without MCS using video analysis, activity logging, and food retrieval tasks. Animals were examined using two different stimulation protocols. The first protocol consisted of 1 hour of MCS therapy daily. The second protocol exposed the animal to continuous MCS for more than 24 hours with at least 2 weeks between MCS treatments. Conclusions Daily MCS yielded no consistent change in symptoms, but MCS at 2-week intervals resulted in significant increases in activity. Effects of biweekly MCS disappeared, however, within 24 hours of the onset of continuous MCS. In this study, MCS only temporarily reduced the severity of MPTP-induced parkinsonism.

Responses to acoustic input were recorded from human temporal cortex using subdural electrodes in order to investigate in greater anatomical detail how attentional load modulates exogenous auditory responses. Four patient-volunteers performed a dichotic listening task in which they listened for rare frequency deviants in a series of tones presented to both ears at interstimulus intervals (ISIs) of 400, 800, and 2000 ms. Across all ISIs, stimuli presented contralateral to electrode location produced the strongest deflections in the averaged ERP at approximately 90 and 170 ms post-stimulus on average (labeled N90stg and P170stg). Maximal recording sites for these peaks most often occurred over the Sylvian fissure or the upper bank of the posterior superior temporal gyrus. Neither ISI nor selective attention exhibited substantial effects on peak latencies. However, as presentation rates increased (decreasing ISI), overall averaged event-related potential (ERP) amplitudes declined significantly, while attending to the contralateral stimulus significantly increased both the N90stg and P170stg peaks for most patients. This effect of attention increased with decreasing ISI for both components most clearly in the difference between the grand-average ERPs for attending to vs. ignoring the contralateral stimulus, and even more dramatically in the percentage ratio of that difference over the mean peak amplitude. This amplifying effect of attention with increasing load, along with its anatomical location, suggests that attention can enhance exogenous sources in auditory cortex.

Learning involves not only the establishment of memory per se, but also the specific details of its contents. In classical conditioning, the former concerns whether an association was learned while the latter discloses what was learned. The neural bases of associativity have been studied extensively while neural mechanisms of memory specificity have been neglected. Stimulation of the cholinergic nucleus basalis (NBs) paired with a preceding tone induces CS-specific associative memory. As different levels of acetylcholine may be released naturally during different learning situations, we asked whether the level of activation of the cholinergic neuromodulatory system can control the degree of detail that is encoded and retrieved. Adult male rats were tested pre- and post-training for behavioral responses (interruption of ongoing respiration) to tones of various frequencies (1-15 kHz, 70 dB, 2 s). Training consisted of 200 trials/day of tone (8.0 kHz, 70 dB, 2 s) either paired or unpaired with NBs (CS-NBs = 1.8 s) at moderate (65.7+/-9.0 microA, one day) or weak (46.7+/-12.1 microA, three training days) levels of stimulation, under conditions of controlled behavioral state (pre-trial stable respiration rate). Post-training (24 h) responses to tones revealed that moderate activation induced both associative and CS-specific behavioral memory, whereas weak activation produced associative memory lacking frequency specificity. The degree of memory specificity 24 h after training was positively correlated with the magnitude of CS-elicited increase in gamma activity within the EEG during training, but only in the moderate NBs group. Thus, a low level of acetylcholine released by the nucleus basalis during learning is sufficient to induce associativity whereas a higher level of release enables the storage of greater experiential detail. gamma waves, which are thought to reflect the coordinated activity of cortical cells, appear to index the encoding of CS detail. The findings demonstrate that the amount of detail in memory can be directly controlled by neural intervention.

Hippocampal sharp waves (SPWs) are among the earliest neural population patterns observed in infant mammals. Similarly, startles are among the earliest behavioral events observed. Here we provide evidence indicating that these two events are linked mechanistically soon after birth in freely moving and head‐fixed 1 to 4‐day‐old rats. EMG electrodes and intrahippocampal silicon depth electrodes were used to detect the presence of startles and SPWs, respectively. In intact pups, the majority of sharp waves were preceded by startles (average latency: 161 ms). When the hippocampal formation was surgically separated from the brainstem, however, sharp waves and startles still occurred, but now independently. In addition, unrelated to startles or SPWs, gamma oscillations were detected in several subjects, as were neocortical “spindles” that propagated passively into the hippocampus. The cooccurrence of sharp waves and startles provides the opportunity for Hebbian changes in synaptic efficacy and, thus, is poised to contribute to the assembly of neural circuits early in development. © 2006 Wiley‐Liss, Inc.

Sleep is a poorly understood behavior that predominates during infancy but is studied almost exclusively in adults. One perceived impediment to investigations of sleep early in ontogeny is the absence of state-dependent neocortical activity. Nonetheless, in infant rats, sleep is reliably characterized by the presence of tonic (i.e., muscle atonia) and phasic (i.e., myoclonic twitching) components; the neural circuitry underlying these components, however, is unknown. Recently, we described a medullary inhibitory area (MIA) in week-old rats that is necessary but not sufficient for the normal expression of atonia. Here we report that the infant MIA receives projections from areas containing neurons that exhibit state-dependent activity. Specifically, neurons within these areas, including the subcoeruleus (SubLC), pontis oralis (PO), and dorsolateral pontine tegmentum (DLPT), exhibit discharge profiles that suggest causal roles in the modulation of muscle tone and the production of myoclonic twitches. Indeed, lesions in the SubLC and PO decreased the expression of muscle atonia without affecting twitching (resulting in "REM sleep without atonia"), whereas lesions of the DLPT increased the expression of atonia while decreasing the amount of twitching. Thus, the neural substrates of infant sleep are strikingly similar to those of adults, a surprising finding in light of theories that discount the contribution of supraspinal neural elements to sleep before the onset of state-dependent neocortical activity.