Mapping morphological cortical networks with joint probability distributions from multiple morphological features.

Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs.

Exercise intervention in middle-aged and elderly individuals with insomnia improves sleep and restores connectivity in the motor network.

Exercise is a potential treatment to improve sleep quality in middle-aged and elderly individuals. Understanding exercise-induced changes in functional plasticity of brain circuits that underlie improvements in sleep among middle-aged and older adults can inform treatment of sleep problems. The aim of the study is to identify the effects of a 12-week exercise program on sleep quality and brain functional connectivity in middle-aged and older adults with insomnia. The trial was registered with Chinese Clinical Trial Register (ChiCTR2000033652). We recruited 84 healthy sleepers and 85 individuals with insomnia. Participants with insomnia were assigned to receive either a 12-week exercise intervention or were placed in a 12-week waitlist control condition. Thirty-seven middle-aged and older adults in the exercise group and 30 in the waitlist group completed both baseline and week 12 assessments. We found that middle-aged and older adults with insomnia showed significantly worse sleep quality than healthy sleepers. At the brain circuit level, insomnia patients showed decreased connectivity in the widespread motor network. After exercise intervention, self-reported sleep was increased in the exercise group (P < 0.001) compared to that in the waitlist group. We also found increased functional connectivity of the motor network with the cerebellum in the exercise group (P < 0.001). Moreover, we observed significant correlations between improvement in subjective sleep indices and connectivity changes within the motor network. We highlight exercise-induced improvement in sleep quality and functional plasticity of the aging brain.

An atmospheric water harvesting system based on the "Optimal Harvesting Window" design for worldwide water production.

Atmospheric water harvesting (AWH) is a promising solution to the water shortage problem. Current sorption-based AWH (SAWH) systems seldom obtain both wide climatic adaptability and high energy efficiency due to the lack of thermodynamic optimization. To achieve the ideal harvesting circulation in SAWH systems, the "optimal harvesting window" (OHW) design based on thermodynamic analysis was first proposed and validated by our prototype. The "OHW" theory indicates the water production rate and energy efficiency could be improved by properly reducing the adsorption temperature. As the humidity increases, the optimal adsorption temperature should be closer to the dew point of the environment. Experimental results revealed that, loaded with 3 kg widely adopted silica gel, the daily water production could reach 5.76-17.64 L/d with ultrahigh energy efficiency of 0.46-1.5 L/kWh. This prototype could also achieve optimal performance in wide climatic conditions in terms of 13-35 °C and 18%-72% RH. Lastly, the performance of photovoltaic (PV)-driven SAWH was evaluated. Results showed that a 1 m2 PV panel could generate 0.66-2 L water per day in Shanghai throughout the year, the highest in opening literature. Notably, this work introduces a promising concept that can help achieve large-scale, ultra-fast, energy-efficient AWH worldwide.

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies.

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates.

Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.

Single-subject cortical morphological brain networks across the adult lifespan.

Age-related changes in focal cortical morphology have been well documented in previous literature; however, how interregional coordination patterns of the focal cortical morphology reorganize with advancing age is not well established. In this study, we performed a comprehensive analysis of the topological changes in single-subject morphological brain networks across the adult lifespan. Specifically, we constructed four types of single-subject morphological brain networks for 650 participants (aged from 18 to 88 years old), and characterized their topological organization using graph-based network measures. Age-related changes in the network measures were examined via linear, quadratic, and cubic models. We found profound age-related changes in global small-world attributes and efficiency, local nodal centralities, and interregional similarities of the single-subject morphological brain networks. The age-related changes were mainly embodied in cortical thickness networks, involved in frontal regions and highly connected hubs, concentrated on short-range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal, and ventral attention networks. Intriguingly, nonlinear (i.e., quadratic or cubic) age-related changes were frequently found in the insula and limbic regions, and age-related cubic changes preferred long-range morphological connections. Finally, we demonstrated that the morphological similarity in cortical thickness between two frontal regions mediated the relationship between age and cognition measured by Cattell scores. Taken together, these findings deepen our understanding of adaptive changes of the human brain with advancing age, which may account for interindividual variations in behaviors and cognition.

Thalamic network under wakefulness after sleep onset and its coupling with daytime fatigue in insomnia disorder: An EEG-fMRI study.

BACKGROUND: Fatigue is the most common daytime impairment of insomnia disorder (ID). Thalamus is acknowledged as the key brain region closely associated with fatigue. However, the thalamus-based neurobiological mechanisms of fatigue in patients with ID remain unknown. METHODS: Forty-two ID patients and twenty-eight well-matched healthy controls (HCs) underwent simultaneous electroencephalography--functional magnetic resonance imaging. We calculated the functional connectivity (FC) between the thalamic seed and each voxel across the whole brain in two conditions of wakefulness--after sleep onset (WASO) and before sleep onset. A linear mixed effect model was used to determine the condition effect of the thalamic FC. The correlation between daytime fatigue and the thalamic connectivity was explored. RESULTS: After sleep onset, the connectivity with the bilateral thalamus was increased in the cerebellar and cortical regions. Compared with HCs, ID patients showed significantly lower FC between left thalamus and left cerebellum under the WASO condition. Furthermore, thalamic connectivity with cerebellum under the WASO condition was negatively correlated with Fatigue Severity Scale scores in the pooled sample. CONCLUSIONS: These findings contribute to an emerging framework that reveals the link between insomnia-related daytime fatigue and the altered thalamic network after sleep onset, further highlighting the possibility that this neural pathway is a therapeutic target for meaningfully mitigating fatigue.

Proper use of light environments for mitigating the effects of COVID-19 and other prospective public health emergency lockdowns on sleep quality and fatigue in adolescents.

Coronavirus disease 2019 (COVID-19) remains a public health emergency of international concern, and some countries still implement strict regional lockdowns. Further, the upcoming 2023 Asian Games and World University Games will implement a closed-loop management system. Quarantine can harm mental and physical health, to which adolescents are more vulnerable compared with adults. Previous studies indicated that light can affect our psychology and physiology, and adolescents were exposed to the artificial light environment in the evening during the lockdown. Thus, this study aimed to establish and assess appropriate residential light environments to mitigate the effects of lockdowns on sleep quality and fatigue in adolescents. The participants were 66 adolescents (12.15 ± 2.45 years of age) in a closed-loop management environment, who participated in a 28-day (7-day baseline, 21-day light intervention) randomized controlled trial of a light-emitting diode (LED) light intervention. The adolescents were exposed to different correlated color temperature (CCT) LED light environments (2000 K or 8000 K) for 1 h each evening. The results for self-reported daily sleep quality indicated that the low CCT LED light environment significantly improved sleep quality (p < 0.05), and the blood test results for serum urea and hemoglobin indicated that this environment also significantly reduced fatigue (p < 0.05) and moderately increased performance, compared to the high CCT LED light environment. These findings can serve as a springboard for further research that aims to develop interventions to reduce the effects of public health emergency lockdowns on mental and physical health in adolescents, and provide a reference for participants in the upcoming Asian Games and World University Games.

Acute Tai Chi Chuan exercise enhances sustained attention and elicits increased cuneus/precuneus activation in young adults.

BACKGROUND: The potential for acute exercise to enhance attention has been discussed in the literature. However, the neural mechanisms by which acute exercise affects attention remain elusive. METHOD: In this study, we first identified an optimized acute Tai Chi Chuan (ATCC) exercise protocol that enhances sustained attention performance and then aimed to determine the neural substrates of exercise-enhanced attention. Reaction time (RT) from the psychomotor vigilance test (PVT) was used to evaluate sustained attention. In Experiment 1, improvements in RTs were compared among six different exercise protocols. In Experiment 2, the participants completed the PVT in an MRI scanner on both rest and exercise days. RESULTS: Experiment 1 showed that practicing TCC 3 times for a total of 20 minutes, followed by 10-minute rest periods, resulted in the largest improvements in RTs. Experiment 2 showed that ATCC enhanced sustained attention, as evidenced by shorter RTs, and resulted in greater cuneus/precuneus activation after exercise than in the rest condition. Exercise-induced changes in brain activities across a distributed network exhibited significant correlations with attention. CONCLUSION: Therefore, this study indicates that ATCC effectively enhances sustained attention and underscores the key role of the cuneus/precuneus and frontoparietal-cerebellar regions in facilitating vigilance among young adults.

State-dependent and region-specific alterations of cerebellar connectivity across stable human wakefulness and NREM sleep states.

Sleep regulation and functioning may rely on systematic coordination throughout the whole brain, including the cerebellum. However, whether and how interactions between the cerebellum and other brain regions vary across sleep stages remain poorly understood. Here, using simultaneous EEG-fMRI recordings captured from 73 participants during wakefulness and non-rapid eye movement (NREM) sleep, we constructed cerebellar connectivity among intrinsic functional networks with intra-cerebellar, neocortical and subcortical regions. We uncovered that cerebellar connectivity exhibited sleep-dependent alterations: slight differences between wakefulness and N1/N2 sleep and greater changes in N3 sleep than other states. Region-specific cerebellar connectivity changes between N2 sleep and N3 sleep were also revealed: general breakdown of intra-cerebellar connectivity, enhancement of limbic-cerebellar connectivity and alterations of cerebellar connectivity with spatially specific neocortices. Further correlation analysis showed that functional connectivity between the cerebellar Control II network and regions (including the insula, hippocampus, and amygdala) correlated with delta power during N3 and beta power during N2 sleep. These findings systematically reveal altered cerebellar connectivity among intrinsic networks from wakefulness to deep sleep and highlight the potential role of the cerebellum in sleep regulation and functioning.

Increased connectivity of the anterior cingulate cortex is associated with the tendency to awakening during N2 sleep in patients with insomnia disorder.

STUDY OBJECTIVES: To investigate the relationship between sleep transition dynamics and stage-specific functional connectivity (FC) of the anterior cingulate cortex (ACC) in patients with insomnia disorder (ID). METHODS: Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) data from 37 patients with ID and 30 well-matched healthy controls (HCs) were recorded during wakefulness and different sleep stages and subsequently analyzed. A Markov chain model was used to estimate the transition probability between each stage. The FC between the ACC (set as the seed) and voxels across the whole brain was calculated. A linear mixed effect model was used to determine the group-by-stage interaction of the seed-based connectivity. The correlation between the sleep-stage transition probability and the ACC-based connectivity was explored. RESULTS: Patients with ID exhibited a higher likelihood of transitioning from N2 to wakefulness than HCs. A significant group-by-stage interaction of connectivity with the bilateral ACC was observed in the cerebellar, subcortical, and cortical regions. Moreover, a significant positive correlation was found in patients with ID between the transition probability from N2 to wakefulness and the FC of the ACC with the anterior cerebellum in N2 (r = 0.48). CONCLUSIONS: This exploratory analysis indicates that enhanced FC between the ACC and cerebellum represents a potential neural pathway underlying the greater likelihood of patients with ID waking during N2 sleep. These findings contribute to an emerging framework that reveals the link between sleep maintenance difficulty and ACC function, further highlighting the possibility that N2 sleep is a therapeutic target for meaningfully reducing sleep disruption.

Reorganizations of latency structures within the white matter from wakefulness to sleep.

Previous resting-state functional magnetic resonance imaging (fMRI) studies have revealed highly reproducible latency structures, reflecting the lead/lag relationship of BOLD fMRI signals in white matter (WM). With simultaneous electroencephalography and fMRI data from 35 healthy subjects who were instructed to sleep during imaging, we explored alterations of latency structures in the WM across wakefulness and nonrapid eye movement (NREM) sleep stages. Lagged cross-covariance was computed among voxelwise time series, followed by parabolic interpolation to determine the actual in-between latencies. WM regions, including the brainstem, internal capsule, optic radiation, genu of corpus callosum, and corona radiata, inconsistently changed temporal dynamics with respect to the rest of the WM across wakefulness and NREM sleep stages, as demonstrated when these regions were used as seeds for seed-based latency analysis. Latency analysis of resting-state networks, obtained by applying K-means clustering to a group-level functional connectivity matrix, identified a dominant direction of signaling, starting from the brainstem up to the internal capsule and then the corona radiata during wakefulness, which was reorganized according to stage transitions, e.g., the temporal organization of the internal capsule and corona radiata switched from unidirectional to bidirectional in the wakefulness to N3 transition. These findings suggest that WM BOLD signals are slow, dynamically modulated across wakefulness and NREM sleep stages and that they are involved in maintaining different levels of consciousness.

Sleep discrepancy is associated with alterations in the salience network in patients with insomnia disorder: An EEG-fMRI study.

BACKGROUND: Positron emission tomography - computed tomography (PET-CT) research has shown that sleep discrepancy recorded by self-report and polysomnography (PSG) may be related to the altered metabolic rate of the anterior insula (aINS) during non-rapid eye movement (NREM) sleep in patients with insomnia disorder. We aim to explore the functional connectivity of aINS across wake and NREM sleep in the patients and to reveal the association between aINS connectivity and sleep discrepancy. METHODS: Patients with insomnia disorder (n = 33) and healthy controls (n = 31) underwent simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) during nighttime sleep, and aINS-based connectivity was calculated across wake and NREM sleep. A linear mixed-effects model was used to assess the main effect of group and group-by-stage (wake, NREM stages 1-3) interaction effect on aINS connectivity. Similar mixed models were used to assess the potential correlation between aINS connectivity and the sleep misperception index (MI). RESULTS: A significant group-by-stage interaction effect on aINS-based connectivity was observed in the bilateral frontal gyrus, right inferior temporal gyrus, bilateral middle occipital gyrus and right postcentral gyrus (p < 0.05, corrected). There was also a significant group-by-MI interaction effect on aINS connectivity with the putamen and thalamus during wakefulness (p < 0.05 corrected); MI was significantly associated with aINS-putamen/thalamus connectivity in the control group, whereas the association was weak or even nonsignificant in the patient group. There was no significant main effect of group. CONCLUSION: The waking activity of a neural pathway containing the aINS, putamen, and thalamus may underlie sleep perception, potentially providing important perspectives to reveal complex mechanisms of sleep discrepancy between self-report and PSG.

Disrupted neural tracking of sound localization during non-rapid eye movement sleep.

Spatial hearing in humans is a high-level auditory process that is crucial to rapid sound localization in the environment. Both neurophysiological models with animals and neuroimaging evidence from human subjects in the wakefulness stage suggest that the localization of auditory objects is mainly located in the posterior auditory cortex. However, whether this cognitive process is preserved during sleep remains unclear. To fill this research gap, we investigated the sleeping brain's capacity to identify sound locations by recording simultaneous electroencephalographic (EEG) and magnetoencephalographic (MEG) signals during wakefulness and non-rapid eye movement (NREM) sleep in human subjects. Using the frequency-tagging paradigm, the subjects were presented with a basic syllable sequence at 5 Hz and a location change that occurred every three syllables, resulting in a sound localization shift at 1.67 Hz. The EEG and MEG signals were used for sleep scoring and neural tracking analyses, respectively. Neural tracking responses at 5 Hz reflecting basic auditory processing were observed during both wakefulness and NREM sleep, although the responses during sleep were weaker than those during wakefulness. Cortical responses at 1.67 Hz, which correspond to the sound location change, were observed during wakefulness regardless of attention to the stimuli but vanished during NREM sleep. These results for the first time indicate that sleep preserves basic auditory processing but disrupts the higher-order brain function of sound localization.

A-PASS: an automated pipeline to analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep.

Objective.Concurrent electroencephalography and functional magnetic resonance imaging (EEG-fMRI) signals can be used to uncover the nature of brain activities during sleep. However, analyzing simultaneously acquired EEG-fMRI data is extremely time consuming and experience dependent. Thus, we developed a pipeline, which we named A-PASS, to automatically analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep.Approach.A deep learning model was trained on a sleep EEG-fMRI dataset from 45 subjects and used to perform sleep stage scoring. Various fMRI indices can be calculated with A-PASS to depict the neurophysiological characteristics across different sleep stages. We tested the performance of A-PASS on an independent sleep EEG-fMRI dataset from 28 subjects. Statistical maps regarding the main effect of sleep stages and differences between each pair of stages of fMRI indices were generated and compared using both A-PASS and manual processing methods.Main results.The deep learning model implemented in A-PASS achieved both an accuracy and F1-score higher than 70% for sleep stage classification on EEG data acquired during fMRI scanning. The statistical maps generated from A-PASS largely resembled those produced from manually scored stages plus a combination of multiple software programs.Significance.A-PASS allowed efficient EEG-fMRI data processing without manual operation and could serve as a reliable and powerful tool for simultaneous EEG-fMRI studies on sleep.

Decreasing integration within face network and segregation beyond the face network in the aging brain.

Face processing is known to decline in older adults; however, a clear understanding of the brain networks behind this cognitive decline is still lacking. In this study, we investigated the neural correlates of the declined face processing with aging from a resting-state brain network perspective. Nineteen healthy old adults and 22 young adults were recruited and underwent two functional magnetic resonance imaging (fMRI) scanning sessions (i.e., resting-state and localizer task) and two behavioral tests (face matching and symbol-form matching). We examined age-related alterations in resting-state functional connectivity (FC) within face network as well as between face network and other networks, and tested their associations with behavioral performance of face and symbol-form processing. We found that (a) compared with young adults, old adults exhibited decreased FC between face-selective regions (fusiform face area and occipital face area), but increased FC between face-selective regions and non-face-selective regions; (b) these age-related FC alterations were correlated with individuals' behavioral performance of face and symbol-form processing. Collectively, these findings suggest the declines of face processing are associated with a mixture of decreased integration within the face network and segregation beyond the face network in the aging brain, and provide evidence for a neural basis of cognitive aging in face processing from an intrinsic brain network perspective.

Changes in white matter functional networks during wakefulness and sleep.

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.

Spindle-related brain activation in patients with insomnia disorder: An EEG-fMRI study.

Sleep spindles have been implicated in sleep protection, depression and anxiety. However, spindle-related brain imaging mechanism underpinning the deficient sleep protection and emotional regulation in insomnia disorder (ID) remains elusive. The aim of the current study is to investigate the relationship between spindle-related brain activations and sleep quality, symptoms of depression and anxiety in patients with ID. Participants (n = 46, 28 females, 18-60 years) were recruited through advertisements including 16 with ID, according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 30 matched controls. Group differences in spindle-related brain activations were analyzed using multimodality data acquired with simultaneous electroencephalography and functional magnetic resonance imaging during sleep. Compared with controls, patients with ID showed significantly decreased bilateral spindle-related brain activations in the cingulate gyrus (familywise error corrected p ˂ 0.05, cluster size 4401 mm3). Activations in the cingulate gyrus were negatively correlated with Pittsburgh Sleep Quality Index scores (r = -0.404, p = 0.005) and Self-Rating Anxiety Scale scores (r = -0.364, p = 0.013), in the pooled sample. These findings underscore the key role of spindle-related brain activations in the cingulate gyrus in subjective sleep quality and emotional regulation in ID.

Repetitive transcranial magnetic stimulation modulates cortical-subcortical connectivity in sensorimotor network.

Repetitive transcranial magnetic stimulation (rTMS) holds the ability to modulate the connectivity within the stimulated network. However, whether and how the rTMS targeted over the primary motor cortex (M1) could affect the connectivity within the sensorimotor network (SMN) is not fully elucidated. Hence, in this study, we investigated the after-effects of rTMS over left M1 at different frequencies on connectivity within SMN. Forty-five healthy participants were recruited and randomly divided into three groups according to rTMS frequencies (high-frequency [HF], 3 Hz; low-frequency [LF], 1 Hz; and SHAM). Participants received 1-Hz, 3-Hz or sham stimulation and underwent two functional magnetic resonance imaging (fMRI) scanning sessions before and after rTMS intervention. Using resting-state functional connectivity (FC) approach, we found that high- and low-frequency rTMS had opposing effects on FC within the SMN, especially for connectivity with subcortical regions (i.e., putamen, thalamus and cerebellum). Specifically, the reductions in connectivity between cortical and subcortical regions within cortico-basal ganglia thalamo-cortical circuits and the cognitive loop of cerebellum, and increased connectivity between cortical and subdivisions within the sensorimotor loop of cerebellum were observed after high-frequency rTMS intervention, whereas the thalamus and cognitive cerebellum subdivisions exhibited increased connectivity, and sensorimotor cerebellum subdivisions showed decreased connectivity with stimulated target after low-frequency stimulation. Collectively, these findings demonstrated the alterations of connectivity within SMN after rTMS intervention at different frequencies and may help to understand the mechanisms of rTMS treatment for movement disorders associated with deficits in subcortical regions such as Parkinson's disease, Huntington's disease and Tourette's syndrome.