Artificial intelligence applications in psychoradiology

Multimodal frontal neuroimaging markers predict longitudinal craving reduction in abstinent individuals with heroin use disorder

The variation in improvement among individuals with addiction after abstinence is a critical issue. Here, we aimed to identify robust multimodal markers associated with high response to 8-month abstinence in the individuals with heroin use disorder (HUD) and explore whether the identified markers could be generalized to the individuals with methamphetamine use disorder (MUD). According to the median of craving changes, 53 individuals with HUD with 8-month abstinence were divided into two groups: higher craving reduction and lower craving reduction. At baseline, clinical variables, cortical thickness and subcortical volume, fractional anisotropy (FA) of fibers and resting-state functional connectivity (RSFC) were extracted. Different strategies (single metric, multimodal neuroimaging fusion and multimodal neuroimaging-clinical data fusion) were used to identify reliable features for discriminating the individuals with HUD with higher craving reduction from those with lower reduction. The generalization ability of the identified features was validated in the 21 individuals with MUD. Multimodal neuroimaging-clinical fusion features with best performance was achieved an 87.1 ± 3.89% average accuracy in individuals with HUD, with a moderate accuracy of 66.7% when generalizing to individuals with MUD. The multimodal neuroimaging features, primarily converging in frontal regions (e.g., the left superior frontal (LSF) thickness, FA of the LSF-occipital tract, and RSFC of left middle frontal-right superior temporal lobe), collectively contributed to prediction alongside dosage and attention impulsiveness. In this study, we identified the validated multimodal frontal neuroimaging markers associated with higher response to long-term abstinence and revealed insights for the neural mechanisms of addiction abstinence, contributing to clinical strategies and treatment for addiction.

Research progress on the psychological burden and intervention measures in cancer patients

In the past 40 years, the gradually increasing incidence and mortality rates of malignant tumors have severely impacted the quality of life of patients, bringing significant physical and psychological burdens and becoming an increasingly serious social issue. With the development of medical standards, new methods for cancer detection and treatment have been continuously proposed. Although it has been proven that cancer is related to increased psychological burden and suicidal behaviors in patients, current research on the psychological burden caused by cancer is insufficient. Clinicians often overlook the psychological health issues of patients while treating their physical diseases. Considering the high incidence of cancer, this review will outline the psychological burdens of cancer patients worldwide in recent years and its high-risk factors. Moreover, this review will summarize the common methods for evaluating psychological burdens, present current predictive models and treatment methods for the psychological burden of cancer patients, aiming to provide a research basis and future direction for the timely and accurate assessment of the psychological burden in cancer patients.

Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains

The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.

More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method

The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.

Common and distinct cortical thickness alterations in youth with autism spectrum disorder and attention-deficit/hyperactivity disorder

BACKGROUND

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental disorders with overlapping behavioral features and genetic etiology. While brain cortical thickness (CTh) alterations have been reported in ASD and ADHD separately, the degree to which ASD and ADHD are associated with common and distinct patterns of CTh changes is unclear.

METHODS

We searched PubMed, Web of Science, Embase, and Science Direct from inception to 8 December 2023 and included studies of cortical thickness comparing youth (age less than 18) with ASD or ADHD with typically developing controls (TDC). We conducted a comparative meta-analysis of vertex-based studies to identify common and distinct CTh alterations in ASD and ADHD.

RESULTS

Twelve ASD datasets involving 458 individuals with ASD and 10 ADHD datasets involving 383 individuals with ADHD were included in the analysis. Compared to TDC, ASD showed increased CTh in bilateral superior frontal gyrus, left middle temporal gyrus, and right superior parietal lobule (SPL) and decreased CTh in right temporoparietal junction (TPJ). ADHD showed decreased CTh in bilateral precentral gyri, right postcentral gyrus, and right TPJ relative to TDC. Conjunction analysis showed both disorders shared reduced TPJ CTh located in default mode network (DMN). Comparative analyses indicated ASD had greater CTh in right SPL and TPJ located in dorsal attention network and thinner CTh in right TPJ located in ventral attention network than ADHD.

CONCLUSIONS

These results suggest shared thinner TPJ located in DMN is an overlapping neurobiological feature of ASD and ADHD. This alteration together with SPL alterations might be related to altered biological motion processing in ASD, while abnormalities in sensorimotor systems may contribute to behavioral control problems in ADHD. The disorder-specific thinner TPJ located in disparate attention networks provides novel insight into distinct symptoms of attentional deficits associated with the two neurodevelopmental disorders.

TRIAL REGISTRATION

PROSPERO CRD42022370620. Registered on November 9, 2022.

Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease

Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia.

Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder

Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.

Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review

Chronic Painful Temporomandibular Disorders (TMD) are challenging to diagnose and manage due to their complexity and lack of understanding of brain mechanism. In the past few decades' neural mechanisms of pain regulation and perception have been clarified by neuroimaging research. Advances in the neuroimaging have bridged the gap between brain activity and the subjective experience of pain. Neuroimaging has also made strides toward separating the neural mechanisms underlying the chronic painful TMD. Recently, Artificial Intelligence (AI) is transforming various sectors by automating tasks that previously required humans' intelligence to complete. AI has started to contribute to the recognition, assessment, and understanding of painful TMD. The application of AI and neuroimaging in understanding the pathophysiology and diagnosis of chronic painful TMD are still in its early stages. The objective of the present review is to identify the contemporary neuroimaging approaches such as structural, functional, and molecular techniques that have been used to investigate the brain of chronic painful TMD individuals. Furthermore, this review guides practitioners on relevant aspects of AI and how AI and neuroimaging methods can revolutionize our understanding on the mechanisms of painful TMD and aid in both diagnosis and management to enhance patient outcomes.

Distinct pre-COVID brain structural signatures in COVID-19-related post-traumatic stress symptoms and post-traumatic growth

Post-traumatic stress symptoms and post-traumatic growth are common co-occurring psychological responses following exposure to traumatic events (such as COVID-19 pandemic), their mutual relationship remains unclear. To explore this relationship, structural magnetic resonance imaging data were acquired from 115 general college students before the COVID-19 pandemic, and follow-up post-traumatic stress symptoms and post-traumatic growth measurements were collected during the pandemic. Voxel-based morphometry was conducted and individual structural covariance networks based on gray matter volume were further analyzed using graph theory and partial least squares correlation. Behavioral correlation found no significant relationship between post-traumatic stress symptoms and post-traumatic growth. Voxel-based morphometry analyses showed that post-traumatic stress symptoms were positively correlated with gray matter volume in medial prefrontal cortex/dorsal anterior cingulate cortex, and post-traumatic growth was negatively correlated with gray matter volume in left dorsolateral prefrontal cortex. Structural covariance network analyses found that post-traumatic stress symptoms were negatively correlated with the local efficiency and clustering coefficient of the network. Moreover, partial least squares correlation showed that post-traumatic stress symptoms were correlated with pronounced nodal properties patterns in default mode, sensory and motor regions, and a marginal correlation of post-traumatic growth with a nodal property pattern in emotion regulation-related regions. This study advances our understanding of the neurobiological substrates of post-traumatic stress symptoms and post-traumatic growth, and suggests that they may have different neuroanatomical features.

Structural and functional alterations in the brains of patients with anisometropic and strabismic amblyopia: a systematic review of magnetic resonance imaging studies

Amblyopia is the most common cause of vision loss in children and can persist into adulthood in the absence of effective intervention. Previous clinical and neuroimaging studies have suggested that the neural mechanisms underlying strabismic amblyopia and anisometropic amblyopia may be different. Therefore, we performed a systematic review of magnetic resonance imaging studies investigating brain alterations in patients with these two subtypes of amblyopia; this study is registered with PROSPERO (registration ID: CRD42022349191). We searched three online databases (PubMed, EMBASE, and Web of Science) from inception to April 1, 2022; 39 studies with 633 patients (324 patients with anisometropic amblyopia and 309 patients with strabismic amblyopia) and 580 healthy controls met the inclusion criteria (e.g., case-control designed, peer-reviewed articles) and were included in this review. These studies highlighted that both strabismic amblyopia and anisometropic amblyopia patients showed reduced activation and distorted topological cortical activated maps in the striate and extrastriate cortices during task-based functional magnetic resonance imaging with spatial-frequency stimulus and retinotopic representations, respectively; these may have arisen from abnormal visual experiences. Compensations for amblyopia that are reflected in enhanced spontaneous brain function have been reported in the early visual cortices in the resting state, as well as reduced functional connectivity in the dorsal pathway and structural connections in the ventral pathway in both anisometropic amblyopia and strabismic amblyopia patients. The shared dysfunction of anisometropic amblyopia and strabismic amblyopia patients, relative to controls, is also characterized by reduced spontaneous brain activity in the oculomotor cortex, mainly involving the frontal and parietal eye fields and the cerebellum; this may underlie the neural mechanisms of fixation instability and anomalous saccades in amblyopia. With regards to specific alterations of the two forms of amblyopia, anisometropic amblyopia patients suffer more microstructural impairments in the precortical pathway than strabismic amblyopia patients, as reflected by diffusion tensor imaging, and more significant dysfunction and structural loss in the ventral pathway. Strabismic amblyopia patients experience more attenuation of activation in the extrastriate cortex than in the striate cortex when compared to anisometropic amblyopia patients. Finally, brain structural magnetic resonance imaging alterations tend to be lateralized in the adult anisometropic amblyopia patients, and the patterns of brain alterations are more limited in amblyopic adults than in children. In conclusion, magnetic resonance imaging studies provide important insights into the brain alterations underlying the pathophysiology of amblyopia and demonstrate common and specific alterations in anisometropic amblyopia and strabismic amblyopia patients; these alterations may improve our understanding of the neural mechanisms underlying amblyopia.

Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

Background

Social anxiety (SA) is a negative emotional response that can lead to mental health issues, which some have experienced during the coronavirus disease 2019 (COVID-19) pandemic. Little attention has been given to the neurobiological mechanisms underlying inter-individual differences in SA alterations related to COVID-19. This study aims to identify neurofunctional markers of COVID-specific SA development.

Methods

110 healthy participants underwent resting-state magnetic resonance imaging and behavioral tests before the pandemic (T1, October 2019 to January 2020) and completed follow-up behavioral measurements during the pandemic (T2, February to May 2020). We constructed individual functional networks and used graph theoretical analysis to estimate their global and nodal topological properties, then used Pearson correlation and partial least squares correlations examine their associations with COVID-specific SA alterations.

Results

In terms of global network parameters, SA alterations (T2-T1) were negatively related to pre-pandemic brain small-worldness and normalized clustering coefficient. In terms of nodal network parameters, SA alterations were positively linked to a pronounced degree centrality pattern, encompassing both the high-level cognitive networks (dorsal attention network, cingulo-opercular task control network, default mode network, memory retrieval network, fronto-parietal task control network, and subcortical network) and low-level perceptual networks (sensory/somatomotor network, auditory network, and visual network). These findings were robust after controlling for pre-pandemic general anxiety, other stressful life events, and family socioeconomic status, as well as by treating SA alterations as categorical variables.

Conclusions

The individual functional network associated with SA alterations showed a disrupted topological organization with a more random state, which may shed light on the neurobiological basis of COVID-related SA changes at the network level.

Cerebellum drives functional dysfunctions in restless leg syndrome

OBJECTIVE

Restless legs syndrome (RLS) has serious effects on patients' sleep quality, physical and mental health. However, the pathophysiological mechanisms of RLS remain unclear. This study utilized both static and dynamic functional activity and connectivity analyses approaches as well as effective connectivity analysis to reveal the neurophysiological basis of RLS.

METHODS

The resting-state functional MRI (rs-fMRI) data from 32 patients with RLS and 33 age-, and gender-matched healthy control (HC) were collected. Dynamic and static amplitude of low frequency fluctuation (ALFF), functional connectivity (FC), and Granger causality analysis (GCA) were employed to reveal the abnormal functional activities and couplings in patients with RLS.

RESULTS

RLS patients showed over-activities in left parahippocampus and right cerebellum, hyper-connectivities of right cerebellum with left basal ganglia, left postcentral gyrus and right precentral gyrus, and enhanced effective connectivity from right cerebellum to left postcentral gyrus compared to HC.

CONCLUSIONS

Abnormal cerebellum-basal ganglia-sensorimotor cortex circuit may be the underlying neuropathological basis of RLS. Our findings highlight the important role of right cerebellum in the onset of RLS and suggest right cerebellum may be a potential target for precision therapy.

Distinct gray matter abnormalities in children/adolescents and adults with history of childhood maltreatment

Gray matter (GM) abnormalities have been reported in both adults and children/adolescents with histories of childhood maltreatment (CM). A comparison of effects in youth and adulthood may be informative regarding life-span effects of CM. Voxel-wise meta-analyses of whole-brain voxel-based morphometry studies were conducted in all datasets and age-based subgroups respectively, followed by a quantitative comparison of the subgroups. Thirty VBM studies (31 datasets) were included. The pooled meta-analysis revealed increased GM in left supplementary motor area, and reduced GM in bilateral cingulate/paracingulate gyri, left occipital lobe, and right middle frontal gyrus in maltreated individuals compared to the controls. Maltreatment-exposed youth showed less GM in the cerebellum, and greater GM in bilateral middle cingulate/paracingulate gyri and bilateral visual cortex than maltreated adults. Opposite GM alterations in bilateral middle cingulate/paracingulate gyri were found in maltreatment-exposed adults (decreased) and children/adolescents (increased). Our findings demonstrate different patterns of GM changes in youth closer to maltreatment events than those seen later in life, suggesting detrimental effects of CM on the developmental trajectory of brain structure.

Shared and Disorder-Specific Alterations of Brain Temporal Dynamics in Obsessive-Compulsive Disorder and Schizophrenia

BACKGROUND

Obsessive-compulsive disorder (OCD) and schizophrenia have distinct but also overlapping symptoms. Few studies have examined the shared and disorder-specific disturbances in dynamic brain function in the 2 disorders.

STUDY DESIGN

Resting-state functional magnetic resonance imaging data of 31 patients with OCD and 49 patients with schizophrenia, all untreated, and 45 healthy controls (HCs) were analyzed using spatial group independent component (IC) analysis. Time-varying degree centrality patterns across the whole brain were clustered into 3 reoccurring states, and state transition metrics were obtained. We further explored regional temporal variability of degree centrality for each IC across all time windows.

STUDY RESULTS

Patients with OCD and patients with schizophrenia both showed decreased occurrence of a state having the highest centrality in the sensorimotor and auditory networks. Additionally, patients with OCD and patients with schizophrenia both exhibited reduced dynamics of degree centrality in the superior frontal gyrus than controls, while dynamic degree centrality of the cerebellum was lower in patients with schizophrenia than with OCD and HCs. Altered dynamics of degree centrality nominally correlated with symptom severity in both patient groups.

CONCLUSIONS

Our study provides evidence of transdiagnostic and clinically relevant functional brain abnormalities across OCD and schizophrenia in neocortex, as well as functional dynamic alterations in the cerebellum specific to schizophrenia. These findings add to the recognition of overlap in neocortical alterations in the 2 disorders, and indicate that cerebellar alterations in schizophrenia may be specifically important in schizophrenia pathophysiology via impact on cerebellar thalamocortical circuitry.

Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis

Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD.

Pre-COVID brain functional connectome features prospectively predict emergence of distress symptoms after onset of the COVID-19 pandemic

BACKGROUND

Persistent psychological distress associated with the coronavirus disease 2019 (COVID-19) pandemic has been well documented. This study aimed to identify pre-COVID brain functional connectome that predicts pandemic-related distress symptoms among young adults.

METHODS

Baseline neuroimaging studies and assessment of general distress using the Depression, Anxiety and Stress Scale were performed with 100 healthy individuals prior to wide recognition of the health risks associated with the emergence of COVID-19. They were recontacted for the Impact of Event Scale-Revised and the Posttraumatic Stress Disorder Checklist in the period of community-level outbreaks, and for follow-up distress evaluation again 1 year later. We employed the network-based statistic approach to identify connectome that predicted the increase of distress based on 136-region-parcellation with assigned network membership. Predictive performance of connectome features and causal relations were examined by cross-validation and mediation analyses.

RESULTS

The connectome features that predicted emergence of distress after COVID contained 70 neural connections. Most within-network connections were located in the default mode network (DMN), and affective network-DMN and dorsal attention network-DMN links largely constituted between-network pairs. The hippocampus emerged as the most critical hub region. Predictive models of the connectome remained robust in cross-validation. Mediation analyses demonstrated that COVID-related posttraumatic stress partially explained the correlation of connectome to the development of general distress.

CONCLUSIONS

Brain functional connectome may fingerprint individuals with vulnerability to psychological distress associated with the COVID pandemic. Individuals with brain neuromarkers may benefit from the corresponding interventions to reduce the risk or severity of distress related to fear of COVID-related challenges.

Psychological and neurological predictors of acupuncture effect in patients with chronic pain: a randomized controlled neuroimaging trial

ABSTRACT

Chronic pain has been one of the leading causes of disability. Acupuncture is globally used in chronic pain management. However, the efficacy of acupuncture treatment varies across patients. Identifying individual factors and developing approaches that predict medical benefits may promise important scientific and clinical applications. Here, we investigated the psychological and neurological factors collected before treatment that would determine acupuncture efficacy in knee osteoarthritis. In this neuroimaging-based randomized controlled trial, 52 patients completed a baseline assessment, 4-week acupuncture or sham-acupuncture treatment, and an assessment after treatment. The patients, magnetic resonance imaging operators, and outcome evaluators were blinded to treatment group assignment. First, we found that patients receiving acupuncture treatment showed larger pain intensity improvements compared with patients in the sham-acupuncture arm. Second, positive expectation, extraversion, and emotional attention were correlated with the magnitude of clinical improvements in the acupuncture group. Third, the identified neurological metrics encompassed striatal volumes, posterior cingulate cortex (PCC) cortical thickness, PCC/precuneus fractional amplitude of low-frequency fluctuation (fALFF), striatal fALFF, and graph-based small-worldness of the default mode network and striatum. Specifically, functional metrics predisposing patients to acupuncture improvement changed as a consequence of acupuncture treatment, whereas structural metrics remained stable. Furthermore, support vector machine models applied to the questionnaire and brain features could jointly predict acupuncture improvement with an accuracy of 81.48%. Besides, the correlations and models were not significant in the sham-acupuncture group. These results demonstrate the specific psychological, brain functional, and structural predictors of acupuncture improvement and may offer opportunities to aid clinical practices.

The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies

Background

Depression is generally accompanied by a disturbed conscious processing of emotion, which manifests as a negative bias to facial/voice emotion information and a decreased accuracy in emotion recognition tasks. Several studies have proved that abnormal brain activation was responsible for the deficit function of conscious emotion recognition in depression. However, the altered brain activation related to the conscious processing of emotion in depression was incongruent among studies. Therefore, we conducted an activation likelihood estimation (ALE) analysis to better understand the underlying neurophysiological mechanism of conscious processing of emotion in depression.

Method

Electronic databases were searched using the search terms "depression," "emotion recognition," and "neuroimaging" from inceptions to April 10th, 2023. We retrieved trials which explored the neuro-responses of depressive patients to explicit emotion recognition tasks. Two investigators independently performed literature selection, data extraction, and risk of bias assessment. The spatial consistency of brain activation in conscious facial expressions recognition was calculated using ALE. The robustness of the results was examined by Jackknife sensitivity analysis.

Results

We retrieved 11,365 articles in total, 28 of which were included. In the overall analysis, we found increased activity in the middle temporal gyrus, superior temporal gyrus, parahippocampal gyrus, and cuneus, and decreased activity in the superior temporal gyrus, inferior parietal lobule, insula, and superior frontal gyrus. In response to positive stimuli, depressive patients showed hyperactivity in the medial frontal gyrus, middle temporal gyrus, and insula (uncorrected p < 0.001). When receiving negative stimuli, a higher activation was found in the precentral gyrus, middle frontal gyrus, precuneus, and superior temporal gyrus (uncorrected p < 0.001).

Conclusion

Among depressive patients, a broad spectrum of brain areas was involved in a deficit of conscious emotion processing. The activation of brain regions was different in response to positive or negative stimuli. Due to potential clinical heterogeneity, the findings should be treated with caution.

Systematic review registration

https://inplasy.com/inplasy-2022-11-0057/, identifier: 2022110057.

Altered single-subject gray matter structural networks in social anxiety disorder

Previous fMRI studies have reported more random brain functional graph configurations in social anxiety disorder (SAD). However, it is still unclear whether the same configurations would occur in gray matter (GM) graphs. Structural MRI was performed on 49 patients with SAD and on 51 age- and gender-matched healthy controls (HC). Single-subject GM networks were obtained based on the areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared, and the structure-function coupling was examined. These network measures were further correlated with the clinical characteristics in the SAD group. Compared with controls, the SAD patients demonstrated globally decreased clustering coefficient and characteristic path length. Altered topological properties were found in the fronto-limbic and sensory processing systems. Altered metrics were associated with the illness duration of SAD. Compared with the HC group, the SAD group exhibited significantly decreased structural-functional decoupling. Furthermore, structural-functional decoupling was negatively correlated with the symptom severity in SAD. These findings highlight less-optimized topological configuration of the brain structural networks in SAD, which may provide insights into the neural mechanisms underlying the excessive fear and avoidance of social interactions in SAD.

Effects of randomness in the development of machine learning models in neuroimaging studies of schizophrenia

Numerous studies have used machine learning with neuroimaging data for identifying individuals with a schizophrenia diagnosis. However, inconsistent results have limited the ability of the psychiatric community to objectively judge and accept the value of this approach. One factor that has contributed to the inconsistency, but has long been ignored, is randomness in the practice of machine learning. This is manifest when executing the same machine learning pipeline multiple times on the same dataset but getting different results. In the current study, a dataset of anatomical MRI scans from 158 patients with first-episode medication-naïve schizophrenia and 166 matched controls was used to investigate the effect of randomness on classifier performance estimates under different algorithm complexity and data splitting ratios. The maximum discriminatory accuracy that could be reached was 62.6 % ± 4.7 % (43.5 %-79.3 %) obtained when using extra-trees classifiers without feature normalization. Regions contributing to discrimination were located at bilateral temporal lobes and right frontal lobe. The results show that randomness has a significant impact on the precision of model performance estimates, especially when the size of test set is small. Current neuroimaging feature engineering combined with machine learning still falls short of being able to make diagnoses in the clinical context, but has value in revealing patterns of regional brain alteration associated with the illness. The current results indicate that effects of randomness on model performance should be reported and considered in interpreting model utility and it is necessary to evaluate models on large test sets to obtain valid estimates of model performance.

A voxel-based meta-analysis comparing medication-naive patients of major depression with treated longer-term ill cases

Structural neuroimaging studies have identified brain areas implicated in the pathogenesis of major depressive disorder (MDD). However, findings have been inconsistent, potentially due to variable illness duration and effects of antidepressant treatment. Using a meta-analytic approach, we compared gray matter (GM) volumes in patients grouped by medication status (naïve and treated) and illness duration (early course and long-term ill) to identify potential treatment and illness duration effects on brain structure. A total of 70 studies were included, including 3682 patients and 3469 controls. The pooled analysis found frontal, temporal and limbic regions with decreased GM volume in MDD patients. Additional analyses indicated that larger GM volume in the right striatum and smaller GM volume in the right precuneus are likely to be associated with drug effects, while smaller GM volume in the right temporal gyrus may correlate with longer illness duration. Similar GM decreases in bilateral medial frontal cortex between patient subgroups suggest that this alteration may persist over the course of illness and drug treatment.

Abnormal brain functional network dynamics in sleep-related hypermotor epilepsy

AIMS

This study aimed to use resting-state functional magnetic resonance imaging (rs-fMRI) to determine the temporal features of functional connectivity states and changes in connectivity strength in sleep-related hypermotor epilepsy (SHE).

METHODS

High-resolution T1 and rs-fMRI scanning were performed on all the subjects. We used a sliding-window approach to construct a dynamic functional connectivity (dFC) network. The k-means clustering method was performed to analyze specific FC states and related temporal properties. Finally, the connectivity strength between the components was analyzed using network-based statistics (NBS) analysis. The correlations between the abovementioned measures and disease duration were analyzed.

RESULTS

After k-means clustering, the SHE patients mainly exhibited two dFC states. The frequency of state 1 was higher, which was characterized by stronger connections within the networks; state 2 occurred at a relatively low frequency, characterized by stronger connections between networks. SHE patients had greater fractional time and a mean dwell time in state 2 and had a larger number of state transitions. The NBS results showed that SHE patients had increased connectivity strength between networks. None of the properties was correlated with illness duration among patients with SHE.

CONCLUSION

The patterns of dFC patterns may represent an adaptive and protective mode of the brain to deal with epileptic seizures.

Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference

Lateralization is a fundamental characteristic of many behaviors and the organization of the brain, and atypical lateralization has been suggested to be linked to various brain-related disorders such as autism and schizophrenia. Right-handedness is one of the most prominent markers of human behavioural lateralization, yet its neurobiological basis remains to be determined. Here, we present a large-scale analysis of handedness, as measured by self-reported direction of hand preference, and its variability related to brain structural and functional organization in the UK Biobank (N = 36,024). A multivariate machine learning approach with multi-modalities of brain imaging data was adopted, to reveal how well brain imaging features could predict individual's handedness (i.e., right-handedness vs. non-right-handedness) and further identify the top brain signatures that contributed to the prediction. Overall, the results showed a good prediction performance, with an area under the receiver operating characteristic curve (AUROC) score of up to 0.72, driven largely by resting-state functional measures. Virtual lesion analysis and large-scale decoding analysis suggested that the brain networks with the highest importance in the prediction showed functional relevance to hand movement and several higher-level cognitive functions including language, arithmetic, and social interaction. Genetic analyses of contributions of common DNA polymorphisms to the imaging-derived handedness prediction score showed a significant heritability (h²=7.55%, p <0.001) that was similar to and slightly higher than that for the behavioural measure itself (h²=6.74%, p <0.001). The genetic correlation between the two was high (r_g=0.71), suggesting that the imaging-derived score could be used as a surrogate in genetic studies where the behavioural measure is not available. This large-scale study using multimodal brain imaging and multivariate machine learning has shed new light on the neural correlates of human handedness.

Large-scale dysfunctional white matter and grey matter networks in patients with social anxiety disorder

Dysfunction of large-scale brain networks has been implicated in social anxiety disorder (SAD); most work has focused on grey matter (GM) functional connectivity (FC) abnormalities, whereas white matter (WM) FC alterations remain unclear. Here, using a K-means clustering algorithm, we obtained 8 GM and 10 WM functional networks from a cohort dataset (48 SAD patients and 48 healthy controls). By calculating and comparing FC matrices between SAD group and healthy controls, we demonstrated disrupted connections between the limbic and dorsal prefrontal, lateral temporal, and sensorimotor networks, and between the visual and sensorimotor networks. Furthermore, there were negative correlations between HAMD scores and limbic-dorsal prefrontal and limbic-sensorimotor networks, and between illness duration and sensorimotor-visual networks. These findings reflect the critical role of limbic network, with its extensive connections to other networks, and the neurobiology of disordered cognition processing and emotional regulation in SAD.

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Anomalous interactions between large-scale functional networks were identified in SAD

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The limbic, prefrontal, and perceptual networks underlie the neurobiology of SAD

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The white matter functional network is physiologically important

The protective effect of grit on clinical nurses’ occupational psychological distress: Mediating and suppressing effects of Hope

As at a high-risk group of psychological distress, nurses generally experience varying degrees of stress, anxiety, and depression. This paper identifies the positive factors that may negatively regulate the psychological pain of clinical nurses and their mechanisms of action, providing reliable references for clinical nurse support management. The effects and mechanisms of hope and the two components of grit consistency of interest and perseverance of effort) on clinical nurses’ psychological distress (depression, anxiety, and stress) were observed in this study. A total of 635 Chinese clinical nurses (90.4% female) completed an anonymous questionnaire for the survey. As expected, hope, consistency of interest, and perseverance of effort were negatively correlated with the three indicators of psychological distress (r = −0.21 ~ −0.38, p < 0.01). Path analysis results showed that hope significantly mediated the negative effect of consistency of interest on psychological distress, with an effect of 12.96%. Hope also covered up the perseverance of effort on psychological distress, the effect of 110.63%. In the influence of consistency of interest and perseverance of effort on psychological distress, hope contributed a vital mediating. Based on these results, it can be concluded that grit and hope have protective effects on psychological distress in clinical nurses. Significantly increasing the level of hope or grit may effectively prevent and reduce psychological distress in clinical nurses.

Multilayer Network Analysis of Dynamic Network Reconfiguration in Adults With Posttraumatic Stress Disorder

BACKGROUND

Brain functional network abnormalities are reported in posttraumatic stress disorder (PTSD). Most resting-state functional magnetic resonance imaging studies have assumed that the functional networks remain static during the scans. How these might change dynamically in PTSD remains unclear.

METHODS

Resting-state functional magnetic resonance imaging data were collected from 71 noncomorbid, treatment-naïve patients with PTSD and 70 demographically matched, trauma-exposed non-PTSD control subjects. Network switching rate was used to characterize dynamic changes of individual resting-state functional networks. Results were analyzed by comparing switching rates between the PTSD and trauma-exposed non-PTSD groups, testing for diagnosis × sex interactions, and examining correlations with PTSD symptom severity.

RESULTS

At the global level, the PTSD group showed significantly lower network switching rates than the trauma-exposed non-PTSD group. These were observed mainly in the frontoparietal, default mode, and limbic networks at the subnetwork level and in the frontal and temporal regions at the nodal level. These network switching rate alterations were correlated with PTSD symptom severity. There were no significant effects of sex.

CONCLUSIONS

These disruptions of dynamic functional network stability, reflected by lower network switching rates in the resting state, are a feature of PTSD and suggest that the frontoparietal, default mode, and limbic networks may play a critical role in the underlying neural mechanisms.

Distinct brain structural abnormalities in attention-deficit/hyperactivity disorder and substance use disorders: A comparative meta-analysis

As two common mental disorders during the period of adolescence that extend to early adulthood, attention-deficit/hyperactivity disorder (ADHD) and substance use disorders (SUDs) have considerable diagnostic co-occurrence and shared neuropsychological impairments. Our study aimed to identify overlapping and distinct brain structural abnormalities associated with ADHD and SUDs among adolescents and young adults. A systematic literature search on voxel-based morphometry (VBM) studies of ADHD and SUDs was conducted in PubMed and Web of Science. Data were extracted and analyzed to identify brain abnormalities using Seed-based d-Mapping software. Data-driven functional decoding was conducted to identify the psychophysiological functioning associated with brain alterations. 13 and 14 VBM studies for ADHD (619 patients and 483 controls) and SUDs (516 patients and 413 controls), respectively, were included. Patterns of decreased gray matter volume (GMV) were found in the left precentral gyrus, bilateral superior frontal gyri, and left inferior frontal gyrus in the ADHD group compared to the control group. In contrast, individuals with SUDs, relative to controls, were characterized by increased GMV in the left putamen and insula. Comparative analysis indicated larger regional GMV in the right inferior parietal lobule and smaller volumes in the left putamen and left precentral gyrus in the ADHD group than in the SUDs group. Dissociable brain structural abnormalities in adolescents and young adults with ADHD and SUDs potentially implicate different pathogeneses and provide a reference for differential diagnosis and early detection for shared symptomology and comorbidity.

COVID-19 vicarious traumatization links functional connectome to general distress

As characterized by repeated exposure of others' trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals' vicarious traumatization scores (q² = -0.18, MSE = 617, r _{[predicted, actual]} = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization.

Common and Distinct Neural Patterns of Attention-Deficit/Hyperactivity Disorder and Borderline Personality Disorder: A Multimodal Functional and Structural Meta-analysis

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) and borderline personality disorder (BPD) have partially overlapping symptom profiles and are highly comorbid in adults. However, whether the behavioral similarities correspond to shared neurobiological substrates is not known.

METHODS

An overlapping meta-analysis of 58 ADHD and 66 BPD whole-brain articles incorporating observations from 3401 adult patients and 3238 healthy participants was performed by seed-based d mapping. Brain maps were subjected to meta-analytic connectivity modeling and data-driven functional decoding analyses to identify associated neural circuit alterations and relations to behavioral dimensions.

RESULTS

Both groups exhibited hypoactivated abnormalities in the left inferior parietal lobule, and altered clusters of the bilateral superior temporal gyrus were disjunctive in ADHD and BPD. No overlapping structural abnormalities were found. Multimodal alterations of ADHD were located in the right putamen and of BPD in the left orbitofrontal cortex.

CONCLUSIONS

The transdiagnostic neural bases of ADHD and BPD in temporoparietal circuitry may underlie overlapping problems of behavioral control, while disorder-specific substrates in frontostriatal circuitry may account for their distinguishing features in motor and emotion domains, respectively.

Cortical Thickness Abnormalities at Different Stages of the Illness Course in Schizophrenia: A Systematic Review and Meta-analysis

IMPORTANCE

Questions of whether and how cortical thickness (CTh) alterations differ over the course of schizophrenia (SCZ) have yet to be resolved.

OBJECTIVE

To characterize CTh alterations across illness stages in SCZ.

DATA SOURCES

PubMed, Embase, Web of Science, and Science Direct were screened for CTh studies published before June 15, 2021.

STUDY SELECTION

Original studies comparing whole-brain CTh alterations from healthy controls in individuals at clinical high-risk (CHR), first episode of psychosis (FEP), and long-term illness stages of SCZ were included.

DATA EXTRACTION AND SYNTHESIS

This preregistered systematic review and meta-analysis followed PRISMA reporting guidelines. Separate and pooled meta-analyses were performed using seed-based d mapping. Meta-regression analyses were conducted.

MAIN OUTCOMES AND MEASURES

Cortical thickness differences from healthy control individuals across illness stages.

RESULTS

Ten studies comprising 859 individuals with CHR (mean [SD] age, 21.02 [2.66] years; male, 573 [66.7%]), 12 studies including 671 individuals with FEP (mean [SD] age, 22.87 [3.99] years; male, 439 [65.4%]), and 10 studies comprising 579 individuals with long-term SCZ (mean [SD] age, 41.58 [6.95] years; male, 396 [68.4%]) were included. Compared with healthy control individuals, individuals with CHR showed cortical thinning in bilateral medial prefrontal cortex (z = -1.01; P < .001). Individuals with FEP showed cortical thinning in right lateral superior temporal cortex (z = -1.34; P < .001), right anterior cingulate cortex (z = -1.44; P < .001), and right insula (z = -1.14; P = .002). Individuals with long-term SCZ demonstrated CTh reductions in right insula (z = -3.25; P < .001), right inferior frontal cortex (z = -2.19; P < .001), and left (z = -2.37; P < .001) and right (z = -1.94; P = .002) temporal pole. There were no significant CTh differences between CHR and FEP. Individuals with long-term SCZ showed greater cortical thinning in right insula (z = -2.58; P < .001), right inferior frontal cortex (z = -2.32; P < .001), left lateral temporal cortex (z = -1.91; P = .002), and right temporal pole (z = -1.82; P = .002) than individuals with FEP. Combining all studies on SCZ, accelerated age-related CTh reductions were found in bilateral lateral middle temporal cortex and right pars orbitalis in inferior frontal cortex.

CONCLUSIONS AND RELEVANCE

The absence of significant differences between FEP and CHR noted in this systematic review and meta-analysis suggests that the onset of psychosis was not associated with robust CTh reduction. The greater cortical thinning in long-term SCZ compared with FEP with accelerated age-related reduction in CTh suggests progressive neuroanatomic alterations following illness onset. Caution in interpretation is needed because heterogeneity in samples and antipsychotic treatment may confound these results.

Applications of Artificial Intelligence to Popularize Legal Knowledge and Publicize the Impact on Adolescents' Mental Health Status

Artificial intelligence (AI) advancements have radically altered human production and daily living. When it comes to AI's quick rise, it facilitates the growth of China's citizens, and at the same moment, a lack of intelligence has led to several concerns regarding regulations and laws. Current investigations regarding AI on legal knowledge do not have consistent benefits in predicting adolescents' psychological status, performance, etc. The study's primary purpose is to examine the influence of AI on the legal profession and adolescent mental health using a novel cognitive fuzzy K-nearest neighbor (CF-KNN). Initially, the legal education datasets are gathered and are standardized in the pre-processing stage through the normalization technique to retrieve the unwanted noises or outliers. When normalized data are transformed into numerical features, they can be analyzed using a variational autoencoder (VAE) approach. Multi-gradient ant colony optimization (MG-ACO) is applied to select a proper subset of the features. Tree C4.5 (T-C4.5) and fitness-based logistic regression analysis (F-LRA) techniques assess the adolescent's mental health conditions. Finally, our proposed work's performance is examined and compared with classical techniques to gain our work with the greatest effectiveness. Findings are depicted in chart formation by employing the MATLAB tool.

Magnetization transfer imaging alterations and its diagnostic value in antipsychotic-naïve first-episode schizophrenia

Magnetization transfer imaging (MTI) may provide more sensitivity and mechanistic understanding of neuropathological changes associated with schizophrenia than volumetric MRI. This study aims to identify brain magnetization transfer ratio (MTR) changes in antipsychotic-naïve first-episode schizophrenia (FES), and to correlate MTR findings with clinical symptom severity. A total of 143 individuals with antipsychotic-naïve FES and 147 healthy controls (HCs) were included and underwent 3.0 T brain MTI between August 2005 and July 2014. Voxelwise analysis was performed to test for MTR differences with family-wise error corrections. Relationships of these differences to symptom severity were assessed using partial correlations. Exploratory analyses using a support vector machine (SVM) classifier were conducted to discriminate FES from HCs using MTR maps. Model performance was examined using a 10-fold stratified cross-validation. Compared with HCs, individuals with FES exhibited higher MTR values in left thalamus, precuneus, cuneus, and paracentral lobule, that were positively correlated with schizophrenia symptom severity [precuneus (r = 0.34, P = 0.0004), cuneus (r = 0.33, P = 0.0006) and paracentral lobule (r = 0.37, P = 0.001)]. Whole-brain MTR maps identified individuals with FES with overall accuracy 75.5% (219 of 290 individuals) based on SVM approach. In antipsychotic-naïve FES, clinically relevant biophysical abnormalities detected by MTI mainly in the left parieto-occipital regions are informative about local brain pathology, and have potential as diagnostic markers.

Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites

Background

Establishing objective and quantitative neuroimaging biomarkers at individual level can assist in early and accurate diagnosis of major depressive disorder (MDD). However, most previous studies using machine learning to identify MDD were based on small sample size and did not account for the brain connectome that is associated with the pathophysiology of MDD. Here, we addressed these limitations by applying graph convolutional network (GCN) in a large multi-site MDD dataset.

Methods

Resting-state functional MRI scans of 1586 participants (821 MDD vs. 765 controls) across 16 sites of Rest-meta-MDD consortium were collected. GCN model was trained with individual whole-brain functional network to identify MDD patients from controls, characterize the most salient regions contributing to classification, and explore the relationship between topological characteristics of salient regions and clinical measures.

Findings

GCN achieved an accuracy of 81·5% (95%CI: 80·5–82·5%, AUC: 0·865), which was higher than other common machine learning classifiers. The most salient regions contributing to classification were primarily identified within the default mode, fronto-parietal, and cingulo-opercular networks. Nodal topologies of the left inferior parietal lobule and left dorsolateral prefrontal cortex were associated with depressive severity and illness duration, respectively.

Interpretation

These findings based on a large, multi-site dataset support the feasibility and effectiveness of GCN in characterizing MDD, and also illustrate the potential utility of GCN for enhancing understanding of the neurobiology of MDD by detecting clinically-relevant disruption in functional network topology.

Funding

This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 82027808, 81820108018).

Altered single-subject gray matter structural networks in drug-naïve attention deficit hyperactivity disorder children

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

Cortical thickness abnormalities in patients with bipolar disorder: A systematic review and meta-analysis

BACKGROUND

An increasing number of neuroimaging studies report alterations of cortical thickness (CT) related to the neuropathology of bipolar disorder (BD). We provide here a whole-brain vertex-wise meta-analysis, which may help improve the spatial precision of these identifications.

METHODS

A comprehensive meta-analysis was performed to investigate the differences in CT between patients with BD and healthy controls (HCs) by using a newly developed mask for CT analysis in seed-based d mapping (SDM) meta-analytic software. We used meta-regression to explore the effects of demographics and clinical characteristics on CT. This meta-review was conducted in accordance with PRISMA guideline.

RESULTS

We identified 21 studies meeting criteria for the systematic review, of which 11 were eligible for meta-analysis. The meta-analysis comprising 649 BD patients and 818 HCs showed significant cortical thinning in the left insula extending to left Rolandic operculum and Heschl gyrus, the orbital part of left inferior frontal gyrus (IFG), the medial part of left superior frontal gyrus (SFG) as well as bilateral anterior cingulate cortex (ACC) in BD. In meta-regression analyses, mean patient age was negatively correlated with reduced CT in the left insula.

LIMITATIONS

All enrolled studies were cross-sectional; we could not explore the potential effects of medication and mood states due to the limited data.

CONCLUSIONS

Our results suggest that BD patients have significantly thinner frontoinsular cortex than HCs, and the results may be helpful in revealing specific neuroimaging biomarkers of BD patients.

Patterns of a structural covariance network associated with dispositional optimism during late adolescence

Dispositional optimism (hereinafter, optimism), as a vital character strength, reflects the tendency to hold generalized positive expectancies for future outcomes. A great number of studies have consistently shown the importance of optimism to a spectrum of physical and mental health outcomes. However, less attention has been given to the intrinsic neurodevelopmental patterns associated with interindividual differences in optimism. Here, we investigated this important question in a large sample comprising 231 healthy adolescents (16-20 years old) via structural magnetic resonance imaging and behavioral tests. We constructed individual structural covariance networks based on cortical gyrification using a recent novel approach combining probability density estimation and Kullback-Leibler divergence and estimated global (global efficiency, local efficiency and small-worldness) and regional (betweenness centrality) properties of these constructed networks using graph theoretical analysis. Partial correlations adjusted for age, sex and estimated total intracranial volume showed that optimism was positively related to global and local efficiency but not small-worldness. Partial least squares correlations indicated that optimism was positively linked to a pronounced betweenness centrality pattern, in which twelve cognition-, emotion-, and motivation-related regions made robust and reliable contributions. These findings remained basically consistent after additionally controlling for family socioeconomic status and showed significant correlations with optimism scores from 2.5 years before, which replicated the main findings. The current work, for the first time, delineated characteristics of the cortical gyrification covariance network associated with optimism, extending previous neurobiological understandings of optimism, which may navigate the development of interventions on a neural network level aimed at raising optimism.

Structural and functional deficits and couplings in the cortico-striato-thalamo-cerebellar circuitry in social anxiety disorder

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

Cortical thickness abnormalities in patients with post-traumatic stress disorder: A vertex-based meta-analysis

Neuroimaging studies report altered cortical thickness in patients with post-traumatic stress disorder (PTSD), but the results are inconsistent. Using anisotropic effect-size seed-based d mapping (AES-SDM) software with its recently-developed meta-analytic thickness mask, we conducted a meta-analysis of published studies which used whole-brain surface-based morphometry, in order to define consistent cortical thickness alterations in PTSD patients. Eleven studies with 438 patients and 396 controls were included. Compared with all controls, patients with PTSD showed increased cortical thickness in right superior temporal gyrus, and in left and right superior frontal gyrus; the former survived in subgroup analysis of adult patients, and in subgroup comparison with only non-PTSD trauma-exposed controls, the latter in subgroup comparison with only non-trauma-exposed healthy controls. Cortical thickness in right superior frontal gyrus was positively associated with percentage of female patients, and cortical thickness in left superior frontal gyrus was positively associated with symptom severity measured by the clinician-administered PTSD scale. These robust results may help to elucidate the pathophysiology of PTSD.

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.

Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disease that severely affects the activities of daily living in aged individuals, which typically needs to be diagnosed at an early stage. Generative adversarial networks (GANs) provide a new deep learning method that show good performance in image processing, while it remains to be verified whether a GAN brings benefit in AD diagnosis. The purpose of this research is to systematically review psychoradiological studies on the application of a GAN in the diagnosis of AD from the aspects of classification of AD state and AD-related image processing compared with other methods. In addition, we evaluated the research methodology and provided suggestions from the perspective of clinical application. Compared with other methods, a GAN has higher accuracy in the classification of AD state and better performance in AD-related image processing (e.g. image denoising and segmentation). Most studies used data from public databases but lacked clinical validation, and the process of quantitative assessment and comparison in these studies lacked clinicians' participation, which may have an impact on the improvement of generation effect and generalization ability of the GAN model. The application value of GANs in the classification of AD state and AD-related image processing has been confirmed in reviewed studies. Improvement methods toward better GAN architecture were also discussed in this paper. In sum, the present study demonstrated advancing diagnostic performance and clinical applicability of GAN for AD, and suggested that the future researchers should consider recruiting clinicians to compare the algorithm with clinician manual methods and evaluate the clinical effect of the algorithm.

Cortical thickness abnormalities in patients with first episode psychosis: a meta-analysis of psychoradiologic studies and replication in an independent sample

BACKGROUND

Abnormalities of cortical thickness (CTh) in patients with their first episode psychosis (FEP) have been frequently reported, but findings are inconsistent.

OBJECTIVE

To define the most consistent CTh changes in patients with FEP by meta-analysis of published whole-brain studies.

METHODS

The meta-analysis used seed-based d mapping (SDM) software to obtain the most prominent regional CTh changes in FEP, and meta-regression analyses to explore the effects of demographics and clinical characteristics. The meta-analysis results were verified in an independent sample of 142 FEP patients and 142 age- and sex-matched healthy controls (HCs), using both a vertex-wise and a region of interest analysis, with multiple comparisons correction.

RESULTS

The meta-analysis identified lower CTh in the right middle temporal cortex (MTC) extending to superior temporal cortex (STC), insula, and anterior cingulate cortex (ACC) in FEP compared with HCs. No significant correlations were identified between CTh alterations and demographic or clinical variables. These results were replicated in the independent dataset analysis.

CONCLUSION

This study identifies a robust pattern of cortical abnormalities in FEP and extends understanding of gray matter abnormalities and pathological mechanisms in FEP.

Dysconnectivity of the amygdala and dorsal anterior cingulate cortex in drug-naive post-traumatic stress disorder

Convergent studies have highlighted the amygdala-based and dorsal anterior cingulate cortex (dACC)-based circuit or network dysfunction in post-traumatic stress disorder (PTSD). However, previous studies are often complicated by various traumatic types, psychiatric comorbidities, chronic illness duration, and medication effect on brain function. Besides, little is known whether the functional integration with amygdala-dACC interaction disrupted or not in PTSD. Here, we investigated effective connectivity (EC) between the amygdala-dACC and rest of the cortex by applying psycho-physiological interaction (PPI) approach to resting-state functional magnetic resonance imaging data of 63 drug-naive PTSD patients and 74 matched trauma-exposed non-PTSD controls. Pearson correlation analysis was performed between EC values extracted from regions with between-group difference and clinical profiles in PTSD patients. We observed distinct amygdala-dACC interaction pattern between PTSD group and the control group, which is composed primarily by positive EC in the former and negative in the latter. In addition, compared with non-PTSD controls, PTSD patients showed increased EC between amygdala-dACC and the prefrontal cortex, left inferior parietal lobule, and bilateral ventral occipital cortex, and decreased EC between amygdala-dACC and the left fusiform gyrus. The EC increase between amygdala-dACC and the right middle frontal gyrus was negatively correlated with the clinician-administered PTSD scale scores in PTSD patients. Aberrent communication between amgydala-dACC and brain regions involved in central executive network and visual systems might be associated with the pathophysiology of PTSD. Further, these findings suggested that dysconnectivity of the amygdala and dACC could be adapted as a relatively early course diagnostic biomarker of PTSD.

Using deep learning to classify pediatric posttraumatic stress disorder at the individual level

BACKGROUND

Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metrics in pediatric PTSD patients relative to healthy controls (HC). Here we aimed to apply deep learning (DL) models to neuroimaging markers of classification which may be of assistance in diagnosis of pediatric PTSD.

METHODS

We studied 33 pediatric PTSD and 53 matched HC. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was used to examine the topological properties of the functional connectome. A DL algorithm then used this measure to classify pediatric PTSD vs HC.

RESULTS

Graphic topological measures using DL provide a potentially clinically useful classifier for differentiating pediatric PTSD and HC (overall accuracy 71.2%). Frontoparietal areas (central executive network), cingulate cortex, and amygdala contributed the most to the DL model's performance.

CONCLUSIONS

Graphic topological measures based on fMRI data could contribute to imaging models of clinical utility in distinguishing pediatric PTSD from HC. DL model may be a useful tool in the identification of brain mechanisms PTSD participants.

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.

Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level

Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD.