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The messenger RNA (mRNA)-based therapy, especially mRNA vaccines, has shown its superiorities in versatile design, rapid development and scale production, since the outbreak of coronavirus disease 2019 (COVID-19). Although the Pfizer-BioNTech and Moderna COVID-19 mRNA vaccines had been approved for application, unexpected adverse events were reported to be most likely associated with the mRNA delivery systems. Thus, the development of mRNA delivery system with good efficacy and safety remains a challenge. Here, for the first time, we report that the neutral cytidinyl lipid, 2-(4-amino-2-oxopyrimidin-1-yl)-N-(2,3-dioleoyl-oxypropyl) acetamide (DNCA), and the cationic lipid, dioleoyl-3,3′-disulfanediylbis-[2-(2,6-diaminohexanamido)] propanoate (CLD), could encapsulate and deliver the COVID-19 mRNA-1096 into the cytoplasm to induce robust adaptive immune response. In the formulation, the molar ratio of DNCA/CLD to a single nucleotide of COVID-19 mRNA-1096 was about 0.9: 0.5: 1 (the N/P ratio was about 7: 1). The DNCA/CLD-mRNA-1096 lipoplexes were rationally prepared by the combination of the lipids DNCA/CLD with the aqueous mRNA solution under mild sonication to stimulate multiple interactions, including H-bonding, π-stacking and electrostatic force between the lipids and the mRNA. After intramuscular applications of the DNCA/CLD-mRNA-1096 lipoplexes, robust neutralizing antibodies and long-lived Th1-biased SARS-CoV-2-specific cell immunity were detected in the immunized mice, thus suggesting the DNCA/CLD a promising mRNA delivery system. Moreover, our study might also inspire better ideas for developing mRNA delivery systems., Graphical abstract Unlabelled Image

The default mode network (DMN) reflects spontaneous activity in the resting human brain. Previous studies examined the difference in static functional connectivity (sFC) of the DMN between eyes-closed (EC) and eyes-open (EO) using the resting-state functional magnetic resonance imaging (rs-fMRI) data. However, it remains unclear about the difference in dynamic FC (dFC) of the DMN between EC and EO. To this end, we acquired rs-fMRI data from 19 subjects in two different statues (EC and EO) and selected a seed region-of-interest (ROI) at the posterior cingulate cortex (PCC) to generate the sFC map. We identified the DMN consisting of ten clusters that were significantly correlated with the PCC. By using a sliding-window approach, we analyzed the dFC of the DMN. Then, the Newman's modularity algorithm was applied to identify dFC states based on nodal total connectivity strength in each sliding-window. In addition, graph-theory based network analysis was applied to detect dynamic topological properties of the DMN. We identified three group-level dFC states (State1, 2 and 3) that reflects the strength of dFC within the DMN between EC and EO in different time. The following results were reached: (1) no significant difference in sFC between EC and EO, (2) dFC was lower in State2 but higher in State3 in EC than in EO, (3) lower clustering coefficient, local efficiency, and global efficiency, but higher characteristic path length in State2 in EC than in EO, and (4) lower nodal strength in the precuneus (PCUN), PCC, angular gyrus (ANG), middle temporal gyrus (MTG) and medial prefrontal cortex (MPFC) in State3 in EC. These results suggested different resting statuses, EC and EO, may induce different time-varying neural activity in the DMN.

Background: Resting-state functional MRI (fMRI) studies have provided much evidence for abnormal intrinsic brain activity in schizophrenia, but results have been inconsistent. Methods: We conducted a meta-analysis of whole-brain, resting-state fMRI studies that explored differences in amplitude of low-frequency fluctuation (ALFF) between people with schizophrenia (including first episode and chronic) and healthy controls. Results: A systematic literature search identified 24 studies comparing a total of 1249 people with schizophrenia and 1179 healthy controls. Overall, patients with schizophrenia displayed decreased ALFF in the bilateral postcentral gyrus, bilateral precuneus, left inferior parietal gyri and right occipital lobe, and increased ALFF in the right putamen, right inferior frontal gyrus, left inferior temporal gyrus and right anterior cingulate cortex. In the subgroup analysis, patients with first-episode schizophrenia demonstrated decreased ALFF in the bilateral inferior parietal gyri, right precuneus and left medial prefrontal cortex, and increased ALFF in the bilateral putamen and bilateral occipital gyrus. Patients with chronic schizophrenia showed decreased ALFF in the bilateral postcentral gyrus, left precuneus and right occipital gyrus, and increased ALFF in the bilateral inferior frontal gyri, bilateral superior frontal gyrus, left amygdala, left inferior temporal gyrus, right anterior cingulate cortex and left insula. Limitations: The small sample size of our subgroup analysis, predominantly Asian samples, processing steps and publication bias could have limited the accuracy of the results. Conclusion: Our comprehensive meta-analysis suggests that findings of aberrant regional intrinsic brain activity during the initial stages of schizophrenia, and much more widespread damage with the progression of disease, may contribute to our understanding of the progressive pathophysiology of schizophrenia.

PurposeThe aim of this study is to compare the blood oxygen level–dependent (BOLD) fluctuation power in 96 frequency points ranging from 0 to 0.25 Hz between benign and malignant musculoskeletal (MSK) tumorsviapower spectrum analyses using functional magnetic resonance imaging (fMRI).Materials and methodsBOLD-fMRI and T1-weighted imaging (T1WI) of 92 patients with benign or malignant MSK tumors were acquired by 1.5-T magnetic resonance scanner. For each patient, the tumor-related BOLD time series were extracted, and then, the power spectrum of BOLD time series was calculated and was then divided into 96 frequency points. A two-samplet-test was used to assess whether there was a significant difference in the powers (the “power” is the square of the BOLD fluctuation amplitude with arbitrary unit) of each frequency point between benign and malignant MSK tumors. The receiver operator characteristic (ROC) analysis was used to assess the diagnostic capability of distinguishing between benign and malignant MSK tumors.ResultsThe result of the two-samplet-test showed that there was significant difference in the power between benign and malignant MSK tumor at frequency points of 58 (0.1508 Hz,P= 0.036), 59 (0.1534 Hz,P= 0.032), and 95 (0.247 Hz,P= 0.014), respectively. The ROC analysis of mean power of three frequency points showed that the area of under curve is 0.706 (P =0.009), and the cutoff value is 0.73130. If the power of the tumor greater than or equal to 0.73130 is considered the possibility of benign tumor, then the diagnostic sensitivity and specificity values are 83% and 59%, respectively. Thepost hocanalysis showed that the merged power of 0.1508 and 0.1534 Hz in benign MSK tumors was significantly higher than that in malignant ones (P= 0.014). The ROC analysis showed that, if the benign MSK tumor was diagnosed with the power greater than or equal to the cutoff value of 1.41241, then the sensitivity and specificity were 67% and 68%, respectively.ConclusionThe mean power of three frequency points at 0.1508, 0.1534, and 0.247 Hz may potentially be a biomarker to differentiate benign from malignant MSK tumors. By combining the power of 0.1508 and 0.1534 Hz, we could better detect the difference between benign and malignant MSK tumors with higher specificity.

Freezing of gait (FOG), a disabling symptom of Parkinson's disease (PD), severely affects PD patients' life quality. Previous studies found neuropathologies in functional connectivity related to FOG, but few studies detected abnormal regional activities related to FOG in PD patients. In the present study, we analyzed the amplitude of low-frequency fluctuations (ALFF) to detect brain regions showing abnormal activity in PD patients with FOG (PD-with-FOG) and without FOG (PD-without-FOG). As different frequencies of neural oscillations in brain may reflect distinct brain functional and physiological properties, we conducted this study in three frequency bands, slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), and classical frequency band (0.01-0.08 Hz). We acquired rs-fMRI data from 18 PD-with-FOG patients, 18 PD-without-FOG patients, and 17 healthy controls, then calculated voxel-wise ALFF across the whole brain and compared ALFF among the three groups in each frequency band. We found: (1) in slow-5, both PD-with-FOG and PD-without-FOG patients showed lower ALFF in the bilateral putamen compared to healthy controls, (2) in slow-4, PD-with-FOG patients showed higher ALFF in left inferior temporal gyrus (ITG) and lower ALFF in right middle frontal gyrus (MFG) compared to either PD-without-FOG patients or healthy controls, (3) in classical frequency band, PD-with-FOG patients also showed higher ALFF in ITG compared to either PD-without-FOG patients or healthy controls. Furthermore, we found that ALFF in MFG and ITG in slow-4 provided the highest classification accuracy (96.7%) in distinguishing PD-with-FOG from PD-without-FOG patients by using a stepwise multivariate pattern analysis. Our findings indicated frequency-specific regional spontaneous neural activity related to FOG, which may help to elucidate the pathogenesis of FOG.

Bipolar disorder (BD) is a mental disorder with severe implications for those affected and their families. Previous studies detected brain structural and functional alterations in BD patients. However, very few studies conducted a multimodal MRI fusion analysis, and little is known about the role of common anomalies in the connectivity of BD.We collected sMRI, rs-fMRI, and DTI data from 56 patients with unmedicated BD-II depression and 72 age-, sex- and handedness-matched healthy controls. We applied data-driven approaches to analyze multimodal MRI data and detected brain areas with significant group differences in cortical thickness (CT), amplitude of low frequency fluctuations (ALFF), and fractional anisotropy (FA) of the superficial white matter. We observed the common abnormal areas and took these areas as seeds to analyze the resting-state functional connectivity (RSFC) patterns in BD patients by overlapping these abnormal areas.The BD patients showed two common abnormal areas: (1) the left anterior insula (AI) with abnormal CT and FA, and (2) the left posterior cingulate cortex (PCC) with abnormal CT and ALFF. Seed-based analyses showed RSFC between the left AI and left occipital sensory cortex, the left AI and left superior and inferior parietal cortex, and the left PCC and right medial prefrontal cortex were uniformly lower in the BD patients than controls. Correlation analyses showed negative correction between AI's FA and disease episodes and between AI's FA and disease duration in depressed BD-II patients.We observed abnormal brain structural and functional properties in the left AI and left PCC in BD patients. The abnormal RSFC patterns may suggest sensory and cognitive dysfunction in BD.

The eyes are our windows to the brain. There are differences in brain activity between people who have their eyes closed (EC) and eyes open (EO). Previous studies focused on differences in brain functional properties between these eyes conditions based on an assumption that brain activity is a static phenomenon. However, the dynamic nature of the brain activity in different eyes conditions is still unclear. In this study, we collected resting-state fMRI data from 21 healthy subjects in the EC and EO conditions. Using a sliding time window approach and a k-means clustering algorithm, we calculated the temporal properties of dynamic functional connectivity (dFC) states in the eyes conditions. We also used graph theory to estimate the dynamic topological properties of functional networks in the two conditions. We detected two dFC states, a hyper-connected State 1 and a hypo-connected State 2. We showed the following results: (i) subjects in the EC condition stayed longer in the hyper-connected State 1 than those in the EO; (ii) subjects in the EO condition stayed longer in the hypo-connected State 2 than those in the EC; and (iii) the dFC state transformed into the other state more frequently during EC than during EO. We also found the variance of the characteristic path length was higher during EC than during EO in the hyper-connected State 1. These results indicate that brain activity may be more active and unstable during EC than during EO. Our findings may provide insights into the dynamic nature of the resting-state brain and could be a useful reference for future rs-fMRI studies.

A number of studies have indicated that disorders of consciousness result from multifocal injuries as well as from the impaired functional and anatomical connectivity between various anterior forebrain regions. However, the specific causal mechanism linking these regions remains unclear. In this study, we used spectral dynamic causal modeling to assess how the effective connections (ECs) between various regions differ between individuals. Next, we used connectome-based predictive modeling to evaluate the performance of the ECs in predicting the clinical scores of DOC patients. We found increased ECs from the striatum to the globus pallidus as well as from the globus pallidus to the posterior cingulate cortex, and decreased ECs from the globus pallidus to the thalamus and from the medial prefrontal cortex to the striatum in DOC patients as compared to healthy controls. Prediction of the patients' outcome was effective using the negative ECs as features. In summary, the present study highlights a key role of the thalamo-basal ganglia-cortical loop in DOCs and supports the anterior forebrain mesocircuit hypothesis. Furthermore, EC could be potentially used to assess the consciousness level.

BackgroundBipolar disorder (BD) has been associated with altered brain structural and functional connectivity. However, little is known regarding alterations of the structural brain connectome in BD. The present study aimed to use diffusion-tensor imaging (DTI) and graph theory approaches to investigate the rich club organization and white matter structural connectome in BD.MethodsForty-two patients with unmedicated BD depression and 59 age-, sex- and handedness-matched healthy control participants underwent DTI. The whole-brain structural connectome was constructed by a deterministic fiber tracking approach. Graph theory analysis was used to examine the group-specific global and nodal topological properties, and rich club organizations, and then nonparametric permutation tests were used for group comparisons of network parameters.ResultsCompared with healthy control participants, the patients with BD showed abnormal global properties, including increased characteristic path length, and decreased global efficiency and local efficiency. Locally, the patients with BD showed abnormal nodal parameters (nodal strength, nodal efficiency, and nodal betweenness) predominantly in the parietal, orbitofrontal, occipital, and cerebellar regions. Moreover, the patients with BD showed decreased rich club and feeder connectivity density.ConclusionsOur results may reflect the disrupted white matter topological organization in the whole-brain, and abnormal regional connectivity supporting cognitive and affective functioning in depressed BD, which, in part, be due to impaired rich club connectivity.

Differential brain structural abnormalities between bipolar disorder (BD) and major depressive disorder (MDD) may reflect different pathological mechanisms underlying these two brain disorders. However, few studies have directly compared the brain structural properties, especially in white matter (WM) tracts, between BD and MDD. Using automated fiber-tract quantification (AFQ), we utilized diffusion tensor images (DTI) from 67 unmedicated depressed patients, including 31 BD and 36 MDD, and 45 healthy controls (HC) to create fractional anisotropy (FA) tract profiles along 20 major WM tracts. Then, we compared between-group differences in FA values at each node along the fiber tracts. To differentiate the BD and the MDD, we enrolled the diffusion measures of the tract profiles into support vector machine (SVM), a type of machine learning algorithm. The BD showed lower FA in the insular cortex portion of the right uncinate fasciculus (UF) compared to the MDD and in the prefrontal lobe portion of the right UF compared to the HC. The MDD showed lower FA in the prefrontal lobe portion of the left anterior thalamic radiation (ATR) compared to the HC. Using the SVM approach, we found the FA tract profile of the left ATR can be used to differentiate the BD and the MDD at an accuracy up to 68.33% (p = 0.018). These findings suggested that the BD and the MDD may be characterized by different abnormalities in specific segments of brain WM tracts, especially in two frontal-situated tracts, the right UF and the left ATR.

Long-term intensive gymnastic training can induce brain structural and functional reorganization. Previous studies have identified structural and functional network differences between world class gymnasts (WCGs) and non-athletes at the whole-brain level. However, it is still unclear how interactions within and between functional networks are affected by long-term intensive gymnastic training. We examined both intra- and inter-network functional connectivity of gymnasts relative to non-athletes using resting-state fMRI (R-fMRI). R-fMRI data were acquired from 13 WCGs and 14 non-athlete controls. Group-independent component analysis (ICA) was adopted to decompose the R-fMRI data into spatial independent components and associated time courses. An automatic component identification method was used to identify components of interest associated with resting-state networks (RSNs). We identified nine RSNs, the basal ganglia network (BG), sensorimotor network (SMN), cerebellum (CB), anterior and posterior default mode networks (aDMN/pDMN), left and right fronto-parietal networks (lFPN/rFPN), primary visual network (PVN), and extrastriate visual network (EVN). Statistical analyses revealed that the intra-network functional connectivity was significantly decreased within the BG, aDMN, lFPN, and rFPN, but increased within the EVN in the WCGs compared to the controls. In addition, the WCGs showed uniformly decreased inter-network functional connectivity between SMN and BG, CB, and PVN, BG and PVN, and pDMN and rFPN compared to the controls. We interpret this generally weaker intra- and inter-network functional connectivity in WCGs during the resting state as a result of greater efficiency in the WCGs' brain associated with long-term motor skill training.

BackgroundPrevious studies have analyzed brain functional connectivity to reveal the neural physiopathology of bipolar disorder (BD) and major depressive disorder (MDD) based on the triple-network model [involving the salience network, default mode network (DMN), and central executive network (CEN)]. However, most studies assumed that the brain intrinsic fluctuations throughout the entire scan are static. Thus, we aimed to reveal the dynamic functional network connectivity (dFNC) in the triple networks of BD and MDD.MethodsWe collected resting state fMRI data from 51 unmedicated depressed BD II patients, 51 unmedicated depressed MDD patients, and 52 healthy controls. We analyzed the dFNC by using an independent component analysis, sliding window correlation and k-means clustering, and used the parameters of dFNC state properties and dFNC variability for group comparisons.ResultsThe dFNC within the triple networks could be clustered into four configuration states, three of them showing dense connections (States 1, 2, and 4) and the other one showing sparse connections (State 3). Both BD and MDD patients spent more time in State 3 and showed decreased dFNC variability between posterior DMN and right CEN (rCEN) compared with controls. The MDD patients showed specific decreased dFNC variability between anterior DMN and rCEN compared with controls.ConclusionsThis study revealed more common but less specific dFNC alterations within the triple networks in unmedicated depressed BD II and MDD patients, which indicated their decreased information processing and communication ability and may help us to understand their abnormal affective and cognitive functions clinically.

Background Previous studies analyzed brain functional connectivity (FC) based on resting-state fMRI (RS-fMRI) data to reveal the neuropathology of bipolar disorder (BD) and suggested that their FC alterations are at widespread network-level. However, few studies have analyzed the dynamic functional network connectivity (dFNC) in BD. Thus, we aimed to reveal the dFNC properties of BD in this study. Methods The RS-fMRI data were collected from 51 unmedicated depressed BD II patients and 50 healthy controls. We analyzed the dFNC properties by using an independent component analysis, sliding window correlation, k-means clustering, and graph theory methods. Results The intrinsic brain FNC could be clustered into three configuration states, one with sparse connections between all functional networks (State 1), another with negative correlations between the salience network, cerebellum, basal ganglia and the sensory networks (State 2), and a third with negative correlations between the default mode network and the other functional networks (State 3). The BD patients had increased time in State 2, decreased time in State 3, and increased transition number between states. And the time spent in State 2 was positively correlated with the HDRS24 score in the BD patients. In addition, the BD patients had increased dynamic variance in the small-world properties of FNC. Limitations This study did not examine data from BD patients in other episodes and other BD types. Conclusions This study detected abnormal dFNC properties in BD, which indicated their FNC unstability and provided new insights into the neuropathology of their affective and cognitive deficits.

Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) in clinical practice, especially during depressive episodes. A unifying triple-network model, involving the default mode network (DMN), central executive network (CEN) and salience network (SN), has been proposed to explain the neural physiopathology of psychiatric and neurological disorders. Although several studies revealed shared and specific alterations between BD and MDD in key regions of DMN, CEN, and SN, and a few studies used different measures to detect detailed alterations in the triple networks in BD and MDD, their shared and specific patterns of altered functional connectivity (FC) in the triple networks has remained unclear. In this study, we acquired resting-state fMRI (R-fMRI) data from 38 unmedicated BD and 35 unmedicated MDD patients during depressive episodes along with 47 healthy controls. We first determined the spatially independent components of the DMN, SN, and CEN by using independent component analysis (ICA); then we estimated the inter-ROI and inter-network FC for each group. By comparing the differences between the three groups, we obtained the following results: (1) both the BD and MDD patients showed shared weaker intra-network FC in the left mPFC and right precuneus within the DMN as well as weaker inter-ROI FC between the left AI and right AI compared with the healthy controls; (2) the BD had weaker while the MDD had stronger intra-network FC in the right dlPFC within the rCEN as well as stronger inter-ROI FC between the right dlPFC and right ANG compared with the healthy controls; (3) the BD showed specific, stronger inter-ROI FC between the left PPC and right AI as well as stronger inter-network FC between the lCEN and SN compared with either the MDD or the control group. Our findings provide new information for understanding the neural physiopathology and clinical symptoms of depressed BD and MDD patients.

Spatial navigation is a crucial ability involving the interplay of multiple cognitive processes and is related to input spatial information (such as landmarks and orientation cues) and output navigational strategies (such as route-based and map-based). It is still not clear where is the spatial navigation system and whether these tasks evoke different activation patterns in human brain. Thus, we analyzed the reported brain activations of 33 related functional neuroimaging studies by using activation likelihood estimation (ALE) meta-analyses. Statistical analysis revealed a navigational system including the hippocampus, parahippocampal place area (PPA), retrosplenial complex (RSC), occipital place area (OPA), and insula. More likely, landmarks activate the left secondary motor cortex (SMC), whereas orientation cues activate the right somatosensory associative cortex (SAC). Although no region showed stronger activation likelihood in route- than map-based navigation, the map-based navigation had stronger activation likelihood in the right SAC than route-based navigation. These findings revealed the brain representation of spatial navigational system and suggested that different parts of this system are involved with the specific navigational tasks.

Sleep-related attentional bias is thought to play a role in the maintenance of insomnia. However, this concept has been questioned by several studies that did not show the presence of sleep-related attentional bias in clinical insomnia or poor sleepers. Our goal in the present study was to test whether the mood state of individuals with insomnia affects the presence of sleep-related attentional bias. To this end, 31 individuals with insomnia and 34 good sleepers were randomly assigned to a negative mood-inducing condition or a control condition. They then completed a visual probe task with three types of pictorial stimuli (general threat, sleep-related negative pictures and sleep-related positive pictures). Vigilance, maintenance and the overall bias indexes were calculated based on the reaction time. We found individuals with insomnia only showed a greater overall bias compared with good sleepers following a negative mood induction, regardless of the pictures presented. In addition, we found that a negative mood state was significantly correlated with the overall attentional bias in good sleepers but not in individuals with insomnia. These findings suggest that sleep-related attentional bias in insomnia can be modulated by mood state. This effect may reflect the dysregulation of top-down attentional control in individuals with insomnia.

Bipolar disorder (BD) is characterized by emotion dysregulation and involves changes in the gray matter (GM) and white matter (WM). Although previous diffusion tensor imaging (DTI) studies reported changes in the diffusion properties of the deep WM (DWM) in BD patients, the diffusion properties of the superficial WM (SWM) are rarely investigated. In this study, we tried to determine whether the diffusion parameters of the SWM were altered in BD patients compared to controls and whether the changes were associated with the disrupted emotion regulation of the BD patients. We collected DTI data from 37 BD patients and 42 gender- and age-matched healthy controls (HC). Using probabilistic tractography, we defined a population-based SWM mask based on all the subjects. After performing the tract-based spatial statistical (TBSS) analyses, we identified the SWM areas in which the BD patients differed from the controls. This study showed significantly reduced fractional anisotropy in the SWM (FASWM) in the BD patients compared to the HC in the bilateral dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), medial prefrontal cortex (mPFC), and the left parietal cortex. Moreover, compared to the controls, the BD patients showed significantly increased mean diffusivity (MDSWM) and radial diffusivity (RDSWM) in the SWM in the right frontal cortex. This study presents altered cortico-cortical connections proximal to the regions related to the emotion dysregulation of BD patients, which indicated that the SWM may serve as the brain's structural basis underlying the disrupted emotion regulation of BD patients. The disrupted FASWM in the parietal cortex may indicate that the emotion dysregulation in BD patients is related to the cognitive control network., Highlights • BD patients showed altered FASWM in the regions related to emotion dysregulation. • Disrupted SWM may be the brain's structural basis underlying emotion dysregulation in BD patients. • FASWM between the vlPFC and dlPFC was negatively correlated with disease exacerbations in BD patients. • Emotion dysregulation in BD patients may be related to a disrupted cognitive control network.

Understanding the neural mechanisms of disorders of consciousness (DOC) is essential for estimating the conscious level and diagnosing DOC patients. Although previous studies reported brain functional connectivity (FC) and spontaneous neural activity patterns associated with consciousness, the relationship between them remains unclear. In this study, we identified the abnormal brain regions in DOC patients by performing voxel-wise FC strength (FCS) and fractional amplitude of low-frequency fluctuations (fALFF) analyses on resting-state functional magnetic resonance imaging data of 15 DOC patients and 24 healthy controls. Furthermore, we detected spatial intersections between two measures and estimated the correlations between either the FCS or the fALFF and the subscales of the Coma Recovery Scale-Revised (CRS-R). We found that the right superior frontal gyrus, left thalamus and right precuneus in which the DOC patients had a lower local FCS and fALFF than healthy controls, are coincident with regions of the mesocircuit model. In the right precuneus, the local FCS/fALFF was significantly positively correlated with the oromotor and motor scores/motor score of the CRS-R. Our findings may indicate that the co-occurrent pattern of spontaneous neural activity and functional connectivity in the thalamo-frontal circuit and the precuneus are associated with motor function in DOC patients.

Worldwide, stroke is the leading cause of mortality and disability, with hypertension being an independent risk factor for a secondary stroke. Acupuncture for the treatment of hypertension gains more attention in alternative and complementary medicine, but the results are inconsistent. Few studies regarding the secondary prevention of stroke by managing hypertension with acupuncture have been carried out as there are some problems regarding the antihypertensive drug status in the secondary prevention of stroke. Still, the potential of acupuncture in regulating the blood pressure for secondary stroke prevention deserves our focus. This review is based on papers recorded in the PubMed, Embase, and Web of Science databases, from their inception until March 28, 2017, and retrieved with the following search terms: hypertension and acupuncture, limited in spontaneously hypertensive rats (SHRs), stress-induced (or cold-induced) hypertensive or pre-hypertensive models. We find that, in these hypertensive animals, acupuncture could mainly influence factors related to the nervous system, oxidative stress, the endocrine system, cardiovascular function, and hemorheology, which are closely associated with the stroke outcome. This trend may give us a hint that acupuncture might well participate in the secondary prevention of stroke through these pathways when used in the management of hypertension.

The cerebellum plays an important role in depression. Cerebro-cerebellar circuits have been found to show aberrance in bipolar disorder (BD) and major depressive disorder (MDD). However, whether the cerebro-cerebellar connectivity contributes equally to the pathologic mechanisms of BD and MDD remains unknown. We recruited 33 patients with MDD, 32 patients with BD, and 43 healthy controls (HC). We selected six seed regions (three per hemisphere) in the cerebrum, corresponding to the affective, cognitive control, and default mode networks, to establish cerebro-cerebellar functional connectivity maps. Relative to the HC, both the BD and MDD patients exhibited weaker negative connectivity between the right subgenual anterior cingulate cortex and the cerebellar vermis IV_V (pBD = 0.03, pMDD = 0.001) and weaker positive connectivity between the left precuneus and the left cerebellar lobule IX (pBD = 0.043, pMDD = 0.000). Moreover, the MDD patients showed weaker positive connectivity in the left precuneus—left cerebellar lobule IX circuit than the BD patients (p = 0.049). In addition, the BD patients showed weaker positive connectivity in the right dorsolateral prefrontal cortex—left cerebellar lobule Crus Ι circuit compared to the HC (p = 0.002) or the MDD patients (p = 0.013). Receiver operating characteristic curves analyses showed that the altered cerebro-cerebellar connectivities could be used to distinguish the patients from the HC with relatively high accuracy. Our findings suggested that differences in connectivity of cerebro-cerebellar circuits, which are involved in affective or cognitive functioning, significantly contributed to BD and MDD.

Brain white matter (WM) could be generally categorized into two types, deep and superficial WM. Studies combining these two types WM are important for a better understanding of brain plasticity induced by motor training. In this study, we applied both univariate and multivariate approaches to study gymnastic training-induced plasticity in brain WM. Specifically, we acquired diffusion tensor imaging data from 13 world class gymnasts and 14 non-athlete normal controls, reconstructed brain deep and superficial WM tracts, estimated and compared their fractional anisotropy (FA) difference between the two groups. Taking FA values as the features, we applied logistic regression and support vector machine to distinguish the gymnasts from the controls. Compared to the controls, the gymnasts showed lower FA in four regional deep WM tracts, including the occipital lobe portion of left inferior fronto-occipital fasciculus (IFOF.L), occipital and temporal lobe portion of right inferior longitudinal fasciculus (ILF.R), insular cortex portion of right uncinate fasciculus (UF.R), and parietal lobe portion of right arcuate fasciculus (AF.R). Meanwhile, we found lower FA in the superficial U-shaped tracts within the frontal lobe in the gymnasts compared to the controls. In addition, we detected that mean FA in either the AF.R or the U-shaped tracts connecting the left pars triangularis and superior frontal gyrus was negatively correlated with years of training in the gymnasts. Classification analyses indicated FA in deep WM hold higher potential to distinguish the gymnasts from the controls. Overall, our findings provide a more complete picture of training-induced plasticity in brain WM.

To detect the abnormal cortical thickness and disrupted brain resting-state functional connectivity (RSFC) in patients with systemic lupus erythematosus (SLE) without neuropsychiatric symptoms (non-NPSLE). Using T1-weighted 3D brain structural data, we first determined the regions with abnormal cortical thickness in a cohort of 33 adult female non-NPSLE patients. By taking brain regions with significantly reduced cortical thickness as the seeds, we calculated their RSFC based on the resting-fMRI data and detected the relationship between the RSFC and cortical thickness in the non-NPSLE patients. Compared to the controls, the non-NPSLE patients showed significantly cortical thinning in the left fusiform gyrus (FUS.L), left lingual gyrus (LING.L), right lingual gyrus (LING.R) and left superior frontal cortex (SFC.L). As for the RSFC, statistical analyses indicated that the abnormal cortical thickness in LING.L is associated with increased RSFC in the left posterior cingulate cortex (PCC.L), and cortical thinning in SFC.L associated with decreased RSFC in left cerebellum 6 (CRBL 6.L) in non-NPSLE patients. In addition, in non-NPSLE patients, the decreased cortical thickness in LING.L was correlated to the increased RSFC in PCC.L, and decreased cortical thickness in SFC.L was correlated to the decreased RSFC in CRBL 6.L. Our findings suggest that the cortical abnormalities may affect brain intrinsic connectivity in non-NPSLE patients.

The amplitude of low-frequency fluctuation (ALFF) measures low-frequency oscillations of the blood-oxygen-level-dependent signal, characterizing local spontaneous activity during the resting state. ALFF is a commonly used measure for resting-state functional magnetic resonance imaging (rs-fMRI) in numerous basic and clinical neuroscience studies. Using a test-retest rs-fMRI dataset consisting of 21 healthy subjects and three repetitive scans, we found that several key brain regions with high ALFF intensities (or magnitude) had poor reliability. Such regions included the posterior cingulate cortex, the medial prefrontal cortex in the default mode network, parts of the right and left thalami, and the primary visual and motor cortices. The above finding was robust with regard to different sample sizes (number of subjects), different scanning parameters (repetition time) and variations of test-retest intervals (i.e., intra-scan, intra-session, and inter-session reliability), as well as with different scanners. Moreover, the qualitative, map-wise results were validated further with a region-of-interest-based quantitative analysis using “canonical” coordinates as reported previously. Therefore, we suggest that the reliability assessments be incorporated in future ALFF studies, especially for the brain regions with a large ALFF magnitude as listed in our paper. Splitting single data into several segments and assessing within-scan “test-retest” reliability is an acceptable alternative if no “real” test-retest datasets are available. Such evaluations might become more necessary if the data are collected with clinical scanners whose performance is not as good as those that are used for scientific research purposes and are better maintained because the lower signal-to-noise ratio may further dampen ALFF reliability.