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

Resting EEG source-level connectivity pattern to predict anhedonia improvement with agomelatine treatment in patients with major depression

BACKGROUND

Neuroimaging studies have revealed that dysfunction of reward circuitry in the brain underlies anhedonia, a core symptom of major depressive disorder (MDD) that is related to treatment outcomes. However, the relationship between the brain network at the level of neuronal oscillations and the longitudinal improvement in the severity of anhedonia is still unknown.

METHODS

The study enrolled 84 unmedicated patients with MDD. Anhedonia severity was measured using the Snaith-Hamilton Pleasure Scale (SHAPS). EEG data in the resting state was obtained both at baseline and following an 8-week course of agomelatine 25 mg taken once daily. Whole-brain functional connectivity (FC) of source-level resting-state EEG and FC-derived graph metrics (i.e., global topological properties: global efficiency and local efficiency) were calculated in distinct frequency bands.

RESULTS

SHAPS scores were significantly improved from baseline to 8 weeks. Concurrently, there was a decrease in alpha-1 (8.5-10 Hz) connectivity between the right-hemisphere precuneus (PreC) and the left-hemisphere inferior frontal gyrus (IFG). Reduced alpha-2 (10.5-12 Hz) connectivity between the right-hemisphere transverse temporal gyrus (TTG) and the left-hemisphere superior frontal gyrus (SFG) and middle frontal gyrus (MFG) was observed. Global efficiency in the alpha-1 (p < 0.001) and alpha-2 (p = 0.003) frequency bands and local efficiency in the alpha-1 frequency band (p = 0.003) were reduced. Correlation analyses showed that alpha-1 local efficiency at baseline predicted improvement in SHAPS scores (r = -0.261, p = 0.017).

CONCLUSION

Global topological properties of source-level EEG FC can predict anhedonia improvement during antidepressant treatment, which might help guide treatment decisions and advance precision psychopharmacology.

Understanding and treating postpartum depression: a narrative review

Postpartum depression (PPD) is an increasingly prevalent but still poorly characterized disorder. Causal and modulating factors include hormones fluctuations, such as estrogen, progesterone, and allopregnolone, pathways imbalances, such as oxytocin and kynurenine, chronobiological factors, and brain imaging alterations. Treatment may differ from the traditional major depression management, while selective serotonin reuptake inhibitors such as sertraline are commonly used and suggested by guidelines, neurosteroids such as brexanolone and the more convenient zuranolone have been recently approved. Newer neurosteroids such as ganaxolone, valaxanolone, and lysaxanolone are currently under development, but also esketamine and psychedelics are promising potential treatments. Other somatic treatments including brain stimulation techniques and light therapy also showed benefit. PPD is therefore increasingly understood as, at least partially, independent from major depressive disorder. Specific and individualized treatments including pharmacological and non-pharmacological therapies are progressively being introduced in the routine clinical practice.

Disrupted interhemispheric functional and structural connectivity in patients with major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is associated with disrupted interhemispheric cooperation. However, the relationship between structural and functional alterations in interhemispheric cooperation in patients with MDD remains unclear. We investigated the associations between voxel-mirrored homotopic connectivity (VMHC) and radial diffusivity (RD) within the corpus callosum (CC) and their links to depressive symptoms in patients with MDD.

METHODS

Sixty patients with MDD and 38 healthy controls (HCs) were assessed using resting-state functional MRI (rs-fMRI) and diffusion MRI (dMRI) to evaluate interhemispheric functional connectivity (VMHC) and structural integrity (RD) in the CC subregions. Group comparisons, correlation analyses, and mediation analyses were conducted to identify the significant differences, relationships, and indirect effects.

RESULTS

Patients with MDD showed significantly reduced VMHC in the bilateral postcentral gyrus and lingual gyrus and increased RD in the CC subregions CC3, CC4, and CC5, indicating impaired functional and structural connectivity. Lower VMHC in the lingual gyrus was negatively correlated with depressive severity, whereas increased RD in the CC4 and CC5 was positively correlated with depressive symptoms. Mediation analysis revealed that the VMHC in the lingual gyrus fully mediated the relationship between RD in CC5 and depressive symptoms, suggesting a pathway through which structural impairments may affect mood through abnormal functional connectivity.

LIMITATIONS

The cross-sectional design limits the assessment of changes over time, and focusing solely on interhemispheric connectivity may overlook other networks involved in MDD.

CONCLUSION

These findings provide preliminary evidence for disrupted interhemispheric coordination in MDD, with both functional and structural connectivity impairments linked to depressive symptoms. The mediating effect of the VMHC in the lingual gyrus highlights the potential role of interhemispheric connectivity in the pathophysiology of MDD. Our results provide an integrative perspective on the functional and microstructural organization of the brain in patients with MDD.

Depressive mixed state and anxious distress as risk factors for suicidal behavior during major depressive episodes

Accurately assessing and predicting suicidal behavior in patients with depression are challenging for researchers and clinicians. We examined various risk factors for suicidal behavior during major depressive episodes (MDE), especially focusing on depressive mixed state (DMX) and anxious distress (AD). We recruited 187 patients with MDE and divided them into two groups-with and without suicidal behavior-defined as the cut-off score of 1 or more on the suicidal behavior sub-item in the quick inventory of depressive symptomatology-self report. The presence of DMX was defined as a total score of 13 or more on the self-administered 8-item questionnaire for DMX. We used multivariate logistic regression analysis with the presence or absence of suicidal behavior as a dependent variable for investigating factors associated with suicidal behavior. The with suicidal behavior group was younger and indicated a greater proportion of past suicide attempts, AD, and DMX than the without suicidal behavior group. Logistic regression analysis revealed that AD (P = 0.020) and DMX (P = 0.018) were significantly associated with suicidal behavior. AD and DMX may promote suicidal behavior during MDE. These two psychopathological features should be carefully monitored and intensively treated for the prevention of suicide-related events.

Integrating virtual reality, electroencephalography, and transcranial magnetic stimulation to study the neural correlates of awe experiences: The SUBRAIN protocol

INTRODUCTION

Awe is a complex emotion unveiling a positive and mixed nature, which resembles the Romantic feeling of the Sublime. It has increasingly become the object of scientific investigation in the last twenty years. However, its underlying brain mechanisms are still unclear. To fully capture its nature in the lab, researchers have increasingly relied on virtual reality (VR) as an emotion-elicitation method, which can resemble even complex phenomena in a limited space. In this work, a multidisciplinary team proposed a novel experimental protocol integrating VR, electroencephalography (EEG), and transcranial magnetic stimulation (TMS) to investigate the brain mechanisms of this emotion.

METHODS

A group of bioengineers, psychologists, psychiatrists, and philosophers designed the SUBRAIN study, a single-center, one-arm, non-randomized interventional study to explore the neural processes underlying awe experiences. The study is ongoing and is expected to enroll fifty adults between 20 and 40 years of age. Currently, more than 40 individuals have been enrolled. The experimental protocol includes different steps: (i) screening, (ii) enrollment, (iii) pre-experimental assessment, (iv) VR experimental assessment, and (v) post-experimental debriefing. The brain's electrical activity is recorded using the EEG while participants navigated three immersive awe-inducing VR environments and a neutral one. At the same time, the cortical excitability and connectivity is investigated by performing a TMS-EEG session right after each VR navigation. Along with cerebral signals, self-reported questionnaires were used to assess the VR-induced changes in the emotional state of the subjects. This data is then analyzed to delve into the cerebral mechanisms of awe.

DISCUSSION

This study protocol is the first one that tries to fully understand the neural bases of awe by eliciting and studying this phenomenon in VR. The pairing of awe-inducing VR experiences and questionnaires investigating participants' affect and emotions, with non-invasive neural techniques, can provide a novel and extensive knowledge on this complex phenomenon. The protocol can inform on the combination of different instruments showing a reproducible and reliable setting for the investigation of induced complex emotions.

Psychobiological Stress Regulation in Depressive Women Achieved Through Group Music Therapy: Results From the Randomised-Controlled Music Therapy for Depression Study

Major depressive disorder (MDD) is a stress-related disease that affects women more often than men. Music therapy (MT) has been shown to be effective in the treatment of MDD. However, clinical trials investigating the effects of MT on psychological and psychobiological stress-related outcomes in women suffering from MDD are still scarce. This study was conducted as a randomised controlled trial, with participants assigned to either an intervention group (IG), which received group MT (GMT), or a waitlist control group (CG), which received GMT 6 months later. The primary objective was to assess the impact of GMT on psychological stress outcomes (chronic stress, stress coping, and stress experienced in daily life) and psychobiological stress markers (diurnal salivary cortisol levels and circadian heart rate variability), considering the effects of both group allocation and time. Outcome measurements were taken before, immediately after, and-for some variables-10 weeks following the intervention period. A total of 102 women 18-65 years old and diagnosed with current MDD took part in the study. Overall, the IG demonstrated significantly stronger stress-reducing effects than the CG. Significant improvements were observed in general stress coping, positive thinking, daily life stress, and cortisol levels. GMT is a cost-effective and non-invasive approach to effectively address the stress-related psychological and psychobiological burden associated with MDD. To demonstrate long-term effects and gain a better understanding of the underlying mechanisms, further methodologically robust studies are needed. TRAIL REGISTRATION: The MUSED study was pre-registered at the German Clinical Trials Registry (DRKS00016616). All study-related procedures were published in detail in a study protocol.

Framing major depressive disorder as a condition of network imbalance at the compartment level: a proof-of-concept study

Major depressive disorder (MDD) is associated with hypoactivity in the frontoparietal (FP) system and hyperactivity in the limbic (LM) system. The widely accepted limbic-cortical dysregulation model has recently been extended by the concept of imbalanced reciprocal suppression between these 2 systems. This study investigates the refined theoretical framework. Neuroimaging datasets from 60 MDD and 60 healthy controls were obtained from the Canadian Biomarker Integration Network in Depression database, including structural magnetic resonance imaging (MRI) and resting-state functional MRI (rsfMRI). The cerebral cortex was parcellated using the modular analysis and similarity measurements (MOSI) technique. For each node, the average amplitude of low-frequency fluctuation (avgALFF) and nodal strength were calculated. Correlation analyses were conducted to establish an adjacency matrix and assess the relationship between nodal power and strength. The results indicated that the LM system in MDD displayed higher partition numbers and avgALFF (P < 0.005). A significant negative correlation between nodal strength and power was replicated (P < 1E-10), suggesting that greater functional input enhances regional neural suppression. Notably, MDD participants exhibited a higher negative correlation between FP nodal power and LM-FP connectivity (stronger suppression) but a lower negative correlation between LM nodal power and FP-LM connectivity (weaker suppression). These findings support the theory of abnormal cortical signal organization and reciprocal suppression in MDD.

Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression

Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD's complex and varied neuropathology. Identifying biomarkers for antidepressant treatment requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined with advanced data-driven methods, can enhance our understanding of the neurobiological processes influencing treatment outcomes. To address this, we analyzed resting-state fMRI and EEG connectivity data from 130 patients treated with sertraline and 135 patients with placebo from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks to integrate data-augmented connectivity and cross-modality correlation, aiming to predict individual symptom changes by revealing multimodal brain network signatures. The results showed that our model demonstrated promising prediction accuracy, with an R2 value of 0.24 for sertraline and 0.20 for placebo. It also exhibited potential in transferring predictions using only EEG. Key brain regions identified for predicting sertraline response included the inferior temporal gyrus (fMRI) and posterior cingulate cortex (EEG), while for placebo response, the precuneus (fMRI) and supplementary motor area (EEG) were critical. Additionally, both modalities identified the superior temporal gyrus and posterior cingulate cortex as significant for sertraline response, while the anterior cingulate cortex and postcentral gyrus were common predictors in the placebo arm. Additionally, variations in the frontoparietal control, ventral attention, dorsal attention, and limbic networks were notably associated with MDD treatment. By integrating fMRI and EEG, our study established novel multimodal brain network signatures to predict individual responses to sertraline and placebo in MDD, providing interpretable neural circuit patterns that may guide future targeted interventions. Trial Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) ClinicalTrials.gov Identifier: NCT#01407094.

Examining anterior prefrontal cortex resting-state functional connectivity patterns associated with depressive symptoms in chronic moderate-to-severe traumatic brain injury

In chronic moderate-to-severe TBI (msTBI), depression is one of the most common psychiatric consequences. Yet to date, there is limited understanding of its neural underpinnings. This study aimed to better understand this gap by examining seed-to-voxel connectivity in depression, with all voxel-wise associations seeded to the bilateral anterior prefrontal cortices (aPFC). In a secondary analysis of 32 patients with chronic msTBI and 17 age-matched controls acquired from the Toronto Rehab TBI Recovery Study database, the Personality Assessment Inventory Depression scale scores were used to group patients into an msTBI-Dep group (T ≥ 60; n = 13) and an msTBI-Non-Dep group (T < 60; n = 19). Resting-state fMRI scans were analyzed using seed-based connectivity analyses. F-tests, controlling for age and education, were used to assess differences in bilateral aPFC rsFC across the 3 groups. After nonparametric permutation testing, the left aPFC demonstrated significantly increased rsFC with the left (p = 0.041) and right (p = 0.013) fusiform gyri, the right superior temporal lobe (p = 0.032), and the right precentral gyrus (p = 0.042) in the msTBI-Dep group compared to controls. The msTBI-Non-Dep group had no significant rsFC differences with either group. To our knowledge, this study is the first to examine aPFC rsFC in a sample of patients with msTBI exclusively. Our preliminary findings suggest a role for the aPFC in the pathophysiology of depressive symptoms in patients with chronic msTBI. Increased aPFC-sensory/motor rsFC could be associated with vulnerability to depression post-TBI, a hypothesis that warrants further investigation.

Objective outcome prediction in depression through functional MRI brain network dynamics

RESEARCH PURPOSE

Subjective clinical decision-making in major depressive disorder (MDD) may result in low treatment effectiveness. This study aims to identify objective predictors of MDD outcome using resting-state functional MRI scans, acquired from 25 MDD patients at baseline. Over a year, patients were assessed every 3 months, labeled as positive or negative outcome (change in depression severity). Group independent component analysis (GICA) identified (sub)networks at different orders, from which static and dynamic (wavelet) fMRI features were extracted. Binary classifiers performed MDD outcome prediction at each follow-up.

PRINCIPAL RESULTS

The total coherence feature, reflecting network interactivity, yielded the highest performance (area under the curve (AUC) of 0.70). In the positive outcome group, total coherence between the default mode network and ventral salience network was increased at all follow-ups. Classification using this feature alone further demonstrated its discriminating capability (AUC of 0.76 ± 0.10 over all follow-ups). These results suggest that a higher switching capability between internal and external brain states predicts symptom improvement. Higher GICA orders, where major networks are divided into subnetworks, yielded optimal classification performance.

MAJOR CONCLUSIONS

Total coherence, a dynamic fMRI measure, achieved the highest classification performance. These findings contribute to the identification of prognostic biomarkers in MDD.

Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.

Quantification of Glutathione and Its Associated Spontaneous Neuronal Activity in Major Depressive Disorder and Obsessive-Compulsive Disorder

BACKGROUND

Glutathione (GSH) is a crucial antioxidant in the human brain. Although proton magnetic resonance spectroscopy using the Mescher-Garwood point-resolved spectroscopy sequence is highly recommended, limited literature has measured cortical GSH using this method in major psychiatric disorders.

METHODS

By combining magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging, we quantified brain GSH and glutamate in the medial prefrontal cortex and precuneus and explored relationships between GSH levels and intrinsic neuronal activity as well as clinical symptoms among healthy control (HC) participants (n = 30), people with major depressive disorder (MDD) (n = 28), and people with obsessive-compulsive disorder (OCD) (n = 28).

RESULTS

GSH concentrations were lower in the medial prefrontal cortex and precuneus in both the MDD and OCD groups than in the HC group. In the HC group, positive correlations were noted between GSH and glutamate levels and between GSH and fractional amplitude of low-frequency fluctuations in both regions. However, while these correlations were absent in both patient groups, there was a weak positive correlation between glutamate and fractional amplitude of low-frequency fluctuations. Moreover, GSH levels were negatively correlated with depressive and compulsive symptoms in MDD and OCD, respectively.

CONCLUSIONS

These findings suggest that reduced GSH levels and an imbalance between GSH and glutamate could increase oxidative stress and alter neurotransmitter signaling, thereby leading to disruptions in GSH-related neurochemical-neuronal coupling and psychopathologies across MDD and OCD. Understanding these mechanisms could provide valuable insights into the processes that underlie these disorders and potentially become a springboard for future directions and advancing our knowledge of their neurobiological foundations.

Altered electroencephalography-based source functional connectivity in drug-free patients with major depressive disorder

BACKGROUND

Compared to functional magnetic resonance imaging (fMRI), source localization of a scalp-recorded electroencephalogram (EEG) provides higher temporal resolution and frequency synchronization to better understand the potential neurophysiological origins of disrupted functional connectivity (FC) in major depressive disorder (MDD). The present study aimed to investigate EEG-sourced measures to examine the FC in drug-free patients with MDD.

METHOD

Resting-state 32-channel EEG were recorded in 84 drug-free patients with MDD and 143 healthy controls, and the cortical source signals were estimated. Exact low-resolution brain electromagnetic tomography (eLORETA) was used to compute the intracortical activity from regions within the default mode network (DMN) and frontoparietal network (PFN). Lagged phase synchronization was used as a measure of functional connectivity.

RESULTS

Compared with control subjects, the MDD group showed greater within-DMN alpha 1 and 2 bands and within-FPN alpha 1, 2, and beta 3 bands. Furthermore, the MDD group showed hyperconnectivity between the DMN and the FPN in the alpha 1 and 2 bands. Finally, higher levels of anhedonia were associated with higher between-network DMN and FPN connectivity in the alpha-1 band.

LIMITATIONS

Due to the inherent limitations of eLORETA with predefined seeds, we could not exclude connectivity between regions of interest (ROIs), which may be related to the activity from regions adjacent to the ROIs.

CONCLUSIONS

The present findings support the importance of phase-lagged functional dysconnectivity in the neurophysiological mechanisms underlying MDD. Exploring the potential of these patterns as surrogates for treatment responses may advance targeted interventions for depression.

Assessment of Resting-state functional Magnetic Resonance Imaging Connectivity Among Patients with Major Depressive Disorder: A Comparative Study

Background

Resting-state functional connectivity analysis has a potential to unearth the putative neuronal underpinnings of various disorders of the brain. Major depressive disorder (MDD) is regarded as a disorder arising from alterations in functional networks of the brain.

Purpose

There is paucity of literature on resting-state functional magnetic resonance imaging (Rs-fMRI) in MDD, especially from the Indian subcontinent. The purpose of our study was to elucidate the differences in Rs-fMRI connectivity between MDD patients and age and gender matched healthy controls (HC).

Methods

In this prospective single institute-based study, the patients were recruited consecutively based on Hamilton depression rating scale (HAM-D). Age and gender matched HC were also recruited. Rs-fMRI and anatomical MRI images were acquired for all the subjects (MDD and HC group) and subsequent analysis was done using the CONN toolbox.

Results

A total of 49 subjects were included in the final analysis (MDD = 28 patients, HC = 21). HAM-D score was noted to be 24.4 ± 4.8 in the MDD group. There was no significant difference between MDD and HC groups as far as age, gender, employment status, and level of education is concerned. Region-of-interest-based analysis of Rs-fMRI data showed a significantly lower connectivity between the left insula and left nucleus accumbens and between left paracingulate gyrus and bilateral posterior middle temporal gyri in MDD group as compared to HC group.

Conclusion

There is reduced connectivity between certain key regions of the brain in MDD patients, that is, between the left insular cortex and the left nucleus accumbens and between the left paracingulate gyrus and the bilateral posterior middle temporal gyrus. These findings could explain the basis of clinical features of MDD such as anhedonia, rumination of thoughts, reduced visuo-spatial comprehension, reduced language function, and response to external stimuli.

Brain functional effects of cognitive behavioral therapy for depression: A systematic review of task-based fMRI studies

BACKGROUND

Depressive disorders are associated with alterations in brain function, affecting processes such as affective and reward processing and emotion regulation. However, the influence of Cognitive Behavioral Therapy (CBT) on the neuronal patterns remains inadequately understood. Therefore, this review systematically summarizes longitudinal fMRI brain activity changes in depressive patients treated with CBT and their association with symptom remission.

METHODS

This systematic review was conducted according to the PRISMA statement. Out of 2149 results of the literature search, N = 14 studies met the inclusion criteria (e.g., diagnosis of a current depressive disorder, assessment of longitudinal task-based fMRI, and the analysis of functional changes before and after CBT).

RESULTS

The findings reveal (1) diminished limbic reactivity following CBT across various tasks, (2) increased striatal activity during reward processing, but decreased activity during affective processing and future thinking, and (3) alterations in cingulate and prefrontal cortex activity across tasks. Partially, these results are associated with symptom remission, especially in the subgenual anterior cingulate cortex.

LIMITATIONS

There are heterogenous results especially in cortical areas that might partially be due to methodological issues like differences across the studies in terms of task content, statistical evaluation, and interventions. Thus, future research should focus on the standardization of methodologies.

CONCLUSIONS

The results indicate that CBT partially normalizes the neural patterns of depressive patients, particularly within regions involved in affective and reward processing and the development of negative cognitive biases. Overall, potential neural mechanisms underlying CBT were identified, underscoring its effectiveness on an objective neurobiological basis.

Boosting Psychotherapy With Noninvasive Brain Stimulation: The Whys and Wherefores of Modulating Neural Plasticity to Promote Therapeutic Change

The phenomenon of neural plasticity pertains to the intrinsic capacity of neurons to undergo structural and functional reconfiguration through learning and experiential interaction with the environment. These changes could manifest themselves not only as a consequence of various life experiences but also following therapeutic interventions, including the application of noninvasive brain stimulation (NIBS) and psychotherapy. As standalone therapies, both NIBS and psychotherapy have demonstrated their efficacy in the amelioration of psychiatric disorders' symptoms, with a certain variability in terms of effect sizes and duration. Consequently, scholars suggested the convenience of integrating the two interventions into a multimodal treatment to boost and prolong the therapeutic outcomes. Such an approach is still in its infancy, and the physiological underpinnings substantiating the effectiveness and utility of combined interventions are still to be clarified. Therefore, this opinion paper aims to provide a theoretical framework consisting of compelling arguments as to why adding NIBS to psychotherapy can promote therapeutic change. Namely, we will discuss the physiological effects of the two interventions, thus providing a rationale to explain the potential advantages of a combined approach.

Brain Insights and Resolution of Youth Depression through Neurotechnology

As per United Nations, the youth constitute 16% of total population globally whereas World Health Organization reported that one in every seven young individual suffers from depression. Among various tested therapeutic solutions for depression management, the efficacy of transcranial Direct Current Stimulation (tDCS) is still unexplored specifically in young participants. Therefore, this study aims to investigate the cross hemispheric tDCS intervention with a smaller number of sessions in youth population by means of neurological, neuropsychological, and behavioural measures. A total of 50 young participants were recruited comprising of 25 healthy and 25 depressed individuals. The participants of depressed group were randomly assigned to active tDCS and sham tDCS sub groups and completed 150 min of training over 5 consecutive days. The active tDCS group received stimulation of 2 mA over dorsolateral prefrontal cortex. Unlike healthy individuals, depressed participants demonstrated reduced difference of brain activity between eyes opened and closed resting conditions which gets restored following the intervention in active group. Additionally, the tDCS intervention effectively modified the previously reduced alpha asymmetry observed in depressed participants compared to healthy individuals. These neurological outcomes may also be supported with enhanced neuropsychological score of depression (t = 5.47, P < .01) in active group. The attention score (t = 5.14, P < .01) and reaction time (t = 2.22, P = .02) evaluated through behavioural measure of Stroop task were also significantly improved in active group post tDCS intervention. The reported outcomes of the study highlighted the ability of tDCS for prompt and efficient youth depression management.

Identifying dysfunctional cell types and circuits in animal models for psychiatric disorders with calcium imaging

A central goal of neuroscience is to understand how the brain transforms external stimuli and internal bodily signals into patterns of activity that underlie cognition, emotional states, and behavior. Understanding how these patterns of activity may be disrupted in mental illness is crucial for developing novel therapeutics. It is well appreciated that psychiatric disorders are complex, circuit-based disorders that arise from dysfunctional activity patterns generated in discrete cell types and their connections. Recent advances in large-scale, cell-type specific calcium imaging approaches have shed new light on the cellular, circuit, and network-level dysfunction in animal models for psychiatric disorders. Here, we highlight a series of recent findings over the last ~10 years from in vivo calcium imaging studies that show how aberrant patterns of activity in discrete cell types and circuits may underlie behavioral deficits in animal models for several psychiatric disorders, including depression, anxiety, autism spectrum disorders, and schizophrenia. These advances in calcium imaging in pre-clinical models demonstrate the power of cell-type-specific imaging tools in understanding the underlying dysfunction in cell types, activity patterns, and neural circuits that may contribute to disease and provide new blueprints for developing more targeted therapeutics and treatment strategies.

Multi-modal MRI for objective diagnosis and outcome prediction in depression

RESEARCH PURPOSE

The low treatment effectiveness in major depressive disorder (MDD) may be caused by the subjectiveness in clinical examination and the lack of quantitative tests. Objective biomarkers derived from magnetic resonance imaging (MRI) may support clinical experts during decision-making. Numerous studies have attempted to identify such MRI-based biomarkers. However, the majority is uni-modal (based on a single MRI modality) and focus on either MDD diagnosis or outcome. Uncertainty remains regarding whether key features or classification models for diagnosis may also be used for outcome prediction. Therefore, we aim to find multi-modal predictors of both, MDD diagnosis and outcome. By addressing these research questions using the same dataset, we eliminate between-study confounding factors. Various structural (T1-weighted, T2-weighted, diffusion tensor imaging (DTI)) and functional (resting-state and task-based functional MRI) scans were acquired from 32 MDD and 31 healthy control (HC) subjects during the first visit at the investigational site (baseline). Depression severity was assessed at baseline and 6 months later. Features were extracted from the baseline MRI images with different modalities. Binary 6-months negative and positive outcome (NO; PO) classes were defined based on relative (to baseline) change in depression severity. Support vector machine models were employed to separate MDD from HC (diagnosis) and NO from PO subjects (outcome). Classification was performed through a uni-modal (features from a single MRI modality) and multi-modal (combination of features from different modalities) approach.

PRINCIPAL RESULTS

Our results show that DTI features yielded the highest uni-modal performance for diagnosis and outcome prediction: mean diffusivity (AUC (area under the curve) = 0.701) and the sum of streamline weights (AUC = 0.860), respectively. Multi-modal ensemble classifiers with T1-weighted, resting-state functional MRI and DTI features improved classification performance for both diagnosis and outcome (AUC = 0.746 and 0.932, respectively). Feature analyses revealed that the most important features were located in frontal, limbic and parietal areas. However, the modality or location of these features was different between diagnostic and prognostic models.

MAJOR CONCLUSIONS

Our findings suggest that combining features from different MRI modalities predict MDD diagnosis and outcome with higher performance. Furthermore, we demonstrated that the most important features for MDD diagnosis were different and located in other brain regions than those for outcome. This longitudinal study contributes to the identification of objective biomarkers of MDD and its outcome. Follow-up studies may further evaluate the generalizability of our models in larger or multi-center cohorts.

Enhancing Major Depressive Disorder Diagnosis With Dynamic-Static Fusion Graph Neural Networks

Major Depressive Disorder (MDD) is a debilitating, complex mental condition with unclear mechanisms hindering diagnostic progress. Research links MDD to abnormal brain connectivity using functional magnetic resonance imaging (fMRI). Yet, existing fMRI-based MDD models suffer from limitations, including neglecting dynamic network traits, lacking interpretability, and struggling with small datasets. We present DSFGNN, a novel graph neural network framework addressing these issues for improved MDD diagnosis. DSFGNN employs a graph isomorphism encoder to model static and dynamic brain networks, achieving effective fusion of temporal and spatial information through a spatiotemporal attention mechanism, thereby enhancing interpretability. Furthermore, we incorporate a causal disentangling module and orthogonal regularization module to augment the model's expressiveness. We evaluate DSFGNN on the Rest-meta-MDD dataset, yielding superior results compared to the best baseline. Besides, extensive ablation studies and interpretability analysis confirm DSFGNN's effectiveness and potential for biomarker discovery.

Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or 'biotypes' to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free (n = 801) and after randomization to pharmacotherapy or behavioral therapy (n = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.

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.

Neuromelanin-sensitive magnetic resonance imaging in the study of mental disorder: A systematic review

Dopamine and norepinephrine are implicated in the pathophysiology of mental disorders, but non-invasive study of their neuronal function remains challenging. Recent research suggests that neuromelanin-sensitive magnetic resonance imaging (NM-MRI) techniques may overcome this limitation by enabling the non-invasive imaging of the substantia nigra (SN)/ ventral tegmental area (VTA) dopaminergic and locus coeruleus (LC) noradrenergic systems. A review of 19 studies that met the criteria for NM-MRI application in mental disorders found that despite the use of heterogeneous sequence parameters and metrics, nearly all studies reported differences in contrast ratio (CNR) of LC or SN/VTA between patients with mental disorders and healthy controls. These findings suggest that NM-MRI is a valuable tool in psychiatry, but the differences in sequence parameters across studies hinder comparability, and a standardized analysis pipeline is needed to improve the reliability of results. Further research using standardized methods is needed to better understand the role of dopamine and norepinephrine in mental disorders.

Test-Retest Reliability of Functional Connectivity in Adolescents With Depression

BACKGROUND

The test-retest reliability of functional magnetic resonance imaging is critical to identifying reproducible biomarkers for psychiatric illness. Recent work has shown how reliability limits the observable effect size of brain-behavior associations, hindering detection of these effects. However, while a fast-growing literature has explored both univariate and multivariate reliability in healthy individuals, relatively few studies have explored reliability in populations with psychiatric illnesses or how this interacts with age.

METHODS

Here, we investigated functional connectivity reliability over the course of 1 year in a longitudinal cohort of 88 adolescents (age at baseline = 15.63 ± 1.29 years; 64 female) with major depressive disorder (MDD) and without MDD (healthy volunteers [HVs]). We compared a univariate metric, intraclass correlation coefficient, and 2 multivariate metrics, fingerprinting and discriminability.

RESULTS

Adolescents with MDD had marginally higher mean intraclass correlation coefficient (μMDD = 0.34, 95% CI, 0.12-0.54; μHV = 0.27, 95% CI, 0.05-0.52), but both groups had poor average intraclass correlation coefficients (<0.4). Fingerprinting index was greater than chance and did not differ between groups (fingerprinting indexMDD = 0.75; fingerprinting indexHV = 0.91; Poisson tests p < .001). Discriminability indicated high multivariate reliability in both groups (discriminabilityMDD = 0.80; discriminabilityHV = 0.82; permutation tests p < .01). Neither univariate nor multivariate reliability was associated with symptom severity or edge-level effect size of group differences.

CONCLUSIONS

Overall, we found little evidence for a relationship between depression and reliability of functional connectivity during adolescence. These findings suggest that biomarker identification in depression is not limited due to reliability compared with healthy samples and support the shift toward multivariate analysis for improved power and reliability.

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.

A review of machine learning applications for the proton MR spectroscopy workflow

This literature review presents a comprehensive overview of machine learning (ML) applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the MR field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.

Increased thalamic gray matter volume induced by repetitive transcranial magnetic stimulation treatment in patients with major depressive disorder

Purpose

Repetitive transcranial magnetic stimulation (rTMS) is an effective therapy in improving depressive symptoms in MDD patients, but the intrinsic mechanism is still unclear. In this study, we investigated the influence of rTMS on brain gray matter volume for alleviating depressive symptoms in MDD patients using structural magnetic resonance imaging (sMRI) data.

Methods

Patients with first episode, unmedicated patients with MDD (n = 26), and healthy controls (n = 31) were selected for this study. Depressive symptoms were assessed before and after treatment by using the HAMD-17 score. High-frequency rTMS treatment was conducted in patients with MDD over 15 days. The rTMS treatment target is located at the F3 point of the left dorsolateral prefrontal cortex. Structural magnetic resonance imaging (sMRI) data were collected before and after treatment to compare the changes in brain gray matter volume.

Results

Before treatment, patients with MDD had significantly reduced gray matter volumes in the right fusiform gyrus, left and right inferior frontal gyrus (triangular part), left inferior frontal gyrus (orbital part), left parahippocampal gyrus, left thalamus, right precuneus, right calcarine fissure, and right median cingulate gyrus compared with healthy controls (P < 0.05). After rTMS treatment, significant growth in gray matter volume of the bilateral thalamus was observed in depressed patients (P < 0.05).

Conclusion

Bilateral thalamic gray matter volumes were enlarged in the thalamus of MDD patients after rTMS treatment and may be the underlying neural mechanism for the treatment of rTMS on depression.

Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review

Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.

Research on major depression in China: A perspective from bibliometric analysis

BACKGROUND

Major depressive disorder (MDD) is a major psychiatric disorder with increasing research intensity. However, papers written in languages other than English are less accessible to international readers. This study examined the bibliometric features of English and Chinese language research papers about major depressive disorder in China.

METHODS

The Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) databases were searched for eligible studies. Authorship collaboration networks and keyword co-occurrences were estimated and visualized.

RESULTS

There were 2,220 and 63,306 publications on MDD in the WoS and CNKI between 1990 and 2021, respectively. The number of papers increased annually during the period. For papers written in English, the Journal of Affective Disorders (201; 9.05 %) had the highest activity and the Shanghai Jiao Tong University had the most publications (232; 10.45 %). For papers in Chinese, the highest activity was with the Journal of Clinical Psychiatry (1,025; 1.62 %) and the Beijing University of Chinese Medicine (1,098; 1.73 %). Xiang YT (68; 3.06 %) and Yuan YG (179; 0.28 %) were the most productive authors in the English and Chinese languages, respectively. Keyword analysis showed that English and Chinese publications differed in emphasis (English: related psychiatric conditions, study design, clinical aspects, and assessment instruments; Chinese: somatic comorbidities, antidepressants, related psychiatric conditions, treatment of depression, and electrophysiological).

CONCLUSIONS

The number of scientific papers on MDD increased yearly, and Chinese authors writing in English have an increasing influence. Except for a few authors, productivity and influence were dominated by national universities and specialized medical universities.