Twenty healthy subjects (12 males and 8 females; age range = 20–29, mean age = 23.8 ± 2.45 years) were selected as volunteers from a university student population. The requirements for inclusion were: regular sleep duration and schedule (habitual sleep time: 12:00 am–8:00 am ± 1 h), no daytime nap habits, no excessive daytime sleepiness and no other sleep, medical, neurological or psychiatric disorder, as assessed by a 1-week sleep log and by a clinical interview.

Participants were asked to keep constant their sleep/wake cycle during the week before the experimental night, and their compliance was controlled by daily sleep log and actigraphic recordings.

All subjects provided written informed consent. The study was approved by the Institutional Ethics Committee of the Department of Psychology of “Sapienza” University of Rome and was conducted in accordance with the Declaration of Helsinki.

Each subject participated in this study across two consecutive nights. The first night was considered of adaptation, while the following was the experimental night. Polysomnographic (PSG) recordings performed during the experimental night only was considered for the present analyses.

The sleep recordings were carried out in an electrically shielded, sound-proof and temperature-controlled room. Bedtime was scheduled at midnight and ended after 7.5 h of accumulated sleep, as visually checked online by expert sleep researchers.

PSG was recorded using an Esaote Biomedica VEGA 24 polygraph. EEG signals were analogically filtered (high-pass filter at 0.50 Hz and antialiasing low-pass filter at 30 Hz [−30 dB/octave]). The 19 unipolar EEG derivations (Fp1, Fp2, F7, F8, F3, F4, Fz, C3, C4, Cz, P3, P4, Pz, T3, T4, T5, T6, O1, O2) were placed according to the international 10–20 system and recorded from scalp electrodes with linked mastoid electrodes as a reference. Bipolar horizontal electrooculogram (EOG) was recorded from electrodes placed approximately 1 cm from the medial and lateral canthi of the dominant eye. The bipolar submental electromyogram (EMG) was also recorded. The time constants were as follow: 0.03 s for EMGs and 1 s for EOGs. Impedance of these electrodes was maintained below 5 kOhm.

The whole-night sleep recordings (19 EEG channels, EOG, and EMG) were digitalized with a sampling rate of 128 Hz.

The results of source estimation of spindle activity from the 19-channel EEG recordings are depicted in Figure 1.

Figure 1B provides a grand average of CSD maps for slow and fast spindles during NREM sleep. LORETA processing revealed two broadly distinct cortical areas for slow and fast spindles, respectively placed in the anterior and posterior cortical brain areas. Multiple cortical sources for slow spindles were identified, scattered throughout the frontal areas. These areas included the superior and medial frontal gyrus (frontal lobe) and anterior cingulate (limbic lobe), with a maximum CSD value in the superior frontal gyrus (BA10). Multiple sources were also identified for fast spindles, mostly restricted and situated in the parietal areas (superior and inferior parietal lobule and post-central gyrus), with the superior parietal lobule (BA7) as the most active region.

The localization and MNI coordinates of local maxima CSD for each detected source of the two types of spindles are reported in Table 4.

Current source density maps of group average in each NREM sleep episodes are depicted in Figure 2. Despite the varying intensity across cycles, we observed that the local maxima of CSD distribution for both slow and fast spindles remained stable.

The repeated measures ANOVA Spindle Type × Sleep Cycle on CSD values (Figure 3) showed a significant main effect of the Sleep Cycle (F_3,57 = 3.279, p = 0.0273), in the direction of a clear reduction of CSD values in the fourth cycle compared to the first (p = 0.0223), second (p = 0.0099) and third (p = 0.0113) cycle. No significant main effect of Spindle Type was observed.

A significant interaction effect between the two factors was also observed (F_3,57 = 4.264, p = 0.0087). LSD post hoc comparison showed that while CSD in BA10 (slow spindle source) progressively decreases over time (Cycle 1 vs. Cycle 4: p = 0.0012; Cycle 2 vs. Cycle 4: p = 0.0008; Cycle 2 vs. Cycle 3: p = 0.0504), CSD in BA7 (fast spindle source) showed a different trend. After an initial increase, statistically significant between Cycle 1 and Cycle 3 (p = 0.0266), we found a decrease from Cycle 3 to Cycle 4 (p = 0.0117). CSD of the two types of spindle was different in the first and third cycle, with a prevalence of BA10 within Cycle 1 (p = 0.0399) and of BA7 within Cycle 3 (p = 0.0477).

Figure 4 depicts intra-cycle variations of CSD computed from BA10 (slow spindle source) and BA7 (fast spindle source) during the first four sleep cycles.

We carried out four separate ANOVAs, one for each cycle, considering Spindle Type and Cycle Interval as within factors. A statistically significant main effect for the factor Cycle Interval was found for the first (F_4,76 = 3.334, p = 0.0142) and second (F_4,76 = 3.866, p = 0.0065) NREM cycle. No effect of Spindle Type or interaction Spindle Type × Cycle Interval was found in any NREM cycle.

LSD post hoc testing for the first NREM cycle showed a significant prevalence of CSD in the first interval as compared to the last three cycle intervals (Interval 1 vs. Interval 3: p = 0.0015; Interval 1 vs. Interval 4: p = 0.0083; Interval 1 vs. Interval 5: p = 0.01258). In the second cycle, we found the same decrease of CSD across different intervals (Interval 1 vs. Interval 3: p = 0.0031; Interval 1 vs. Interval 4: p = 0.0013; Interval 1 vs. Interval 5: p = 0.0296) and a prevalence of CSD in Interval 2 as compared to the Interval 4 (p = 0.0286).

We computed CSD maps in the frequency domain considering a wide frequency range (10.0–16.0 Hz) (Figure 1A), with a bin resolution of 0.5 Hz. This frequency-specific analysis identified the ranges 11.0–12.5 and 13.0–14.5 Hz as the frequency intervals with the highest values of CSD, respectively for the “slow” and “fast” spindle type. The frequency resolution allowed us to observe the smoothly changing of detected sources across consecutive bins, with a shift from the anterior activating areas (slower frequencies) to the posterior sources (faster frequencies).

The most active source of slow spindles was found in BA10, in the frontal cortex, while fast spindles range reached the absolute maximum in the parietal cortex (BA7) (Figure 1B).

The pattern observed at cortical level mirrors the appearance of spindles at the scalp level. This is consistent with the idea that EEG activity reflects the synchronous activation of brain electrical generators. Indeed, the slow spindle oscillations are predominant throughout the anterior derivations, while fast spindles prevail posteriorly (Werth et al., 1996; Zeitlhofer et al., 1997).

Despite the limited number of scalp electrodes (19) may have affected the precision of the inverse solution implemented by LORETA, the preferential localization of slow and fast spindles sources corresponds to those previously reported in high-density EEG studies (Del Felice et al., 2013, 2014). A strength of the current study is the use of a data-driven approach. Instead of a priori division of sleep spindles according to their topographical scalp distribution, the classification was only performed after the source reconstruction through LORETA. The aim was to avoid the pre-selection based on EEG activity.

To describe the temporal dynamics of such detected generators across the night, we first computed the average LORETA images for the first four NREM sleep cycles and then for the separate intervals within each cycle.

The maximum value of CSD remained systematically located in BA10 (slow spindle source) and BA7 (fast spindle source) along successive NREM sleep cycles, showing topographical stability of the detected generators (Figure 2). However, a gradual decline of CSD in BA10 over consecutive NREM episodes was observed. This decline was paralleled by a concomitant slight increase of CSD in BA7, reaching its peak around the third cycle and then decreasing again (Figure 3). The results provided by LORETA showed great inter-individual variability (see SEs in Figures 3, 4). Therefore, the possible absence of consistent pattern across subjects could make it difficult to generalize the observed findings.

Several evidences showed that while EEG power in slow spindle frequency range decreases over the night, an opposite trend is observed for higher frequency power (Tanaka et al., 1997; Cox et al., 2017). Similar results have also been reported for the amount (density) of slow and fast spindles across the night (Werth et al., 1996; Bódizs et al., 2009). Our results on active sources suggest an interesting resemblance with temporal dynamics exhibited by slow and fast spindles on scalp topography (spectral power, density).

The variations within cycles followed a “U” shaped curve for both spindle sources, with a trough around third and fourth interval (middle part) and the highest values at the beginning and the end of the time window investigated (Figure 4). As previously reported in EEG studies, such peculiar temporal pattern was also mirrored by EEG topography (Aeschbach et al., 1997; De Gennaro et al., 2000b; Himanen et al., 2002, 2003). The amount of sleep spindles and sigma activity power decreases with the deepening of sleep and increases closer to SO or REM transition (Silverstein and Levy, 1976; Guazzelli et al., 1986; Uchida et al., 1991, 1994; Purcell et al., 2017). In line with this evidence, numerous human and animal studies reported a negative temporal relationship between spindle activity and the low-frequency (0.5–4.0 Hz) and high-amplitude (≥75 μV) slow wave activity (SWA), which are predominant during SWS. Intracellular recordings suggest that spindle and delta waves (1.0–4.0 Hz) are generated by a common thalamocortical mechanism and their differentiation depends on the degree of membrane hyperpolarization of thalamocortical neurons (Steriade et al., 1993; Andrillon et al., 2011).

Moreover, also sleep pressure-related courses of SWA and spindle-frequency activity are negative correlated (Borbély et al., 1981; Dijk et al., 1993; De Gennaro et al., 2000a; Finelli et al., 2001). However, the response to sleep deprivation across the spindle frequency range is not uniform. The reduction of spindle activity only affects high-frequency spindle (>13.0 Hz) (Knoblauch et al., 2002), possibly reflecting a different sensitivity to homeostatic processes for the two types of spindles.

Therefore, the mutual relationship between these two EEG activities could explain both the variations of slow/fast spindle sources between NREM cycles (homeostatic decrease of slow spindle) and in the course of a NREM episode (reduced spindle activity in mid-cycle). However, the lack of comparison analysis between scalp- and source-recorded activities makes this interpretation speculative.

Hashemi et al. (2019) recently examined the interrelationship between sleep-spindles activity and higher frequencies (∼25.0 Hz). In their model, the cortical high frequency activity and its interaction with thalamic oscillation at fast spindle are proposed as key mechanism for the generation of slow spindles. Investigating the relationship between spindle sources and other patterns of brain activity could help understand how the local pattern of spindle activity (e.g., fast vs. slow) emerges. However, an exhaustive description of spindles local features is not possible with LORETA algorithm, due to its limited potential for the identification and localization of multiple and concomitant cortical sources (Nir et al., 2011; Piantoni et al., 2017; Hagler et al., 2018). Moreover, LORETA does not take into account co-localized sources, possibly explaining the key role of modulation of cortical spontaneous activity in the genesis of spindle (Hashemi et al., 2019).

Despite the observed time-related variations, the statistical comparison between slow and fast spindle sources was not significant. The two kinds of spindles, even if distinct, still showed strong overlapping time courses within each NREM episode. This can be interpreted in terms of similar cortical innervation patterns, regardless of different subcortical thalamic generators (Ujma et al., 2019). As highlighted in previous studies (Anderer et al., 2001; Bersagliere et al., 2013; Del Felice et al., 2014), the frontal and parietal spindle sources were directly connected with specific thalamic nuclei, where the spindles are initiated. Subsequent regional variability may stem from local neuronal activity, as suggested by previous researches with intracranial recordings (Andrillon et al., 2011; Nir et al., 2011; Ujma et al., 2019).

The debate about the existence of one or two sleep spindle generators should be considered open. The exploration of the spatiotemporal dynamics of cortical spindle sources could provide new information on this issue. However, given the key role of subcortical sources (thalamus, hippocampus) in spindle generation (McCormick and Bal, 1997; Sarasso et al., 2014), the LORETA solution space restricted to the cortex may represent an important limitation.