Pre-treatment Resting-State Functional MR Imaging Predicts the Long-Term Clinical Outcome After Short-Term Paroxtine Treatment in Post-traumatic Stress Disorder

Neuroimaging of posttraumatic stress disorder in adults and youth: progress over the last decade on three leading questions of the field

Almost three decades have passed since the first posttraumatic stress disorder (PTSD) neuroimaging study was published. Since then, the field of clinical neuroscience has made advancements in understanding the neural correlates of PTSD to create more efficacious treatment strategies. While gold-standard psychotherapy options are available, many patients do not respond to them, prematurely drop out, or never initiate treatment. Therefore, elucidating the neurobiological mechanisms that define the disorder can help guide clinician decision-making and develop individualized mechanisms-based treatment options. To this end, this narrative review highlights progress made in the last decade in adult and youth samples on three outstanding questions in PTSD research: (1) Which neural alterations serve as predisposing (pre-exposure) risk factors for PTSD development, and which are acquired (post-exposure) alterations? (2) Which neural alterations can predict treatment outcomes and define clinical improvement? and (3) Can neuroimaging measures be used to define brain-based biotypes of PTSD? While the studies highlighted in this review have made progress in answering the three questions, the field still has much to do before implementing these findings into clinical practice. Overall, to better answer these questions, we suggest that future neuroimaging studies of PTSD should (A) utilize prospective longitudinal designs, collecting brain measures before experiencing trauma and at multiple follow-up time points post-trauma, taking advantage of multi-site collaborations/consortiums; (B) collect two scans to explore changes in brain alterations from pre-to-post treatment and compare changes in neural activation between treatment groups, including longitudinal follow up assessments; and (C) replicate brain-based biotypes of PTSD. By synthesizing recent findings, this narrative review will pave the way for personalized treatment approaches grounded in neurobiological evidence.

Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis

Objective

College students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health.

Methods

Publications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends.

Results

Spanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications.

Conclusion

This study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.

Shared and distinctive dysconnectivity patterns underlying pure generalized anxiety disorder (GAD) and comorbid GAD and depressive symptoms

The resting-state connectivity features underlying pure generalized anxiety disorder (GAD, G1) and comorbid GAD and depressive symptoms (G2) have not been directly compared. Furthermore, it is unclear whether these features might serve as potential prognostic biomarkers and change with treatment. Degree centrality (DC) in G1 (40 subjects), G2 (58 subjects), and healthy controls (HCs, 54 subjects) was compared before treatment, and the DC of G1 or G2 at baseline was compared with that after 4 weeks of paroxetine treatment. Using support vector regression (SVR), voxel-wise DC across the entire brain and abnormal DC at baseline were employed to predict treatment response. At baseline, G1 and G2 exhibited lower DC in the left mid-cingulate cortex and vermis IV/V compared to HCs. Additionally, compared to HCs, G1 had lower DC in the left middle temporal gyrus, while G2 showed higher DC in the right inferior temporal/fusiform gyrus. However, there was no significant difference in DC between G1 and G2. The SVR based on abnormal DC at baseline could successfully predict treatment response in responders in G2 or in G1 and G2. Notably, the predictive performance based on abnormal DC at baseline surpassed that based on DC across the entire brain. After treatment, G2 responders showed lower DC in the right medial orbital frontal gyrus, while no change in DC was identified in G1 responders. The G1 and G2 showed common and distinct dysconnectivity patterns and they could potentially serve as prognostic biomarkers. Furthermore, DC in patients with GAD could change with treatment.

Imaging the cerebellum in post-traumatic stress and anxiety disorders: a mini-review

Post-traumatic stress disorder (PTSD) and anxiety disorders are among the most prevalent psychiatric conditions worldwide sharing many clinical manifestations and, most likely, neural mechanisms as suggested by neuroimaging research. While the so-called fear circuitry and traditional limbic structures of the brain, particularly the amygdala, have been extensively studied in sufferers of these disorders, the cerebellum has been relatively underexplored. The aim of this paper was to present a mini-review of functional (task-activity or resting-state connectivity) and structural (gray matter volume) results on the cerebellum as reported in magnetic resonance imaging studies of patients with PTSD or anxiety disorders (49 selected studies in 1,494 patients). While mixed results were noted overall, e.g., regarding the direction of effects and anatomical localization, cerebellar structures like the vermis seem to be highly involved. Still, the neurofunctional and structural alterations reported for the cerebellum in excessive anxiety and trauma are complex, and in need of further evaluation.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach

Background

Transient ischemic attack (TIA) is a known risk factor for stroke. Abnormal alterations in the low-frequency range of the gray matter (GM) of the brain have been studied in patients with TIA. However, whether there are abnormal neural activities in the low-frequency range of the white matter (WM) in patients with TIA remains unknown. The current study applied two resting-state metrics to explore functional abnormalities in the low-frequency range of WM in patients with TIA. Furthermore, a reinforcement learning method was used to investigate whether altered WM function could be a diagnostic indicator of TIA.

Methods

We enrolled 48 patients with TIA and 41 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) and clinical/physiological/biochemical data were collected from each participant. We compared the group differences between patients with TIA and HCs in the low-frequency range of WM using two resting-state metrics: amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). The altered ALFF and fALFF values were defined as features of the reinforcement learning method involving a Q-learning algorithm.

Results

Compared with HCs, patients with TIA showed decreased ALFF in the right cingulate gyrus/right superior longitudinal fasciculus/left superior corona radiata and decreased fALFF in the right cerebral peduncle/right cingulate gyrus/middle cerebellar peduncle. Based on these two rs-fMRI metrics, an optimal Q-learning model was obtained with an accuracy of 82.02%, sensitivity of 85.42%, specificity of 78.05%, precision of 82.00%, and area under the curve (AUC) of 0.87.

Conclusion

The present study revealed abnormal WM functional alterations in the low-frequency range in patients with TIA. These results support the role of WM functional neural activity as a potential neuromarker in classifying patients with TIA and offer novel insights into the underlying mechanisms in patients with TIA from the perspective of WM function.

Spontaneous brain activity, graph metrics, and head motion related to prospective post-traumatic stress disorder trauma-focused therapy response

Introduction

Trauma-focused psychotherapy for post-traumatic stress disorder (PTSD) is effective in about half of all patients. Investigating biological systems related to prospective treatment response is important to gain insight in mechanisms predisposing patients for successful intervention. We studied if spontaneous brain activity, brain network characteristics and head motion during the resting state are associated with future treatment success.

Methods

Functional magnetic resonance imaging scans were acquired from 46 veterans with PTSD around the start of treatment. Psychotherapy consisted of trauma-focused cognitive behavioral therapy (tf-CBT), eye movement desensitization and reprocessing (EMDR), or a combination thereof. After intervention, 24 patients were classified as treatment responders and 22 as treatment resistant. Differences between groups in spontaneous brain activity were evaluated using amplitude of low-frequency fluctuations (ALFF), while global and regional brain network characteristics were assessed using a minimum spanning tree (MST) approach. In addition, in-scanner head motion was assessed.

Results

No differences in spontaneous brain activity and global network characteristics were observed between the responder and non-responder group. The right inferior parietal lobule, right putamen and left superior parietal lobule had a more central position in the network in the responder group compared to the non-responder group, while the right dorsolateral prefrontal cortex (DLPFC), right inferior frontal gyrus and left inferior temporal gyrus had a less central position. In addition, responders showed less head motion.

Discussion

These results show that areas involved in executive functioning, attentional and action processes, learning, and visual-object processing, are related to prospective PTSD treatment response in veterans. In addition, these findings suggest that involuntary micromovements may be related to future treatment success.

The cerebellum and psychological trauma: A systematic review of neuroimaging studies

Psychological trauma is highly prevalent among psychiatric disorders, however, the relationship between trauma, neurobiology and psychopathology is not yet fully understood. The cerebellum has been recognized as a crucial structure for cognition and emotion, however, it has been relatively ignored in the literature of psychological trauma, as it is not considered as part of the traditional fear neuro-circuitry. The aim of this review is to investigate how psychological trauma affects the cerebellum and to make conclusive remarks on whether the cerebellum forms part of the trauma-affected brain circuitry. A total of 267 unique records were screened and 39 studies were included in the review. Structural cerebellar alterations and aberrant cerebellar activity and connectivity in trauma-exposed individuals were consistently reported across studies. Early-onset of adverse experiences was associated with cerebellar alterations in trauma-exposed individuals. Several studies reported alterations in connectivity between the cerebellum and nodes of large-brain networks, which are implicated in several psychiatric disorders, including the default mode network, the salience network and the central executive network. Also, trauma-exposed individuals showed altered resting state and task based cerebellar connectivity with cortical and subcortical structures that are involved in emotion and fear regulation. Our preferred interpretation of the results is through the lens of the Universal Cerebellar Transform, the hypothesis that the cerebellum, given its homogeneous cytoarchitecture, performs a common computation for motor, cognitive and emotional functions. Therefore, trauma-induced alterations in this computation might set the ground for a variety of psychiatric symptoms.

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.

Resting-state dynamic functional connectivity predicts the psychosocial stress response

Acute stress triggers a complex cascade of psychological, physiological, and neural responses, which show large and enduring individual differences. Although previous studies have examined the relationship between the stress response and dynamic features of the brain's resting state, no study has used the brain's dynamic activity in the resting state to predict individual differences in the psychosocial stress response. In the current study, resting-state scans of forty-eight healthy participants were collected, and then their individual acute stress responses during the Montreal Imaging Stress Test (MIST) paradigm were recorded. Results defined a connectivity state (CS) characterized by positive correlations across the whole brain during resting-state that could negatively predict participants' feelings of social evaluative threat during stress tasks. Another CS characterized by negative correlations between the frontal-parietal network (FPN) and almost all other networks, except the dorsal attentional network (DAN), could predict participants' subjective stress, feelings of uncontrollability, and feelings of social evaluative threat. However, no CS could predict participants' salivary cortisol stress response. Overall, these results suggested that the brain state characterized as attentional regulation, linking self-control, and top-down regulation ability, could predict the psychosocial stress response. This study also developed an objective indicator for predicting human stress responses.

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.

The resting-state functional connectivity of amygdala subregions associated with post-traumatic stress symptom and sleep quality in trauma survivors

Neuroimaging findings suggest that the amygdala plays a primary role in both the psychopathology of posttraumatic stress disorder (PTSD) and poor sleep quality, which are common in trauma survivors. However, the neural mechanisms of these two problems in trauma survivors associated with amygdala remain unclear. In the current study, we aimed to explore the role of functional connectivity of amygdala subregions in both PTSD symptoms and poor sleep quality. A total of 94 trauma-exposed subjects were scanned on a 3T MR system using resting-state functional magnetic resonance imaging. Both Pittsburgh Sleep Quality Index and Clinician-Administered PTSD Scale scores were negatively correlated with the resting-state functional connectivity between the left basolateral amygdala-left medial prefrontal cortex and the right basolateral amygdala-right medial prefrontal cortex. Our findings suggest a shared amygdala subregional neural circuitry underlying the neuropathological mechanisms of PTSD symptoms and poor sleep quality in trauma survivors.

Repeated cocaine exposure prior to fear conditioning induces persistency of PTSD-like symptoms and enhancement of hippocampal and amygdala cell density in male rats

Pre- and post-trauma drug use can interfere with recovery from post-traumatic stress disorder (PTSD). However, the biological underpinnings of this interference are poorly understood. Here we examined the effect of pre-fear conditioning cocaine self-administration on PTSD-like symptoms in male rats, and defined impairment of fear extinction as difficulty to recover from PTSD. We also examined cell density changes in brain regions suspected of being involved in resistance to PTSD recovery. Before footshock stress testing, rats were trained to self-administer cocaine during 20 consecutive days, after which they were exposed to footshocks, while other rats continued to self-administer cocaine until the end of the experiment. Upon assessment of three PTSD-like symptoms (fear during situational reminders, anxiety-like behavior, and impairment of recognition memory) and fear extinction learning and memory, changes in cell density were measured in the medial prefrontal cortex, hippocampus, and amygdala. Results show that pre-footshock cocaine exposure did not affect fear during situational reminders. Fear conditioning did not lead to an increase in cocaine consumption. However, in footshock stressed rats, cocaine induced a reduction of anxiety-like behavior, an aggravation of recognition memory decline, and an impairment of extinction memory. These behavioral alterations were associated with increased cell density in the hippocampal CA1, CA2, and CA3 regions and basolateral amygdala, but not in the medial prefrontal cortex. Our findings suggest that enhancement of cell density in the hippocampus and amygdala may be changes associated with drug use, interfering with PTSD recovery.

The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Neural correlates of treatment effect and prediction of treatment outcome in patients with PTSD and comorbid personality disorder: study design

BACKGROUND

Neural alterations related to treatment outcome in patients with both post-traumatic stress disorder (PTSD) and comorbid personality disorder are unknown. Here we describe the protocol for a neuroimaging study of treatment of patients with PTSD and comorbid borderline (BPD) or cluster C (CPD) personality disorder traits. Our specific aims are to 1) investigate treatment-induced neural alterations, 2) predict treatment outcome using structural and functional magnetic resonance imaging (MRI) and 3) study neural alterations associated with BPD and CPD in PTSD patients. We hypothesize that 1) all treatment conditions are associated with normalization of limbic and prefrontal brain activity and hyperconnectivity in resting-state brain networks, with additional normalization of task-related activation in emotion regulation brain areas in the patients who receive trauma-focused therapy and personality disorder treatment; 2) Baseline task-related activation, together with structural brain measures and clinical variables predict treatment outcome; 3) dysfunction in task-related activation and resting-state connectivity of emotion regulation areas is comparable in PTSD patients with BPD or CPD, with a hypoconnected central executive network in patients with PTSD+BPD.

METHODS

We aim to include pre- and post-treatment 3 T-MRI scans in 40 patients with PTSD and (sub) clinical comorbid BPD or CPD. With an expected attrition rate of 50%, at least 80 patients will be scanned before treatment. MRI scans for 30 matched healthy controls will additionally be acquired. Patients with PTSD and BPD were randomized to either EMDR-only or EMDR combined with Dialectical Behaviour Therapy. Patients with PTSD and CPD were randomized to Imaginary Rescripting (ImRs) or to ImRs combined with Schema Focused Therapy. The scan protocol consists of a T1-weighted structural scan, resting state fMRI, task-based fMRI during an emotional face task and multi-shell diffusion weighted images. For data analysis, multivariate mixed-models, regression analyses and machine learning models will be used.

DISCUSSION

This study is one of the first to use neuroimaging measures to predict and better understand treatment response in patients with PTSD and comorbid personality disorders. A heterogeneous, naturalistic sample will be included, ensuring generalizability to a broad group of treatment seeking PTSD patients.

TRIAL REGISTRATION

Clinical Trials, NCT03833453 & NCT03833531 . Retrospectively registered, February 2019.

Defining focal brain stimulation targets for PTSD using neuroimaging

INTRODUCTION

Focal brain stimulation has potential as a treatment for posttraumatic stress disorder (PTSD). In this review, we aim to inform selection of focal brain stimulation targets for treating PTSD by examining studies of the functional neuroanatomy of PTSD and treatment response. We first briefly review data on brain stimulation interventions for PTSD. Although published data suggest good efficacy overall, the neurobiological rationale for each stimulation target is not always clear.

METHODS

Therefore, we assess pre- and post-treatment (predominantly psychotherapy) functional neuroimaging studies in PTSD to determine which brain changes seem critical to treatment response. Results of these studies are presented within a previously proposed functional neural systems model of PTSD.

RESULTS

While not completely consistent, research suggests that downregulating the fear learning and threat and salience detection circuits (i.e., amygdala, dorsal anterior cingulate cortex and insula) and upregulating the emotion regulation and executive function and contextual processing circuits (i.e., prefrontal cortical regions and hippocampus) may mediate PTSD treatment response.

CONCLUSION

This literature review provides some justification for current focal brain stimulation targets. However, the examination of treatment effects on neural networks is limited, and studies that include the stimulation targets are lacking. Further, additional targets, such as the cingulate, medial prefrontal cortex, and inferior parietal lobe, may also be worth investigation, especially when considering how to achieve network level changes. Additional research combining PTSD treatment with functional neuroimaging will help move the field forward by identifying and validating novel targets, providing better rationale for specific treatment parameters and personalizing treatment for PTSD.

Intranasal vasopressin modulates resting state brain activity across multiple neural systems: Evidence from a brain imaging machine learning study

Arginine vasopressin (AVP), a neuropeptide with widespread receptors in brain regions important for socioemotional processing, is critical in regulating various mammalian social behavior and emotion. Although a growing body of task-based brain imaging studies have revealed the effects of AVP on brain activity associated with emotion processing, social cognition and behaviors, the potential modulations of AVP on resting-state brain activity remain largely unknown. Here, the current study addressed this issue by adopting a machine learning approach to distinguish administration of AVP and placebo, employing the amplitude of low-frequency fluctuation (ALFF) as a measure of resting-state brain activity. The brain regions contributing to the classification were then subjected to functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that ALFF across multiple neural systems were sufficient to distinguish between AVP and placebo at individual level, with the contributing regions distributed across the social cognition network, sensorimotor regions and emotional processing network. These findings suggest that the role of AVP in socioemotional functioning recruits multiple brain networks distributed across the whole brain rather than specific localized neural pathways. Beyond these findings, the current data-driven approach also opens a novel avenue to delineate neural underpinnings of various neuropeptides or hormones.

Altered dynamic parahippocampus functional connectivity in patients with post-traumatic stress disorder

OBJECTIVES

This study investigated dynamic brain functional alterations in post-traumatic stress disorder (PTSD) patients with resting state functional magnetic resonance imaging.

METHODS

Degree centrality (DC) and seed-based functional connectivity (FC) analyses were conducted among typhoon survivours with (n = 27) and without PTSD (n = 33) and healthy controls (HC) (n = 30) to assess the intrinsic dysconnectivity pattern and network-level brain function.

RESULTS

Both the PTSD group and the trauma-exposed control (TEC) group had increased DC in the left parahippocampal gyrus relative to the HC group. More increased DC in the left parahippocampal gyrus was found in the PTSD group. Both traumatised groups exhibited decreased left parahippocampal gyrus dynamic FC with the bilateral middle frontal gyrus and superior frontal gyrus relative to the HC group. The Checklist-Civilian Version score was positively correlated with dynamic FC between the parahippocampal gyrus and left superior frontal gyrus but was negatively correlated with dynamic FC between the parahippocampal gyrus and right middle frontal gyrus.

CONCLUSIONS

Trauma exposure may lead to an altered dynamic FC in individuals with or without PTSD. An altered DC in the parahippocampal gyrus may be an important risk factor for PTSD development following trauma exposure. A more prominently increased DC in the parahippocampal gyrus might be a common trait in the PTSD group.

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.

The use of machine learning techniques in trauma-related disorders: a systematic review

Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice and in academic research, due to clinical and biological heterogeneity. Machine learning (ML) techniques can be applied to improve classification of disorders, to predict outcomes or to determine person-specific treatment selection. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with ASD or PTSD. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to May 2019. We found 806 abstracts and included 49 studies in our review. Most of the included studies used multiple levels of biological data to predict risk factors or to identify early symptoms related to PTSD. Other studies used ML classification techniques to distinguish individuals with ASD or PTSD from other psychiatric disorder or from trauma-exposed and healthy controls. We also found studies that attempted to define outcome profiles using clustering techniques and studies that assessed the relationship among symptoms using network analysis. Finally, we proposed a quality assessment in this review, evaluating methodological and technical features on machine learning studies. We concluded that etiologic and clinical heterogeneity of ASD/PTSD patients is suitable to machine learning techniques and a major challenge for the future is to use it in clinical practice for the benefit of patients in an individual level.

Metacognitive Training Modulates Default-Mode Network Homogeneity During 8-Week Olanzapine Treatment in Patients With Schizophrenia

BACKGROUND

Previous studies have revealed the efficacy of metacognitive training for schizophrenia. However, the underlying mechanisms of metacognitive training on brain function alterations, including the default-mode network (DMN), remain unknown. The present study explored treatment effects of metacognitive training on functional connectivity of the brain regions in the DMN.

METHODS

Forty-one patients with schizophrenia and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. Patients were randomly assigned to drug plus psychotherapy (DPP) and drug therapy (DT) groups. The DPP group received olanzapine and metacognitive training, and the DT group received only olanzapine for 8 weeks. Network homogeneity (NH) was applied to analyze the imaging data, and pattern classification techniques were applied to test whether abnormal NH deficits at baseline might be used to discriminate patients from healthy controls. Abnormal NH in predicting treatment response was also examined in each patient group.

RESULTS

Compared with healthy controls, patients at baseline showed decreased NH in the bilateral ventral medial prefrontal cortex (MPFC), right posterior cingulate cortex (PCC)/precuneus, and bilateral precuneus and increased NH in the right cerebellum Crus II and bilateral superior MPFC. NH values in the right PCC/precuneus increased in the DPP group after 8 weeks of treatment, whereas no substantial difference in NH value was observed in the DT group. Support vector machine analyses showed that the accuracy, sensitivity, and specificity for distinguishing patients from healthy controls were more than 0.7 in the NH values of the right PCC/precuneus, bilateral ventral MPFC, bilateral superior MPFC, and bilateral precuneus regions. Support vector regression analyses showed that high NH levels at baseline in the bilateral superior MPFC could predict symptomatic improvement of positive and negative syndrome scale (PANSS) after 8 weeks of DPP treatment. No correlations were found between alterations in the NH values and changes in the PANSS scores/cognition parameters in the patients.

CONCLUSION

This study provides evidence that metacognitive training is related to the modulation of DMN homogeneity in schizophrenia.

Individual prediction of psychotherapy outcome in posttraumatic stress disorder using neuroimaging data

Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30-50% of patients do not benefit sufficiently. We investigated whether structural and resting-state functional magnetic resonance imaging (MRI/rs-fMRI) data could distinguish between treatment responders and non-responders on the group and individual level. Forty-four male veterans with PTSD underwent baseline scanning followed by trauma-focused psychotherapy. Voxel-wise gray matter volumes were extracted from the structural MRI data and resting-state networks (RSNs) were calculated from rs-fMRI data using independent component analysis. Data were used to detect differences between responders and non-responders on the group level using permutation testing, and the single-subject level using Gaussian process classification with cross-validation. A RSN centered on the bilateral superior frontal gyrus differed between responders and non-responder groups (P_FWE < 0.05) while a RSN centered on the pre-supplementary motor area distinguished between responders and non-responders on an individual-level with 81.4% accuracy (P < 0.001, 84.8% sensitivity, 78% specificity and AUC of 0.93). No significant single-subject classification or group differences were observed for gray matter volume. This proof-of-concept study demonstrates the feasibility of using rs-fMRI to develop neuroimaging biomarkers for treatment response, which could enable personalized treatment of patients with PTSD.

The cerebellum under stress

Stress-related psychiatric conditions are one of the main causes of disability in developed countries. They account for a large portion of resource investment in stress-related disorders, become chronic, and remain difficult to treat. Research on the neurobehavioral effects of stress reveals how changes in certain brain areas, mediated by a number of neurochemical messengers, markedly alter behavior. The cerebellum is connected with stress-related brain areas and expresses the machinery required to process stress-related neurochemical mediators. Surprisingly, it is not regarded as a substrate of stress-related behavioral alterations, despite numerous studies that show cerebellar responsivity to stress. Therefore, this review compiles those studies and proposes a hypothesis for cerebellar function in stressful conditions, relating it to stress-induced psychopathologies. It aims to provide a clearer picture of stress-related neural circuitry and stimulate cerebellum-stress research. Consequently, it might contribute to the development of improved treatment strategies for stress-related disorders.