Enhanced baseline activity in the left ventromedial putamen predicts individual treatment response in drug-naive, first-episode schizophrenia: Results from two independent study samples

The role and molecular mechanisms of the early growth response 3 gene in schizophrenia

Schizophrenia is a chronic, debilitating mental illness caused by both genetic and environmental factors. Genetic factors play a major role in schizophrenia development. Early growth response 3 (EGR3) is a member of the EGR family, which is associated with schizophrenia. Accumulating studies have investigated the relationship between EGR3 and schizophrenia. However, the role of EGR3 in schizophrenia pathogenesis remains unclear. In the present review, we focus on the progress of research related to the role of EGR3 in schizophrenia, including association studies between EGR3 and schizophrenia, abnormal gene expressional analysis of EGR3 in schizophrenia, biological function studies of EGR3 in schizophrenia, the molecular regulatory mechanism of EGR3 and schizophrenia susceptibility candidate genes, and possible role of EGR3 in the immune system function in schizophrenia. In summary, EGR3 is a schizophrenia risk candidate factor and has comprehensive regulatory roles in schizophrenia pathogenesis. Further studies investigating the molecular mechanisms of EGR3 in schizophrenia are warranted for understanding the pathophysiology of this disorder as well as the development of new therapeutic strategies for the treatment and control of this disorder.

Functional Connectivity Between Auditory and Medial Temporal Lobe Networks in Antipsychotic-Naïve Patients With First-Episode Schizophrenia Predicts the Effects of Dopamine Antagonism on Auditory Verbal Hallucinations

Background

Understanding how antipsychotic medication ameliorates auditory verbal hallucinations (AVHs) through modulation of brain circuitry is pivotal for understanding the pathophysiology of psychosis and for predicting treatment response.

Methods

This case-control study included examinations at baseline and at follow-up after 6 weeks. Initially, antipsychotic-naïve patients with first-episode schizophrenia who were experiencing AVHs were recruited together with healthy control participants. Antipsychotic treatment with the relatively selective D2 receptor antagonist amisulpride was administered as monotherapy. Functional connectivity measured by resting-state functional magnetic resonance imaging between networks of interest was used to study the effects of D2 blockade on brain circuitry and predict clinical treatment response. Hallucinations were rated with the Positive and Negative Syndrome Scale.

Results

Thirty-two patients experiencing AVHs and 34 healthy control participants were scanned at baseline. Twenty-two patients and 34 healthy control participants were rescanned at follow-up. Connectivity between the auditory network and the medial temporal lobe network was increased in patients at baseline (p = .002) and normalized within 6 weeks of D2 blockade (p = .018). At baseline, the connectivity between these networks was positively correlated with ratings of hallucinations (t = 2.67, p = .013). Moreover, baseline connectivity between the auditory network and the medial temporal lobe network predicted reduction in hallucinations (t = 2.34, p = .032).

Conclusions

Functional connectivity between the auditory network and the medial temporal lobe predicted response to initial antipsychotic treatment. These findings demonstrate that connectivity between networks involved in auditory processing, internal monitoring, and memory is associated with the clinical effect of dopamine antagonism.

A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis

OBJECTIVE

Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker.

METHODS

In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design.

RESULTS

The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems.

CONCLUSIONS

This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.

Predicting new onset thought disorder in early adolescence with optimized deep learning implicates environmental-putamen interactions

Background

Thought disorder (TD) is a sensitive and specific marker of risk for schizophrenia onset. Specifying factors that predict TD onset in adolescence is important to early identification of youth at risk. However, there is a paucity of studies prospectively predicting TD onset in unstratified youth populations.

Study Design

We used deep learning optimized with artificial intelligence (AI) to analyze 5,777 multimodal features obtained at 9-10 years from youth and their parents in the ABCD study, including 5,014 neural metrics, to prospectively predict new onset TD cases at 11-12 years. The design was replicated for all prevailing TD cases at 11-12 years.

Study Results

Optimizing performance with AI, we were able to achieve 92% accuracy and F1 and 0.96 AUROC in prospectively predicting the onset of TD in early adolescence. Structural differences in the left putamen, sleep disturbances and the level of parental externalizing behaviors were specific predictors of new onset TD at 11-12 yrs, interacting with low youth prosociality, the total parental behavioral problems and parent-child conflict and whether the youth had already come to clinical attention. More important predictors showed greater inter-individual variability.

Conclusions

This study provides robust person-level, multivariable signatures of early adolescent TD which suggest that structural differences in the left putamen in late childhood are a candidate biomarker that interacts with psychosocial stressors to increase risk for TD onset. Our work also suggests that interventions to promote improved sleep and lessen parent-child psychosocial stressors are worthy of further exploration to modulate risk for TD onset.

Machine learning methods to predict outcomes of pharmacological treatment in psychosis

In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.

Frontal lobe fALFF measured from resting-state fMRI as a prognostic biomarker in first-episode psychosis

Clinical response to antipsychotic drug treatment is highly variable, yet prognostic biomarkers are lacking. The goal of the present study was to test whether the fractional amplitude of low-frequency fluctuations (fALFF), as measured from baseline resting-state fMRI data, can serve as a potential biomarker of treatment response to antipsychotics. Patients in the first episode of psychosis (n = 126) were enrolled in two prospective studies employing second-generation antipsychotics (risperidone or aripiprazole). Patients were scanned at the initiation of treatment on a 3T MRI scanner (Study 1, GE Signa HDx, n = 74; Study 2, Siemens Prisma, n = 52). Voxelwise fALFF derived from baseline resting-state fMRI scans served as the primary measure of interest, providing a hypothesis-free (as opposed to region-of-interest) search for regions of the brain that might be predictive of response. At baseline, patients who would later meet strict criteria for clinical response (defined as two consecutive ratings of much or very much improved on the CGI, as well as a rating of ≤3 on psychosis-related items of the BPRS-A) demonstrated significantly greater baseline fALFF in bilateral orbitofrontal cortex compared to non-responders. Thus, spontaneous activity in orbitofrontal cortex may serve as a prognostic biomarker of antipsychotic treatment.

Neural substrates of reward anticipation and outcome in schizophrenia: a meta-analysis of fMRI findings in the monetary incentive delay task

Dysfunction of the mesocorticolimbic dopaminergic reward system is a core feature of schizophrenia (SZ), yet its precise contributions to different stages of reward processing and their relevance to disease symptomology are not fully understood. We performed a coordinate-based meta-analysis, using the monetary incentive delay task, to identify which brain regions are implicated in different reward phases in functional magnetic resonance imaging in SZ. A total of 17 studies (368 SZ and 428 controls) were included in the reward anticipation, and 10 studies (229 SZ and 281 controls) were included in the reward outcome. Our meta-analysis revealed that during anticipation, patients showed hypoactivation in the striatum, anterior cingulate cortex, median cingulate cortex (MCC), amygdala, precentral gyrus, and superior temporal gyrus compared with controls. Striatum hypoactivation was negatively associated with negative symptoms and positively associated with the proportion of second-generation antipsychotic users (percentage of SGA users). During outcome, patients displayed hyperactivation in the striatum, insula, amygdala, hippocampus, parahippocampal gyrus, cerebellum, postcentral gyrus, and MCC, and hypoactivation in the dorsolateral prefrontal cortex (DLPFC) and medial prefrontal cortex (mPFC). Hypoactivity of mPFC during outcome was negatively associated with positive symptoms. Moderator analysis showed that the percentage of SGA users was a significant moderator of the association between symptom severity and brain activity in both the anticipation and outcome stages. Our findings identified the neural substrates for different reward phases in SZ and may help explain the neuropathological mechanisms underlying reward processing deficits in the disorder.

Regional Homogeneity Abnormalities and Its Correlation With Impulsivity in Male Abstinent Methamphetamine Dependent Individuals

Methamphetamine (MA) use affects the brain structure and function. However, no studies have investigated the relationship between changes in regional homogeneity (ReHo) and impulsivity in MA dependent individuals (MADs). The aim of this study was to investigate the changes of brain activity under resting state in MADs and their relationship to impulsivity using ReHo method. Functional magnetic resonance imaging (fMRI) was performed to collect data from 46 MADs and 44 healthy controls (HCs) under resting state. ReHo method was used to investigate the differences in average ReHo values between the two groups. The ReHo values abnormalities of the brain regions found in inter-group comparisons were extracted and correlated with impulsivity. Compared to the HCs, MADs showed significant increased ReHo values in the bilateral striatum, while the ReHo values of the bilateral precentral gyrus and the bilateral postcentral gyrus decreased significantly. The ReHo values of the left precentral gyrus were negatively correlated with the BIS-attention, BIS-motor, and BIS-nonplanning subscale scores, while the ReHo values of the postcentral gyrus were only negatively correlated with the BIS-motor subscale scores in MADs. The abnormal spontaneous brain activity in the resting state of MADs revealed in this study may further improve our understanding of the neuro-matrix of MADs impulse control dysfunction and may help us to explore the neuropathological mechanism of MADs related dysfunction and rehabilitation.

Abnormal global-brain functional connectivity and its relationship with cognitive deficits in drug-naive first-episode adolescent-onset schizophrenia

Abnormal functional connectivity (FC) has been reported in drug-naive first-episode adolescent-onset schizophrenia (AOS) with inconsistent results due to differently selected regions of interest. The voxel-wise global-brain functional connectivity (GFC) analysis can help explore abnormal FC in an unbiased way in AOS. A total of 48 drug-naive first-episode AOS as well as 31 sex-, age- and education-matched healthy controls were collected. Data were subjected to GFC, correlation analysis and support vector machine analyses. Compared with healthy controls, the AOS group exhibited increased GFC in the right middle frontal gyrus (MFG), and decreased GFC in the right inferior temporal gyrus, left superior temporal gyrus (STG)/precentral gyrus/postcentral gyrus, right posterior cingulate cortex /precuneus and bilateral cuneus. After the Benjamini-Hochberg correction, significantly negative correlations between GFC in the bilateral cuneus and Trail-Making Test: Part A (TMT-A) scores (r=-0.285, p=0.049), between GFC in the left STG/precentral gyrus/postcentral gyrus and TMT-A scores (r=-0.384, p=0.007), and between GFC in the right MFG and the fluency scores (r=-0.335, p=0.020) in the patients. GFC in the left STG/precentral gyrus/postcentral gyrus has a satisfactory accuracy (up to 86.08%) in classifying patients from controls. AOS shows abnormal GFC in the brain areas of multiple networks, which bears cognitive significance. These findings suggest potential abnormalities in processing self-monitoring and sensory prediction, which further elucidate the pathophysiology of AOS.

Vortioxetine Modulates the Regional Signal in First-Episode Drug-Free Major Depressive Disorder at Rest

BACKGROUND

Previous studies on brain functional alterations associated with antidepressants for major depressive disorder (MDD) have produced conflicting results because they involved short treatment periods and a variety of compounds.

METHODS

Resting-state functional magnetic resonance imaging scans were obtained from 25 first-episode drug-free patients with MDD and 25 healthy controls. The patients, who were treated with vortioxetine for 8 weeks, were scanned at two-time points (baseline and week 8 of treatment). The amplitude of low-frequency fluctuation (ALFF) in the imaging data was used to analyze local brain signal alterations associated with antidepressant treatment.

RESULTS

Compared with the controls, the patients at baseline showed decreased ALFF values in the right inferior temporal gyrus and increased ALFF values in the left inferior cerebellum, right cingulate gyrus and postcentral gyrus. After 8 weeks of vortioxetine treatment, patients showed increased ALFF values in the bilateral cingulate gyrus, middle temporal gyrus, medial superior frontal gyrus, and inferior cerebellum.

CONCLUSION

This study provided evidence that vortioxetine modulates brain signals in MDD sufferers. These findings contribute to the understanding of how antidepressants effect brain function.

Comparing Brain Functional Activities in Patients With Blepharospasm and Dry Eye Disease Measured With Resting-State fMRI

Background: Blepharospasm (BSP) and dry eye disease (DED) are clinically common diseases characterized by an increased blinking rate. A sustained eyelid muscle activity may alter the cortical sensorimotor concordance and lead to secondary functional changes. This study aimed to explore the central mechanism of BSP by assessing brain functional differences between the two groups and comparing them with healthy controls.

Methods: In this study, 25 patients with BSP, 22 patients with DED, and 23 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) scan. The amplitude of low-frequency fluctuations (ALFF) was applied to analyze the imaging data.

Results: Analysis of covariance (ANCOVA) revealed widespread differences in ALFF across the three groups. In comparison with healthy controls, patients with BSP showed abnormal ALFF in the sensorimotor integration related-brain regions, including the bilateral supplementary motor area (SMA), left cerebellar Crus I, left fusiform gyrus, bilateral superior medial prefrontal cortex (MPFC), and right superior frontal gyrus (SFG). In comparison with patients with DED, patients with BSP exhibited a significantly increased ALFF in the left cerebellar Crus I and left SMA. ALFF in the left fusiform gyrus/cerebellar Crus I was positively correlated with symptomatic severity of BSP.

Conclusions: Our results reveal that the distinctive changes in the brain function in patients with BSP are different from those in patients with DED and healthy controls. The results further emphasize the primary role of sensorimotor integration in the pathophysiology of BSP.

Resting-state functional connectivity predictors of treatment response in schizophrenia - A systematic review and meta-analysis

We aimed to systematically synthesize and quantify the utility of pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) in predicting antipsychotic response in schizophrenia. We searched the PubMed/MEDLINE database for studies that examined the magnitude of association between baseline rs-fMRI assessment and subsequent response to antipsychotic treatment in persons with schizophrenia. We also performed meta-analyses for quantifying the magnitude and accuracy of predicting response defined continuously and categorically. Data from 22 datasets examining 1280 individuals identified striatal and default mode network functional segregation and integration metrics as consistent determinants of treatment response. The pooled correlation coefficient for predicting improvement in total symptoms measured continuously was ~0.47 (12 datasets; 95% CI: 0.35 to 0.59). The pooled odds ratio of predicting categorically defined treatment response was 12.66 (nine datasets; 95% CI: 7.91-20.29), with 81% sensitivity and 76% specificity. rs-fMRI holds promise as a predictive biomarker of antipsychotic treatment response in schizophrenia. Future efforts need to focus on refining feature characterization to improve prediction accuracy, validate prediction models, and evaluate their implementation in clinical practice.

Increased Homotopic Connectivity in the Prefrontal Cortex Modulated by Olanzapine Predicts Therapeutic Efficacy in Patients with Schizophrenia

Background

Previous studies have revealed the abnormalities in homotopic connectivity in schizophrenia. However, the relationship of these deficits to antipsychotic treatment in schizophrenia remains unclear. This study explored the effects of antipsychotic therapy on brain homotopic connectivity and whether the homotopic connectivity of these regions might predict individual treatment response in schizophrenic patients.

Methods

A total of 21 schizophrenic patients and 20 healthy controls were scanned by the resting-state functional magnetic resonance imaging. The patients received olanzapine treatment and were scanned at two time points. Voxel-mirrored homotopic connectivity (VMHC) and pattern classification techniques were applied to analyze the imaging data.

Results

Schizophrenic patients presented significantly decreased VMHC in the temporal and inferior frontal gyri, medial prefrontal cortex (MPFC), and motor and low-level sensory processing regions (including the fusiform gyrus and cerebellum lobule VI) relative to healthy controls. The VMHC in the superior/middle MPFC was significantly increased in the patients after eight weeks of treatment. Support vector regression (SVR) analyses revealed that VMHC in the superior/middle MPFC at baseline can predict the symptomatic improvement of the positive and negative syndrome scale after eight weeks of treatment.

Conclusions

This study demonstrated that olanzapine treatment may normalize decreased homotopic connectivity in the superior/middle MPFC in schizophrenic patients. The VMHC in the superior/middle MPFC may predict individual response for antipsychotic therapy. The findings of this study conduce to the comprehension of the therapy effects of antipsychotic medications on homotopic connectivity in schizophrenia.

Altered patterns of fractional amplitude of low-frequency fluctuation and regional homogeneity in abstinent methamphetamine-dependent users

Methamphetamine (MA) could induce functional and structural brain alterations in dependent subjects. However, few studies have investigated resting-state activity in methamphetamine-dependent subjects (MADs). We aimed to investigate alterations of brain activity during resting-state in MADs using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo). We analyzed fALFF and ReHo between MADs (n = 70) and healthy controls (HCs) (n = 84) and performed regression analysis using MA use variables. Compared to HCs, abstinent MADs showed increased fALFF and ReHo values in the bilateral striatum, decreased fALFF in the left inferior frontal gyrus, and decreased ReHo in the bilateral anterior cingulate cortex, sensorimotor cortex, and left precuneus. We also observed the fALFF values of bilateral striatum were positively correlated with the age of first MA use, and negatively correlated with the duration of MA use. The fALFF value of right striatum was also positively correlated with the duration of abstinence. The alterations of spontaneous cerebral activity in abstinent MADs may help us probe into the neurological pathophysiology underlying MA-related dysfunction and recovery. Since MADs with higher fALFF in the right striatum had shorter MA use and longer abstinence, the increased fALFF in the right striatum might implicate early recovery during abstinence.

Pattern classification as decision support tool in antipsychotic treatment algorithms

Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.

Increased regional homogeneity modulated by metacognitive training predicts therapeutic efficacy in patients with schizophrenia

Previous studies have demonstrated the efficacy of metacognitive training (MCT) in schizophrenia. However, the underlying mechanisms related to therapeutic effect of MCT remain unknown. The present study explored the treatment effects of MCT on brain regional neural activity using regional homogeneity (ReHo) and whether these regions' activities could predict individual treatment response in schizophrenia. Forty-one patients with schizophrenia and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. Patients were randomly divided into drug therapy (DT) and drug plus psychotherapy (DPP) groups. The DT group received only olanzapine treatment, whereas the DPP group received olanzapine and MCT for 8 weeks. The results revealed that ReHo in the right precuneus, left superior medial prefrontal cortex (MPFC), right parahippocampal gyrus and left rectus was significantly increased in the DPP group after 8 weeks of treatment. Patients in the DT group showed significantly increased ReHo in the left ventral MPFC/anterior cingulate cortex (ACC), left superior MPFC/middle frontal gyrus (MFG), left precuneus, right rectus and left MFG, and significantly decreased ReHo in the bilateral cerebellum VIII and left inferior occipital gyrus (IOG) after treatment. Support vector regression analyses showed that high ReHo levels at baseline in the right precuneus and left superior MPFC could predict symptomatic improvement of Positive and Negative Syndrome Scale (PANSS) after 8 weeks of DPP treatment. Moreover, high ReHo levels at baseline and alterations of ReHo in the left ventral MPFC/ACC could predict symptomatic improvement of PANSS after 8 weeks of DT treatment. This study suggests that MCT is associated with the modulation of ReHo in schizophrenia. ReHo in the right precuneus and left superior MPFC may predict individual therapeutic response for MCT in patients with schizophrenia.

A Multimodal Fusion Analysis of Pretreatment Anatomical and Functional Cortical Abnormalities in Responsive and Non-responsive Schizophrenia

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.

Abnormal Spontaneous Brain Activities of Limbic-Cortical Circuits in Patients With Dry Eye Disease

Whether brain function is altered in patients with dry eye disease (DED) remains unclear. Twenty patients with DED and 23 healthy controls (HCs) were scanned using resting-state functional magnetic resonance imaging. Regional homogeneity (ReHo) and support vector machine (SVM) were used to analyze the imaging data. Relative to the HCs, the patients with DED showed significantly increased ReHo values in the left inferior occipital gyrus (IOG), left superior temporal gyrus, and right superior medial prefrontal cortex, and significantly decreased ReHo values in the right superior frontal gyrus/middle frontal gyrus and bilateral middle cingulum (MC). SVM results indicated that the combination of ReHo values in the left MC and the left IOG in distinguishing patients with DED from HCs had a sensitivity of 95.00%, a specificity of 91.30%, and an accuracy of 93.02%. The present study found that the patients with DED had abnormal ReHo values in the limbic-cortical circuits. A combination of ReHo values in the left MC and the left IOG could be applied as a potential imaging biomarker to distinguish patients with DED from HCs. The dysfunction of limbic-cortical circuits may play an important role in the pathophysiology of DED.

Gray Matter Volume and Functional Connectivity in Hypochondriasis: A Magnetic Resonance Imaging and Support Vector Machine Analysis

Objective: Patients with hypochondriasis hold unexplainable beliefs and a fear of having a lethal disease, with poor compliances and treatment response to psychotropic drugs. Although several studies have demonstrated that patients with hypochondriasis demonstrate abnormalities in brain structure and function, gray matter volume (GMV) and functional connectivity (FC) in hypochondriasis still remain unclear. Methods: The present study collected T1-weighted and resting-state functional magnetic resonance images from 21 hypochondriasis patients and 22 well-matched healthy controls (HCs). We first analyzed the difference in the GMV between the two groups. We then used the regions showing a difference in GMV between two groups as seeds to perform functional connectivity (FC) analysis. Finally, a support vector machine (SVM) was applied to the imaging data to distinguish hypochondriasis patients from HCs. Results: Compared with the HCs, the hypochondriasis group showed decreased GMV in the left precuneus, and increased GMV in the left medial frontal gyrus. FC analyses revealed decreased FC between the left medial frontal gyrus and cuneus, and between the left precuneus and cuneus. A combination of both GMV and FC in the left precuneus, medial frontal gyrus, and cuneus was able to discriminate the hypochondriasis patients from HCs with a sensitivity of 0.98, specificity of 0.93, and accuracy of 0.95. Conclusion: Our study suggests that smaller left precuneus volumes and decreased FC between the left precuneus and cuneus seem to play an important role of hypochondriasis. Future studies are needed to confirm whether this finding is generalizable to patients with hypochondriasis.

Less reduced gray matter volume in the subregions of superior temporal gyrus predicts better treatment efficacy in drug-naive, first-episode schizophrenia

Decreased gray matter volume (GMV) in the superior temporal gyrus (STG) has been implicated in the neurophysiology of schizophrenia. However, it remains unclear whether volumetric reduction in the subregions of the STG can predict treatment efficacy for schizophrenia. Our cohort included 44 drug-naive, first-episode patients, 42 unaffected siblings and 44 healthy controls. Voxel-based morphometry and pattern classification were utilized to analyze the acquired imaging data as per the anatomical subdivision by a well-defined brainnetome atlas. The patients presented lower GMV values in left TE1.0/1.2 (TE, anterior temporal visual association area) than the siblings, and lower GMV values in the left/right TE1.0/1.2 and left A22r (rostral area 22) than the controls. A positive correlation is observed between the GMV values in the right A38l (lateral area 38) and baseline Positive and Negative Syndrome Scale (PANSS) total scores in the patients. Support vector regression (SVR) results exhibited a significant association between predicted (based on the GMV values in the right A38l) and actual symptomatic improvement based on the reduction ratio of the PANSS total scores (r = 0.498, p = 0.001). Our results suggest that normal structure in the right A38l of the STG may be an important factor indicative of the effects of antipsychotic drugs, which can be potentially used to monitor drug effects for first-episode patients at an early stage in clinical practice.

Fractional amplitude of low frequency fluctuation in drug-naïve first-episode patients with anorexia nervosa: A resting-state fMRI study

To characterize the fractional amplitude of low-frequency fluctuation (fALFF) in drug-naïve first-episode female patients with anorexia nervosa (AN) using resting-state functional magnetic resonance imaging (rs-fMRI).Whole brain rs-fMRI data were collected from 7 drug-naïve first-episode female patients with DSM-5 AN and 14 age-matched healthy female controls. fALFF values were calculated and compared between the two groups using a two-sample t test. Correlation analysis between the fALFF values in the entire brain and body mass index (BMI) was performed.Compared with the healthy controls, increased fALFF values were observed in the AN patients in their right hippocampus and left superior frontal gyrus, while decreased fALFF values were observed in their left rectus and left middle occipital gyrus. Moreover, low BMI was significantly associated with decreased fALFF in the left inferior frontal gyrus but increased fALFF in the left calcarine. In particular, the z-standardized fALFF (zfALFF) value of the left rectus was positive associated with BMI.Our findings suggest that spontaneous brain activity in the frontal region, hippocampus and rectus, characterized by fALFF values, was altered in drug-naïve, first-episode female patients with AN.

Reduced Brain Activity in the Right Putamen as an Early Predictor for Treatment Response in Drug-Naive, First-Episode Schizophrenia

Antipsychotic medications can have a significant effect on brain function after only several days of treatment. It is unclear whether such an acute effect can serve as an early predictor for treatment response in schizophrenia. Thirty-two patients with drug-naive, first-episode schizophrenia and 32 healthy controls underwent resting-state functional magnetic resonance imaging. Patients were treated with olanzapine and were scanned at baseline and 1 week of treatment. Healthy controls were scanned once at baseline. Symptom severity was assessed within the patient group using the Positive and Negative Syndrome Scale (PANSS) at three time points (baseline, 1 week of treatment, and 8 weeks of treatment). The fractional amplitude of low frequency fluctuation (fALFF) and support vector regression (SVR) methods were used to analyze the data. Compared with the control group, the patient group showed increased levels of fALFF in the bilateral putamen at baseline. After 1week of olanzapine treatment, the patient group showed decreased levels of fALFF in the right putamen relative to those at baseline. The SVR analysis found a significantly positive relationship between the reduction in fALFF after 1 week of treatment and the improvement in positive symptoms after 8 weeks of treatment (r = 0.431, p = 0.014). The present study provides evidence that early reduction and normalization of fALFF in the right putamen may serve as a predictor for treatment response in patients with schizophrenia.