Aberrant Functional Network Connectivity as a Biomarker of Generalized Anxiety Disorder

Advances in neuroscience research and big data's analysis on anxiety disorder

Anxiety disorder is a complex disease with the influence of environmental and genetic factors and multimolecular participation, and it is also one of the most common mental disorders. The causes of disorders are not clear but may include a variety of social, psychological, and biological factors. Therefore, neither genetics, neurobiology, nor neuroimaging can independently explain the pathological mechanism. By searching the Web of Science databases, Derwent Innovation Patent database, ClinicalTrials.gov database, and Cortellis database, we analyze the current situation of papers, patents, clinical trials, and drugs of anxiety disorder. Second, the existing literature was reviewed to summarize the neurophysiological mechanism, brain imaging, gene, anti-anxiety drugs, and other aspects of anxiety disorders. This article reviews the research status of anxiety disorders. The heterogeneity of the disease, lack of treatment effectiveness, and gaps in translational medicine still present barriers to further advancement. Thus, in-depth explorations of the underlying biological mechanisms of anxiety disorders, the detection and intervention of biological targets, and further developments based on existing intervention strategies will drive future research on anxiety disorders. This article is categorized under: Neuroscience > Clinical.

A review of studies on constructing classification models to identify mental illness using brain effective connectivity

Brain effective connectivity (EC) is a functional measurement that reflects the causal effects and topological relationships of neural activities. Recent research has increasingly focused on the classification for mental illnesses and healthy controls using brain EC; however, no comprehensive reviews have synthesized these studies. Therefore, the aim of this review is to thoroughly examine the existing literature on constructing diagnosis model for mental illnesses using brain EC. We first conducted a systematical literature search and thirty-five papers met the inclusion criteria. Subsequently, we summarized the approaches for estimating EC, the classification and validation methods used, the accuracies of models, and the main findings. Finally, we discussed the limitations of current research and the challenges in future research. These summaries and discussion provide references for future research on mental illnesses identification based on brain EC.

Deciphering the spatiotemporal transcriptional landscape of intestinal diseases (Review)

The intestines are the largest barrier organ in the human body. The intestinal barrier plays a crucial role in maintaining the balance of the intestinal environment and protecting the intestines from harmful bacterial invasion. Single‑cell RNA sequencing technology allows the detection of the different cell types in the intestine in two dimensions and the exploration of cell types that have not been fully characterized. The intestinal mucosa is highly complex in structure, and its proper functioning is linked to multiple structures in the proximal‑distal intestinal and luminal‑mucosal axes. Spatial localization is at the core of the efforts to explore the interactions between the complex structures. Spatial transcriptomics (ST) is a method that allows for comprehensive tissue analysis and the acquisition of spatially separated genetic information from individual cells, while preserving their spatial location and interactions. This approach also prevents the loss of fragile cells during tissue disaggregation. The emergence of ST technology allows us to spatially dissect enzymatic processes and interactions between multiple cells, genes, proteins and signals in the intestine. This includes the exchange of oxygen and nutrients in the intestine, different gradients of microbial populations and the role of extracellular matrix proteins. This regionally precise approach to tissue studies is gaining more acceptance and is increasingly applied in the investigation of disease mechanisms related to the gastrointestinal tract. Therefore, this review summarized the application of ST in gastrointestinal diseases.

Abnormal regional activity in the prefrontal-limbic circuit at rest: Potential imaging markers and treatment predictors in drug-naive anxiety disorders

BACKGROUND

Previous research has identified functional impairments within the prefrontal-limbic circuit in individuals with anxiety disorders. However, the link between these deficiencies, clinical symptoms, and responses to antipsychotic treatment is still not fully understood. This study aimed to investigate abnormal regional activity within the prefrontal-limbic circuit among drug-naive individuals diagnosed with generalized anxiety disorder (GAD) and panic disorder (PD) and to analyze changes following treatment.

METHODS

Resting-state magnetic resonance imaging was performed on a cohort of 118 anxiety disorder patients (64 GAD, 54 PD) and 61 healthy controls (HCs) at baseline. Among them, 52 patients with GAD and 44 patients with PD underwent a 4-week treatment regimen of paroxetine. Fractional amplitude of low-frequency fluctuation (fALFF) measurements and pattern classification techniques were employed to analyze the data in accordance with the human Brainnetome atlas.

RESULTS

Both patients with GAD and PD demonstrated decreased fALFF in the right cHipp subregion of the hippocampus and increased fALFF in specified subregions of the cingulate and orbitofrontal lobe. Notably, patients with PD exhibited significantly higher fALFF in the left A24cd subregion compared to patients with GAD, while other ROI subregions showed no significant variations between the two patient groups. Whole-brain analysis revealed abnormal fALFF in both patient groups, primarily in specific areas of the cingulate and parasingulate gyrus, as well as the inferior and medial orbitofrontal gyrus (OFG). Following a 4-week treatment period, specific subregions in the GAD and PD groups showed a significant decrease in fALFF. Further analysis using support vector regression indicated that fALFF measurements in the right A13 and right A24cd subregions may be predictive of treatment response among anxiety disorder patients.

CONCLUSIONS

Aberrant functional activity in certain subregions of the prefrontal-limbic circuit appears to be linked to the manifestation of anxiety disorders. These findings suggest potential imaging indicators for individual responses to antipsychotic treatment.

An amygdala-centered effective connectivity network in trait anxiety

Previous studies have established that the amygdala plays an important role in trait anxiety. However, there remains limited knowledge regarding the changes in amygdala-centered effective connectivity network associated with this trait. The current study employed the Granger Causal analysis to investigate the directional connectivity patterns involving the amygdala in relation to trait anxiety in a large cohort of young adults (N = 424). The results revealed a negative association between trait anxiety scores and the Granger causality from the left middle frontal gyrus and right superior frontal gyrus to the right amygdala. Conversely, higher trait anxiety levels were found to be associated with increased effective connectivity from the left amygdala to the left hippocampus. These results demonstrated the significance of the prefrontal cortex-amygdala-hippocampus neural circuitry in the neurobiological mechanisms underlying trait anxiety. Our findings advance the comprehension of this characteristic, holding promise for informing strategies in the prevention and treatment of related mental disorders.

Machine learning applied to functional magnetic resonance imaging in anxiety disorders

BACKGROUND

Brain functional abnormalities have been commonly reported in anxiety disorders, including generalized anxiety disorder, social anxiety disorder, panic disorder, agoraphobia, and specific phobias. The role of functional abnormalities in the discrimination of these disorders can be tested with machine learning (ML) techniques. Here, we aim to provide a comprehensive overview of ML studies exploring the potential discriminating role of functional brain alterations identified by functional magnetic resonance imaging (fMRI) in anxiety disorders.

METHODS

We conducted a search on PubMed, Web of Science, and Scopus of ML investigations using fMRI as features in patients with anxiety disorders. A total of 12 studies (resting-state fMRI n = 5, task-based fMRI n = 6, resting-state and task-based fMRI n=1) met our inclusion criteria.

RESULTS

Overall, the studies showed that, regardless of the classifiers, alterations in functional connectivity and aberrant neural activation involving the amygdala, anterior cingulate cortex, hippocampus, insula, orbitofrontal cortex, temporal pole, cerebellum, default mode network, dorsal attention network, sensory network, and affective network were able to discriminate patients with anxiety from controls, with accuracies spanning from 36 % to 94 %.

LIMITATIONS

The small sample size, different ML approaches and heterogeneity in the selection of regions included in the multivariate pattern analyses limit the conclusions of the present review.

CONCLUSIONS

ML methods using fMRI as features can distinguish patients with anxiety disorders from healthy controls, indicating that these techniques could be used as a helpful tool for the diagnosis and the development of more targeted treatments for these disorders.

A systematic review and meta-analysis of resting-state fMRI in anxiety disorders: Need for data sharing to move the field forward

Anxiety disorders are among the most prevalent psychiatric disorders. Neuroimaging findings remain uncertain, and resting state functional magnetic resonance (rs-fMRI) connectivity is of particular interest since it is a scalable functional imaging modality. Given heterogeneous past findings for rs-fMRI in anxious individuals, we characterize patterns across anxiety disorders by conducting a systematic review and meta-analysis. Studies were included if they contained at the time of scanning both a healthy group and a patient group. Due to insufficient study numbers, the quantitative meta-analysis only included seed-based studies. We performed an activation likelihood estimation (ALE) analysis that compared patients and healthy volunteers. All analyses were corrected for family-wise error with a cluster-level threshold of p < .05. Patients exhibited hypo-connectivity between the amygdala and the medial frontal gyrus, anterior cingulate cortex, and cingulate gyrus. This finding, however, was not robust to potential file-drawer effects. Though limited by strict inclusion criteria, our results highlight the heterogeneous nature of reported findings. This underscores the need for data sharing when attempting to detect reliable patterns of disruption in brain activity across anxiety disorders.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

BACKGROUND

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

METHODS

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.

RESULTS

A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  - 2.75, p < .001, R²_adj = 0.40; r =  - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF₁₀ > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance.

CONCLUSIONS

Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Abnormal causal connectivity of prefrontal-limbic circuit by structural deficits in drug-naive anxiety disorders

BACKGROUND

Structural and functional deficits in the prefrontal-limbic circuit have been revealed in patients with anxiety disorders. However, the effect of structural abnormalities on causal connectivity within this circuit remains unclear. This study aimed to investigate causal connectivity in the prefrontal-limbic circuit associated with structural deficits in drug-naive patients with generalized anxiety disorder (GAD) and panic disorder (PD) and the changes after treatment.

METHODS

A total of 64 GAD patients, 54 PD patients and 61 healthy controls (HCs) completed the resting-state magnetic resonance imaging scans at baseline. Among them, 96 patients with anxiety disorders (52 in GAD group and 44 in PD group) completed a 4-week paroxetine treatment. Voxel-based morphometry and Granger causality analysis (GCA) were applied to analyze the data based on the human brainnetome atlas.

RESULTS

Patients with GAD and PD showed decreased gray matter volume (GMV) in the bilateral A24cd subregions of cingulate gyrus. Whole-brain analysis revealed decreased GMV in the left cingulate gyrus in patients with PD. Hence, the left A24cd subregion was selected as a seed. Compared with HCs, unidirectional causal connectivity between the limbic-superior temporal gyrus (STG) temporal pole and the limbic-precentral/middle frontal gyrus was enhanced in patients with GAD and PD (from the left A24cd subregion of cingulate gyrus to the right STG temporal pole, and from the left A24cd subregion of cingulate gyrus to the right precentral/middle frontal gyrus). Compared with patients with PD, the limbic-precuneus unidirectional causal connectivity was enhanced in patients with GAD, and the cerebellum crus1 - limbic connectivity has a positive feedback effect.

CONCLUSIONS

The anatomical defects in the left A24cd subregion of cingulate gyrus may partially affect the prefrontal-limbic circuit, and the unidirectional causal effect from the left A24cd subregion to the right STG temporal pole may be an imaging change shared by anxiety disorders. The causal effect of the left A24cd subregion of cingulate gyrus to precuneus might be related to the neurobiology of GAD.

Reduced vmPFC-insula functional connectivity in generalized anxiety disorder: a Bayesian confirmation study

Differences in the correlated activity of networked brain regions have been reported in individuals with generalized anxiety disorder (GAD) but an overreliance on null-hypothesis significance testing (NHST) limits the identification of disorder-relevant relationships. In this preregistered study, we applied both a Bayesian statistical framework and NHST to the analysis of resting-state fMRI scans from females with GAD and matched healthy comparison females. Eleven a-priori hypotheses about functional connectivity (FC) were evaluated using Bayesian (multilevel model) and frequentist (t-test) inference. Reduced FC between the ventromedial prefrontal cortex (vmPFC) and the posterior-mid insula (PMI) was confirmed by both statistical approaches and was associated with anxiety sensitivity. FC between the vmPFC-anterior insula, the amygdala-PMI, and the amygdala-dorsolateral prefrontal cortex (dlPFC) region pairs did not survive multiple comparison correction using the frequentist approach. However, the Bayesian model provided evidence for these region pairs having decreased FC in the GAD group. Leveraging Bayesian modeling, we demonstrate decreased FC of the vmPFC, insula, amygdala, and dlPFC in females with GAD. Exploiting the Bayesian framework revealed FC abnormalities between region pairs excluded by the frequentist analysis and other previously undescribed regions in GAD, demonstrating the value of applying this approach to resting-state FC data in clinical investigations.

820-nm Transcranial Near-infrared Stimulation on the Left DLPFC Relieved Anxiety: A Randomized, Double-blind, Sham-controlled Study

OBJECTIVE

Generalized anxiety disorder (GAD) is a chronic mood disease associated with abnormal brain network connections, including decreased activity in the left dorsolateral prefrontal cortex (DLPFC). Cortical excitability can be increased with 820-nm transcranial near-infrared stimulation (tNIRS), while transcranial magnetic stimulation with electroencephalography (TMS-EEG) can help evaluate time-varying brain network connectivity. A randomized, double-blind, sham-controlled trial was conducted to assess the efficacy of tNIRS on the left DLPFC and the impact on time-varying brain network connections in GAD patients.

METHODS

A total of 36 GAD patients were randomized to receive active or sham tNIRS for 2 weeks. Clinical psychological scales were assessed before, after, and at the 2-, 4-, and 8-week follow-ups. TMS-EEG was performed for 20minutes before and immediately after tNIRS treatment. The healthy controls did not receive tNIRS and only had TMS-EEG data collected once in the resting state.

RESULTS

The Hamilton Anxiety Scale (HAMA) scores of the active stimulation group decreased post-treatment compared with the sham group (P=0.021). The HAMA scores of the active stimulation group at the 2-, 4-, and 8-week follow-up assessments were lower than those before treatment (P<0.05). The time-varying EEG network pattern showed an information outflow from the left DLPFC and the left posterior temporal region after active treatment.

CONCLUSION

Herein, 820-nm tNIRS targeting the left DLPFC had significant positive effects on therapy for GAD that lasted at least 2 months. tNIRS may reverse the abnormality of time-varying brain network connections in GAD.

Multimodal Brain MRI of Deep Gray Matter Changes Associated With Inflammatory Bowel Disease

BACKGROUND

Behavioral symptoms, including mood disorders, substantially impact the quality of life of patients with inflammatory bowel disease (IBD), even when clinical remission is achieved. Here, we used multimodal magnetic resonance imaging (MRI) to determine if IBD is associated with changes in the structure and function of deep gray matter brain regions that regulate and integrate emotional, cognitive, and stress responses.

METHODS

Thirty-five patients with ulcerative colitis (UC) or Crohn's disease (CD) and 32 healthy controls underwent 3 Tesla MRIs to assess volume, neural activity, functional connection strength (connectivity), inflammation, and neurodegeneration of key deep gray matter brain regions (thalamus, caudate, pallidum, putamen, amygdala, hippocampus, and hypothalamus) involved in emotional, cognitive and stress processing. Associations with sex, presence of pain, disease activity, and C-reactive protein (CRP) concentration were examined.

RESULTS

Significantly increased activity and functional connectivity were observed in cognitive and emotional processing brain regions, including parts of the limbic system, basal ganglia, and hypothalamus of IBD patients compared with healthy controls. Inflammatory bowel disease patients exhibited significantly increased volumes of the amygdala and hypothalamus, as well as evidence of neurodegeneration in the putamen and pallidum. Hippocampal neural activity was increased in IBD patients with active disease. The volume of the thalamus was positively correlated with CRP concentration and was increased in females experiencing pain.

CONCLUSIONS

Patients with IBD exhibit functional and structural changes in the limbic and striatal systems. These changes may be targets for assessing or predicting the response to therapeutic interventions aimed at improving comorbid emotional and cognitive symptoms.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Dysfunctional mitochondrial processes contribute to energy perturbations in the brain and neuropsychiatric symptoms

Mitochondria are complex endosymbionts that evolved from primordial purple nonsulfur bacteria. The incorporation of bacteria-derived mitochondria facilitates a more efficient and effective production of energy than what could be achieved based on previous processes alone. In this case, endosymbiosis has resulted in the seamless coupling of cytochrome c oxidase and F-ATPase to maximize energy production. However, this mechanism also results in the generation of reactive oxygen species (ROS), a phenomenon that can have both positive and negative ramifications on the host. Recent studies have revealed that neuropsychiatric disorders have a pro-inflammatory component in which ROS is capable of initiating damage and cognitive malfunction. Our current understanding of cognition suggests that it is the product of a neuronal network that consumes a substantial amount of energy. Thus, alterations or perturbations of mitochondrial function may alter not only brain energy supply and metabolite generation, but also thought processes and behavior. Mitochondrial abnormalities and oxidative stress have been implicated in several well-known psychiatric disorders, including schizophrenia (SCZ) and bipolar disorder (BPD). As cognition is highly energy-dependent, we propose that the neuronal pathways underlying maladaptive cognitive processing and psychiatric symptoms are most likely dependent on mitochondrial function, and thus involve brain energy translocation and the accumulation of the byproducts of oxidative stress. We also hypothesize that neuropsychiatric symptoms (e.g., disrupted emotional processing) may represent the vestiges of an ancient masked evolutionary response that can be used by both hosts and pathogens to promote self-repair and proliferation via parasitic and/or symbiotic pathways.

Socioeconomic deprivation in early life and symptoms of depression and anxiety in young adulthood: mediating role of hippocampal connectivity

BACKGROUND

Experience of early-life socioeconomic deprivation (ELSD) may increase the risk of mental disorders in young adulthood. This association may be mediated by structural and functional alterations of the hippocampus.

METHODS

We conducted a prospective cohort study on 122 participants of the European Longitudinal Study of Pregnancy and Childhood. Information about ELSD was collected via questionnaire from mothers during the first 18 months of participants' lives. At age 23-24, participants underwent examination by structural magnetic resonance imaging, resting-state functional connectivity and assessment of depressive symptoms (Mood and Feelings Questionnaire) and anxiety (Spielberger State-Trait Anxiety Inventory). The association of ELSD with brain outcomes in young adulthood was assessed with correlations, linear regression (adjusting for sex, socioeconomic position and mother's mental health) and moderated mediation analysis.

RESULTS

Higher ELSD was associated with greater depressive symptoms (B = 0.22; p = 0.001), trait anxiety (B = 0.07; p = 0.02) and lower global connectivity of the right hippocampus (B = -0.01; p = 0.02). These associations persisted when adjusted for covariates. In women, lower global connectivity of the right hippocampus was associated with stronger trait anxiety (B = -4.14; p = 0.01). Global connectivity of the right hippocampus as well as connectivity between the right hippocampus and the left middle temporal gyrus mediated the association between ELSD and trait anxiety in women. Higher ELSD correlated with a lower volume of the right hippocampus in men, but the volume of the right hippocampus was not related to mental health.

CONCLUSIONS

Early preventive strategies targeted at children from socioeconomically deprived families may yield long-lasting benefits for the mental health of the population.

Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity

Medication and other therapies for psychiatric disorders show unsatisfying efficacy, in part due to the significant clinical/ biological heterogeneity within each disorder and our over-reliance on categorical clinical diagnoses. Alternatively, dimensional transdiagnostic studies have provided a promising pathway toward realizing personalized medicine and improved treatment outcomes. One factor that may influence response to psychiatric treatments is cognitive function, which is reflected in one's intellectual capacity. Intellectual capacity is also reflected in the organization and structure of intrinsic brain networks. Using a large transdiagnostic cohort (n = 1721), we sought to discover neuroimaging biomarkers by developing a resting-state functional connectome-based prediction model for a key intellectual capacity measure, Full-Scale Intelligence Quotient (FSIQ), across the diagnostic spectrum. Our cross-validated model yielded an excellent prediction accuracy (r = 0.5573, p < 0.001). The robustness and generalizability of our model was further validated on three independent cohorts (n = 2641). We identified key transdiagnostic connectome signatures underlying FSIQ capacity involving the dorsal-attention, frontoparietal and default-mode networks. Meanwhile, diagnosis groups showed disorder-specific biomarker patterns. Our findings advance the neurobiological understanding of cognitive functioning across traditional diagnostic categories and provide a new avenue for neuropathological classification of psychiatric disorders.

Classification of Alzheimer’s Disease Based on Abnormal Hippocampal Functional Connectivity and Machine Learning

Objective

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive deterioration of memory and cognition. Mild cognitive impairment (MCI) has been implicated as a prodromal phase of AD. Although abnormal functional connectivity (FC) has been demonstrated in AD and MCI, the clinical differentiation of AD, MCI, and normal aging remains difficult, and the distinction between MCI and normal aging is especially problematic. We hypothesized that FC between the hippocampus and other brain structures is altered in AD and MCI, and that measurement of abnormal FC could have diagnostic utility for the classification of different AD stages.

Methods

Elderly adults aged 60–85 years were assigned to AD, MCI, or normal control (NC) groups based on clinical criteria. Functional magnetic resonance scanning was completed by 119 subjects. Five dimension reduction/classification methods were applied, using hippocampus-derived FC strengths as input features. Classification performance of the five dimensionality reduction methods was compared between AD, MCI, and NC groups.

Results

FCs between the hippocampus and left insula, left thalamus, cerebellum, right lingual gyrus, posterior cingulate cortex, and precuneus were significantly reduced in AD and MCI. Support vector machine learning coupled with sparse principal component analysis demonstrated the best discriminative performance, yielding classification accuracies of 82.02% (AD vs. NC), 81.33% (MCI vs. NC), and 81.08% (AD vs. MCI).

Conclusion

Hippocampus-seed-based FCs were significantly different between AD, MCI, and NC groups. FC assessment combined with widely used machine learning methods can improve AD differential diagnosis, and may be especially useful to distinguish MCI from normal aging.

Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis

Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.

Neuropeptide Y Variation Is Associated With Altered Static and Dynamic Functional Connectivity of the Salience Network

Neuropeptide Y (NPY) is a neurotransmitter that has been implicated in the development of anxiety and mood disorders. Low levels of NPY have been associated with risk for these disorders, and high levels with resilience. Anxiety and depression are associated with altered intrinsic functional connectivity of brain networks, but the effect of NPY on functional connectivity is not known. Here, we test the hypothesis that individual differences in NPY expression affect resting functional connectivity of the default mode and salience networks. We evaluated static connectivity using graph theoretical techniques and dynamic connectivity with Leading Eigenvector Dynamics Analysis (LEiDA). To increase our power of detecting NPY effects, we genotyped 221 individuals and identified 29 healthy subjects at the extremes of genetically predicted NPY expression (12 high, 17 low). Static connectivity analysis revealed that lower levels of NPY were associated with shorter path lengths, higher global efficiency, higher clustering, higher small-worldness, and average higher node strength within the salience network, whereas subjects with high NPY expression displayed higher modularity and node eccentricity within the salience network. Dynamic connectivity analysis showed that the salience network of low-NPY subjects spent more time in a highly coordinated state relative to high-NPY subjects, and the salience network of high-NPY subjects switched between states more frequently. No group differences were found for static or dynamic connectivity of the default mode network. These findings suggest that genetically driven individual differences in NPY expression influence risk of mood and anxiety disorders by altering the intrinsic functional connectivity of the salience network.

Aberrant Topological Patterns of Structural Covariance Networks in Cognitively Normal Elderly Adults With Mild Behavioral Impairment

Mild behavioral impairment (MBI), characterized by the late-life onset of sustained and meaningful neuropsychiatric symptoms, is increasingly recognized as a prodromal stage of dementia. However, the underlying neural mechanisms of MBI remain unclear. Here, we examined alterations in the topological organization of the structural covariance networks of patients with MBI (N = 32) compared with normal controls (N = 38). We found that the gray matter structural covariance networks of both the patients with MBI and controls exhibited a small-world topology evidenced by sigma value larger than one. The patients with MBI had significantly decreased clustering coefficients at several network densities and local efficiency at densities ranging from 0.05 to 0.26, indicating decreased local segregation. No significant differences in the characteristic path length, gamma value, sigma value, or global efficiency were detected. Locally, the patients with MBI showed significantly decreased nodal betweenness centrality in the left middle frontal gyrus, right inferior frontal gyrus (opercular part), and left Heschl gyrus and increased betweenness centrality in the left gyrus rectus, right insula, bilateral precuneus, and left thalamus. Moreover, the difference in the bilateral precuneus survived after correcting for multiple comparisons. In addition, a different number and distribution of hubs was identified in patients with MBI, showing more paralimbic hubs than observed in the normal controls. In conclusion, we revealed abnormal topological patterns of the structural covariance networks in patients with MBI and offer new insights into the network dysfunctional mechanisms of MBI.

EEG correlates of attentional control in anxiety disorders: A systematic review of error-related negativity and correct-response negativity findings

BACKGROUND

Anxiety disorders are highly prevalent and cause substantial personal, social and economic burden. Altered attentional control has been shown to be present across anxiety disorders and is associated with specific changes in brain activity which can be recorded by electroencephalogram (EEG). These include changes in the EEG markers of error-related negativity (ERN) and correct-response negativity (CRN), both believed to reflect response monitoring and attentional control pathophysiology in anxiety. The aim of this review was to systematically assess the research on ERN and CRN in attentional control in individuals with clinical anxiety and healthy controls, across emotional and non-emotional attentional control.

METHODS

A comprehensive literature search was conducted for studies published prior to October 22^nd, 2020. Details of the protocol for this systematic review were registered on PROSPERO (CRD42019144885).

RESULTS

66 studies had their data extracted. All 66 studies measured ERN, with 85% finding significantly increased ERN amplitudes associated with clinical anxiety. Only 44 of the extracted studies analysed CRN and only ~20% of these found significant changes in CRN amplitude associated with individuals with clinical anxiety.

LIMITATIONS

There were several anxiety disorders that had either limited literature (i.e. specific phobia, separation anxiety disorder or agoraphobia) or nil literature (i.e. selective mutism) available. No extracted studies included samples of older adults (i.e. aged 60+ years), and only six extracted studies included measures of emotional attentional control.

CONCLUSIONS

Findings indicate the promising utility of ERN of attentional control as a robust, transdiagnostic trait marker of clinical anxiety.

Incerto-thalamic modulation of fear via GABA and dopamine

Fear generalization and deficits in extinction learning are debilitating dimensions of Post-Traumatic Stress Disorder (PTSD). Most understanding of the neurobiology underlying these dimensions comes from studies of cortical and limbic brain regions. While thalamic and subthalamic regions have been implicated in modulating fear, the potential for incerto-thalamic pathways to suppress fear generalization and rescue deficits in extinction recall remains unexplored. We first used patch-clamp electrophysiology to examine functional connections between the subthalamic zona incerta and thalamic reuniens (RE). Optogenetic stimulation of GABAergic ZI → RE cell terminals in vitro induced inhibitory post-synaptic currents (IPSCs) in the RE. We then combined high-intensity discriminative auditory fear conditioning with cell-type-specific and projection-specific optogenetics in mice to assess functional roles of GABAergic ZI → RE cell projections in modulating fear generalization and extinction recall. In addition, we used a similar approach to test the possibility of fear generalization and extinction recall being modulated by a smaller subset of GABAergic ZI → RE cells, the A13 dopaminergic cell population. Optogenetic stimulation of GABAergic ZI → RE cell terminals attenuated fear generalization and enhanced extinction recall. In contrast, optogenetic stimulation of dopaminergic ZI → RE cell terminals had no effect on fear generalization but enhanced extinction recall in a dopamine receptor D1-dependent manner. Our findings shed new light on the neuroanatomy and neurochemistry of ZI-located cells that contribute to adaptive fear by increasing the precision and extinction of learned associations. In so doing, these data reveal novel neuroanatomical substrates that could be therapeutically targeted for treatment of PTSD.

Early life adversity predicts brain-gut alterations associated with increased stress and mood

Alterations in the brain-gut system have been implicated in various disease states, but little is known about how early-life adversity (ELA) impacts development and adult health as mediated by brain-gut interactions. We hypothesize that ELA disrupts components of the brain-gut system, thereby increasing susceptibility to disordered mood. In a sample of 128 healthy adult participants, a history of ELA and current stress, depression, and anxiety were assessed using validated questionnaires. Fecal metabolites were measured using liquid chromatography tandem mass spectrometry-based untargeted metabolomic profiling. Functional brain connectivity was evaluated by magnetic resonance imaging. Sparse partial least squares-discriminant analysis, controlling for sex, body mass index, age, and diet was used to predict brain-gut alterations as a function of ELA. ELA was correlated with four gut-regulated metabolites within the glutamate pathway (5-oxoproline, malate, urate, and glutamate gamma methyl ester) and alterations in functional brain connectivity within primarily sensorimotor, salience, and central executive networks. Integrated analyses revealed significant associations between these metabolites, functional brain connectivity, and scores for perceived stress, anxiety, and depression. This study reveals a novel association between a history of ELA, alterations in the brain-gut axis, and increased vulnerability to negative mood and stress. Results from the study raise the hypothesis that select gut-regulated metabolites may contribute to the adverse effects of critical period stress on neural development via pathways related to glutamatergic excitotoxicity and oxidative stress.

The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches

A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.

Relation of Decreased Functional Connectivity Between Left Thalamus and Left Inferior Frontal Gyrus to Emotion Changes Following Acute Sleep Deprivation

Objective: The thalamus is a key node for sleep-wake pathway gate switching during acute sleep deprivation (ASD), and studies have shown that it plays a certain role in emotion changes. However, there are no studies on the association between the thalamus and emotion changes in ASD. In this study, we used resting-state functional magnetic resonance imaging (R-fMRI) to explore whether changes in the functional connections between the thalamus and other brain regions are related to emotion changes and further explored the function of the thalamus under total ASD conditions.

Method: Thirty healthy, right-handed adult men underwent emotional assessment according to the Profile of Mood States Scale and R-fMRI scans before and after ASD. The correlations between changes in functional connectivity between the thalamus and other brain regions and emotion changes were then studied.

Results: Positive emotions and psychomotor performance were reduced, and negative emotions were increased following ASD. The functional connections between the left thalamus and left middle temporal gyrus, left inferior frontal gyrus, right thalamus, right inferior temporal gyrus, left middle temporal pole gyrus, right calcarine, left cuneus, left rectus and left medial superior frontal gyrus were significantly altered. Decreased functional connectivity between left thalamus and left inferior frontal gyrus related to emotion changes following ASD.

Conclusion: This study finds that functional changes in the thalamus are associated with emotion changes during ASD, suggesting that the left thalamus probably plays an essential role in emotion changes under ASD conditions.

Fear-induced brain activations distinguish anxious and trauma-exposed brains

Translational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

Anxiety in Parkinson's disease: Abnormal resting activity and connectivity

Anxiety is a very common yet poorly understood symptom of Parkinson's disease. We investigated whether Parkinson's disease patients experiencing anxiety share neural mechanisms described in the general population with involvement of critical regions for the control of behaviour and movement. Thirty-nine patients with PD were recruited for this study, 20 with higher anxiety scores and 19 with lower anxiety scores. They all underwent a resting-state fMRI scan, while they were on medication. The amplitude of low-frequency fluctuation (ALFF) and seed-based connectivity were investigated to reveal the changes of the spontaneous activity and the interaction among different related regions. The results provided evidence that anxiety in Parkinson's disease is associated with the over-activation of the amygdala and impaired inter-relationship of regions involved in behavior (i.e. medial prefrontal cortex, insula) and motor control (i.e. basal ganglia).

SARS-CoV-2, Trait Anxiety, and the Microbiome

During the COVID-19 pandemic, research on the relationships between the virus and its human host has become fundamental to understand this pathology and its effects. Attaining this profound understanding is critical for the effective containment and treatment of infections caused by the virus. In this review, we present some possible mechanisms by which psychopathological symptoms emerge following viral infections of the central nervous system (CNS). These proposed mechanisms are based on microbial communication and the induced priming of microglial antibody activation within the CNS through Toll-like receptor signaling. In this process, chronic microglial activation causes increased glutamate release in virally-altered, high-density neuronal structures, thereby modulating cognitive networks and information integration processes. This modulation, in turn, we suggest, affects the accuracy of sensory integration and connectivity of major control networks, such as the default mode network. The chronic activation of immunological responses and neurochemical shifts toward an elevated glutamate/gamma-aminobutyric acid ratio lead to negative reinforcement learning and suboptimal organismic functioning, for example, maintaining the body in an anxious state, which can later become internalized as trait anxiety. Therefore, we hypothesize that the homeostatic relationship between host, microbiome, and virome, would be decisive in determining the efficiency of subsequent immunological responses, disease susceptibility, and long-term psychopathological effects of diseases that impact the CNS, such as the COVID-19.

White matter structural network disturbances in first-episode, drug-naïve adolescents with generalized anxiety disorder

BACKGROUND

Previous studies have suggested that individuals with generalized anxiety disorder (GAD) would show inefficient whole-brain communication and dysconnectivity in the fronto-parietal-subcortical sub-networks in the white matter (WM) structural network. However, these hypotheses have yet to be tested.

METHODS

Individual WM structural networks were constructed based on diffusion MRI data and deterministic tractography in 34 first-episode, medication-naïve adolescents with GAD and 27 healthy controls (HCs). Graph theory was applied to investigate the topological organization alterations of the structural network.

RESULTS

GAD patients showed disrupted small-world configurations (i.e., increased path length and decreased clustering coefficient) and hub organization (i.e., less connection strength in the feeder and local connections). A decreased connection strength was found in a GAD-related sub-network (mainly involving the frontal-subcortical circuits), which was able to distinguish GAD patients from HCs with higher accuracy (area under the curve of 0.96, sensitivity of 94%, specificity of 89%) than clinical scores and other topological alternations.

LIMITATIONS

The current study just compared GAD patients with HCs based on a small sample, leaving whether the alternations found here are specific to GAD still an open question. Future studies are recommended to recruit patients with other anxiety disorders (e.g., social anxiety disorder) and/or comorbid mood disorders to identify the GAD-specific WM alterations using a larger sample.

CONCLUSIONS

Our findings highlight the disruption of the topological organization of the whole-brain WM structural network (especially the frontal-subcortical circuits) in GAD, and suggest the potential of using structural connectivity of the GAD-related sub-network as a biomarker for GAD patients.

Peer victimization and its impact on adolescent brain development and psychopathology

Chronic peer victimization has long-term impacts on mental health; however, the biological mediators of this adverse relationship are unknown. We sought to determine whether adolescent brain development is involved in mediating the effect of peer victimization on psychopathology. We included participants (n = 682) from the longitudinal IMAGEN study with both peer victimization and neuroimaging data. Latent profile analysis identified groups of adolescents with different experiential patterns of victimization. We then associated the victimization trajectories and brain volume changes with depression, generalized anxiety, and hyperactivity symptoms at age 19. Repeated measures ANOVA revealed time-by-victimization interactions on left putamen volume (F = 4.38, p = 0.037). Changes in left putamen volume were negatively associated with generalized anxiety (t = -2.32, p = 0.020). Notably, peer victimization was indirectly associated with generalized anxiety via decreases in putamen volume (95% CI = 0.004-0.109). This was also true for the left caudate (95% CI = 0.002-0.099). These data suggest that the experience of chronic peer victimization during adolescence might induce psychopathology-relevant deviations from normative brain development. Early peer victimization interventions could prevent such pathological changes.

A conceptual model of risk and protective factors associated with internalizing symptoms in autism spectrum disorder: A scoping review, synthesis, and call for more research

This paper reviews and synthesizes key areas of research related to the etiology, development, and maintenance of internalizing symptoms in children, adolescents, and adults with autism spectrum disorder (ASD). In developing an integrated conceptual model, we draw from current conceptual models of internalizing symptoms in ASD and extend the model to include factors related to internalizing within other populations (e.g., children that have experienced early life stress, children with other neurodevelopmental conditions, typically developing children) that have not been systematically examined in ASD. Our review highlights the need for more research to understand the developmental course of internalizing symptoms, potential moderators, and the interplay between early risk and protective factors. Longitudinal studies incorporating multiple methods and both environmental and biological factors will be important in order to elucidate these mechanisms.

Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample

BACKGROUND

The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The recent application of machine learning techniques to develop neuroimaging-based biomarkers has yielded promising preliminary results. However, much of the work in this domain has not met best practice standards from the field of machine learning. This is especially true for studies of anxiety, creating uncertainty about the potential for anxiety biomarker development.

METHODS

We applied machine learning tools to predict trait anxiety from neuroimaging measurements in humans. Using publicly available data from the Brain Genomics Superstruct Project, we compared a suite of neuroimaging-based machine learning models predicting anxiety within a discovery sample (n = 531, 307 women) via k-fold cross-validation, and we tested the final model (a stacked model incorporating region-to-region functional connectivity, amygdala seed-to-voxel connectivity, and volumetric and cortical thickness data) in a held-out, unseen test sample (n = 348, 209 women).

RESULTS

Though the best model was able to predict anxiety within the discovery sample (cross-validated R2 of .06, permutation test p < .001), the generalization test within the holdout sample failed (R2 of -.04, permutation test p > .05).

CONCLUSIONS

In this study, we did not find evidence of a generalizable anxiety biomarker. However, we encourage other researchers to investigate this topic, utilizing large samples and proper methodology, to clarify the potential of neuroimaging-based anxiety biomarkers.

Functional connectome and neuropsychiatric symptom clusters of Alzheimer's disease

BACKGROUND

Neuropsychiatric symptoms (NPSs) are important aspects of Alzheimer's disease (AD). Investigation of the effect of functional network abnormalities on clustered NPSs may uncover loci of altered connectivity for more targeted pharmacological and behavioral interventions in AD. The study aimed to investigate functional connectivity in AD and the clustered NPSs, as assessed by the Neuropsychiatric Inventory (NPI).

METHODS

In one hundred and fifty-nine patients with mild dementia stage of AD, graph metrics measuring functional connectivity at global network- and local network-level were assessed by closeness-centrality, betweenness-centrality, average-path-length, local-efficiency, and clustering-coefficient, respectively. The relationship between the NPI composite score and functional connectivity was assessed.

RESULTS

In AD, an increase in behavioral composite score was associated with changes in functional connectivity at local network-level, and regions displayed the changes was left lingual gyrus, left sub-genual ACC nodes, and left supra-genual ACC nodes (P < 0.05). An increase in affective composite score was associated with changes in functional connectivity at global network-level, and regions displayed the change was right caudate (P = 0.014). An increase in psychotic composite score was associated with changes in functional connectivity at global network-level, and regions displayed the change was left precuneus and right dorsolateral superior frontal gyrus (P < 0.05).

LIMITATIONS

Cognitively normal elderly subjects and longitudinal follow-up will be needed to see the evolution of NPS clusters and pathological changes in the functional connectivity at global or local network-level.

CONCLUSIONS

Different NPS clusters corresponded to distinct changes in functional connectivity at global and local network-level.

Insula shows abnormal task-evoked and resting-state activity in first-episode drug-naïve generalized anxiety disorder

BACKGROUND

Interoception is associated with neural activity in the insula of healthy humans. On the basis of the somatic symptoms in generalized anxiety disorder (GAD), especially abnormal heartbeat perception, we hypothesized that abnormal activity in the insula was associated with interoceptive awareness in patients with GAD.

METHODS

We investigated the psychological correlates of interoceptive awareness in a sample of 34 patients with first-onset, drug-naïve GAD and 30 healthy controls (HCs). Furthermore, we compared blood oxygenation level-dependent responses between the two groups during a heartbeat perception task to assess task-evoked activity and its relationship with psychological measures. We also examined between-group differences in insular subregions resting-state functional connectivity (rsFC), and its relationship with anxiety severity.

RESULTS

Patients with GAD had significantly higher body perception scores than HCs. They also exhibited greater task-evoked activity in the left anterior insula, left posterior insula, and right anterior insula during interoceptive awareness than HCs. Left anterior insula activity was positively correlated with body awareness in patients with GAD, and rsFC between the left anterior insula and left medial prefrontal gyrus was negatively correlated with somatic anxiety severity.

CONCLUSIONS

Investigating a sample of first-episode, drug-naïve patients, our study demonstrated abnormal interoceptive awareness in patients with GAD and that this was related to abnormal anterior insular activity during both rest and task. These results shed new light on the psychological and neural substrates of somatic symptoms in GAD, and they may serve to establish abnormal interoceptive awareness as a neural and psychological marker of GAD.

Review of EEG, ERP, and Brain Connectivity Estimators as Predictive Biomarkers of Social Anxiety Disorder

Social anxiety disorder (SAD) is characterized by a fear of negative evaluation, negative self-belief and extreme avoidance of social situations. These recurrent symptoms are thought to maintain the severity and substantial impairment in social and cognitive thoughts. SAD is associated with a disruption in neuronal networks implicated in emotional regulation, perceptual stimulus functions, and emotion processing, suggesting a network system to delineate the electrocortical endophenotypes of SAD. This paper seeks to provide a comprehensive review of the most frequently studied electroencephalographic (EEG) spectral coupling, event-related potential (ERP), visual-event potential (VEP), and other connectivity estimators in social anxiety during rest, anticipation, stimulus processing, and recovery states. A search on Web of Science provided 97 studies that document electrocortical biomarkers and relevant constructs pertaining to individuals with SAD. This study aims to identify SAD neuronal biomarkers and provide insight into the differences in these biomarkers based on EEG, ERPs, VEP, and brain connectivity networks in SAD patients and healthy controls (HC). Furthermore, we proposed recommendations to improve methods of delineating the electrocortical endophenotypes of SAD, e.g., a fusion of EEG with other modalities such as functional magnetic resonance imaging (fMRI) and magnetoencephalograms (MEG), to realize better effectiveness than EEG alone, in order to ultimately evolve the treatment selection process, and to review the possibility of using electrocortical measures in the early diagnosis and endophenotype examination of SAD.

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

Functional and Structural Brain Alterations in Encephalitis With LGI1 Antibodies

Objective: The purpose of this study was to examine the neural substrates and mechanisms that generate memory deficits, seizures and neuropsychiatric abnormalities in encephalitis with LGI1 antibodies using a data-driven, multimodal magnetic resonance imaging (MRI) approach. Methods: Functional MRI data were acquired from 14 anti-LGI1 encephalitis patients and 14 age and gender matched normal controls. Independent component analysis with hierarchical partner matching (HPM-ICA) was used to assess the whole-brain intrinsic functional connectivity. Granger causality (GC) was applied to investigate the effective connectivity among the brain regions that identified by HPM-ICA. Diffusion tensor imaging (DTI) was utilized to investigate white matter microstructural changes of the patients. Results: Participants with LGI1 antibodies encephalitis presented reduced functional connectivity in the brain areas associated with memory, cognition and motion circuits, while increased functional connectivity in putamen and caudate in comparison to the normal controls. Moreover, the effective connectivity in patients was decreased from the frontal cortex to supplementary motor area. Finally, patients had significant reductions in fractional anisotropy (FA) for the corpus callosum, internal capsule, corona radiata and superior longitudinal fasciculus, accompanied by increases in mean diffusivity (MD) for these regions as compared to controls. Conclusion: Our findings suggest that the neural disorder and behavioral deficits of anti-LGI1 encephalitis may be associated with extensive changes in brain connectivity and microstructure. These pathological alterations affect the basal ganglia and limbic system besides the temporal and frontal lobe.

Oscillotherapeutics - Time-targeted interventions in epilepsy and beyond

Oscillatory brain activities support many physiological functions from motor control to cognition. Disruptions of the normal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson's disease, Alzheimer's disease, schizophrenia, anxiety/trauma-related disorders, major depressive disorders, and drug addiction. Therefore, these disorders can be considered as common oscillation defects despite having distinct behavioral manifestations and genetic causes. Recent technical advances of neuronal activity recording and analysis have allowed us to study the pathological oscillations of each disorder as a possible biomarker of symptoms. Furthermore, recent advances in brain stimulation technologies enable time- and space-targeted interventions of the pathological oscillations of both neurological disorders and psychiatric disorders as possible targets for regulating their symptoms.

Biological and Clinical Markers to Differentiate the Type of Anxiety Disorders

The present chapter is an overview of possible biomarkers which distinguish anxiety disorders as classified by the DSM-5. Structural or activity changes in the brain regions; changes in N-acetylaspartate/creatine, dopamine, serotonin, and oxytocin; hearth rate variability; hypothalamic-pituitary-adrenal axis activity; error-related negativity; respiratory regulation; and genetic variants are proposed. However, their clinical utility is questionable due to low specificity and sensitivity: the majority does not distinguish subjects with different anxiety disorders, and they might be influenced by stress, comorbidity, physical activity, and psychotropic medications. In this framework, the staging model, a clinimetric tool which allows to define the degree of progression of a disease at a point in time and where the patient is located on the continuum of the course of the disease, is proposed since several DSM anxiety disorders take place at different stages of the same syndrome according to the staging model. Thus, a stage-specific biomarker model for anxiety disorders is hypothesized and illustrated.

Dynamic changes of amplitude of low-frequency fluctuations in patients with generalized anxiety disorder

Previous neuroimaging studies have mainly focused on alterations of static and dynamic functional connectivity in patients with generalized anxiety disorder (GAD). However, the characteristics of local brain activity over time in GAD are poorly understood. This study aimed to investigate the abnormal time‐varying local brain activity of GAD by using the amplitude of low‐frequency fluctuation (ALFF) method combined with sliding‐window approach. Group comparison results showed that compared with healthy controls (HCs), patients with GAD exhibited increased dynamic ALFF (dALFF) variability in widespread regions, including the bilateral dorsomedial prefrontal cortex, hippocampus, thalamus, striatum; and left orbital frontal gyrus, inferior parietal lobule, temporal pole, inferior temporal gyrus, and fusiform gyrus. The abnormal dALFF could be used to distinguish between patients with GAD and HCs. Increased dALFF variability values in the striatum were positively correlated with GAD symptom severity. These findings suggest that GAD patients are associated with abnormal temporal variability of local brain activity in regions implicated in executive, emotional, and social function. This study provides insight into the brain dysfunction of GAD from the perspective of dynamic local brain activity, highlighting the important role of dALFF variability in understanding neurophysiological mechanisms and potentially informing the diagnosis of GAD.

Systematic review and meta-analyses of neural structural and functional differences in generalized anxiety disorder and healthy controls using magnetic resonance imaging

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PFC-amygdala FC is altered in GAD, indicating top-down processing deficits.

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GAD had reduced activity for emotion regulation and working memory in the culmen.

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Salience, default, and central executive nodes have altered structure and function.

Objective

To compare structure, functional connectivity (FC) and task-based neural differences in subjects with generalized anxiety disorder (GAD) compared to healthy controls (HC).

Methods

The Embase, Ovid Medline, PsycINFO, Scopus, and Web of Science databases were searched from inception until March 12, 2018. Two reviewers independently screened titles, abstracts, and full-text articles. Data were extracted from records directly contrasting GAD and HC that included structure (connectivity and local indices such as volume, etc.), FC, or task-based magnetic resonance imaging data. Meta-analyses were conducted, as applicable, using AES-SDM software.

Results

The literature search produced 4,645 total records, of which 85 met the inclusion criteria for the systematic review. Records included structural (n = 35), FC (n = 33), and task-based (n = 42) findings. Meta-analyses were conducted on voxel-based morphometry and task-based results.

Discussion

The systematic review confirms and extends findings from previous reviews. Although few whole-brain resting state studies were conducted, key nodes of resting state networks have altered physiology: the hippocampus (default network), ACC and amygdala (salience network), have reduced volume, and the dlPFC (central executive network) and ACC have reduced FC with the amygdala in GAD. Nodes in the sensorimotor network are also altered with greater pre- and postcentral volume, reduced supplementary motor area volume, and reduced FC in anterior and increased FC in posterior cerebellum.

Conclusions

Despite limitations due to sample size, the meta-analyses highly agree with the systematic review and provide evidence of widely distributed neural differences in subjects with GAD, compared to HC. Further research optimized for meta-analyses would greatly improve large-scale comparisons.

Resting-state connectivity of the bed nucleus of the stria terminalis and the central nucleus of the amygdala in clinical anxiety

Background

The central nucleus of the amygdala and bed nucleus of the stria terminalis are involved primarily in phasic and sustained aversive states. Although both structures have been implicated in pathological anxiety, few studies with a clinical population have specifically focused on them, partly because of their small size. Previous work in our group used high-resolution imaging to map the restingstate functional connectivity of the bed nucleus of the stria terminalis and the central nucleus of the amygdala in healthy subjects at 7 T, confirming and extending structural findings in humans and animals, while providing additional insight into cortical connectivity that is potentially unique to humans.

Methods

In the current follow-up study, we contrasted resting-state functional connectivity in the bed nucleus of the stria terminalis and central nucleus of the amygdala at 7 T between healthy volunteers (n = 30) and patients with generalized and/or social anxiety disorder (n = 30).

Results

Results revealed significant voxel-level group differences. Compared with healthy volunteers, patients showed stronger resting-state functional connectivity between the central nucleus of the amygdala and the lateral orbitofrontal cortex and superior temporal sulcus. They also showed weaker resting-state functional connectivity between the bed nucleus of the stria terminalis and the dorsolateral prefrontal cortex and occipital cortex.

Limitations

These findings depart from a previous report of resting-state functional connectivity in the central nucleus of the amygdala and bed nucleus of the stria terminalis under sustained threat of shock in healthy volunteers.

Conclusion

This study provides functional MRI proxies of the functional dissociation of the bed nucleus of the stria terminalis and central nucleus of the amygdala, and suggests that resting-state functional connectivity of key structures in the processing of defensive responses do not recapitulate changes related to induced state anxiety. Future work needs to replicate and further probe the clinical significance of these findings.

Psychiatric disorders in multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is characterized by a large spectrum of symptoms, involving all functional systems. Psychiatric symptoms are common in people with MS (pwMS) having an important impact on quality of life and on some features of MS (fatigue, sleep, disability, adherence to disease-modifying drugs). The main psychiatric disturbances in MS are depressive, bipolar, anxiety, schizophrenic and obsessive-compulsive syndromes.

METHODS

Literature search for original articles and review in the databases, including PubMed and Scopus from 1959 to 2019.

RESULTS AND CONCLUSION

Studies answering the aim of this review were selected and reported. Epidemiological and clinical aspects of psychiatric syndromes (PS) in MS as well as self-report diagnostic scales and radiological correlates of PS in MS are described. Moreover, some radiological studies about primary psychiatric disorders (PD) are reported to underline how gray matter atrophy, white matter abnormalities and corpus callosum involvement in these diseases, as features in common with MS, may explain the more frequent occurrence of PD in MS than in the general population.

A failed top-down control from the prefrontal cortex to the amygdala in generalized anxiety disorder: Evidence from resting-state fMRI with Granger causality analysis

In generalized anxiety disorder (GAD), abnormal top-down control from the prefrontal cortex (PFC) to the amygdala is a widely accepted hypothesis through which an "emotional dysregulation model" may be explained. However, whether and how the PFC directly exerts abnormal top-down control on the amygdala remains largely unknown. We aimed to investigate the amygdala-based effective connectivity by using Granger causality analysis (GCA). Thirty-five drug-naive patients with GAD and thirty-six healthy controls (HC) underwent resting-state functional MR imaging. We used seed-based Granger causality analysis to examine the effective connectivity between the bilateral amygdala and the whole brain. The amygdala-based effective connectivity was compared between the HC and GAD groups. The results showed that, in the HC group, the left middle frontal gyrus exerted an inhibitory influence on the right amygdala, while in the GAD group, this influence was disrupted (single voxel P < 0.001, Gaussian random field corrected with P < 0.01). Our findings support and advance the "insufficient top-down control" hypothesis by identifying a failed top-down control from the prefrontal cortex to the amygdala in GAD.

Structural and functional neuroimaging studies in generalized anxiety disorder: a systematic review

OBJECTIVES

Brain imaging studies carried out in patients suffering from generalized anxiety disorder (GAD) have contributed to better characterize the pathophysiological mechanisms underlying this disorder. The present study reviews the available functional and structural brain imaging evidence on GAD, and suggests further strategies for investigations in this field.

METHODS

A systematic literature review was performed in PubMed, PsycINFO, and Google Scholar, aiming to identify original research evaluating GAD patients with the use of structural and functional magnetic resonance imaging as well as diffusion tensor imaging.

RESULTS

The available studies have shown impairments in ventrolateral and dorsolateral prefrontal cortex, anterior cingulate, posterior parietal regions, and amygdala in both pediatric and adult GAD patients, mostly in the right hemisphere. However, the literature is often tentative, given that most studies have employed small samples and included patients with comorbidities or in current use of various medications. Finally, different methodological aspects, such as the type of imaging equipment used, also complicate the generalizability of the findings.

CONCLUSIONS

Longitudinal neuroimaging studies with larger samples of both juvenile and adult GAD patients, as well as at risk individuals and unaffected relatives, should be carried out in order to shed light on the specific biological signature of GAD.

Disrupted interhemispheric resting-state functional connectivity and structural connectivity in first-episode, treatment-naïve generalized anxiety disorder

BACKGROUND

Aberrant functional and structural connectivity are considered to be involved in the underlying neural mechanism of generalized anxiety disorder (GAD). However, alterations in functional and structural interactions between the bilateral hemispheres are rarely examined. The current study aimed to characterized interhemispheric resting-state functional connectivity and white matter microstructural integrity of the corpus callosum in patients with GAD.

METHODS

Resting-state Blood oxygen level-dependent and diffusion tensor image were acquired for patients with GAD and healthy subjects. The two groups were matched in age, gender, education years. The voxel-mirrored homotopic connectivity (VMHC) of whole brain and white matter integrity of the corpus callosum (CC) were compared between the two groups. Their correlations with clinical measures were further performed.

RESULTS

Compare to controls, decreased resting-state VMHC were found in the precentral gyrus, middle cingulate gyrus and insula/putamen in patients with GAD. No regions of increased VMHC were detected in GAD. Compared to controls, GAD patients showed decreased fractional anisotropy (FA) values in CC2. In GAD group, further Pearson's correlation analyses showed that VMHC of the midcingulate gyrus positively correlated with FA of CC2, FA of CC2 negatively correlated with anxiety severity. Further mediation analyses demonstrated that attenuated VMHC in bilateral midcingulate gyrus partly mediated the association between white matter integrity of CC2 sub-region and anxiety severity.

CONCLUSION

Our findings suggested impairment of interhemispheric coordination in GAD. Moreover, disrupted interhemispheric connectivity correlated with anxiety severity in GAD. Our findings provided a novel clue about the neural mechanism of GAD, and may contribute to further deep exploration and treatment of GAD.

LIMITATIONS

The study was lack of comparison with non-GAD anxiety disorders.

Frontoparietal network abnormalities of gray matter volume and functional connectivity in patients with generalized anxiety disorder

We hypothesized that the frontoparietal region would exhibit differences in gray matter volume (GMV) and resting-state functional connectivity (rs-FC) in patients with generalized anxiety disorder (GAD) versus healthy controls (HCs). We also aimed to report on correlations between these neuroradiological findings and HAMA scores. We recruited 27 patients with GAD and 28 HCs, matched for gender, age and education. GMV was estimated using voxel-based morphometry (VBM). We found decreased GMV in the precentral gyrus (PrCG) and the superior frontal gyrus (SFG) in patients with GAD, which were used as regions of interest (ROI) for rs-FC analyses. We detected enhanced rs-FC in the inferior frontal gyrus (IFG) based on an increase in negative connections, and reduced rs-FC in the superior temporal gyrus (STG) based on a decrease in positive connections compared to HCs. The right PrCG may be a candidate biomarker in patients with GAD, as well as a potential stimulation target for improvement of anxiety symptoms. By combining GMV and rs-FC analyses, our findings help to understand the pathophysiology of GAD by combining GMV and rs-FC.

Dynamic functional abnormalities in generalized anxiety disorders and their increased network segregation of a hyperarousal brain state modulated by insomnia

BACKGROUND

Insomnia is frequently accompanied by the generalized anxiety disorder (GAD) but mostly fMRI studies investigated their aberrant functional connectivity (FC) without this issue. Recently, dynamic FC approach is prevailing to capture the time-varying fluctuations of spontaneous brain activities. Nevertheless, it is unclear how the dynamic FC characteristics are altered by insomnia in GAD.

METHODS

We acquired resting state fMRI and neuropsychological tests for the 17 comorbid GAD with insomnia (GAD/IS), 15 GAD and 24 healthy controls (HC). Then, based on the sliding window correlations, we estimated distinct brain states and statistically compared their dynamic properties. Further combining with graph theory, their network properties of each state among groups were accessed. Lastly, we examined associations between abnormal parameters and neuropsychological tests.

RESULTS

We identified four brain states but did not observe significance on the state transitions. The mean dwell time and fraction of one globally hypoactive state accounted for high proportion of brain activities were significantly different (GAD > HC > GAD/IS). Meanwhile, we found gradual decreases in a brain state representing slight sleep/drowsiness (HC > GAD/IS > GAD). Additionally, we observed the GAD/IS patients had significantly increased network segregation and posterior cingulate cortex in a hyperarousal state, as well as significant associations with anxiety and insomnia severity.

LIMITATIONS

The influences of depression on dynamic FC properties in GAD are unclear yet and more subjects should be recruited.

CONCLUSIONS

These results provide new insights about the temporal features in GAD and offer potential biomarkers to evaluate the impacts of insomnia.