Progress in psychoradiology, the clinical application of psychiatric neuroimaging

Decreased intrinsic neural timescale in treatment-naïve adolescent depression

BACKGROUND

Major depressive disorder (MDD) is mainly characterized by its core dysfunction in higher-order brain cortices involved in emotional and cognitive processes, whose neurobiological basis remains unclear. In this study, we applied a relatively new developed resting-state functional magnetic resonance imaging (rs-fMRI) method of intrinsic neural timescale (INT), which reflects how long neural information is stored in a local brain area and reflects an ability of information integration, to investigate the local intrinsic neural dynamics using univariate and multivariate analyses in adolescent depression.

METHOD

Based on the rs-fMRI data of sixty-six treatment-naïve adolescents with MDD and fifty-two well-matched healthy controls (HCs), we calculated an INT by assessing the magnitude of autocorrelation of the resting-state brain activity, and then compared the difference of INT between the two groups. Correlation between abnormal INT and clinical features was performed. We also utilized multivariate pattern analysis to determine whether INT could differentiate MDD patients from HCs at the individual level.

RESULT

Compared with HCs, patients with MDD showed shorter INT widely distributed in cortical and partial subcortical regions. Interestingly, the decreased INT in the left hippocampus was related to disease severity of MDD. Furthermore, INT can distinguish MDD patients from HCs with the most discriminative regions located in the dorsolateral prefrontal cortex, angular, middle occipital gyrus, and cerebellar posterior lobe.

CONCLUSION

Our research aids in advancing understanding the brain abnormalities of treatment-naïve adolescents with MDD from the perspective of the local neural dynamics, highlighting the significant role of INT in understanding neurophysiological mechanisms. This study shows that the altered intrinsic timescales of local neural signals widely distributed in higher-order brain cortices regions may be the neurodynamic basis of cognitive and emotional disturbances in MDD patients, and provides preliminary support for the suggestion that these could be used to aid the identification of MDD patients in clinical practice.

Trait anxiety mediates the association between hippocampal-insula functional connectivity and anxiety symptom severity in adults with and without generalized anxiety disorder

BACKGROUND

Trait anxiety is a vulnerability factor for the development of generalized anxiety disorder (GAD). The hippocampus has been implicated in trait anxiety in normal and GAD populations. However, the exact neural mechanism of hippocampal functional connectivity (FC) and its association with clinical symptoms and trait anxiety in GAD patients remains unknown.

METHODS

We recruited 68 participants (37 drug-naïve non-comorbidity GAD patients and 31 matched healthy controls (HC)), assessed their trait and state anxiety, scanned them with structural and resting-state functional magnetic resonance imaging (fMRI), and compared their hippocampal FC and volumes. We explored the relationships between hippocampal FC, clinical symptoms, and trait anxiety using partial correlation analyses; we also investigated the mediating effects of trait anxiety on the association between hippocampal FC and GAD symptom severity.

RESULTS

The GAD group showed increased right hippocampal FC with left insula, which was positively correlated with the Self-Rating Anxiety Scale (SAS), State Anxiety Inventory (SAI), and Trait Anxiety Inventory (TAI). Trait anxiety mediated the relationship between hippocampal FC and anxiety levels. We found no significant difference in hippocampal volumes between GAD and HC.

LIMITATIONS

The sample size was moderate. The exclusion of comorbidity may reduce the generalizability of our results in normal clinical settings.

CONCLUSIONS

The GAD patients showed no structural change but had functional alterations in the hippocampus. More importantly, future psychotherapy for this disorder should consider that trait anxiety might play a crucial role in the altered hippocampal FC in GAD.

Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis

Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.

Suprachiasmatic nucleus functional connectivity related to insomnia symptoms in adolescents with major depressive disorder

Background

Insomnia is a commonly seen symptom in adolescents with major depressive disorder (MDD). The suprachiasmatic nucleus (SCN), which is the circadian rhythm regulation center, plays a crucial role in the regulation of sleep-wake circulation. Nevertheless, how SCN function contributes to the exact neural mechanisms underlying the associations between insomnia and depressive symptoms has not been explored in adolescents. In the current study, we aimed to explore the relationship between SCN functional connectivity (FC) and insomnia symptoms in adolescents with MDD using a seed-based FC method.

Methods

In the current study, we recruited sixty-eight first-episode drug-naïve adolescents with MDD and classified them into high insomnia (MDD-HI) and low insomnia (MDD-LI) groups according to the sleep disturbance subscale of the Hamilton Depression Rating Scale (HAMD-S). Forty-three age/gender-matched healthy controls (HCs) were also recruited. SCN FC maps were generally for all subjects and compared among three groups using one-way ANOVA with age, gender and adjusted HAMD score as covariates. We used partial correlations to explore associations between altered FC and clinical symptoms, including sleep quality scores.

Results

Adolescents with MDD showed worse sleep quality, which positively correlated with the severity of depression. Compared to MDD-LI and HCs, MDD-HI adolescents demonstrated significantly decreased FC between the right SCN and bilateral precuneus, and there was no significant difference between the MDD-LI and HC groups. The HAMD-S scores were negatively correlated with bilateral SCN-precuneus connectivity, and the retardation factor score of HAMD was negatively correlated with right SCN-precuneus connectivity.

Conclusion

The altered FC between the SCN and precuneus may underline the neural mechanism of sleep-related symptoms in depressive adolescents and provide potential targets for personalized treatment strategies.

Recent advances in psychoradiology

Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brain in vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.

Biological Predictors of Treatment Response in Adult Attention Deficit Hyperactivity Disorder (ADHD): A Systematic Review

Background: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent condition with onset in childhood and in many cases persisting into adulthood. Even though an increasing number of studies have investigated the efficacy of pharmacotherapy in the management of adult ADHD, few authors have tried to identify the biological predictors of treatment response. Objectives: To summarize the available data about the biological markers of treatment response in adults affected by ADHD. Methods: A search on the main biomedical and psychological archives (PubMed, Embase, Scopus, and PsycINFO) was performed. Manuscripts in English, published up to May 2022 and having the biological predictors of treatment response in adults with ADHD as their main topic, were included. Results: A total of 3855 articles was screened. Twenty-two articles were finally included. Most of the manuscripts studied neuroimaging and electrophysiological factors as potential predictors of treatment response in adult ADHD patients. No reliable markers were identified until now. Promising findings on this topic regard genetic polymorphisms in snap receptor (SNARE) proteins and default mode network-striatum connectivity. Conclusions: Even though some biological markers seem promising for the prediction of treatment response in adults affected by ADHD, further studies are needed to confirm the available data in the context of precision medicine.

Robustness of radiomics to variations in segmentation methods in multimodal brain MRI

Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods.

Sex-specific alterations of cortical morphometry in treatment-naïve patients with major depressive disorder

Major depressive disorder (MDD) shows sex differences in terms of incidence and symptoms, but the neurobiological basis underlying these sex differences remains to be clarified. High resolution T1-weighted Magnetic Resonance Imaging (MRI) scans were obtained from 123 non-comorbid treatment-naïve individuals with MDD and 81 age-, sex-, and handedness-matched healthy controls (HCs). MRI data were preprocessed with FreeSurfer software and four cortical measures were extracted: cortical thickness (CT), surface area (SA), cortical volume (CV), and local gyrification index (LGI). We tested for both sex-specific and sex-nonspecific patterns of cortical anatomic alterations. Regardless of sex, individuals with MDD showed significantly higher LGI in posterior cortex relative to HCs. Significant sex-by-group interactions were observed, and subsequent post-hoc analyses revealed that female individuals with MDD showed significantly lower SA in left ventrolateral prefrontal cortex (vlPFC), lower CV in right rostromedial prefrontal cortex (rmPFC), and higher LGI in left visual cortex compared with sex-matched HCs, whereas the opposite patterns of significant effects were seen in male individuals with MDD relative to their sex-matched HCs. Thus, sex-nonspecific and specific morphometric differences from HCs were found in posterior cortex, while in PFC alterations were highly sex-specific early in the illness course. This may involve sex-specific alterations in brain development or processes related to illness onset. These findings highlight the presence and regional distribution of generalized as well as sex-specific alterations of brain neurobiology in MDD.

Psychoradiological abnormalities in treatment-naive noncomorbid patients with posttraumatic stress disorder

BACKGROUND

Neuroimaging studies in posttraumatic stress disorder (PTSD) have identified various alterations in white matter (WM) microstructural organization. However, it remains unclear whether these are localized to specific regions of fiber tracts, and what diagnostic value they might have. This study set out to explore the spatial profile of WM abnormalities along defined fiber tracts in PTSD.

METHODS

Diffusion tensor images were obtained from 77 treatment-naive noncomorbid patients with PTSD and 76 demographically matched trauma-exposed non-PTSD (TENP) controls. Using automated fiber quantification, tract profiles of fractional anisotropy, axial diffusivity, mean diffusivity, and radial diffusivity were calculated to evaluate WM microstructural organization. Results were analyzed by pointwise comparisons, by correlation with symptom severity, and for diagnosis-by-sex interactions. Support vector machine analyses assessed the ability of tract profiles to discriminate PTSD from TENP.

RESULTS

Compared to TENP, PTSD showed lower fractional anisotropy accompanied by higher radial diffusivity and mean diffusivity in the left uncinate fasciculus, and lower fractional anisotropy accompanied by higher radial diffusivity in the right anterior thalamic radiation. Tract profile alterations were correlated with symptom severity, suggesting a pathophysiological relevance. There were no significant differences in diagnosis-by-sex interaction. Tract profiles allowed individual classification of PTSD versus TENP with significant accuracy, of potential diagnostic utility.

CONCLUSIONS

These findings add to the knowledge of the neuropathological basis of PTSD. WM alterations based on a tract-profile quantification approach are a potential biomarker for PTSD.

Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis

OBJECTIVE

There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate.

METHODS

Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms.

RESULTS

Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient Cp (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small-worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%.

CONCLUSION

Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.

Temporal variability of regional intrinsic neural activity in drug-naïve patients with obsessive-compulsive disorder

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.

Left superior temporal sulcus morphometry mediates the impact of anxiety and depressive symptoms on sleep quality in healthy adults

Anxiety and depressive symptoms may predispose individuals to sleep disturbance. Understanding how these emotional symptoms affect sleep quality, especially the underlying neural basis, could support the development of effective treatment. The aims of the present study were therefore to investigate potential changes in brain morphometry associated with poor sleep quality and whether this structure played a mediating role between the emotional symptoms and sleep quality. One hundred and forty-one healthy adults (69 women, mean age = 26.06 years, SD = 6.36 years) were recruited. A structural magnetic resonance imaging investigation was performed, and self-reported measures of anxiety, depressive symptoms and sleep quality were obtained for each participant. Whole-brain regression analysis revealed that worse sleep quality was associated with thinner cortex in left superior temporal sulcus (STS). Furthermore, the thickness of left STS mediated the association between the emotional symptoms and sleep quality. A subsequent commonality analysis showed that physiological component of the depressive symptoms had the greatest influence on sleep quality. In conclusion, thinner cortex in left STS may represent a neural substrate for the association between anxiety and depressive symptoms and poor sleep quality and may thus serve as a potential target for neuromodulatory treatment of sleep problems.

Psychiatric disorders in China: strengths and challenges of contemporary research and clinical services

China accounts for 17% of the global disease burden attributable to mental, neurological and substance use disorders. As a country undergoing profound societal change, China faces growing challenges to reduce the disease burden caused by psychiatric disorders. In this review, we aim to present an overview of progress in neuroscience research and clinical services for psychiatric disorders in China during the past three decades, analysing contributing factors and potential challenges to the field development. We first review studies in the epidemiological, genetic and neuroimaging fields as examples to illustrate a growing contribution of studies from China to the neuroscience research. Next, we introduce large-scale, open-access imaging genetic cohorts and recently initiated brain banks in China as platforms to study healthy brain functions and brain disorders. Then, we show progress in clinical services, including an integration of hospital and community-based healthcare systems and early intervention schemes. We finally discuss opportunities and existing challenges: achievements in research and clinical services are indispensable to the growing funding investment and continued engagement in international collaborations. The unique aspect of traditional Chinese medicine may provide insights to develop a novel treatment for psychiatric disorders. Yet obstacles still remain to promote research quality and to provide ubiquitous clinical services to vulnerable populations. Taken together, we expect to see a sustained advancement in psychiatric research and healthcare system in China. These achievements will contribute to the global efforts to realize good physical, mental and social well-being for all individuals.

Artificial intelligence applications in psychoradiology

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness, prediction of prognosis before treatment, and guidance for selection of effective treatments that target patient-relevant pathophysiological features. This is the primary aim of the field of Psychoradiology. Using databases collected from large samples at multiple centers, sophisticated artificial intelligence (AI) algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose, predict, and make treatment decisions. In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be considered in future translational research.

Efficacy and tolerability of repetitive transcranial magnetic stimulation for the treatment of obsessive-compulsive disorder in adults: a systematic review and network meta-analysis

Repetitive transcranial magnetic stimulation (rTMS) has been widely used as an alternative treatment for obsessive-compulsive disorder (OCD). However, the most effective rTMS parameters, such as the targets and stimulation frequencies, remain controversial. Therefore, we aimed to compare and rank the efficacy and tolerability of different rTMS strategies for OCD treatment. We searched five electronic databases from the date of their inception to March 25, 2020. Pairwise meta-analyses and network meta-analyses were performed to synthesize data. We assessed the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Twenty-two eligible randomized controlled trials (RCTs) were included. For efficacy, low-frequency (LF) rTMS over the dorsolateral prefrontal cortex (DLPFC; mean difference (MD) 6.34, 95% credible interval (CrI) 2.12-10.42) and supplementary motor area (MD 4.18, 95% CrI 0.83-7.62), and high-frequency rTMS over the DLPFC (MD 3.75, 95% CrI 1.04-6.81) were more effective than sham rTMS. Regarding tolerability, all rTMS treatment strategies were similar to the sham rTMS. The estimated ranking probabilities of treatments showed that LF-rTMS over the DLPFC might be the most effective intervention among all rTMS strategies. However, the quality of evidence regarding efficacy was evaluated as very low. Current evidence suggested a marginal advantage for LF-rTMS over the DLPFC on OCD treatment. High-quality RCTs with low selection and performance bias are needed to further verify the efficacy of specific rTMS strategies for the OCD treatment.

Drug Response-Related DNA Methylation Changes in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Pharmacotherapy is the most common treatment for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). Pharmacogenetic studies have achieved results with limited clinical utility. DNA methylation (DNAm), an epigenetic modification, has been proposed to be involved in both the pathology and drug treatment of these disorders. Emerging data indicates that DNAm could be used as a predictor of drug response for psychiatric disorders. In this study, we performed a systematic review to evaluate the reproducibility of published changes of drug response-related DNAm in SCZ, BD and MDD. A total of 37 publications were included. Since the studies involved patients of different treatment stages, we partitioned them into three groups based on their primary focuses: (1) medication-induced DNAm changes (n = 8); (2) the relationship between DNAm and clinical improvement (n = 24); and (3) comparison of DNAm status across different medications (n = 14). We found that only BDNF was consistent with the DNAm changes detected in four independent studies for MDD. It was positively correlated with clinical improvement in MDD. To develop better predictive DNAm factors for drug response, we also discussed future research strategies, including experimental, analytical procedures and statistical criteria. Our review shows promising possibilities for using BDNF DNAm as a predictor of antidepressant treatment response for MDD, while more pharmacoepigenetic studies are needed for treatments of various diseases. Future research should take advantage of a system-wide analysis with a strict and standard analytical procedure.

Neurostructural correlates of hope: dispositional hope mediates the impact of the SMA gray matter volume on subjective well-being in late adolescence

There has been increasing interest in identifying factors to predict subjective well-being in the emerging field of positive psychology over the past two decades. Dispositional hope, which reflects one’s goal-directed tendencies, including both pathway thinking (planning to meet goals) and agency thinking (goal-directed determination), has emerged as a stable predictor for subjective well-being. However, the neurobiological substrates of dispositional hope and the brain-hope mechanism for predicting subjective well-being remain unclear. Here, we examined these issues in 231 high school graduates within the same grade by estimating cortical gray matter volume (GMV) utilizing a voxel-based morphometry method based on structural magnetic resonance imaging. Whole-brain regression analyses and prediction analyses showed that higher dispositional hope was stably associated with greater GMV in the left supplementary motor area (SMA). Furthermore, mediation analyses revealed that dispositional hope mediated the relation between left SMA volume and subjective well-being. Critically, our results were obtained after adjusting for age, sex, family socioeconomic status and total GMV. Altogether, our study presents novel evidence for the neuroanatomical basis of dispositional hope and suggests an underlying indirect effect of dispositional hope on the link between brain gray matter structure and subjective well-being.

Correlation of abnormalities in resting state fMRI with executive functioning in chronic schizophrenia

BACKGROUND

Although previous studies have consistently demonstrated that neurocognitive and social cognitive impairments are commonly observed in schizophrenia, the neural substrates of deficits of cognitive function remain unclear, especially for the chronic schizophrenia. There has been little resting-state functional magnetic resonance imaging (rs-fMRI) study of cognitive function in chronic schizophrenia. In this study we aimed to investigate the changes of rs-fMRI signals with regional homogeneity (ReHo), and explore the correlations between abnormal regional activity and cognitive function in chronic schizophrenia.

METHODS

Altogether 76 subjects, 37 patients with chronic schizophrenia and 39 normal controls matched approximately for age, gender and education level were enrolled. All subjects were evaluated psychotic symptoms by Positive and Negative Syndrome Scale (PANSS) and cognitive function by Wisconsin Card Sorting Test (WCST). Conventional MRI and rs-fMRI were performed in all subjects. ReHo was calculated to measure the temporal synchronization of a given voxel and its neighboring voxels based on Kendall coefficient of concordance (KCC) in the rs-fMRI.

RESULTS

For the numbers of achieved categories, percentage of conceptual level response in the scores of WCST, the patient group was significantly lower than the control group (p<0.05). For the total errors, perseverative errors, non-perseverative errors, the patient group was significantly higher than the control group (p<0.05). Significant differences in ReHo were found in 11 regions (included five activated and five with decreased activity in the cerebrum and one with decreased activity in the cerebellum) in the chronic schizophrenia patients when compared with the normal controls. The ReHo map clusters that were significantly different between the two groups showed no significant correlation with clinical symptoms. Correlation of the whole brain with subscores of PANSS-T, PANSS-P, PANSS-N and WCST were significantly found in some regions.

CONCLUSIONS

The study identified five increased and six decreased spontaneous synchrony in the cerebrum and cerebellum in chronic schizophrenia patients compared to the normal matched controls, which were associated with positive, negative symptoms, and deficits of executive functioning.

Disrupted morphological grey matter networks in early-stage Parkinson's disease

While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.

Abnormalities of intrinsic brain activity in essential tremor: A meta-analysis of resting-state functional imaging

Neuroimaging studies using a variety of techniques have demonstrated abnormal patterns of spontaneous brain activity in patients with essential tremor (ET). However, the findings are variable and inconsistent, hindering understanding of underlying neuropathology. We conducted a meta‐analysis of whole‐brain resting‐state functional neuroimaging studies in ET compared to healthy controls (HC), using anisotropic effect‐size seed‐based d mapping, to identify the most consistent brain activity alterations and their relation to clinical features. After systematic literature search, we included 13 studies reporting 14 comparisons, describing 286 ET patients and 254 HC. Subgroup analyses were conducted considering medication status, head tremor status, and methodological factors. Brain activity in ET is altered not only in the cerebellum and cerebral motor cortex, but also in nonmotor cortical regions including prefrontal cortex and insula. Most of the results remained unchanged in subgroup analyses of patients with head tremor, medication‐naive patients, studies with statistical threshold correction, and the large subgroup of studies using functional magnetic resonance imaging. These findings not only show consistent and robust abnormalities in specific brain regions but also provide new information on the biology of patient heterogeneity, and thus help to elucidate the pathophysiology of ET.

Alterations in hippocampal subfield and amygdala subregion volumes in posttraumatic subjects with and without posttraumatic stress disorder

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.

Altered brain functional network dynamics in obsessive-compulsive disorder

Obsessive–compulsive disorder (OCD) is a debilitating and disabling neuropsychiatric disorder, whose neurobiological basis remains unclear. Although traditional static resting‐state magnetic resonance imaging (rfMRI) studies have found aberrant functional connectivity (FC) in OCD, alterations in whole‐brain FC and topological properties in the context of brain dynamics remain relatively unexplored. The rfMRI data of 29 patients with OCD and 40 healthy controls were analyzed using group independent component analysis to obtain independent components (ICs) and a sliding‐window approach to generate dynamic functional connectivity (dFC) matrices. dFC patterns were clustered into three reoccurring states, and state transition metrics were obtained. Then, graph‐theory methods were applied to dFC matrices to calculate the variability of network topological organization. The occurrence of a state (State 1) with the highest modularity index and lowest mean FC between networks was increased significantly in OCD, and the fractional time in brain State 1 was positively correlated with anxiety level in patients. State 1 was characterized by having positive connections within default mode (DMN) and salience networks (SAN), and negative coupling between the two networks. Additionally, ICs belonging to DMN and SAN showed lower temporal variability of nodal degree centrality and efficiency in patients, which was related to longer illness duration and higher current obsession ratings. Our results provide evidence of clinically relevant aberrant dynamic brain activity in OCD. Increased functional segregation among networks and impaired functional flexibility in connections among brain regions in DMN and SAN may play important roles in the neuropathology of OCD.

Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies

Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.

Effect of experimental orthodontic pain on gray and white matter functional connectivity

Aim

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.

Methods

Resting‐state functional magnetic resonance imaging (rsfMRI) was performed in 44 healthy subjects 24 hours after an elastic separator had been introduced between the first and the second molar on the right side of the lower jaw and in 49 age‐ and sex‐matched healthy control (HC) subjects. A K‐means clustering algorithm was used to identify functional gray matter (GM) and white matter (WM) resting‐state networks, and differences in functional connectivity (FC) of GM and WM between the group of subjects with experimental orthodontic pain and HC were analyzed.

Results

Twelve GM networks and 14 WM networks with high stability were identified. Compared with HC, subjects with orthodontic pain showed significantly increased FC between WM12, which includes posterior thalamic radiation and posterior cingulum bundle, and most GM networks. Besides, the WM12 network showed significant differences in FC with three GM‐WM loops involving the default mode network, dorsal attention network, and salience network, respectively.

Conclusions

Orthodontic pain is shown to produce an alteration of FC in networks relevant to pain processing, which may be mediated by a WM network relevant to emotion perception and cognitive processing.

Individualized Prediction of PTSD Symptom Severity in Trauma Survivors From Whole-Brain Resting-State Functional Connectivity

Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting-state functional magnetic resonance imaging (fMRI) and symptom severity in post-traumatic stress disorder. However, few studies have used brain-based measures to identify imaging associations with illness severity at the level of individual patients. This study applied connectome-based predictive modeling (CPM), a recently developed data-driven and subject-level method, to identify brain function features that are related to symptom severity of trauma survivors. Resting-state fMRI scans and clinical ratings were obtained 10-15 months after the earthquake from 122 earthquake survivors. Symptom severity of post-traumatic stress disorder features for each survivor was evaluated using the Clinician Administered Post-traumatic Stress Disorder Scale (CAPS-IV). A functionally pre-defined atlas was applied to divide the human brain into 268 regions. Each individual's functional connectivity 268 × 268 matrix was created to reflect correlations of functional time series data across each pair of nodes. The relationship between CAPS-IV scores and brain functional connectivity was explored in a CPM linear model. Using a leave-one-out cross-validation (LOOCV) procedure, findings showed that the positive network model predicted the left-out individual's CAPS-IV scores from resting-state functional connectivity. CPM predicted CAPS-IV scores, as indicated by a significant correspondence between predicted and actual values (r = 0.30, P = 0.001) utilizing primarily functional connectivity between visual cortex, subcortical-cerebellum, limbic, and motor systems. The current study provides data-driven evidence regarding the functional brain features that predict symptom severity based on the organization of intrinsic brain networks and highlights its potential application in making clinical evaluation of symptom severity at the individual level.

Resting-State Functional Connectivity: Signal Origins and Analytic Methods

Resting state functional connectivity (RSFC) has been widely studied in functional magnetic resonance imaging (fMRI) and is observed by a significant temporal correlation of spontaneous low-frequency signal fluctuations (SLFs) both within and across hemispheres during rest. Different hypotheses of RSFC include the biophysical origin hypothesis and cognitive origin hypothesis, which show that the role of SLFs and RSFC is still not completely understood. Furthermore, RSFC and age studies have shown an "age-related compensation" phenomenon. RSFC data analysis methods include time domain analysis, seed-based correlation, regional homogeneity, and principal and independent component analyses. Despite advances in RSFC, the authors also discuss challenges and limitations, ranging from head motion to methodological limitations.

White matter disruption in obsessive-compulsive disorder revealed by meta-analysis of tract-based spatial statistics

BACKGROUND

Exploring white matter (WM) microstructural alterations is a momentous step for gaining insights about underlying mechanisms of obsessive-compulsive disorder (OCD) and improving the efficacy of therapies for this condition. Many tract-based spatial statistics (TBSS) studies have revealed abnormalities of fractional anisotropy (FA; an index of WM integrity) in OCD. However, research works have not drawn robust conclusions. Therefore, we integrated the findings of TBSS studies to identify the most consistent FA changes in OCD using meta-analytical approach.

METHODS

Online databases were systematically searched for all TBSS studies comparing FA between patients with OCD and controls. A coordinate-based meta-analysis was performed using anisotropic effect size version of the seed-based d mapping software. Meanwhile, meta-regression was used to explore the potential association of clinical characteristics with regional FA abnormalities.

RESULTS

Our meta-analysis included 488 OCD patients and 519 controls across 17 datasets. FA reductions were identified in the genu of the corpus callosum and the left orbitofrontal WM in OCD patients relative to controls. Metaregression analyses showed that the FA in the left orbitofrontal WM was negatively and independently correlated with symptom severity and illness duration in patients with OCD.

CONCLUSIONS

The current study provides a quantitative overview of TBSS findings in OCD and demonstrates the most prominent and replicable WM abnormalities in OCD are in the anterior part of the brain including interhemispheric connection and orbitofrontal region. Additionally, our findings suggest that FA reduction in the orbitofrontal WM might be a potential biomarker in predicting disease severity and progression in patients with OCD.

Widespread Morphometric Abnormalities in Major Depression: Neuroplasticity and Potential for Biomarker Development

Major depression is common and debilitating. Identifying neurobiological subtypes that comprise the disorder and predict clinical outcome are key challenges. Genetic and environmental factors leading to major depression are expressed in neural structure and function. Volumetric decreases in gray matter have been demonstrated in corticolimbic circuits involved in emotion regulation. MR imaging observable abnormalities reflect cytoarchitectonic alterations within a local neuroendocrine milieu with systemic effects. Multivariate pattern analysis offers the potential to identify the neurobiological subtypes and predictors of clinical outcome. It is essential to characterize disease heterogeneity by incorporating data-driven inductive and symptom-based deductive approaches in an iterative process.

Neurostructural correlates of optimism: Gray matter density in the putamen predicts dispositional optimism in late adolescence

Dispositional optimism reflects one's generalized positive expectancies for future outcomes and plays a crucial role in personal developmental outcomes and health (e.g., counteracting related mental disorders such as depression and anxiety). Increasing evidence has suggested that extraversion is an important personality factor contributing to dispositional optimism. However, less is known about the association between dispositional optimism and brain structure and the role of extraversion in this association. Here, we examined these issues in 231 healthy high school students aged 16 to 20 years (110 males, mean age = 18.48 years, SD = 0.54) by estimating regional gray matter density (rGMD) using a voxel‐based morphometry method via structural magnetic resonance imaging. Whole‐brain regression analyses revealed a significant positive correlation between dispositional optimism and the rGMD of the bilateral putamen after adjusting for age, sex, family socioeconomic status (SES), general intelligence, and total gray matter volume (TGMV). Moreover, prediction analyses using fourfold balanced cross‐validation combined with linear regression confirmed a significant connection between dispositional optimism and putamen density after adjusting for age, sex, and family SES. More importantly, subsequent mediation analysis showed that extraversion may account for the association between putamen density and dispositional optimism after adjusting for age, sex, family SES, general intelligence, TGMV, and the other four Big Five personality traits. Taken together, the current study provides new evidence regarding the neurostructural basis underlying dispositional optimism in adolescents and underscores the importance of extraversion as an essential personality factor for dispositional optimism acquisition.

Magnetic Resonance Spectroscopy for Psychiatry: Progress in the Last Decade

Psychiatric disorders are common and can be severe. There is a need to identify biomarkers of psychiatric disorders to better diagnose and treat patients with psychiatric symptoms. Magnetic resonance spectroscopy (MRS) is a tool used to measure the levels of various metabolites in the human brain, and MRS studies of psychiatric disorders have identified potentially useful biomarkers of psychiatric illness. There have been significant advances in the way that psychiatric disorders are understood, classified, and researched as well as improvements in magnetic resonance imaging/MRS technology. MRS as a tool has not yet proved helpful to individual patients with psychiatric symptoms.

Psychoradiological Biomarkers for Psychopharmaceutical Effects

The application of personalized medicine to psychiatry is challenging. Psychoradiology could provide biomarkers based on objective tests in support of the diagnostic classifications and treatment planning. We review potential psychoradiological biomarkers for psychopharmaceutical effects. Although none of the biomarkers reviewed are yet of sufficient clinical utility to inform the selection of a specific pharmacologic compound for an individual patient, there is strong consensus that advanced multimodal approaches will contribute to discovery of novel treatment predictors in psychiatric disorders. Progress has been sufficient to warrant enthusiasm, in which application of neuroimaging-based biomarkers would represent a paradigm shift and modernization of psychiatric practice.

The Neurodevelopment of Autism from Infancy Through Toddlerhood

Autism spectrum disorder (ASD) emerges during early childhood and is marked by a relatively narrow window in which infants transition from exhibiting normative behavioral profiles to displaying the defining features of the ASD phenotype in toddlerhood. Prospective brain imaging studies in infants at high familial risk for autism have revealed important insights into the neurobiology and developmental unfolding of ASD. In this article, we review neuroimaging studies of brain development in ASD from birth through toddlerhood, relate these findings to candidate neurobiological mechanisms, and discuss implications for future research and translation to clinical practice.

Imaging-Based Subtyping for Psychiatric Syndromes

Despite considerable research evidence demonstrating significant neurobiological alterations in psychiatric disorders, incorporating neuroimaging approaches into clinical practice remains challenging. There is an urgent need for biologically validated psychiatric disease constructs that can inform diagnostic algorithms and targeted treatment development. In this article, we present a conceptual review of the most robust and impactful findings from studies that use neuroimaging methods in efforts to define distinct disease subtypes, while emphasizing cross-diagnostic and dimensional approaches. In addition, we discuss current challenges in psychoradiology and outline potential future strategies for clinically applicable translation.

Individual-Specific Analysis for Psychoradiology

In neuroimaging research, averaging data at the level of the group results in blurring of potentially meaningful individual differences. A more widespread use of an individual-specific approach is advocated for, which involves a more thorough investigation of each individual in a group, and characterization of idiosyncrasies at the level of behavior, cognition, and symptoms, as well as at the level of brain organization. It is hoped that such an approach, focused on individuals, will provide convergent findings that will help identify the underlying pathologic condition in various psychiatric disorders and help in the development of treatments individualized for each patient.

Neuroimaging in Schizophrenia

Schizophrenia is a chronic psychotic disorder with a lifetime prevalence of about 1%. Onset is typically in adolescence or early adulthood; characteristic symptoms include positive symptoms, negative symptoms, and impairments in cognition. Neuroimaging studies have shown substantive evidence of brain structural, functional, and neurochemical alterations that are more pronounced in the association cortex and subcortical regions. These abnormalities are not sufficiently specific to be of diagnostic value, but there may be a role for imaging techniques to provide predictions of outcome. Incorporating multimodal imaging datasets using machine learning approaches may offer better diagnostic and predictive value in schizophrenia.

Imaging of Posttraumatic Stress Disorder

This review summarizes current knowledge obtained from psychoradiological studies of posttraumatic stress disorder (PTSD). We first focus on 3 key anatomic structures (hippocampus, amygdala, and medial prefrontal cortex) and the functional circuits to which they contribute. In addition, we discuss the triple-network model, a widely accepted neurobiological model of PTSD that explains the vast majority of neuroimaging findings, as well as their interactions and relationships to functional disruptions in PTSD.

Clinical Strategies and Technical Challenges in Psychoradiology

Psychoradiology is an emerging discipline at the intersection between radiology and psychiatry. It holds promise for playing a role in clinical diagnosis, evaluation of treatment response and prognosis, and illness risk prediction for patients with psychiatric disorders. Addressing complex issues, such as the biological heterogeneity of psychiatric syndromes and unclear neurobiological mechanisms underpinning radiological abnormalities, is a challenge that needs to be resolved. With the advance of multimodal imaging and more efforts in standardization of image acquisition and analysis, psychoradiology is becoming a promising tool for the future of clinical care for patients with psychiatric disorders.

Disrupted brain functional networks in drug-naïve children with attention deficit hyperactivity disorder assessed using graph theory analysis

Neuroimaging studies have revealed functional brain network abnormalities in attention deficit hyperactivity disorder (ADHD), but the results have been inconsistent, potentially related to confounding medication effects. Furthermore, specific topological alterations in functional networks and their role in behavioral inhibition dysfunction remain to be established. Resting-state functional magnetic resonance imaging was performed on 51 drug-naïve children with ADHD and 55 age-matched healthy controls. Brain functional networks were constructed by thresholding the partial correlation matrices of 90 brain regions, and graph theory was used to analyze network topological properties. The Stroop test was used to assess cognitive inhibitory abilities. Nonparametric permutation tests were used to compare the topological architectures in the two groups. Compared with healthy subjects, brain networks in ADHD patients demonstrated altered topological characteristics, including lower global (FDR q = 0.01) and local efficiency (p = 0.032, uncorrected) and a longer path length (FDR q = 0.01). Lower nodal efficiencies were found in the left inferior frontal gyrus and anterior cingulate cortex in the ADHD group (FDR both q < 0.05). Altered global and nodal topological efficiencies were associated with the severity of inhibitory cognitive control deficits and hyperactivity symptoms in ADHD (p <0 .05). Alterations in network topologies in drug-naïve ADHD patients indicate weaker small-worldization with decreased segregation and integration of functional brain networks. Deficits in the cingulo-fronto-parietal attention network were associated with inhibitory control deficits.