White Matter Microstructure in Pediatric Bipolar Disorder and Disruptive Mood Dysregulation Disorder

Structure-function coupling in network connectivity and associations with negative affectivity in a group of transdiagnostic adolescents

The study of brain connectivity, both functional and structural, can inform us on the development of psychopathology. The use of multimodal MRI methods allows us to study associations between structural and functional connectivity, and how this relates to psychopathology. This may be especially useful during childhood and adolescence, a period where most forms of psychopathology manifest for the first time. The current paper explores structure-function coupling, measured through diffusion and resting-state functional MRI, and quantified as the correlation between structural and functional connectivity matrices. We investigate associations between psychopathology and coupling in a transdiagnostic group of adolescents, including many treatment-seeking youth with relatively high levels of symptoms (n = 72, Mage = 13.3). We used a bifactor model to extract our main outcome measure, Negative Affectivity, from anxiety and irritability ratings. This provided the principal measure of psychopathology. Supplementary analyses investigated 'domain-specific' factors of anxiety and irritability. Findings indicate a positive association between negative affectivity and structure-function coupling between the default mode and the fronto-parietal control networks. Higher structure-function coupling may indicate heightened structural constraints on function, which limit functional network reorganization during adolescence required for healthy psychological outcomes.

White matter changes in paediatric bipolar disorder: A systematic review of diffusion magnetic resonance imaging studiesA systematic review of diffusion magnetic resonance imaging studies

BACKGROUND

Paediatric bipolar disorder (PBD) is characterized by severe mood fluctuations that deviate from typical childhood emotional development. Despite the efforts, the pathophysiology of this disorder is not well understood yet. In this review, we aimed to synthesize existing diffusion magnetic resonance imaging (dMRI) research findings in PBD.

METHODS

A literature search was conducted using PubMed, Embase, Scopus, and Web of Science databases to identify relevant studies published before April 2024.

RESULTS

A total of 23 studies were included in the review. The findings showed variations of fractional anisotropy (FA), axial diffusivity, radial diffusivity, and apparent diffusion coefficient in PBD compared to healthy controls (HCs). Key findings included decreased FA in the anterior cingulate, anterior corona radiata, and corpus callosum, particularly the genu, which correlated with clinical symptoms. Furthermore, longitudinal studies emphasized the significance of the uncinate fasciculus as having atypical developmental trajectories in PBD compared to HCs. In addition, graph analysis revealed widespread changes in structural connectivity, especially affecting the orbitofrontal cortex, frontal gyrus, and basal ganglia. Lastly, machine learning models showed promising results in differentiating PBD from HCs.

LIMITATIONS

Cross-sectional design of the studies, small sample sizes, and different imaging protocols preclude integration of the findings.

CONCLUSION

PBD seems to be associated with widespread structural changes compared to HC. Understanding these changes, which might account for the clinical manifestations of this disorder, increase our knowledge of the neurobiological underpinnings of PBD. This, in turn, may help develop more effective treatments for this disorder.

Modeling Shared and Specific Variances of Irritability, Inattention, and Hyperactivity Yields Novel Insights Into White Matter Perturbations

OBJECTIVE

Irritability, inattention, and hyperactivity, which are common presentations of childhood psychopathology, have been associated with perturbed white matter microstructure. However, similar tracts have been implicated across these phenotypes; such non-specificity could be rooted in their high co-occurrence. To address this problem, we use a bifactor approach parsing unique and shared components of irritability, inattention, and hyperactivity, which we then relate to white matter microstructure.

METHOD

We developed a bifactor model based on the Conners Comprehensive Behavioral Rating Scale in a sample of youth with no psychiatric diagnosis or a primary diagnosis of attention-deficit/hyperactivity disorder or disruptive mood dysregulation disorder (n = 521). We applied the model to an independent yet sociodemographically and clinically comparable sample (n = 152), in which we tested associations between latent variables and fractional anisotropy (FA).

RESULTS

The bifactor model fit well (comparative fit index = 0.99; root mean square error of approximation = 0.07). The shared factor was positively associated with an independent measure of impulsivity (ρS = 0.88, pFDR < .001) and negatively related to whole-brain FA (r = -0.20), as well as FA of the corticospinal tract (all pFWE < .05). FA increased with age and deviation from this curve, indicating that altered white matter maturation was associated with the hyperactivity-specific factor (r = -0.16, pFWE < .05). Inattention-specific and irritability-specific factors were not linked to FA.

CONCLUSION

Perturbed white matter microstructure may represent a shared neurobiological mechanism of irritability, inattention, and hyperactivity related to heightened impulsivity. Furthermore, hyperactivity might be uniquely associated with a delay in white matter maturation.

PLAIN LANGUAGE SUMMARY

In this study, researchers developed a model identifying shared aspects of key symptoms of disruptive mood dysregulation disorder (DMDD) and attention-deficit hyperactivity disorder (ADHD), more specifically irritability, inattention, and hyperactivity. In 521 participants, impulsivity emerged as a shared factor. Applied to 152 youth with brain imaging data, shared impulsivity, not specific symptoms, related to atypical brain structure. Additionally, hyperactivity was linked to delayed white matter maturation. Study findings suggest this approach might identify mechanisms of these childhood disorders that remain hidden when relying on traditional diagnostic categories.

Shared and Distinct White Matter Alterations in Major Depression and Bipolar Disorder: A Systematic Review and Meta-Analysis

BACKGROUND

Identifying white matter (WM) microstructural similarities and differences between major depressive disorder (MDD) and bipolar disorder (BD) is an important way to understand the potential neuropathological mechanism in emotional disorders. Numerous diffusion tensor imaging (DTI) studies over recent decades have confirmed the presence of WM anomalies in these two affective disorders, but the results were inconsistent. This study aimed to determine the statistical consistency of DTI findings for BD and MDD by using the coordinate-based meta-analysis (CBMA) approach.

METHODS

We performed a systematic search of tract-based spatial statistics (TBSS) studies comparing MDD or BD with healthy controls (HC) as of June 30, 2024. The seed-based d-mapping (SDM) was applied to investigate fractional anisotropy (FA) changes. Meta-regression was then used to analyze the potential correlations between demographics and neuroimaging alterations.

RESULTS

Regional FA reductions in the body of the corpus callosum (CC) were identified in both of these two diseases. Besides, MDD patients also exhibited decreased FA in the genu and splenium of the CC, as well as the left anterior thalamic projections (ATP), while BD patients showed FA reduction in the left median network, and cingulum in addition to the CC.

CONCLUSIONS

The results highlighted that altered integrity in the body of CC served as the shared basis of MDD and BD, and distinct microstructural WM abnormalities also existed, which might induce the various clinical manifestations of these two affective disorders. The study was registered on PROSPERO (http://www.crd.york.ac.uk/PROSPERO), registration number: CRD42022301929.

Reduced myelin content in bipolar disorder: A study of inhomogeneous magnetization transfer

BACKGROUND

Previous neuroimaging and pathological studies have found myelin-related abnormalities in bipolar disorder (BD), which prompted the use of magnetic resonance (MR) imaging technology sensitive to neuropathological changes to explore its neuropathological basis. We holistically investigated alterations in myelin within BD patients by inhomogeneous magnetization transfer (ihMT), which is sensitive and specific to myelin content.

METHODS

Thirty-one BD and 42 healthy controls (HC) were involved. Four MR metrics, i.e., ihMT ratio (ihMTR), pseudo-quantitative ihMT (qihMT), magnetization transfer ratio and pseudo-quantitative magnetization transfer (qMT), were compared between groups using analysis methods based on whole-brain voxel-level and white matter regions of interest (ROI), respectively.

RESULTS

The voxel-wise analysis showed significantly inter-group differences of ihMTR and qihMT in the corpus callosum. The ROI-wise analysis showed that ihMTR, qihMT, and qMT values in BD group were significantly lower than that in HC group in the genu and body of corpus callosum, left anterior limb of the internal capsule, left anterior corona radiate, and bilateral cingulum (p < 0.001). And the qihMT in genu of corpus callosum and right cingulum were negatively correlated with depressive symptoms in BD group.

LIMITATIONS

This study is based on cross-sectional data and the sample size is limited.

CONCLUSION

These findings suggest the reduced myelin content of anterior midline structure in the bipolar patients, which might be a critical pathophysiological feature of BD.

White matter microstructural integrity is associated with retinal vascular caliber in adolescents with bipolar disorder

OBJECTIVE

Reduced white matter integrity is observed in bipolar disorder (BD), and is associated with cardiovascular risk in adults. This topic is underexplored in youth, and in BD, where novel microvascular measures may help to inform understanding of the vascular-brain connection. We therefore examined the association of retinal vascular caliber with white matter integrity in a cross-sectional sample of adolescents with and without BD.

METHODS

Eighty-four adolescents (n = 42 BD, n = 42 controls) completed retinal imaging, yielding arteriolar and venular caliber. Diffusion tensor imaging measured white matter fractional anisotropy (FA). Multiple linear regression tested associations between retinal vascular caliber and FA in regions-of-interest; corpus callosum, anterior thalamic radiation, uncinate fasciculus, and superior longitudinal fasciculus. Complementary voxel-wise analyses were performed.

RESULTS

Arteriolar caliber was elevated in adolescents with BD relative to controls (F(1,79) = 6.15, p = 0.02, η2p = 0.07). In the overall sample, higher venular caliber was significantly associated with lower corpus callosum FA (β = -0.24, puncorrected = 0.04). In voxel-wise analyses, higher arteriolar caliber was significantly associated with lower corpus callosum and forceps minor FA in the overall sample (β = -0.46, p = 0.03). A significant diagnosis-by-venular caliber interaction on FA was noted in 5 clusters including the right retrolenticular internal capsule (β = 0.72, p = 0.03), corticospinal tract (β = 0.72, p = 0.04), and anterior corona radiata (β = 0.63, p = 0.04). In each instance, venular caliber was more positively associated with FA in BD vs. controls.

CONCLUSION

Retinal microvascular measures are associated with white matter integrity in BD, particularly in the corpus callosum. This study was proof-of-concept, designed to guide future studies focused on the vascular-brain interface in BD.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Persistent Frustration-Induced Reconfigurations of Brain Networks Predict Individual Differences in Irritability

OBJECTIVE

Aberrant responses to frustration are central mechanisms of pediatric irritability, which is a common reason for psychiatric consultation and a risk factor for affective disorders and suicidality. This pilot study aimed to characterize brain network configuration during and after frustration and test whether characteristics of networks formed during or after frustration relate to irritability.

METHOD

During functional magnetic resonance imaging, a transdiagnostic sample enriched for irritability (N = 66, mean age = 14.0 years, 50% female participants) completed a frustration-induction task flanked by pretask and posttask resting-state scans. We first tested whether and how the organization of brain regions (ie, nodes) into networks (ie, modules) changes during and after frustration. Then, using a train/test/held-out procedure, we aimed to predict past-week irritability from global efficiency (Eglob) (ie, capacity for parallel information processing) of these modules.

RESULTS

Two modules present in the baseline pretask resting-state scan (one encompassing anterior default mode and temporolimbic regions and one consisting of frontoparietal regions) contributed most to brain circuit reorganization during and after frustration. Only Eglob of modules in the posttask resting-state scans (ie, after frustration) predicted irritability symptoms. Self-reported irritability was predicted by Eglob of a frontotemporal-limbic module. Parent-reported irritability was predicted by Eglob of ventral-prefrontal-subcortical and somatomotor-parietal modules.

CONCLUSION

These pilot results suggest the importance of the postfrustration recovery period in the pathophysiology of irritability. Eglob in 3 specific posttask modules, involved in emotion processing, reward processing, or motor function, predicted irritability. These findings, if replicated, could represent specific intervention targets for irritability.

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

IMPORTANCE

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

OBJECTIVE

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

EVIDENCE REVIEW

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

FINDINGS

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

CONCLUSIONS AND RELEVANCE

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

Diagnostic instruments for the assessment of disruptive mood dysregulation disorder: a systematic review of the literature

Disruptive mood dysregulation disorder (DMDD) involves non-episodic irritability and frequent severe temper outbursts in children. Since the inclusion of the diagnosis in the DSM-5, there is no established gold-standard in the assessment of DMDD. In this systematic review of the literature, we provide a synopsis of existing diagnostic instruments for DMDD. Bibliographic databases were searched for any studies assessing DMDD. The systematic search of the literature yielded K = 1167 hits, of which n = 110 studies were included. The most frequently used measure was the Kiddie Schedule for Affective Disorders and Schizophrenia DMDD module (25%). Other studies derived diagnostic criteria from interviews not specifically designed to measure DMDD (47%), chart review (7%), clinical diagnosis without any specific instrument (6%) or did not provide information about the assessment (9%). Three structured interviews designed to diagnose DMDD were used in six studies (6%). Interrater reliability was reported in 36% of studies (ranging from κ = 0.6-1) while other psychometric properties were rarely reported. This systematic review points to a variety of existing diagnostic measures for DMDD with good reliability. Consistent reporting of psychometric properties of recently developed DMDD interviews, as well as their further refinement, may help to ascertain the validity of the diagnosis.

Disruptive mood dysregulation disorder: does variance in treatment responses also add to the conundrum? The widening gap in the evidence is a signal needing attention

The new diagnosis of disruptive mood dysregulation disorder (DMDD) was introduced in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, to address the overdiagnosis of bipolar disorder in children and adolescents. However, there are ongoing debates about its nosology given chronic persistent irritability in children and adolescents has contextual valence. Those meeting the criteria for DMDD may, in fact, have an oppositional defiant disorder, attention deficit hyperactivity disorder, or other behavioral disorders. Similarly, in the last few years, there are many different types of treatment studies that have also yielded mixed results. These counterintuitive findings need a meticulous review for a wider debate given its clinical utility for patients, families, and practicing clinicians.

Transmembrane protein 108 inhibits the proliferation and myelination of oligodendrocyte lineage cells in the corpus callosum

Background

Abnormal white matter is a common neurobiological change in bipolar disorder, and dysregulation of myelination in oligodendrocytes (OLs) is the cause. Transmembrane protein 108 (Tmem108), as a susceptible gene of bipolar disorder, is expressed higher in OL lineage cells than any other lineage cells in the central nervous system. Moreover, Tmem108 mutant mice exhibit mania-like behaviors, belonging to one of the signs of bipolar disorder. However, it is unknown whether Tmem108 regulates the myelination of the OLs.

Results

Tmem108 expression in the corpus callosum decreased with the development, and OL progenitor cell proliferation and OL myelination were enhanced in the mutant mice. Moreover, the mutant mice exhibited mania-like behavior after acute restraint stress and were susceptible to drug-induced epilepsy.

Conclusions

Tmem108 inhibited OL progenitor cell proliferation and mitigated OL maturation in the corpus callosum, which may also provide a new role of Tmem108 involving bipolar disorder pathogenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13041-022-00918-7.

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review

Background

Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.

Objective

This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.

Methods

The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.

Results

We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.

Conclusions

This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.

Prevention of Depression in Children, Adolescents, and Young Adults: The Role of Teachers and Parents

Major depressive disorder (MDD) and other affective disorders may surreptitiously arise in children and adolescents during their school period and impair their social and educational functioning. Besides the social and personal burden, which are increased during the SARS-CoV-2 pandemic, the onset of depression may compromise the future of the growing person with chronicity and recurrence. In this context, educators’ training is essential to detect early the onset of a depressive disorder, to spare later consequences through the timely establishment of adequate treatment. The educational staff should receive adequate training to be able to work closely with healthcare providers and parents, thus directing the young person with an affective disorder to the right psychological and pharmacological treatment provider, i.e., a specialized psychologist or psychiatrist. The first approach should be to establish a trustful relationship with the adolescent and his/her classmates, to reduce social and self-stigma and inform about mental illness. If symptoms do not subside and the suffering child or adolescent fails to reintegrate within his/her school environment, cognitive–behavioral interventions are recommended that are individual, group, or computer-based. When needed, these should be implemented with individualized pharmacotherapy.

Functional connectivity during frustration: a preliminary study of predictive modeling of irritability in youth

Irritability cuts across many pediatric disorders and is a common presenting complaint in child psychiatry; however, its neural mechanisms remain unclear. One core pathophysiological deficit of irritability is aberrant responses to frustrative nonreward. Here, we conducted a preliminary fMRI study to examine the ability of functional connectivity during frustrative nonreward to predict irritability in a transdiagnostic sample. This study included 69 youths (mean age = 14.55 years) with varying levels of irritability across diagnostic groups: disruptive mood dysregulation disorder (n = 20), attention-deficit/hyperactivity disorder (n = 14), anxiety disorder (n = 12), and controls (n = 23). During fMRI, participants completed a frustrating cognitive flexibility task. Frustration was evoked by manipulating task difficulty such that, on trials requiring cognitive flexibility, "frustration" blocks had a 50% error rate and some rigged feedback, while "nonfrustration" blocks had a 10% error rate. Frustration and nonfrustration blocks were randomly interspersed. Child and parent reports of the affective reactivity index were used as dimensional measures of irritability. Connectome-based predictive modeling, a machine learning approach, with tenfold cross-validation was conducted to identify networks predicting irritability. Connectivity during frustration (but not nonfrustration) blocks predicted child-reported irritability (ρ = 0.24, root mean square error = 2.02, p = 0.03, permutation testing, 1000 iterations, one-tailed). Results were adjusted for age, sex, medications, motion, ADHD, and anxiety symptoms. The predictive networks of irritability were primarily within motor-sensory networks; among motor-sensory, subcortical, and salience networks; and between these networks and frontoparietal and medial frontal networks. This study provides preliminary evidence that individual differences in irritability may be associated with functional connectivity during frustration, a phenotype-relevant state.

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review (Preprint).. [DOI: 10.2196/preprints.29749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.

OBJECTIVE

This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.

METHODS

The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.

RESULTS

We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.

CONCLUSIONS

This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.

Editorial: White Matter Matters: Neurobiological Differences Between Pediatric Bipolar Disorder and Disruptive Mood Dysregulation Disorder

Think about this: Linke et al.¹ are publishing the "first" ever study using diffusion tensor imaging (DTI) in children with disruptive mood dysregulation disorder (DMDD). Child and adolescent psychiatrists know that these children are not new and are not uncommonly seen in both inpatient and outpatient mental health settings. So, why does the first paper examining the microstructural differences in white matter tracts in children with DMDD emerge in 2019? If you search PubMed using the key words "autism" and "diffusion tensor imaging" (as children with autistic symptoms have likely been around for as long as children with symptoms of dysregulation), you end up with 305 papers. So, what's the story? Was aberrant white matter microstructure never considered as a putative hypothesis for DMDD? Or did DMDD fall under a different name, and, as Shakespeare once wrote, "a rose by any other name would smell as sweet"? Or, in our loose psychiatric-based translation from Old English, "a pediatric bipolar child by any other name is just as dysregulated?" Indeed, DMDD has had a circuitous and somewhat shady history, with its roots in the name "pediatric bipolar disorder."² Because a number of long-term studies showed that few children with chronic irritable mood problems progressed to develop adult bipolar affective disorder (BPAD),³ DMDD emerged in the DSM-5.⁴.

White matter microstructure in youth with and at risk for bipolar disorder

OBJECTIVES

Bipolar disorder (BD) and familial risk for BD have been associated with aberrant white matter (WM) microstructure in the corpus callosum and fronto-limbic pathways. These abnormalities might constitute trait or state marker and have been suggested to result from aberrant maturation and to relate to difficulties in emotion regulation.

METHODS

To determine whether WM alterations represent a trait, disease or resilience marker, we compared youth at risk for BD (n = 36 first-degree relatives, REL) to youth with BD (n = 36) and healthy volunteers (n = 36, HV) using diffusion tensor imaging.

RESULTS

Individuals with BD and REL did not differ from each other in WM microstructure and, compared to HV, showed similar aberrations in the superior corona radiata (SCR)/corticospinal tract (CST) and the body of the corpus callosum. WM microstructure of the anterior CC showed reduced age-related in-creases in BD compared to REL and HV. Further, individuals with BD and REL showed in-creased difficulties in emotion regulation, which were associated with the microstructure of the anterior thalamic radiation.

DISCUSSION

Alterations in the SCR/CST and the body of the corpus callosum appear to represent a trait marker of BD, whereas changes in other WM tracts seem to be a disease state marker. Our findings also support the role of aberrant developmental trajectories of WM microstructure in the risk architecture of BD, although longitudinal studies are needed to confirm this association. Finally, our findings show the relevance of WM microstructure for difficulties in emotion regulation-a core characteristic of BD.