Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting-state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low-frequency fluctuation, regional homogeneity and two connectome-wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10-fold cross-validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.

Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value.

Mild cognitive impairment (MCI) is common in Parkinson's disease (PD), and graph theory approaches can be performed to investigate the topographic organization in newly diagnosed drug-naïve PD patients with MCI.

Mild cognitive impairment in Parkinson's disease (PD-M) is related to a high risk of dementia. This study explored the whole-brain functional networks in early-stage PD-M. Forty-one patients with PD classified as cognitively normal (PD-N, n = 17) and PD-M (n = 24) and 24 demographically matched healthy controls (HC) underwent clinical and neuropsychological evaluations and resting-state functional magnetic resonance imaging. The global, regional, and modular topological characteristics were assessed in the brain functional networks, and their relationships to cognitive scores were tested. At the global level, PD-M and PD-N exhibited higher characteristic path length and lower clustering coefficient, local and global efficiency relative to HC. At the regional level, PD-M and PD-N showed lower nodal centrality in sensorimotor regions relative to HC. At the modular level, PD-M showed lower intramodular connectivity in default mode and cerebellum modules, and lower intermodular connectivity between default mode and frontoparietal modules than PD-N, correlated with Montreal Cognitive Assessment scores. Early-stage PD patients showed weaker small-worldization of brain networks. Modular connectivity alterations were mainly observed in patients with PD-M. These findings highlight the shared and distinct brain functional network dysfunctions in PD-M and PD-N, and yield insight into the neurobiology of cognitive decline in PD.

Although functional and structural abnormalities in brain regions involved in the neurobiology of fear and anxiety have been observed in patients with social anxiety disorder (SAD), the findings have been heterogeneous due to small sample sizes, demographic confounders, and methodological differences. Besides, multimodal neuroimaging studies on structural-functional deficits and couplings are rather scarce. Herein, we aimed to explore functional network anomalies in brain regions with structural deficits and the effects of structure-function couplings on the SAD diagnosis. High-resolution structural magnetic resonance imaging (MRI) and resting-state functional MRI images were obtained from 49 non-comorbid patients with SAD and 53 demography-matched healthy controls. Whole-brain voxel-based morphometry analysis was conducted to investigate structural alterations, which were subsequently used as seeds for the resting-state functional connectivity analysis. In addition, correlation and mediation analyses were performed to probe the potential roles of structural-functional deficits in SAD diagnosis. SAD patients had significant gray matter volume reductions in the bilateral putamen, right thalamus, and left parahippocampus. Besides, patients with SAD demonstrated widespread resting-state dysconnectivity in cortico-striato-thalamo-cerebellar circuitry. Moreover, dysconnectivity of the putamen with the cerebellum and the right thalamus with the middle temporal gyrus/supplementary motor area partially mediated the effects of putamen/thalamus atrophy on the SAD diagnosis. Our findings provide preliminary evidence for the involvement of structural and functional deficits in cortico-striato-thalamo-cerebellar circuitry in SAD, and may contribute to clarifying the underlying mechanisms of structure-function couplings for SAD. Therefore, they could offer insights into the neurobiological substrates of SAD.

Background: Individuals with autism spectrum disorder (ASD) show elevated levels of motor variability that are associated with clinical outcomes. Cortical-cerebellar networks involved in visuomotor control have been implicated in postmortem and anatomical imaging studies of ASD. However, the extent to which these networks show intrinsic functional alterations in patients, and the relationship between intrinsic functional properties of cortical-cerebellar networks and visuomotor impairments in ASD have not yet been clarified. Methods: We examined the amplitude of low-frequency fluctuation (ALFF) of cortical and cerebellar brain regions during resting-state functional MRI (rs-fMRI) in 23 individuals with ASD and 16 typically developing (TD) controls. Regions of interest (ROIs) with ALFF values significantly associated with motor variability were identified for for patients and controls respectively, and their functional connectivity (FC) to each other and to the rest of the brain was examined. Results: For TD controls, greater ALFF in bilateral cerebellar crus I, left superior temporal gyrus, left inferior frontal gyrus, right supramarginal gyrus, and left angular gyrus each were associated with greater visuomotor variability. Greater ALFF in cerebellar lobule VIII was associated with less visuomotor variability. For individuals with ASD, greater ALFF in right calcarine cortex, right middle temporal gyrus (including MT/V5), left Heschl's gyrus, left post-central gyrus, right pre-central gyrus, and left precuneus was related to greater visuomotor variability. Greater ALFF in cerebellar vermis VI was associated with less visuomotor variability. Individuals with ASD and TD controls did not show differences in ALFF for any of these ROIs. Individuals with ASD showed greater posterior cerebellar connectivity with occipital and parietal cortices relative to TD controls, and reduced FC within cerebellum and between lateral cerebellum and pre-frontal and other regions of association cortex. Conclusion: Together, these findings suggest that increased resting oscillations within visuomotor networks in ASD are associated with more severe deficits in controlling variability during precision visuomotor behavior. Differences between individuals with ASD and TD controls in the topography of networks showing relationships to visuomotor behavior suggest atypical patterns of cerebellar-cortical specialization and connectivity in ASD that underlies previously documented visuomotor deficits.

Primary monosymptomatic nocturnal enuresis (PMNE) is a common developmental disorder in children. Previous literature has suggested that PMNE not only is a micturition disorder but also is characterized by cerebral structure abnormalities and dysfunction. However, the biological mechanisms underlying the disease are not thoroughly understood. Graph theoretical analysis has provided a unique tool to reveal the intrinsic attributes of the connectivity patterns of a complex network from a global perspective. Resting-state fMRI was performed in 20 children with PMNE and 20 healthy controls. Brain networks were constructed by computing Pearson's correlations for blood oxygenation level-dependent temporal fluctuations among the 2 groups, followed by graph-based network analyses. The functional brain networks in the PMNE patients were characterized by a significantly lower clustering coefficient, global and local efficiency, and higher characteristic path length compared with controls. PMNE patients also showed a reduced nodal efficiency in the bilateral calcarine sulcus, bilateral cuneus, bilateral lingual gyri, and right superior temporal gyrus. Our findings suggest that PMNE includes brain network alterations that may affect global communication and integration.

Published MRI evidence of structural and resting-state functional brain abnormalities in MDD has been inconsistent. To eliminate interference by repeated disease episodes and antidepressant treatment, we conducted the first multimodal voxel-wise meta-analysis of studies of voxel-based morphometry (VBM) and the amplitude of low-frequency fluctuation (ALFF) in first-episode drug-naive MDD patients, using the Seed-based d Mapping method (SDM). Fifteen VBM data sets and 11 ALFF data sets were included. SDM-based multimodal meta-analysis was used to highlight brain regions with both structural and functional abnormalities. This identified conjoint structural and functional abnormalities in left lateral orbitofrontal cortex and right supplementary motor area, and also dissociated abnormalities of structure (decreased grey matter in right dorsolateral prefrontal cortex and right inferior temporal gyrus; increased grey matter in right insula, right putamen, left temporal pole, and bilateral thalamus) and function (increased brain activity in left supplementary motor area, left parahippocampal gyrus, and hippocampus; decreased brain activity in right lateral orbitofrontal cortex). This study reveals a complex pattern of conjoint and dissociated structural and functional abnormalities, supporting the involvement of basal ganglia-thalamocortical circuits, representing emotional, cognitive and psychomotor abnormalities, in the pathophysiology of early-stage MDD. Specifically, this study adds to Psychoradiology, an emerging subspecialty of radiology, which seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with mental disorder.

White matter (WM) abnormalities have long been suspected in major depressive disorder (MDD). Tract-based spatial statistics (TBSS) studies have detected abnormalities in fractional anisotropy (FA) in MDD, but the available evidence has been inconsistent. We performed a quantitative meta-analysis of TBSS studies contrasting MDD patients with healthy control subjects (HCS). A total of 17 studies with 18 datasets that included 641 MDD patients and 581 HCS were identified. Anisotropic effect size-signed differential mapping (AES-SDM) meta-analysis was performed to assess FA alterations in MDD patients compared to HCS. FA reductions were identified in the genu of the corpus callosum (CC) extending to the body of the CC and left anterior limb of the internal capsule (ALIC) in MDD patients relative to HCS. Descriptive analysis of quartiles, sensitivity analysis and subgroup analysis further confirmed these findings. Meta-regression analysis revealed that individuals with more severe MDD were significantly more likely to have FA reductions in the genu of the CC. This study provides a thorough profile of WM abnormalities in MDD and evidence that interhemispheric connections and frontal-striatal-thalamic pathways are the most convergent circuits affected in MDD.

Focal epilepsy accounts for most epilepsy cases, and frontal lobe epilepsy (FLE) accounts for the largest proportion of cases of extratemporal epilepsy syndrome. The epileptogenic zone is usually not easy to locate, contributing to a lack of imaging studies. The objective of this study was to evaluate functional connectivity patterns to explore the underlying pathological mechanisms of this disorder.

The cerebral haemodynamic status of idiopathic generalized epilepsy (IGE) is a very complicated process. Little attention has been paid to cerebral blood flow (CBF) alterations in IGE detected by arterial spin labelling (ASL) perfusion magnetic resonance imaging (MRI). However, the selection of an optimal delay time is difficult for single-delay ASL. Multi-delay multi-parametric ASL perfusion MRI overcomes the limitations of single-delay ASL. We applied multi-delay multi-parametric ASL perfusion MRI to investigate the patterns of postictal cerebral perfusion in IGE patients with absence seizures. A total of 21 IGE patients with absence seizures and 24 healthy control subjects were enrolled. IGE patients exhibited prolonged arterial transit time (ATT) in the left superior temporal gyrus. The mean CBF of IGE patients was significantly increased in the left middle temporal gyrus, left parahippocampal gyrus and left fusiform gyrus. Prolonged ATT in the left superior temporal gyrus was negatively correlated with the age at onset in IGE patients. This study demonstrated that cortical dysfunction in the temporal lobe and fusiform gyrus may be related to epileptic activity in IGE patients with absence seizures. This information can play an important role in elucidating the pathophysiological mechanism of IGE from a cerebral haemodynamic perspective.

A large body of evidence indicates that both the altered cytokines that mediate the immune-inflammatory process and abnormal gray matter are associated with schizophrenia. Whether peripheral cytokines are related to cerebral structural abnormality remains unclear. Therefore, we aimed to investigate the association of peripheral cytokine levels with gray matter abnormalities at the whole brain level in schizophrenia. Forty-four outpatients with schizophrenia and 44 controls were recruited. The serum levels of interleukin-1 beta (IL-1β), IL-2, IL-6, IL-8, interferon-gamma (IFN-γ), transforming growth factor-beta (TGF-β), and IL-10 were measured using a quantitative chemiluminescence assay. High-resolution T1 weighted images were acquired from all subjects and processed using FreeSurfer software to obtain the cortical thickness, surface area, and cortical and subcortical gray matter volumes. The cytokines and cerebral structures were compared between patients and controls using analysis of covariance (ANCOVA). The association between the cytokines and whole cerebral structures was performed using stepwise linear regression. Patients had higher levels of IL-2, IL-6, IL-8, and IL-10 than controls. In patients, the IL-6 level was significantly associated with the cortical thickness in the left pars opercularis, right pars triangularis, left superior temporal gyrus, and right middle temporal gyrus, which showed structural differences between the two groups. Altered cytokine levels may be associated with particular but not all cortical abnormalities in schizophrenia, especially IL-6, which was significantly associated with the abnormal cortical thickness of the bilateral Broca's area and temporal gyrus, which provided neuroimaging evidence to support the relationship between peripheral cytokines and the cerebral cortex in schizophrenia.

Different acupoints exhibiting similar therapeutic effects are a common phenomenon in acupuncture clinical practice. However, the mechanism underlying this phenomenon remains unclear. This study aimed to investigate the similarities and differences in cerebral activities elicited through stimulation of CV12 and ST36, the two most commonly used acupoints, in the treatment of gastrointestinal diseases, so as to partly explore the mechanism of the different acupoints with similar effects. Thirty-eight eligible functional dyspepsia (FD) patients were randomly assigned into either group A (CV12 group) or group B (ST36 group). Each patient received five acupuncture treatments per week for 4 weeks. The Symptom Index of Dyspepsia (SID), Nepean Dyspepsia Symptom Index (NDSI), and Nepean Dyspepsia Life Quality Index (NDLQI) were used to assess treatment efficacy. Functional MRI (fMRI) scans were performed to detect cerebral activity changes at baseline and at the end of the treatment. The results demonstrated that (1) improvements in NDSI, SID, and NDLQI were found in both group A and group B (p < 0.05). However, there were no significant differences in the improvements of the SID, NDSI, and NDLQI scores between group A and group B (p > 0.05); (2) all FD patients showed significantly increased amplitude of low-frequency fluctuation (ALFF) in the left postcentral gyrus after acupuncture treatment, and the changes of ALFF in the left postcentral gyrus were significantly related to the improvements of SID scores (r = 0.358, p = 0.041); and (3) needling at CV12 significantly decreased the resting-state functional connectivity (rsFC) between the left postcentral gyrus and angular gyrus, caudate, middle frontal gyrus (MFG), and cerebellum, while needling at ST36 significantly increased the rsFC between the left postcentral gyrus with the precuneus, superior frontal gyrus (SFG), and MFG. The results indicated that CV12 and ST36 shared similar therapeutic effects for dyspepsia, with common modulation on the activity of the postcentral gyrus in FD patients. However, the modulatory pattern on the functional connectivity of the postcentral gyrus was different. Namely, stimulation of CV12 primarily involved the postcentral gyrus-reward network, while stimulation of ST36 primarily involved the postcentral gyrus-default mode network circuitry.

Altered topological organization of brain structural covariance networks has been observed in attention deficit hyperactivity disorder (ADHD). However, results have been inconsistent, potentially related to confounding medication effects. In addition, since structural networks are traditionally constructed at the group level, variabilities in individual structural features remain to be well characterized. Structural brain imaging with MRI was performed on 84 drug-naïve children with ADHD and 83 age-matched healthy controls. Single-subject gray matter (GM) networks were obtained based on areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared using nonparametric permutation testing. Compared with healthy subjects, GM networks in ADHD patients demonstrated significantly altered topological characteristics, including higher global and local efficiency and clustering coefficient, and shorter path length. In addition, ADHD patients exhibited abnormal centrality in corticostriatal circuitry including the superior frontal gyrus, orbitofrontal gyrus, medial superior frontal gyrus, precentral gyrus, middle temporal gyrus, and pallidum (all p < .05, false discovery rate [FDR] corrected). Altered global and nodal topological efficiencies were associated with the severity of hyperactivity symptoms and the performance on the Stroop and Wisconsin Card Sorting Test tests (all p < .05, FDR corrected). ADHD combined and inattention subtypes were differentiated by nodal attributes of amygdala (p < .05, FDR corrected). Alterations in GM network topologies were observed in drug-naïve ADHD patients, in particular in frontostriatal loops and amygdala. These alterations may contribute to impaired cognitive functioning and impulsive behavior in ADHD.

Obsessive-compulsive disorder (OCD) is a disabling neuropsychiatric disorder whose neurobiological basis remains unclear. Magnetic resonance imaging (MRI) studies have reported functional and structural alterations of the anterior cingulate cortex (ACC) in OCD. In this study, we explored the functional activity of subregions of the ACC and effective connectivity (EC) between ACC subregions and the whole brain in OCD. We used a Granger causality analysis (GCA) to identify the direction of information flow and whether the impact of that flow was excitatory or inhibitory. We performed resting-state functional MRI in 31 patients with OCD and 36 healthy controls and analyzed the amplitude of low-frequency fluctuation (ALFF) and coefficient-based GCA. The left pregenual ACC (pACC) in patients with OCD showed decreased ALFF relative to controls. There was significantly decreased excitatory output from the left pACC to both right dorsal superior frontal gyrus (dSFG) and left precuneus in patients compared with controls. Patients also had decreased inhibitory input to left pACC from left ventral SFG and left thalamus and caudate relative to controls. Results were similar in drug-naive patients and those with prior but not current psychopharmacological treatment. In patients, path coefficients of GCA from left pACC to right dSFG showed significant negative correlations with obsession and anxiety ratings. Decreased spontaneous neural activity and altered EC of pACC with widely distributed cortical circuitry, and associations with clinical ratings highlight the importance of pACC functional alteration in OCD.

Perfectionism is correlated with the occurrence of exercise dependence. We aim to reveal the role of functional connectivity (FC) between gray matter (GM) and white matter (WM) networks in the association between perfectionism and exercise dependence.

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies of obsessive-compulsive disorder (OCD) have facilitated our understanding of OCD pathophysiology based on its intrinsic activity. However, whether the group difference derived from univariate analysis could be useful for informing the diagnosis of individual OCD patients remains unclear. We aimed to apply multivariate pattern analysis of different rs-fMRI parameters to distinguish drug-naive patients with OCD from healthy control subjects (HCS). Fifty-four drug-naive OCD patients and 54 well-matched HCS were recruited. Four different rs-fMRI parameter maps, including the amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo) and functional connectivity strength (FCS), were calculated. Training of a support vector machine (SVM) classifier using rs-fMRI maps produced voxelwise discrimination maps. Overall, the classification accuracies were acceptable for the four rs-fMRI parameters. Excellent performance was achieved when ALFF maps were employed (accuracy, 95.37%, p < 0.01), good performance was achieved by using ReHo maps, weaker performance was achieved by using fALFF maps, and fair performance was achieved by using FCS maps. The brain regions showing the greatest discriminative power included the prefrontal cortex, anterior cingulate cortex, precentral gyrus, and occipital lobes. The application of SVM to rs-fMRI features may provide potential power for OCD classification.

The present study aimed to compare the regional homogeneity (ReHo) of resting-state brain function between subjects with strabismic amblyopia and normal controls. A total of 12 adult patients with strabismic amblyopia and 34 age- and gender-matched normal controls were enrolled in the present study. All patients were subjected to resting-state magnetic resonance imaging using a Siemens Trio 3.0T magnetic resonance scanner. ReHo values of the whole brain were calculated and standardized. An independent-samples t-test was used to analyze the differences in ReHo values between the two groups. Patients with strabismic amblyopia exhibited reduced ReHo values in certain parts of the occipital lobe, including the lingual gyrus, cuneus and superior occipital gyrus. Increased ReHo values were observed in the precuneus and certain parts of the prefrontal cortex of patients with strabismic amblyopia, including the superior frontal gyrus and middle frontal gyrus. The ReHo index of the precuneus was negatively correlated with age. However, there was no correlation between the ReHo values and the visual acuity of patients with strabismic amblyopia. ROC curve analysis demonstrated that the greatest area under curve (AUC) value was in the medial prefrontal cortex (AUC=0.864). The results of the present study suggested that visual information processing may be impaired in visual areas V1 and V2. Furthermore, adult patients with strabismic amblyopia exhibited brain plasticity that compensated for visuomotor coordination and visuospatial imagery deficits.

Whilst acupuncture has been shown to be an effective treatment for functional dyspepsia (FD), its efficacy varies significantly among patients. Knowing beforehand how each patient responds to acupuncture treatment will facilitate the ability to produce personalized prescriptions, therefore, improving acupuncture efficacy. The objective of this study was to construct the prediction model, based on the clinical-neuroimaging signature, to forecast the individual symptom improvement of FD patients following a 4-week acupuncture treatment and to identify the critical predictive features that could potentially serve as biomarkers for predicting the efficacy of acupuncture for FD. Clinical-functional brain connectivity signatures were extracted from samples in the training-test set (100 FD patients) and independent validation set (60 FD patients). Based on these signatures and support vector machine algorithms, prediction models were developed in the training test set, followed by model performance evaluation and predictive features extraction. Subsequently, the external robustness of the extracted predictive features in predicting acupuncture efficacy was evaluated by the independent validation set. The developed prediction models possessed an accuracy of 88% in predicting acupuncture responders, as well as an R2 of 0.453 in forecasting symptom relief. Factors that contributed significantly to stronger responsiveness of patients to acupuncture therapy included higher resting-state functional connectivity associated with the orbitofrontal gyrus, caudate, hippocampus, and anterior insula, as well as higher baseline scores of the Symptom Index of Dyspepsia and shorter durations of the condition. Furthermore, the robustness of these features in predicting the efficacy of acupuncture for FD was verified through various machine learning algorithms and independent samples and remained stable in univariate and multivariate analyses. These findings suggest that it is both feasible and reliable to predict the efficacy of acupuncture for FD based on the pre-treatment clinical-neuroimaging signature. The established prediction framework will promote the identification of suitable candidates for acupuncture treatment, thereby improving the efficacy and reducing the cost of acupuncture for FD.

The current study is to characterize the alterations of peripheral cytokines and anatomical brain changes, and their relationships in untreated nasopharyngeal carcinoma (NPC) patients with depressive symptoms. Twenty-nine newly diagnosed NPC patients without any treatment and 46 matched healthy comparisons were recruited, scanned with high-resolution T1 images and assessed psychologically using Hamilton Rating Scale for Depression (HAMD). Serum levels of interleukin-1 beta (IL-1β), IL-2, IL-6, IL-8, IL-10, interferon-gamma (IFN-γ) and transforming growth factor-beta (TGF-β) were measured by quantitative chemiluminescence assay. Inter-group comparisons of anatomical brain measures were performed, and regions with significant inter-group differences were correlated to HAMD scores and cytokines in NPC patients. A subgroup analysis especially within NPC patients with depression was conducted to precisely characterize the associations among serum cytokines, brain changes and depressive symptoms. Relative to healthy subjects, NPC patients showed significantly decreased cortical thickness in the left parahippocampal gyrus, increased surface area in the right superior parietal lobule and precentral gyrus, and increased gray matter volume in the right postcentral gyrus, bilateral caudate nucleus and right thalamus, as well as significantly elevated IL-1β, IL-2 and IL-10. The elevated IL-2 and IL-10 were negatively correlated with surface area in right superior parietal lobule, whilst IL-1β level was positively correlated to HAMD scores. In patients with depression, specific brain changes and evaluated IL-1β were identified, and the IL-1β interacted with right precentral gyrus to significantly affect the depressive symptoms. Our findings provide novel evidence indicating potential effects of inflammation on brain structure and behavior in NPC patients.