A sleep spindle is a distinctive feature of electroencephalogram (EEG), which is characterized by progressively increased and gradually decreased 10~16 Hz oscillations [1]. It occurs during non-rapid eye movement (NREM) sleep and is temporally locked to the K-complex and vertex sharp wave [1]. In human EEG, sleep spindle is divided into two types, slow spindle (~12 Hz) in the frontal and fast spindle (~14 Hz) in parietal and central brain region [2,3,4,5,6]. Each type of spindle is affected differently by the same variables, such as age, lifestyle and medical history [7,8,9]. The differences between the two types of spindles suggest that each spindle is generated by a different mechanism. However, a different mechanism of spindle generation remains unclear due to lack of spatial information of sleep spindle propagation.

Previously we reported that regionally specific sleep spindles exist in rodents, as they do in human [10]. Also, we reported corticothalamic connection among cortex, thalamic nucleus, and reticular nucleus has distinctive patterns for anterior and posterior spindles. This suggests that each type of spindle generation in the mouse had a different generation mechanism. We believe that the knowledge of rodent spindle can be an effective channel to understand human spindle. However, for mice, the comprehensive method for neuroimaging and EEG topographic measure is limited due to the small size of mouse brain. Recently, we established the source localization technique [11] in mice with the developed high density EEG [12,13].

In this study, we have chosen to apply our novel neuroimage technique to each type of sleep spindle and visualized the dynamics of spindles.

We used the high density EEG data during spontaneous sleep which were collected, detected, and classified in our previous study [10]. Briefly, the high density EEG was obtained from polyimide-based microarray in freely moving mice [12,13]. The EEG was band-pass filtered and then the spindle detection algorithm was applied to the filtered signals based on a threshold method. The anterior and posterior spindles were classified based on K-mean clustering algorithm.

We used high density EEG signals of the anterior and posterior spindles, which were clustered based on K-means cluster analysis in our previous study [10]. The representative traces of anterior and posterior spindles were presented in Fig. 2. In each panel, the left column contains the raw signals and the right column contains the filtered signals. The topographies of total power of the corresponding anterior and posterior spindles overlay the cortical surface of mouse brain in (C) and (D), respectively.

We followed the procedures for the dipole source localizations described in our previous work (Fig. 1 in [11]). Briefly, the boundary element model for the mouse brain was built based on mouse MRI to compute the leadfield matrix. Four different source localization algorithms (minimum norm [16], LORETA [17,18], sLORETA [19] and eLORETA [20]) were applied and their performance was compared. As a demonstration, the posterior spindle shown in Fig. 2B was applied and the axial and coronal view of its estimated source distributions are shown in Fig. 3A and 3B, respectively. While the spatial distributions of horizontal axis were similar for all algorithms, the spatial distributions of vertical axis were different depending on the algorithms. The LORETA or modified LORETA algorithms estimated the source deeper compared to MN. In our previous study based on optogenetic stimulation, the LORETA algorithm estimated the source deeper than the real source position [11]. On the other hand, the minimum norm algorithm estimated the sources successfully confined to the cortex.

The advantage of source localization is in the ability to visualize the propagation of potential sources generating the cortical potentials. Therefore, we employed the weighted norm algorithm to sleep spindles to visualize the propagation of cortical sources as time passes and presented in cine mode for anterior and posterior sleep spindles in Fig. 4 and Fig. 5, respectively. The cine mode presentation shows the spatio-temporal patterns of spindle evolution over time. For example, the sources of posterior spindles were confined to the posterior regions, but their positions fluctuated within the posterior cortex. A movie with high temporal resolution showed that the first slow wave of spindles was generated in the somatosensory cortex and propagated to the motor cortex, but the last slow wave of spindle was generated in the motor cortex and then propagated to the somatosensory cortex, showing complex patterns of onset and propagation of sleep spindles. This visualization confirms that the spindles are dynamical phenomena rather than an event of one entity.

In this study, we evaluated the performance of four different algorithms for source localization of spindles. The flow direction of source cluster was similar, but LORETA or modified LORETA algorithms overshoot the depth of source. Generally, cortical dipole was regarded as the source of EEG [21]. Many studies have suggested that the rodent EEG has identical characteristics as the human EEG [10,22]. Also, an electrode with the impedance of several hundred ohms can only measure the signal in 250 µm [23]. Additionally, we used common average reference. This method cancels out the signal from deep brain region. Therefore, the subcortical source was not reasonable. One of the major possible sources of error is the electrode position. All our electrodes were located above the upper surface. Therefore, depth of source localization solution could be biased. The MN is used to analyze evoked responses that involve wide-spread neuronal activation over time. In addition, MN tends to reconstruct sources that are superficial [24,25]. Therefore, we recommended that MN method is suitable for mouse brain.

As a comparable tool for noninvasive functional brain mapping, functional magnetic resonance imaging (fMRI) has been used in human brain mapping. However, the fMRI is based on blood oxygen level dependenet level rather than neural activity like EEG. EEG is the sum of synchronized post-synaptic activities, whereas BOLD response represents the change of hemodynamics that was related to the total synaptic activity. Additionally, EEG and the BOLD signal were caused by different cellular populations [26]. Previous research shows that average distance between local field potential and centroid of functional MRI was 1 cm in half of the monkey sensory cortex recording [27]. As previously mentioned, EEG is more related to functional neural activity and have a better temporal resolution to understand fast brain dynamics. Besides, the temporal resolution of fMRI is inappropriate to trace the fast activities like sleep spindles.

In sum, this study employed the equivalent dipole source estimation method in visualizing the sleep spindles in mouse brain. By applying source localization method, we have shown that the anterior and posterior spindles do not have identical functional brain mapping and temporal change of spindle dynamics. Our result suggests the possibility of the minimally invasive functional approach of the spindle network analysis. Also, our approach may advance our understanding for the functional study of cortical network in mice.