Sustained stimulation of sensory organs results in adaptation of the neuronal response along the sensory pathway. Whether or not cortical adaptation affects equally excitation and inhibition is poorly understood. We examined this question using patch recordings of neurons in the barrel cortex of anesthetized rats while repetitively stimulating the principal whisker. We found that inhibition adapts more than excitation, causing the balance between them to shift toward excitation. A comparison of the latency of thalamic firing and evoked excitation and inhibition in the cortex strongly suggests that adaptation of inhibition results mostly from depression of inhibitory synapses rather than adaptation in the firing of inhibitory cells. The differential adaptation of the evoked conductances that shifts the balance toward excitation may act as a gain mechanism which enhances the subthreshold response during sustained stimulation, despite a large reduction in excitation.

Neurons in the barrel cortex and the thalamus respond preferentially to stimulation of one whisker (the principal whisker) and weakly to several adjacent whiskers. Cortical neurons, unlike thalamic cells, gradually adapt to repeated whisker stimulations. Whether cortical adaptation is specific to the stimulated whisker is not known. The aim of this intracellular study was to determine whether the response of a cortical cell to stimulation of an adjacent whisker would be affected by previous adaptation induced by stimulation of the principal whisker and vice versa. Using a high-frequency stimulation that causes substantial adaptation in the cortex and much less adaptation in the thalamus, we show that cortical adaptation evoked by a train of stimuli applied to one whisker does not affect the synaptic response to subsequent stimulation of a neighboring whisker. Our data indicate that intrinsic mechanisms are not involved in cortical adaptation. Thalamic recordings obtained under the same conditions demonstrated that an adjacent whisker response was not generated in the thalamus, indicating that the observed whisker-specific adaptation results from diverging thalamic inputs or from cortical integration.

Although the neural circuitry underlying homeostatic sleep regulation is little understood, cortical neurons immunoreactive for neuronal nitric oxide synthase (nNOS) and the neurokinin-1 receptor (NK1) have been proposed to be involved in this physiological process. By systematically manipulating the durations of sleep deprivation and subsequent recovery sleep, we show that activation of cortical nNOS/NK1 neurons is directly related to non-rapid eye movement (NREM) sleep time, NREM bout duration, and EEG δ power during NREM sleep, an index of preexisting homeostatic sleep drive. Conversely, nNOS knockout mice show reduced NREM sleep time, shorter NREM bouts, and decreased power in the low δ range during NREM sleep, despite constitutively elevated sleep drive. Cortical NK1 neurons are still activated in response to sleep deprivation in these mice but, in the absence of nNOS, they are unable to up-regulate NREM δ power appropriately. These findings support the hypothesis that cortical nNOS/NK1 neurons translate homeostatic sleep drive into up-regulation of NREM δ power through an NO-dependent mechanism.T he electrical activity of the cerebral cortex has been used to distinguish sleep vs. wakefulness since the earliest EEG studies of sleep (1). Several neural circuits have been implicated in the synchronization and desynchronization of cortical activity that distinguish non-rapid eye movement sleep (NREM) from wakefulness and rapid eye movement sleep (REM). Input from the basal forebrain (BF), likely from both cholinergic and noncholinergic neurons, is critical for the desynchronized EEG characteristic of wakefulness and REM (2, 3). Synchronization of the EEG during NREM depends on thalamic as well as intrinsic cortical oscillators (4).The firing rate of cortical neurons has generally been reported to be reduced during NREM relative to wakefulness and REM (5, 6). A few studies have reported cortical neurons with the opposite pattern. For example, 4 of 177 neurons in the monkey orbitofrontal cortex increased their firing rates during NREM (7). In the cat parietal cortex, 25% of neurons discharged in phase with NREM slow waves during up states but ceased firing during quiet wakefulness (8).Using Fos immunohistochemistry as a marker of cellular activity, we described a population of cortical GABAergic interneurons that are activated during sleep in three species (9, 10). These neurons express neuronal nitric oxide synthase (nNOS) and thus likely release nitric oxide (NO) as well as GABA (11). The percentage of activated cortical nNOS neurons was proportional to NREM δ energy (NRDE), the product of NREM time and NREM EEG δ power. Because NREM time and δ power increase in response to prolonged wakefulness through a regulated process referred to as sleep homeostasis, NRDE is an electrophysiological marker of homeostatic sleep "drive." Consequently, activation of cortical nNOS neurons during sleep seems to be related to the sleep need that accrues during wakefulness.Cortical nNOS neurons receive cholinergic (12), monoamin...

L-dopa-induced dyskinesias (LIDs) are a serious complication of L-dopa therapy for Parkinson's disease. Emerging evidence indicates that the nicotinic cholinergic system plays a role in LIDs, although the pathways and mechanisms are poorly understood. Here we used optogenetics to investigate the role of striatal cholinergic interneurons in LIDs. Mice expressing cre-recombinase under the control of the choline acetyltransferase promoter (ChAT-Cre) were lesioned by unilateral injection of 6-hydroxydopamine. AAV5-ChR2-eYFP or AAV5-control-eYFP was injected into the dorsolateral striatum, and optical fibers implanted. After stable virus expression, mice were treated with L-dopa. They were then subjected to various stimulation protocols for 2 h and LIDs rated. Continuous stimulation with a short duration optical pulse (1-5 ms) enhanced LIDs. This effect was blocked by the general muscarinic acetylcholine receptor (mAChR) antagonist atropine indicating it was mAChR-mediated. By contrast, continuous stimulation with a longer duration optical pulse (20 ms to 1 s) reduced LIDs to a similar extent as nicotine treatment (~50%). The general nicotinic acetylcholine receptor (nAChR) antagonist mecamylamine blocked the decline in LIDs with longer optical pulses showing it was nAChR-mediated. None of the stimulation regimens altered LIDs in control-eYFP mice. Lesion-induced motor impairment was not affected by optical stimulation indicating that cholinergic transmission selectively regulates LIDs. Longer pulse stimulation increased the number of c-Fos expressing ChAT neurons, suggesting that changes in this immediate early gene may be involved. These results demonstrate that striatal cholinergic interneurons play a critical role in LIDs and support the idea that nicotine treatment reduces LIDs via nAChR desensitization.

A robust, automated pattern recognition system for polysomnography data targeted to the sleep-waking state and stage identification is presented. Five patterns were searched for: slow-delta and theta wave predominance in the background electro-encephalogram (EEG) activity; presence of sleep spindles in the EEG; presence of rapid eye movements in an electro-oculogram; and presence of muscle tone in an electromyogram. The performance of the automated system was measured indirectly by evaluating sleep staging, based on the experts' accepted methodology, to relate the detected patterns in infants over four months of post-term age. The set of sleep-waking classes included wakefulness, REM sleep and non-REM sleep stages I, II, and III-IV. Several noise and artifact rejection methods were implemented, including filters, fuzzy quality indices, windows of variable sizes and detectors of limb movements and wakefulness. Eleven polysomnographic recordings of healthy infants were studied. The ages of the subjects ranged from 6 to 13 months old. Six recordings counting 2665 epochs were included in the training set. Results on a test set (2,369 epochs from five recordings) show an overall agreement of 87.7% (kappa 0.840) between the automated system and the human expert. These results show significant improvements compared with previous work.