Distinctive Neuroanatomical Substrates for Depression in Bipolar Disorder versus Major Depressive Disorder

Neural Functioning in Late-Life Depression: An Activation Likelihood Estimation Meta-Analysis

Late-life depression (LLD) is a relatively common and debilitating mental disorder, also associated with cognitive dysfunctions and an increased risk of mortality. Considering the growing elderly population worldwide, LLD is increasingly emerging as a significant public health issue, also due to the rise in direct and indirect costs borne by healthcare systems. Understanding the neuroanatomical and neurofunctional correlates of LLD is crucial for developing more targeted and effective interventions, both from a preventive and therapeutic standpoint. This ALE meta-analysis aims to evaluate the involvement of specific neurofunctional changes in the neurophysiopathology of LLD by analysing functional neuroimaging studies conducted on patients with LLD compared to healthy subjects (HCs). We included 19 studies conducted on 844 subjects, divided into 439 patients with LLD and 405 HCs. Patients with LLD, compared to HCs, showed significant hypoactivation of the right superior and medial frontal gyri (Brodmann areas (Bas) 8, 9), left cingulate cortex (BA 24), left putamen, and left caudate body. The same patients exhibited significant hyperactivation of the left superior temporal gyrus (BA 42), left inferior frontal gyrus (BA 45), right anterior cingulate cortex (BA 24), right cerebellar culmen, and left cerebellar declive. In summary, we found significant changes in activation patterns and brain functioning in areas encompassed in the cortico-limbic-striatal network in LLD. Furthermore, our results suggest a potential role for areas within the cortico-striatal-cerebellar network in the neurophysiopathology of LLD.

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

Differences in acute outcomes of suicide patients by psychiatric disorder: Retrospective observational study

Suicide is a social problem with significant economic losses, the victims of which are mainly from the productive population. There are numerous reports on the assessment of suicide risk, but most focus on long-term management. Therefore, factors influencing the severity of physical impairments in the acute phase and the prognosis of suicidal patients have not been sufficiently investigated. This is a single-center retrospective observational study. We collected data on suicidal patients admitted to our emergency department. The effect of age, gender, psychiatric history, method of suicide, alcohol consumption, and hospital admission on the outcome of suicide was assessed. Outcomes were assessed using the hospital mortality scale and the cerebral performance category scale for in-hospital mortality within 28 days. Methods of suicide with a high mortality rate (hanging, jumping, carbon monoxide poisoning, and burns) were defined as lethal methods. A detailed risk assessment of outcomes was performed for patients with schizophrenia, mood disorders, and somatoform disorders. We identified 340 suicide patients from computerized medical records and analyzed 322 records without missing data. The non-survivor group predominantly comprised older adults, men, and patients without a history of psychiatric treatment. Contrastingly, more patients drank alcohol before suicide in the survivor group. In the subgroup analysis, patients with schizophrenia had unfavorable neurological outcomes. Patients with mood disorders had worse in-hospital mortality than other psychiatric patients, as did patients who chose the lethal method. By disease, patients with stress-related and somatoform disorders tended to have higher survival rates, although their psychiatric hospitalization rates were lower. Conversely, patients with mood disorders had a higher rate of hospital visits but a lower survival rate. The results suggest that usual outpatient treatment alone may not be sufficient to reduce suicide mortality in patients with mood disorders who are considered to be at high risk of suicide.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Differences in intracellular protein levels in monocytes and CD4+ lymphocytes between bipolar depressed patients and healthy controls: A pilot study with tyramine-based signal-amplified flow cytometry

BACKGROUND

Molecular biomarkers for bipolar disorder (BD) that distinguish it from other manifestations of depressive symptoms remain unknown. The aim of this study was to determine if a very sensitive tyramine-based signal-amplification technology for flow cytometry (CellPrint™) could facilitate the identification of cell-specific analyte expression profiles of peripheral blood cells for bipolar depression (BPD) versus healthy controls (HCs).

METHODS

The diagnosis of psychiatric disorders was ascertained with Mini International Neuropsychiatric Interview for DSM-5. Expression levels for eighteen protein analytes previously shown to be related to bipolar disorder were assessed with CellPrint™ in CD4⁺ T cells and monocytes of bipolar patients and HCs. Implementation of protein-protein interaction (PPI) network and pathway analysis was subsequently used to identify new analytes and pathways for subsequent interrogations.

RESULTS

Fourteen drug-naïve or -free patients with bipolar I or II depression and 17 healthy controls (HCs) were enrolled. The most distinguishable changes in analyte expression based on t-tests included GSK3β, HMGB1, IRS2, phospho-GSK3αβ, phospho-RELA, and TSPO in CD4⁺ T cells and calmodulin, GSK3β, IRS2, and phospho-HS1 in monocytes. Subsequent PPI and pathway analysis indicated that prolactin, leptin, BDNF, and interleukin-3 signal pathways were significantly different between bipolar patients and HCs.

LIMITATION

The sample size of the study was small and 2 patients were on medications.

CONCLUSION

In this pilot study, CellPrint™ was able to detect differences in cell-specific protein levels between BPD patients and HCs. A subsequent study including samples from patients with BPD, major depressive disorder, and HCs is warranted.

An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges

Objective: Automatic diagnosis of psychiatric disorders such as bipolar disorder (BD) through machine learning techniques has attracted substantial attention from psychiatric and artificial intelligence communities. These approaches mostly rely on various biomarkers extracted from electroencephalogram (EEG) or magnetic resonance imaging (MRI)/functional MRI (fMRI) data. In this paper, we provide an updated overview of existing machine learning-based methods for bipolar disorder (BD) diagnosis using MRI and EEG data. Method : This study is a short non-systematic review with the aim of describing the current situation in automatic diagnosis of BD using machine learning methods. Therefore, an appropriate literature search was conducted via relevant keywords for original EEG/MRI studies on distinguishing BD from other conditions, particularly from healthy peers, in PubMed, Web of Science, and Google Scholar databases. Results: We reviewed 26 studies, including 10 EEG studies and 16 MRI studies (including structural and functional MRI), that used traditional machine learning methods and deep learning algorithms to automatically detect BD. The reported accuracies for EEG studies is about 90%, while the reported accuracies for MRI studies remains below the minimum level for clinical relevance, i.e. about 80% of the classification outcome for traditional machine learning methods. However, deep learning techniques have generally achieved accuracies higher than 95%. Conclusion: Research utilizing machine learning applied to EEG signals and brain images has provided proof of concept for how this innovative technique can help psychiatrists distinguish BD patients from healthy people. However, the results have been somewhat contradictory and we must keep away from excessive optimistic interpretations of the findings. Much progress is still needed to reach the level of clinical practice in this field.

Decreased dorsal attention network homogeneity as a potential neuroimaging biomarker for major depressive disorder

BACKGROUND

Gaining insight into abnormal functional brain network homogeneity (NH) has the potential to aid efforts to target or otherwise study major depressive disorder (MDD). The NH of the dorsal attention network (DAN) in first-episode treatment-naive MDD patients, however, has yet to be studied. As such, the present study was developed to explore the NH of the DAN in order to determine the ability of this parameter to differentiate between MDD patients and healthy control (HC) individuals.

METHODS

This study included 73 patients with first-episode treatment-naive MDD and 73 age-, gender-, and educational level-matched healthy controls. All participants completed the attentional network test (ANT), Hamilton Rating Scale for Depression (HRSD), and resting-state functional magnetic resonance imaging (rs-fMRI) analyses. A group independent component analysis (ICA) was used to identify the DAN and to compute the NH of the DAN in patients with MDD. Spearman's rank correlation analyses were used to explore relationships between significant NH abnormalities in MDD patients, clinical parameters, and executive control reaction time.

RESULTS

Relative to HCs, patients exhibited reduced NH in the left supramarginal gyrus (SMG). Support vector machine (SVM) analyses and receiver operating characteristic curves indicated that the NH of the left SMG could be used to differentiate between HCs and MDD patients with respective accuracy, specificity, sensitivity, and AUC values of 92.47 %, 91.78 %, 93.15 %, and 65.39 %. A significant positive correlation was observed between the left SMG NH values and HRSD scores among MDD patients.

CONCLUSIONS

These results suggest that NH changes in the DAN may offer value as a neuroimaging biomarker capable of differentiating between MDD patients and healthy individuals.

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

IMPORTANCE

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

OBJECTIVE

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

EVIDENCE REVIEW

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

FINDINGS

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

CONCLUSIONS AND RELEVANCE

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

Dissecting the molecular mechanisms underlying the antidepressant activities of herbal medicines through the comprehensive review of the recent literatures

Depression is clinically defined as a mood disorder with persistent feeling of sadness, despair, fatigue, and loss of interest. The pathophysiology of depression is tightly regulated by the biosynthesis, transport and signaling of neurotransmitters [e.g., serotonin, norepinephrine, dopamine, or γ-aminobutyric acid (GABA)] in the central nervous system. The existing antidepressant drugs mainly target the dysfunctions of various neurotransmitters, while the efficacy of antidepressant therapeutics is undermined by different adverse side-effects. The present review aimed to dissect the molecular mechanisms underlying the antidepressant activities of herbal medicines toward the development of effective and safe antidepressant drugs. Our strategy involved comprehensive review and network pharmacology analysis for the active compounds and associated target proteins. As results, 45 different antidepressant herbal medicines were identified from various in vivo and in vitro studies. The antidepressant mechanisms might involve multiple signaling pathways that regulate neurotransmitters, neurogenesis, anti-inflammation, antioxidation, endocrine, and microbiota. Importantly, herbal medicines could modulate broader spectrum of the cellular pathways and processes to attenuate depression and avoid the side-effects of synthetic antidepressant drugs. The present review not only recognized the antidepressant potential of herbal medicines but also provided molecular insights for the development of novel antidepressant drugs.

Psychosomatic symptoms related to exacerbation of fatigue in patients with medically unexplained symptoms

Background

Medically unexplained symptoms (MUS) are common conditions that cause various somatic complaints and are often avoided in primary care. Fatigue frequently occurs in patients with MUS. However, the somatic and psychiatric symptoms associated with fatigue in patients with MUS are unknown. This study aimed to clarify the intensity of fatigue and the related somatic and psychiatric symptoms in patients with MUS.

Methods

A total of 120 patients with MUS aged 20-64 years who visited the Department of Psychosomatic Medicine, Toho University Medical Center Omori Hospital, between January and March 2021 were considered. The participants' medical conditions were assessed using the Chalder Fatigue Scale (CFS), Somatic Symptom Scale-8 (SSS-8), and Hospital Anxiety and Depression Scale (HADS). We estimated the relationship between CFS, SSS-8 and HADS by using Spearman's rank correlation. Additionally, linear multiple regression analysis with CFS as the objective variable was used to identify symptoms related to fatigue.

Results

Fatigue was significantly associated with all symptoms observed (p < 0.01). Linear multiple regression analysis revealed that "dizziness," "headache," and "Sleep medication" were extracted as relevant somatic symptoms (p < 0.05), independent of anxiety and depression, which were already known to be associated with fatigue in MUS.

Conclusion

The intensity of anxiety, depression, headache, and dizziness were all associated with the intensity of fatigue in MUS patients. On the contrary, sleeping medication was associated with lower levels of fatigue in MUS.

Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients

The diagnosis based on clinical assessment of pediatric bipolar disorder (PBD) may sometimes lead to misdiagnosis in clinical practice. For the past several years, machine learning (ML) methods were introduced for the classification of bipolar disorder (BD), which were helpful in the diagnosis of BD. In this study, brain cortical thickness and subcortical volume of 33 PBD-I patients and 19 age-sex matched healthy controls (HCs) were extracted from the magnetic resonance imaging (MRI) data and set as features for classification. The dimensionality reduced feature subset, which was filtered by Lasso or f_classif, was sent to the six classifiers (logistic regression (LR), support vector machine (SVM), random forest classifier, naïve Bayes, k-nearest neighbor, and AdaBoost algorithm), and the classifiers were trained and tested. Among all the classifiers, the top two classifiers with the highest accuracy were LR (84.19%) and SVM (82.80%). Feature selection was performed in the six algorithms to obtain the most important variables including the right middle temporal gyrus and bilateral pallidum, which is consistent with structural and functional anomalous changes in these brain regions in PBD patients. These findings take the computer-aided diagnosis of BD a step forward.

Combined fractional anisotropy and subcortical volumetric deficits in patients with mild-to-moderate depression: Evidence from the treatment of antidepressant traditional Chinese medicine

Numerous neuroimaging studies have demonstrated that diverse brain structural plasticity could occur in a human brain during a depressive episode. However, there is a lack of knowledge regarding the underlying mechanisms of mild-to-moderate depression (MMD), especially the changes of brain structural characteristics after treatment with the Shuganjieyu capsule (SG), a kind of traditional Chinese medicine that has been recommended for the specialized treatment of MMD. In this study, we investigated the structural brain plasticity in MMD that have been undergoing 8 weeks of SG treatment compared with age- and sex-matched healthy controls (HCs) and assessed the relationship between these brain structural alternations and clinical symptoms in MMD. At the baseline, we found that: (1) fractional anisotropy (FA) values in patients with MMD were found to be significantly increased in the regions of anterior limb of internal capsule (ALIC) [MNI coordinates: Peak (x/y/z) = 102, 126, 77; MMD FA_peak (Mean ± SD) = 0.621 ± 0.043; HCs FA_peak (Mean ± SD) = 0.524 ± 0.052; MMD > HCs, t = 9.625, p < 0.001] and posterior limb of internal capsule (PLIC) [MNI coordinates: Peak (x/y/z) = 109, 117, 87; MMD FA_peak (Mean ± SD) = 0.694 ± 0.042; HCs FA_peak (Mean ± SD) = 0.581 ± 0.041; MMD > HCs, t = 12.90, p < 0.001], and FA values were significantly positively correlated with HAMD scores in patients with MMD. (2) Patients with MMD showed smaller gray matter volume (GMV) of the dorsolateral prefrontal cortex (DLPFC), frontal cortex, occipital cortex, and precuneus, and the GMV of DLPFC was negatively correlated with HAMD scores. After SG treatment, we found that (1) the HAMD scores decreased; (2) FA values were significantly decreased in the regions of the ALIC and PLIC compared to those at baseline and TBSS revealed no significant differences in FA values between patients with MMD and HCs. (3) The structural characteristics of DLPFC in patients with MMD obtained at the 8th week were improved, e.g., no significant differences in GMV of DLPFC between the two groups. Taken together, our results provided neuroimaging evidence suggesting that SG is an effective treatment for patients with MMD. Moreover, alterations of GMV after 8 weeks of SG treatment indicated a potential modulation mechanism in brain structural plasticity within the DLPFC in patients with MMD.

Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review

OBJECTIVES

Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar depression characterize pathophysiological differences between these conditions. However, it is difficult to interpret the current literature due to differences in MRI modalities, analysis methods, and study designs.

METHODS

We conducted a systematic review of publications using MRI to compare individuals with bipolar and unipolar depression. We grouped studies according to MRI modality and task design. Within the discussion, we critically evaluated and summarized the functional MRI research and then further complemented these findings by reviewing the structural MRI literature.

RESULTS

We identified 88 MRI publications comparing participants with bipolar depression and unipolar depressive disorder. Compared to individuals with unipolar depression, participants with bipolar disorder exhibited heightened function, increased within network connectivity, and reduced grey matter volume in salience and central executive network brain regions. Group differences in default mode network function were less consistent but more closely associated with depressive symptoms in participants with unipolar depression but distractibility in bipolar depression.

CONCLUSIONS

When comparing mood disorder groups, the neuroimaging evidence suggests that individuals with bipolar disorder are more influenced by emotional and sensory processing when responding to their environment. In contrast, depressive symptoms and neurofunctional response to emotional stimuli were more closely associated with reduced central executive function and less adaptive cognitive control of emotionally oriented brain regions in unipolar depression. Researchers now need to replicate and refine network-level trends in these heterogeneous mood disorders and further characterize MRI markers associated with early disease onset, progression, and recovery.

SPAMRI: A MATLAB Toolbox for Surface-Based Processing and Analysis of Magnetic Resonance Imaging

Structural magnetic resonance imaging (MRI) has elicited increasing attention in morphological surface studies due to its stability and sensitivity to neurodegenerative processes, particularly in exploring brain aging and psychiatric disease. However, a user-friendly toolbox for the surface-based analysis of structural MRI is still lacking. On the basis of certain software functions in FreeSurfer, CAT and ANTs, a MATLAB toolbox called “surface-based processing and analysis of MRI” (SPAMRI) has been developed, which can be performed in Windows, Linux and Mac-OS. SPAMRI contains several features as follows: (1) open-source MATLAB-based package with a graphical user interface (GUI); (2) a set of images that can be generated for quality checking, such as Talairach transform, skull strip, and surface reconstruction; (3) user-friendly GUI with capabilities on statistical analysis, multiple comparison corrections, reporting of results, and surface measurement extraction; and (4) provision of a conversion tool between surface files (e.g., mesh files) and volume files (e.g., NIFTI files). SPAMRI is applied to a publicly released structural MRI dataset of 44 healthy young adults and 39 old adults. Findings showed that old people have decreased cortical thickness, especially in prefrontal cortex, relative to those of young adults, thereby suggesting a cognitive decline in the former. SPAMRI is anticipated to substantially simplify surface-based image processing and MRI dataset analyses and subsequently open new opportunities to investigate structural morphologies.

Loss of superiority illusion in bipolar depressive disorder: A combined functional and structural MRI study

Superiority illusion (SI) is a positive cognitive bias related to self, manifested as overestimated self-appraisal. Negative self-schema is a core feature of the cognitive model of depression, including bipolar depressive disorder (BDD). However, only little research has explored the impaired self-processing in BDD. The potential alteration of positive self-bias and the corresponding neural mechanism in BDD remains unclear. This study aimed to investigate the underlying neural mechanism of self-processing in BDD combining task-related functional magnetic resonance imaging and high-resolution T1 structural imaging. Forty-three BDD and forty-eight healthy controls were recruited and underwent a self-related task, where participants were required to evaluate how they compared with their average peers on a serial of positive and negative traits. We defined the ratio of neural activation and gray matter volume (GMV) in a region as the functional-structural coupling index to detect the changes of brain image in BDD. Furthermore, we used moderation analysis to explore the relationship among functional-structural coupling, behavioral scores and depression symptoms. BDD exhibited decreased task activation, GMV, and functional-structural coupling in bilateral anterior insula (AI) and inferior parietal lobule (IPL). The associations between functional-structural coupling in the right AI, IPL and negative trait self-rating scores were moderated by depressive symptom severity. The study revealed disturbed self-related processing and provided new evidences to neuropsychological dysfunction in BDD.

Brain Neural Activity Patterns in an Animal Model of Antidepressant-Induced Manic Episodes

Background: In the treatment of patients with bipolar disorder (BP), antidepressant-induced mania is usually observed. The rate of phase switching (from depressive to manic) in these patients exceeds 22%. The exploration of brain activity patterns during an antidepressant-induced manic phase may aid the development of strategies to reduce the phase-switching rate. The use of a murine model to explore brain activity patterns in depressive and manic phases can help us to understandthe pathological features of BP. The novel object recognition preference ratio is used to assess cognitive ability in such models.

Objective: To investigate brain Ca²⁺ activity and behavioral expression in the depressive and manic phases in the same murine model, to aid understanding of brain activity patterns in phase switching in BP.

Methods: In vivo two-photon imaging was used to observe brain activity alterations in a murine model in which induce depressive-like and manic-like behaviors were induced sequentially. The immobility time was used to assess depressive-like symptoms and the total distance traveled was used to assess manic-like symptoms.

Results: In vivo two-photon imaging revealed significantly reduced brain Ca²⁺ activity in temporal cortex pyramidal neurons in the depressive phase in mice exposed to chronic unpredictable mild stress compared with naïve controls. The brain Ca²⁺ activity correlated negatively with the novel object recognition preference ratio within the immobility time. Significantly increased brain Ca²⁺ activity was observed in the ketamine-induced manic phase. However, this activity did not correlate with the total distance traveled. The novel object recognition preference ratio correlated negatively with the total distance traveled in the manic phase.

Shared and distinct changes in local dynamic functional connectivity patterns in major depressive and bipolar depressive disorders

BACKGROUND

Distinguishing bipolar depressive disorder (BDD) from major depressive disorder (MDD) solely relying on clinical clues is a challenge. Evidence in neuroimaging have revealed potential neurological markers for the differential diagnosis.

METHODS

We aimed to characterize common and specific alterations in the dynamic local functional connectivity pattern in BDD and MDD by using the dynamic regional phase synchrony (DRePS), a newly developed method for assessing intrinsic dynamic local functional connectivity. A total of 98 patients with MDD and 56 patients with BDD patients, and 97 age-, gender-, and education-matched healthy controls (HC) were included and underwent the resting-state functional magnetic resonance imaging.

RESULTS

Compared with HC, patients with two disorders shared decreased DRePS value in the bilateral orbitofrontal cortex (OFC) extends to insula, the right insula extends to hippocampus, the left hippocampus, the right inferior frontal gyrus (IFG), the left thalamus extends to caudate, the right caudate, the bilateral superior frontal gyrus (SFG), and the right medial frontal gyrus (MFG). Furthermore, patients with MDD exhibited specific decreased DRePS value in the left caudate. Moreover, voxel signals in these regions during the support vector machine analysis contributed to the classification of the two diagnoses.

CONCLUSIONS

Our findings provided new insight into the neural mechanism of patients with MDD and BDD and could potentially inform the diagnosis and the treatment of this disease.

Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study®

Background

Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health.

Methods

We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers.

Results

The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4–100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately.

Conclusions

The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society.

Calcium imaging reveals depressive- and manic-phase-specific brain neural activity patterns in a murine model of bipolar disorder: a pilot study

Brain pathological features during manic/hypomanic and depressive episodes in the same patients with bipolar disorder (BPD) have not been described precisely. The study aimed to investigate depressive and manic-phase-specific brain neural activity patterns of BPD in the same murine model to provide information guiding investigation of the mechanism of phase switching and tailored prevention and treatment for patients with BPD. In vivo two-photon imaging was used to observe brain activity alterations in the depressive and manic phases in the same murine model of BPD. Two-photon imaging showed significantly reduced Ca²⁺ activity in temporal cortex pyramidal neurons in the depression phase in mice exposed to chronic unpredictable mild stress (CUMS), but not in the manic phase in mice exposed to CUMS and ketamine. Total integrated calcium values correlated significantly with immobility times. Brain Ca²⁺ hypoactivity was observed in the depression and manic phases in the same mice exposed to CUMS and ketamine relative to naïve controls. The novel object recognition preference ratio correlated negatively with the immobility time in the depression phase and the total distance traveled in the manic phase. With recognition of its limitations, this study revealed brain neural activity impairment indicating that intrinsic emotional network disturbance is a mechanism of BPD and that brain neural activity is associated with cognitive impairment in the depressive and manic phases of this disorder. These findings are consistent with those from macro-imaging studies of patients with BPD. The observed correlation of brain neural activity with the severity of depressive, but not manic, symptoms need to be investigated further.

Brain correlates of depression, post-traumatic distress, and inflammatory biomarkers in COVID-19 survivors: A multimodal magnetic resonance imaging study

Psychiatric sequelae substantially contribute to the post-acute burden of disease associated with COVID-19, persisting months after clearance of the virus. Brain imaging shows white matter (WM) hypodensities/hyperintensities, and the involvement of grey matter (GM) in prefrontal, anterior cingulate (ACC) and insular cortex after COVID, but little is known about brain correlates of persistent psychopathology. With a multimodal approach, we studied whole brain voxel-based morphometry, diffusion-tensor imaging, and resting-state connectivity, to correlate MRI measures with depression and post-traumatic distress (PTSD) in 42 COVID-19 survivors without brain lesions, at 90.59 ​± ​54.66 days after COVID. Systemic immune-inflammation index (SII) measured in the emergency department, which reflects the immune response and systemic inflammation based on peripheral lymphocyte, neutrophil, and platelet counts, predicted worse self-rated depression and PTSD, widespread lower diffusivity along the main axis of WM tracts, and abnormal functional connectivity (FC) among resting state networks. Self-rated depression and PTSD inversely correlated with GM volumes in ACC and insula, axial diffusivity, and associated with FC. We observed overlapping associations between severity of inflammation during acute COVID-19, brain structure and function, and severity of depression and post-traumatic distress in survivors, thus warranting interest for further study of brain correlates of the post-acute COVID-19 syndrome. Beyond COVID-19, these findings support the hypothesis that regional GM, WM microstructure, and FC could mediate the relationship between a medical illness and its psychopathological sequelae, and are in agreement with current perspectives on the brain structural and functional underpinnings of depressive psychopathology.

Altered Homotopic Functional Connectivity Within White Matter in the Early Stages of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with memory loss and cognitive impairment. The white matter (WM) BOLD signal has recently been shown to provide an important role in understanding the intrinsic cerebral activity. Although the altered homotopic functional connectivity within gray matter (GM-HFC) has been examined in AD, the abnormal HFC to WM remains unknown. The present study sought to identify changes in the WM-HFC and anatomic characteristics by combining functional magnetic resonance imaging with diffusion tensor imaging (DTI). Resting-state and DTI magnetic resonance images were collected from the OASIS-3 dataset and consisted of 53 mild cognitive impairment (MCI) patients, 90 very MCI (VMCI), and 100 normal cognitive (NC) subjects. Voxel-mirrored HFC was adopted to examine whether WM-HFC was disrupted in VMCI and MCI participants. Moreover, the DTI technique was used to investigate whether specific alterations of WM-HFC were associated with anatomic characteristics. Support vector machine analyses were used to identify the MCI and VMCI participants using the abnormal WM-HFC as the features. Compared with NC, MCI, and VMCI participants showed significantly decreased GM-HFC in the middle occipital gyrus and inferior parietal gyrus and decreased WM-HFC in the bilateral middle occipital and parietal lobe-WM. In addition, specific WM-functional network alteration for the bilateral sub-lobar-WM was found in MCI subjects. MCI subjects showed abnormal anatomic characteristics for bilateral sub-lobar and parietal lobe-WM. Results of GM-HFC mainly showed common neuroimaging features for VMCI and MCI subjects, whereas analysis of WM-HFC showed specific clinical neuromarkers and effectively compensated for the lack of GM-HFC to distinguish NC, VMCI, and MCI subjects.

Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age

Few neuroimaging studies are sufficiently large to adequately describe population‐wide variations. This study's primary aim was to generate neuroanatomical norms and individual markers that consider age, sex, and brain size, from 629 cerebral measures in the UK Biobank (N = 40,028). The secondary aim was to examine the effects and interactions of sex, age, and brain allometry—the nonlinear scaling relationship between a region and brain size (e.g., total brain volume)—across cerebral measures. Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and was largely stable across age and sex. Sex differences occurred in 67% of cerebral measures (median |β| = .13): 37% of regions were larger in males and 30% in females. Brain measures (49%) generally decreased with age, although aging effects varied across regions and sexes. While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. Finally, we contribute to the reproducibility of research on sex differences in the brain by replicating previous studies examining cerebral sex differences. This large‐scale study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.

Cellular correlates of gray matter volume changes in magnetic resonance morphometry identified by two-photon microscopy

Magnetic resonance imaging (MRI) of the brain combined with voxel-based morphometry (VBM) revealed changes in gray matter volume (GMV) in various disorders. However, the cellular basis of GMV changes has remained largely unclear. We correlated changes in GMV with cellular metrics by imaging mice with MRI and two-photon in vivo microscopy at three time points within 12 weeks, taking advantage of age-dependent changes in brain structure. Imaging fluorescent cell nuclei allowed inferences on (i) physical tissue volume as determined from reference spaces outlined by nuclei, (ii) cell density, (iii) the extent of cell clustering, and (iv) the volume of cell nuclei. Our data indicate that physical tissue volume alterations only account for 13.0% of the variance in GMV change. However, when including comprehensive measurements of nucleus volume and cell density, 35.6% of the GMV variance could be explained, highlighting the influence of distinct cellular mechanisms on VBM results.

Examining the relationship between gray matter volume and a continuous measure of bipolarity in unmedicated unipolar and bipolar depression

BACKGROUND

It has been argued that unipolar major depressive disorder (MDD) and bipolar disorder (BD) exist on a continuous spectrum, given their overlapping symptomatology and genetic diatheses. The Bipolarity Index (BI) is a scale that considers bipolarity as a continuous construct and was developed to assess confidence in bipolar diagnosis. Here we investigated whether BI scores correlate with gray matter volume (GMV) in a sample of unmedicated unipolar and bipolar depressed individuals.

METHODS

158 subjects (139 with MDD, 19 with BD) in a major depressive episode at time of scan were assigned BI scores. T1-weighted Magnetic Resonance Imaging scans were obtained and processed with Voxel-Based Morphometry using SPM12 (CAT12 toolbox) to assess GMV. Regression was performed at the voxel level to identify clusters of voxels whose GMV was associated with BI score, (p<0.001, family-wise error-corrected cluster-level p<0.05), with age, sex and total intracranial volume as covariates.

RESULTS

GMV was inversely correlated with BI score in four clusters located in left lateral occipital cortex, bilateral angular gyri and right frontal pole. Clusters were no longer significant after controlling for diagnosis. GMV was not correlated with BI score within the MDD cohort alone.

LIMITATIONS

Incomplete clinical data required use of a modified BI scale.

CONCLUSION

BI scores were inversely correlated with GMV in unmedicated subjects with MDD and BD, but these correlations appeared driven by categorical diagnosis. Future work will examine other imaging modalities and focus on elements of the BI scale most likely to be related to brain structure and function.

Probing the clinical and brain structural boundaries of bipolar and major depressive disorder

Bipolar disorder (BD) and major depressive disorder (MDD) have both common and distinct clinical features, that pose both conceptual challenges in terms of their diagnostic boundaries and practical difficulties in optimizing treatment. Multivariate machine learning techniques offer new avenues for exploring these boundaries based on clinical neuroanatomical features. Brain structural data were obtained at 3 T from a sample of 90 patients with BD, 189 patients with MDD, and 162 healthy individuals. We applied sparse partial least squares discriminant analysis (s-PLS-DA) to identify clinical and brain structural features that may discriminate between the two clinical groups, and heterogeneity through discriminative analysis (HYDRA) to detect patient subgroups with reference to healthy individuals. Two clinical dimensions differentiated BD from MDD (area under the curve: 0.76, P < 0.001); one dimension emphasized disease severity as well as irritability, agitation, anxiety and flight of ideas and the other emphasized mostly elevated mood. Brain structural features could not distinguish between the two disorders. HYDRA classified patients in two clusters that differed in global and regional cortical thickness, the distribution proportion of BD and MDD and positive family history of psychiatric disorders. Clinical features remain the most reliable discriminant attributed of BD and MDD depression. The brain structural findings suggests that biological partitions of patients with mood disorders are likely to lead to the identification of subgroups, that transcend current diagnostic divisions into BD and MDD and are more likely to be aligned with underlying genetic variation. These results set the foundation for future studies to enhance our understanding of brain-behavior relationships in mood disorders.

Exploring the Effects of Temperament on Gray Matter Volume of Frontal Cortex in Patients with Mood Disorders

BACKGROUND

Patients with bipolar disorder (BD) and patients with major depressive disorder (MDD) have relatively specific temperament and structural abnormalities of brain regions related to emotion and cognition. However, the effects of temperament factors on the structure of frontal and temporal cortex is still unclear. The aims of this study were to explore the differences and relationships between temperament characteristics and the gray matter volume of frontal and temporal cortex in patients with BD or MDD.

METHODS

T1-weighted magnetic resonance imaging (MRI) data, demographic and clinical information were obtained from 279 depressed patients (90 patients with BD, 189 patients with MDD) and 162 healthy controls (HC). Temperament was assessed with the Chinese short version of Temperament Evaluation of Memphis, Pisa and San Diego - Auto questionnaire (TEMPS-A). The Desikan-Killiany atlas was used for yielding gray matter volume by FreeSurfer 6.0 software suite. A total of 22 frontal and temporal regions were chosen as regions of interest (ROIs).

RESULTS

Compared with patients with MDD, patients with BD had higher TEMPS-A total scores and scores on cyclothymic, irritable and hyperthymic subscales. The gray matter volume in bilateral rostral middle frontal gyrus (RMFG), left temporal pole and right superior frontal gyrus were reduced in patients with BD. Patients with MDD only had lower gray matter volume in bilateral temporal pole. In the pooled patients, there were negative associations between hyperthymia and gray matter volume in right RMFG.

CONCLUSION

Patients with BD and MDD had different temperament characteristics. The prominent temperament subscales in patients with BD were cyclothymia, irritable and hyperthymia. Patients with greater hyperthymia had lower gray matter volume in right frontal gyrus. Temperament may reflect an endophenotype in patients with mood disorders, especially in BD.

Neuroanatomic and Functional Neuroimaging Findings

The search for brain morphology findings that could explain behavioral disorders has gone through a long path in the history of psychiatry. With the advance of brain imaging technology, studies have been able to identify brain morphology and neural circuits associated with the pathophysiology of mental illnesses, such as bipolar disorders (BD). Promising results have also shown the potential of neuroimaging findings in the identification of outcome predictors and response to treatment among patients with BD. In this chapter, we present brain imaging structural and functional findings associated with BD, as well as their hypothesized relationship with the pathophysiological aspects of that condition and their potential clinical applications.

Low-Dose Testosterone Augmentation for Antidepressant-Resistant Major Depressive Disorder in Women: An 8-Week Randomized Placebo-Controlled Study

OBJECTIVE

Low-dose testosterone has been shown to improve depression symptom severity, fatigue, and sexual function in small studies in women not formally diagnosed with major depressive disorder. The authors sought to determine whether adjunctive low-dose transdermal testosterone improves depression symptom severity, fatigue, and sexual function in women with antidepressant-resistant major depression. A functional MRI (fMRI) substudy examined effects on activity in the anterior cingulate cortex (ACC), a brain region important in mood regulation.

METHODS

The authors conducted an 8-week randomized double-blind placebo-controlled trial of adjunctive testosterone cream in 101 women, ages 21-70, with antidepressant-resistant major depression. The primary outcome measure was depression symptom severity as assessed by the Montgomery-Åsberg Depression Rating Scale (MADRS). Secondary endpoints included fatigue, sexual function, and safety measures. The primary outcome of the fMRI substudy (N=20) was change in ACC activity.

RESULTS

The participants' mean age was 47 years (SD=14) and their mean baseline MADRS score was 26.6 (SD=5.9). Eighty-seven (86%) participants completed 8 weeks of treatment. MADRS scores decreased in both study arms from baseline to week 8 (testosterone arm: from 26.8 [SD=6.3] to 15.3 [SD=9.6]; placebo arm: from 26.3 [SD=5.4] to 14.4 [SD=9.3]), with no significant difference between groups. Improvement in fatigue and sexual function did not differ between groups, nor did side effects. fMRI results showed a relationship between ACC activation and androgen levels before treatment but no difference in ACC activation with testosterone compared with placebo.

CONCLUSIONS

Adjunctive transdermal testosterone, although well tolerated, was not more effective than placebo in improving symptoms of depression, fatigue, or sexual dysfunction. Imaging in a subset of participants demonstrated that testosterone did not result in greater activation of the ACC.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

OBJECTIVES

The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement.

METHOD

We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019.

RESULT

We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%.

CONCLUSIONS

Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.

Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging

One of the greatest challenges in providing early effective treatment in mood disorders is the early differential diagnosis between major depression (MDD) and bipolar disorder (BD). A remarkable need exists to identify reliable biomarkers for these disorders. We integrate structural neuroimaging techniques (i.e. Tract-based Spatial Statistics, TBSS, and Voxel-based morphometry) in a multiple kernel learning procedure in order to define a predictive function of BD against MDD diagnosis in a sample of 148 patients. We achieved a balanced accuracy of 73.65% with a sensitivity for BD of 74.32% and specificity for MDD of 72.97%. Mass-univariates analyses showed reduced grey matter volume in right hippocampus, amygdala, parahippocampal, fusiform gyrus, insula, rolandic and frontal operculum and cerebellum, in BD compared to MDD. Volumes in these regions and in anterior cingulate cortex were also reduced in BD compared to healthy controls (n = 74). TBSS analyses revealed widespread significant effects of diagnosis on fractional anisotropy, axial, radial, and mean diffusivity in several white matter tracts, suggesting disruption of white matter microstructure in depressed patients compared to healthy controls, with worse pattern for MDD. To best of our knowledge, this is the first study combining grey matter and diffusion tensor imaging in predicting BD and MDD diagnosis. Our results prompt brain quantitative biomarkers and multiple kernel learning as promising tool for personalized treatment in mood disorders.

Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI

OBJECTIVE

Misdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD.

METHOD

The module allegiance from resting-fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD.

RESULTS

Based on our classification model, difference mainly reflected in default-mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well-overlapped, except for cognitive control network (CCN).

CONCLUSION

The functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.

Regional brain volume reductions in major depressive disorder and bipolar disorder: An analysis by voxel-based morphometry

OBJECTIVES

The present study investigated the usefulness of evaluating the existence of volume reduction in brain regions using voxel-based morphometry (VBM) to dissociate major depressive disorder (MDD) from bipolar disorder (BD).

METHODS/DESIGN

This study enrolled 92 individuals with MDD, 32 individuals with BD, and 43 healthy controls (HCs). We focused on gray matter volume (GMV) of the subgenual anterior cingulate cortex (sgACC), subcallosal area (SCA), and hippocampus. The degree of volume reduction in these brain regions was calculated as the z score, and the differences of z scores in these regions were investigated among the MDD, BD, and HC groups. We then performed a receiver operating characteristic curve analysis to dissociate the individuals with MDD and BD from the HCs based on the z scores in the GMV of these brain regions.

RESULTS

While there were no significant differences in the z scores of the hippocampus among the three groups, the z score of the sgACC was significantly higher in the MDD group than in the BD and HC groups, and the SCA z score was significantly higher in the MDD and BD groups than in the HC group.

CONCLUSIONS

Our findings suggest that VBM evaluation of GMV reduction in the sgACC may be useful as an objective adjunctive tool to distinguish between MDD and BD.

Principles of inflammasome priming and inhibition: Implications for psychiatric disorders

The production of inflammatory proteins by the innate immune system is a tightly orchestrated procedure that allows the body to efficiently respond to exogenous and endogenous threats. Recently, accumulating evidence has indicated that disturbances in the inflammatory response system not only provoke autoimmune disorders, but also can have deleterious effects on neuronal function and mental health. As inflammation in the brain is primarily mediated by microglia, there has been an expanding focus on the mechanisms through which these cells initiate and propagate neuroinflammation. Microglia can enter persistently active states upon their initial recognition of an environmental stressor and are thereafter prone to elicit amplified and persistent inflammatory responses following subsequent exposures to stressors. A recent focus on why primed microglia cells are susceptible to environmental insults has been the NLRP3 inflammasome. Its function within the innate immune system is regulated in such a manner that supports a role for the complex in gating neuroinflammatory responses. The activation of NLRP3 inflammasome in microglia results in the cleavage of zymogen inflammatory interleukins into functional forms that elicit a number of consequential effects in the local neuronal environment. There is evidence to support the principle that within primed neuroimmune systems a lowered threshold for NLRP3 activation can cause persistent neuroinflammation or the amplified production of inflammatory cytokines, such as IL-1β and IL-18. Over the course of an individual's lifetime, persistent neuroinflammation can subsequently lead to the pathophysiological signatures that define psychological disorders. Therefore, targeting the NLRP3 inflammasome complex may represent an innovative and consequential approach to limit neuroinflammatory states in psychiatric disorders, such as major depressive disorder.