The hippocampus and amygdala are important structures in the posttraumatic stress disorder (PTSD); however, the exact relationship between these structures and stress or PTSD remains unclear. Moreover, they consist of several functionally distinct subfields/subregions that may serve different roles in the neuropathophysiology of PTSD. Here we present a subregional profile of the hippocampus and amygdala in 145 survivors of a major earthquake and 56 non-traumatized healthy controls (HCs). We found that the bilateral hippocampus and left amygdala were significantly smaller in survivors than in HCs, and there was no difference between survivors with (n = 69) and without PTSD (trauma-exposed controls [TCs], n = 76). Analyses revealed similar results in most subfields/subregions, except that the right hippocampal body (in a head-body-tail segmentation scheme), right presubiculum, and left amygdala medial nuclei (Me) were significantly larger in PTSD patients than in TCs but smaller than in HCs. Larger hippocampal body were associated with the time since trauma in PTSD patients. The volume of the right cortical nucleus (Co) was negatively correlated with the severity of symptoms in the PTSD group but positively correlated with the same measurement in the TC group. This correlation between symptom severity and Co volume was significantly different between the PTSD and TCs. Together, we demonstrated that generalized smaller volumes in the hippocampus and amygdala were more likely to be trauma-related than PTSD-specific, and their subfields/subregions were distinctively affected. Notably, larger left Me, right hippocampal body and presubiculum were PTSD-specific; these could be preexisting factors for PTSD or reflect rapid posttraumatic reshaping.

Impaired working memory is thought to be a core feature of attention deficit hyperactivity disorder (ADHD). Previous imaging studies investigating working memory in ADHD have used tasks involving different cognitive resources and ignoring the categorical judgments about objects that are essential parts of performance in visual working memory tasks, thus complicating the interpretation of their findings. In the present study, we explored differential neural activation in children and adolescents with ADHD and in healthy controls using functional magnetic resonance imaging (fMRI) with the categorical n-back task (CN-BT), which maximized demands for executive reasoning while holding memory demands constant.

Studies of dynamic functional connectivity (dFC) and topology can provide novel insights into the neurophysiology of brain dysfunction in schizophrenia and its relation to core symptoms of psychosis. Limited investigations of these disturbances have been conducted with never-treated first-episode patients to avoid the confounds of treatment or chronic illness. Therefore, we recruited 95 acutely ill, first-episode, never-treated patients with schizophrenia and examined brain dFC patterns relative to healthy controls using resting-state functional magnetic resonance imaging and a sliding-window approach. We compared the dynamic attributes at the group level and found patients spent more time in a hypoconnected state and correspondingly less time in a hyperconnected state. Patients demonstrated decreased dynamics of nodal efficiency and eigenvector centrality (EC) in the right medial prefrontal cortex, which was associated with psychosis severity reflected in Positive and Negative Syndrome Scale ratings. We also observed increased dynamics of EC in temporal and sensorimotor regions. These findings were supported by validation analysis. To supplement the group comparison analyses, a support vector classifier was used to identify the dynamic attributes that best distinguished patients from controls at the individual level. Selected features for case-control classification were highly coincident with the properties having significant between-group differences. Our findings provide novel neuroimaging evidence about dynamic characteristics of brain physiology in acute schizophrenia. The clinically relevant atypical pattern of dynamic shifting between brain states in schizophrenia may represent a critical aspect of illness pathophysiology underpinning its defining cognitive, behavioral, and affective features.

Diffusion tensor imaging (DTI) has been widely used in psychiatric research and has provided evidence of white matter abnormalities in first episode schizophrenia (FES). The goal of the present meta-analysis was to identify white matter deficits by DTI in FES.

Periventricular nodular heterotopia (PNH) is a neural migration disorder which often presents clinically with seizures. However, the underlying functional neural basis of PNH is still unclear. We aimed to explore the underlying pathological mechanism of PNH by combining both whole brain functional connectivity (FC) and seed-based FC analyses. We utilized resting-state fMRI to measure functional connectivity strength (FCS) in 38 patients with PNH-related epilepsy and 38 control subjects. The regions with FCS alterations were selected as seeds in the following FC analyses. Pearson correlation analyses were performed to explore associations between these functional neural correlates and clinical features. In comparison with controls, PNH patients showed lower FCS in bilateral insula (P < 0.05, family wise error (FWE) correction), higher FC in the default mode network and lower FC in the fronto-limbic-cerebellar circuits (P < 0.05, FWE correction). Pearson correlation analyses revealed that FCS in bilateral insula was negatively correlated with the epilepsy duration (P < 0.05); medial prefronto-insular connectivity was negatively correlated with Hamilton Anxiety Scale (P < 0.05) and cerebellar-insular connectivity was also negatively correlated with Hamilton Depression Scale (P < 0.05). Using the resting-state FCS analytical approach, we identified significant insular hypoactivation in PNH patients, which suggests that the insula might represent the cortical hub of the whole-brain networks in this condition. Additionally, disruption of resting state FC in large-scale neural networks pointed to a connectivity-based neuropathological process in PNH.

Background: The chronic phase of post-traumatic stress disorder (PTSD) and the limited effectiveness of existing treatments creates the need for the development of potential biomarkers to predict response to antidepressant medication at an early stage. However, findings at present focus on acute therapeutic effect without following-up the long-term clinical outcome of PTSD. So far, studies predicting the long-term clinical outcome of short-term treatment based on both pre-treatment and post-treatment functional MRI in PTSD remains limited. Methods: Twenty-two PTSD patients were scanned using resting-state functional MRI (rs-fMRI) before and after 12 weeks of treatment with paroxetine. Twenty patients were followed up using the same psychopathological assessments 2 years after they underwent the second MRI scan. Based on clinical outcome, the follow-up patients were divided into those with remitted PTSD or persistent PTSD. Amplitude of low-frequency fluctuations (ALFF) and degree centrality (DC) derived from pre-treatment and post-treatment rs-fMRI were used as classification features in a support vector machine (SVM) classifier. Results: Prediction of long-term clinical outcome by combined ALFF and DC features derived from pre-treatment rs-fMRI yielded an accuracy rate of 72.5% (p < 0.005). The most informative voxels for outcome prediction were mainly located in the precuneus, superior temporal area, insula, dorsal medial prefrontal cortex, frontal orbital cortex, supplementary motor area, lingual gyrus, and cerebellum. Long-term outcome could not be successfully classified by post-treatment imaging features with accuracy rates <50%. Conclusions: Combined information from ALFF and DC from rs-fMRI data before treatment could predict the long-term clinical outcome of PTSD, which is critical for defining potential biomarkers to customize PTSD treatment and improve the prognosis.

Obsessive-compulsive disorder (OCD) displays alterations in regional brain activity represented by the amplitude of low-frequency fluctuation (ALFF), but the time-varying characteristics of this local neural activity remain to be clarified. We aimed to investigate the dynamic changes of intrinsic brain activity in a relatively large sample of drug-naïve OCD patients using univariate and multivariate analyses. We applied a sliding-window approach to calculate the dynamic ALFF (dALFF) and compared the difference between 73 OCD patients and age- and sex-matched healthy controls (HCs). We also utilized multivariate pattern analysis to determine whether dALFF could differentiate OCD patients from HCs at the individual level. Compared with HCs, OCD patients exhibited increased dALFF mainly within regions of the cortical-striatal-thalamic-cortical (CSTC) circuit, including the bilateral dorsal anterior cingulate cortex, medial prefrontal cortex and striatum, and right dorsolateral prefrontal cortex (dlPFC). Decreased dALFF was identified in the bilateral inferior parietal lobule (IPL), posterior cingulate cortex, insula, fusiform gyrus, and cerebellum. Moreover, we found negative correlations between illness duration and dALFF values in the right IPL and between dALFF values in the left cerebellum and Hamilton Depression Scale scores. Furthermore, dALFF can distinguish OCD patients from HCs with the most discriminative regions located in the IPL, dlPFC, middle occipital gyrus, and cuneus. Taken together, in the current study, we demonstrated a characteristic pattern of higher variability of regional brain activity within the CSTC circuits and lower variability in regions outside the CSTC circuits in drug-naïve OCD patients.

Male and female show significant differences in important behavioral features such as shyness, yet the neural substrates of these differences remain poorly understood. Previous neuroimaging studies have demonstrated that both shyness and social anxiety in healthy subjects are associated with increased activation in the fronto-limbic and cognitive control areas. However, it remains unknown whether these brain abnormalities would be shared by different genders. Therefore, in the current study, we used resting-state fMRI (r-fMRI) to investigate sex differences in intrinsic cerebral activity that may contribute to shyness and social anxiety. Sixty subjects (28 males, 32 females) participated in r-fMRI scans, and the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) were used to measure the spontaneous regional cerebral activity in all subjects. We first compared the differences between male and female both in the ALFF and fALFF and then we also examined the whole brain correlation between the ALFF/fALFF and the severity of shyness as well as social anxiety by genders. Referring to shyness measure, we found a significant positive correlation between shyness scores (CBSS) and ALFF/fALFF value in the frontoparietal control network and a negative correlation in the cingulo-insular network in female; while in male, there is no such correlation. For the social anxiety level, we found positive correlations between Leibowitz Social Anxiety Scale (LSAS) scores and spontaneous activity in the frontal-limbic network in male and negative correlation between the frontal-parietal network; however, such correlation was not prominent in female. This pattern suggests that shy female individuals engaged a proactive control process, driven by a positive association with activity in frontoparietal network and negative association in cingulo-insular network, whereas social anxiety males relied more on a reactive control process, driven by a positive correlation of frontal-limbic network and negative correlation of frontoparietal network. Our results reveal that shyness or social anxiety is associated with disrupted spontaneous brain activity patterns and that these patterns are influenced by sex.

Over 90% of patients receiving orthodontic treatment experience clinically significant pain. However, little is known about the neural correlates of orthodontic pain and which has therefore been investigated in the present study of healthy subjects using an experimental paradigm.

Background: Antipsychotic medications provide limited long-term benefit to ~30% of schizophrenia patients. Multimodal magnetic resonance imaging (MRI) data have been used to investigate brain features between responders and nonresponders to antipsychotic treatment; however, these analytical techniques are unable to weigh the interrelationships between modalities. Here, we used multiset canonical correlation and joint independent component analysis (mCCA + jICA) to fuse MRI data to examine the shared and specific multimodal features between the patients and healthy controls (HCs) and between the responders and non-responders. Method: Resting-state functional and structural MRI data were collected from 55 patients with drug-naïve first-episode schizophrenia (FES) and demographically matched HCs. Based on the decrease in Positive and Negative Syndrome Scale scores from baseline to the 1-year follow-up, FES patients were divided into a responder group (RG) and a non-responder group (NRG). Gray matter volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) maps were used as features in mCCA + jICA. Results: Between FES patients and HCs, there were three modality-specific discriminative independent components (ICs) showing the difference in mixing coefficients (GMV-IC7, GMV-IC8, and fALFF-IC5). The fusion analysis indicated one modality-shared IC (GMV-IC2 and ReHo-IC2) and three modality-specific ICs (GMV-IC1, GMV-IC3, and GMV-IC6) between the RG and NRG. The right postcentral gyrus showed a significant difference in GMV features between FES patients and HCs and modality-shared features (GMV and ReHo) between responders and nonresponders. The modality-shared component findings were highlighted by GMV, mainly in the bilateral temporal gyrus and the right cerebellum associated with ReHo in the right postcentral gyrus. Conclusions: This study suggests that joint anatomical and functional features of the cortices may reflect an early pathophysiological mechanism that is related to a 1-year treatment response.

Adolescent binge drinking has been associated with higher risks for the development of many health problems throughout the lifespan. Adolescents undergo multiple changes that involve the co-development processes of brain, personality and behavior; therefore, certain behavior, such as alcohol consumption, can have disruptive effects on both brain development and personality maturation. However, these effects remain unclear due to the scarcity of longitudinal studies. In the current study, we used multivariate approaches to explore discriminative features in brain functional architecture, personality traits, and genetic variants in 19-year-old individuals (n = 212). Taking advantage of a longitudinal design, we selected features that were more drastically altered in drinkers with an earlier onset of binge drinking. With the selected features, we trained a hierarchical model of support vector machines using a training sample (n = 139). Using an independent sample (n = 73), we tested the model and achieved a classification accuracy of 71.2%. We demonstrated longitudinally that after the onset of binge drinking the developmental trajectory of improvement in impulsivity slowed down. This study identified the disrupting effects of adolescent binge drinking on the developmental trajectories of both brain and personality.

The hippocampus, a key structure with distinct subfield functions, is strongly implicated in the pathophysiology of post-traumatic stress disorder (PTSD); however, few studies of hippocampus subfields in PTSD have focused on pediatric patients. We therefore investigated the hippocampal subfield volume using an automated segmentation method and explored the subfield-centered functional connectivity aberrations related to the anatomical changes, in a homogenous population of traumatized children with and without PTSD. To investigate the potential diagnostic value in individual patients, we used a machine learning approach to identify features with significant discriminative power for diagnosis of PTSD using random forest classifiers. Compared to controls, we found significant mean volume reductions of 8.4% and 9.7% in the right presubiculum and hippocampal tail in patients, respectively. These two subfields' volumes were the most significant contributors to group discrimination, with a mean classification accuracy of 69% and a specificity of 81%. These anatomical alterations, along with the altered functional connectivity between (pre)subiculum and inferior frontal gyrus, may underlie deficits in fear circuitry leading to dysfunction of fear extinction and episodic memory, causally important in post-traumatic symptoms such as hypervigilance and re-experience. For the first time, we suggest that hippocampal subfield volumes might be useful in discriminating traumatized children with and without PTSD.

To evaluate the performance of the Decipher test in predicting lymph node invasion (LNI) on radical prostatectomy (RP) specimens.

To investigate whether aberrant interactions between brain structure and function present similarly or differently across probands with psychotic illnesses [schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar I disorder with psychosis (BP)] and whether these deficits are shared with their first-degree non-psychotic relatives. A total of 1199 subjects were assessed, including 220 SZ, 147 SAD, 180 psychotic BP, 150 first-degree relatives of SZ, 126 SAD relatives, 134 BP relatives, and 242 healthy controls (1). All subjects underwent structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) scanning. Joint-independent component analysis (jICA) was used to fuse sMRI gray matter and rs-fMRI amplitude of low-frequency fluctuations data to identify the relationship between the two modalities. jICA revealed two significantly fused components. The association between functional brain alteration in a prefrontal-striatal-thalamic-cerebellar network and structural abnormalities in the default mode network was found to be common across psychotic diagnoses and correlated with cognitive function, social function, and schizo-bipolar scale scores. The fused alteration in the temporal lobe was unique to SZ and SAD. The above effects were not seen in any relative group (including those with cluster-A personality). Using a multivariate-fused approach involving two widely used imaging markers, we demonstrate both shared and distinct biological traits across the psychosis spectrum. Furthermore, our results suggest that the above traits are psychosis biomarkers rather than endophenotypes.

Abnormal neuroanatomic brain networks have been reported in schizophrenia, but their characterization across patients with psychotic disorders, and their potential alterations in nonpsychotic relatives, remain to be clarified. Participants recruited by the Bipolar and Schizophrenia Network for Intermediate Phenotypes consortium included 326 probands with psychotic disorders (107 with schizophrenia (SZ), 87 with schizoaffective disorder (SAD), 132 with psychotic bipolar disorder (BD)), 315 of their nonpsychotic first-degree relatives and 202 healthy controls. Single-subject gray matter graphs were extracted from structural MRI scans, and whole-brain neuroanatomic organization was compared across the participant groups. Compared with healthy controls, psychotic probands showed decreased nodal efficiency mainly in bilateral superior temporal regions. These regions had altered morphological relationships primarily with frontal lobe regions, and their network-level alterations were associated with positive symptoms of psychosis. Nonpsychotic relatives showed lower nodal centrality metrics in the prefrontal cortex and subcortical regions, and higher nodal centrality metrics in the left cingulate cortex and left thalamus. Diagnosis-specific analysis indicated that individuals with SZ had lower nodal efficiency in bilateral superior temporal regions than controls, probands with SAD only exhibited lower nodal efficiency in the left superior and middle temporal gyrus, and individuals with psychotic BD did not show significant differences from healthy controls. Our findings provide novel evidence of clinically relevant disruptions in the anatomic association of the superior temporal lobe with other regions of whole-brain networks in patients with psychotic disorders, but not in their unaffected relatives, suggesting that it is a disease-related trait. Network disorganization primarily involving frontal lobe and subcortical regions in nonpsychotic relatives may be related to familial illness risk.

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.

Postpartum depression (PPD) is the most common psychological health issue among women, which often comorbids with anxiety (PPD-A). PPD and PPD-A showed highly overlapping clinical symptoms. Identifying disorder-specific neurophysiological markers of PDD and PPD-A is important for better clinical diagnosis and treatments. Here, we performed functional connectivity density (FCD) and resting-state functional connectivity (rsFC) analyses in 138 participants (45 unmedicated patients with first-episode PPD, 31 PDD-A patients and 62 healthy postnatal women, respectively). FCD mapping revealed specifically weaker long-range FCD in right lingual gyrus (LG.R) for PPD patients and significantly stronger long-range FCD in left ventral striatum (VS.L) for PPD-A patients. The follow-up rsFC analyses further revealed reduced functional connectivity between dorsomedial prefrontal cortex (dmPFC) and VS.L in both PPD and PPD-A. PPD showed specific changes of rsFC between LG.R and dmPFC, right angular gyrus and left precentral gyrus, while PPD-A represented specifically abnormal rsFC between VS.L and left ventrolateral prefrontal cortex. Moreover, the altered FCD and rsFC were closely associated with depression and anxiety symptoms load. Taken together, our study is the first to identify common and disorder-specific neural circuit disruptions in PPD and PPD-A, which may facilitate more effective diagnosis and treatments.

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.