Disorder-specific volumetric brain difference in adolescent major depressive disorder and bipolar depression

Alterations of subcortical structural volume in pediatric bipolar disorder patients with and without psychotic symptoms

BACKGROUND

Pediatric bipolar disorder (PBD) with psychotic symptoms may predict more severe impairment in social functioning, but the underlying biological mechanisms remain unclear. The aim of this study was to investigate alterations in subcortical structural volume in PBD with and without psychotic symptoms.

METHODS

We recruited 24 psychotic PBD (P-PBD) patients, 24 non-psychotic PBD (NP-PBD) patients, and 18 healthy controls (HCs). All participants underwent scanning with a 3.0 T Siemens Trio scanner. The FreeSurfer 7.4.0 software was employed to calculate the volume of each subcortical structure. An analysis of covariance (ANCOVA) was performed to identify brain regions with significant volume differences among the three groups, and then the inter-group comparisons were calculated. Partial correlation analyses were conducted to identify relationships between subcortical structural volumes and clinical features. Finally, receiver operating characteristic curve (ROC) analysis was employed to verify the capacity to distinguish between P-PBD and NP-PBD, P-PBD and HCs, and NP-PBD and HCs.

RESULTS

ANCOVA revealed significant differences in the volumes of bilateral lateral ventricles, third ventricle, left thalamus, and right pallidum among three groups. Compared with HC, the third ventricle volume was increased in both groups of PBD patients, whereas the left thalamus and right pallidum volumes were decreased, and the bilateral lateral ventricles were enlarged in P-PBD patients. In contrast, only the third ventricle showed further enlargement in the group of P-PBD patients compared with NP-PBD patients. Partial correlation analyses revealed that episode times were associated with the third ventricle volume in P-PBD patients. Furthermore, ROC analyses indicated that volume in the left lateral ventricle exhibited the greatest capacity to distinguish between the P-PBD and NP-PBD, and the third ventricle performed best in distinguishing both the P-PBD group from HCs and the NP-PBD group from HCs. The combined metrics demonstrated greater diagnostic value in two-by-two comparisons.

CONCLUSION

Current research suggests that PBD with psychotic symptoms may have more extensive lateral and third ventricular volume enlargement. Bilateral lateral ventricles may serve as potential neurobiomarkers to distinguish P- PBD patients from NP-PBD patients.

Functional magnetic resonance imaging studies in bipolar disorder in resting state: A coordinates-based meta-analysis

Exploring changes in the intrinsic activity of the brain in people with bipolar disorder (BD) is necessary. However, the findings have not yet led to consistent conclusions. In this regard, this paper aims to extract more obvious differential brain areas and neuroimaging markers, for the purpose of providing assistance for early clinical diagnosis and subsequent treatment. We conducted a meta-analysis of whole-brain resting-state functional magnetic resonance imaging (rs-fMRI) studies using seed-based d-mapping software that examined differences in amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) between patients with BD and healthy controls (HCs). Seed-based d-Mapping (formerly Signed Differential Mapping) with Permutation of Subject Images, or SDM-PSI, is a statistical technique for meta-analyzing studies of differences in brain activity or structure. A total of 16 articles involving 1112 individuals were included in this study for meta-analysis. This paper confidently analyzes the correlation between the clinical scales HAMD, HAMA, and YMRS, and the area of difference. We found significant changes that increased activation in the anterior connective and left lens nucleus, the nucleus of the shell, and BA 48 in BD patients compared with HC (P < 0.05, uncorrected), as well as a significant correlation between HAMD and the left superior frontal gyrus (after FWE correction P < 0.05). Therefore, basal ganglia and frontal cortex may have important significance in the pathogenesis and pathological basis of BD, making it an important issue to be attached importance to.

Brain functional networks and structures that categorize type 2 bipolar disorder and major depression

BACKGROUND

Distinguishing between type 2 bipolar disorder (BD II) and major depressive disorder (MDD) poses a significant clinical challenge due to their overlapping symptomatology. This study aimed to investigate neurobiological markers that differentiate BD II from MDD using multimodal neuroimaging techniques.

METHODS

Fifty-nine individuals with BD II, 114 with MDD, and 117 healthy controls participated in the study, undergoing structural and functional magnetic resonance imaging. Functional connectivity (FC) analysis used regions from Shen's whole-brain FC-based atlas. Feature selection was carried out using independent t-tests and ReliefF algorithms, followed by classification using Support Vector Machine and wide neural network.

RESULTS

Significant differences in brain structure and function were observed among patients with BD II, MDD, and healthy controls. Both structural and functional alterations were more pronounced in BD II compared to MDD, particularly in regions associated with sensory processing, motor function, and the cerebellum. Classification based on neurobiological markers achieved a mean testing accuracy of 88.24%, with the t-test selected features outperforming those selected by ReliefF. Dysconnectivity patterns correlated with symptom severity and functioning in BD II but not MDD.

CONCLUSION

Our findings suggest that neurobiological markers derived from multimodal imaging techniques can effectively differentiate patients with BD II from those with MDD. The identified alterations in brain structure and function, particularly in sensory-motor processing networks, may serve as potential biomarkers for distinguishing between these mood disorders. However, the influence of psychotropic medications and daily functioning severity on these neurobiological markers warrants further investigation.

Pathophysiology in cortico-amygdala circuits and excessive aversion processing: the role of oligodendrocytes and myelination

Stress-related psychiatric illnesses, such as major depressive disorder, anxiety and post-traumatic stress disorder, present with alterations in emotional processing, including excessive processing of negative/aversive stimuli and events. The bidirectional human/primate brain circuit comprising anterior cingulate cortex and amygdala is of fundamental importance in processing emotional stimuli, and in rodents the medial prefrontal cortex-amygdala circuit is to some extent analogous in structure and function. Here, we assess the comparative evidence for: (i) Anterior cingulate/medial prefrontal cortex<->amygdala bidirectional neural circuits as major contributors to aversive stimulus processing; (ii) Structural and functional changes in anterior cingulate cortex<->amygdala circuit associated with excessive aversion processing in stress-related neuropsychiatric disorders, and in medial prefrontal cortex<->amygdala circuit in rodent models of chronic stress-induced increased aversion reactivity; and (iii) Altered status of oligodendrocytes and their oligodendrocyte lineage cells and myelination in anterior cingulate/medial prefrontal cortex<->amygdala circuits in stress-related neuropsychiatric disorders and stress models. The comparative evidence from humans and rodents is that their respective anterior cingulate/medial prefrontal cortex<->amygdala circuits are integral to adaptive aversion processing. However, at the sub-regional level, the anterior cingulate/medial prefrontal cortex structure-function analogy is incomplete, and differences as well as similarities need to be taken into account. Structure-function imaging studies demonstrate that these neural circuits are altered in both human stress-related neuropsychiatric disorders and rodent models of stress-induced increased aversion processing. In both cases, the changes include altered white matter integrity, albeit the current evidence indicates that this is decreased in humans and increased in rodent models. At the cellular-molecular level, in both humans and rodents, the current evidence is that stress disorders do present with changes in oligodendrocyte lineage, oligodendrocytes and/or myelin in these neural circuits, but these changes are often discordant between and even within species. Nonetheless, by integrating the current comparative evidence, this review provides a timely insight into this field and should function to inform future studies-human, monkey and rodent-to ascertain whether or not the oligodendrocyte lineage and myelination are causally involved in the pathophysiology of stress-related neuropsychiatric disorders.

Vulnerable brain regions in adolescent major depressive disorder: A resting-state functional magnetic resonance imaging activation likelihood estimation meta-analysis

BACKGROUND

Adolescent major depressive disorder (MDD) is a significant mental health concern that often leads to recurrent depression in adulthood. Resting-state functional magnetic resonance imaging (rs-fMRI) offers unique insights into the neural mechanisms underlying this condition. However, despite previous research, the specific vulnerable brain regions affected in adolescent MDD patients have not been fully elucidated.

AIM

To identify consistent vulnerable brain regions in adolescent MDD patients using rs-fMRI and activation likelihood estimation (ALE) meta-analysis.

METHODS

We performed a comprehensive literature search through July 12, 2023, for studies investigating brain functional changes in adolescent MDD patients. We utilized regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) analyses. We compared the regions of aberrant spontaneous neural activity in adolescents with MDD vs healthy controls (HCs) using ALE.

RESULTS

Ten studies (369 adolescent MDD patients and 313 HCs) were included. Combining the ReHo and ALFF/fALFF data, the results revealed that the activity in the right cuneus and left precuneus was lower in the adolescent MDD patients than in the HCs (voxel size: 648 mm3, P < 0.05), and no brain region exhibited increased activity. Based on the ALFF data, we found decreased activity in the right cuneus and left precuneus in adolescent MDD patients (voxel size: 736 mm3, P < 0.05), with no regions exhibiting increased activity.

CONCLUSION

Through ALE meta-analysis, we consistently identified the right cuneus and left precuneus as vulnerable brain regions in adolescent MDD patients, increasing our understanding of the neuropathology of affected adolescents.

Shared and specific neurobiology in bipolar disorder and unipolar disorder: Evidence based on the connectome gradient and a transcriptome-connectome association study

BACKGROUND

Distinguishing bipolar disorder (BD) and unipolar disorder (UD) remains challenging. To identify the common and diagnosis-specific neuropathological alterations and their potential molecular mechanisms in patients with UD and BD (with a current depressive episode).

METHODS

Resting-state functional magnetic resonance imaging was obtained from 279 participants (95 BD patients, 107 UD patients and 77 health controls). Connectome gradients analysis was performed to explore the shared and diagnosis-specific gradient alterations in BD and UD. The Allen Human Brain Atlas data was used to explore the potential gene mechanisms of the gradient alterations.

RESULTS

BD and UD had shared hierarchical disorganisation, including downgrading and contraction from the unimodal sensory networks (vision and sensorimotor) to the transmodal cognitive networks (limbic, frontoparietal, dorsal attention, and default) (all P < 0.05, FDR corrected) in gradient 1 and gradient 2. The BD patients had specific connectome gradient dysfunction in the subcortical network. Moreover, the hierarchical disorganisation was closely correlated with profiles of gene expression specific to the neuroglial cells in the prefrontal cortex in BD and UD, while the most correlated gene ontology biological processes and function were concentrated in synaptic signalling, calcium ion binding, and transmembrane transporter activity.

CONCLUSION

These findings reveal the shared and diagnosis-specific neurobiological mechanism underlying BD and UD patients, which advances our understanding of the neuromechanisms of these disorders.

Differentiation between bipolar disorder and major depressive disorder in adolescents: from clinical to biological biomarkers

Background

Mood disorders are very common among adolescents and include mainly bipolar disorder (BD) and major depressive disorder (MDD), with overlapping depressive symptoms that pose a significant challenge to realizing a rapid and accurate differential diagnosis in clinical practice. Misdiagnosis of BD as MDD can lead to inappropriate treatment and detrimental outcomes, including a poorer ultimate clinical and functional prognosis and even an increased risk of suicide. Therefore, it is of great significance for clinical management to identify clinical symptoms or features and biological markers that can accurately distinguish BD from MDD. With the aid of bibliometric analysis, we explore, visualize, and conclude the important directions of differential diagnostic studies of BD and MDD in adolescents.

Materials and methods

A literature search was performed for studies on differential diagnostic studies of BD and MDD among adolescents in the Web of Science Core Collection database. All studies considered for this article were published between 2004 and 2023. Bibliometric analysis and visualization were performed using the VOSviewer and CiteSpace software.

Results

In total, 148 publications were retrieved. The number of publications on differential diagnostic studies of BD and MDD among adolescents has been generally increasing since 2012, with the United States being an emerging hub with a growing influence in the field. Boris Birmaher is the top author in terms of the number of publications, and the Journal of Affective Disorders is the most published journal in the field. Co-occurrence analysis of keywords showed that clinical characteristics, genetic factors, and neuroimaging are current research hotspots. Ultimately, we comprehensively sorted out the current state of research in this area and proposed possible research directions in future.

Conclusion

This is the first-ever study of bibliometric and visual analyses of differential diagnostic studies of BD and MDD in adolescents to reveal the current research status and important directions in the field. Our research and analysis results might provide some practical sources for academic scholars and clinical practice.

Identification of structural brain alterations in adolescents with depressive symptomatology

INTRODUCTION

Depressive symptoms can emerge as early as childhood and may lead to adverse situations in adulthood. Studies have examined structural brain alternations in individuals with depressive symptoms, but findings remain inconclusive. Furthermore, previous studies have focused on adults or used a categorical approach to assess depression. The current study looks to identify grey matter volumes (GMV) that predict depressive symptomatology across a clinically concerning sample of adolescents.

METHODS

Structural MRI data were collected from 338 clinically concerning adolescents (mean age = 15.30 SD=2.07; mean IQ = 101.01 SD=12.43; 132 F). Depression symptoms were indexed via the Mood and Feelings Questionnaire (MFQ). Freesurfer was used to parcellate the brain into 68 cortical regions and 14 subcortical regions. GMV was extracted from all 82 brain areas. Multiple linear regression was used to look at the relationship between MFQ scores and region-specific GMV parameter. Follow up regressions were conducted to look at potential effects of psychiatric diagnoses and medication intake.

RESULTS

Our regression analysis produced a significant model (R2 = 0.446, F(86, 251) = 2.348, p < 0.001). Specifically, there was a negative association between GMV of the left parahippocampal (B = -0.203, p = 0.005), right rostral anterior cingulate (B = -0.162, p = 0.049), and right frontal pole (B = -0.147, p = 0.039) and a positive association between GMV of the left bank of the superior temporal sulcus (B = 0.173, p = 0.029). Follow up analyses produced results proximal to the main analysis.

CONCLUSIONS

Altered regional brain volumes may serve as biomarkers for the development of depressive symptoms during adolescence. These findings suggest a homogeneity of altered cortical structures in adolescents with depressive symptoms.

Gray matter alterations in adolescent major depressive disorder and adolescent bipolar disorder

BACKGROUND

Gray matter volume (GMV) alterations in several emotion-related brain areas are implicated in mood disorders, but findings have been inconsistent in adolescents with major depressive disorder (MDD) or bipolar disorder (BD).

METHODS

We conducted a comprehensive meta-analysis of 35 region-of-interest (ROI) and 18 whole-brain voxel-based morphometry (VBM) MRI studies in adolescent MDD and adolescent BD, and indirectly compared the results in the two groups. The effects of age, sex, and other demographic and clinical scale scores were explored using meta-regression analysis.

RESULTS

In the ROI meta-analysis, right putamen volume was decreased in adolescents with MDD, while bilateral amygdala volume was decreased in adolescents with BD compared to healthy controls (HC). In the whole-brain VBM meta-analysis, GMV was increased in right middle frontal gyrus and decreased in left caudate in adolescents with MDD compared to HC, while in adolescents with BD, GMV was increased in left superior frontal gyrus and decreased in limbic regions compared with HC. MDD vs BD comparison revealed volume alteration in the prefrontal-limbic system.

LIMITATION

Different clinical features limit the comparability of the samples, and small sample size and insufficient clinical details precluded subgroup analysis or meta-regression analyses of these variables.

CONCLUSIONS

Distinct patterns of GMV alterations in adolescent MDD and adolescent BD could help to differentiate these two populations and provide potential diagnostic biomarkers.

Neurostructural Differences in Adolescents With Treatment-Resistant Depression and Treatment Effects of Transcranial Magnetic Stimulation

BACKGROUND

Despite its morbidity and mortality, the neurobiology of treatment-resistant depression (TRD) in adolescents and the impact of treatment on this neurobiology is poorly understood.

METHODS

Using automatic segmentation in FreeSurfer, we examined brain magnetic resonance imaging baseline volumetric differences among healthy adolescents (n = 30), adolescents with major depressive disorder (MDD) (n = 19), and adolescents with TRD (n = 34) based on objective antidepressant treatment rating criteria. A pooled subsample of adolescents with TRD were treated with 6 weeks of active (n = 18) or sham (n = 7) 10-Hz transcranial magnetic stimulation (TMS) applied to the left dorsolateral prefrontal cortex. Ten of the adolescents treated with active TMS were part of an open-label trial. The other adolescents treated with active (n = 8) or sham (n = 7) were participants from a randomized controlled trial.

RESULTS

Adolescents with TRD and adolescents with MDD had decreased total amygdala (TRD and MDD: -5%, P = .032) and caudal anterior cingulate cortex volumes (TRD: -3%, P = .030; MDD: -.03%, P = .041) compared with healthy adolescents. Six weeks of active TMS increased total amygdala volumes (+4%, P < .001) and the volume of the stimulated left dorsolateral prefrontal cortex (+.4%, P = .026) in adolescents with TRD.

CONCLUSIONS

Amygdala volumes were reduced in this sample of adolescents with MDD and TRD. TMS may normalize this volumetric finding, raising the possibility that TMS has neurostructural frontolimbic effects in adolescents with TRD. TMS also appears to have positive effects proximal to the site of stimulation.

Peripheral biomarkers to predict the diagnosis of bipolar disorder from major depressive disorder in adolescents

The onset of bipolar disorder (BD) occurs in childhood or adolescence in half of the patients. Early stages of BD usually present depressive episodes, which makes it difficult to be distinguished from major depressive disorder (MDD). Objective biomarkers for discriminating BD from MDD in adolescent patients are limited. We collected basic demographic data and the information of the first blood examination performed after the admission to psychiatry unit of BD and MDD inpatients during 2009-2018. We recruited 261 adolescents (aged from 10 to 18), including 160 MDD and 101 BD. Forward-Stepwise Selection of binary logistic regression was used to construct predictive models for the total sample and subgroups by gender. Independent external validation was made by 255 matched patients from another hospital in China. Regression models of total adolescents, male and female subgroups showed accuracy of 73.3%, 70.6% and 75.2%, with area under curves (AUC) as 0.785, 0.816 and 0.793, respectively. Age, direct bilirubin (DBIL), lactic dehydrogenase (LDH), free triiodothyronine (FT3) and C-reactive protein (CRP) were final factors included into the models. The discrimination was well at external validation (AUC = 0.714). This study offers the evidence that accessible information of common clinical laboratory examination might be valuable in distinguishing BD form MDD in adolescents. With good diagnostic accuracies and external validation, the total regression equation might potentially be applied to individualized clinical inferences on adolescent BD patients.

Personality mediates the association between juvenile conduct problems and adulthood mood disorders

This study aimed to examine the association between conduct problems and mood disorders, and to evaluate the mediating roles of personality traits in it. Adult participants (N = 309), for which patients with major depressive disorder (MDD) or bipolar disorder (BD), and controls without major psychiatric history were recruited. Juvenile conduct problem was defined by the items in Composite International Diagnosis Interview. We assessed personality traits of extraversion and neuroticism. Multiple mediation model was performed to investigate the intervening effect of personality traits between juvenile conduct problems and adulthood mood disorders. Participants had on average 2.7 symptoms of conduct problems, and 43.4% had conduct problems. Having more symptoms of conduct problems was associated with a higher likelihood of BD (OR = 1.20). Higher neuroticism was associated with elevated risks of both MDD and BD. There was no direct effect of binary conduct problems on the risk of BD, and showed significant total indirect effect mediated by neuroticism for BD (OR = 1.49; bias-corrected and accelerated 95% CI = 1.10–2.05), but not through extraversion. Conduct problems defined as a continuous variable had a direct effect on the risk of adult MDD (OR = 1.36; bias-corrected and accelerated 95% CI = 1.05–1.76), while had an indirect effect on the risk of BD via the mediation of neuroticism (OR = 1.08; bias-corrected and accelerated 95% CI = 1.02–1.14). Neuroticism mediates between the association of juvenile conduct problems and adult BD. This finding raises our attention to assess personality traits in individuals with juvenile conduct problems for timely intervention strategies of reducing the vulnerability for developing mood disorders.

Left amygdala structure mediates longitudinal associations between exposure to threat and long-term psychiatric symptomatology in youth

Traumatic experiences during childhood can have profound effects on stress sensitive brain structures (e.g., amygdala and hippocampus) and the emergence of psychiatric symptoms. Recent theoretical and empirical work has delineated dimensions of trauma (i.e., threat and deprivation) as having distinct neural and behavioral effects, although there are few longitudinal examinations. A sample of 243 children and adolescents were followed for three time points, with each assessment approximately 1 year apart (ages 9–15 years at Time 1; 120 males). Participants or their caregiver reported on youths' threat exposure, perceived stress (Time 1), underwent a T1‐weighted structural high‐resolution MRI scan (Time 2), and documented their subsequent psychiatric symptoms later in development (Time 3). The primary findings indicate that left amygdala volume, in particular, mediated the longitudinal association between threat exposure and subsequent internalizing and externalizing symptomatology. Greater threat exposure related to reduced left amygdala volume, which in turn differentially predicted internalizing and externalizing symptoms. Decreased bilateral hippocampal volume was related to subsequently elevated internalizing symptoms. These findings suggest that the left amygdala is highly threat‐sensitive and that stress‐related alterations may partially explain elevated psychopathology in stress‐exposed adolescents. Uncovering potential subclinical and/or preclinical predictive biomarkers is essential to understanding the emergence, progression, and eventual targeted treatment of psychopathology following trauma exposure.

Shared and specific patterns of dynamic functional connectivity variability of striato-cortical circuitry in unmedicated bipolar and major depressive disorders

BACKGROUND

Accumulating studies have found structural and functional abnormalities of the striatum in bipolar disorder (BD) and major depressive disorder (MDD). However, changes in intrinsic brain functional connectivity dynamics of striato-cortical circuitry have not been investigated in BD and MDD. This study aimed to investigate the shared and specific patterns of dynamic functional connectivity (dFC) variability of striato-cortical circuitry in BD and MDD.

METHODS

Brain resting-state functional magnetic resonance imaging data were acquired from 128 patients with unmedicated BD II (current episode depressed), 140 patients with unmedicated MDD, and 132 healthy controls (HCs). Six pairs of striatum seed regions were selected: the ventral striatum inferior (VSi) and the ventral striatum superior (VSs), the dorsal-caudal putamen (DCP), the dorsal-rostral putamen (DRP), and the dorsal caudate and the ventral-rostral putamen (VRP). The sliding-window analysis was used to evaluate dFC for each seed.

RESULTS

Both BD II and MDD exhibited increased dFC variability between the left DRP and the left supplementary motor area, and between the right VRP and the right inferior parietal lobule. The BD II had specific increased dFC variability between the right DCP and the left precentral gyrus compared with MDD and HCs. The MDD had increased dFC variability between the left VSi and the left medial prefrontal cortex compared with BD II and HCs.

CONCLUSIONS

The patients with BD and MDD shared common dFC alteration in the dorsal striatal-sensorimotor and ventral striatal-cognitive circuitries. The patients with MDD had specific dFC alteration in the ventral striatal-affective circuitry.

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.

Anterior cingulate cortex in individuals with depressive symptoms: A structural MRI study

Several magnetic resonance imaging (MRI) studies have reported reduction in anterior cingulate cortex (ACC) volume in individuals with major depressive disorder (MDD). However, some MRI studies did not find significant ACC volumetric changes in MDD, and sample sizes were generally small. This cross-sectional structural MRI study examined the relationship between current depressive symptoms and ACC volume in a large community sample of 1803 adults. A series of multiple linear regression analyses were conducted to predict right and left ACC volumes using Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) scores, intracranial volume, age, sex, race/ethnicity, alcohol use, tobacco use, and psychotropic medications as predictor variables. Right ACC volume was significantly negatively associated with QIDS-SR scores, while no significant association was found between left ACC volume and QIDS-SR scores. In addition, there was a significant negative association between QIDS-SR scores and right but not left ACC volumes in males, and no significant association between QIDS-SR scores and right or left ACC volumes in females. These findings suggest that right ACC volume is reduced in people with greater self-reported depressive symptom severity, and that this association is only significant in men.

Sex Differences in the Behavioral, Molecular, and Structural Effects of Ketamine Treatment in Depression

Major depressive disorder (MDD) is a common psychiatric illness that manifests in sex-influenced ways. Men and women may experience depression differently and also respond to various antidepressant treatments in sex-influenced ways. Ketamine, which is now being used as a rapid-acting antidepressant, is likely the same. To date, the majority of studies investigating treatment outcomes in MDD do not disaggregate the findings in males and females, and this is also true for ketamine. This review aims to highlight that gap by exploring pre-clinical data-at a behavioral, molecular, and structural level-and recent clinical trials. Sex hormones, particularly estrogen and progesterone, influence the response at all levels examined, and sex is therefore a critical factor to examine when looking at ketamine response. Taken together, the data show females are more sensitive to ketamine than males, and it might be possible to monitor the phase of the menstrual cycle to mitigate some risks associated with the use of ketamine for females with MDD. Based on the studies reviewed in this article, we suggest that ketamine should be administered adhering to sex-specific considerations.

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.

Hippocampus shape deformation: a potential diagnostic biomarker for chronic back pain in women

ABSTRACT

Sex differences in the quality and prevalence of chronic pain are manifold, with women generally presenting higher incidence and severity. Uncovering chronic pain-related sex differences inform neural mechanisms and may lead to novel treatment routes. In a multicenter morphological study (total n = 374), we investigated whether the shape of subcortical regions would reflect sex differences in back pain. Given the hormone-dependent functions of the hippocampus, and its role in the transition to chronic pain, this region constituted our primary candidate. We found that the anterior part of the left hippocampus (alHP) presented outer deformation in women with chronic back pain (CBP), identified in CBP in the United States (n = 77 women vs n = 78 men) and validated in a Chinese data set (n = 29 women vs n = 58 men with CBP, in contrast to n = 53 female and n = 43 male healthy controls). Next, we examined this region in subacute back pain who persisted with back pain a year later (SBPp; n = 18 women vs n = 18 men) and in a subgroup with persistent back pain for 3 years. Weeks after onset of back pain, there was no deformation within alHP, but at 1 and 3 years women exhibited a trend for outer deformation. The alHP partly overlapped with the subiculum and entorhinal cortex, whose functional connectivity, in healthy subjects, was associated with emotional and episodic memory related terms (Neurosynth, reverse inference). These findings suggest that in women the alHP undergoes anatomical changes with pain persistence, highlighting sexually dimorphic involvement of emotional and episodic memory-related circuitry with chronic pain.

Temporal trajectory of brain tissue property changes induced by electroconvulsive therapy

BACKGROUND

After more than eight decades of electroconvulsive therapy (ECT) for pharmaco-resistant depression, the mechanisms governing its anti-depressant effects remain poorly understood. Computational anatomy studies using longitudinal T1-weighted magnetic resonance imaging (MRI) data have demonstrated ECT effects on hippocampus volume and cortical thickness, but they lack the interpretational specificity about underlying neurobiological processes.

METHODS

We sought to fill in the gap of knowledge by acquiring quantitative MRI indicative for brain's myelin, iron and tissue water content at multiple time-points before, during and after ECT treatment. We adapted established tools for longitudinal spatial registration of MRI data to the relaxometry-based multi-parameter maps aiming to preserve the initial total signal amount and introduced a dedicated multivariate analytical framework.

RESULTS

The whole-brain voxel-based analysis based on a multivariate general linear model showed that there is no brain tissue oedema contributing to the predicted ECT-induced hippocampus volume increase neither in the short, nor in the long-term observations. Improvements in depression symptom severity over time were associated with changes in both volume estimates and brain tissue properties expanding beyond mesial temporal lobe structures to anterior cingulate cortex, precuneus and striatum.

CONCLUSION

The obtained results stemming from multi-contrast MRI quantitative data provided a fingerprint of ECT-induced brain tissue changes over time that are contrasted against the background of established morphometry findings. The introduced data processing and statistical testing algorithms provided a reliable analytical framework for longitudinal multi-parameter brain maps. The results, particularly the evidence of lack of ECT impact on brain tissue water, should be considered preliminary considering the small sample size of the study.

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

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

Application of a diathesis-stress model to the interplay of cortical structural development and emerging depression in youth

Cross-sectional studies in adults have long identified differences in cortical structure in adults with depression compared to healthy adults, with most studies identifying reductions in grey matter volume, cortical thickness, and surface area in primarily frontal cortical regions including the OFC, ACC, and variable sub-regions of the PFC. However, when, why, and for whom these neural correlates of depression emerge remains poorly understood, necessitating developmental study of associations between depression and cortical structure. We systematically reviewed studies examining these associations in child/adolescent samples, and applied a developmentally-focused diathesis-stress model to understand the impacts of depressogenic risk-factors and stressors on the development of structural neural correlates of depression. Cross-sectional findings in youth are generally similar to those found in adults, but vary in magnitude and direction of effects. Preliminary evidence suggests that age, sex, severity, and comorbidity moderate these associations. Longitudinal studies show depression prospectively predicting cortical structure and structure predicting emerging depression. Consistent with a diathesis-stress model, associations have been noted between risk-factors for depression (e.g., genetic risk, family risk) and environmental stressors (e.g., early life stress) and structural neural correlates. Further investigation of these associations across development with attention to vulnerability factors and stressors is indicated.

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.

The deleterious effect of stress-induced depression on rat liver: Protective role of resveratrol and dimethyl fumarate via inhibiting the MAPK/ERK/JNK pathway

This study aimed to uncover the protective potentiality of resveratrol and dimethyl fumarate (DMF) in the liver of a chronic unpredictable mild stress (CUMS)-induced depression animal model. Resveratrol and DMF significantly alleviated CUMS-induced behavioral abnormalities in stressed rats through improving sucrose preference in sucrose preference test and decreasing immobility time in a forced swimming test. They also mitigated serum corticosterone levels and elevated serum serotonin levels, which were formerly disturbed in CUMS rats. The hepatoprotective effect is evidenced by improvement in hepatic histopathological examinations, as well as normalized serum alanine aminotransferase and aspartate aminotransferase activities. Molecular signaling of resveratrol and DMF was estimated by diminishing hepatic expression of phosphorylated p38 mitogen-activated protein kinase (MAPK), extracellular signal-regulated kinase1/2 (ERK1/2), and c-Jun N-terminal kinase (JNK). Consequently, they improved the hepatic antioxidant and anti-inflammatory activities as elaborated by the normalization of total antioxidant capacity, glutathione, malondialdehyde, nuclear factor-κB, tumor necrosis factor-α, and myeloperoxidase levels. In addition, they inhibited hepatocyte apoptosis as evidenced by the increased expression of B-cell lymphoma 2, the decreased expression of Bax, as well as the suppressed activity of caspase-3. In conclusion, resveratrol and DMF purveyed a significant anti-depressant effect, which may be mediated, at least in part, via inhibiting the MAPK/ERK/JNK pathway in the CUMS rat model.

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.

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.

Increased activation of the fear neurocircuitry in children exposed to violence

Most studies investigating the effect of childhood trauma on the brain are retrospective and mainly focus on maltreatment, whereas different types of trauma exposure such as growing up in a violent neighborhood, as well as developmental stage, could have differential effects on brain structure and function. The current magnetic resonance imaging study assessed the effect of trauma exposure broadly and violence exposure more specifically, as well as developmental stage on the fear neurocircuitry in 8- to 14-year-old children and adolescents (N = 69). We observed reduced hippocampal and increased amygdala volume with increasing levels of trauma exposure. Second, higher levels of violence exposure were associated with increased activation in the amygdala, hippocampus, and ventromedial prefrontal cortex during emotional response inhibition. This association was specifically observed in children younger than 10 years. Finally, increased functional connectivity between the amygdala and brainstem was associated with higher levels of violence exposure. Based on the current findings, it could be hypothesized that trauma exposure during childhood results in structural changes that are associated with later risk for psychiatric disorders. At the same time, it could be postulated that growing up in an unsafe environment leads the brain to functionally adapt to this situation in a way that promotes survival, where the long-term costs or consequences of these adaptations are largely unknown and an area for future investigations.

Studies on the potential link between antidepressant effect of Xiaoyao San and its pharmacological activity of hepatoprotection based on multi-platform metabolomics

ETHNOPHARMACOLOGICAL RELEVANCE

In traditional Chinese medicine (TCM) theory, depression is considered to be "liver qi stagnation", and relieving "liver qi stagnation" is regarded as an effective method for treating depression. Xiaoyao San (XYS) is a well-known TCM formula for the treatment of depression by relieving "liver qi stagnation". This formula consists of Radix Paeoniae Alba (Paeonia lactiflora Pall.), Radix Bupleuri (Bupleurum chinense DC.), Poria (Poria cocos (Schw.) Wolf), Rhizoma Atractylodis Macrocephalae (Atractylodes macrocephala Koidz.), Radix Angelicae Sinensis (Angelica sinensis (Oliv.) Diels), Radix Glycyrrhizae (Glycyrrhiza uralensis Fisch.), Rhizoma Zingiberis Recens (Zingiber officinale Roscoe) and Herba Menthae Haplocalycis (Mentha haplocalyx Briq.).

AIM OF THE STUDY

Several studies have suggested that depression is associated with liver injury. XYS was a well-known TCM formula for the treatment of depression and liver stagnancy. However, it was still unknown whether the antidepressant effect of XYS is related to the pharmacological activity of hepatoprotection. The aim of this study was to elucidate the potential link between the antidepressant and hepatoprotective effect of XYS.

MATERIALS AND METHODS

A depression rat model was established by the CUMS (chronic unpredictable mild stress) procedure. The antidepressant effect of XYS was assessed by the behavioral indicators, and the hepatoprotective effect of XYS was evaluated through biochemical assays. ¹H-NMR and LC/MS-based liver metabolomics were performed to discover key metabolic pathways involved in the antidepressant and hepatoprotective effects of XYS. Further, the key pathway was validated using commercial kits.

RESULTS

The results demonstrated that XYS pretreatment could significantly improve the depressive symptom induced by CUMS. More importantly, the results demonstrated that liver injury was observed in the CUMS model rats, and XYS had a hepatoprotective effect by reducing the activities of AST and ALT in serum, increasing the levels of SOD and GSH-Px and reducing the contents of MDA, IL-6, and IL-1β in the liver. In addition, the NMR and LC/MS-based metabolomics results indicated that XYS improved 23 of the 35 perturbed potential liver biomarkers that were induced by CUMS. Among them, 9 biomarkers were significantly correlated with both depression and liver pathology, according to Pearson correlation analysis. Metabolic pathway analyses of these 9 biomarkers showed that glutamine and glutamate metabolism were the most important metabolic pathways. Furthermore, to verify glutamine and glutamate metabolism, the levels of glutamine and glutamate, and the activity of glutamine synthetase (GS) and glutaminase (GLS) were quantitatively determined in the liver by commercial kits, and these results were consistent with the metabolomics results.

CONCLUSIONS

XYS could significantly improve the depressive and liver injury symptoms induced by CUMS. The metabolomics results indicate that the regulation of glutamine and glutamate metabolism to maintain the balance of ammonia and promote energy metabolism is a potential junction between the antidepressant and hepatoprotective effects of XYS.

Altered biochemical metabolism and its lateralization in the cortico-striato-cerebellar circuit of unmedicated bipolar II depression

OBJECTIVES

Evidence of the relationship between neurometabolic changes in the cortico-striato-cerebellar (CSC) circuit and bipolar disorder (BD) is still limited. To elucidate the pathogenesis of BD, we investigated the underlying neurometabolic changes and their effect on CSC lateralization circuits in unmedicated patients with bipolar II depression.

METHODS

Forty unmedicated participants with bipolar II depression and forty healthy controls underwent proton magnetic resonance spectroscopy (¹H-MRS). We obtained bilateral metabolic ratios of N-acetylaspartate (NAA)/creatine (Cr) and choline (Cho)/Cr in the prefrontal white matter (PWM), anterior cingulate cortex (ACC), basal ganglia (BG) and the cerebellum. Metabolic ratios were characterized using a laterality index (LI) for left-right asymmetry.

RESULTS

Overall, aberrant lateralization in the CSC circuit was characteristic in patients with bipolar II depression. Patients with bipolar II depression showed significantly lower NAA/Cr ratios in the left PWM, right ACC, left BG and left cerebellum when compared with the healthy controls. For bipolar II depression, we found lower NAA/Cr LI in the PWM, BG, and cerebellum, higher NAA/Cr LI in the ACC, and higher Cho/Cr LI in the BG and cerebellum when compared to the standard value (1.0). For healthy controls, we found lower NAA/Cr LI only in the BG and higher Cho/Cr LI in the cerebellum when compared to 1.0.

LIMITATIONS

As a cross-sectional study with a small sample size, progressive changes and complex metabolic interactions with treatment were not observed.

CONCLUSIONS

Our findings suggest that abnormal biochemical metabolism with aberrant lateralization in the CSC circuit may be an underlying pathophysiology of bipolar II depression.

Neurobiological mechanisms underlying sex-related differences in stress-related disorders: Effects of neuroactive steroids on the hippocampus

Men and women differ in their vulnerability to a variety of stress-related illnesses, but the underlying neurobiological mechanisms are not well understood. This is likely due to a comparative dearth of neurobiological studies that assess male and female rodents at the same time, while human neuroimaging studies often don't model sex as a variable of interest. These sex differences are often attributed to the actions of sex hormones, i.e. estrogens, progestogens and androgens. In this review, we summarize the results on sex hormone actions in the hippocampus and seek to bridge the gap between animal models and findings in humans. However, while effects of sex hormones on the hippocampus are largely consistent in animals and humans, methodological differences challenge the comparability of animal and human studies on stress effects. We summarise our current understanding of the neurobiological mechanisms that underlie sex-related differences in behavior and discuss implications for stress-related illnesses.

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 resting-state cerebral-limbic functional connectivity in bipolar depression and unipolar depression

Background

Distinctive patterns of functional connectivity (FC) abnormalities in neural circuitry has been reported in patients with bipolar depression (BD) and unipolar depression (UD). However, it is unclear that whether this distinct functional connectivity patterns are diagnosis specific between BD and UD. This study aimed to compare patterns of functional connectivity among BD, UD and healthy controls (HC) and determine the distinct functional connectivity patterns which can differentiate BD from UD.

Method

Totally 23 BD, 22 UD, and 24 HC were recruited to undergo resting-state fMRI scanning. FC between each pair of brain regions was calculated and compared among the three groups, the associations of FC with depressive symptom were also analyzed.

Results

Both patient groups showed significantly decreased cerebral-limbic FC located between the default mode network [posterior cingulated gyrus (PCG) and precuneus] and limbic regions (hippocampus, amygdala and thalamus) than HC. Moreover, the BD group exhibited more decreased FC mainly in the cortical regions (middle temporal gyrus, PCG, medial superior frontal gyrus, inferior occipital gyrus and superior temporal gyrus), but the UD group is more associated with limbic alterations. These decreased FCs were negatively correlated with HAMD scores in both BD and UD patients.

Conclusions

BD and UD patients demonstrate different patterns of abnormal cerebral-limbic FC, reflected by decreased FC within cerebral cortex and limbic regions in BD and UD, respectively. The distinct FC abnormal pattern of the cerebral-limbic circuit might be applied as biomarkers to differentiate these two depressive patient groups.

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.

Cortical Volume and Thickness Across Bipolar Disorder Subtypes in Adolescents: A Preliminary Study

OBJECTIVES

Neuroimaging studies of adults with bipolar disorder (BD) have identified several BD subtype distinctions, including greater deficits in prefrontal gray matter volumes in BD-I (bipolar I disorder) compared to BD-II (bipolar II disorder). We sought to investigate BD subtype differences in brain structure among adolescents and young adults.

METHODS

Forty-four youth with BD (14 BD-I, 16 BD-II, and 14 BD-not otherwise specified [NOS], mean age 17) underwent 3T-MRI and images were analyzed using FreeSurfer software. Cortical volume and thickness were analyzed for region of interest (ROI): ventrolateral prefrontal cortex, ventromedial prefrontal cortex, anterior cingulate cortex (ACC), subgenual cingulate cortex, and amygdala, controlling for age, sex, and total intracranial volume. ROIs were selected as found to be implicated in BD in prior studies. A whole brain vertex-wise exploratory analysis was also performed. Uncorrected results are presented.

RESULTS

There were group differences in ACC thickness (F = 3.88, p = 0.03, η² = 0.173 uncorrected), which was reduced in BD-II in comparison to BD-I (p = 0.027 uncorrected) and BD-NOS (p = 0.019 uncorrected). These results did not survive correction for multiple comparisons and no other group differences were observed. The exploratory vertex-wise analysis found a similar pattern of lower cortical thickness in BD-II in the left and right superior frontal gyrus and left caudal middle frontal gyrus.

CONCLUSIONS

This study found reduced cortical thickness for youth with BD-II, relative to BD-I, in regions associated with cognitive control. Further neurostructural differences between subtypes may emerge later during the course of illness.

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.

Anxiety, depression, impulsivity, and brain structure in children and adolescents

The unique neuroanatomical underpinnings of internalizing symptoms and impulsivity during childhood are not well understood. In this study, we examined associations of brain structure with anxiety, depression, and impulsivity in children and adolescents. Participants were 7- to 21-year-olds (N = 328) from the Pediatric Imaging, Neurocognition, and Genetics (PING) study who completed high-resolution, 3-Tesla, T1-weighted MRI and self-report measures of anxiety, depression, and/or impulsivity. Cortical thickness and surface area were examined across cortical regions-of-interest (ROIs), and exploratory whole-brain analyses were also conducted. Gray matter volume (GMV) was examined in subcortical ROIs. When considered separately, higher depressive symptoms and impulsivity were each significantly associated with reduced cortical thickness in ventromedial PFC/medial OFC, but when considered simultaneously, only depressive symptoms remained significant. Higher impulsivity, but not depressive symptoms, was associated with reduced cortical thickness in the frontal pole, rostral middle frontal gyrus, and pars orbitalis. No differences were found for regional surface area. Higher depressive symptoms, but not impulsivity, were significantly associated with smaller hippocampal GMV and larger pallidal GMV. There were no significant associations between anxiety symptoms and brain structure. Depressive symptoms and impulsivity may be linked with cortical thinning in overlapping and distinct regions during childhood and adolescence.

•

Internalizing problems and impulsivity may have shared and distinct neuroanatomical substrates in childhood.

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Higher depressive symptoms were uniquely associated with reduced cortical thickness in vmPFC/medial OFC.

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Higher impulsivity was uniquely associated with reduced cortical thickness in lateral PFC regions.

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Higher depressive symptoms were associated with smaller hippocampal volume and larger pallidal volume.

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These shared and distinct neuroanatomical correlates may inform the design of prevention and intervention strategies.

Alteration of cortico-limbic-striatal neural system in major depressive disorder and bipolar disorder

BACKGROUND

It is often difficult to differentiate major depressive disorder (MDD) and bipolar disorder (BD) merely according to clinical symptoms. Similarities and differences in neural activity between the two disorders remain unclear. In current study, we use amplitude of low-frequency fluctuations (ALFF) to compare neural activation changes between MDD and BD patients.

METHODS

One hundred and eighty-three adolescents and young adults (57 MDD, 46 BD and 80 healthy controls, HC) were scanned during resting state. The ALFF for each participant was calculated, and were then compared among all groups using voxel-based analysis.

RESULTS

There was a significant effect of diagnosis in the core regions of cortico-limbic-striatal neural system. Furthermore, MDD showed left-sided abnormal neural activity while BD showed a bilateral abnormality in this neural system.

LIMITATIONS

This study was underpowered to consider medications, mood states and neural developmental effects on the neural activation.

CONCLUSIONS

Differences in lateralization of ALFF alterations were found. Alterations predominated in the left hemisphere for MDD, whereas alterations were bilateral for BD.

Microstructural Abnormalities of Basal Ganglia and Thalamus in Bipolar and Unipolar Disorders: A Diffusion Kurtosis and Perfusion Imaging Study

OBJECTIVE

Bipolar disorder (BD) is often misdiagnosed as unipolar depression (UD), leading to mistreatment and poor clinical outcomes. However, little is known about the similarities and differences in subcorticalgray matter regions between BD and UD.

METHODS

Thirty-five BD patients, 30 UD patients and 40 healthy controls underwent diffusional kurtosis imaging (DKI) and three dimensional arterial spin labeling (3D ASL). The parameters including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), radial diffusivity (Dr) and cerebral blood flow (CBF) were measured by using regions-of-interest analysis in the caudate, putamen and thalamus of the subcortical gray matter regions.

RESULTS

UD exhibited differences from controls for DKI measures and CBF in the left putamen and caudate. BD showed differences from controls for DKI measures in the left caudate. Additionally, BD showed lower Ka in right putamen, higher MD in right caudate compared with UD. Receiver operating characteristic analysis revealed the Kr of left caudate had the highest predictive power for distinguishing UD from controls.

CONCLUSION

The two disorders may have overlaps in microstructural abnormality in basal ganglia. The change of caudate may serve as a potential biomarker for UD.

A Comparison of Neuroimaging Abnormalities in Multiple Sclerosis, Major Depression and Chronic Fatigue Syndrome (Myalgic Encephalomyelitis): is There a Common Cause?

There is copious evidence of abnormalities in resting-state functional network connectivity states, grey and white matter pathology and impaired cerebral perfusion in patients afforded a diagnosis of multiple sclerosis, major depression or chronic fatigue syndrome (CFS) (myalgic encephalomyelitis). Systemic inflammation may well be a major element explaining such findings. Inter-patient and inter-illness variations in neuroimaging findings may arise at least in part from regional genetic, epigenetic and environmental variations in the functions of microglia and astrocytes. Regional differences in neuronal resistance to oxidative and inflammatory insults and in the performance of antioxidant defences in the central nervous system may also play a role. Importantly, replicated experimental findings suggest that the use of high-resolution SPECT imaging may have the capacity to differentiate patients afforded a diagnosis of CFS from those with a diagnosis of depression. Further research involving this form of neuroimaging appears warranted in an attempt to overcome the problem of aetiologically heterogeneous cohorts which probably explain conflicting findings produced by investigative teams active in this field. However, the ionising radiation and relative lack of sensitivity involved probably preclude its use as a routine diagnostic tool.

Amplitude of low-frequency fluctuations in first-episode, drug-naïve depressive patients: A 5-year retrospective study

Despite different treatments and courses of illness, depressive symptoms appear similar in bipolar disorder (BD) and major depressive disorder (MDD), causing BD with an onset of depressive episode being frequently misdiagnosed as MDD, and leading to inappropriate treatment and poor clinical outcomes. Therefore, there is an urgent need to explore underlying neural basis to distinguish BD from MDD. The medical records of 80 first-episode, drug-naïve depressive patients with an initial diagnosis of MDD and illness duration of at least 5 years were reviewed retrospectively for this study. Fourteen bipolar depressed patients with a diagnosis conversion from MDD to BD, 14 patients with diagnosis of MDD, and 14 healthy subjects demographically matched with the BD group, were selected to participate in the study. Firstly, we examined whether there were differences among the three groups in whole brain fALFF during resting state. Secondly, clusters showing group differences in fALFF in any two groups were chosen as regions of interest (ROI) and then correlation between clinical features and fALFF values of ROIs were calculated. The BD group showed increased fALFF in bilateral putamen relative to both the MDD group and controls, while the MDD group exhibited decreased fALFF in left superior frontal gyrus (SFG) relative to both the BD group and controls (p < 0.05, corrected). Positive correlations between abnormality in the putamen and symptom severity were observed (significant for the MDD group, p = 0.043; marginally significant for the BD group, p = 0.060/0.076). These results implicate that abnormalities of key regions in the striatum and prefrontal areas may be trait markers for BD and MDD.

Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study

BACKGROUND

Generalized anxiety disorder (GAD) is difficult to recognize and hard to separate from major depression (MD) in clinical settings. Biomarkers might support diagnostic decisions. This study used machine learning on multimodal biobehavioral data from a sample of GAD, MD and healthy subjects to differentiate subjects with a disorder from healthy subjects (case-classification) and to differentiate GAD from MD (disorder-classification).

METHODS

Subjects with GAD (n = 19), MD without GAD (n = 14), and healthy comparison subjects (n = 24) were included. The sample was matched regarding age, sex, handedness and education and free of psychopharmacological medication. Binary support vector machines were used within a nested leave-one-out cross-validation framework. Clinical questionnaires, cortisol release, gray matter (GM), and white matter (WM) volumes were used as input data separately and in combination.

RESULTS

Questionnaire data were well-suited for case-classification but not disorder-classification (accuracies: 96.40%, p < .001; 56.58%, p > .22). The opposite pattern was found for imaging data (case-classification GM/WM: 58.71%, p = .09/43.18%, p > .66; disorder-classification GM/WM: 68.05%, p = .034/58.27%, p > .15) and for cortisol data (38.02%, p = .84; 74.60%, p = .009). All data combined achieved 90.10% accuracy (p < .001) for case-classification and 67.46% accuracy (p = .0268) for disorder-classification.

CONCLUSIONS

In line with previous evidence, classification of GAD was difficult using clinical questionnaire data alone. Particularly cortisol and GM volume data were able to provide incremental value for the classification of GAD. Findings suggest that neurobiological biomarkers are a useful target for further research to delineate their potential contribution to diagnostic processes.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

A pilot study of hippocampal N-acetyl-aspartate in youth with treatment resistant major depression

BACKGROUND

Smaller hippocampal volumes, as assessed by magnetic resonance imaging (MRI), and proton magnetic resonance spectroscopy (¹H-MRS) indexed alterations in brain metabolites have been identified in adults with major depressive disorder (MDD). Our group has found similar effects in MDD youth. However, this has not been studied in youth with treatment resistant MDD (TRD), nor has the interaction between regional N-acetyl-aspartate and volume deficits. N-acetyl-aspartate is an amino acid in the synthesis pathway of glutamate, and serves a marker of neuronal viability/number.

METHODS

Fifteen typically developing youth (16-22 years of age; 7 males, 8 females) and eighteen youth with TRD (14-22 years of age; 8 males, 10 females) underwent ¹H-MRS and MRI on a 3T scanner. A short echo PRESS protocol was used with voxels in the right and left hippocampi (6mL each). Hippocampal volume was evaluated using FreeSurfer.

RESULTS

Compared with the typically developing group, youth with TRD had lower concentrations of N-acetyl-aspartate in the left hippocampus (p=0.004), and a trend for smaller left hippocampal volume (p=0.067). In TRD subjects, hippocampal N-acetyl-aspartate was inversely correlated with left (r=-0.68, p=0.003) but not right hippocampal volume. Right hippocampal glutamate+glutamine was greater in TRD youth compared to typically developing controls (p=0.007).

CONCLUSIONS

These results suggest a neurochemical and structural deficit in the hippocampi of youth with TRD. These findings fit with the role of N-acetyl-aspartate in glutamate neurotransmission and the effect of glutamate on brain morphology.