Exploring The Brain

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Diffusion MRI (or dMRI) came into existence in the mid-1980s. During the last 25 years, diffusion MRI has been extraordinarily successful (with more than 300,000 entries on Google Scholar for diffusion MRI). Its main clinical domain of application has been neurological disorders, especially for the management of patients with acute stroke. It is also rapidly becoming a standard for white matter disorders, as diffusion tensor imaging (DTI) can reveal abnormalities in white matter fiber structure and provide outstanding maps of brain connectivity. The ability to visualize anatomical connections between different parts of the brain, non-invasively and on an individual basis, has emerged as a major breakthrough for neurosciences. The driving force of dMRI is to monitor microscopic, natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. Water molecules are thus used as a probe that can reveal microscopic details about tissue architecture, either normal or in a diseased state.

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This study investigates the brain functional connectivity in the rest and sleep states. We collected EEG, EOG, and fNIRS signals simultaneously during rest and sleep phases. The rest phase was defined as a quiet wake-eyes open (w_o) state, while the sleep phase was separated into three states; quiet wake-eyes closed (w_c), non-rapid eye movement sleep stage 1 (N1), and non-rapid eye movement sleep stage 2 (N2) using the EEG and EOG signals. The fNIRS signals were used to calculate the cerebral hemodynamic responses (oxy-, deoxy-, and total hemoglobin). We grouped 133 fNIRS channels into five brain regions (frontal, motor, temporal, somatosensory, and visual areas). These five regions were then used to form fifteen brain networks. A network connectivity was computed by calculating the Pearson correlation coefficients of the hemodynamic responses between fNIRS channels belonging to the network. The fifteen networks were compared across the states using the connection ratio and connection strength calculated from the normalized correlation coefficients. Across all fifteen networks and three hemoglobin types, the connection ratio was high in the w_c and N1 states and low in the w_o and N2 states. In addition, the connection strength was similar between the w_c and N1 states and lower in the w_o and N2 states. Based on our experimental results, we believe that fNIRS has a high potential to be a main tool to study the brain connectivity in the rest and sleep states.

With more than twenty years of history and many important findings, fMRI has demonstrated its key role in the examination of the brain functional connectivity. However, fMRI is very expensive, it has a low temporal resolution, and it restricts patients who have metal implanted devices. An alternative modality to study the brain networking is functional near-infrared spectroscopy (fNIRS). Compared to fMRI, fNIRS has a relatively low cost, a high temporal resolution, and no subject restriction. In addition, the fNIRS system is usually portable, which enables bedside monitoring13. Furthermore, in terms of the hemodynamic responses, while fMRI provides only information about blood oxygenation level dependent (BOLD) change, fNIRS provides more information including oxy-hemoglobin (HbO), deoxy-hemoglobin (Hb), and total hemoglobin (THb)14.

Apart from the resting-state connectivity, the sleep connectivity also plays an important role in understanding the brain functional connectivity. However, so far, there has been little work using either EEG, fMRI, or both to investigate the cerebral connectivity during sleep, and the results found from these papers are still controversial. While most studies demonstrated a decrease, decoupling, or breakdown of the DMN during sleep23,24, Larson-Prior et al. established the preservation of this network throughout all the examined arousal states25. Hence, further investigation of the sleep connectivity is needed. In this study, we collect fNIRS signals and examine the brain connectivity during the rest and sleep phases. According to our best knowledge, this is the first report on the cerebral functional connectivity in the sleep phase using fNIRS. We believe that the present work will help to interpret the spontaneous activity of the brain in the rest and sleep states.

In order to have a general view of the change of the connectivity in different states, we averaged the connectivity of the whole brain over all subjects. Though the threshold range was from 0.2 to 0.7, in Fig. 1, we displayed the brain map (calculated from HbO) with a threshold of 0.3 for the purpose of better visualizing the difference among states. After thresholding, the number of significant connections was high in the w-c and N1 states and lower in the w-o and N2 states in all networks. In addition, the somatosensory and its related networks altered most among the four states.

Figure 2 displays the averaged connection ratio (CR) from 18 subjects derived from HbO for 15 networks in four states. In all 15 networks, the CR was high in the w_c and N1 states and low in the w_o and N2 states. When the threshold was low (from 0.2 to 0.5), the ANOVA test and the post-hoc t-test were statistically significant in most networks (except the visual, temporal, and motor-temporal networks). However, with a high threshold, the number of the networks that had a significant difference in different states decreased. At the threshold of 0.6, the statistical tests revealed a significant difference in the frontal, somatosensory, and their related networks. Additionally, the threshold of 0.7 resulted in no statistically significant difference among the four states.

Similar to the CR derived from HbO, the CR derived from Hb was high in the w_c and N1 states and low in the w_o and N2 states for all networks with all thresholds (Fig. 3). When the threshold was 0.2, the statistical tests were significant in all networks. However, the higher the threshold was, the less the number of the networks, which were significantly different among states, was. At 0.5 threshold, the significant difference was found in the somatosensory, visual, and their related networks. The threshold of 0.6 and 0.7 revealed no significant difference in any networks.

In agreement with the other two hemodynamic responses, the CR derived from THb was highest in the w_c and N1 states, lower in the w_o state, and lowest in the N2 state with all threshold values. The ANOVA test revealed no statistical significance in any networks. A figure plotting the averaged CR of 18 subjects in 15 networks derived from THb is added as the Supplementary Fig. 1.

In all networks, the connection strength (CS) derived from HbO was similar between the w_c and N1 states, lower in the w_o state, and lowest in the N2 state (Fig. 4a). The ANOVA tests were statistically significant in the frontal, somatosensory, and their related networks. The post-hoc t-test revealed a significantly higher CS in the w_c state compared to the w_o states in these networks.

In agreement with the CR, the CS derived from Hb was high in the w_c and N1 states and low in the other two states for all networks (Fig. 4b). The ANOVA tests were statistically significant in the somatosensory, visual, somatosensory-related, and motor-temporal networks. The post-hoc t-test revealed a significantly higher CS in the w_c state than in the w_o state in the somatosensory, visual, and somatosensory-related networks. In addition, the CS in the N1 state was statistically significantly stronger than the CS in the N2 state in the motor-temporal and temporal-somatosensory networks.

Unlike the CS derived from HbO and Hb, the averaged CS derived from THb was similar in the four states in all networks. The ANOVA tests revealed no statistically significant difference in any networks. The averaged CS of 18 subjects in 15 networks derived from THb is added as the Supplementary Fig. 2.

The brain functional networks were investigated in the rest and sleep states: w_o, w_c, N1, and N2. Fifteen networks formed from five brain regions were compared among four states using an ANOVA test and between two states using a post-hoc t-test based on the CR and CS. The brain functional connectivity was found to be strong in the w_c and N1 states and weaker in the w_o and N2 states. The statistical analysis revealed the statistically and significantly higher CR and CS in the w_c and N1 states compared to the w_o and N2 states in the somatosensory and its related networks.

The previous studies using EEG and fMRI reported an increase in the brain functional connectivity in light sleep and a decrease in these networks in slow wave sleep23,24,25,26,27. Larson-Prior found an increase in the functional connectivity in the dorsal attention network in light sleep25, and Horovitz et al. and Smann et al. reported a breakdown of connectivity in the prefrontal cortex in slow-wave sleep23,24. Similar to the previous reports, our study showed that across all fifteen networks, the averaged CR and CS of 18 subjects were high in the w_c and N1 states and low in the N2 state. In detail, compared to the N2 state, the N1 state showed a significantly higher CS in the motor-temporal and temporal-somatosensory networks. Our findings of the decrease of the brain functional connectivity in the N2 state compared to the w-c and N1 states help to explain the reduced response of a sleep subject to the external environment.

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