Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches

Discriminative analysis of schizophrenia and major depressive disorder using fNIRS

BACKGROUND

Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions.

METHODS

A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists.

RESULTS

Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively.

CONCLUSION

This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.

Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task

One of the most prevalent psychiatric disorders is major depressive disorder (MDD), which increases the probability of suicidal ideation or untimely demise. Abnormal frontal hemodynamic changes detected by functional near-infrared spectroscopy (fNIRS) during verbal fluency task (VFT) have the potential to be used as an objective indicator for assessing clinical symptoms. However, comprehensive quantitative and objective assessment instruments for individuals who exhibit symptoms suggestive of depression remain undeveloped. Drawing from a total of 467 samples in a large-scale dataset comprising 289 MDD patients and 178 healthy controls, fNIRS measurements were obtained throughout the VFT. To identify unique MDD biomarkers, this research introduced a data representation approach for extracting spatiotemporal features from fNIRS signals, which were subsequently utilized as potential predictors. Machine learning classifiers (e.g., Gradient Boosted Decision Trees (GBDT) and Multilayer Perceptron) were implemented to assess the ability to predict selected features. The mean and standard deviation of the cross-validation indicated that the GBDT model, when combined with the 180-feature pattern, distinguishes patients with MDD from healthy controls in the most effective manner. The accuracy of correct classification for the test set was 0.829 ± 0.053, with an AUC of 0.895 (95 % CI: 0.864-0.925) and a sensitivity of 0.914 ± 0.051. Channels that made the most important contribution to the identification of MDD were identified using Shapley Additive Explanations method, located in the frontopolar area and the dorsolateral prefrontal cortex, as well as pars triangularis Broca's area. Assessment of abnormal prefrontal activity during the VFT in MDD serves as an objectively measurable biomarker that could be utilized to evaluate cognitive deficits and facilitate early screening for MDD. The model suggested in this research could be applied to large-scale case-control fNIRS datasets to detect unique characteristics of MDD and offer clinicians an objective biomarker-based analytical instrument to assist in the evaluation of suspicious cases.

Analysis of functional network asymmetry in major depressive disorder under four fNIRS tasks

BACKGROUND

Research in functional asymmetry of Major Depressive Disorder (MDD) under different tasks is crucial for clinical diagnose.

METHODS

Fifty individuals with MDD and twenty healthy controls (HCS) were recruited for hemodynamic data collection under four fNIRS tasks (Emotional picture, Verbal fluency, Fingering and Negative emotional picture description task). Integral values and functional connectivity strength were employed to probe neural activation and functional connectivity in frontal and temporal lobes in MDD. Following, asymmetry characteristic of the frontal cortex between MDD and HCS under four tasks were carefully analyzed and compared.

RESULTS

Individuals with MDD demonstrated heightened connectivity between the frontal and right temporal lobes and reduced connectivity between the frontal and left temporal lobes compared to HCS in all tasks. Additionally, MDD exhibited attenuated activation in the left frontal lobes and exaggerated activation in the right frontal lobes, diverging from HCS. Furthermore, the disparities in left-right asymmetry characteristic of frontal cortex activation between MDD and HCS were more pronounced during the combined task.

LIMITATIONS

Further research is required to grasp the neurophysiological mechanisms governing left-right asymmetry across various tasks and the influence of task-induced brain fatigue on cerebral cortex hemodynamics in MDD.

CONCLUSION

The left-right asymmetry feature provides valuable neurophysiological insights for diagnosing MDD clinically. Variations in activation patterns and functional connectivity features between MDD and HCS are closely tied to the task chosen. Thus, in clinical practice, carefully selecting appropriate fNIRS tasks and relevant features can significantly improve the diagnostic accuracy of MDD.

Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study

Alcohol use disorder (AUD) is a disorder of clinical and public health significance requiring novel and improved therapeutic solutions. Both environmental and genetic factors play a significant role in its pathophysiology. However, the underlying epigenetic molecular mechanisms that link the gene-environment interaction in AUD remain largely unknown. In this proof-of-concept study, we showed, for the first time, the neuroepigenetic biomarker capability of non-invasive imaging of class I histone deacetylase (HDAC) epigenetic enzymes in the in vivo brain for classifying AUD patients from healthy controls using a machine learning approach in the context of precision diagnosis. Eleven AUD patients and 16 age- and sex-matched healthy controls completed a simultaneous positron emission tomography-magnetic resonance (PET/MR) scan with the HDAC-binding radiotracer [11C]Martinostat. Our results showed lower HDAC expression in the anterior cingulate region in AUD. Furthermore, by applying a genetic algorithm feature selection, we identified five particular brain regions whose combined [11C]Martinostat relative standard uptake value (SUVR) features could reliably classify AUD vs. controls. We validate their promising classification reliability using a support vector machine classifier. These findings inform the potential of in vivo HDAC imaging biomarkers coupled with machine learning tools in the objective diagnosis and molecular translation of AUD that could complement the current diagnostic and statistical manual of mental disorders (DSM)-based intervention to propel precision medicine forward.

Gut proinflammatory bacteria is associated with abnormal functional connectivity of hippocampus in unmedicated patients with major depressive disorder

Accumulating evidence has revealed the gut bacteria dysbiosis and brain hippocampal functional and structural alterations in major depressive disorder (MDD). However, the potential relationship between the gut microbiota and hippocampal function alterations in patients with MDD is still very limited. Data of resting-state functional magnetic resonance imaging were acquired from 44 unmedicated MDD patients and 42 demographically matched healthy controls (HCs). Severn pairs of hippocampus subregions (the bilateral cornu ammonis [CA1-CA3], dentate gyrus (DG), entorhinal cortex, hippocampal-amygdaloid transition area, and subiculum) were selected as the seeds in the functional connectivity (FC) analysis. Additionally, fecal samples of participants were collected and 16S rDNA amplicon sequencing was used to identify the altered relative abundance of gut microbiota. Then, association analysis was conducted to investigate the potential relationships between the abnormal hippocampal subregions FC and microbiome features. Also, the altered hippocampal subregion FC values and gut microbiota levels were used as features separately or together in the support vector machine models distinguishing the MDD patients and HCs. Compared with HCs, patients with MDD exhibited increased FC between the left hippocampus (CA2, CA3 and DG) and right hippocampus (CA2 and CA3), and decreased FC between the right hippocampal CA3 and bilateral posterior cingulate cortex. In addition, we found that the level of proinflammatory bacteria (i.e., Enterobacteriaceae) was significantly increased, whereas the level of short-chain fatty acids producing-bacteria (i.e., Prevotellaceae, Agathobacter and Clostridium) were significantly decreased in MDD patients. Furthermore, FC values of the left hippocampal CA3- right hippocampus (CA2 and CA3) was positively correlated with the relative abundance of Enterobacteriaceae in patients with MDD. Moreover, altered hippocampal FC patterns and gut microbiota level were considered in combination, the best discrimination was obtained (AUC = 0.92). These findings may provide insights into the potential role of gut microbiota in the underlying neuropathology of MDD patients.

Characteristics of oxyhemoglobin during the verbal fluency task in subthreshold depression: A multi-channel near-infrared spectroscopy study

OBJECTIVE

Subthreshold depression is an essential precursor and risk factor for major depressive disorder, and its accurate identification and timely intervention are important for reducing the prevalence of major depressive disorder. Therefore, we used functional near-infrared spectroscopic imaging (fNIRS) to explore the characteristics of the brain neural activity of college students with subthreshold depression in the verbal fluency task.

METHODS

A total of 72 subthreshold depressed college students (SDs) and 67 healthy college students (HCs) were recruited, and all subjects were subjected to a verbal fluency task (VFT) while a 53-channel fNIRS device was used to collect the subjects' cerebral blood oxygenation signals.

RESULTS

The results of the independent samples t-test showed that the mean oxyhemoglobin in the right dorsolateral prefrontal (ch34, ch42, ch45) and Broca's area (ch51, ch53) of SDs was lower than that of HCs. The peak oxygenated hemoglobin of SDs was lower in the right dorsolateral prefrontal (ch34) and Broca's area (ch51, ch53).The brain functional connectivity strength was lower than that of HCs. Correlation analysis showed that the left DLPFC and Broca's area were significantly negatively correlated with the depression level.

CONCLUSION

SDs showed abnormally low, inadequate levels of brain activation and weak frontotemporal brain functional connectivity. The right DLPFC has a higher sensitivity for the differentiation of depressive symptoms and is suitable as a biomarker for the presence of depressive symptoms. Dysfunction in Broca's area can be used both as a marker of depressive symptoms and as a biomarker, indicating the severity of depressive symptoms.

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.

Helpful activities during the COVID-19 pandemic: Perceived benefits and actual association with psychiatric symptoms and psychological wellbeing

BACKGROUND

Few studies have rigorously examined the effectiveness of commonly reported coping activities during the COVID-19 pandemic. This study was designed to assess perceived helpful activities during the pandemic and to investigate the extent to which these activities were associated with psychological outcomes.

METHOD

Adults living in the US (N = 204), who were part of a longitudinal family study of depression responded to an online survey. They reported on their perceived helpful activities during the pandemic. General linear regression models (GLM) were used to evaluate the association between perceived helpful activities and current psychiatric symptoms, controlling for demographic factors, and pre-pandemic psychiatric history and symptoms.

RESULTS

The top perceived helpful activity during COVID-19 was communicating with friends/family via telephone text or video (75.5 %). However, of the top five activities endorsed, cooking/baking was associated with the most clinical outcomes, including lower anxiety/depression and greater psychological wellbeing (all ps < 0.05). These relationships were most prominent among younger individuals < age 40 years, females, and those with recent psychiatric history, although they extended to younger males, and individuals at high or low depression risk.

LIMITATIONS

Close ended items limited variability in coping activities reported. The study lacked data on substance use. The sample was racially and ethnically homogenous.

CONCLUSIONS

These findings move beyond anecdotal evidence that cooking/baking as a coping activity yields protection against psychopathology. Its ready accessibility and ability to confer benefits across a range of individual characteristics, make it a useful adjunct in therapeutic interventions for people confined to their homes.

Predicting outcome with Intranasal Esketamine treatment: A machine-learning, three-month study in Treatment-Resistant Depression (ESK-LEARNING)

Treatment-resistant depression (TRD) represents a severe clinical condition with high social and economic costs. Esketamine Nasal Spray (ESK-NS) has recently been approved for TRD by EMA and FDA, but data about predictors of response are still lacking. Thus, a tool that can predict the individual patients' probability of response to ESK-NS is needed. This study investigates sociodemographic and clinical features predicting responses to ESK-NS in TRD patients using machine learning techniques. In a retrospective, multicentric, real-world study involving 149 TRD subjects, psychometric data (Montgomery-Asberg-Depression-Rating-Scale/MADRS, Brief-Psychiatric-Rating-Scale/BPRS, Hamilton-Anxiety-Rating-Scale/HAM-A, Hamilton-Depression-Rating-Scale/HAMD-17) were collected at baseline and at one month/T1 and three months/T2 post-treatment initiation. We trained three different random forest classifiers, able to predict responses to ESK-NS with accuracies of 68.53% at T1 and 66.26% at T2 and remission at T2 with 68.60% of accuracy. Features like severe anhedonia, anxious distress, mixed symptoms as well as bipolarity were found to positively predict response and remission. At the same time, benzodiazepine usage and depression severity were linked to delayed responses. Despite some limitations (i.e., retrospective study, lack of biomarkers, lack of a correct interrater-reliability across the different centers), these findings suggest the potential of machine learning in personalized intervention for TRD.

A century of research on neuromodulation interventions: A scientometric analysis of trends and knowledge maps

Interest in neurostimulation interventions has significantly grown in recent decades, yet a scientometric analysis objectively mapping scientific knowledge and recent trends remains unpublished. Using relevant keywords, we conducted a search in the Web of Science Core Collection on September 23, 2022, retrieving a total of 47,681 documents with 987,979 references. We identified two prominent research trends: 'noninvasive brain stimulation' and 'invasive brain stimulation.' These methods have interconnected over time, forming a cluster focused on evidence synthesis. Noteworthy emerging research trends encompassed 'transcutaneous auricular vagus nerve stimulation,' 'DBS/epilepsy in the pediatric population,' 'spinal cord stimulation,' and 'brain-machine interface.' While progress has been made for various neurostimulation interventions, their approval as adjuvant treatments remains limited, and optimal stimulation parameters lack consensus. Enhancing communication between experts of both neurostimulation types and encouraging novel translational research could foster further development. These findings offer valuable insights for funding agencies and research groups, guiding future directions in the field.

Correlation between trait emotional intelligence and prefrontal activation during a verbal fluency task: A functional near-infrared spectroscopy study

Stress is inevitable in humans and stress changes our physical and mental states. Stress has been studied epidemiologically, biologically, and psychologically. First defined in 1990, emotional intelligence (EI) affects psychological stress management. In contrast, the prefrontal cortex (PFC) is suggested to play a vital role in stress management. Human PFC activity can be inferred from the balance of oxygenated and deoxygenated hemoglobin in cerebral blood flow, which can be measured and calculated using functional near-infrared spectroscopy (fNIRS). An important cognitive activation task to activate the PFC is the verbal fluency task (VFT). Therefore, if the PFC is activated by the VFT and monitored by fNIRS, and the activity correlates with EI, fNIRS can be used to measure EI. In this study, Psychological tests using the self-rating depression scale, state-trait anxiety inventory (STAI), and trait emotional intelligence questionnaire-short form (TEIQue-SF) were conducted to evaluate the correlation with VFT performance. Relative oxygenated and deoxygenated hemoglobin concentrations were measured using an fNIRS device, and their correlation with VFT performance was tested. Spearman correlation coefficient was used to determine correlations. Results were as follows. Although VFT performance did not correlate with the oxygenated hemoglobin concentration ([Oxy-Hb]) changes, [Oxy-Hb] was elevated in all channels. VFT performance was significantly negatively correlated with the Zung self-rating depression scale (ρ = 0.063, P = .759), trait anxiety or anxiety level as a personal characteristic of STAI (ρ = 0.243, P = .232), and state anxiety or anxiety about an event of STAI (ρ = -0.138, P = .500), whereas no correlation was found with the TEIQue-SF (ρ = 0.303, P = .132). Healthy individuals PFC activity is not severely affected by their mental state and cognitive activation successfully activates the PFC, supporting the hypothesis that EI is correlated with frontal cortical activation during the VFT in a nonclinical population. EI may play a vital role in reducing stress associated with depression and anxiety in our social lives. Although we failed to show a statistical correlation between TEIQue-SF and [Oxy-Hb] due to a sample size shortage, our preliminary study was the first to attempt to show the PFC activity of EI through a hemodynamic response. Future research may elucidate the role of EI in reducing psychological stress in social life.

Differences and correlations of biochemical index levels in patients with bipolar disorder and major depressive disorder during a stable period

The differences and correlation of biochemical indexes between bipolar disorder (BPD) and major depressive disorder (MDD) in stable stage were analyzed and discussed. Patients diagnosed with BPD and MDD in the Third People's Hospital of Foshan from January 2019 to December 2021 were selected as the research subjects, with 200 cases in each. Fasting serum was collected from patients and then detected regarding TC, TG, high-density lipoprotein, low-density lipoprotein (LDL), aspartate aminotransferase, lactic dehydrogenase, creatine kinase, creatine kinase-MB, urea, creatinine, uric acid, alanine aminotransferase, glucose (GLU), hemoglobin A1c, prolactin, high-sensitivity C-reactive protein, homocysteine. The results showed that the mean age and serum LDL, GLU, and HbAc1 levels of the MDD group were significantly higher than those of the BPD group (P < .05), while there was no significant difference in other indexes (P > .05). The prevalence of BPD was significantly negatively correlated with patient age (r = -0.164, P = .020), LDL (r = -0.150, P = .034), GLU (r = -0.140, P = .048), and HbAc1 (r = -0.215, P = .002) (P < .05). There were no significant differences in serum Hcy and high-sensitivity C-reactive protein levels between the BPD and MDD groups. The age, fasting blood glucose, glycosylated hemoglobin, and LDL of BPD patients were negatively correlated with their incidence.

Transdiagnostic factors in symptoms of depression and post-traumatic stress: a systematic review

The current systematic review sought to identify quantitative empirical studies that focused on the transdiagnostic factors of intolerance of uncertainty, emotional dysregulation and rumination, and their relation with depression and post-traumatic stress disorder (PTSD). The overall research aim was to examine the relationship between these transdiagnostic factors and their relation with depression and PTSD symptoms. The systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Out of the 768 articles initially identified, 55 met the inclusion criteria for the current review. The results determined that intolerance of uncertainty is indirectly related to depression and PTSD symptoms, mainly through other factors including emotion dysregulation and rumination. Additionally, emotional dysregulation is a significant predictor of both depression and PTSD symptoms. Rumination is a robust factor related to depression and PTSD symptoms, this relationship was significant in cross-sectional and longitudinal studies. This review provides evidence on the transdiagnostic factors of intolerance of uncertainty, emotional dysregulation and rumination in the relationship with depression and PTSD symptoms.

Depression affects working memory performance: A Functional Near Infrared Spectroscopy (fNIRS) Study

Depression is a complex disorder that can be caused by psychosocial and biological conditions, and it not only affects to emotional life, but also cognitive functions, specifically the executive functions, attention, psychomotor speed, and memory. Some results of the studies in the literature show that depressed individuals perform worse on cognitive tasks than healthy individuals, while others indicate that there is no difference. Moreover, there is also no consensus about the depressed people brain functionalities. We aimed to compare the people who has high and low depression score measured with Beck Depression Inventory in terms of their working memory performance by using n-back paradigm and their brain activity by using optical imaging with this study. The age of lower BDI group (n = 11) is 23,9 ± 3,04 and higher BDI group (n = 23) is 22,2 + 2,28. The fNIRS were recorded from each subject while neutral words-faces and emotional words-faces are given to the subjects in the visuospatial 2-back WM task. There are no significant differences between the two groups behaviorally during the working memory performance, however, the high BDI group's PFC activation in right hemisphere is founded to be higher than the lower group. Our findings support the literature who is claiming the difference brain activity in depressed brain but not cognitive performance. Though, the small group size and the task difficulty (easy) could be the explanation of the behavioral results.

The diagnostic utility of miRNA and elucidation of pathological mechanisms in major depressive disorder

AIMS

Our study aims to explore how miRNAs can elucidate the molecular mechanisms of major depressive disorder (MDD) by comparing the miRNA levels in the blood serum of patients with depression and healthy individuals. It also explores the potential of miRNAs to differentiate between depressed patients and healthy controls.

METHODS

60 healthy controls (n = 45 females) were matched to 60 depressed patients (n = 10 unmedicated) for age (±7), sex, ethnicity, and years of education. Depression severity was measured using the Hamilton Depression Rating Scale, and venous blood was collected using PAXgene Blood RNA tubes for miRNA profiling. To further identify the depression-related biological pathways that are influenced by differentially expressed miRNAs, networks were constructed using QIAGEN Ingenuity Pathway Analysis. Receiver operating characteristic (ROC) analyses were also conducted to examine the discriminative ability of miRNAs to distinguish between depressed and healthy individuals.

RESULTS

Six miRNAs (miR-542-3p, miR-181b-3p, miR-190a-5p, miR-33a-3p, miR-3690 and miR-6895-3p) showed to be considerably down-regulated in unmedicated depressed patients relative to healthy controls. miR-542-3p, in particular, also has experimentally verified mRNA targets that are predicted to be associated with MDD. ROC analyses found that a panel combining miR-542-3p, miR-181b-3p and miR-3690 produced an area under the curve value of 0.67 in distinguishing between depressed and healthy individuals.

CONCLUSIONS

miRNAs - most notably, miR-542-3p, miR-181b-3p and miR-3690 - may be biomarkers with targets that are implicated in the pathophysiology of depression. They could also be used to distinguish between depressed and healthy individuals with reasonable accuracy.

Study on brain function of the frontal lobe in patients with functional gastroduodenal disease by near-infrared functional imaging

OBJECTIVE

functional gastroduodenal disease is the main type of functional gastrointestinal disease in the clinical department of Gastroenterology and psychosomatic medicine at present, which accounts for a large proportion of outpatients in gastroenterology. The main manifestations are epigastric pain, dyspepsia, belching, chronic nausea, and vomiting. The purpose of this study is to explore the changes in brain function in patients with functional gastroduodenal diseases through experiments to reveal the possible central etiology and development process.

METHODS

the functional changes of the prefrontal lobe in patients with functional gastroduodenal diseases and normal controls were detected and analyzed by near-infrared brain imaging. At the same time, SCL-90 was used to evaluate the mental health status of patients with functional gastroduodenal diseases and normal controls. The changes in the autonomic nerve system in patients and normal controls were detected and compared by heart rate variability trend chart.

RESULTS

the activity of left prefrontal lobe areas s8-d8, s10-d4, s10-d10 and s10-d15 in patients with functional gastroduodenal disease was significantly lower than normal controls (p < 0.05). The SCL-90 scale showed that there were significant differences between patients with functional gastroduodenal disease and normal controls, especially in depression, compulsion, anxiety, somatization, interpersonal sensitivity and hostility (p < 0.05). There was no significant difference in lf/hf values detected by the HRV trend chart (p > 0.05).

CONCLUSION

the function of the left frontal lobe is decreased in patients with functional gastroduodenal disease. The autonomic nervous system may be related to the connection system between the brain center and internal organs.

Pediatric Crohn's disease diagnosis aid via genomic analysis and machine learning

Introduction

Determination of pediatric Crohn's disease (CD) remains a major diagnostic challenge. However, the rapidly emerging field of artificial intelligence has demonstrated promise in developing diagnostic models for intractable diseases.

Methods

We propose an artificial neural network model of 8 gene markers identified by 4 classification algorithms based on Gene Expression Omnibus database for diagnostic of pediatric CD.

Results

The model achieved over 85% accuracy and area under ROC curve value in both training set and testing set for diagnosing pediatric CD. Additionally, immune infiltration analysis was performed to address why these markers can be integrated to develop a diagnostic model.

Conclusion

This study supports further clinical facilitation of precise disease diagnosis by integrating genomics and machine learning algorithms in open-access database.

Systematic metabolic characterization of mental disorders reveals age‐related metabolic disturbances as potential risk factors for depression in older adults

Mental disorders are associated with dysregulated metabolism, but comprehensive investigations of their metabolic similarities and differences and their clinical relevance are few. Here, based on the plasma metabolome and lipidome of subcohort1, comprising 100 healthy participants, 55 cases with anxiety, 52 persons with depression, and 41 individuals with comorbidity, which are from WCHAT, a perspective cohort study of community‐dwelling older adults aged over 50, multiple metabolites as potential risk factors of mental disorders were identified. Furthermore, participants with mental illnesses were classified into three subtypes (S1, S2, and S3) by unsupervised classification with lipidomic data. Among them, S1 showed higher triacylglycerol and lower sphingomyelin, while S2 displayed opposite features. The metabolic profile of S3 was like that of the normal group. Compared with S3, individuals in S1 and S2 had worse quality of life, and suffered more from sleep and cognitive disorders. Notably, an assessment of 6,467 individuals from the WCHAT showed an age‐related increase in the incidence of depression. Seventeen depression‐related metabolites were significantly correlated with age, which were validated in an independent subcohort2. Collectively, this work highlights the clinical relevance of metabolic perturbation in mental disorders, and age‐related metabolic disturbances may be a bridge‐linking aging and depressive.

Effects of long-term COVID-19 confinement and music stimulation on mental state and brain activity of young people

The Corona Virus Disease 2019 (COVID-19) pandemic may have had a negative emotional impact on individuals. This study investigated the effect of long-term lockdown and music on young people’s mood and neurophysiological responses in the prefrontal cortex (PFC). Fifteen healthy young adults were recruited and PFC activation was acquired using functional near-infrared spectroscopy during the conditions of resting, Stroop and music stimulation. The Depression Anxiety Stress Scales mental scale scores were simultaneously recorded. Mixed effect models, paired t-tests, one-way ANOVAs and Spearman analyses were adopted to analyse the experimental parameters. Stress, anxiety and depression levels increased significantly from Day 30 to Day 40. In terms of reaction time, both Stroop1 and Stroop2 were faster on Day 40 than on Day 30 (P = 0.01, P = 0.003). The relative concentration changes of oxyhemoglobin were significantly higher during premusic conditions than music stimulation and postmusic Stroop. The intensity of functional connectivity shifted from inter- to intracerebral over time. In conclusion, the reduced hemodynamic response of the PFC in healthy young adults is associated with negative emotions, especially anxiety, during lockdown. Immediate music stimulation appears to improve efficiency by altering the pattern of connections in PFC.

The right prefrontal cortex (PFC) can distinguish anxious depression from non-anxious depression: A promising functional near infrared spectroscopy study (fNIRS)

BACKGROUND

Anxious depression is a serious mental disorder characterized by comorbidity of anxiety and depression, and its symptoms are similar to those of non-anxious depression. This study aimed to use functional near-infrared spectroscopy (fNIRS) as a tool to distinguish between patients with anxious and non-anxious depression based on differences in hemodynamic changes in the right prefrontal cortex during the verbal fluency task. It is helpful to improve the diagnostic accuracy of the two disorders to further promote their therapeutic effect and prognosis.

METHODS

A total of 105 subjects, comprising 39 patients with anxious depression, 32 patients with non-anxious depression, and 32 healthy controls, were evaluated using 53-channel fNIRS and the Depression and Anxiety Clinical Scale.

RESULTS

Hemodynamic activation was significantly enhanced in the right dorsolateral prefrontal cortex (DLPFC) and right frontopole cortex (FPC) in the anxious depressed group compared with the non-anxious depressed and healthy groups.

LIMITATIONS

First, Hospital Anxiety and Depression Scale (HADS) was used to evaluate the scores of anxiety and depression among the three groups in our study. Different scales may result in different research results. Therefore, other scales (HAM, the Montgomery Asberg Depression Rating Scale, or the Beck Depression Inventory) should be used for further verification. Second, although all the samples we have chosen were patients with the diagnosis of anxious depression or no-anxious depression, we did not distinguish between different severity of anxious depression or no-anxious depression. Third, pure anxiety was not included as the control condition in our study.

CONCLUSIONS

There are significant differences in activation patterns of the right DLPFC and right FPC areas between patients with and without anxious depression. Moreover, the right FPC area is promising as a brain region to assess the severity of anxious depression. fNIRS may be a potential tool to improve diagnostic accuracy for both disorders.

Prefrontal cortex activities during verbal fluency and emotional words tasks in major depressive, adjustment, and bipolar disorders with depressive states

BACKGROUND

It can be difficult to differentiate psychiatric disorders from depressive states, with little knowledge on how to differentiate them. This study aimed to evaluate changes in brain activity during cognitive and emotional tasks in patients with depressive state to help with differential diagnoses.

METHODS

Sixty-two patients with depressive states [17 with adjustment disorder (AD), 27 with major depressive disorder (MDD), and 18 with bipolar disorder (BD)] and 34 healthy controls (HC) were recruited. We used a verbal fluency task (VFT) and emotional word tasks with happy and threat words. Functional near-infrared spectroscopy measured the relative change in oxygenated hemoglobin in the frontotemporal areas.

RESULTS

During the VFT, patients with AD or MDD showed significantly reduced activation in the bilateral frontotemporal region (all p < 0.01), whereas patients with BD demonstrated significantly reduced activation in the right frontotemporal areas compared to HC (p < 0.01). During the emotional words task with happy words, patients with MDD showed significantly increased activity in the frontopolar area compared to HC (p = 0.023). Binary logistic regression analysis showed that MDD or BD was significantly associated with brain activity during the happy word task. In distinguishing MDD or BD from HC, the happy words task performed equally well, with an area under the curve of 0.70.

LIMITATIONS

All study patients were taking psychotropic drugs.

CONCLUSIONS

Brain activation in response to a combination of cognitive or emotional stimuli could assist in distinguishing patients with depressive states from healthy controls.

Socioeconomic status and depressive symptoms: An individual-participant data meta-analysis on range restriction and measurement in the United States

INTRODUCTION

The association between socioeconomic status (SES) and depressive symptoms is well documented, yet less attention has been paid to the methodological factors contributing to between-study variability. We examined the moderating role of range restriction and the depressive-symptom measurement instrument used in estimating the correlation between components of SES and depressive symptoms.

METHODS

We conducted an individual participant data meta-analysis of nationally-representative, public-access datasets in the United States. We identified 123 individual datasets with a total of 1,655,991 participants (56.8 % female, mean age = 40.33).

RESULTS

The presence of range restriction was associated with larger correlations between income and depressive symptoms and with smaller correlations between years of education and depressive symptoms. The measurement instrument of depressive symptoms moderated the association for income, years of education, and occupational status/prestige. The Center for Epidemiological Studies-Depression scale consistently produced larger correlations. Higher measurement reliability was also associated with larger correlations.

LIMITATIONS

This study was not a comprehensive review of all measurement instruments of depressive symptoms, focused on datasets from the United States, and did not examine the moderating role of sample characteristics.

DISCUSSION

Methodological characteristics, including range restriction of SES and instrument of depressive symptoms, meaningfully influence the observed magnitude of association between SES and depressive symptoms. Clinicians and researchers designing future studies should consider which instrument of depressive symptoms is suitable for their purpose and population.

Time and gender measurement invariance in the modified Calderon depression scale

Background

Assessing change and comparing groups requires high quality and invariant scales. However, there is limited evidence of simultaneous longitudinal and gender measurement invariance for depression scales. This evidence is even more scant with long-established panel studies from low and middle-income countries.

Methods

In this paper, we used three waves (years 2002, 2005, and 2009) of a nationally representative panel study to examine the psychometric properties of the modified Calderon Depression Scale (CAL-DM)—a one-item exclusion of a depression scale designed for a population residing in a middle-income country (i.e., Mexico). Our analytical sample included 16,868 participants: 7,696 men and 9,172 women. Using Confirmatory Factor Analysis (CFA), we first examined overall fit in each wave, and then we tested time, gender, and time-gender measurement invariance across three waves. We also estimated and compared depression score means by gender and time. Finally, we examined the association between depression scores and self-rated health.

Results

Our analyses indicated the CAL-DM is a robust scale, suitable for time, gender, and time by gender comparisons. Mean comparisons exemplified how the scale can be used as a latent variable or a summative score. Women have higher depression scores than men and the gap is narrowing from 3.4 in 2002 to 2.5 in 2009.

Conclusions

The CAL-DM is a reliable instrument to measure depression in the Mexican general population that can be used for epidemiological research. Our results will contribute to a burgeoning line of research that examines the social determinants of depression, and the risk factors associated with different individuals’ depression trajectories over the life course.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12955-022-02007-8.