Resting-state fMRI in depressive and (hypo)manic mood states in bipolar disorders: A systematic review

Distinct global brain connectivity alterations in depressed adolescents with subthreshold mania and the relationship with processing speed: Evidence from sBEAD Cohort

BACKGROUND

Bipolar disorder (BD) is a progressive condition. Investigating the neuroimaging mechanisms in depressed adolescents with subthreshold mania (SubMD) facilitates the early identification of BD. However, the global brain connectivity (GBC) patterns in SubMD patients, as well as the relationship with processing speed before the onset of full-blown BD, remain unclear.

METHODS

The study involved 72 SubMD, 77 depressed adolescents without subthreshold mania (nSubMD), and 69 gender- and age-matched healthy adolescents (HCs). All patients underwent a clinical follow-up ranging from six to twelve months. We calculated the voxel-based graph theory analysis of the GBC map and conducted the TMT-A test to measure the processing speed.

RESULTS

Compared to HCs and nSubMD, SubMD patients displayed distinctive GBC index patterns: GBC index decreased in the right Medial Superior Frontal Gyrus (SFGmed.R)/Superior Frontal Gyrus (SFG) while increased in the right Precuneus and left Postcentral Gyrus. Both patient groups showed increased GBC index in the right Inferior Temporal Gyrus. An increased GBC value in the right Supplementary Motor Area was exclusively observed in the nSubMD-group. There were opposite changes in the GBC index in SFGmed.R/SFG between two patient groups, with an AUC of 0.727. Additionally, GBC values in SFGmed.R/SFG exhibited a positive correlation with TMT-A scores in SubMD-group.

LIMITATIONS

Relatively shorter follow-up duration, medications confounding, and modest sample size.

CONCLUSION

These findings suggest that adolescents with subthreshold BD have specific impairments patterns at the whole brain connectivity level associated with processing speed impairments, providing insights into early identification and intervention strategies for BD.

Right ventrolateral and left dorsolateral 10 Hz transcranial magnetic stimulation as an add-on treatment for bipolar I and II depression: a double-blind, randomised, three-arm, sham-controlled study

OBJECTIVES

Despite the clinical importance of bipolar depression (BDE), effective treatment options are still limited. Transcranial magnetic stimulation (rTMS) has proven of moderate efficacy in major depression, but the evidence remains inconclusive for BDE.

METHODS

A 4-week, double-blind, randomised, parallel-group, sham-controlled study (trial ID ISRCTN77188420) explored the benefits of 10 Hz MRI-guided right ventrolateral (RVL) rTMS and left dorsolateral (LDL) rTMS as add-on treatments for BDE. Outcome measures included changes in the Montgomery-Åsberg Depression Rating Scale (MADRS) score, self-assessment, response and remission rates, and side effects.

RESULTS

Sixty patients were randomly assigned to study groups, and forty-six completed the double-blind phase. The mean change from baseline to Week 4 in MADRS was greater in both active groups compared to the sham, yet differences did not achieve significance (RVL vs sham: -4.50, 95%CI -10.63 to 1.64, p = 0.3; LDL vs sham: -4.07, 95%CI -10.24 to 2.10, p = 0.4). None of the other outcome measures yielded significant results.

CONCLUSIONS

While not demonstrating the superiority of either 10 Hz rTMS over sham, with the limited sample size, we can not rule out a moderate yet clinically meaningful effect. Further well-powered studies are essential to elucidate the role of rTMS in managing BDE.

A systematic review of the biomarkers associated with cognition and mood state in bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a severe psychiatric disorder characterized by changes in mood that alternate between (hypo) mania or depression and mixed states, often associated with functional impairment and cognitive dysfunction. But little is known about biomarkers that contribute to the development and sustainment of cognitive deficits. The aim of this study was to review the association between neurocognition and biomarkers across different mood states.

METHOD

Search databases were Web of Science, Scopus and PubMed. A systematic review was carried out following the PRISMA guidelines. Risk of bias was assessed with the Newcastle-Ottawa Scale. Studies were selected that focused on the correlation between neuroimaging, physiological, genetic or peripheral biomarkers and cognition in at least two phases of BD: depression, (hypo)mania, euthymia or mixed. PROSPERO Registration No.: CRD42023410782.

RESULTS

A total of 1824 references were screened, identifying 1023 published articles, of which 336 were considered eligible. Only 16 provided information on the association between biomarkers and cognition in the different affective states of BD. The included studies found: (i) Differences in levels of total cholesterol and C reactive protein depending on mood state; (ii) There is no association found between cognition and peripheral biomarkers; (iii) Neuroimaging biomarkers highlighted hypoactivation of frontal areas as distinctive of acute state of BD; (iv) A deactivation failure has been reported in the ventromedial prefrontal cortex (vmPFC), potentially serving as a trait marker of BD.

CONCLUSION

Only a few recent articles have investigated biomarker-cognition associations in BD mood phases. Our findings underline that there appear to be central regions involved in BD that are observed in all mood states. However, there appear to be underlying mechanisms of cognitive dysfunction that may vary across different mood states in BD. This review highlights the importance of standardizing the data and the assessment of cognition, as well as the need for biomarkers to help prevent acute symptomatic phases of the disease, and the associated functional and cognitive impairment.

Longitudinal changes in resting-state functional connectivity as markers of vulnerability or resilience in first-degree relatives of patients with bipolar disorder

BACKGROUND

There is a significant contribution of genetic factors to the etiology of bipolar disorder (BD). Unaffected first-degree relatives of patients (UR) with BD are at increased risk of developing mental disorders and may manifest cognitive impairments and alterations in brain functional and connective dynamics, akin to their affected relatives.

METHODS

In this prospective longitudinal study, resting-state functional connectivity was used to explore stable and progressive markers of vulnerability i.e. abnormalities shared between UR and BD compared to healthy controls (HC) and resilience i.e. features unique to UR compared to HC and BD in full or partial remission (UR n = 72, mean age = 28.0 ± 7.2 years; HC n = 64, mean age = 30.0 ± 9.7 years; BD patients n = 91, mean age = 30.6 ± 7.7 years). Out of these, 34 UR, 48 BD, and 38 HC were investigated again following a mean time of 1.3 ± 0.4 years.

RESULTS

At baseline, the UR showed lower connectivity values within the default mode network (DMN), frontoparietal network, and the salience network (SN) compared to HC. This connectivity pattern in UR remained stable over the follow-up period and was not present in BD, suggesting a resilience trait. The UR further demonstrated less negative connectivity between the DMN and SN compared to HC, abnormality that remained stable over time and was also present in BD, suggesting a vulnerability marker.

CONCLUSION

Our findings indicate the coexistence of both vulnerability-related abnormalities in resting-state connectivity, as well as adaptive changes possibly promoting resilience to psychopathology in individual at familial risk.

Study on exosomes for identifying bipolar disorder in early stage: A cross-sectional and validation study protocol

BACKGROUND

The difficulty is remained to accurately distinguish bipolar disorder (BD) from major depressive disorder (MDD) in early stage, with a delayed diagnosis for 5-10 years. BD patients are often treated with antidepressants systematically due to being diagnosed with MDD, affecting the disease course and clinical outcomes. The current study aims to explore the role of plasma exosomes as biomarker to distinguish BD from MDD in early stage.

METHODS

Two stages are included. The first stage is a cross-sectional study, comparing the concentrations of plasma exosome microRNA and related proteins among BD group, MDD group, and healthy controls (HC) group (n = 40 respectively), to identify target biomarkers preliminarily. The "Latent Class Analysis" and "Receiver Operating Characteristic" analysis will be performed to determine the optimal concentration range for each biomarker. The second stage is to validate target markers in subjects, coming from an ongoing study focusing on patients with a first depressive episode. All target biomarkers will be test in plasma samples reserved at the initial stage to detect whether the diagnosis indicated by biomarker level is consistent with the diagnosis by DSM-5. Furthermore, the correlation between specific biomarkers and the manic episode, suicidal ideation, and adverse reactions will also be observed.

DISCUSSION

Exosome-derived microRNA and related proteins have potential in serving as a good medium for exploring mental disorders because it can pass through the blood-brain barrier bidirectionally and convey a large amount of information stably. Improving the early diagnosis of BD would help implement appropriate intervention strategy as early as possible and significantly reduce the burden of disease.

Brain functional changes across mood states in bipolar disorder: from a large-scale network perspective

BACKGROUND

Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis.

RESULTS

A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients.

CONCLUSIONS

Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.

Advances in functional MRI research in bipolar disorder: from the perspective of mood states

Bipolar disorder is characterised by recurrent and alternating episodes of mania/hypomania and depression. Current breakthroughs in functional MRI techniques have uncovered the functional neuroanatomy of bipolar disorder. However, the pathophysiology underlying mood instability, mood switching and the development of extreme mood states is less well understood. This review presents a comprehensive overview of current evidence from functional MRI studies from the perspective of mood states. We first summarise the disrupted brain activation patterns and functional connectivity that have been reported in bipolar disorder, irrespective of the mood state. We next focus on research that solely included patients in a single mood state for a better understanding of the pathophysiology of bipolar disorder and research comparing patients with different mood states to dissect mood state-related effects. Finally, we briefly summarise current theoretical models and conclude this review by proposing potential avenues for future research. A comprehensive understanding of the pathophysiology with consideration of mood states could not only deepen our understanding of how acute mood episodes develop at a neurophysiological level but could also facilitate the identification of biological targets for personalised treatment and the development of new interventions for bipolar disorder.

Regional Homogeneity of the Left Posterior Cingulate Gyrus May Be a Potential Imaging Biomarker of Manic Episodes in First-Episode, Drug-Naive Bipolar Disorder

Introduction

Abnormal brain networks with emotional response in bipolar disorder (BD). However, there have been few studies on the local consistency between manic episodes in drug-naive first-episode BD patients and healthy controls (HCs). The purpose of this study is to evaluate the utility of neural activity values analyzed by Regional Homogeneity (ReHo).

Methods

Thirty-seven manic episodes in first-episode, drug-naive BD patients and 37 HCs participated in resting-state functional magnetic resonance rescanning and scale estimation. Reho and receiver operating characteristic (ROC) curve methods were used to analyze the imaging data. Support vector machine (SVM) method was used to analyze ReHo in different brain regions.

Results

Compared to HCs, ReHo increased in the left middle temporal gyrus (MTG.L), posterior cingulate gyrus (PCG), inferior parietal gyrus, and bilateral angular gyrus, and decreased in the left dorsolateral superior frontal gyrus in target group. The ROC results showed that the ReHo value of the left PCG could discriminate the target group from the HCs, and the AUC was 0.8766. In addition, the results of the support vector machine show that the increase in ReHo value in the left PCG can effectively discriminate the patients from the controls, with accuracy, sensitivity, and specificity of 86.02%, 86.49%, and 81.08%, respectively.

Conclusion

The increased activity of the left PCG may contribute new evidence of participation in the pathophysiology of manic episodes in first-episode, drug-naive BD patients. The Reho value of the left posterior cingulate gyrus may be a potential neuroimaging biomarker to discriminate target group from HCs.

Enhanced Dynamic Laterality Based on Functional Subnetworks in Patients with Bipolar Disorder

An ocean of studies have pointed to abnormal brain laterality changes in patients with bipolar disorder (BD). Determining the altered brain lateralization will help us to explore the pathogenesis of BD. Our study will fill the gap in the study of the dynamic changes of brain laterality in BD patients and thus provide new insights into BD research. In this work, we used fMRI data from 48 BD patients and 48 normal controls (NC). We constructed the dynamic laterality time series by extracting the dynamic laterality index (DLI) at each sliding window. We then used k-means clustering to partition the laterality states and the Arenas-Fernandez-Gomez (AFG) community detection algorithm to determine the number of states. We characterized subjects' laterality characteristics using the mean laterality index (MLI) and laterality fluctuation (LF). Compared with NC, in all windows and state 1, BD patients showed higher MLI in the attention network (AN) of the right hemisphere, and AN in the left hemisphere showed more frequent laterality fluctuations. AN in the left hemisphere of BD patients showed higher MLI in all windows and state 3 compared to NC. In addition, in the AN of the right hemisphere in state 1, higher MLI in BD patients was significantly associated with patient symptoms. Our study provides new insights into the understanding of BD neuropathology in terms of brain dynamic laterality.

Neuroanatomical and functional substrates of the hypomanic personality trait and its prediction on aggression

Hypomanic personality manifests a close link with several psychiatric disorders and its abnormality is a risk indicator for developing bipolar disorders. We systematically investigated the potential neuroanatomical and functional substrates underlying hypomanic personality trait (HPT) and its sub-dimensions (i.e., Social Vitality, Mood Volatility, and Excitement) combined with structural and functional imaging data as well as their corresponding brain networks in a large non-clinical sample across two studies (n = 464). Behaviorally, HPT, specifically Mood Volatility and Excitement, was positively associated with aggressive behaviors in both studies. Structurally, sex-specific morphological characteristics were further observed in the motor and top-down control networks especially for Mood Volatility, although HPT was generally positively associated with grey matter volumes (GMVs) in the prefrontal, temporal, visual, and limbic systems. Functionally, brain activations related to immediate or delayed losses were found to predict individual variability in HPT, specifically Social Vitality and Excitement, on the motor and prefrontal-parietal cortices. Topologically, connectome-based prediction model analysis further revealed the predictive role of individual-level morphological and resting-state functional connectivity on HPT and its sub-dimensions, although it did not reveal any links with general brain topological properties. GMVs in the temporal, limbic (e.g., amygdala), and visual cortices mediated the effects of HPT on behavioral aggression. These findings suggest that the imbalance between motor and control circuits may be critical for HPT and provide novel insights into the neuroanatomical, functional, and topological mechanisms underlying the specific temperament and its impacts on aggression.

The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis

Objective

Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD.

Methods

English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity.

Results

Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity.

Conclusion

ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.

Antidepressant-induced mania in panic disorder: a single-case study of clinical and functional connectivity characteristics

Background

Mental health issues, including panic disorder (PD), are prevalent and often co-occur with anxiety and bipolar disorders. While panic disorder is characterized by unexpected panic attacks, and its treatment often involves antidepressants, there is a 20-40% risk of inducing mania (antidepressant-induced mania) during treatment, making it crucial to understand mania risk factors. However, research on clinical and neurological characteristics of patients with anxiety disorders who develop mania is limited.

Methods

In this single case study, we conducted a larger prospective study on panic disorder, comparing baseline data between one patient who developed mania (PD-manic) and others who did not (PD-NM group). We enrolled 27 patients with panic disorder and 30 healthy controls (HCs) and examined alterations in amygdala-based brain connectivity using a seed-based whole-brain approach. We also performed exploratory comparisons with healthy controls using ROI-to-ROI analyses and conducted statistical inferences at a threshold of cluster-level family-wise error-corrected p < 0.05, with the cluster-forming threshold at the voxel level of uncorrected p < 0.001.

Results

The patient with PD-mania showed lower connectivity in brain regions related to the default mode network (left precuneous cortex, maximum z-value within the cluster = -6.99) and frontoparietal network (right middle frontal gyrus, maximum z-value within the cluster = -7.38; two regions in left supramarginal gyrus, maximum z-value within the cluster = -5.02 and -5.86), and higher in brain regions associated with visual processing network (right lingual gyrus, maximum z-value within the cluster = 7.86; right lateral occipital cortex, maximum z-value within the cluster = 8.09; right medial temporal gyrus, maximum z-value within the cluster = 8.16) in the patient with PD-mania compared to the PD-NM group. One significantly identified cluster, the left medial temporal gyrus (maximum z-value within the cluster = 5.82), presented higher resting-state functional connectivity with the right amygdala. Additionally, ROI-to-ROI analysis revealed that significant clusters between PD-manic and PD-NM groups differed from HCs in the PD-manic group but not in the PD-NM group.

Conclusion

Here, we demonstrate altered amygdala-DMN and amygdala-FPN connectivity in the PD-manic patient, as reported in bipolar disorder (hypo) manic episodes. Our study suggests that amygdala-based resting-state functional connectivity could serve as a potential biomarker for antidepressant-induced mania in panic disorder patients. Our findings provide an advance in understanding the neurological basis of antidepressant-induced mania, but further research with larger cohorts and more cases is necessary for a broader perspective on this issue.

Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm

Emotion analysis has emerged as one of the most prominent study areas in the field of Brain Computer Interface (BCI) due to the critical role that the human brain plays in the creation of human emotions. In this study, a Multi-objective Immunogenetic Community Division Algorithm Based on Memetic Framework (MFMICD) was suggested to study different emotions from the perspective of brain networks. To improve convergence and accuracy, MFMICD incorporates the unique immunity operator based on the traditional genetic algorithm and combines it with the taboo search algorithm. Based on this approach, we examined how the structure of people’s brain networks alters in response to different emotions using the electroencephalographic emotion database. The findings revealed that, in positive emotional states, more brain regions are engaged in emotion dominance, the information exchange between local modules is more frequent, and various emotions cause more varied patterns of brain area interactions than in negative brain states. A brief analysis of the connections between different emotions and brain regions shows that MFMICD is reliable in dividing emotional brain functional networks into communities.

Resting-state functional connectivity and spontaneous brain activity in early-onset bipolar disorder: A review of functional Magnetic Resonance Imaging studies

BACKGROUND

Early-onset bipolar disorder (BD) is a complex psychiatric illness characterized by mood swings, irritability and functional impairments. To improve our understanding of the pathophysiology of the disorder, we collected the existing resting-state functional Magnetic Resonance Imaging (rs-fMRI) studies exploring resting-state functional connectivity (rs-FC) and spontaneous activity alterations in children and adolescents with BD.

METHODS

A search on PubMed, Web of Science and Scopus was conducted to identify all the relevant rs-fMRI investigations conducted in early-onset BD. A total of 14 studies employing different methodological approaches to explore rs-FC and spontaneous activity in early-onset BD were included (independent component analysis, n = 1; seed-based analysis, n = 7; amplitude of low frequency fluctuations analysis, n = 2; regional homogeneity analysis, n = 4).

RESULTS

Overall, the studies showed abnormalities within the Default Mode Network (DMN) and between the DMN and the Salience Network (SN). Moreover, widespread alterations in rs-FC and spontaneous brain activity within and between cortico-limbic structures, involving primarily the occipital and frontal lobes, amygdala, hippocampus, insula, thalamus and striatum were also reported.

LIMITATIONS

The small sample sizes, the use of medications, the presence of comorbidities and the heterogeneity in methods hamper the integration of the study findings.

CONCLUSIONS

Early-onset BD seems to be characterized by selective rs-FC and spontaneous activity dysfunctions in DMN and SN as well as in the cortico-limbic and cortico-striatal circuits, which could explain the emotive and cognitive deficits observed in this disabling psychiatric illness.