Fronto-limbic neuroimaging biomarkers for diagnosis and prediction of treatment responses in major depressive disorder

Advances in biosensors for major depressive disorder diagnostic biomarkers

Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.

Intracranial Directed Connectivity Links Subregions of the Prefrontal Cortex to Major Depression

Understanding the neural basis of major depressive disorder (MDD) is vital to guiding neuromodulatory treatments. The available evidence supports the hypothesis that MDD is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neural systems lead to diminished emotion regulation and intrusive ruminations. Recent research also points towards network changes in the brain, especially within the prefrontal cortex (PFC), as primary sources of MDD etiology. However, due to limitations in spatiotemporal resolution and clinical opportunities for intracranial recordings, this hypothesis has not been directly tested. We recorded intracranial EEG from the dorsolateral (dlPFC), orbitofrontal (OFC), and anterior cingulate cortices (ACC) in neurosurgical patients with MDD. We measured daily fluctuations in self-reported depression severity alongside directed connectivity between these PFC subregions. We focused primarily on delta oscillations (1-3 Hz), which have been linked to GABAergic inhibitory control and intracortical communication. Depression symptoms worsened when connectivity within the left vs. right PFC became imbalanced. In the left hemisphere, all directed connectivity towards the ACC, from the dlPFC and OFC, was positively correlated with depression severity. In the right hemisphere, directed connectivity between the OFC and dlPFC increased with depression severity as well. This is the first evidence that delta oscillations flowing between prefrontal subregions transiently increase intensity when people are experiencing more negative mood. These findings support the overarching hypothesis that MDD worsens with prefrontal disinhibition.

Leptin's Inverse Association With Brain Morphology and Depressive Symptoms: A Discovery and Confirmatory Study Across 2 Independent Samples

BACKGROUND

Major depressive disorder has a complex, bidirectional relationship with metabolic dysfunction, but the neural correlates of this association are not well understood.

METHODS

In this cross-sectional investigation, we used a 2-step discovery and confirmatory strategy utilizing 2 independent samples (sample 1: 288 participants, sample 2: 196 participants) to examine the association between circulating indicators of metabolic health (leptin and adiponectin) and brain structures in individuals with major depressive disorder.

RESULTS

We found a replicable inverse correlation between leptin levels and cortical surface area within essential brain areas responsible for emotion regulation, such as the left posterior cingulate cortex, right pars orbitalis, right superior temporal gyrus, and right insula (standardized beta coefficient range: -0.27 to -0.49, puncorrected < .05). Notably, this relationship was independent of C-reactive protein levels. We also identified a significant interaction effect of leptin levels and diagnosis on the cortical surface area of the right superior temporal gyrus (standardized beta coefficient = 0.26 in sample 1, standardized beta coefficient = 0.30 in sample 2, puncorrected < .05). We also observed a positive correlation between leptin levels and atypical depressive symptoms in both major depressive disorder groups (r = 0.14 in sample 1, r = 0.29 in sample 2, puncorrected < .05).

CONCLUSIONS

The inverse association between leptin and cortical surface area in brain regions that are important for emotion processing and leptin's association with atypical depressive symptoms support the hypothesis that metabolic processes may be related to emotion regulation. However, the molecular mechanisms through which leptin may exert these effects should be explored further.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Computational Modeling of the Prefrontal-Cingulate Cortex to Investigate the Role of Coupling Relationships for Balancing Emotion and Cognition

Within the prefrontal-cingulate cortex, abnormalities in coupling between neuronal networks can disturb the emotion-cognition interactions, contributing to the development of mental disorders such as depression. Despite this understanding, the neural circuit mechanisms underlying this phenomenon remain elusive. In this study, we present a biophysical computational model encompassing three crucial regions, including the dorsolateral prefrontal cortex, subgenual anterior cingulate cortex, and ventromedial prefrontal cortex. The objective is to investigate the role of coupling relationships within the prefrontal-cingulate cortex networks in balancing emotions and cognitive processes. The numerical results confirm that coupled weights play a crucial role in the balance of emotional cognitive networks. Furthermore, our model predicts the pathogenic mechanism of depression resulting from abnormalities in the subgenual cortex, and network functionality was restored through intervention in the dorsolateral prefrontal cortex. This study utilizes computational modeling techniques to provide an insight explanation for the diagnosis and treatment of depression.

Functional Connectivity Differences in the Resting-state of the Amygdala in Alcohol-dependent Patients with Depression

RATIONALE AND OBJECTIVES

The mechanism of comorbidity between alcohol dependence and depressive disorders are not well understood. This study investigated differences in the brain function of alcohol-dependent patients with and without depression by performing functional connectivity analysis using resting-state functional magnetic resonance imaging.

MATERIALS AND METHODS

A total of 29 alcohol-dependent patients with depression, 31 alcohol-dependent patients without depression and 31 healthy control subjects were included in this study. The resting-state functional connectivity between the amygdala and the whole brain was compared among the three groups. Additionally, we examined the correlation between functional connectivity values in significantly different brain regions and levels of alcohol dependence and depression.

RESULTS

The resting-state functional connectivity between the left amygdala and the right caudate nucleus was decreased in alcohol-dependent patients. Additionally, the resting-state functional connectivity of the right amygdala with the right caudate nucleus, right transverse temporal gyrus, right temporal pole: superior temporal gyrus were also decreased. In alcohol-dependent patients with depression, not only was functional connectivity between the above brain regions significantly decreased, but so was functional connectivity between the right amygdala and the left middle temporal gyrus. Also, there was no significant correlation between the resting-state functional connectivity values in statistically significant brain regions and the levels of alcohol dependence and depression.

CONCLUSION

The impairment of the functional connectivity of the amygdala with caudate nucleus and partial temporal lobe may be involved in the neural mechanism of alcohol dependence comorbidity depressive disorders.

Constructing hierarchical attentive functional brain networks for early AD diagnosis

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing FBN in a hierarchical manner for more effective analysis of the complementary diagnostic insights at different scales. To this end, this paper proposes to build hierarchical FBNs adaptively within the Transformer framework. Specifically, a sparse attention-based node-merging module is designed to work alongside the conventional network feature extraction modules in each layer. The proposed module generates coarser nodes for further FBN construction and analysis by combining fine-grained nodes. By stacking multiple such layers, a hierarchical representation of FBN can be adaptively learned in an end-to-end manner. The hierarchical structure can not only integrate the complementary information from multiscale FBN for joint analysis, but also reduce the model complexity due to decreasing node sizes. Moreover, this paper argues that the nodes defined by the existing atlases are not necessarily the optimal starting level to build FBN hierarchy and exploring finer nodes may further enrich the FBN representation. In this regard, each predefined node in an atlas is split into multiple sub-nodes, overcoming the scale limitation of the existing atlases. Extensive experiments conducted on various data sets consistently demonstrate the superior performance of the proposed method over the competing methods.

Predicting depression risk in early adolescence via multimodal brain imaging

Depression is an incapacitating psychiatric disorder with increased risk through adolescence. Among other factors, children with family history of depression have significantly higher risk of developing depression. Early identification of pre-adolescent children who are at risk of depression is crucial for early intervention and prevention. In this study, we used a large longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) Study (2658 participants after imaging quality control, between 9-10 years at baseline), we applied advanced machine learning methods to predict depression risk at the two-year follow-up from the baseline assessment, using a set of comprehensive multimodal neuroimaging features derived from structural MRI, diffusion tensor imaging, and task and rest functional MRI. Prediction performance underwent a rigorous cross-validation method of leave-one-site-out. Our results demonstrate that all brain features had prediction scores significantly better than expected by chance, with brain features from rest-fMRI showing the best classification performance in the high-risk group of participants with parental history of depression (N = 625). Specifically, rest-fMRI features, which came from functional connectomes, showed significantly better classification performance than other brain features. This finding highlights the key role of the interacting elements of the connectome in capturing more individual variability in psychopathology compared to measures of single brain regions. Our study contributes to the effort of identifying biological risks of depression in early adolescence in population-based samples.

Distinctive intrinsic functional connectivity alterations of anterior cingulate cortex subdivisions in major depressive disorder: A systematic review and meta-analysis

Evidence of whether the intrinsic functional connectivity of anterior cingulate cortex (ACC) and its subregions is altered in major depressive disorder (MDD) remains inconclusive. A systematic review and meta-analysis were therefore performed on the whole-brain resting-state functional connectivity (rsFC) studies using the ACC and its subregions as seed regions in MDD, in order to draw more reliable conclusions. Forty-four ACC-based rsFC studies were included, comprising 25 subgenual ACC-based studies, 11 pregenual ACC-based studies, and 17 dorsal ACC-based studies. Specific alterations of rsFC were identified for each ACC subregion in patients with MDD, with altered rsFC of subgenual ACC in emotion-related brain regions, of pregenual ACC in sensorimotor-related regions, and of dorsal ACC in cognition-related regions. Furthermore, meta-regression analysis revealed a significant negative correlation between the pgACC-caudate hypoconnectivity and percentage of female patients in the study cohort. This meta-analysis provides robust evidence of altered intrinsic functional connectivity of the ACC subregions in MDD, which may hold relevance to understanding the origin of, and treating, the emotional, sensorimotor and cognitive dysfunctions that are often observed in these patients.

Individual resting-state network functional connectivity predicts treatment improvement of repetitive transcranial magnetic stimulation in major depressive disorder: A pilot study

The current pilot study aimed to exploratively investigate whether individual functional connectivity (FC) of the rTMS stimulation site with resting-state networks could predict the individual efficacy of rTMS treatment. We found that rTMS induced an increase of the FC between the stimulation site and the limbic network (LN) in healthy participants, and that this individualized FC was negatively correlated with the rTMS treatment improvement in MDD patients. Moreover, the LN successfully guided the personalized rTMS therapy. These findings highlighted the crucial role of the LN in understanding the mechanisms underlying rTMS treatment improvement, and the personalized therapy in MDD patients.

Investigation of altered spontaneous brain activity in patients with bronchial asthma using the percent amplitude of fluctuation method: a resting-state functional MRI study

Purpose

To explore the regions of aberrant spontaneous brain activity in asthma patients and their potential impacts using the Percent amplitude of fluctuation (PerAF) analysis method.

Patients and methods

In this study, a total of 31 bronchial asthma (BA) patients were ultimately included, comprising 17 males and 14 females. Subsequently, 31 healthy control subjects (HCS) were recruited, consisting of 17 males and 14 females, and they were matched with the BA group based on age, sex, and educational status. The PerAF analysis technique was employed to study the differences in spontaneous brain activity between the two groups. The SPM12 toolkit was used to carry out a two sample t-test on the collected fMRI data, in order to examine the differences in PerAF values between the asthma patients and the healthy controls. We employed the Montreal Cognitive Assessment (MoCA) scale and the Hamilton Depression Scale (HAMD) to evaluate the cognitive and emotional states of the two groups. Pearson correlation analysis was utilized to ascertain the relationship between changes in the PerAF values within specific brain regions and cognitive as well as emotional conditions.

Results

Compared with the healthy control group, areas of the brain with reduced PerAF in asthma patients included the inferior cerebellum, fusiform gyrus, right inferior orbital frontal gyrus, left middle orbital frontal gyrus, left/right middle frontal gyrus (MFG), dorsal lateral superior frontal gyrus (SFGdl), left superior temporal gyrus (STG), precuneus, right inferior parietal lobule (IPL), and left/right angular gyrus. BA patients exhibit mild cognitive impairments and a propensity for emotional disturbances. Furthermore, the perAF values of the SFGdl region are significantly positively correlated with the results of the MoCA cognitive assessment, while negatively correlated with the HAMD evaluation.

Conclusion

Through the application of PerAF analysis methods, we discovered that several brain regions in asthma patients that control the amplitude of respiration, vision, memory, language, attention, and emotional control display abnormal changes in intrinsic brain activity. This helps characterize the neural mechanisms behind cognitive, sensory, and motor function impairments in asthma patients, providing valuable insights for potential therapeutic targets and disease management strategies.

Aberrant intrinsic hippocampal and orbitofrontal connectivity in drug-naive adolescent patients with major depressive disorder

Alterations in resting-state functional connectivity (rsFC) of hippocampus and orbitofrontal cortex (OFC) have been highly implicated in major depressive disorder (MDD) and the researches have penetrated to the subregional level. However, relatively little is known about the intrinsic connectivity patterns of these two regions in adolescent MDD (aMDD), especially that of their functional subregions. Therefore, in the current study, we recruited 68 first-episode drug-naive aMDD patients and 43 matched typically developing controls (TDC) to characterize the alterations of whole-brain rsFC patterns in hippocampus and OFC at both regional and subregional levels in aMDD. The definition of specific functional subregions in hippocampus and OFC were based on the prior functional clustering-analysis results. Furthermore, the relationship between rsFC alterations and clinical features was also explored. Compared to TDC group, aMDD patients showed decreased connectivity of the left whole hippocampus with bilateral OFC and right inferior temporal gyrus at the regional level and increased connectivity between one of the right hippocampal subregions and right posterior insula at the subregional level. Reduced connectivity of OFC was only found in the subregion of left OFC with left anterior insula extending to lenticula in aMDD patients relative to TDC group. Our study identifies that the aberrant hippocampal and orbitofrontal rsFC was predominantly located in the insular cortex and could be summarized as an altered hippo-orbitofrontal-insular circuit in aMDD, which may be the unique features of brain network dysfunction in depression at this particular age stage. Moreover, we observed the distinct rsFC alterations in adolescent depression at the subregional level, especially the medial and lateral OFC.

Altered Effective Connectivity Among the Cerebellum and Cerebrum in Patients with Major Depressive Disorder Using Multisite Resting-State fMRI

Major depressive disorder (MDD) is a serious and widespread psychiatric disorder. Previous studies mainly focused on cerebrum functional connectivity, and the sample size was relatively small. However, functional connectivity is undirected. And, there is increasing evidence that the cerebellum is also involved in emotion and cognitive processing and makes outstanding contributions to the symptomology and pathology of depression. Therefore, we used a large sample size of resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate the altered effective connectivity (EC) among the cerebellum and other cerebral cortex in patients with MDD. Here, from the perspective of data-driven analysis, we used two different atlases to divide the whole brain into different regions and analyzed the alterations of EC and EC networks in the MDD group compared with healthy controls group (HCs). The results showed that compared with HCs, there were significantly altered EC in the cerebellum-neocortex and cerebellum-basal ganglia circuits in MDD patients, which implied that the cerebellum may be a potential biomarker of depressive disorders. And, the alterations of EC brain networks in MDD patients may provide new insights into the pathophysiological mechanisms of depression.

Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI

BACKGROUND

Due to individual differences and lack of objective biomarkers, only 30-40% patients with major depressive disorder (MDD) achieve remission after initial antidepressant medication (ADM). We aimed to employ radiomics analysis after ComBat harmonization to predict early improvement to ADM in adolescents with MDD by using brain multiscale structural MRI (sMRI) and identify the radiomics features with high prediction power for selection of selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs).

METHODS

121 MDD patients were recruited for brain sMRI, including three-dimensional T1 weighted imaging (3D-T₁WI)and diffusion tensor imaging (DTI). After receiving SSRIs or SNRIs for 2 weeks, the subjects were divided into ADM improvers (SSRIs improvers and SNRIs improvers) and non-improvers according to reduction rate of the Hamilton Depression Rating Scale, 17 item (HAM-D₁₇) score. Then, sMRI data were preprocessed, and conventional imaging indicators and radiomics features of gray matter (GM) based on surface-based morphology (SBM) and voxel-based morphology (VBM) and diffusion properties of white matter (WM) were extracted and harmonized with ComBat harmonization. Two-level reduction strategy with analysis of variance (ANOVA) and recursive feature elimination (RFE) was utilized sequentially to decrease high-dimensional features. Support vector machine with radial basis function kernel (RBF-SVM) was used to integrate multiscale sMRI features to construct models for early improvement prediction. Area under the curve (AUC), accuracy, sensitivity, and specificity based on the leave-one-out cross-validation (LOO-CV) and receiver operating characteristic (ROC) curve analysis were calculated to evaluate the model performance. Permutation tests were used for assessing the generalization rate.

RESULTS

After 2-week ADM, 121 patients were divided into 67 ADM improvers (31 SSRIs improvers and 36 SNRIs improvers) and 54 ADM non-improvers. After two-level dimensionality reduction, 8 conventional indicators (2 VBM-based features and 6 diffusion features) and 49 radiomics features (16 VBM-based features and 33 diffusion features) were selected. The overall accuracy of RBF-SVM models based on conventional indicators and radiomics features was 74.80% and 88.19%. The radiomics model achieved the AUC, sensitivity, specificity, and accuracy of 0.889, 91.2%, 80.1% and 85.1%, 0.954, 89.2%, 87.4% and 88.5%, 0.942, 91.9%, 82.5% and 86.8% for predicting ADM improvers, SSRIs improvers and SNRIs improvers, respectively. P value of permutation tests were less than 0.001. The radiomics features predicting ADM improver were mainly located in the hippocampus, medial orbitofrontal gyrus, anterior cingulate gyrus, cerebellum (lobule vii-b), body of corpus callosum, etc. The radiomics features predicting SSRIs improver were primarily distributed in hippocampus, amygdala, inferior temporal gyrus, thalamus, cerebellum (lobule vi), fornix, cerebellar peduncle, etc. The radiomics features predicting SNRIs improver were primarily located in the medial orbitofrontal cortex, anterior cingulate gyrus, ventral striatum, corpus callosum, etc. CONCLUSIONS: These findings suggest the radiomics analysis based on brain multiscale sMRI after ComBat harmonization could effectively predict the early improvement of ADM in adolescent MDD patients with a high accuracy, which was superior to the model based on the conventional indicators. The radiomics features with high prediction power may help for the individual selection of SSRIs and SNRIs.

Common and unique alterations of functional connectivity in major depressive disorder and bipolar disorder

OBJECTIVE

Major depressive disorder (MDD) and bipolar disorder (BD) are considered whole-brain disorders with some common clinical and neurobiological features. It is important to investigate neural mechanisms to distinguish between the two disorders. However, few studies have explored the functional dysconnectivity between the two disorders from the whole brain level.

METHODS

In this study, 117 patients with MDD, 65 patients with BD, and 116 healthy controls completed resting-state functional magnetic resonance imaging (R-fMRI) scans. Both edge-based network construction and large-scale network analyses were applied.

RESULTS

Results found that both the BD and MDD groups showed decreased FC in the whole brain network. The shared aberrant network across patients involves the visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and ventral attention network (VAN), which is related to the processing of external stimuli. The default mode network (DMN) and the limbic network (LN) abnormalities were only found in patients with MDD. Furthermore, results showed the highest decrease in edges of patients with MDD in between-network FC in SMN-VN, whereas in VAN-VN of patients with BD.

CONCLUSIONS

Our findings indicated that both MDD and BD are extensive abnormal brain network diseases, mainly aberrant in those brain networks correlated to the processing of external stimuli, especially the attention network. Specific altered functional connectivity also was found in MDD and BD groups, respectively. These results may provide possible trait markers to distinguish the two disorders.

Biotypes of major depressive disorder identified by a multiview clustering framework

BACKGROUND

The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy.

METHODS

We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls).

RESULTS

Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed.

LIMITATIONS

The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes.

CONCLUSIONS

Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.

Molecular imaging findings for treatment resistant depression

Approximately 40% of patients with major depressive disorder (MDD) had limited response to conventional antidepressant treatments, resulting in treatment-resistant depression (TRD), a debilitating subtype that yielded a significant disease burden worldwide. Molecular imaging techniques, such as positron emission tomography (PET) and single photon emission tomography (SPECT), can measure targeted macromolecules or biological processes in vivo. These imaging tools provide a unique possibility to explore the pathophysiology and treatment mechanisms underlying TRD. This work reviewed and summarized prior PET and SPECT studies to examine the neurobiology and treatment-induced changes of TRD. A total of 51 articles were included with supplementary information from studies for MDD and healthy controls (HC). We found that there were altered regional blood flow or metabolic activity in several brain regions, such as the anterior cingulate cortex, prefrontal cortex, insula, hippocampus, amygdala, parahippocampus, and striatum. These regions have been suggested to engage in the pathophysiology or treatment resistance of depression. There was also limited data to demonstrate the changes in the markers of serotonin, dopamine, amyloid, and microglia over some regions in TRD. Moreover, several observed abnormal imaging indices were linked to treatment outcomes, supporting their specificity and clinical relevance. To address the limitations of the included studies, we proposed that future studies needed longitudinal designs, multimodal approaches, and radioligands targeting specific neural substrates for TRD to evaluate their baseline and treatment-related alterations in TRD. Adequate data sharing and reproducible data analysis can facilitate advances in this field.

Serotonin 4 Receptor Brain Binding in Major Depressive Disorder and Association With Memory Dysfunction

Importance

The cerebral serotonin 4 (5-HT4) receptor is a promising novel target for treatment of major depressive disorder (MDD), and pharmacological stimulation of the 5-HT4 receptor has been associated with improved learning and memory in healthy individuals.

Objective

To map the neurobiological signatures of patients with untreated MDD compared with healthy controls and to examine the association between cerebral 5-HT4 receptor binding and cognitive functions in the depressed state.

Design, Setting, and Participants

This case-control study used baseline data from the NeuroPharm clinical depression trial in Denmark. Adult participants included antidepressant-free outpatients with a current moderate to severe depressive episode and healthy controls. All participants completed positron emission tomography (PET) scanning with [11C]SB207145 for quantification of brain 5-HT4 receptor binding, but only the patients underwent cognitive testing. Data analyses were performed from January 21, 2020, to April 22, 2022.

Main Outcomes and Measures

The main study outcome was the group difference in cerebral 5-HT4 receptor binding between patients with MDD and healthy controls. In addition, the association between 5-HT4 receptor binding and verbal memory performance in the patient group was tested. Other cognitive domains (working memory, reaction time, emotion recognition bias, and negative social emotions) were assessed as secondary outcomes.

Results

A total of 90 patients with untreated MDD (mean [SD] age, 27.1 [8.2] years; 64 women [71.1%]) and 91 healthy controls (mean [SD] age, 27.1 [8.0] years; 55 women [60.4%]) were included in the analysis. Patients with current MDD had significantly lower cerebral 5-HT4 receptor binding than healthy controls (-7.0%; 95% CI, -11.2 to -2.7; P = .002). In patients with MDD, there was a correlation between cerebral 5-HT4 receptor binding and verbal memory (r = 0.29; P = .02).

Conclusions and Relevance

Results of this study show that cerebral 5-HT4 receptor binding was lower in patients with MDD than in healthy controls and that the memory dysfunction in patients with MDD was associated with lower cerebral 5-HT4 receptor binding. The cerebral 5-HT4 receptor is a promising treatment target for memory dysfunction in patients with MDD.

Integrating functional neuroimaging and serum proteins improves the diagnosis of major depressive disorder

BACKGROUND

The lack of effective objective diagnostic biomarkers for major depressive disorder (MDD) leads to high misdiagnosis. Compared with healthy controls (HC), abnormal brain functions and protein levels are often observed in MDD. However, it is unclear whether combining these changed multidimensional indicators could help improve the diagnosis of MDD.

METHODS

Sixty-three MDD and eighty-one HC subjects underwent resting-state fMRI scans, among whom 37 MDD and 45 HC provided blood samples. Amplitudes of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and serum levels of brain-derived neurotrophic factor (BDNF), cortisol, and multiple cytokines were measured and put into the linear discriminant analysis (LDA) to construct corresponding MDD diagnostic models. The area under the receiver operating characteristic curve (AUC) of 5-fold cross-validation was calculated to evaluate each model's performance.

RESULTS

Compared with HC, MDD patients' spontaneous brain activity, serum BDNF, cortisol, interleukin (IL)-4, IL-6, and IL-10 levels changed significantly. The combinations of unidimensional multi-indicator had better diagnostic performance than a single one. The model consisted of multidimensional multi-indicator further exhibited conspicuously superior diagnostic efficiency than those constructed with unidimensional multi-indicator, and its AUC, sensitivity, specificity, and accuracy of 5-fold cross-validation were 0.99, 92.0 %, 100.0 %, and 96.3 %, respectively.

LIMITATIONS

This cross-sectional study consists of relatively small samples and should be replicated in larger samples with follow-up data to optimize the diagnostic model.

CONCLUSIONS

MDD patients' neuroimaging features and serum protein levels significantly changed. The model revealed by LDA could diagnose MDD with high accuracy, which may serve as an ideal diagnostic biomarker for MDD.

A Microbial-Based Approach to Mental Health: The Potential of Probiotics in the Treatment of Depression

Probiotics are currently the subject of intensive research pursuits and also represent a multi-billion-dollar global industry given their vast potential to improve human health. In addition, mental health represents a key domain of healthcare, which currently has limited, adverse-effect prone treatment options, and probiotics may hold the potential to be a novel, customizable treatment for depression. Clinical depression is a common, potentially debilitating condition that may be amenable to a precision psychiatry-based approach utilizing probiotics. Although our understanding has not yet reached a sufficient level, this could be a therapeutic approach that can be tailored for specific individuals with their own unique set of characteristics and health issues. Scientifically, the use of probiotics as a treatment for depression has a valid basis rooted in the microbiota-gut-brain axis (MGBA) mechanisms, which play a role in the pathophysiology of depression. In theory, probiotics appear to be ideal as adjunct therapeutics for major depressive disorder (MDD) and as stand-alone therapeutics for mild MDD and may potentially revolutionize the treatment of depressive disorders. Although there is a wide range of probiotics and an almost limitless range of therapeutic combinations, this review aims to narrow the focus to the most widely commercialized and studied strains, namely Lactobacillus and Bifidobacterium, and to bring together the arguments for their usage in patients with major depressive disorder (MDD). Clinicians, scientists, and industrialists are critical stakeholders in exploring this groundbreaking concept.

Psychopathological network for early-onset post-stroke depression symptoms

BACKGROUND

Post-stroke depression (PSD) can be conceptualized as a complex network where PSD symptoms (PSDS) interact with each other. The neural mechanism of PSD and interactions among PSDS remain to be elucidated. This study aimed to investigate the neuroanatomical substrates of, as well as the interactions between, individual PSDS to better understand the pathogenesis of early-onset PSD.

METHODS

A total of 861 first-ever stroke patients admitted within 7 days poststroke were consecutively recruited from three independent hospitals in China. Sociodemographic, clinical and neuroimaging data were collected upon admission. PSDS assessment with Hamilton Depression Rating Scale was performed at 2 weeks after stroke. Thirteen PSDS were included to develop a psychopathological network in which central symptoms (i.e. symptoms most strongly correlated with other PSDS) were identified. Voxel-based lesion-symptom mapping (VLSM) was performed to uncover the lesion locations associated with overall PSDS severity and severities of individual PSDS, in order to test the hypothesis that strategic lesion locations for central symptoms could significantly contribute to higher overall PSDS severity.

RESULTS

Depressed mood, Psychiatric anxiety and Loss of interest in work and activities were identified as central PSDS at the early stage of stroke in our relatively stable PSDS network. Lesions in bilateral (especially the right) basal ganglia and capsular regions were found significantly associated with higher overall PSDS severity. Most of the above regions were also correlated with higher severities of 3 central PSDS. The other 10 PSDS could not be mapped to any certain brain region.

CONCLUSIONS

There are stable interactions among early-onset PSDS with Depressed mood, Psychiatric anxiety and Loss of interest as central symptoms. The strategic lesion locations for central symptoms may indirectly induce other PSDS via the symptom network, resulting in higher overall PSDS severity.

TRIAL REGISTRATION

URL: http://www.chictr.org.cn/enIndex.aspx ; Unique identifier: ChiCTR-ROC-17013993.

Structural and functional features of treatment-resistant depression: A systematic review and exploratory coordinate-based meta-analysis of neuroimaging studies

OBJECTIVES

A third of people suffering from major depressive disorder do not experience a significant improvement in their symptoms even after adequate treatment with two different antidepressant medications. This common condition, termed treatment-resistant depression (TRD), severely affects the quality of life of millions of people worldwide, causing long-lasting interpersonal problems and social costs. Given its epidemiological and clinical relevance and the little consensus on whether the neurobiological underpinnings of TRD differ from treatment-sensitive depression (TSD), we sought to highlight the convergent morphometric and functional neuroimaging correlates of TRD.

METHODS

We systematically reviewed the published literature on structural and resting-state functional neuroimaging of TRD compared to TSD and healthy controls (HC) and performed exploratory coordinate-based meta-analyses (CBMA) of significant results separately for each modality and multimodally ("all-effects"). CBMAs were also performed for each direction and combining both directions of group contrasts.

RESULTS

Out of the initial 1929 studies, only eight involving 555 participants (189 patients with TRD, 156 with TSD, and 210 HC) were included. In all-effects CBMA, precentral/superior frontal gyrus showed a significant difference between TRD and HC. Functional and structural imaging meta-analyses did not yield statistically significant results. A marginally significant cluster of altered intrinsic activity was found between TRD and HC in the cerebellum/pons.

CONCLUSIONS

Frontal, cerebellar, and brainstem functions can be involved in the pathophysiology of TRD. However, the design and heterogeneity of the (scarce) published literature hinder the generalizability of the findings.

Neural activations to loss anticipation mediates the association between difficulties in emotion regulation and screen media activities among early adolescent youth: A moderating role for depression

BACKGROUND

Screen media activities (SMAs; e.g., watching videos, playing videogames) have become increasingly prevalent among youth as ways to alleviate or escape from negative emotional states. However, neural mechanisms underlying these processes in youth are incompletely understood.

METHOD

Seventy-nine youth aged 11-15 years completed a monetary incentive delay task during fMRI scanning. Neural correlates of reward/loss processing and their associations with SMAs were explored. Next, brain activations during reward/loss processing in regions implicated in the processing of emotions were examined as potential mediating factors between difficulties in emotion regulation (DER) and engagement in SMAs. Finally, a moderated mediation model tested the effects of depressive symptoms in such relationships.

RESULT

The emotional components associated with SMAs in reward/loss processing included activations in the left anterior insula (AI) and right dorsolateral prefrontal cortex (DLPFC) during anticipation of working to avoid losses. Activations in both the AI and DLPFC mediated the relationship between DER and SMAs. Moreover, depressive symptoms moderated the relationship between AI activation in response to loss anticipation and SMAs.

CONCLUSION

The current findings suggest that DER link to SMAs through loss-related brain activations implicated in the processing of emotions and motivational avoidance, particularly in youth with greater levels of depressive symptoms. The findings suggest the importance of enhancing emotion-regulation tendencies/abilities in youth and, in particular, their regulatory responses to negative emotional situations in order to guide moderate engagement in SMAs.

Structural disconnection-based prediction of poststroke depression

Poststroke depression (PSD) is a common complication of stroke. Brain network disruptions caused by stroke are potential biological determinants of PSD but their conclusive roles are unavailable. Our study aimed to identify the strategic structural disconnection (SDC) pattern for PSD at three months poststroke and assess the predictive value of SDC information. Our prospective cohort of 697 first-ever acute ischemic stroke patients were recruited from three hospitals in central China. Sociodemographic, clinical, psychological and neuroimaging data were collected at baseline and depression status was assessed at three months poststroke. Voxel-based disconnection-symptom mapping found that SDCs involving bilateral temporal white matter and posterior corpus callosum, as well as white matter next to bilateral prefrontal cortex and posterior parietal cortex, were associated with PSD. This PSD-specific SDC pattern was used to derive SDC scores for all participants. SDC score was an independent predictor of PSD after adjusting for all imaging and clinical-sociodemographic-psychological covariates (odds ratio, 1.25; 95% confidence interval, 1.07, 1.48; P = 0.006). Split-half replication showed the stability and generalizability of above results. When added to the clinical-sociodemographic-psychological prediction model, SDC score significantly improved the model performance and ranked the highest in terms of predictor importance. In conclusion, a strategic SDC pattern involving multiple lobes bilaterally is identified for PSD at 3 months poststroke. The SDC score is an independent predictor of PSD and may improve the predictive performance of the clinical-sociodemographic-psychological prediction model, providing new evidence for the brain-behavior mechanism and biopsychosocial theory of PSD.

How a transdiagnostic approach can improve the treatment of emotional disorders: Insights from clinical psychology and neuroimaging

Multiple psychological treatments for emotional disorders have been developed and implemented, improving the quality of life of individuals. Nevertheless, relapse and poor response to psychotherapy are common. This article argues that a greater understanding of both the psychological and neurobiological mechanisms of change in psychotherapy is essential to improve treatment for emotional disorders. It aims to demonstrate how an understanding of these mechanisms provides a basis for (i) reconceptualizing some mental disorders, (ii) refining and establishing the evidence for existing therapeutic techniques and (iii) designing new techniques that precisely target the processes that maintain these disorders. Possible future directions for researchers and practitioners working at the intersection of neuropsychology and clinical psychology are discussed.

Neural Substrates of Poststroke Depression: Current Opinions and Methodology Trends

Poststroke depression (PSD), affecting about one-third of stroke survivors, exerts significant impact on patients’ functional outcome and mortality. Great efforts have been made since the 1970s to unravel the neuroanatomical substrate and the brain-behavior mechanism of PSD. Thanks to advances in neuroimaging and computational neuroscience in the past two decades, new techniques for uncovering the neural basis of symptoms or behavioral deficits caused by focal brain damage have been emerging. From the time of lesion analysis to the era of brain networks, our knowledge and understanding of the neural substrates for PSD are increasing. Pooled evidence from traditional lesion analysis, univariate or multivariate lesion-symptom mapping, regional structural and functional analyses, direct or indirect connectome analysis, and neuromodulation clinical trials for PSD, to some extent, echoes the frontal-limbic theory of depression. The neural substrates of PSD may be used for risk stratification and personalized therapeutic target identification in the future. In this review, we provide an update on the recent advances about the neural basis of PSD with the clinical implications and trends of methodology as the main features of interest.

Treatment of major depressive disorder with bilateral theta burst stimulation: study protocol for a randomized, double-blind, placebo-controlled multicenter trial (TBS-D)

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (dlPFC) is currently evolving as an effective and safe therapeutic tool in the treatment of major depressive disorder (MDD). However, already established rTMS treatment paradigms are rather time-consuming. With theta burst stimulation (TBS), a patterned form of rTMS, treatment time can be substantially reduced. Pilot studies and a randomized controlled trial (RCT) demonstrate non-inferiority of TBS to 10 Hz rTMS and support a wider use in MDD. Still, data from placebo-controlled multicenter RCTs are lacking. In this placebo-controlled multicenter study, 236 patients with MDD will be randomized to either intermittent TBS (iTBS) to the left and continuous TBS (cTBS) to the right dlPFC or bilateral sham stimulation (1:1 ratio). The treatment will be performed with 80% resting motor threshold intensity over six consecutive weeks (30 sessions). The primary outcome is the treatment response rate (Montgomery-Asberg Depression Rating Scale reduction ≥ 50%). The aim of the study is to confirm the superiority of active bilateral TBS compared to placebo treatment. In two satellite studies, we intend to identify possible MRI-based and (epi-)genetic predictors of responsiveness to TBS therapy. Positive results will support the clinical use of bilateral TBS as an advantageous, efficient, and well-tolerated treatment and pave the way for further individualization of MDD therapy.Trial registration: ClinicalTrials.gov (NCT04392947).