experienced previously [11,37]. Interestingly, recent intracranial EEG evidences suggested that all DMN areas displayed transient increases and decreases of broadband gamma (60–140 Hz) power during goal-directed behavior [38]. Therefore, gamma oscillations during SWS could recurrently restore an intrinsic form of large- scale brain dynamics, typical of wakefulness, and trigger over time an activity-dependent reinforcement pathway like LTP, leading to a progressive consolidation of local cortical circuits [39], thus complementing the roles of gamma oscillations in encoding and retrieval of memory traces during wakefulness [40,41]. Indeed, as previously reported [11], these cortical gamma oscillations may be also triggered by hippocampal ripples-sharp waves and may coordinate, at the cortical level, the reactivation of hippocampus- dependent memories [42,43]. In this context, as it has been proposed by others [5], while gamma oscillations during ‘‘UP’’ states (IN-phase pattern) may facilitate the hippocampal-neocor- tical transfer of previously coded information via parahippocampal pathways [11], neocortical gamma oscillations during ‘‘DOWN’’ states (ANTI-phase pattern) may reflect an intrinsic hippocampal replay and a modification of intrahippocampal and entorhinal cortical connectivity. Additional research is here justified to clarify the role of IN-phase or ANTI-phase gamma events in the supposed iterative mechanisms of memory reprocessing during SWS. It is anticipated that further insights into the role of gamma activities during sleep will accrue from studying phenomena such as lucid dreaming [44] and sleep-state misperception [45], in which patients report the subjective experience of wakefulness during polysomnographically defined sleep. We have reported here the presence of intracranial gamma oscillations in epileptic patients recorded during seizure-free periods and we have shown that these oscillations coincide with non epileptic activities such as slow sleep oscillations. Nevertheless, epileptic oscillations in the same frequency range are well known in intracranial EEG recordings of the epileptic regions of patients with temporal and extra-temporal epilepsy, at seizure onset [46,47] or throughout the interictal period [48]. Furthermore, as already reported in patients with frontal lobe seizures [49], epileptic gamma oscillations appear to be closely related to sleep, especially in the prefrontal and orbitofrontal cortex. In addition to these observations, experimental models have led to the proposal that these fast oscillations could be involved in the initiation of seizures [50]. This raises the question about the relations between physiological and epileptic gamma oscillations. From our point of view [11] and as also suggested by other authors [48], the increased high frequency activity seen at the onset of some seizures could be the result of an aberration of the same physiological mechanisms underlying gamma activations in SWS. Indeed, strong gamma oscillations may be driven in the epileptic cortex by transient paroxysmal neuronal depolarization, in a similar way than the strong potential fluctuations which occur during the normal sleep. Furthermore, epileptic processes are well-known to be characterized by a general tendency of hypersynchronization of normal oscillations, as seen for example in the epileptic facilitation of sleep spindles [51] or ripples [52]. This aberrant hyper- expression may possibly imply the same physiological mechanisms underlying memory consolidation, leading to a progressive reinforcement of local epileptogenic circuits [48]. Materials and Methods Subjects and recordings We analyzed twenty subjects (10 females; age range, 18–49 years; mean age, 32 years) with refractory partial epilepsy undergoing presurgical evaluation, hospitalized between February Human Gamma Oscillations during Slow Wave Sleep 2002 and July 2007 in the epilepsy unit at the Pitie´-Salpeˆtrie`re hospital in Paris. Each patient was continuously recorded during several days (duration range, 9–20 days; mean duration, 15 days) with intracranial and scalp electrodes (Nicolet acquisition system, CA, US). Both scalp and depth EEG data were continuously recorded at a sampling rate of 400 Hz. The placement of scalp electrodes was adapted to each patient in order to facilitate the implantation of intracranial electrodes but the montage always involved electrodes at T9, TP9, T10, TP10, FPZ, FP1 and FP2 locations. Depth electrodes were composed of 4 to 10 cylindrical contacts 2.3-mm long, 1-mm in diameter, 10-mm apart center-to- center, mounted on a 1 mm wide flexible plastic probe. Subdural electrodes were strips with 4 to 8 one-sided circular contacts, 2.3 mm in diameter and with a center to center separation of 10 mm. Pre and post implantation MRI scans were evaluated to anatomically and precisely locate each contact along the electrode trajectory. Talairach coordinates of intracranial contacts could be successfully estimated for 11 subjects through BrainVisa/Anato- mist (http://brainvisa.info/index_f.html). The placement of elec- trodes within each patient was determined solely by clinical criteria; however, the routine clinical use of broad anatomical coverage for intracranial recordings provided a large sample of electrophysiological data from tissue outside of the epileptogenic zone (Table S1). Common average reference montage was chosen as our previous work on intracranial high-frequency activities [25]. This montage was chosen in order to avoid the introduction of unrelated information from the two referential time-series into the bipolar time-series, or from the removal or alteration of information common to the two referential time-series in the bipolar time-series. Noisy electrodes and those presenting high epileptic activity were excluded from the average by visual inspection. Digital bandpass filtering between frequencies f12f2 was, in all cases, implemented through a forward-backward digital infinite impulse response (IIR) type II Chebyshev filter (passband: f1#f#f2 Hz, attenuation #1 dB, monotonic; stopband: f#(f12k) or f$(f2+k) Hz, where k = 0.5 for f1,2$1 Hz or k = 0.05 otherwise, attenuation $100 dB, equiripple). Electrical line noise at 50 Hz was suppressed by a bandstop filter of the same type. All analyses were implemented in MATLABH 7.5 (The MathWorksTM, MA, USA). All patients gave their written informed consent and procedures were approved by the local ethical committee (CCP). Sleep studies Two seizure-free nights with at least two complete sleep cycles were chosen from all subjects. Each night was scored for sleep stages using the software SomnologicaTM Studio (Embla Systems, Inc, CO, USA) and visually confirmed by a time-frequency analysis. Slow waves detection Slow waves were first detected from a single scalp electrode and only from segments during NREM sleep. Since not all subjects shared exactly the same number of scalp electrodes, FP1 was chosen for the analysis because it was systematically recorded. For the automatic detection of the slow waves, the signal was first down-sampled to 40 Hz and then bandpass-filtered between 0.1– 4 Hz. The criteria for the detection of the slow waves were similar to those used in [53] and [16]: (1) a negative wave between two succeeding zero-crossings separated by 0.125–1 sec and presenting only one main peak #280 mV and, optionally, other negative peaks not exceeding 50% of the main one (in absolute value); (2) a subsequent (or antecedent) positive wave between two succeeding zero-crossings separated by 0.125–1 sec; (3) a negative-to-positive (or positive-to-negative) peak-to-peak amplitude $140 mV (see PLoS ONE | www.plosone.org 11 April 2012 | Volume 7 | Issue 4 | e33477