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Anorexia nervosa (AN) is a complex psychiatric disorder with poorly understood etiology. Numerous voxel‐based morphometry (VBM) and resting‐state functional imaging studies have provided strong evidence of abnormal brain structure and intrinsic and functional activities in AN, but with inconsistent conclusions. Herein, a whole‐brain meta‐analysis was conducted on VBM (660 patients with AN, and 740 controls) and resting‐state functional imaging (425 patients with AN, and 461 controls) studies that measured differences in the gray matter volume (GMV) and intrinsic functional activity between patients with AN and healthy controls (HCs). Overall, patients with AN displayed decreased GMV in the bilateral median cingulate cortex (extending to the bilateral anterior and posterior cingulate cortex), and left middle occipital gyrus (extending to the left inferior parietal lobe). In resting‐state functional imaging studies, patients with AN displayed decreased resting‐state functional activity in the bilateral anterior cingulate cortex and bilateral median cingulate cortex, and increased resting‐state functional activity in the right parahippocampal gyrus. This multimodal meta‐analysis identified reductions of gray matter and functional activity in the anterior and median cingulate in patients with AN, which contributes to further understanding of the pathophysiology of AN. This meta‐analysis demonstrated a significant reduction in the functional activity and gray matter in the cingulate cortex in patients with AN, particularly in the ACC and MCC, which imply that structural changes may underlie functional alterations. These results expand the current understanding of functional and structural brain abnormalities in AN patients, which would provide additional potential targets for therapeutic intervention.

Functional magnetic resonance imaging has been widely used to investigate the effects of acupuncture on neural activity. However, most functional magnetic resonance imaging studies have focused on acute changes in brain activation induced by acupuncture. Thus, the time course of the therapeutic effects of acupuncture remains unclear. In this study, 32 patients with amnestic mild cognitive impairment were randomly divided into two groups, where they received either Tiaoshen Yizhi acupuncture or sham acupoint acupuncture. The needles were either twirled at Tiaoshen Yizhi acupoints, including Sishencong（EX-HN1）, Yintang（EX-HN3）, Neiguan（PC6）, Taixi（KI3）, Fenglong（ST40）, and Taichong（LR3）, or at related sham acupoints at a depth of approximately 15 mm, an angle of ± 60°, and a rate of approximately 120 times per minute. Acupuncture was conducted for 4 consecutive weeks, five times per week, on weekdays. Resting-state functional magnetic resonance imaging indicated that connections between cognition-related regions such as the insula, dorsolateral prefrontal cortex, hippocampus, thalamus, inferior parietal lobule, and anterior cingulate cortex increased after acupuncture at Tiaoshen Yizhi acupoints. The insula, dorsolateral prefrontal cortex, and hippocampus acted as central brain hubs. Patients in the Tiaoshen Yizhi group exhibited improved cognitive performance after acupuncture. In the sham acupoint acupuncture group, connections between brain regions were dispersed, and we found no differences in cognitive function following the treatment. These results indicate that acupuncture at Tiaoshen Yizhi acupoints can regulate brain networks by increasing connectivity between cognition-related regions, thereby improving cognitive function in patients with mild cognitive impairment.

Differentiating the parkinsonian variant of multiple system atrophy (MSA‐P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA‐P patients and use machine learning methods to explore the diagnostic utility of these features. Resting‐state fMRI data were acquired from 88 healthy controls, 76 MSA‐P patients, and 53 IPD patients using a 3.0 T scanner. The whole‐brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA‐P patients. Regarding graph metrics, early IPD and MSA‐P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum‐basal ganglia‐cortical network, but these disconnections were mainly in the cortico‐thalamo‐cerebellar circuits in MSA‐P patients and the basal ganglia‐thalamo‐cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA‐P and IPD patients show similar whole‐brain network topological alterations. MSA‐P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum‐basal ganglia‐cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA‐P and IPD. We investigated brain functional connectome abnormalities in patients with idiopathic Parkinson's disease (IPD) and parkinsonian variant of multiple system atrophy (MSA‐P) using resting‐state functional magnetic resonance imaging, demonstrating that “disconnection” within the cortico‐basal ganglia‐cerebellar network serve as the cornerstone of a differential diagnosis. IPD patients have decreased functional connectivity (FC) in the basal ganglia‐cortical circuit, whereas early‐stage MSA‐P patients have reduced FC in the cerebellum‐cortical circuit.

Although previous neuroimaging evidence has confirmed the brain functional disturbances in thyroid‐associated ophthalmopathy (TAO), the dynamic characteristics of brain activity and functional connectivity (FC) in TAO were rarely concerned. The present study aims to investigate the alterations of temporal variability of brain activity and FC in TAO using resting‐state functional magnetic resonance imaging (rs‐fMRI). Forty‐seven TAO patients and 30 age‐, gender‐, education‐, and handedness‐matched healthy controls (HCs) were enrolled and underwent rs‐fMRI scanning. The dynamic amplitude of low‐frequency fluctuation (dALFF) was first calculated using a sliding window approach to characterize the temporal variability of brain activity. Based on the dALFF results, seed‐based dynamic functional connectivity (dFC) analysis was performed to identify the temporal variability of efficient communication between brain regions in TAO. Additionally, correlations between dALFF and dFC and the clinical indicators were analyzed. Compared with HCs, TAO patients displayed decreased dALFF in the left superior occipital gyrus (SOG) and cuneus (CUN), while showing increased dALFF in the left triangular part of inferior frontal gyrus (IFGtriang), insula (INS), orbital part of inferior frontal gyrus (ORBinf), superior temporal gyrus (STG) and temporal pole of superior temporal gyrus (TPOsup). Furthermore, TAO patients exhibited decreased dFC between the left STG and the right middle occipital gyrus (MOG), as well as decreased dFC between the left TPOsup and the right calcarine fissure and surrounding cortex (CAL) and MOG. Correlation analyses showed that the altered dALFF in the left SOG/CUN was positively related to visual acuity (r = .409, p = .004), as well as the score of QoL for visual functioning (r = .375, p = .009). TAO patients developed abnormal temporal variability of brain activity in areas related to vision, emotion, and cognition, as well as reduced temporal variability of FC associated with vision deficits. These findings provided additional insights into the neurobiological mechanisms of TAO. We explored the spatiotemporal alterations of both brain activity and connectivity in thyroid‐associated ophthalmopathy (TAO) by using dynamic amplitude of low‐frequency fluctuation and dynamic functional connectivity, respectively. We also identified correlations between the abnormal dynamic brain activity in the left occipital area and the visual deficits in TAO patients.

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting‐state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep‐deprived subjects showed increased regional‐level temporal variability in large‐scale brain regions, and decreased regional‐level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra‐network temporal variability in the default mode network (DMN) and increased inter‐network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter‐network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = −.42, p = 5.57 × 10−4) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep‐deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD. Regional‐level and network‐level temporal variabilities were significantly increased after SD and correlated with the psychomotor vigilance test performance. These findings suggested that sleep‐deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.

Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter‐cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study‐specific imaging indices. We proposed a novel framework for inter‐cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting‐state functional MRI (fMRI) between MCI converters (MCI_C) and non‐converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3‐year follow‐up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross‐validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter‐network hypo‐connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging‐based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi‐modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter‐cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future. Inter‐cohort validation was performed for MRI‐based prediction of MCI conversion. The framework integrated structural, static, and dynamic functional features. Altered dynamic functional features were reported in MCI converters.

Objectives Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are known as the preclinical and early stage of Alzheimer's disease (AD). The dorsal attention network (DAN) is mainly responsible for the “top‐down” attention process. However, previous studies mainly focused on single functional modality and limited structure. This study aimed to investigate the multimodal alterations of DAN in SCD and aMCI to assess their diagnostic value in preclinical and early‐stage AD. Methods Resting‐state functional magnetic resonance imaging (MRI) was carried out to measure the fractional amplitude of low‐frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Structural MRI was used to calculate the gray matter volume (GMV) and cortical thickness. Moreover, receiver‐operating characteristic (ROC) analysis was used to distinguish these alterations in SCD and aMCI. Results The SCD and aMCI groups showed both decreased ReHo in the right middle temporal gyrus (MTG) and decreased GMV compared to healthy controls (HCs). Especially in the SCD group, there were increased fALFF and increased ReHo in the left inferior occipital gyrus (IOG), decreased fALFF and increased FC in the left inferior parietal lobule (IPL), and reduced cortical thickness in the right inferior temporal gyrus (ITG). Furthermore, functional and structural alterations in the SCD and aMCI groups were closely related to episodic memory (EM), executive function (EF), and information processing speed (IPS). The combination of multiple indicators of DAN had a high accuracy in differentiating clinical stages. Conclusions Our current study demonstrated functional and structural alterations of DAN in SCD and aMCI, especially in the MTG, IPL, and SPL. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early‐stage AD for their high diagnostic value. Our current study demonstrated obviously functional and structural alterations of DAN in SCD and aMCI. We found that the abnormalities of the functional alterations were especially located in the IPL, IOG, and MTG, whereas structural alterations were mainly in the SPL and ITG. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early‐stage AD for their high diagnostic value.

Attention‐deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age‐inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting‐state functional magnetic resonance (rs‐fMRI) have more discriminative power for the diagnosis of ADHD. The rs‐fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD‐200 Consortium. A total of four preprocessed rs‐fMRI images including regional homogeneity (ReHo), amplitude of low‐frequency fluctuation (ALFF), voxel‐mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve = 0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs‐fMRI information to distinguish ADHD from healthy controls. The rs‐fMRI‐based radiomics features have the potential to be neuroimaging biomarkers for ADHD. Radiomics features extracted from rs‐fMRI metrics can be used for distinguishing ADHD patients from HC with better classification performance. There was a significant correlation between radiomics features and clinical measures, thus radiomics may be potential neuroimaging biomarkers of ADHD.

Aims To explore the potential relationships among gut microbiota (GM), local brain spontaneous activity, and neuropsychological characteristics in amnestic mild cognitive impairment (aMCI) patients. Methods Twenty aMCI and 22 healthy control (HC) subjects were recruited. The GM composition was determined by 16S ribosomal RNA gene sequencing. Resting‐state functional magnetic resonance imaging scans were performed, and fractional amplitude of low‐frequency fluctuations (fALFF) was calculated across different frequencies. The Spearman or Pearson correlation analysis was used to analyze the relationship between spontaneous brain activity and cognitive function, and GM composition. Results aMCI patients had altered GM state and local spontaneous brain activity as compared with HC subjects. Correlation analysis showed that aMCI and HC groups had different “GM‐intrinsic brain activity interaction” patterns. In aMCI group, at the typical band (0.01‐0.08 Hz), the relative abundance (RA) of Bacteroides from phylum to genus level was negatively correlated with fALFF value of cerebellar vermis IV‐V, and the Ruminococcaceae RA was negatively correlated with fALFF values of left lenticular nucleus and pallidum. The Clostridiaceae RA and Blautia RA were positively correlated with the left cerebellum lobules IV‐V at the slow‐4 band (0.027‐0.073 Hz). The Veillonellaceae RA was positively correlated with fALFF values of left precentral gyrus at the slow‐5 band (0.073‐0.08 Hz). Correlation analysis showed that Clostridium members (Lachnospiraceae and Blautia) were positively, while Veillonellaceae was negatively, correlated with cognition test. Bacteroides was positively correlated with attention and computation, and negatively correlated with the three‐stage command score. Conclusions aMCI patients have a specific GM‐intrinsic brain activity‐cognitive function interaction pattern. Mounting evidences suggest the crosstalk exists between the gut microbiota (GM) and the brain. In this study, amnestic mild cognitive impairment (aMCI) subjects show changes in resting state brain activity and cognitive function that parallel to specific GM bacterial taxal populations, which suggests aMCI patients have a specific interaction pattern of gut microbiota‐intrinsic brain activity‐cognition.

Functional connectivity (FC) disruption is a remarkable characteristic of schizophrenia. However, heterogeneous patterns reported across sites severely hindered its clinical generalization. Based on qualified nodal‐based FC of 340 schizophrenia patients (SZ) and 348 normal controls (NC) acquired from seven different scanners, this study compared four commonly used site‐effect correction methods in removing the site‐related heterogeneities, and then tried to cluster the abnormal FCs into several replicable and independent disrupted subnets across sites, related them to clinical symptoms, and evaluated their potentials in schizophrenia classification. Among the four site‐related heterogeneity correction methods, ComBat harmonization (F1 score: 0.806 ± 0.145) achieved the overall best balance between sensitivity and false discovery rate in unraveling the aberrant FCs of schizophrenia in the local and public data sets. Hierarchical clustering analysis identified three replicable FC disruption subnets across the local and public data sets: hypo‐connectivity within sensory areas (Net1), hypo‐connectivity within thalamus, striatum, and ventral attention network (Net2), and hyper‐connectivity between thalamus and sensory processing system (Net3). Notably, the derived composite FC within Net1 was negatively correlated with hostility and disorientation in the public validation set (p < .05). Finally, the three subnet‐specific composite FCs (Best area under the receiver operating characteristic curve [AUC] = 0.728) can robustly and meaningfully discriminate the SZ from NC with comparable performance with the full identified FCs features (best AUC = 0.765) in the out‐of‐sample public data set (Z = −1.583, p = .114). In conclusion, ComBat harmonization was most robust in detecting aberrant connectivity for schizophrenia. Besides, the three subnet‐specific composite FC measures might be replicable neuroimaging markers for schizophrenia. This study reported that ComBat harmonization was most robust in detecting aberrant connectivity while controlling for the false discoveries for schizophrenia. Besides, the identified three subnet‐specific composite functional connectivity measures might be considered replicable neuroimaging markers for schizophrenia.