Distinguishing medication-free subjects with unipolar disorder from subjects with bipolar disorder: state matters

Diagnostic value of structural, functional and effective connectivity in bipolar disorder

INTRODUCTION

The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS.

RESULTS

A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria.

DISCUSSION

Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP)

Traditional diagnostic formulations of psychotic disorders have low correspondence with underlying disease neurobiology. This has led to a growing interest in using brain-based biomarkers to capture biologically-informed psychosis constructs. Building upon our prior work on the B-SNIP Psychosis Biotypes, we aimed to examine whether structural MRI (an independent biomarker not used in the Biotype development) can effectively classify the Biotypes. Whole brain voxel-wise grey matter density (GMD) maps from T1-weighted images were used to train and test (using repeated randomized train/test splits) binary L2-penalized logistic regression models to discriminate psychosis cases (n = 557) from healthy controls (CON, n = 251). A total of six models were evaluated across two psychosis categorization schemes: (i) three Biotypes (B1, B2, B3) and (ii) three DSM diagnoses (schizophrenia (SZ), schizoaffective (SAD) and bipolar (BD) disorders). Above-chance classification accuracies were observed in all Biotype (B1 = 0.70, B2 = 0.65, and B3 = 0.56) and diagnosis (SZ = 0.64, SAD = 0.64, and BD = 0.59) models. However, the only model that showed evidence of specificity was B1, i.e., the model was able to discriminate B1 vs. CON and did not misclassify other psychosis cases (B2 or B3) as B1 at rates above nominal chance. The GMD-based classifier evidence for B1 showed a negative association with an estimate of premorbid general intellectual ability, regardless of group membership, i.e. psychosis or CON. Our findings indicate that, complimentary to clinical diagnoses, the B-SNIP Psychosis Biotypes may offer a promising approach to capture specific aspects of psychosis neurobiology.

Distinguishing major depressive disorder from bipolar disorder in remission: A brain structural network analysis

BACKGROUND

It is challenging to differentiate major depressive disorder (MDD) from bipolar disorder (BD) in depression and remission. To exclude the potential influence of depressive episodes, we compared the white matter (WM) network between MDD and BD patients in remission to find disease-specific alterations in MDD and BD, and then distinguish these two affective disorders.

METHODS

We recruited 33 patients with remitted MDD (rMDD), 54 patients with remitted BD (rBD), and 60 healthy controls (HCs). Diffusion tensor imaging and high-resolution 3D T1-weighted image were acquired. Global and nodal topological parameters were used to depict the alterations of the whole-brain WM network.

RESULTS

We found that rMDD displayed increased global network efficiency (E_glob) and local network efficiency (E_loc) compared with HCs, whereas we found no significance between rBD and HCs. Compared with rBD and HCs, patients in the rMDD group showed increased nodal degree and nodal efficiency, and decreased nodal shortest path length in the four cerebral regions, including the right calcarine fissure (CAL.R), right cuneus (CUN.R), left lingual gyrus (LING.L), and left middle occipital gyrus (MOG.L). We did not find any rBD specific changes of nodal topological metrics.

LIMITATIONS

The main limitation is the possible effects of medication and BD subtypes on the results.

CONCLUSIONS

Our findings indicate that rMDD exhibited elevated global properties compared with HCs group, and increased nodal properties in the CAL.R, CUN.R, LING.L, and MOG.L specifically compared with rBD and HCs, which may underlie the distinction of the two affective disorders in remission.

Global hippocampus functional connectivity as a predictive neural marker for conversion to future mood disorder in unaffected offspring of bipolar disorder parents

OBJECTIVES

Hippocampus-related functional alteration in genetically at-risk individuals may reflect an endophenotype of a mood disorder. Herein, we performed a prospective study to investigate whether baseline hippocampus functional connectivity (FC) in offspring of patients with bipolar disorder (BD) would predict subsequent conversion to mood disorder.

METHODS

Eighty bipolar offspring and 40 matched normal controls (NC) underwent resting state functional MRI (rsfMRI) scanning on a 3.0 Tesla MR scanner. The offspring were subdivided into asymptomatic offspring (AO) (n = 41) and symptomatic offspring (SO) (n = 39) according to whether they manifested subthreshold mood symptoms. After identifying the different hippocampus FCs between the AO and SO, a logistic regression analysis was conducted to investigate whether the baseline hippocampus FCs predicted a future mood disorder during a 6-year follow-up.

RESULTS

We identified seven baseline para/hippocampus FCs that showed differences between AO and SO, which were entered as predictive features in the logistic regressive model. Of the 80 bipolar offspring entering the analysis, the FCs between left hippocampus and left precuneus, and between right hippocampus and left posterior cingulate, showed a discriminative capacity for predicting future mood disorder (area-under-curve, or AUC=75.76 % and 75.00 % respectively), and for predicting BD onset (AUC=77.46 % and 81.63 %, respectively).

CONCLUSIONS

The present findings revealed high predictive utility of the hippocampus resting state FCs for future mood disorder and BD onset in individuals at familial risk. These neural markers can potentially improve early detection of individuals carrying particularly high risk for future mood disorder.

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.

Multimodal MRI reveals alterations of the anterior insula and posterior cingulate cortex in bipolar II disorders: A surface-based approach

BACKGROUND

Bipolar disorder (BD) is a mental disorder with severe implications for those affected and their families. Previous studies detected brain structural and functional alterations in BD patients. However, very few studies conducted a multimodal MRI fusion analysis, and little is known about the role of common anomalies in the connectivity of BD.

METHODS

We collected sMRI, rs-fMRI, and DTI data from 56 patients with unmedicated BD-II depression and 72 age-, sex- and handedness-matched healthy controls. We applied data-driven approaches to analyze multimodal MRI data and detected brain areas with significant group differences in cortical thickness (CT), amplitude of low frequency fluctuations (ALFF), and fractional anisotropy (FA) of the superficial white matter. We observed the common abnormal areas and took these areas as seeds to analyze the resting-state functional connectivity (RSFC) patterns in BD patients by overlapping these abnormal areas.

RESULTS

The BD patients showed two common abnormal areas: (1) the left anterior insula (AI) with abnormal CT and FA, and (2) the left posterior cingulate cortex (PCC) with abnormal CT and ALFF. Seed-based analyses showed RSFC between the left AI and left occipital sensory cortex, the left AI and left superior and inferior parietal cortex, and the left PCC and right medial prefrontal cortex were uniformly lower in the BD patients than controls. Correlation analyses showed negative correction between AI's FA and disease episodes and between AI's FA and disease duration in depressed BD-II patients.

CONCLUSIONS

We observed abnormal brain structural and functional properties in the left AI and left PCC in BD patients. The abnormal RSFC patterns may suggest sensory and cognitive dysfunction in BD.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Divergent and convergent imaging markers between bipolar and unipolar depression based on Machine Learning

Distinguishing bipolar depression (BD) from unipolar depression (UD) based on symptoms only is challenging. Brain functional connectivity (FC), especially dynamic FC, has emerged as a promising approach to identify possible imaging markers for differentiating BD from UD. However, most of such studies utilized conventional FC and group-level statistical comparisons, which may not be sensitive enough to quantify subtle changes in the FC dynamics between BD and UD. In this paper, we present a more effective individualized differentiation model based on machine learning and the whole-brain high-order functional connectivity (HOFC) network. The HOFC, capturing temporal synchronization among the dynamic FC time series, a more complex chronnectome metric compared to the conventional FC, was used to classify 52 BD, 73 UD, and 76 healthy controls (HC). We achieved a satisfactory accuracy (70.40%) in BD vs. UD differentiation. The resultant contributing features revealed the involvement of the coordinated flexible interactions among sensory (e.g., olfaction, vision, and audition), motor, and cognitive systems. Despite sharing common chronnectome of cognitive and affective impairments, BD and UD also demonstrated unique dynamic FC synchronization patterns. UD is more associated with abnormal visual-somatomotor inter-network connections, while BD is more related to impaired ventral attention-frontoparietal inter-network connections. Moreover, we found that the illness duration modulated the BD vs. UD separation, with the differentiation performance hampered by the secondary disease effects. Our findings suggest that BD and UD may have divergent and convergent neural substrates, which further expand our knowledge of the two different mental disorders.

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.

Anhedonia difference between major depressive disorder and bipolar disorder II

OBJECTIVE

This study aims to explore the difference in anhedonia between Major Depressive Disorder (MDD) and Bipolar Disorder II (BD-II), and attempt to distinguish the two diseases through Snaith-Hamilton Pleasure Scale (SHAPS).

METHODS

A total of 164 drug-free depressive patients (98 MDD patients, 66 BD-II patients) completed the investigation. 17-item Hamilton Depression Scale (HAMD-17) and Hamilton Anxiety Scale (HAMA) and SHAPS were assessed in all participants.

RESULTS

Our results showed that BD-II patients had higher SHAPS scores than MDD patients. The stepwise logistic regression analysis further revealed that SHAPS score, drinking habit, and extroversion as influencing factors for the identification of BD-II. The ROC curve analysis indicated that SHAPS could differentiate BD-II from MDD patients (AUC = 0.655, P = 0.001, 95% CI = 0.568 to 0.742), with the best screening cutoff at 26, and the corresponding sensitivity and specificity was 0.788 and 0.520, respectively.

CONCLUSION

Our results suggest that BD-II patients had more severe anhedonia compared to MDD patients, and the difference in anhedonia may help clinicians preliminary identify BD patients from MDD patients. The preliminary findings are worthly of further exploration.

Deep learning model of fMRI connectivity predicts PTSD symptom trajectories in recent trauma survivors

Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method's performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization.

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.

Shared and specific dynamics of brain segregation and integration in bipolar disorder and major depressive disorder: A resting-state functional magnetic resonance imaging study

BACKGROUND

When bipolar disorder (BD) presents as the depressive state, it is often misdiagnosed as major depressive disorder (MDD). However, few studies have focused on dynamic differences in local brain activity and connectivity between BD and MDD. Therefore, the present study explored shared and specific patterns of abnormal dynamic brain segregation and integration in BD and MDD patients.

METHODS

BD Patients (n = 106), MDD patients (n = 114), and 130 healthy controls (HCs) underwent resting state functional magnetic resonance imaging (fMRI). We first used a sliding window analysis to evaluate the dynamic amplitude of low-frequency fluctuations (dALFF) and, based on the altered dALFF, further analyzed the dynamic functional connectivity (dFC) using a seed-based approach.

RESULTS

Both the BD and MDD groups showed decreased temporal variability of the dALFF (less dynamic segregation) in the bilateral posterior cingulate cortex (PCC)/precuneus compared with the HCs. The MDD group showed increased temporal variability of the dALFF (more dynamic segregation) in the left putamen compared with the controls, but there was no significant difference between the BD and HCs. The dFC analysis also showed that both the BD and MDD groups had reduced dFC (less dynamic integration) between the bilateral PCC/ precuneus and the left inferior parietal lobule compared with the HCs.

LIMITATIONS

This study was cross-sectional and did not examine data from remitted BD and MDD patients.

CONCLUSION

Our findings indicated disrupted dynamic balance between segregation and integration within the default mode network in both BD and MDD. Moreover, we found MDD-specific abnormal brain dynamics in the putamen.

White matter integrity in young medication-naïve bipolar II depressed adults

It is unknown if young medication-naïve bipolar II (BPII) depressed patients have increased white matter (WM) disruptions. 27 each of young (average 23 years) and treatment-naïve BPII depressed, unipolar depressed (UD) patients and age–sex–education matched healthy controls (HC) underwent 3 T MRIs with diffusion tensor imaging. Diagnostic ratings included Structured Clinical Interview for DSM Disorders (SCID), Montgomery-Åsberg Depression Rating Scale (MADRS), Young Mania Rating Scale (YMRS) and Hamilton Anxiety Rating Scale (HAM-A). Patients were clinically depressed (MADRS-BPII: 26.15 [SD9.25], UD: 25.56 [5.24], p = 0.86). Compared to UD, BPII had increased family bipolarity (BPII 13.6% vs UD 2.5%, p = 0.01, φc = 0.28), hypomanic symptoms (YMRS-BPII: 4.22 [4.24], UD: 1.33 [2], p = 0.02, d = 0.87), lifetime number of depressive episodes (BPII: 2.37 [1.23], UD: 1.44 [0.75], p = 0.02, d = 0.91), lifetime and current-year number of episodes (lifetime BPII: 50.85 [95.47], UD: 1.7 [1.03]; current-year BPII: 9.93 [16.29], UD: 1.11 [0.32], ps = 0.04, ds = 0.73–0.77) and longer illness duration (BPII: 4.96 years [3.96], UD: 2.99 [3.33], p = 0.15, d = 0.54). BPII showed no increased WM disruptions vs UD or HC in any of the 15 a priori WM tracts. UD had lower right superior longitudinal fasciculus (SLF) (temporal) axial diffusivity (AD) (1.14 vs 1.17 (BPII), 1.16 (HC); F = 6.93, 95% CI of\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${F}_{B}$$\end{document}FB: 0.00073, 5.22, ηp² = 0.15). Principal component analysis followed by exploratory linear discriminant analysis showed that increased R-SLF (temporal) AD, YMRS and family bipolarity distinguished BPII from UD (81.5% sensitivity, 85.2% specificity) independent of episode number and frequency. Young, medication-naïve adults with BPII depression did not show the WM disruptions distinguishing more chronically ill BP patients from UD. These WM disruptions may therefore be partly attributable to illness chronicity. Longitudinal studies should examine the trajectory of WM changes in BPII and UD and predictive validity of these baseline clinical and imaging parameters.

Structural and functional alterations in untreated patients with major depressive disorder and bipolar disorder experiencing first depressive episode: A magnetic resonance imaging study combined with follow-up

BACKGROUND

Magnetic resonance imaging (MRI) could assist in identifying objective biomarkers and follow-up study could effectively improve subjective diagnostic accuracy. By combining MRI with follow-up, this study aims to determine the shared and distinct alterations between major depressive disorder (MDD) and bipolar disorder (BD).

METHODS

Untreated patients with MDD experiencing the first episode were subjected to MRI and subsequent follow-up. Fifteen patients with mania or hypomania were regrouped into BD group. Twenty patients were still grouped as MDD after an average of 37.95 months follow-up. Thirty healthy controls (HCs) were recruited to match the patients. Gray matter volume (GMV) and amygdala-seed functional connectivity (FC) in the whole brain were detected and compared among the three groups.

RESULTS

GMV analysis revealed that the MDD and BD groups presented reduced GMV predominantly in the parietal, occipital, and frontal regions in the bilateral cerebrum compared with the HCs. The BD group had reduced GMV predominantly in the parietal, temporal, insular regions and the Rolandic operculum in the right-side cerebrum compared with MDD and HC groups. FC analysis revealed that the MDD and BD patients displayed increased FC values mainly in the bilateral parietal, and left occipital regions. Only the BD group displayed increased FC values in the temporal, occipital, parietal and limbic regions in the right-side cerebrum relative to HCs.

LIMITATIONS

The main limitation is the relatively small sample size.

CONCLUSIONS

Alterations in the cortical regions and cortico-limbic neural system may provide the scientific basis for differential diagnosis in affective disorders.

EEG Frontal Asymmetry and Theta Power in Unipolar and Bipolar Depression

BACKGROUND

Distinguishing between unipolar and bipolar depression is of high clinical relevance. However, there is sparse research directly comparing these groups in terms of EEG activity.

METHOD

We investigated 87 participants' left and right EEG frontal alpha-1, alpha-2, and theta activity related to happy and sad face stimuli in unipolar (UD, n=33) and bipolar (BD, n=22) depressed participants, and controls without depression (HC, n=32).

RESULTS

Post-hoc analysis of an observed hemisphere x group interaction (p< .037) showed significant differences in alpha-1 asymmetry only for the comparison of UD and HC (p< .006). Further analysis of a significant emotion x group interaction (p= .001) revealed a differential impact of stimulus valence on theta power between the groups (p< .001). The valence dependent theta power of the BD differed from that of the UD (p< .0002) and the HC (p< .004). Alpha-1 asymmetry classified HC and both depressed groups with an accuracy of .69. Valence-related theta classified BD from UD with an accuracy of .83. Leave-one-out cross validation resulted in slightly reduced accuracy.

LIMITATIONS

Important limitations were the small sample size and that subjects were not medication-free.

CONCLUSIONS

Our results demonstrate the value of simple, task related EEG activity for differentiating not only healthy individuals from those with depression, but also individuals with unipolar depression from those with bipolar depression.

Default mode network activity in bipolar disorder

Since its discovery in 1997, the default mode network (DMN) and its components have been extensively studied in both healthy individuals and psychiatric patients. Several studies have investigated possible DMN alterations in specific mental conditions such as bipolar disorder (BD). In this review, we describe current evidence from resting-state functional magnetic resonance imaging studies with the aim to understand possible changes in the functioning of the DMN in BD. Overall, several types of analyses including seed-based and independent component have been conducted on heterogeneous groups of patients highlighting different results. Despite the differences, findings seem to indicate that BD is associated with alterations in both frontal and posterior DMN structures, mainly in the prefrontal, posterior cingulate and inferior parietal cortices. We conclude this review by suggesting possible future research directions.

Anterior cingulate cortex, insula and amygdala seed-based whole brain resting-state functional connectivity differentiates bipolar from unipolar depression

OBJECTIVE

The frontal-limbic circuit is hypothesized as sub-serving emotional regulation. We performed whole brain resting-state functional connectivity (rs-FC) analysis by studying the key hubs of frontal-limbic circuit: anterior cingulate cortex (ACC), bilateral insula subregions, bilateral amygdala (Amy) as seeds, separately, to discriminate bipolar depression (BipD) from unipolar depression (UniD).

METHODS

We compared seed-based rs-FC of the frontal-limbic seeds with whole brain among 23 BipD participants; 23 age, gender, and depression severity matched patients with UniD, and 23 healthy controls (HCs). We also used support vector machine learning to study classification based on the rs-FC of ACC, bilateral insula subregions, and bilateral Amy seeds with whole brain.

RESULTS

BipD showed increased rs-FC between the left ventral anterior insula (vAI) seed and the left anterior supramarginal gyrus (aSMG) and left postcentral gyrus, as well as increased rs-FC between left amygdala seed and the left aSMG when compared to HCs and UniD. Compared to UniD, BipD was associated with increased rs-FC between right dorsal anterior insula seed and right superior frontal gyrus, as well as increased rs-FC between left posterior insula seed and right precentral gyrus and right thalamus. Combined rs-FC of ACC, bilateral insula subregions and bilateral Amy seeds with the whole brain discriminated BipD from UniD with an accuracy of 91.30%.

CONCLUSIONS

Rs-FC of the emotional regulation circuit is more widely disturbed in BipD than UniD. Using rs-FC with this circuit may lead to further developments in diagnostic decision-making.

Resting state networks activity in euthymic bipolar disorder

OBJECTIVES

Bipolar disorder (BD) is a psychiatric condition causing shifts in mood, energy and activity levels severely altering the quality of life of the patients even in the euthymic phase. Although widely accepted, the neurobiological bases of the disorder in the euthymic phase remain elusive. This study aims at characterizing resting state functional activity of the BD euthymic phase in order to better understand the pathogenesis of the disease and build future neurobiological models.

METHODS

Fifteen euthymic BD patients (10 females; mean age 40.2; standard deviation 13.5; range 20-61) and 27 healthy controls (HC) (21 females; mean age 37; standard deviation 10.6; range 22-60) underwent a 3T functional MRI scan at rest. Resting state activity was extracted through independent component analysis (ICA) run with automatic dimensionality estimation.

RESULTS

ICA identified 22 resting state networks (RSNs). Within-network analysis revealed decreased connectivity in the visual, temporal, motor and cerebellar RSNs of BD patients vs HC. Between-network analysis showed increased connectivity between motor area and the default mode network (DMN) partially overlapping with the fronto-parietal network (FPN) in BD patients.

CONCLUSION

Within-network analysis confirmed existing evidence of altered cerebellar, temporal, motor and visual networks in BD. Increased connectivity between the DMN and the motor area network suggests the presence of alterations of the fronto-parietal regions, precuneus and cingulate cortex in the euthymic condition. These findings indicate that specific connectivity alterations might persist even in the euthymic state suggesting the importance of examining both within and between-network connectivity to achieve a global understanding of the BD euthymic condition.

Towards a brain-based predictome of mental illness

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

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

OBJECTIVES

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

METHOD

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

RESULT

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

CONCLUSIONS

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

Investigating the neural basis of cognitive control dysfunction in mood disorders

OBJECTIVES

Dysfunction of cognitive control is a feature of both bipolar disorder (BP) and major depression (MDD) and persists through to remission. However, it is unknown whether these disorders are characterized by common or distinct disruptions of cognitive control function and its neural basis. We investigated this gap in knowledge in asymptomatic BP and MDD participants, interpreted within a framework of normative function.

METHODS

Participants underwent fMRI scans engaging cognitive control through a working memory task and completed a cognitive battery evaluating performance across multiple subdomains of cognitive control, including attention, impulsivity, processing speed, executive function, and memory. Analysis was performed in two stages: (i) cognitive control-related brain activation and deactivation were correlated with cognitive control performance in 115 healthy controls (HCs), then, (ii) significantly correlated regions from (i) were compared between 25 asymptomatic BP, 25 remitted MDD, and with 25 different HCs, matched for age and gender.

RESULTS

Impulsivity and executive function performance were significantly worse in BP compared to both MDD and HCs. Both BP and MDD had significantly poorer memory performance compared to HCs. Greater deactivation of the medial prefrontal cortex (MPFC) during the fMRI task was associated with better executive function in healthy controls. Significantly less deactivation in this region was present in both BP and MDD compared to HCs.

CONCLUSIONS

Failure to deactivate the MPFC, a key region of the default mode network, during working memory processing is a shared neural feature present in both bipolar and major depression and could be a source of common cognitive dysfunction.

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

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

Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning

Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central executive network (CEN), and the salience network (SN) have been shown to be aberrant in patients with posttraumatic stress disorder (PTSD). The purpose of the current study was to a) compare ICN functional connectivity between PTSD, dissociative subtype PTSD (PTSD+DS) and healthy individuals; and b) to examine the use of multivariate machine learning algorithms in classifying PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our neuroimaging dataset consisted of resting-state fMRI scans from 186 participants [PTSD (n = 81); PTSD + DS (n = 49); and healthy controls (n = 56)]. We performed group-level independent component analyses to evaluate functional connectivity differences within each ICN. Multiclass Gaussian Process Classification algorithms within PRoNTo software were then used to predict the diagnosis of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. When comparing the functional connectivity of ICNs between PTSD, PTSD+DS and healthy controls, we found differential patterns of connectivity to brain regions involved in emotion regulation, in addition to limbic structures and areas involved in self-referential processing, interoception, bodily self-consciousness, and depersonalization/derealization. Machine learning algorithms were able to predict with high accuracy the classification of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our results suggest that alterations within intrinsic connectivity networks may underlie unique psychopathology and symptom presentation among PTSD subtypes. Furthermore, the current findings substantiate the use of machine learning algorithms for classifying subtypes of PTSD illness based on ICNs.

Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach

BACKGROUND

The field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition methods have been applied recently to predict many psychiatric disorders, these techniques have not been utilized to predict subtypes of posttraumatic stress disorder (PTSD), including the dissociative subtype of PTSD (PTSD + DS).

METHODS

Using Multiclass Gaussian Process Classification within PRoNTo, we examined the classification accuracy of: (i) the mean amplitude of low-frequency fluctuations (mALFF; reflecting spontaneous neural activity during rest); and (ii) seed-based amygdala complex functional connectivity within 181 participants [PTSD (n = 81); PTSD + DS (n = 49); and age-matched healthy trauma-unexposed controls (n = 51)]. We also computed mass-univariate analyses in order to observe regional group differences [false-discovery-rate (FDR)-cluster corrected p < 0.05, k = 20].

RESULTS

We found that extracted features could predict accurately the classification of PTSD, PTSD + DS, and healthy controls, using both resting-state mALFF (91.63% balanced accuracy, p < 0.001) and amygdala complex connectivity maps (85.00% balanced accuracy, p < 0.001). These results were replicated using independent machine learning algorithms/cross-validation procedures. Moreover, areas weighted as being most important for group classification also displayed significant group differences at the univariate level. Here, whereas the PTSD + DS group displayed increased activation within emotion regulation regions, the PTSD group showed increased activation within the amygdala, globus pallidus, and motor/somatosensory regions.

CONCLUSION

The current study has significant implications for advancing machine learning applications within the field of psychiatry, as well as for developing objective biomarkers indicative of diagnostic heterogeneity.

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.

Abnormal intrinsic brain functional network dynamics in unmedicated depressed bipolar II disorder

BACKGROUND

Previous studies analyzed brain functional connectivity (FC) based on resting-state fMRI (RS-fMRI) data to reveal the neuropathology of bipolar disorder (BD) and suggested that their FC alterations are at widespread network-level. However, few studies have analyzed the dynamic functional network connectivity (dFNC) in BD. Thus, we aimed to reveal the dFNC properties of BD in this study.

METHODS

The RS-fMRI data were collected from 51 unmedicated depressed BD II patients and 50 healthy controls. We analyzed the dFNC properties by using an independent component analysis, sliding window correlation, k-means clustering, and graph theory methods.

RESULTS

The intrinsic brain FNC could be clustered into three configuration states, one with sparse connections between all functional networks (State 1), another with negative correlations between the salience network, cerebellum, basal ganglia and the sensory networks (State 2), and a third with negative correlations between the default mode network and the other functional networks (State 3). The BD patients had increased time in State 2, decreased time in State 3, and increased transition number between states. And the time spent in State 2 was positively correlated with the HDRS24 score in the BD patients. In addition, the BD patients had increased dynamic variance in the small-world properties of FNC.

LIMITATIONS

This study did not examine data from BD patients in other episodes and other BD types.

CONCLUSIONS

This study detected abnormal dFNC properties in BD, which indicated their FNC unstability and provided new insights into the neuropathology of their affective and cognitive deficits.

Aberrant brain network topology in fronto-limbic circuitry differentiates euthymic bipolar disorder from recurrent major depressive disorder

INTRODUCTION

Previous studies have established graph theoretical analysis of functional network connectivity (FNC) as a potential tool to detect neurobiological underpinnings of psychiatric disorders. Despite the promising outcomes in studies that examined FNC aberrancies in bipolar disorder (BD) and major depressive disorder (MDD), there is still a lack of research comparing both mood disorders, especially in a nondepressed state. In this study, we used graph theoretical network analysis to compare brain network properties of euthymic BD, euthymic MDD and healthy controls (HC) to evaluate whether these groups showed distinct features in FNC.

METHODS

We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 BD patients, 15 patients with recurrent MDD as well as 30 age- and gender-matched HC. Graph theoretical analyses were then applied to investigate functional brain networks on a global and regional network level.

RESULTS

Global network analysis revealed a significantly higher mean global clustering coefficient in BD compared to HC. We further detected frontal, temporal and subcortical nodes in emotion regulation areas such as the limbic system and associated regions exhibiting significant differences in network integration and segregation in BD compared to MDD patients and HC. Participants with MDD and HC only differed in frontal and insular network centrality.

CONCLUSION

In conclusion, our findings indicate that a significantly altered brain network topology in the limbic system might be a trait marker specific to BD. Brain network analysis in these regions may therefore be used to differentiate euthymic BD not only from HC but also from patients with MDD.

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.

Cortico-limbic connectivity as a possible biomarker for bipolar disorder: where are we now?

INTRODUCTION

The fronto-limbic network has been suggested as a key circuitry in the pathophysiology and maintenance of bipolar disorder. In the past decade, a disrupted connectivity within prefrontal-limbic structures was identified as a promising candidate biomarker for the disorder. Areas Covered: In this review, the authors examine current literature in terms of the structural, functional and effective connectivity in bipolar disorder, integrating recent findings of imaging genetics and machine learning. This paper profiles the current knowledge and identifies future perspectives to provide reliable and usable neuroimaging biomarkers for bipolar psychopathology in clinical practice. Expert Opinion: The replication and the translation of acquired knowledge into useful and usable tools represents one of the current greatest challenges in biomarker research applied to psychiatry.

Differentiation of Transformed Bipolar Disorder From Unipolar Depression by Resting-State Functional Connectivity Within Reward Circuit

Previous studies have found that neural functional abnormalities detected by functional magnetic resonance imaging (fMRI) in brain regions implicated in reward processing during reward tasks show promise to distinguish bipolar from unipolar depression (UD), but little is known regarding resting-state functional connectivity (rsFC) within the reward circuit. In this study, we investigated neurobiomarkers for early recognition of bipolar disorder (BD) by retrospectively comparing rsFC within the reward circuit between UD and depressed BD. Sixty-six depressed patients were enrolled, none of whom had ever experienced any manic/hypomanic episodes before baseline. Simultaneously, 40 matched healthy controls (HC) were also recruited. Neuroimaging data of each participant were obtained from resting-state fMRI scans. Some patients began to manifest bipolar disorder (tBD) during the follow-up period. All patients were retrospectively divided into two groups (33 tBD and 33 UD) according to the presence or absence of mania/hypomania in the follow-up. rsFC between key regions of the reward circuit was calculated and compared among groups. Results showed decreased rsFC between the left ventral tegmental area (VTA) and left ventral striatum (VS) in the tBD group compared with the UD group, which showed good accuracy in predicting diagnosis (tBD vs. UD) according to receiver operating characteristic (ROC) analysis. No significant different rsFC was found within the reward circuit between any patient group and HC. Our preliminary findings indicated that bipolar disorder, in early depressive stages before onset of mania/hypomania attacks, already differs from UD in the reward circuit of VTA-VS functional synchronicity at the resting state.

Decreased Functional Connectivity in Insular Subregions in Depressive Episodes of Bipolar Disorder and Major Depressive Disorder

Objective: Clinically, it is very difficult to distinguish between major depressive disorder (MDD) and bipolar disorder (BD) in the period of depression. Increasing evidence shows that the insula plays an important role in depression. We aimed to compare the resting-state functional connectivity (rsFC) of insular subregions in patients with MDD and BD in depressive episodes (BDD), who had never experienced manic or hypomanic episodes when they were scanned to identify biomarkers for the identification of two diseases. Methods: We recruited 21 BDD patients, 40 MDD patients and 70 healthy controls (HC). Resting-state functional magnetic resonance imaging (rs-fMRI) was performed. BDD patients had never had manic or hypomanic episodes when they were scanned, and the diagnoses were determined by follow-up. We divided the insula into three parts including the ventral anterior insular cortex (v-AIN), dorsal anterior insular cortex (d-AIN), and posterior insula (PI). The insular-based rsFC was compared among the three groups, and an analysis of the correlation between the rsFC value and Hamilton depression and anxiety scales was carried out. Results: BDD and MDD patients demonstrated decreased rsFC from the v-AIN to the left superior/middle frontal gyrus compared with the HC group. Versus MDD and HC groups, BDD patients exhibited decreased rsFC from the v-AIN to the area in the left orbital frontal gyrus and left superior temporal gyrus (included temporal pole), from the PI to the right lateral postcentral gyrus and from all three insular subregions to the somatosensory and motor cortex. Meanwhile, a correlation between the rsFC value of the PI-right lateral postcentral gyrus and anxiety score was observed in patients. Conclusion: Our findings show BDD and MDD patients have similar decreases in insular connectivity in the dorsal lateral frontal regions, and BDD patients have specific decreased insular connectivity, especially in the somatosensory and motor cortex, which may be used as imaging evidence for clinical identification.

Abnormal intrinsic cerebro-cerebellar functional connectivity in un-medicated patients with bipolar disorder and major depressive disorder

OBJECTIVE

The cerebellum plays an important role in depression. Cerebro-cerebellar circuits have been found to show aberrance in bipolar disorder (BD) and major depressive disorder (MDD). However, whether the cerebro-cerebellar connectivity contributes equally to the pathologic mechanisms of BD and MDD remains unknown.

METHODS

We recruited 33 patients with MDD, 32 patients with BD, and 43 healthy controls (HC). We selected six seed regions (three per hemisphere) in the cerebrum, corresponding to the affective, cognitive control, and default mode networks, to establish cerebro-cerebellar functional connectivity maps.

RESULTS

Relative to the HC, both the BD and MDD patients exhibited weaker negative connectivity between the right subgenual anterior cingulate cortex and the cerebellar vermis IV_V (p_BD = 0.03, p_MDD = 0.001) and weaker positive connectivity between the left precuneus and the left cerebellar lobule IX (p_BD = 0.043, p_MDD = 0.000). Moreover, the MDD patients showed weaker positive connectivity in the left precuneus-left cerebellar lobule IX circuit than the BD patients (p = 0.049). In addition, the BD patients showed weaker positive connectivity in the right dorsolateral prefrontal cortex-left cerebellar lobule Crus Ι circuit compared to the HC (p = 0.002) or the MDD patients (p = 0.013). Receiver operating characteristic curves analyses showed that the altered cerebro-cerebellar connectivities could be used to distinguish the patients from the HC with relatively high accuracy.

CONCLUSIONS

Our findings suggested that differences in connectivity of cerebro-cerebellar circuits, which are involved in affective or cognitive functioning, significantly contributed to BD and MDD.

Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective

Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states. However, it remains largely unclear whether spontaneous brain activity is effectively related to EO and EC resting states. The amplitude, local functional concordance, inter-hemisphere functional synchronization, and network centrality of spontaneous brain activity were measured by the fraction amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC), respectively. Using the public Eyes-open/Eyes-closed dataset, we employed the support vector machine (SVM) and bootstrap technique to establish linking models for the fALFF, ReHo, VMHC and DC dimensions. The classification accuracies of linking models are 0.72 (0.59, 0.82), 0.88 (0.79, 0.97), 0.82 (0.74, 0.91) and 0.70 (0.62, 0.79), respectively. Specifically, we observed that brain activity in the EO condition is significantly greater in attentional system areas, including the fusiform gyrus, occipital and parietal cortex, but significantly lower in sensorimotor system areas, including the precentral/postcentral gyrus, paracentral lobule (PCL) and temporal cortex compared to the EC condition from the four dimensions. The results consistently indicated that spontaneous brain activity is effectively related to EO and EC resting states, and the two resting states are of opposite brain activity in sensorimotor and occipital regions. It may provide new insight into the neural substrate of the resting state and help computational neuroscientists or neuropsychologists to choose an appropriate resting state condition to investigate various mental disorders from the resting state functional magnetic resonance imaging (fMRI) technique.

Machine learning in major depression: From classification to treatment outcome prediction

AIMS

Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.

DISCUSSIONS

In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.

CONCLUSIONS

We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.

Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder

BACKGROUND

Bipolar Disorder (BD) cannot be reliably distinguished from Major Depressive Disorder (MDD) until the first manic or hypomanic episode. Consequently, many patients with BD are treated with antidepressants without mood stabilizers, a strategy that is often ineffective and carries a risk of inducing a manic episode. We previously reported reduced cortical thickness in right precuneus, right caudal middle-frontal cortex and left inferior parietal cortex in BD compared with MDD.

METHODS

This study extends our previous work by performing individual level classification of BD or MDD in an expanded, currently unmedicated, cohort using gray matter volume (GMV) based on Magnetic Resonance Imaging and a Support Vector Machine. All patients were in a Major Depressive Episode and a leave-two-out analysis was performed.

RESULTS

Nineteen out of 26 BD subjects and 20 out of 26 MDD subjects were correctly identified, for a combined accuracy of 75%. The three brain regions contributing to the classification were higher GMV in bilateral supramarginal gyrus and occipital cortex indicating MDD, and higher GMV in right dorsolateral prefrontal cortex indicating BD.

LIMITATIONS

This analysis included scans performed with two different headcoils and scan sequences, which limited the interpretability of results in an independent cohort analysis.

CONCLUSIONS

Our results add to previously published data which suggest that regional gray matter volume should be investigated further as a clinical diagnostic tool to predict BD before the appearance of a manic or hypomanic episode.

Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression

OBJECTIVE

Depression in bipolar disorder (BipD) requires a therapeutic approach that is from treating unipolar major depressive disorder (UniD), but to date, no reliable methods could separate these two disorders. The aim of this study was to establish the clinical validity and utility of a non-invasive functional MRI-based method to classify BipD from UniD.

METHOD

The degree of connectivity (degree centrality or DC) of every small unit (voxel) with every other unit of the brain was estimated in 22 patients with BipD and 22 age, gender, and depressive severity-matched patients with UniD and 22 healthy controls. Pattern classification analysis was carried out using a support-vector machine (SVM) approach.

RESULTS

Degree centrality pattern from 8-min resting fMRI discriminated BipD from UniD with an accuracy of 86% and diagnostic odds ratio of 9.6. DC was reduced in the left insula and increased in bilateral precuneus in BipD when compared to UniD. In this sample with a high degree of uncertainty (50% prior probability), positive predictive value of the DC test was 79%.

CONCLUSION

Degree centrality maps are potential candidate measures to separate bipolar depression from unipolar depression. Test performance reported here requires further pragmatic evaluation in regular clinical practice.

The impact of machine learning techniques in the study of bipolar disorder: A systematic review

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.

Insula and amygdala resting-state functional connectivity differentiate bipolar from unipolar depression

OBJECTIVE

Distinguishing depressive episodes due to bipolar disorder (BD) or major depressive disorder (MDD) solely on clinical grounds is challenging. We aimed at comparing resting-state functional connectivity (rsFC) of regions subserving emotional regulation in similarly depressed BD and MDD.

METHOD

We enrolled 76 in-patients (BD, n = 36; MDD, n = 40) and 40 healthy controls (HC). A seed-based approach was used to identify regions showing different rsFC with the insula and the amygdala. Insular and amygdalar parcellations were then performed along with diagnostic accuracy of the main findings.

RESULTS

Lower rsFC between the left insula and the left mid-dorsolateral prefrontal cortex and between bilateral insula and right frontopolar prefrontal cortex (FPPFC) was observed in BD compared to MDD and HC. These results were driven by the dorsal anterior and posterior insula (PI). Lower rsFC between the right amygdala and the left anterior hippocampus was observed in MDD compared to BD and HC. These results were driven by the centromedial and laterobasal amygdala. Left PI/right FPPC rsFC showed 78% accuracy differentiating BD and MDD.

CONCLUSION

rsFC of amygdala and insula distinguished between depressed BD and MDD. The observed differences suggest the possibility of differential pathophysiological mechanisms of emotional dysfunction in bipolar and unipolar depression.