The spatial topological properties of cortical regions vary across individuals. Connectivity-based functional and anatomical cortical mapping in individuals will facilitate research on structure–function relationships. However, individual-specific cortical topographic properties derived from anatomical connectivity are less explored than those based on functional connectivity. We aimed to develop a novel individualized anatomical connectivity-based parcellation framework and investigate individual differences in spatial topographic features of cortical regions using diffusion magnetic resonance imaging (dMRI) tractography. Using a high-quality, repeated-session dMRI dataset (42 subjects, 2 sessions per subject), cortical parcels were derived through in vivo anatomical connectivity-based parcellation. These individual-specific parcels demonstrated good within-individual reproducibility and reflected interindividual differences in anatomical brain organization. Connectivity in these individual-specific parcels was significantly more homogeneous than that based on the group atlas. We found that the position, size, and topography of these anatomical parcels were highly variable across individuals and demonstrated nonredundant information about individual differences. Finally, we found that intersubject variability in anatomical connectivity was correlated with the diversity of anatomical connectivity patterns. Overall, we identified cortical parcels that show homogeneous anatomical connectivity patterns. These parcels displayed marked intersubject spatial variability, which may be used in future functional studies to reveal structure–function relationships in the human brain.

Animal experiments indicate that the hypothalamus plays an essential role in regulating the sleep-wake cycle. A recent neuroimaging study conducted under resting wakefulness conditions suggested the presence of a wake-promoting region and a sleep-promoting region in the human posterior hypothalamus and anterior hypothalamus, respectively, and interpreted their anticorrelated organization in resting-state functional networks as evidence for their opposing roles in sleep-wake regulation.However, whether and how the functional networks of the two hypothalamic regions reorganize according to their wake-or sleep-promoting roles during sleep are unclear. Here, we constructed functional networks of the posterior and anterior hypothalamus during wakefulness and nonrapid eye movement (NREM) sleep using simultaneous electroencephalography and functional magnetic resonance imaging data collected from 62 healthy participants. The functional networks of the posterior and anterior hypothalamus exhibited inversely correlated organizations during both wakefulness and NREM sleep. The connectivity strength of the posterior hypothalamic functional network was stronger during wakefulness than during stable sleep.From wakefulness to sleep, the anterior cingulate gyrus, paracingulate gyrus, insular cortex, and fontal operculum cortex showed decreased positive connectivity, while the precentral gyrus and postcentral gyrus showed decreased negative connectivity with the posterior hypothalamus. Additionally, the insular cortex and frontal operculum cortex showed negative connectivity during wakefulness and positive connectivity during sleep with the anterior hypothalamus, exhibiting an increasing trend. These findings provide insights into the correspondence between the functional network organizations and hypothalamic sleep-wake regulation in humans.

Blood oxygenation level‐dependent (BOLD) signals in the white matter (WM) have been demonstrated to encode neural activities by showing structure‐specific temporal correlations during resting‐state and task‐specific imaging of fiber pathways with various degrees of correlations in strength and time delay. Previous neuroimaging studies have shown state‐dependent functional connectivity and regional amplitude of signal fluctuations in brain gray matter across wakefulness and nonrapid eye movement (NREM) sleep cycles. However, the functional characteristics of WM during sleep remain unknown. Using simultaneous electroencephalography and functional magnetic resonance imaging data during wakefulness and NREM sleep collected from 66 healthy participants, we constructed 10 stable WM functional networks using clustering analysis. Functional connectivity between these WM functional networks and regional amplitude of WM signal fluctuations across multiple low‐frequency bands were evaluated. In general, decreased WM functional connectivity between superficial and middle layer WM functional networks was observed from wakefulness to sleep. In addition, functional connectivity between the deep and cerebellar networks was higher during light sleep and lower during both wakefulness and deep sleep. The regional fluctuation amplitude was always higher during light sleep and lower during deep sleep. Importantly, slow‐wave activity during deep sleep negatively correlated with functional connectivity between WM functional networks but positively correlated with fluctuation strength in the WM. These observations provide direct physiological evidence that neural activities in the WM are modulated by the sleep–wake cycle. This study provided the initial mapping of functional changes in WM during sleep.

Objective The assessment system for monitoring systemic lupus erythematosus (SLE) disease activity is complex and lacks reliable laboratory indicators. It is necessary to find rapid and noninvasive biomarkers. The aim of this study was to screen and identify the differentially expressed proteins in urine samples between active SLE and stable SLE and to further explore the expression of light chains. Methods First, we used a label-free quantitative proteomics approach to establish the urine protein expression profile of SLE, and then screened differentially expressed proteins. Subsequently, the expression of overall light chains was examined by immunofixation electrophoresis and immunoturbidimetric methods, respectively. Results Mass spectrometry data analysis found a total of 51 light chain peptides in the urinary protein expression spectrum, of which 27 light chain peptides were differentially expressed between the two groups. The largest difference was IGLV5-45 located in the variable region of the immunoglobulin Lambda light chain. The levels of urinary light chains and serum light chains were both significantly elevated in active SLE, and the levels of urinary light chains increased with the severity of disease activity. Conclusions The measurement of light chains would help to monitor SLE disease activity. Serum light chains had better discriminatory capacity than urinary light chains, while urine light chains were closely related to the severity of disease activity and could be used for dynamically monitoring the progress of disease activity.