The rise and fall of MRI studies in major depressive disorder

Altered dynamic neural activities in individuals with obsessive-compulsive disorder and comorbid depressive symptoms

Objectives

Depressive symptoms are the most prevalent comorbidity in individuals with obsessive-compulsive disorder (OCD). The objective of this study was to investigate the dynamic characteristics of resting-state neural activities in OCD patients with depressive symptoms.

Methods

We recruited 29 OCD patients with depressive symptoms, 21 OCD patients without depressive symptoms, and 27 healthy controls, and collected data via structural and functional magnetic resonance imaging (fMRI). We analyzed the fMRI results using the dynamic amplitude of low-frequency fluctuation (dALFF) and support vector machine (SVM) techniques.

Results

Compared with OCD patients without depressive symptoms, OCD patients with depressive symptoms exhibited an increased dALFF in the left precuneus and decreased dALFF in the right medial frontal gyrus. The SVM indicated that the integration of aberrant dALFF values in the left precuneus and right medial frontal gyrus led to an overall accuracy of 80%, a sensitivity of 79%, and a specificity of 100% in detecting depressive symptoms among OCD patients.

Conclusion

Therefore, our study reveals that OCD patients with depressive symptoms display neural activities with unique dynamic characteristics in the resting state. Accordingly, abnormal dALFF values in the left precuneus and right medial frontal gyrus could be used to identify depressive symptoms in OCD patients.

Deconstructing depression by machine learning: the POKAL-PSY study

Unipolar depression is a prevalent and disabling condition, often left untreated. In the outpatient setting, general practitioners fail to recognize depression in about 50% of cases mainly due to somatic comorbidities. Given the significant economic, social, and interpersonal impact of depression and its increasing prevalence, there is a need to improve its diagnosis and treatment in outpatient care. Various efforts have been made to isolate individual biological markers for depression to streamline diagnostic and therapeutic approaches. However, the intricate and dynamic interplay between neuroinflammation, metabolic abnormalities, and relevant neurobiological correlates of depression is not yet fully understood. To address this issue, we propose a naturalistic prospective study involving outpatients with unipolar depression, individuals without depression or comorbidities, and healthy controls. In addition to clinical assessments, cardiovascular parameters, metabolic factors, and inflammatory parameters are collected. For analysis we will use conventional statistics as well as machine learning algorithms. We aim to detect relevant participant subgroups by data-driven cluster algorithms and their impact on the subjects' long-term prognosis. The POKAL-PSY study is a subproject of the research network POKAL (Predictors and Clinical Outcomes in Depressive Disorders; GRK 2621).

New insights into the effects of serotonin on Parkinson's disease and depression through its role in the gastrointestinal tract

Neuropsychiatric and neurodegenerative disorders are frequently associated with gastrointestinal (GI) co-pathologies. Although the central and enteric nervous systems (CNS and ENS, respectively) have been studied separately, there is increasing interest in factors that may contribute to conditions affecting both systems. There is compelling evidence that serotonin (5-HT) may play an important role in several gut-brain disorders. It is well known that 5-HT is essential for the development and functioning of the CNS. However, most of the body's 5-HT is produced in the GI tract. A deeper understanding of the specific effects of enteric 5-HT on gut-brain disorders may provide the basis for the development of new therapeutic targets. This review summarizes current data focusing on the important role of 5-HT in ENS development and motility, with particular emphasis on novel aspects of 5-HT signaling in conditions where CNS and ENS comorbidities are common, such as Parkinson's disease and depressive disorders.

White Matter Tract Integrity Is Reduced in Depression and in Individuals With Genetic Liability to Depression

BACKGROUND

While major depression has been linked to changes in white matter architecture, it remains unclear whether risk factors for depression are directly associated with these alterations. We reexamined white matter fiber tracts in individuals with depressive symptoms and investigated the connection between genetic and environmental risk for depression and structural changes in the brain.

METHODS

We included 19,183 participants from the UK Biobank imaging cohort, with depression status and adverse life experience based on questionnaire data and genetic liability for depression quantified by polygenic scores. The integrity of 27 white matter tracts was assessed using mean fractional anisotropy derived from diffusion magnetic resonance imaging.

RESULTS

White matter integrity was reduced, particularly in thalamic and intracortical fiber tracts, in individuals with depressive symptoms, independent of current symptom status. In a group of healthy individuals without depression, increasing genetic risk and increasing environmental risk were associated with reduced integrity in relevant fiber tracts, particularly in thalamic radiations. This association was stronger than expected based on statistical dependencies between samples, as confirmed by subsequent in silico simulations and permutation tests.

CONCLUSIONS

White matter alterations in thalamic and association tracts are associated with depressive symptoms and genetic risk for depression in unaffected individuals, suggesting an intermediate phenotype at the brain level.

ER stress in mouse serotonin neurons triggers a depressive phenotype alleviated by ketamine targeting eIF2α signaling

Depression is a devastating mood disorder that causes significant disability worldwide. Current knowledge of its pathophysiology remains modest and clear biological markers are lacking. Emerging evidence from human and animal models reveals persistent alterations in endoplasmic reticulum (ER) homeostasis, suggesting that ER stress-related signaling pathways may be targets for prevention and treatment. However, the neurobiological basis linking the pathways involved in depression-related ER stress remains unknown. Here, we report that an induced model of ER stress in mouse serotonin (5-HT) neurons is associated with reduced Egr1-dependent 5-HT cellular activity and 5-HT neurotransmission, resulting in neuroplasticity deficits in forebrain regions and a depressive-like phenotype. Ketamine administration engages downstream eIF2α signaling to trigger rapid neuroplasticity events that rescue the depressive-like effects. Collectively, these data identify ER stress in 5-HT neurons as a cellular pathway involved in the pathophysiology of depression and show that eIF2α is critical in eliciting ketamine's fast antidepressant effects.

Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks.

Structural Brain Connectivity and Treatment Improvement in Mood Disorder

Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.

Mechanisms Involved in the Link between Depression, Antidepressant Treatment, and Associated Weight Change

Major depressive disorder is a severe mood disorder associated with a marked decrease in quality of life and social functioning, accompanied by a risk of suicidal behavior. Therefore, seeking out and adhering to effective treatment is of great personal and society-wide importance. Weight changes associated with antidepressant therapy are often cited as the reason for treatment withdrawal and thus are an important topic of interest. There indeed exists a significant mechanistic overlap between depression, antidepressant treatment, and the regulation of appetite and body weight. The suggested pathomechanisms include the abnormal functioning of the homeostatic (mostly humoral) and hedonic (mostly dopaminergic) circuits of appetite regulation, as well as causing neuromorphological and neurophysiological changes underlying the development of depressive disorder. However, this issue is still extensively discussed. This review aims to summarize mechanisms linked to depression and antidepressant therapy in the context of weight change.

Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

The molecular mechanism of polysaccharides in combating major depressive disorder: A comprehensive review

Major depressive disorder (MDD) is a complex psychiatric condition with diverse etiological factors. Typical pathological features include decreased cerebral cortex, subcortical structures, and grey matter volumes, as well as monoamine transmitter dysregulation. Although medications exist to treat MDD, unmet needs persist due to limited efficacy, induced side effects, and relapse upon drug withdrawal. Polysaccharides offer promising new therapies for MDD, demonstrating antidepressant effects with minimal side effects and multiple targets. These include neurotransmitter, neurotrophin, neuroinflammation, hypothalamic-pituitary-adrenal axis, mitochondrial function, oxidative stress, and intestinal flora regulation. This review explores the latest advancements in understanding the pharmacological actions and mechanisms of polysaccharides in treating major depression. We discuss the impact of polysaccharides' diverse structures and properties on their pharmacological actions, aiming to inspire new research directions and facilitate the discovery of novel anti-depressive drugs.

Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models

The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.

Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network

Establishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD and the shift to multisite data decreased identification accuracy. To address these issues, the Brain Dynamic Attention Network (BDANet) is innovatively proposed, and analyzed bimodal scans from 2055 participants of the Rest-meta-MDD consortium. The end-to-end BDANet contains two crucial components. The Dynamic BrainGraph Generator dynamically focuses and represents topological relationships between Regions of Interest, overcoming limitations of static methods. The Ensemble Classifier is constructed to obfuscate domain sources to achieve inter-domain alignment. Finally, BDANet dynamically generates sample-specific brain graphs by downstream recognition tasks. The proposed BDANet achieved an accuracy of 81.6%. The regions with high attribution for classification were mainly located in the insula, cingulate cortex and auditory cortex. The level of brain connectivity in p24 region was negatively correlated ( [Formula: see text]) with the severity of MDD. Additionally, sex differences in connectivity strength were observed in specific brain regions and functional subnetworks ( [Formula: see text] or [Formula: see text]). These findings based on a large multisite dataset support the conclusion that BDANet can better solve the problem of the clinical heterogeneity of MDD and the shift of multisite data. It also illustrates the potential utility of BDANet for personalized accurate identification, treatment and intervention of MDD.

A multimodal prediction model for suicidal attempter in major depressive disorder

Background

Suicidal attempts in patients with major depressive disorder (MDD) have become an important challenge in global mental health affairs. To correctly distinguish MDD patients with and without suicidal attempts, a multimodal prediction model was developed in this study using multimodality data, including demographic, depressive symptoms, and brain structural imaging data. This model will be very helpful in the early intervention of MDD patients with suicidal attempts.

Methods

Two feature selection methods, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms, were merged for feature selection in 208 MDD patients. SVM was then used as a classification model to distinguish MDD patients with suicidal attempts or not.

Results

The multimodal predictive model was found to correctly distinguish MDD patients with and without suicidal attempts using integrated features derived from SVM-RFE and RF, with a balanced accuracy of 77.78%, sensitivity of 83.33%, specificity of 70.37%, positive predictive value of 78.95%, and negative predictive value of 76.00%. The strategy of merging the features from two selection methods outperformed traditional methods in the prediction of suicidal attempts in MDD patients, with hippocampal volume, cerebellar vermis volume, and supracalcarine volume being the top three features in the prediction model.

Conclusions

This study not only developed a new multimodal prediction model but also found three important brain structural phenotypes for the prediction of suicidal attempters in MDD patients. This prediction model is a powerful tool for early intervention in MDD patients, which offers neuroimaging biomarker targets for treatment in MDD patients with suicidal attempts.

Subthreshold Depression: A Systematic Review and Network Meta-Analysis of Non-Pharmacological Interventions

Background

Subthreshold depression (StD) is considered to be the "precursor" stage of major depressive disorder (MDD), which could cause higher risk of suicide, disease burden and functional impairment. There have been various non-pharmacological interventions for StD. However, the comparison of their effectiveness still lacks sufficient evidence. We performed a systematic review and network meta-analysis to evaluate and rank the efficacy of multiple non-pharmacological interventions targeting StD.

Methods

We conducted a thorough search across various databases including PubMed, Medline, Embase, Web of Science and PsycINFO from inception to December 2022. All included studies were randomized controlled trials (RCTs) of non-pharmacological interventions for patients with StD compared with control group (CG). Several universal scales for measuring depression severity were used as efficacy outcomes. The surface under the cumulative ranking curve (SUCRA) was used to separately rank each intervention using the "Stata 17.0" software.

Results

A total of thirty-six trials were included, involving twenty-eight interventions and 7417 participants. The research found that most non-pharmacological interventions were superior to controls for StD. In each outcome evaluation by different scales for measuring depression, psychotherapy always ranked first in terms of treatment effectiveness, especially Problem-solving Therapy (PST), Behavioral Activation Therapy (BAT), Cognitive Behavioral Therapy (CBT)/Internet-based CBT (I-CBT)/Telephone-based CBT (T-CBT). Since different groups could not be directly compared, the total optimal intervention could not be determined.

Conclusion

Here, we show that psychotherapy may be the better choice for the treatment of StD. This study provides some evidence on StD management selection for clinical workers. However, to establish its intervention effect more conclusively, the content, format and operators of psychotherapy still require extensive exploration to conduct more effective, convenient and cost-effective implementation in primary healthcare. Notably, further research is also urgently needed to find the biological and neural mechanisms of StD by examining whether psychotherapy alters neuroplasticity in patients with StD.

Learning without contingencies: A loss of synergy between memory and reward circuits in schizophrenia

Motivational deficits in schizophrenia may interact with foundational cognitive processes including learning and memory to induce impaired cognitive proficiency. If such a loss of synergy exists, it is likely to be underpinned by a loss of synchrony between the brains learning and reward sub-networks. Moreover, this loss should be observed even during tasks devoid of explicit reward contingencies given that such tasks are better models of real world performance than those with artificial contingencies. Here we applied undirected functional connectivity (uFC) analyses to fMRI data acquired while participants engaged in an associative learning task without contingencies or feedback. uFC was estimated and inter-group differences (between schizophrenia patients and controls, n = 54 total, n = 28 patients) were assessed within and between reward (VTA and NAcc) and learning/memory (Basal Ganglia, DPFC, Hippocampus, Parahippocampus, Occipital Lobe) sub-networks. The task paradigm itself alternated between Encoding, Consolidation, and Retrieval conditions, and uFC differences were quantified for each of the conditions. Significantly reduced uFC dominated the connectivity profiles of patients across all conditions. More pertinent to our motivations, these reductions were observed within and across classes of sub-networks (reward-related and learning/memory related). We suggest that disrupted functional connectivity between reward and learning sub-networks may drive many of the performance deficits that characterize schizophrenia. Thus, cognitive deficits in schizophrenia may in fact be underpinned by a loss of synergy between reward-sensitivity and cognitive processes.

Assessing volumetric brain differences in migraine and depression patients: a UK Biobank study

BACKGROUND

Migraine and depression are two of the most common and debilitating conditions. From a clinical perspective, they are mostly prevalent in women and manifest a partial overlapping symptomatology. Despite the high level of comorbidity, previous studies hardly investigated possible common patterns in brain volumetric differences compared to healthy subjects. Therefore, the current study investigates and compares the volumetric difference patterns in sub-cortical regions between participants with migraine or depression in comparison to healthy controls.

METHODS

The study included data from 43 930 participants of the large UK Biobank cohort. Using official ICD10 diagnosis, we selected 712 participants with migraine, 1 853 with depression and 23 942 healthy controls. We estimated mean volumetric difference between the groups for the different sub-cortical brain regions using generalized linear regression models, conditioning the model within the levels of BMI, age, sex, ethnical background, diastolic blood pressure, current tobacco smoking, alcohol intake frequency, Assessment Centre, Indices of Multiple Deprivation, comorbidities and total brain volume.

RESULTS

We detected larger overall volume of the caudate (mean difference: 66, 95% CI [-3, 135]) and of the thalamus (mean difference: 103 mm³, 95% CI [-2, 208]) in migraineurs than healthy controls. We also observed that individuals with depression appear to have also larger overall (mean difference: 47 mm³, 95% CI [-7, 100]) and gray matter (mean difference: 49 mm³, 95% CI [2, 95]) putamen volumes than healthy controls, as well as larger amygdala volume (mean difference: 17 mm³, 95% CI [-7, 40]).

CONCLUSION

Migraineurs manifested larger overall volumes at the level of the nucleus caudate and of the thalamus, which might imply abnormal pain modulation and increased migraine susceptibility. Larger amygdala and putamen volumes in participants with depression than controls might be due to increased neuronal activity in these regions.

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model

Objectives

Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD).

Participants

Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study.

Design

Cross-sectional.

Measurements

Structural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes.

Results

Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex.

Conclusions

We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information.

Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study

BACKGROUND

Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease.

METHODS

We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC.

RESULTS

The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls.

CONCLUSION

The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.

Brain function mediates the association between low vitamin D and neurocognitive status in female patients with major depressive disorder

BACKGROUND

Vitamin D is engaged in various neural processes, with low vitamin D linked to depression and cognitive dysfunction. There are gender differences in depression and vitamin D level. However, the relationship between depression, gender, vitamin D, cognition, and brain function has yet to be determined.

METHODS

One hundred and twenty-two patients with major depressive disorder (MDD) and 119 healthy controls underwent resting-state functional MRI and fractional amplitude of low-frequency fluctuations (fALFF) was calculated to assess brain function. Serum concentration of vitamin D (SCVD) and cognition (i.e. prospective memory and sustained attention) were also measured.

RESULTS

We found a significant group-by-gender interaction effect on SCVD whereby MDD patients showed a reduction in SCVD relative to controls in females but not males. Concurrently, there was a female-specific association of SCVD with cognition and MDD-related fALFF alterations in widespread brain regions. Remarkably, MDD- and SCVD-related fALFF changes mediated the relation between SCVD and cognition in females.

CONCLUSION

Apart from providing insights into the neural mechanisms by which low vitamin D contributes to cognitive impairment in MDD in a gender-dependent manner, these findings might have clinical implications for assignment of female patients with MDD and cognitive dysfunction to adjuvant vitamin D supplementation therapy, which may ultimately advance a precision approach to personalized antidepressant choice.

Characterizing heterogeneity in early adolescent reward networks and individualized associations with behavioral and clinical outcomes

Associations between connectivity networks and behavioral outcomes such as depression are typically examined by comparing average networks between known groups. However, neural heterogeneity within groups may limit the ability to make inferences at the individual level as qualitatively distinct processes across individuals may be obscured in group averages. This study characterizes the heterogeneity of effective connectivity reward networks among 103 early adolescents and examines associations between individualized features and multiple behavioral and clinical outcomes. To characterize network heterogeneity, we used extended unified structural equation modeling to identify effective connectivity networks for each individual and an aggregate network. We found that an aggregate reward network was a poor representation of individuals, with most individual-level networks sharing less than 50% of the group-level network paths. We then used Group Iterative Multiple Model Estimation to identify a group-level network, subgroups of individuals with similar networks, and individual-level networks. We identified three subgroups that appear to reflect differences in network maturity, but this solution had modest validity. Finally, we found numerous associations between individual-specific connectivity features and behavioral reward functioning and risk for substance use disorders. We suggest that accounting for heterogeneity is necessary to use connectivity networks for inferences precise to the individual.

Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank

Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45-82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.

The effective on intradermal acupuncture based on changes in biological specificity of acupoints for major depressive disorder: study protocol of a prospective, multicenter, randomized, controlled trial

Background

Antidepressants still have some side effects in treating major depressive disorder (MDD), and acupuncture therapy is a complementary therapy of research interest for MDD. Acupoints are sensitive sites for disease response and stimulation points for acupuncture treatment. Prior studies suggest that the biological specificity of acupoints is altered in physiological and pathological situations. Therefore, we hypothesize that the biological specificity of acupoints is associated with the diagnosis of MDD and that stimulating acupoints with significant biological specificity can achieve a better therapeutic effect than clinical common acupoints. This study aims to investigate the efficacy and safety of intradermal acupuncture (IA) treatment for MDD based on changes in the biological specificity of acupoints.

Methods

The first part of the study will enroll 30 MDD patients and 30 healthy control (HC) participants to assess pain sensitivity and thermal specificity of MDD-related acupoints using a pressure pain threshold gauge (PTG) and infrared thermography (IRT). The potentially superior acupoints for treating MDD will be selected based on the results of PTG and IRT tests and referred to as pressure pain threshold strong response acupoints (PSA) and temperature strong response acupoints (TSA).The second part of the study will enroll 120 eligible MDD patients randomly assigned to waiting list (WL) group, clinical common acupoint (CCA) group, TSA group, and PSA group in a 1:1:1:1 ratio. The change in the Patient Health Questionnaire-9 Items (PHQ-9), the MOS item short-form health survey (SF-36), pressure pain threshold, temperature of acupoints, and adverse effects will be observed. The outcomes of PHQ-9 and SF-36 measures will be assessed before intervention, at 3 and 6 weeks after intervention, and at a 4-week follow-up. The biological specificity of acupoint measures will be assessed before intervention and at 6 weeks after intervention. All adverse effects will be assessed.

Discussion

This study will evaluate the therapeutic effect and safety of IA for MDD based on changes in the biological specificity of acupoints. It will investigate whether there is a correlation between the biological specificity of MDD-related acupoints and the diagnosis of MDD and whether stimulating strong response acupoints is superior to clinical common acupoints in the treatment of MDD. The study's results may provide insights into the biological mechanisms of acupuncture and its potential as a complementary therapy for MDD.

Clinical Trial Registration

ClinicalTrials.gov, identifier: NCT05524519.

A review on multiple sclerosis prognostic findings from imaging, inflammation, and mental health studies

Magnetic resonance imaging (MRI) of the brain is commonly used to detect where chronic and active lesions are in multiple sclerosis (MS). MRI is also extensively used as a tool to calculate and extrapolate brain health by way of volumetric analysis or advanced imaging techniques. In MS patients, psychiatric symptoms are common comorbidities, with depression being the main one. Even though these symptoms are a major determinant of quality of life in MS, they are often overlooked and undertreated. There has been evidence of bidirectional interactions between the course of MS and comorbid psychiatric symptoms. In order to mitigate disability progression in MS, treating psychiatric comorbidities should be investigated and optimized. New research for the prediction of disease states or phenotypes of disability have advanced, primarily due to new technologies and a better understanding of the aging brain.

Abnormal dynamic functional connectivity of thalamic subregions in patients with first-episode, drug-naïve major depressive disorder

Background

Recent studies have shown that major depressive disorder (MDD) is associated with altered intrinsic functional connectivity (FC) of the thalamus; however, investigations of these alterations at a finer time scale and the level of thalamic subregions are still lacking.

Methods

We collected resting-state functional MRI data from 100 treatment-naïve, first-episode MDD patients and 99 age-, gender- and education-matched healthy controls (HCs). Seed-based whole-brain sliding window-based dFC analyses were performed for 16 thalamic subregions. Between-group differences in the mean and variance of dFC were determined using threshold-free cluster enhancement algorithm. For significant alterations, there relationships with clinical and neuropsychological variables were further examined via bivariate and multivariate correlation analyses.

Results

Of all thalamic subregions, only the left sensory thalamus (Stha) showed altered variance of dFC in the patients characterized by increases with the left inferior parietal lobule, left superior frontal gyrus, left inferior temporal gyrus, and left precuneus, and decreases with multiple frontal, temporal, parietal, and subcortical regions. These alterations accounted for, to a great extent, clinical, and neuropsychological characteristics of the patients as revealed by the multivariate correlation analysis. In addition, the bivariate correlation analysis revealed a positive correlation between the variance of dFC between the left Stha and right inferior temporal gurus/fusiform and childhood trauma questionnaires scores (r = 0.562, P < 0.001).

Conclusion

These findings suggest that the left Stha is the most vulnerable thalamic subregion to MDD, whose dFC alterations may serve as potential biomarkers for the diagnosis of the disease.

Large-scale lesion symptom mapping of depression identifies brain regions for risk and resilience

Understanding neural circuits that support mood is a central goal of affective neuroscience, and improved understanding of the anatomy could inform more targeted interventions in mood disorders. Lesion studies provide a method of inferring the anatomical sites causally related to specific functions, including mood. Here, we performed a large-scale study evaluating the location of acquired, focal brain lesions in relation to symptoms of depression. Five hundred and twenty-six individuals participated in the study across two sites (356 male, average age 52.4 ± 14.5 years). Each subject had a focal brain lesion identified on structural imaging and an assessment of depression using the Beck Depression Inventory-II, both obtained in the chronic period post-lesion (>3 months). Multivariate lesion-symptom mapping was performed to identify lesion sites associated with higher or lower depression symptom burden, which we refer to as 'risk' versus 'resilience' regions. The brain networks and white matter tracts associated with peak regional findings were identified using functional and structural lesion network mapping, respectively. Lesion-symptom mapping identified brain regions significantly associated with both higher and lower depression severity (r = 0.11; P = 0.01). Peak 'risk' regions include the bilateral anterior insula, bilateral dorsolateral prefrontal cortex and left dorsomedial prefrontal cortex. Functional lesion network mapping demonstrated that these 'risk' regions localized to nodes of the salience network. Peak 'resilience' regions include the right orbitofrontal cortex, right medial prefrontal cortex and right inferolateral temporal cortex, nodes of the default mode network. Structural lesion network mapping implicated dorsal prefrontal white matter tracts as 'risk' tracts and ventral prefrontal white matter tracts as 'resilience' tracts, although the structural lesion network mapping findings did not survive correction for multiple comparisons. Taken together, these results demonstrate that lesions to specific nodes of the salience network and default mode network are associated with greater risk versus resiliency for depression symptoms in the setting of focal brain lesions.

Adaptogens on Depression-Related Outcomes: A Systematic Integrative Review and Rationale of Synergism with Physical Activity

Depression is considered the most important disorder affecting mental health. The aim of this systematic integrative review was: (i) to describe the effects of supplementation with adaptogens on variables related to depression in adults; and (ii) to discuss the potential combination with physical exercise to aid planning and commissioning future clinical research. An integrative review was developed complementing the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement (PROSPERO registration: CRD42021249682). A total of 41 articles met the inclusion criteria. With a Price index of 46.4%, we found that: (i) Hypericum perforatum (St. John's Wort) is the most studied and supported adaptogen (17/41 [41.46%], three systematic reviews with meta-analysis) followed by Crocus sativus L. or saffron (6/41 [14.63%], three systematic reviews with meta-analysis and two systematic reviews); (ii) it is possible that the significantly better performance of adaptogens over placebo is due to the reduction of allostatic load via the action of secondary metabolites on BDNF regulation; and, (iii) the number of studies reporting physical activity levels is limited or null for those that combine an exercise program with the consumption of adaptogens. Aware of the need for a multidisciplinary approach for depression treatment, this systematic integrative review provides an up-to-date view for supporting the use of St. John's Wort and saffron as non-pharmacological strategies while also help commissioning future research on the efficacy of other adaptogens. It also contributes to the design of future clinical research studies that evaluate the consumption of herbal extracts plus physical exercise, mainly resistance training, as a potentially safe and powerful strategy to treat depression.

Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review

Mental disorders are rapidly increasing each year and have become a major challenge affecting the social and financial well-being of individuals. There is a need for phenotypic characterization of psychiatric disorders with biomarkers to provide a rich signature for Major Depressive Disorder, improving the understanding of the pathophysiological mechanisms underlying these mental disorders. This comprehensive review focuses on depression and relapse detection modalities such as self-questionnaires, audiovisuals, and EEG, highlighting noteworthy publications in the last ten years. The article concentrates on the literature that adopts machine learning by audiovisual and EEG signals. It also outlines preprocessing, feature extraction, and public datasets for depression detection. The review concludes with recommendations that will help improve the reliability of developed models and the determinism of computational intelligence-based systems in psychiatry. To the best of our knowledge, this survey is the first comprehensive review on depression and relapse prediction by self-questionnaires, audiovisual, and EEG-based approaches. The findings of this review will serve as a useful and structured starting point for researchers studying clinical and non-clinical depression recognition and relapse through machine learning-based approaches.

Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder

Recent studies have shown that major depressive disorder (MDD) is accompanied by alterations in functional and structural network gradients. However, whether changes are present in the cortical morphometric similarity (MS) network gradient, and the relationship between alterations of the gradient and gene expression remains largely unknown. In this study, the MS network was constructed, and its gradient was calculated in 71 patients with first-episode, treatment-naive MDD, and 69 demographically matched healthy controls. Between-group comparisons were performed to investigate abnormalities in the MS network gradient, and partial least squares regression analysis was conducted to explore the association between gene expression profiles and MS network gradient-based alternations in MDD. We found that the gradient was primarily significantly decreased in sensorimotor regions in patients with MDD compared with healthy controls, and increased in visual-related regions. In addition, the altered principal MS network gradient in the left postcentral cortex and right lingual cortex exhibited significant correlations with symptom severity. The abnormal gradient pattern was spatially correlated with the brain-wide expression of genes enriched for neurobiologically relevant pathways, downregulated in the MDD postmortem brain, and preferentially expressed in different cell types and cortical layers. These results demonstrated alterations of the principal MS network gradient in MDD and suggested the molecular mechanisms for structural alternations underlying MDD.

EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia

The diagnosis of psychiatric disorders is currently based on a clinical and psychiatric examination (intake). Ancillary tests are used minimally or only to exclude other disorders. Here, we demonstrate a novel mathematical approach based on the field of p-adic numbers and using electroencephalograms (EEGs) to identify and differentiate patients with schizophrenia and depression from healthy controls. This novel approach examines spatio-temporal relations of single EEG electrode signals and characterizes the topological structure of these relations in the individual patient. Our results indicate that the relational topological structures, characterized by either the personal universal dendrographic hologram (DH) signature (PUDHS) or personal block DH signature (PBDHS), form a unique range for each group of patients, with impressive correspondence to the clinical condition. This newly developed approach results in an individual patient signature calculated from the spatio-temporal relations of EEG electrodes signals and might help the clinician with a new objective tool for the diagnosis of a multitude of psychiatric disorders.

Molecular pathways of major depressive disorder converge on the synapse

Major depressive disorder (MDD) is a psychiatric disease of still poorly understood molecular etiology. Extensive studies at different molecular levels point to a high complexity of numerous interrelated pathways as the underpinnings of depression. Major systems under consideration include monoamines, stress, neurotrophins and neurogenesis, excitatory and inhibitory neurotransmission, mitochondrial dysfunction, (epi)genetics, inflammation, the opioid system, myelination, and the gut-brain axis, among others. This review aims at illustrating how these multiple signaling pathways and systems may interact to provide a more comprehensive view of MDD's neurobiology. In particular, considering the pattern of synaptic activity as the closest physical representation of mood, emotion, and conscience we can conceptualize, each pathway or molecular system will be scrutinized for links to synaptic neurotransmission. Models of the neurobiology of MDD will be discussed as well as future actions to improve the understanding of the disease and treatment options.

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

Shared and differential fractional amplitude of low-frequency fluctuation patterns at rest in major depressive disorders with or without sleep disturbance

Objective

Sleep disturbances (SD) are commonly found in patients with major depressive disorder (MDD). This study aims to explore the influence of SD symptoms on clinical characteristics in patients with MDD and to investigate the shared and distinct fractional amplitude of low-frequency fluctuation (fALFF) patterns in these patients with or without SD symptoms.

Methods

Twenty-four MDD patients with SD symptoms (Pa_s), 33 MDD patients without SD symptoms (Pa_ns) and 32 healthy controls (HCs) were included in this study. The fALFF and correlation analyses were applied to analyze the features of imaging and clinical data.

Results

Pa_s showed more severe anxiety and depression than Pa_ns. Compared with Pa_ns, Pa_s exhibited increased fALFF value in the left precuneus. Patients shared abnormal fALFF in the frontal-occipital brain regions. There was a positive correlation between fALFF values of the left precuneus and sleep disturbance scores (r = 0.607, p = 0.0000056734) in all patients in addition to a negative correlation between fALFF values of the left MOG/cuneus and HAMD-17 total scores (r = -0.595, p = 0.002141) in Pa_s. The receiver operating characteristic (ROC) results of the fALFF could be used to discriminate Pa_s from Pa_ns with a specificity of 72.73% and a sensitivity of 70.83%.

Conclusion

Pa_s displayed more serious anxiety and depression symptoms. Patients shared abnormal fALFF in the frontal-occipital brain regions, which may be a common characteristic for MDD. And increased fALFF value in the left precuneus might be a specific neuroimaging feature of MDD patients with SD symptoms.

Subclinical alterations of resting state functional brain network for adjunctive bright light therapy in nonseasonal major depressive disorder: A double blind randomized controlled trial

Introduction

The treatment effect of bright light therapy (BLT) on major depressive disorder (MDD) has been proven, but the underlying mechanism remains unclear. Neuroimaging biomarkers regarding disease alterations in MDD and treatment response are rarely focused on BLT. This study aimed to identify the modulatory mechanism of BLT in MDD using resting-state functional magnetic resonance imaging (rfMRI).

Materials and methods

This double-blind, randomized controlled clinical trial included a dim red light (dRL) control group and a BLT experimental group. All participants received light therapy for 30 min every morning for 4 weeks. The assessment of the Hamilton Depression Rating Scale-24 (HAMD-24) and brain MRI exam were performed at the baseline and the 4-week endpoint. The four networks in interest, including the default mode network (DMN), frontoparietal network (FPN), salience network (SN), and sensorimotor network (SMN), were analyzed. Between-group differences of the change in these four networks were evaluated.

Results

There were 22 and 21 participants in the BLT and dRL groups, respectively. Age, sex, years of education, baseline severity, and improvement in depressive symptoms were not significantly different between the two groups. The baseline rfMRI data did not show any significant functional connectivity differences within the DMN, FPN, SN, and SMN between the two groups. Compared with the dRL group, the BTL group showed significantly increased functional connectivity after treatment within the DMN, FPN, SN, and SMN. Graph analysis of the BLT group demonstrated an enhancement of betweenness centrality and global efficiency.

Conclusion

BLT can enhance intra-network functional connectivity in the DMN, FPN, SN, and SMN for MDD patients. Furthermore, BLT improves the information processing of the whole brain.

Clinical trial registration

The ClinicalTrials.gov identifier was NCT03941301.

Neural markers of familial risk for depression - A systematic review

Structural and functional brain alterations are found in adults with depression. It is not known whether these changes are a result of illness or exist prior to disorder onset. Asymptomatic offspring of parents with depression offer a unique opportunity to research neural markers of familial risk to depression and clarify the temporal sequence between brain changes and disorder onset. We conducted a systematic review to investigate whether asymptomatic offspring at high familial risk have structural and functional brain changes like those reported in adults with depression. Our literature search resulted in 44 studies on 18,645 offspring ranging from 4 weeks to 25 years old. Reduced cortical thickness and white matter integrity, and altered striatal reward processing were the most consistent findings in high-risk offspring across ages. These alterations are also present in adults with depression, suggesting the existence of neural markers of familial risk for depression. Additional studies reproducing current results, streamlining fMRI data analyses, and investigating underexplored topics (i.e intracortical myelin, gyrification, subcortical shape) may be among the next steps required to improve our understanding of neural markers indexing the vulnerability to depression.

Aberrant brain dynamics in major depressive disorder with suicidal ideation

BACKGROUND

Suicidal ideation (SI) is a common symptom of major depressive disorder (MDD). Accumulating studies demonstrated that MDD with SI was associated with static alterations in brain activity and functional connectivity. However, given that brain is a highly dynamic system, the changes of brain dynamic patterns in MDD with SI remain unknown.

METHODS

We included 60 MDD patients with SI (MDD-SI), 58 MDD patients without SI (MDD-NSI), and 58 healthy controls (HCs) who underwent resting-state functional magnetic resonance imaging. The sliding-window approach was used to calculate the dynamic fractional amplitude of low-frequency fluctuation (dfALFF) and dynamic degree centrality (dDC) to characterize the temporal dynamic regional activity and distant functional connectivity. We compared dfALFF and dDC across groups and further conducted correlations between abnormal dynamic metrics and the severity of suicidality.

RESULTS

In terms of the dynamic regional activity, MDD-SI showed decreased dfALFF in the left lingual gyrus and right middle occipital gyrus compared with MDD-NSI; in terms of the dynamic distant connectivity, MDD-SI showed decreased dDC in the right middle frontal gyrus compared with MDD-NSI. The decreased dDC in the right middle frontal gyrus was correlated with increased severity of suicidality.

LIMITATIONS

The relatively small sample size.

CONCLUSIONS

We demonstrate the specific brain dynamic patterns of MDD-SI in regional activity and distant functional connectivity compared to MDD-NSI. Especially the decreased temporal variability of the distant connectivity in the middle frontal gyrus was associated with SI. These altered dynamic patterns may represent a potential neurobiological diathesis of SI in MDD.

Fetal sex-specific epigenetic associations with prenatal maternal depressive symptoms

Prenatal maternal mental health is a global health challenge with poorly defined biological mechanisms. We used maternal blood samples collected during the second trimester from a Singaporean longitudinal birth cohort study to examine the association between inter-individual genome-wide DNA methylation and prenatal maternal depressive symptoms. We found that (1) the maternal methylome was significantly associated with prenatal maternal depressive symptoms only in mothers with a female fetus; and (2) this sex-dependent association was observed in a comparable, UK-based birth cohort study. Qualitative analyses showed fetal sex-specific differences in genomic features of depression-related CpGs and genes mapped from these CpGs in mothers with female fetuses implicated in a depression-associated WNT/β-catenin signaling pathway. These same genes also showed enriched expression in brain regions linked to major depressive disorder. We also found similar female-specific associations with fetal-facing placenta methylome. Our fetal sex-specific findings provide evidence for maternal-fetal interactions as a mechanism for intergenerational transmission.

Driving brain state transitions in major depressive disorder through external stimulation

Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility. We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model. Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.

May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data

Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here, we review and clarify the key concepts of connectivity, computation, criticality and coherence-the 4C's-and outline a potential role for metastability as a common denominator across these propositions. We analyse and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging, to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.

Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects

Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging‐based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network‐based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.

Allostasis, Action, and Affect in Depression: Insights from the Theory of Constructed Emotion

The theory of constructed emotion is a systems neuroscience approach to understanding the nature of emotion. It is also a general theoretical framework to guide hypothesis generation for how actions and experiences are constructed as the brain continually anticipates metabolic needs and attempts to meet those needs before they arise (termed allostasis). In this review, we introduce this framework and hypothesize that allostatic dysregulation is a trans-disorder vulnerability for mental and physical illness. We then review published findings consistent with the hypothesis that several symptoms in major depressive disorder (MDD), such as fatigue, distress, context insensitivity, reward insensitivity, and motor retardation, are associated with persistent problems in energy regulation. Our approach transforms the current understanding of MDD as resulting from enhanced emotional reactivity combined with reduced cognitive control and, in doing so, offers novel hypotheses regarding the development, progression, treatment, and prevention of MDD.

An insight into diagnosis of depression using machine learning techniques: a systematic review

BACKGROUND

In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis.

METHODS

This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis.

RESULTS

A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test.

CONCLUSION

The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.

Brain Networks Involved in Depression in Patients with Frontotemporal Dementia and Parkinson’s Disease: An Exploratory Resting-State Functional Connectivity MRI Study

Depression is characterized by feelings of sadness, loss, or anger that may interfere with everyday activities. Such a neuropsychiatric condition is commonly reported in multiple neurodegenerative disorders, which are quite different from each other. This study aimed at investigating the brain networks involved in depression in patients with frontotemporal dementia (FTD) and Parkinson’s disease (PD) as compared to healthy controls (HC). Fifty participants were included in the study: 17 depressed FTD/PD patients; 17 non-depressed FTD/PD patients; and 16 non-depressed HCs matched for age and gender. We used the Beck depression inventory (BDI-II) to measure depression in all groups. On the same day, 3T brain magnetic resonance with structural and resting-state functional sequences were acquired. Differences in resting-state functional connectivity (FC) between depressed and non-depressed patients in all the experimental groups were assessed by using seed-to-seed and network-to-network approaches. We found a significant seed-to-seed hyperconnectivity patterns between the left thalamus and the left posterior temporal fusiform cortex, which differentiated FTD/PD depressed patients from the HCs. Network-to-network analysis revealed a significant hyperconnectivity among the default-mode network (left lateral-parietal region), the medial prefrontal cortex and the left lateral prefrontal cortex (i.e., part of the central executive network). We investigated whether such FC patterns could be related to the underlying neurodegenerative disorder by replicating the analyses with two independent samples (i.e., non-depressed PD and non-depressed FTD patients) and adding clinical parameters as covariates. We found no FC differences in these groups, thus suggesting how the FC pattern we found may signal a common depression-related neural pathway implicated in both the neurocognitive disorders.

Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data

BACKGROUND

Given that major depressive disorder (MDD) is both biologically and clinically heterogeneous, a diagnostic system integrating neurobiological markers and clinical characteristics would allow for better diagnostic accuracy and, consequently, treatment efficacy.

OBJECTIVE

Our study aimed to evaluate the discriminative and predictive ability of unimodal, bimodal, and multimodal approaches in a total of seven machine learning (ML) models-clinical, demographic, functional near-infrared spectroscopy (fNIRS), combinations of two unimodal models, as well as a combination of all three-for MDD.

METHODS

We recruited 65 adults with MDD and 68 matched healthy controls, who provided both sociodemographic and clinical information, and completed the HAM-D questionnaire. They were also subject to fNIRS measurement when participating in the verbal fluency task. Using the nested cross validation procedure, the classification performance of each ML model was evaluated based on the area under the receiver operating characteristic curve (ROC), balanced accuracy, sensitivity, and specificity.

RESULTS

The multimodal ML model was able to distinguish between depressed patients and healthy controls with the highest balanced accuracy of 87.98 ± 8.84% (AUC = 0.92; 95% CI (0.84-0.99) when compared with the uni- and bi-modal models.

CONCLUSIONS

Our multimodal ML model demonstrated the highest diagnostic accuracy for MDD. This reinforces the biological and clinical heterogeneity of MDD and highlights the potential of this model to improve MDD diagnosis rates. Furthermore, this model is cost-effective and clinically applicable enough to be established as a robust diagnostic system for MDD based on patients' biosignatures.

A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods

BACKGROUND

The present study aimed to explore the difference in the brain function and structure between patients with major depressive disorder (MDD) and healthy controls (HCs) using two-center and multi-modal MRI data, which would be helpful to investigate the pathogenesis of MDD.

METHODS

The subjects were collected from two hospitals. One including 140 patients with MDD and 138 HCs was used as primary cohort. Another one including 29 patients with MDD and 52 HCs was used as validation cohort. Functional and structural magnetic resonance images (MRI) were acquired to extract four types of features: functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV). Then classifiers using different combinations among the four types of selected features were respectively built to discriminate patients from HCs. Different templates were applied and the results under different templates were compared.

RESULTS

The classifier built with the combination of FC, ALFF, and GMV under the AAL template discriminated patients from HCs with the best performance (AUC=0.916, ACC=84.8%). The regions selected in all the different templates were mainly located in the default mode network, affective network, prefrontal cortex.

LIMITATIONS

First, the sample size of the validation cohort was limited. Second, diffusion tensor imaging data were not collected.

CONCLUSION

The performance of classifier was improved by using multi-modal MRI imaging. Different templates would be suitable for different types of analysis. The regions selected in all the different templates are possibly the core regions to investigate the pathophysiology of MDD.

Major Depression and Onset of Frontotemporal Dementia

PURPOSE

Frontotemporal dementia (FTD) is still a clinical challenge with the highest rate of misdiagnosis and poor outcome. The pathogenetic relationship between depression and neurodegeneration remains unclear. This study evaluated depression prevalence before FTD diagnosis.

PATIENTS AND METHODS

The aim was to assess the prevalence and impact of depression on FTD diagnostic process. The clinical characteristics of 72 patients hospitalized in Department of Affective and Psychotic Disorders Medical University of Lodz between 2010 and 2020 with final diagnosis FTD were analyzed. The data referring to first psychiatric diagnosis, time from first psychopathological symptoms to clarification of FTD diagnosis were collected. The patients who did not undergo full neuropsychiatric verification were excluded from the analysis.

RESULTS

About 69% of patients had other concomitant diagnosis of mental disorders which was made prior to FTD diagnosis. Among this subsample, 71% revealed depression diagnosis with at least moderate severity. The patients whose first diagnosis was psychotic depression revealed the longest period from the appearance of the first psychopathological symptoms to the diagnosis of FTD in comparison to the subsample with other psychiatric diagnosis (p=0.034; mean 4.33±3.28 years vs mean 2.68±1.39 years).

CONCLUSION

The severe depressive symptoms in older age may reflect the development of neurodegeneration before full-blown frontotemporal dementia symptomatology. We hypothesized that psychotic depression is a predictor of FTD. Further investigations in this field are required.

Neural mechanism of the relationship between sleep efficiency and clinical improvement in major depressive disorder: A longitudinal functional magnetic resonance imaging study

BACKGROUND

Antidepressants represent the most common treatment of choice for major depressive disorder (MDD). In this study, we aimed to explore the status-related changes (acute vs. remitted status) in brain function in patients with MDD.

METHODS

Regular antidepressant medications (an average of 7 months after the initial visit, remitted status) were received by 48 patients with MDD. All the patients underwent MRI and polysomnography examinations as well as clinical assessment at each visit.

RESULTS

We found that baseline fractional amplitude of low-frequency fluctuations (fALFF) of right superior parietal gyrus (SPG) and middle frontal gyrus could predict depression and anxiety symptoms improvement from acute to remitted status in patients with MDD, respectively. Moreover, we found a significant positive correlation between the fALFF of right SPG and baseline sleep efficiency (SE) in patients with MDD. Further mediation analysis revealed that the fALFF of right SPG mediated the relationship between baseline SE and depressive symptom improvement.

CONCLUSION

Apart from highlighting the fALFF as a potential prognostic indicator to predict and track disease progression in patients with MDD, these findings might provide a neural mechanism basis for improving sleep quality of patients with MDD and thus promoting the recovery of clinical symptoms, as well as provide a practical basis for clinical interventions in patients with MDD with sleep disorders.

Influence of the inflammasome complex on psychiatric disorders: clinical and preclinical studies

INTRODUCTION

The innate immune complex, an inflammasome complex, has a role in the etiology of psychiatric disorders. Preclinical studies have demonstrated that the inflammasome activation leads to psychiatric disorders and clinical studies have proved that specific psychiatric illnesses are associated with aberrant levels of inflammatory cytokines and inflammasome. The inflammasome complex could be a major factor in the progression and pathology of psychiatric disorders.

AREA COVERED

We discuss the pathogenesis of psychiatric disorders with respect to the activation of the inflammasome complex. Inflammasome-associated inflammatory cytokines are observed in patients and animal models of psychiatric disorders. The article also reflects on inflammasome regulatory options for the prevention and treatment of psychiatric disorders. Relevant literature available on PubMed from 1992 to 2021 has been included in this review.

EXPERT OPINION

Modulating the inflammasome complex is a potential therapeutic strategy to treat symptom severity for patients with psychiatric disorders, particularly those with inflammasome-associated disorders. However, the nature of the psychiatric disorders should be considered when targeting inflammasome.

Resting State Functional Magnetic Resonance Imaging Elucidates Neurotransmitter Deficiency in Autism Spectrum Disorder

Resting-state functional magnetic resonance imaging provides dynamic insight into the functional organization of the brains' intrinsic activity at rest. The emergence of resting-state functional magnetic resonance imaging in both the clinical and research settings may be attributed to recent advancements in statistical techniques, non-invasiveness and enhanced spatiotemporal resolution compared to other neuroimaging modalities, and the capability to identify and characterize deep brain structures and networks. In this report we describe a 16-year-old female patient with autism spectrum disorder who underwent resting-state functional magnetic resonance imaging due to late regression. Imaging revealed deactivated networks in deep brain structures involved in monoamine synthesis. Monoamine neurotransmitter deficits were confirmed by cerebrospinal fluid analysis. This case suggests that resting-state functional magnetic resonance imaging may have clinical utility as a non-invasive biomarker of central nervous system neurochemical alterations by measuring the function of neurotransmitter-driven networks. Use of this technology can accelerate and increase the accuracy of selecting appropriate therapeutic agents for patients with neurological and neurodevelopmental disorders.

How environmental enrichment balances out neuroinflammation in chronic pain and comorbid depression and anxiety disorders

Depression and anxiety commonly occur in chronic pain states and the coexistence of these diseases worsens outcomes for both disorders and may reduce treatment adherence and response. Despite the advances in the knowledge of chronic pain mechanisms, pharmacological treatment is still unsatisfactory. Research based on exposure to environmental enrichment is currently under investigation and seems to offer a promising low-cost strategy with no side effects. In this review, we discuss the role of inflammation as a major biological substrate and aetiological factor of chronic pain and depression/anxiety and report a collection of preclinical evidence of the effects and mechanisms of environmental enrichment. As microglia participates in the development of both conditions, we also discuss microglia as a potential target underlying the beneficial actions of environmental enrichment in chronic pain and comorbid depression/anxiety. We also discuss how alternative interventions under clinical guidelines, such as environmental enrichment, may improve treatment compliance and patient outcomes.