Differential Abnormal Pattern of Anterior Cingulate Gyrus Activation in Unipolar and Bipolar Depression: an fMRI and Pattern Classification Approach

Resting-state fMRI is associated with trauma experiences, mood and psychosis in Afro-descendants with bipolar disorder and schizophrenia

BACKGROUND

Bipolar disorder (BD) and schizophrenia (SCZ) may exhibit functional abnormalities in several brain areas, including the medial temporal and prefrontal cortex and hippocampus; however, a less explored topic is how brain connectivity is linked to premorbid trauma experiences and clinical features in non-Caucasian samples of SCZ and BD.

METHODS

Sixty-two individuals with SCZ (n = 20), BD (n = 21), and healthy controls (HC, n = 21) from indigenous and African ethnicity were submitted to clinical screening (Di-PAD), traumata experiences (ETISR-SF), cognitive and functional MRI assessment. The item psychosis/hallucinations in SCZ patients showed a negative correlation with the global efficiency (GE) in the right dorsal attention network. The items mania, irritable mood, and racing thoughts in the Di-PAD scale had a significant negative correlation with the GE in the parietal right default mode network.

CONCLUSIONS

Differences in the activation of specific networks were associated with earlier disease onset, history of physical abuse, and more severe psychotic and mood symptoms in SCZ and BD subjects of indigenous and black ethnicity. Findings provide further evidence on SZ and BD's brain connectivity disturbances, and their clinical significance, in non-Caucasian samples.

Altered effective connectivity among face-processing systems in major depressive disorder

BACKGROUND

Neuroimaging studies have revealed abnormal functional interaction during the processing of emotional faces in patients with major depressive disorder (MDD), thereby enhancing our comprehension of the pathophysiology of MDD. However, it is unclear whether there is abnormal directional interaction among face-processing systems in patients with MDD.

METHODS

A group of patients with MDD and a healthy control group underwent a face-matching task during functional magnetic resonance imaging. Dynamic causal modelling (DCM) analysis was used to investigate effective connectivity between 7 regions in the face-processing systems. We used a Parametric Empirical Bayes model to compare effective connectivity between patients with MDD and controls.

RESULTS

We included 48 patients and 44 healthy controls in our analyses. Both groups showed higher accuracy and faster reaction time in the shape-matching condition than in the face-matching condition. However, no significant behavioural or brain activation differences were found between the groups. Using DCM, we found that, compared with controls, patients with MDD showed decreased self-connection in the right dorsolateral prefrontal cortex (DLPFC), amygdala, and fusiform face area (FFA) across task conditions; increased intrinsic connectivity from the right amygdala to the bilateral DLPFC, right FFA, and left amygdala, suggesting an increased intrinsic connectivity centred in the amygdala in the right side of the face-processing systems; both increased and decreased positive intrinsic connectivity in the left side of the face-processing systems; and comparable task modulation effect on connectivity.

LIMITATIONS

Our study did not include longitudinal neuroimaging data, and there was limited region of interest selection in the DCM analysis.

CONCLUSION

Our findings provide evidence for a complex pattern of alterations in the face-processing systems in patients with MDD, potentially involving the right amygdala to a greater extent. The results confirm some previous findings and highlight the crucial role of the regions on both sides of face-processing systems in the pathophysiology of MDD.

Disrupted limbic-prefrontal effective connectivity in response to fearful faces in lifetime depression

BACKGROUND

Multiple brain imaging studies of negative emotional bias in major depressive disorder (MDD) have used images of fearful facial expressions and focused on the amygdala and the prefrontal cortex. The results have, however, been inconsistent, potentially due to small sample sizes (typically N<50). It remains unclear if any alterations are a characteristic of current depression or of past experience of depression, and whether there are MDD-related changes in effective connectivity between the two brain regions.

METHODS

Activations and effective connectivity between the amygdala and dorsolateral prefrontal cortex (DLPFC) in response to fearful face stimuli were studied in a large population-based sample from Generation Scotland. Participants either had no history of MDD (N=664 in activation analyses, N=474 in connectivity analyses) or had a diagnosis of MDD during their lifetime (LMDD, N=290 in activation analyses, N=214 in connectivity analyses). The within-scanner task involved implicit facial emotion processing of neutral and fearful faces.

RESULTS

Compared to controls, LMDD was associated with increased activations in left amygdala (PFWE=0.031,kE=4) and left DLPFC (PFWE=0.002,kE=33), increased mean bilateral amygdala activation (β=0.0715,P=0.0314), and increased inhibition from left amygdala to left DLPFC, all in response to fearful faces contrasted to baseline. Results did not appear to be attributable to depressive illness severity or antidepressant medication status at scan time.

LIMITATIONS

Most studied participants had past rather than current depression, average severity of ongoing depression symptoms was low, and a substantial proportion of participants were receiving medication. The study was not longitudinal and the participants were only assessed a single time.

CONCLUSIONS

LMDD is associated with hyperactivity of the amygdala and DLPFC, and with stronger amygdala to DLPFC inhibitory connectivity, all in response to fearful faces, unrelated to depression severity at scan time. These results help reduce inconsistency in past literature and suggest disruption of 'bottom-up' limbic-prefrontal effective connectivity in depression.

Abnormal stability of spontaneous neuronal activity as a predictor of diagnosis conversion from major depressive disorder to bipolar disorder

OBJECTIVE

Bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD) in the early stage, which may lead to inappropriate treatment. This study aimed to characterize the alterations of spontaneous neuronal activity in patients with depressive episodes whose diagnosis transferred from MDD to BD.

METHODS

532 patients with MDD and 132 healthy controls (HCs) were recruited over 10 years. During the follow-up period, 75 participants with MDD transferred to BD (tBD), and 157 participants remained with the diagnosis of unipolar depression (UD). After excluding participants with poor image quality and excessive head movement, 68 participants with the diagnosis of tBD, 150 participants with the diagnosis of UD, and 130 HCs were finally included in the analysis. The dynamic amplitude of low-frequency fluctuations (dALFF) of spontaneous neuronal activity was evaluated in tBD, UD and HC using functional magnetic resonance imaging at study inclusion. Receiver operating characteristic (ROC) analysis was performed to evaluate sensitivity and specificity of the conversion prediction from MDD to BD based on dALFF.

RESULTS

Compared to HC, tBD exhibited elevated dALFF at left premotor cortex (PMC_L), right lateral temporal cortex (LTC_R) and right early auditory cortex (EAC_R), and UD showed reduced dALFF at PMC_L, left paracentral lobule (PCL_L), bilateral medial prefrontal cortex (mPFC), right orbital frontal cortex (OFC_R), right dorsolateral prefrontal cortex (DLPFC_R), right posterior cingulate cortex (PCC_R) and elevated dALFF at LTC_R. Furthermore, tBD exhibited elevated dALFF at PMC_L, PCL_L, bilateral mPFC, bilateral OFC, DLPFC_R, PCC_R and LTC_R than UD. In addition, ROC analysis based on dALFF in differential areas obtained an area under the curve (AUC) of 72.7%.

CONCLUSIONS

The study demonstrated the temporal dynamic abnormalities of tBD and UD in the critical regions of the somatomotor network (SMN), default mode network (DMN), and central executive network (CEN). The differential abnormal patterns of temporal dynamics between the two diseases have the potential to predict the diagnosis transition from MDD to BD.

Cerebral glucose hypometabolism and hypoperfusion of cingulate gyrus: an imaging biomarker of autoimmune encephalitis with psychiatric symptoms

BACKGROUND

About 60% of autoimmune encephalitis (AE) patients present psychiatric symptoms, but the underlying mechanism remains unknown. This study examined the role of the cingulate cortex in such patients to identify predictive poor psychiatric factors.

METHODS

In this study, 49 AE patients and 39 healthy controls were enrolled. AE patients were further divided into two groups based on the presence/absence of psychiatric symptoms. The ratio of the standardized uptake value (SUVR) and relative cerebral blood flow (rCBF) in different regions of the cingulate cortex were calculated through positron emission tomography-computed tomography (PET/CT) and arterial spin labeling (ASL) MRI, and the results were compared among the three groups. In addition, we followed-up on the psychiatric outcomes and identified the risk factors for poor psychiatric prognosis, focusing on the cingulate cortex.

RESULTS

More than half of the AE patients (27/49) exhibited psychiatric symptoms. Agitation and thought blocking were typical psychiatric phenotypes, except for anti-glutamic acid decarboxylase 65 (GAD65) encephalitis, which mainly presented with catatonia and a depressed mood. AE patients with psychiatric symptoms experienced reduced metabolism and perfusion of the anterior cingulate cortex (ACC), midcingulate cortex (MCC), and posterior cingulate cortex (PCC). The SUVR of ACC can be used as an independent risk factor of poor psychiatric outcomes, which had an area under the ROC curve (AUC) of 0.865.

CONCLUSION

Impaired cingulate cortex function in AE may be the potential mechanism of psychiatric symptoms. Hypometabolism of ACC is an independent prognostic factor predicting an unfavorable psychiatric prognosis in AE.

How do bipolar disease states affect positive and negative emotion processing? Insights from a meta-analysis on the neural fingerprints of emotional processing

BACKGROUND

Functional magnetic resonance imaging studies on emotion processing in patients with bipolar disorder (BD) show hyperactivity of limbic-striatal brain areas and hypoactivity in inferior frontal areas compared to healthy participants. However, heterogeneous results in patients with different disease states and different valences of emotional stimuli have been identified.

METHODS

To integrate previous results and elucidate the impact of disease state and stimulus valence, we conducted a systematic literature search for journal articles in the Web of Science Core Collection including MEDLINE databases and employed a coordinate-based-meta-analysis of functional-MRI studies comparing emotion processing in BD-patients with healthy participants using seed-based d mapping (SDM) to test for between-subjects-effects. We included 31 studies published before 11/2022 with a total of N = 766 BD-patients and N = 836 controls.

RESULTS

Patients with BD showed hyperactivated regions involved in salience processing of emotional stimuli (e.g., the bilateral insula) and hypoactivation of regions associated with emotion regulation (e.g., inferior frontal gyrus) during emotion processing, compared to healthy participants. A more detailed descriptive analysis revealed a hypoactive (anterior) insula in manic BD-patients specifically for negative in comparison to positive emotion processing.

DISCUSSION

This meta-analysis corroborates the overall tenor of existing literature that patients with BD show an increased emotional reactivity (hyperactivity of salience-processing regions) together with a lower (cognitive) control (hypoactivity of brain areas associated with emotion regulation) over emotional states. Our analysis suggests reduced interoceptive processing of negative stimuli in mania, pointing out the need for longitudinal within-subject analyses of emotion processing.

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.

Reduced fractional anisotropy in bipolar disorder v. major depressive disorder independent of current symptoms

BACKGROUND

Patients with bipolar disorder (BD) show reduced fractional anisotropy (FA) compared to patients with major depressive disorder (MDD). Little is known about whether these differences are mood state-independent or influenced by acute symptom severity. Therefore, the aim of this study was (1) to replicate abnormalities in white matter microstructure in BD v. MDD and (2) to investigate whether these vary across depressed, euthymic, and manic mood.

METHODS

In this cross-sectional diffusion tensor imaging study, n = 136 patients with BD were compared to age- and sex-matched MDD patients and healthy controls (HC) (n = 136 each). Differences in FA were investigated using tract-based spatial statistics. Using interaction models, the influence of acute symptom severity and mood state on the differences between patient groups were tested.

RESULTS

Analyses revealed a main effect of diagnosis on FA across all three groups (ptfce-FWE = 0.003). BD patients showed reduced FA compared to both MDD (ptfce-FWE = 0.005) and HC (ptfce-FWE < 0.001) in large bilateral clusters. These consisted of several white matter tracts previously described in the literature, including commissural, association, and projection tracts. There were no significant interaction effects between diagnosis and symptom severity or mood state (all ptfce-FWE > 0.704).

CONCLUSIONS

Results indicated that the difference between BD and MDD was independent of depressive and manic symptom severity and mood state. Disruptions in white matter microstructure in BD might be a trait effect of the disorder. The potential of FA values to be used as a biomarker to differentiate BD from MDD should be further addressed in future studies using longitudinal designs.

The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies

Background

Depression is generally accompanied by a disturbed conscious processing of emotion, which manifests as a negative bias to facial/voice emotion information and a decreased accuracy in emotion recognition tasks. Several studies have proved that abnormal brain activation was responsible for the deficit function of conscious emotion recognition in depression. However, the altered brain activation related to the conscious processing of emotion in depression was incongruent among studies. Therefore, we conducted an activation likelihood estimation (ALE) analysis to better understand the underlying neurophysiological mechanism of conscious processing of emotion in depression.

Method

Electronic databases were searched using the search terms "depression," "emotion recognition," and "neuroimaging" from inceptions to April 10th, 2023. We retrieved trials which explored the neuro-responses of depressive patients to explicit emotion recognition tasks. Two investigators independently performed literature selection, data extraction, and risk of bias assessment. The spatial consistency of brain activation in conscious facial expressions recognition was calculated using ALE. The robustness of the results was examined by Jackknife sensitivity analysis.

Results

We retrieved 11,365 articles in total, 28 of which were included. In the overall analysis, we found increased activity in the middle temporal gyrus, superior temporal gyrus, parahippocampal gyrus, and cuneus, and decreased activity in the superior temporal gyrus, inferior parietal lobule, insula, and superior frontal gyrus. In response to positive stimuli, depressive patients showed hyperactivity in the medial frontal gyrus, middle temporal gyrus, and insula (uncorrected p < 0.001). When receiving negative stimuli, a higher activation was found in the precentral gyrus, middle frontal gyrus, precuneus, and superior temporal gyrus (uncorrected p < 0.001).

Conclusion

Among depressive patients, a broad spectrum of brain areas was involved in a deficit of conscious emotion processing. The activation of brain regions was different in response to positive or negative stimuli. Due to potential clinical heterogeneity, the findings should be treated with caution.

Systematic review registration

https://inplasy.com/inplasy-2022-11-0057/, identifier: 2022110057.

Association between decreased interhemispheric functional connectivity of the insula and duration of illness in recurrent depression

OBJECTIVE

To investigate the altered interhemispheric functional connectivity in the resting state in patients with recurrent major depressive disorder (MDD).

METHODS

Voxel-mirrored homotopic connectivity (VMHC), a measure of the functional connectivity between any pair of symmetrical interhemispheric voxels, and pattern classification were examined in 41 recurrent MDD patients (22 during the depressive state and 19 during the remitted state) and 60 age, sex, and education level-matched healthy controls (HC) using resting-state functional magnetic resonance imaging (fMRI).

RESULTS

Compared with HC, the recurrent MDD patients exhibited decreased VMHC values in the bilateral fusiform, inferior occipital gyrus, posterior insula, precentral gyrus, precuneus, superior temporal gyrus, and thalamus. A significant negative correlation between the VMHC value of the bilateral posterior insula and illness duration in recurrent MDD was identified. Support vector machine (SVM) analysis showed that VMHC in the fusiform and posterior insula could be used to distinguish recurrent MDD patients from HC with a sensitivity and accuracy >0.6.

CONCLUSION

Our findings revealed a reduction in the resting-state brain activity across several neural networks in patients with recurrent MDD, including within the posterior insula. Lower VMHC values in the posterior insula were associated with longer illness duration, suggesting that impairment in interhemispheric synchronization within the salience network may be due to the accumulated pathology of depression and may contribute to future depression relapse. VMHC changes in the posterior insula may serve as a potential imaging marker to discriminate recurrent MDD patients from HC.

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.

Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data

BACKGROUND

Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD patients showed increased activations in inferior frontal gyrus when instructed to decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data to examine if we could classify conditions within-group as well as HC versus BD.

METHODS

We reanalyzed explicit emotion regulation data using a multivariate pattern recognition approach, as implemented in PRONTO software. The original experimental paradigm consisted of a full 2 × 2 factorial design, with valence (Negative/Neutral) and instruction (Look/Decrease) as within subject factors.

RESULTS

The multivariate models were able to accurately classify different task conditions when HC and BD were analyzed separately (63.24%-75.00%, p = 0.001-0.012). In addition, the models were able to correctly classify HC versus BD with significant accuracy in conditions where subjects were instructed to downregulate their felt emotion (59.60%-60.84%, p = 0.014-0.018). The results for HC versus BD classification demonstrated contributions from the salience network, several occipital and frontal regions, inferior parietal lobes, as well as other cortical regions, to achieve above-chance classifications.

CONCLUSIONS

Our multivariate analysis successfully reproduced some of the main results obtained in the previous univariate analysis, confirming that these findings are not dependent on the analysis approach. In particular, both types of analyses suggest that there is a significant difference of neural patterns between conditions within each subject group. The multivariate approach also revealed that reappraisal conditions provide the most informative activity for differentiating HC versus BD, irrespective of emotional valence (negative or neutral). The current results illustrate the importance of investigating the cognitive control of emotion in BD. We also propose a set of candidate regions for further study of emotional control in BD.

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.

Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data

OBJECTIVE

Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge as effective treatment is quite different for each condition. In this study electroencephalography (EEG) was explored as an objective biomarker for distinguishing MDD from BD using an efficient machine learning algorithm (MLA) trained by a relatively large and balanced dataset.

METHODS

A 3 step MLA was applied: (1) a multi-step preprocessing method was used to improve the quality of the EEG signal, (2) symbolic transfer entropy (STE), an effective connectivity measure, was applied to the resultant EEG and (3) the MLA used the extracted STE features to distinguish MDD (N = 71) from BD (N = 71) subjects.

RESULTS

14 connectivity features were selected by the proposed algorithm. Most of the selected features were related to the frontal, parietal, and temporal lobe electrodes. The major involved regions were the Broca region in the frontal lobe and the somatosensory association cortex in the parietal lobe. These regions are near electrodes FC5 and CPz and are involved in processing language and sensory information, respectively. The resulting classifier delivered an evaluation accuracy of 88.5% and a test accuracy of 89.3%, using 80% of the data for training and evaluation and the remaining 20% for testing, respectively.

CONCLUSIONS

The high evaluation and test accuracies of our algorithm, derived from a large balanced training sample suggests that this method may hold significant promise as a clinical tool.

SIGNIFICANCE

The proposed MLA may provide an inexpensive and readily available tool that clinicians may use to enhance diagnostic accuracy and shorten time to effective treatment.

FMRI study of implicit emotional face processing in patients with MDD with melancholic subtype

Introduction

The accurate perception of facial expressions plays a vital role in daily life, allowing us to select appropriate responses in social situations. Understanding the neuronal basis of altered emotional face processing in patients with major depressive disorder (MDD) may lead to the appropriate choice of individual interventions to help patients maintain social functioning during depressive episodes. Inconsistencies in neuroimaging studies of emotional face processing are caused by heterogeneity in neurovegetative symptoms of depressive subtypes. The aim of this study was to investigate brain activation differences during implicit perception of faces with negative and positive emotions between healthy participants and patients with melancholic subtype of MDD. The neurobiological correlates of sex differences of MDD patients were also examined.

Methods

Thirty patients diagnosed with MDD and 21 healthy volunteers were studied using fMRI while performing an emotional face perception task.

Results

Comparing general face activation irrespective of emotional content, the intensity of BOLD signal was significantly decreased in the left thalamus, right supramarginal gyrus, right and left superior frontal gyrus, right middle frontal gyrus, and left fusiform gyrus in patients with melancholic depression compared to healthy participants. We observed only limited mood-congruence in response to faces of differing emotional valence. Brain activation in the middle temporal gyrus was significantly increased in response to fearful faces in comparison to happy faces in MDD patients. Elevated activation was observed in the right cingulate for happy and fearful faces, in precuneus for happy faces, and left posterior cingulate cortex for all faces in depressed women compared to men. The Inventory for Depressive Symptomatology (IDS) score was inversely correlated with activation in the left subgenual gyrus/left rectal gyrus for sad, neutral, and fearful faces in women in the MDD group. Patients with melancholic features performed similarly to controls during implicit emotional processing but showed reduced activation.

Discussion and conclusion

This finding suggests that melancholic patients compensate for reduced brain activation when interpreting emotional content in order to perform similarly to controls. Overall, frontal hypoactivation in response to implicit emotional stimuli appeared to be the most robust feature of melancholic depression.

Win and Loss Responses in the Monetary Incentive Delay Task Mediate the Link between Depression and Problem Drinking

Depression and alcohol misuse, frequently comorbid, are associated with altered reward processing. However, no study has examined whether and how the neural markers of reward processing are shared between depression and alcohol misuse. We studied 43 otherwise-healthy drinking adults in a monetary incentive delay task (MIDT) during fMRI. All participants were evaluated with the Alcohol Use Disorders Identification Test (AUDIT) and Beck's Depression Inventory (BDI-II) to assess the severity of drinking and depression. We performed whole brain regressions against each AUDIT and BDI-II score to investigate the neural correlates and evaluated the findings at a corrected threshold. We performed mediation analyses to examine the inter-relationships between win/loss responses, alcohol misuse, and depression. AUDIT and BDI-II scores were positively correlated across subjects. Alcohol misuse and depression shared win-related activations in frontoparietal regions and parahippocampal gyri (PHG), and right superior temporal gyri (STG), as well as loss-related activations in the right PHG and STG, and midline cerebellum. These regional activities (β's) completely mediated the correlations between BDI-II and AUDIT scores. The findings suggest shared neural correlates interlinking depression and problem drinking both during win and loss processing and provide evidence for co-morbid etiological processes of depressive and alcohol use disorders.

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.

Increased sensitivity of insula to supraliminal faces in adults with histories of mood disorders and self-injury

BACKGROUND

Mood disorders are associated with neurobiological disruptions in subliminal and supraliminal emotion processing. There may be additional variation based on sex and the presence of self-injurious thoughts and behaviors (SITBs). Examining individuals in remission allows us to understand trait-like emotion processing characteristics that persist in the absence of symptoms. This study investigates neural processing in response to supraliminal and subliminal emotional stimuli based upon mood disorder diagnosis, sex, and SITBs.

METHODS

Seventy-five participants with a history of any mood disorder (AMD; 52 female) and 27 healthy controls (HC; 14 female) completed a fMRI task presenting subliminal and supraliminal facial stimuli. Within the AMD group, 20 had no history of SITBs, 26 had histories of suicidal ideation only, and 27 had histories of both SI and self-injurious behavior. We examined activation of salience network regions of interest including the amygdala, insula, and subgenual anterior cingulate cortex (sgACC) during the task.

RESULTS

AMD showed greater insula activation in response to happy faces relative to sad faces, which was not seen in the HC group. Males exhibited lower insula activation in response to sad faces relative happy faces, a pattern not seen in females. Individuals with SITBs demonstrated a lack of sgACC blunting during supraliminal versus subliminal trials.

CONCLUSIONS

We found different patterns of neural responses related to mood disorder status, sex, and SITBs. Findings highlight the importance of considering heterogeneity within diagnoses and examining neurobiological features in the context of remission.

Neural mechanisms underlying the processing of emotional stimuli in individuals with depression: An ALE meta-analysis study

Depression is the leading cause of physiological problems and suicide. Previous studies have indicated that individuals with depression show abnormal processing of both positive and negative emotional stimuli. However, the common and distinct patterns of brain activity during the processing of positive and negative emotional stimuli in individuals with depression remain controversial. The current meta-analysis study used the activation likelihood estimation method to investigate these issues across 21 functional magnetic resonance imaging studies. Results revealed that, compared with individuals without depression, individuals with depression showed higher activation in the anterior cingulate gyrus, insula, and middle frontal gyrus (MFG) for positive emotional stimuli and higher activation in the MFG, inferior frontal gyrus, and insula for negative emotional stimuli. Moreover, we identified that the MFG was consistently activated in individuals with depression regardless of the type of emotional stimuli. However, we did not find distinct patterns of brain activity between positive and negative emotional stimuli in individuals with depression. Our results demonstrated that both positive and negative emotional stimuli processing shares the same cognitive control-related brain regions in individuals with depression.

Brain Network Alterations in Rectal Cancer Survivors With Depression Tendency: Evaluation With Multimodal Magnetic Resonance Imaging

Surgery and chemotherapy may increase depression tendency in patients with rectal cancer (RC). Nevertheless, few comprehensive studies are conducted on alterations of brain network induced by depression tendency in patients with RC. Resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data were collected from 42 patients with RC with surgery and chemotherapy and 38 healthy controls (HCs). Functional network (FN) was constructed from extracting average time courses in brain regions, and structural network (SN) was established by deterministic tractography. Graph theoretical analysis was used to calculate network properties. Networks resilient of two networks were assessed. Clinical correlation analysis was explored between altered network parameters and Hamilton depression scale (HAMD) score. This study revealed impaired FN and SN at both local and global levels and changed nodal efficiency and abnormal small-worldness property in patients with RC. On the whole, all FNs are more robust than SN. Moreover, compared with HC, patients with RC show less robustness in both networks. Regions with decreased nodal efficiency were associated with HAMD score. These cognitive dysfunctions are mainly attributable to depression-related brain functional and structural network alterations. Brain network reorganization is to prevent patients with RC from more serious depression after surgery and chemotherapy.

Brain Activity in Age-Related Macular Degeneration Patients From the Perspective of Regional Homogeneity: A Resting-State Functional Magnetic Resonance Imaging Study

Objective

In this study, the regional homogeneity (ReHo) method was used to investigate levels of cerebral homogeneity in individuals with age-related macular degeneration (AMD), with the aim of exploring whether these measures are associated with clinical characteristics.

Materials and Methods

Patients with AMD and healthy controls attending the First Affiliated Hospital of Nanchang University were invited to participate. Resting state functional magnetic resonance images were recorded in each participant and levels of synchronous neural activity were evaluated using ReHo. Receiver operating characteristic (ROC) curves were used to evaluate the sensitivity and specificity of this method.

Results

Eighteen patients with AMD (9 males and 9 females) and 15 healthy controls (HCs) were recruited. The two groups were approximately matched in age, gender and weight. Compared with controls, the ReHo values were significantly higher in the AMD group at the limbic lobe and parahippocampal gyrus, and were significantly reduced at the cingulate gyrus, superior frontal gyrus, middle frontal gyrus, inferior parietal lobule, and precentral gyrus. Mean ReHo values at the cingulate gyrus and the superior frontal gyrus were negatively correlated with clinical symptoms.

Conclusion

Brain neural homogeneity dysfunction is a manifestation of visual pathways in AMD patients, and may be one of the pathological mechanisms of chronic vision loss, anxiety and depression in AMD patients. In addition, the ReHo data may be useful for early screening for AMD.

Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection

Major depressive disorder (MDD) is one of the most common mental health disorders that can affect sleep, mood, appetite, and behavior of people. Multimodal neuroimaging data, such as functional and structural magnetic resonance imaging (MRI) scans, have been widely used in computer-aided detection of MDD. However, previous studies usually treat these two modalities separately, without considering their potentially complementary information. Even though a few studies propose integrating these two modalities, they usually suffer from significant inter-modality data heterogeneity. In this paper, we propose an adaptive multimodal neuroimage integration (AMNI) framework for automated MDD detection based on functional and structural MRIs. The AMNI framework consists of four major components: (1) a graph convolutional network to learn feature representations of functional connectivity networks derived from functional MRIs, (2) a convolutional neural network to learn features of T1-weighted structural MRIs, (3) a feature adaptation module to alleviate inter-modality difference, and (4) a feature fusion module to integrate feature representations extracted from two modalities for classification. To the best of our knowledge, this is among the first attempts to adaptively integrate functional and structural MRIs for neuroimaging-based MDD analysis by explicitly alleviating inter-modality heterogeneity. Extensive evaluations are performed on 533 subjects with resting-state functional MRI and T1-weighted MRI, with results suggesting the efficacy of the proposed method.

Altered Brain Activity in Depression of Parkinson’s Disease: A Meta-Analysis and Validation Study

Background

The pathophysiology of depression in Parkinson’s disease (PD) is not fully understood. Studies based upon functional MRI (fMRI) showed the alterations in the blood-oxygen-level-dependent (BOLD) fluctuations in multiple brain regions pertaining to depression in PD. However, large variance was observed across previous studies. Therefore, we conducted a meta-analysis to quantitatively evaluate the results in previous publications and completed an independent regions-of-interests (ROIs)-based analysis using our own data to validate the results of the meta-analysis.

Methods

We searched PubMed, Embase, and Web of Science to identify fMRI studies in PD patients with depression. Using signed differential mapping (SDM) method, we performed a voxel-based meta-analysis. Then, a validation study by using multiscale entropy (MSE) in 28 PD patients with depression and 25 PD patients without depression was conducted. The fMRI scan was completed in anti-depression-medication-off state. The ROIs of the MSE analysis were the regions identified by the meta-analysis.

Results

A total of 126 PD patients with depression and 153 PD patients without depression were included in meta-analysis. It was observed that the resting-state activities within the posterior cingulate gyrus, supplementary motor area (SMA), and cerebellum were altered in depressed patients. Then, in the validation study, these regions were used as ROIs. PD patients with depression had significantly lower MSE of the BOLD fluctuations in these regions (posterior cingulate gyrus: F = 0.856, p = 0.049; SMA: F = 0.914, p = 0.039; cerebellum: F = 0.227, p = 0.043).

Conclusion

Our study revealed that the altered BOLD activity in cingulate, SMA, and cerebellum of the brain were pertaining to depression in PD.

Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis

Applying machine learning (ML) to objective markers may overcome prognosis uncertainty due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA-compliant meta-analysis provides new systematic evidence of the BD classification accuracy reached by different markers and ML algorithms. We focused on neuroimaging, electrophysiological techniques, peripheral biomarkers, genetic data, neuropsychological or clinical measures, and multimodal approaches. PubMed, Embase and Scopus were searched through 3^rd December 2020. Meta-analyses were performed using random-effect models. Overall, 81 studies were included in this systematic review and 65 in the meta-analysis (11,336 participants, 3,903 BD). The overall pooled classification accuracy was 0.77 (95%CI[0.75;0.80]). Despite subgroup analyses for diagnostic comparison group, psychiatric disorders, marker, ML algorithm, and validation procedure were not significant, linear discriminant analysis significantly outperformed support vector machine for peripheral biomarkers (p=0.03). Sample size was inversely related to accuracy. Evidence of publication bias was detected. Ultimately, although ML reached a high accuracy in differentiating BD from other psychiatric disorders, best practices in methodology are needed for the advancement of future studies.

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan

This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.

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.

Association between frontal cortico-limbic white-matter microstructure and risk for pediatric depression

This study aimed to identify white-matter microstructural characteristics associated with risk for pediatric major depressive disorder (MDD) measured by the Child Behavior Checklist (CBCL) Anxiety/Depression scores. Children (N = 32) of both sexes, aged 6-12, underwent T1-weighted whole-head anatomical and diffusion-weighted imaging. Each participant's mean diffusion measure image was generated and thinned to create an alignment-invariant tract representation. Voxel-wise analysis on the resulting map was carried out in Track Based Spatial Statistics (TBSS) using general linear models by regressing the CBCL-Anxiety/Depression score against measures of diffusion tensor imaging (DTI). We also compared these results with prior DTI findings from the same children associated with CBCL-Emotion Dysregulation profile, an indicator for bipolar disorder. TBSS voxel-wise analysis showed a significant negative correlation between fractional anisotropy (FA) and CBCL-Anxiety/Depression scores localized in the right anterior cingulum and connected corpus callosal region. The negative FA correlations in these regions were greater in CBCL-Anxiety/ Depression scores compared to CBCL-Emotional Dysregulation scores. Reduced white-matter connectivity in the anterior cingulum and connected corpus callosal region may represent a biomarker of risk for pediatric MDD. These results may help identify brain differences associated with the development of MDD, and assist with earlier clinical identification of pediatric MDD.

Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features

BACKGROUND

Magnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA).

METHODS

Seventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus.

RESULTS

The primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification.

CONCLUSIONS

The results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

Longitudinal grey matter changes following first episode mania in bipolar I disorder: A systematic review

BACKGROUND

While widespread grey matter (GM) changes are seen in bipolar I disorder (BD-I), it is unclear how early in the illness such changes emerge. To date there has been little synthesis of findings regarding longitudinal grey matter changes early in the course of BD-I. We conducted a systematic review to examine the evolution of GM changes in BD-I patients following the first episode of mania (FEM).

METHODS

Following PRISMA guidelines, we conducted a systematic review of studies examining longitudinal changes in GM volume (GMV), cortical thickness and/or surface area in BD-I patients following FEM. We qualitatively synthesized results regarding longitudinal GM changes in BD-I patients.

RESULTS

Fifteen studies met inclusion criteria, all examining GMV changes. Longitudinal ACC volume decrease following FEM was the most replicated finding, but was only reported in 4 out of 7 studies that examined this region as part of a whole brain/region of interest analysis, with 2 of these positive studies using an overlapping patient sample. The impact of episode recurrence, medications, and other clinical factors was inconsistently examined.

LIMITATIONS

The literature regarding GM changes early in BD-I is highly inconsistent, likely due to heterogeneity in participant characteristics, imaging methodology/analysis and duration of follow up.

CONCLUSIONS

Though there was some suggestion that structural ACC changes may represent a marker for neuroprogression following FEM, results were too inconsistent to draw any conclusions. Larger longitudinal studies examining cortical thickness/surface area, and the influence of relevant clinical factors, are needed to better understand neuroprogression in early BD-I.

Neural Correlates of Positive Emotion Processing That Distinguish Healthy Youths at Familial Risk for Bipolar Versus Major Depressive Disorder

OBJECTIVE

Familial risk for bipolar disorder (BD) or major depressive disorder (MDD) may lead to differential emotion processing signatures, resulting in unique neural vulnerability.

METHOD

Healthy offspring of a parent with BD (n = 29, "BD-risk") or MDD (n = 44, "MDD-risk") and healthy control youths without any personal or family psychopathology (n = 28, "HC") aged 8 to 17 years (13.64 ± 2.59 years) completed an implicit emotion-perception functional magnetic resonance imaging task. Whole-brain voxelwise and psychophysiological interaction analyses examined neural differences in activation and connectivity during emotion processing. Regression modeling tested for neural associations with behavioral strengths and difficulties and conversion to psychopathology at follow-up (3.71 ± 1.91 years).

RESULTS

BD-risk youth showed significantly reduced bilateral putamen activation, and decreased connectivity between the left putamen and the left ventral anterior cingulate cortex (vACC) and the right posterior cingulate cortex (PCC) during positive-valence emotion processing compared to MDD-risk and HC (Z >2.3; p <.001). Decreased left putamen-right PCC connectivity correlated with subsequent peer problems in BD-risk (β = -2.90; p <.05) and MDD-risk (β = -3.64; p < .05) groups. Decreased left (β = -0.09; p < .05) and right putamen activation (β = -0.07; p = .04) were associated with conversion to a mood or anxiety disorder in BD-risk youths. Decreased left putamen-right PCC connectivity was associated with a higher risk of conversion in BD-risk (HR = 8.28 , p < .01) and MDD-risk (HR = 2.31, p = .02) groups.

CONCLUSION

Reduced putamen activation and connectivity during positive emotion processing appear to distinguish BD-risk youths from MDD-risk and HC youths, and may represent a marker of vulnerability.

Integrating Multilevel Functional Characteristics Reveals Aberrant Neural Patterns during Audiovisual Emotional Processing in Depression

Emotion dysregulation is one of the core features of major depressive disorder (MDD). However, most studies in depression have focused on unimodal emotion processing, whereas emotional perception in daily life is highly dependent on multimodal sensory inputs. Here, we proposed a novel multilevel discriminative framework to identify the altered neural patterns in processing audiovisual emotion in MDD. Seventy-four participants underwent an audiovisual emotional task functional magnetic resonance imaging scanning. Three levels of whole-brain functional features were extracted for each subject, including the task-evoked activation, task-modulated connectivity, combined activation and connectivity. Support vector machine classification and prediction models were built to identify MDD from controls and evaluate clinical relevance. We revealed that complex neural networks including the emotion regulation network (prefrontal areas and limbic-subcortical regions) and the multisensory integration network (lateral temporal cortex and motor areas) had the discriminative power. Moreover, by integrating comprehensive information of local and interactive processes, multilevel models could lead to a substantial increase in classification accuracy and depression severity prediction. Together, we highlight the high representational capacity of machine learning algorithms to characterize the complex network abnormalities associated with emotional regulation and multisensory integration in MDD. These findings provide novel evidence for the neural mechanisms underlying multimodal emotion dysregulation of depression.

Deficits in explicit emotion regulation in bipolar disorder: a systematic review

Background

This study aimed to compile and synthesize studies investigating explicit emotion regulation in patients with bipolar disorder and individuals at risk of developing bipolar disorder. The importance of explicit emotion regulation arises from its potential role as a marker for bipolar disorders in individuals at risk and its potent role in therapy for bipolar disorder patients.

Methods

To obtain an exhaustive compilation of studies dealing specifically with explicit emotion regulation in bipolar disorder, we conducted a systematic literature search in four databases. In the 15 studies we included in our review, the emotion-regulation strategies maintenance, distraction, and reappraisal (self-focused and situation-focused) were investigated partly on a purely behavioral level and partly in conjunction with neural measures. The samples used in the identified studies included individuals at increased risk of bipolar disorder, patients with current affective episodes, and patients with euthymic mood state.

Results

In summary, the reviewed studies' results indicate impairments in explicit emotion regulation in individuals at risk for bipolar disorder, patients with manic and depressive episodes, and euthymic patients. These deficits manifest in subjective behavioral measures as well as in neural aberrations. Further, our review reveals a discrepancy between behavioral and neural findings regarding explicit emotion regulation in individuals at risk for bipolar disorders and euthymic patients. While these groups often do not differ significantly in behavioral measures from healthy and low-risk individuals, neural differences are mainly found in frontostriatal networks.

Conclusion

We conclude that these neural aberrations are a potentially sensitive measure of the probability of occurrence and recurrence of symptoms of bipolar disorders and that strengthening this frontostriatal route is a potentially protective measure for individuals at risk and patients who have bipolar disorders.

Abnormal neural activities in adults and youths with major depressive disorder during emotional processing: a meta-analysis

BACKGROUND

Abnormal neural activities during emotional processing have been found in both adults and youths with major depressive disorder. However, findings were inconsistent in each group and cannot be compared to each other.

METHODS

We first identified neuroimaging experiments that revealed abnormal neural activities during emotional processing in patients with major depressive disorder published from January 1997 to January 2019. Then we conducted voxel-wise meta-analyses on adult and youth patients separately and compared the two age groups using direct meta-comparison.

RESULTS

Fifty-four studies comprising 1141 patients and 1242 healthy controls were identified. Both adult and youth patients showed abnormal neural activities in anterior cingulate cortex, insula, superior and middle temporal gyrus, and occipital cortex compared to healthy controls. However, hyperactivities in the superior and middle frontal gyrus, amygdala, and hippocampus were only observed in adult patients, while hyperactivity in the striatum was only found in youth patients compared to controls. In addition, compared with youths, adult patients exhibited significantly greater abnormal activities in insula, middle frontal gyrus, and hippocampus, and significantly lower abnormal activities in middle temporal gyrus, middle occipital gyrus, lingual gyrus, and striatum.

CONCLUSIONS

The common alterations confirmed the negative processing bias in major depressive disorder. Both adult and youth patients were suggested to have disturbed emotional perception, appraisal, and reactivity. However, adult patients might be more subject to the impaired appraisal and reactivity processes, while youth patients were more subject to the impaired perception process. These findings help us understand the progressive pathophysiology of major depressive disorder.

Functional plasticity abnormalities over the lifespan of first-episode patients with major depressive disorder: a resting state fMRI study

Background

Neurodevelopmental and neurodegenerative theories of depression suggest that patients with major depressive disorder (MDD) may follow abnormal developmental, maturational, and aging processes. However, a lack of lifespan studies has precluded verification of these theories. Herein, we analyzed functional magnetic resonance imaging (fMRI) data to comprehensively characterize age-related functional trajectories, as measured by the fractional amplitude of low frequency fluctuations (fALFF), over the course of MDD.

Methods

In total, 235 MDD patients with age-differentiated onsets and 235 age- and sex-matched healthy controls (HC) were included in this study. We determined the pattern of age-related fALFF changes by cross-sectionally establishing the general linear model (GLM) between fALFF and age over a lifespan. Furthermore, the subjects were divided into four age groups to assess age-related neural changes in detail. Inter-group fALFF comparison (MDD vs. HC) was conducted in each age group and Granger causal analysis (GCA) was applied to investigate effective connectivity between regions.

Results

Compared with the HC, no significant quadratic or linear age effects were found in MDD over the entire lifespan, suggesting that depression affects the normal developmental, maturational, and degenerative process. Inter-group differences in fALFF values varied significantly at different ages of onset. This implies that MDD may impact brain functions in a highly dynamic way, with different patterns of alterations at different stages of life. Moreover, the GCA analysis results indicated that MDD followed a distinct pattern of effective connectivity relative to HC, and this may be the neural basis of MDD with age-differentiated onsets.

Conclusions

Our findings provide evidence that normal developmental, maturational, and ageing processes were affected by MDD. Most strikingly, functional plasticity changes in MDD with different ages of onset involved dynamic interactions between neuropathological processes in a tract-specific manner.

Differentiating bipolar disorder from unipolar depression in youth: A systematic literature review of neuroimaging research studies

Differentiating bipolar disorder from unipolar depression is one of the most difficult clinical questions posed in pediatric psychiatric practices, as misdiagnosis can lead to severe repercussions for the affected child. This study aimed to examine the existing literature that investigates brain differences between bipolar and unipolar mood disorders in children directly, across all neuroimaging modalities. We performed a systematic literature search through PubMed, PsycINFO, Embase, and Medline databases with defined inclusion and exclusion criteria. Nine research studies were included in the systematic qualitative review, including three structural MRI studies, five functional MRI studies, and one MR spectroscopy study. Relevant variables were extracted and brain differences between bipolar and unipolar mood disorders in children as well as healthy controls were qualitatively analyzed. Across the nine studies, our review included 228 subjects diagnosed with bipolar disorder, 268 diagnosed with major depressive disorder, and 299 healthy controls. Six of the reviewed studies differentiated between bipolar and unipolar mood disorders. Differentiation was most commonly found in the anterior cingulate cortex (ACC), insula, and dorsal striatum (putamen and caudate) brain areas. Despite its importance, the current neuroimaging literature on this topic is scarce and presents minimal generalizability.

The activity and connectivity of the facial emotion processing neural circuitry in bipolar disorder: a systematic review

BACKGROUND

Facial emotion processing abnormalities may be a trait feature of bipolar disorder (BD). These social cognitive impairments may be due to alterations in the neural processing of facial affective information in visual ("core"), and limbic and prefrontal ("extended") networks, however, the precise neurobiological mechanism(s) underlying these symptoms are unclear.

METHODS

We conducted a systematic review to appraise the literature on the activity and connectivity of the facial emotion processing neural circuitry in BD. Two reviewers undertook a search of the electronic databases PubMed, Scopus and PsycINFO, to identify relevant literature published since inception up until September 2019. Study eligibility criteria included; BD participants, neuroimaging, and facial emotion processing tasks.

RESULTS

Out of an initial yield of 6121 articles, 66 were eligible for inclusion in this review. We identified differences in neural activity and connectivity within and between occipitotemporal, limbic, and prefrontal regions, in response to facial affective stimuli, in BD compared to healthy controls.

LIMITATIONS

The methodologies used across studies varied considerably.

CONCLUSIONS

The findings from this review suggest abnormalities in both the activity and connectivity of facial emotion processing neural circuitry in BD. It is recommended that future research aims to further define the connectivity and spatiotemporal course of neural events within and between occipitotemporal, limbic, and prefrontal regions.

Locus coeruleus connectivity alterations in late-life major depressive disorder during a visual oddball task

•

Patients with late-life MDD show lower global LC connectivity in an oddball task.

•

Lower LC connectivity was observed with the ACC, fusiform gyrus and cerebellum.

•

LC-ACC connectivity correlated with two different measures of depression severity.

The Locus Coeruleus (LC) is the major source of noradrenergic neurotransmission. Structural alterations in the LC have been observed in neurodegenerative disorders and at-risk individuals, although functional connectivity studies between the LC and other brain areas have not been yet performed in these populations. Patients with late-life major depressive disorder (MDD) are indeed at increased risk for neurodegenerative disorders, and here we investigated LC connectivity in late-life MDD in comparison to individuals with amnestic type mild cognitive impairment (aMCI) and healthy controls (HCs). We assessed 20 patients with late-life MDD, 16 patients with aMCI, and 26 HCs, who underwent a functional magnetic resonance scan while performing a visual oddball task. We assessed task-related modulations of LC connectivity (i.e., Psychophysiological Interactions, PPI) with other brain areas. A T1-weighted fast spin-echo sequence for LC localization was also obtained. Patients with late-life MDD showed lower global connectivity during target detection in a cluster encompassing the right caudal LC. Specifically, we observed lower LC connectivity with the left anterior cingulate cortex (ACC), the right fusiform gyrus, and different cerebellar clusters. Moreover, alterations in LC-ACC connectivity correlated negatively with depression severity (i.e., Geriatric Depression Scale and number of recurrences). Reduced connectivity of the LC during oddball performance seems to specifically characterize patients with late-life MDD, but not other populations of aged individuals with cognitive alterations. Such alteration is associated with different measures of disease severity, such as the current presence of symptoms and the burden of disease (number of recurrences).

Common and distinct patterns of intrinsic brain activity alterations in major depression and bipolar disorder: voxel-based meta-analysis

Identification of intrinsic brain activity differences and similarities between major depression (MDD) and bipolar disorder (BD) is necessary. However, results have not yet yielded consistent conclusions. A meta-analysis of whole-brain resting-state functional MRI (rs-fMRI) studies that explored differences in the amplitude of low-frequency fluctuation (ALFF) between patients (including MDD and BD) and healthy controls (HCs) was conducted using seed-based d mapping software. Systematic literature search identified 50 studies comparing 1399 MDD patients and 1332 HCs, and 15 studies comparing 494 BD patients and 593 HCs. MDD patients displayed increased ALFF in the right superior frontal gyrus (SFG) (including the medial orbitofrontal cortex, medial prefrontal cortex [mPFC], anterior cingulate cortex [ACC]), bilateral insula extending into the striatum and left supramarginal gyrus and decreased ALFF in the bilateral cerebellum, bilateral precuneus, and left occipital cortex compared with HCs. BD showed increased ALFF in the bilateral inferior frontal gyrus, bilateral insula extending into the striatum, right SFG, and right superior temporal gyrus (STG) and decreased ALFF in the bilateral precuneus, left cerebellum (extending to the occipital cortex), left ACC, and left STG. In addition, MDD displayed increased ALFF in the left lingual gyrus, left ACC, bilateral precuneus/posterior cingulate gyrus, and left STG and decreased ALFF in the right insula, right mPFC, right fusiform gyrus, and bilateral striatum relative to BD patients. Conjunction analysis showed increased ALFF in the bilateral insula, mPFC, and decreased ALFF in the left cerebellum in both disorders. Our comprehensive meta-analysis suggests that MDD and BD show a common pattern of aberrant regional intrinsic brain activity which predominantly includes the insula, mPFC, and cerebellum, while the limbic system and occipital cortex may be associated with spatially distinct patterns of brain function, which provide useful insights for understanding the underlying pathophysiology of brain dysfunction in affective disorders, and developing more targeted and efficacious treatment and intervention strategies.

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.

Linguistic analysis of autobiographical narratives in unipolar and bipolar mood disorders in light of multiple code theory

BACKGROUND

Discriminating bipolar disorder (BD) from unipolar disorder (UD) is crucial in diagnosing mood disorders. Neurophysiological studies have identified different correlates of emotional regulation in BD and UD. According to the Multiple Code Theory, bodily modifications relate to linguistic styles, as highlighted by studies on the language of depression. Our purpose is to verify the existence in the Italian language of linguistic features of depression differentiating BD from UD to provide tools for clinicians to use beyond self-report measures.

METHODS

The sample included 20 BD, 20 UD (all diagnosed using DSM-5), and 20 Control Group (CG) participants. Participants completed the Profile of Mood States (POMS) and an audio-recorded Relationship Anecdotes Paradigm Interview, transcribed and analyzed by the Discourse Attributes Analysis Program for Referential Process Linguistic Measures.

RESULTS

One-way ANOVAs confirmed that specific linguistic features characterized BD, UD and CG. The use of Sensory-Somatic words was significantly different in the groups: higher in BD, intermediate in UD, and lower in CG. Individuals with BD produced higher scores on the Referential Activity Intensity Index and the use of singular pronoun "I". Negative Affect, as well as several POMS subscales, distinguished UD and BD from CG.

LIMITATIONS

Narrow sample size, use of a single self-report instrument and treatment effects on measures in the clinical groups are limitations of the study.

CONCLUSION

Individuals with UD and BD appear to use sensory-somatic language in predictably different patterns from each other and from the non-clinical population. Observation and assessment of linguistic features could improve diagnostic accuracy.

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.

The anhedonia is differently modulated by structural covariance network of NAc in bipolar disorder and major depressive disorder

During depressive episode, bipolar disorder (BD) patients share indistinguishable depression symptoms with major depressive disorder (MDD).However, whether neural correlates underlying the anhedonia, a core feature of depression, is different between BD and MDD remains unknown. To explore neural correlates underlying the anhedonia in BD and MDD, structural T1-weighted images from 36 depressed BD patients, 40 depressed MDD patients matched for depression severity and 34 health controls (HCs) were scanned. Considering the vital role of nucleus accumbens (NAc) in the anhedonia, we constructed the structural covariance network of NAc for each subject. Then, we explored altered structural covariance network of NAc and its interaction with the anhedonia severity in BD and MDD patients. As a result, BD and MDD patients shared decreased structural covariance of NAc connected to prefrontal gyrus, bilateral striatum extending to bilateral anterior insula. Apart from these regions, BD patients presented specifically increased structural covariance of NAc connected to left hippocampus extending to thalamus. The interaction between structural covariance network of NAc and the anhedonia severity in MDD was mainly associated anterior insula (AIC), amygdala, anterior cingulate cortex (ACC)and caudate while that in BD was mainly located in striatum and prefrontal cortex. Our results found that BD and MDD patients presented commonly and distinctly altered structural covariance network of NAc. What is more, the neural correlates underlying the anhedonia in BD and MDD might be different.

Altered Patterns of the Fractional Amplitude of Low-Frequency Fluctuation in Drug-Naive First-Episode Unipolar and Bipolar Depression

Background: An early and correct diagnosis is crucial for treatment of unipolar depression (UD) and bipolar disorder (BD). The fractional amplitude of low-frequency fluctuations (fALFFs) has been widely used in the study of neuropsychiatric diseases, as it can detect spontaneous brain activities. This study was conducted to survey changes of fALFF within various frequency bands of the UD and BD patients, as well as to explore the effects on changes in fALFF on cognitive function. Methods: In total, 58 drug-naive first-episode patients, including 32 UD and 26 BD, were enrolled in the study. The fALFF values were calculated under slow-5 band (0.01-0.027 Hz) and slow-4 band (0.027-0.073 Hz) among UD patients, BD patients, and healthy control (HC). Additionally, we conducted correlation analyses to examine the association between altered fALFF values and cognitive function. Results: Under the slow-5 band, compared to the HC group, the UD group showed increased fALFF values in the right cerebellum posterior lobe, whereas the BD group showed increased fALFF values in the left middle temporal gyrus (MTG). Under the slow-4 band, in comparison to HC, the UD group showed increased fALFF values in the left superior temporal gyrus, whereas the right inferior parietal lobule (IPL) and BD group showed increased fALFF values in the bilateral postcentral gyrus. Notably, compared to BD, the UD group showed increased fALFF values in the right IPL under the slow-4 band. Furthermore, altered fALFF values in the left MTG and the right IPL were significantly positively correlated with Verbal Fluency Test scores. Conclusions: This current study indicated that there were changes in brain activities in the early UD and BD groups, and changes were related to executive function. The fALFF values can serve as potential biomarker to diagnose and differentiate UD and BD patients.

Common and distinct neural activities in frontoparietal network in first-episode bipolar disorder and major depressive disorder: Preliminary findings from a follow-up resting state fMRI study

BACKGROUND

It is difficult to distinguish bipolar disorder (BD) from major depressive disorder (MDD), especially with the initial depressive episode. In this study, we compared neural activities of BD and MDD patients during the first-episode (FE) to investigate common and distinct neural activities and further explore predictive indicators in the two diseases.

METHODS

FE-MDD patients were performed resting state functional magnetic resonance imaging and followed up after scanning. After follow-up, FE-MDD patients were regrouped into FE-BD and FE-MDD patients. The study included 24 FE-BD patients, 28 FE-MDD patients, and 30 age- and sex-matched healthy controls (HC) to investigate neural activities with regional homogeneity (ReHo) analysis among the 3 groups.

RESULTS

Compared to HC, FE-BD patients displayed significantly higher ReHo values in the superior frontal gyrus, the medial superior frontal gyrus within right-side cerebral hemisphere than FE-MDD patients and HC. Compared to HC, FE-BD and FE-MDD patients displayed significant decreased ReHo values in the paracentral lobule, the precuneus and the median cingulate and paracingulate gyrus within bilateral cerebral hemisphere, and the postcentral gyrus and the precentral gyrus within the right-side. FE-BD displayed significant lower ReHo values than FE-MDD patients in these regions.

LIMITATIONS

The potential effects of medicine, age, course of disease and handedness on results could not be ignored.

CONCLUSIONS

Abnormal neural activities of frontoparietal network may provide common and distinct markers to affective disorders and scientific basis for further prediction researches of affective disorders.

Structural and functional neural correlates of vigilant and avoidant regulation style

BACKGROUND

Regulation of emotional arousal is a relevant factor for mental health. The investigation of neural underpinnings of regulation styles in healthy individuals may provide important insights regarding potential risk factors. To fill the gap of structural correlates of regulation styles and to expand previous results, we focused on the association between brain structure, neural responsiveness and vigilant/avoidant regulation style.

METHODS

In n = 302 healthy individuals regulation style was assessed with the Mainz Coping Inventory (MCI). Participants underwent structural and functional MRI during an emotion-processing paradigm. Structural MRI (voxel-based morphometry) and functional MRI were analysed in two regions of interest (amygdala and anterior cingulate cortex [ACC]).

RESULTS

Regulation styles did not show an association with brain structure after correction for gender, age, trait anxiety, depressive symptoms. During emotion processing, a vigilant regulation style was negatively associated with ACC activation.

LIMITATIONS

The cross-sectional study in a non-pathological sample is not adequate to unveil causalities or draw conclusions regarding prevention interventions.

CONCLUSION

Regulation styles are associated with specific neural activation patterns. The association of a high-vigilant regulation style and low ACC activation during emotion processing in healthy participants might be a potential risk factor.

Transdiagnostic neuroimaging in psychiatry: A review

Transdiagnostic approach has a long history in neuroimaging, predating its recent ascendance as a paradigm for new psychiatric nosology. Various psychiatric disorders have been compared for commonalities and differences in neuroanatomical features and activation patterns, with different aims and rationales. This review covers both structural and functional neuroimaging publications with direct comparison of different psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, conduct disorder, anorexia nervosa, and bulimia nervosa. Major findings are systematically presented along with specific rationales for each comparison.

Differentiating between bipolar and unipolar depression in functional and structural MRI studies

Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification.

Can P300 aid in the differential diagnosis of unipolar disorder versus bipolar disorder depression? A meta-analysis of comparative studies

BACKGROUND

It is difficult to distinguish between bipolar disorder (BD) and unipolar disorder (UD) depression. Given the different pattern of cognitive impairments between BD and UD, P300 is potentially useful for the differential diagnosis. This meta-analysis was performed to estimate the extent of difference in P300 in patients with BD versus UD depression.

METHODS

Studies comparing P300 between depressed BD and UD patients with or without healthy controls (HCs) were retrieved from major English and Chinese databases. Studies with BD and UD samples that were comparable in terms of age, gender, and depression severity, were rated as having high quality. Standardized mean differences (SMDs) of P300 latency and amplitude were calculated.

RESULTS

In total, eight studies with a total of 397 depressed BD patients, 390 depressed UD patients, and 497 HCs, were included. Among included studies, six were rated as having good quality and three followed BD (n = 146) and UD (n = 144) patients during remission. BD patients had significantly longer P300 latency than UD patients during major depressive episode [SMD (95%CI): 0.580 (0.309, 0.850)] and remission [SMD (95%CI): 1.583 (1.322, 1.844)]. Compared to HCs, remitted BD patients still had significantly longer P300 latency [SMD (95%CI): 0.857 (0.059, 1.656)] but P300 latency of remitted UD patients had decreased to normal [SMD (95%CI): 0.536 (-0.272, 1.343)].

LIMITATIONS

Sample sizes of depressed and remitted patients with BD and UD of included studies are small.

CONCLUSIONS

P300 latency can be used as an auxiliary diagnostic marker for differentiating BD from UD depression.

Development of Neuroimaging-Based Biomarkers in Psychiatry

This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.