Structure-function coupling and hierarchy-specific antidepressant response in major depressive disorder.

BACKGROUND: Extensive research has explored altered structural and functional networks in major depressive disorder (MDD). However, studies examining the relationships between structure and function yielded heterogeneous and inconclusive results. Recent work has suggested that the structure-function relationship is not uniform throughout the brain but varies across different levels of functional hierarchy. This study aims to investigate changes in structure-function couplings (SFC) and their relevance to antidepressant response in MDD from a functional hierarchical perspective. METHODS: We compared regional SFC between individuals with MDD (n = 258) and healthy controls (HC, n = 99) using resting-state functional magnetic resonance imaging and diffusion tensor imaging. We also compared antidepressant non-responders (n = 55) and responders (n = 68, defined by a reduction in depressive severity of >50%). To evaluate variations in altered and response-associated SFC across the functional hierarchy, we ranked significantly different regions by their principal gradient values and assessed patterns of increase or decrease along the gradient axis. The principal gradient value, calculated from 219 healthy individuals in the Human Connectome Project, represents a region's position along the principal gradient axis. RESULTS: Compared to HC, MDD patients exhibited increased SFC in unimodal regions (lower principal gradient) and decreased SFC in transmodal regions (higher principal gradient) (p < 0.001). Responders primarily had higher SFC in unimodal regions and lower SFC in attentional networks (median principal gradient) (p < 0.001). CONCLUSIONS: Our findings reveal opposing SFC alterations in low-level unimodal and high-level transmodal networks, underscoring spatial variability in MDD pathology. Moreover, hierarchy-specific antidepressant effects provide valuable insights into predicting treatment outcomes.

Brain-heart interaction disruption in major depressive disorder: disturbed rhythm modulation of the cardiac cycle on brain transient theta bursts.

Brain neurons support arousal and cognitive activity in the form of spectral transient bursts and cooperate with the peripheral nervous system to adapt to the surrounding environment. However, the temporal dynamics of brain-heart interactions have not been confirmed, and the mechanism of brain-heart interactions in major depressive disorder (MDD) remains unclear. This study aimed to provide direct evidence for brain-heart synchronization in temporal dynamics and clarify the mechanism of brain-heart interaction disruption in MDD. Eight-minute resting-state (closed eyes) electroencephalograph and electrocardiogram signals were acquired simultaneously. The Jaccard index (JI) was used to measure the temporal synchronization between cortical theta transient bursts and cardiac cycle activity (diastole and systole) in 90 MDD patients and 44 healthy controls (HCs) at rest. The deviation JI was used to reflect the equilibrium of brain activity between diastole and systole. The results showed that the diastole JI was higher than the systole JI in both the HC and MDD groups; compared to HCs, the deviation JI attenuated at F4, F6, FC2, and FC4 in the MDD patients. The eccentric deviation JI was negatively correlated with the despair factor scores of the HAMD, and after 4 weeks of antidepressant treatment, the eccentric deviation JI was positively correlated with the despair factor scores of the HAMD. It was concluded that brain-heart synchronization existed in the theta band in healthy individuals and that disturbed rhythm modulation of the cardiac cycle on brain transient theta bursts at right frontoparietal sites led to brain-heart interaction disruption in MDD.

Sex differences in anhedonia in bipolar depression: a resting-state fMRI study.

Previous studies about anhedonia symptoms in bipolar depression (BD) ignored the unique role of gender on brain function. This study aims to explore the regional brain neuroimaging features of BD with anhedonia and the sex differences in these patients. The resting-fMRI by applying fractional amplitude of low-frequency fluctuation (fALFF) method was estimated in 263 patients with BD (174 high anhedonia [HA], 89 low anhedonia [LA]) and 213 healthy controls. The effects of two different factors in patients with BD were analyzed using a 3 (group: HA, LA, HC) × 2 (sex: male, female) ANOVA. The fALFF values were higher in the HA group than in the LA group in the right medial cingulate gyrus and supplementary motor area. For the sex-by-group interaction, the fALFF values of the right hippocampus, left medial occipital gyrus, right insula, and bilateral medial cingulate gyrus were significantly higher in HA males than in LA males but not females. These results suggested that the pattern of high activation could be a marker of anhedonia symptoms in BD males, and the sex differences should be considered in future studies of BD with anhedonia symptoms.

Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning.

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self-reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain activity was measured by amplitude of low-frequency fluctuation (ALFF). We trained and tested a two-level k-nearest neighbors (k-NN) model based on resting-state variability of ALFF with fivefold cross-validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity.

Shared and unique imaging-derived endo-phenotypes of two typical antidepressant-applicative depressive patients.

OBJECTIVES: Determining the clinical homogeneous and heterogeneous sets among depressive patients is the key to facilitate individual-level treatment decision. METHODS: The diffusion tensor imaging (DTI) data of 62 patients with major depressive disorder (MDD) and 39 healthy controls were used to construct a Latent Dirichlet Allocation (LDA) Bayesian model. Another 48 MDD patients were used to verify the robustness. The LDA model was employed to identify both shared and unique imaging-derived factors of two typically antidepressant-targeted depressive patients, selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Furthermore, we applied canonical correlation analysis (CCA) between each factor loading and Hamilton depression rating scale (HAMD) sub-score, to explore the potential neurophysiological significance of each factor. RESULTS: The results revealed the imaging-derived connectional fingerprint of all patients could be situated along three latent factor dimensions; such results were also verified by the out-of-sample dataset. Factor 1, uniquely expressed by SNRI-targeted patients, was associated with retardation (r = 0.4, p = 0.037) and characterized by coupling patterns between default mode network and cognitive control network. Factor 3, uniquely expressed by SSRI-targeted patients, was associated with cognitive impairment (r = 0.36, p = 0.047) and characterized by coupling patterns within cognitive control and attention network, and the connectivity between threat and reward network. Shared factor 2, characterized by coupling patterns within default mode network, was associated with anxiety (r = 0.54, p = 0.005) and sleep disturbance (r = 0.37, p = 0.032). CONCLUSIONS: Our findings suggested that quantification of both homogeneity and heterogeneity within MDD may have the potential to inform rational design of pharmacological therapies. KEY POINTS: • The shared and unique manifestations guiding pharmacotherapy of depressive patients are caused by the homogeneity and heterogeneity of underlying structural connections of the brain. • Both shared and unique factor loadings were found in different antidepressant-targeted patients. • Significant correlations between factor loading and HAMD sub-scores were found.

Altered fractional amplitude of low-frequency fluctuations in the superior temporal gyrus: a resting-state fMRI study in anxious depression.

BACKGROUND: Anxious depression, which is a common subtype of major depressive disorder, has distinct clinical features from nonanxious depression. However, little is known about the neurobiological characteristics of anxious depression. In this study, we explored resting-state regional brain activity changes between anxious depression and nonanxious depression. METHOD: Resting-state functional magnetic resonance (rs-fMRI) imaging data were collected from 60 patients with anxious depression, 38 patients with nonanxious depression, and 60 matched healthy controls (HCs). One-way analysis of variance was performed to compare the whole-brain fractional amplitude of low-frequency fluctuation (fALFF) in the three groups. The correlation between the fALFF values and the clinical measures was examined. RESULTS: Compared with those of HCs, the fALFF values in the left superior temporal gyrus (STG) in patients with anxious depression were significantly increased, while the fALFF values in the left middle temporal gyrus (MTG), left STG, and right STG in patients with nonanxious depression were significantly increased. Patients with anxious depression showed reduced fALFF values in the right STG compared with patients with nonanxious depression (p < 0.001, corrected). Within the anxious depression group, fALFF value in the right STG was positively correlated with the cognitive disturbance score (r = 0.36, p = 0.005 corrected). CONCLUSION: The bilateral STG and left MTG, which are related to the default mode network, appear to be key brain regions in nonanxious depression, while the right STG plays an essential role in the neuropathological mechanism of anxious depression.

Attenuated post-movement beta rebound reflects psychomotor alterations in major depressive disorder during a simple visuomotor task: a MEG study.

BACKGROUND: Psychomotor alterations are a common symptom in patients with major depressive disorder (MDD). The primary motor cortex (M1) plays a vital role in the mechanism of psychomotor alterations. Post-movement beta rebound (PMBR) in the sensorimotor cortex is abnormal in patients with motor abnormalities. However, the changes in M1 beta rebound in patients with MDD remain unclear. This study aimed to primarily explore the relationship between psychomotor alterations and PMBR in MDD. METHODS: One hundred thirty-two subjects were enrolled in the study, comprising 65 healthy controls (HCs) and 67 MDD patients. All participants performed a simple right-hand visuomotor task during MEG scanning. PMBR was measured in the left M1 at the source reconstruction level with the time-frequency analysis method. Retardation factor scores and neurocognitive test performance, including the Digit Symbol Substitution Test (DSST), the Making Test Part A (TMT-A), and the Verbal Fluency Test (VFT), were used to measure psychomotor functions. Pearson correlation analyses were used to assess relationships between PMBR and psychomotor alterations in MDD. RESULTS: The MDD group showed worse neurocognitive performance than the HC group in all three neurocognitive tests. The PMBR was diminished in patients with MDD compared to HCs. In a group of MDD patients, the reduced PMBR was negatively correlated with retardation factor scores. Further, there was a positive correlation between the PMBR and DSST scores. PMBR is negatively associated with the TMT-A scores. CONCLUSION: Our findings suggested that the attenuated PMBR in M1 could illustrate the psychomotor disturbance in MDD, possibly contributing to clinical psychomotor symptoms and deficits of cognitive functions.

Hampered gamma oscillations induced by sad emotion underlying suicide attempt in major depressive disorder.

AIM: Major depressive disorder (MDD) is associated with high suicidality, especially for those with suicide attempt (SA). Although impaired oscillatory activity has been previously reported in patients with SA, little is known about precise temporal-spatial variability of its neural dynamics. To solve this, the current study probed the spectral power and network interactions underlying SA in MDD. METHODS: The present study recruited 104 subjects including 56 subjects with MDD (30 with SA and 26 without SA) and 48 healthy controls, who performed sad expressions recognition task during magnetoencephalography (MEG) recording. By investigating source-reconstructed MEG-data, brain states representing different task stages were estimated from a Hidden Markov model. Spectrum power and network connectivity were compared via Gaussian Mixture Models, and fractional occupancy (FO) of states were compared via an independent F-test. RESULTS: Brain states were corresponding to various frequencies (theta/beta/low gamma/ high gamma). In low gamma band (35-45 Hz), the early visual state exhibited increased activation and hyper inter-network connectivity between visual regions and the limbic system, while the middle fronto-parietal state exhibited attenuated activation and decreased intra-network connectivity within fronto-parietal regions in SA group. Crucially, FO values of these two states were significantly correlated with the suicide risks. CONCLUSIONS: Suicide behavior of patients with MDD was significantly associated with aberrant oscillations in low gamma band. Elevated oscillations in occipital cortices and attenuated oscillations in fronto-parietal cortices were significantly associated with SA. Manifesting sadness indulging and reckless decision-making, the hampered temporal characteristics could help explain the neural-electric basis of SA.

An Investigation into the Association Between Dopamine Receptor D1 Multilocus Genetic Variation, Multiparametric Magnetic Resonance Imaging, and Antidepressant Treatment.

BACKGROUND: Combining genetic variants with neuroimaging phenotypes may facilitate understanding of the biological mechanisms for the etiology and pharmacology of antidepressant treatment of major depressive disorder (MDD). PURPOSE: To explore the latent pathway of dopamine gene-hierarchical brain network-antidepressant treatment. STUDY TYPE: Retrospective. POPULATION: One hundred and sixty-eight MDD inpatients divided into responders (N = 98) or nonresponders (N = 70) based on the treatment outcome of antidepressant. FIELD STRENGTH/SEQUENCE: Diffusion tensors imaging and resting-state functional magnetic resonance imaging at 3.0T using echo-planar sequence. ASSESSMENT: Four genetic variations of the dopamine receptor D1 (DRD1) were genotyped. Strengths of rich-club, feeder, and local connections were calculated based on the rich-club organizations of structural and functional brain networks at baseline and following 4 weeks of selective serotonin reuptake inhibitor (SSRI) therapy. STATISTICAL TESTS: Logistic and linear regressions were used to analyze the impact of DRD1 multilocus genetic profile score on the treatment response of SSRI, and their associations with strengths of rich-club, feeder, and local connections. Mediation models were developed to explore the mediation role of rich-club organizations on the relationship between DRD1 and SSRI therapy response. A P value <0.05 was considered to be statistically significant. RESULTS: Multiple genetic variations of DRD1 were significantly related to the strengths of feeder connections both in structural and functional networks, and to the treatment response of SSRI. Furthermore, the strength of the structural feeder connection significantly modulated the effect of DRD1 variants on SSRI treatment outcome. DATA CONCLUSION: DRD1 displayed close connections both with SSRI treatment outcome and rich-club organizations of structural and functional data. Moreover, structural feeder connection played a mediating role in the relationship between DRD1 and antidepressant therapy. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 4.

Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder.

Major depressive disorder (MDD) is associated with increased suicidality, and it's still challenging to identify suicide in clinical practice. Although suicide attempt (SA) is the most relevant precursor with multiple functional abnormalities reported from neuroimaging studies, little is known about how the spontaneous transient activated patterns organize and coordinate brain networks underlying SA. Thus, we obtained resting-state magnetoencephalography data for two MDD subgroups of 44 non-suicide patients and 34 suicide-attempted patients, together with 49 matched health-controls. For the source-space signals, Hidden Markov Model (HMM) helped to capture the sub-second dynamic activity via a hidden sequence of finite number of states. Temporal parameters and spectral activation were acquired for each state and then compared between groups. Here, HMM states characterized the spatiotemporal signatures of eight networks. The activity of suicide attempters switches more frequently into the fronto-temporal network, as the time spent occupancy of fronto-temporal state is increased and interval time is decreased compared with the non-suicide patients. Moreover, these changes are significantly correlated with Nurses' Global Assessment of Suicide Risk scores. Suicide attempters also exhibit increased state-wise activations in the theta band (4-8 Hz) in the posterior default mode network centered on posterior cingulate cortex, which can't be detected in the static spectral analysis. These alternations may disturb the time allocations of cognitive control regulations and cause inflexible decision making to SA. As the better sensitivity of dynamic study in reflecting SA diathesis than the static is validated, dynamic stability could serve as a potential neuronal marker for SA.

Recovery of lymphocyte subpopulations is incomplete in the long-term setting in pediatric solid tumor survivors.

BACKGROUND: Childhood cancer survivors (CCSs) may have comorbidities including a long-term abnormality in the immune system. Immune reconstitution in CCSs after treatment for acute leukemia has been reported previously, while analyses of immune reconstitution in CCSs with solid tumors have been limited. METHODS: Childhood cancer survivors who received chemotherapy for solid tumors and who visited University of Tsukuba Hospital between November 2019 and March 2021 were included the study. Peripheral blood was collected for flow cytometry analysis. RESULTS: Forty-nine samples from 35 CCSs (18 male, 17 female) were included in the study. High-dose chemotherapy and cerebral spinal irradiation were conducted in 14 CCSs (40%) and in five CCSs (14%), respectively. The median time between the completion of chemotherapy and the collection of the present samples was 15.0 months (range, 0-286 months). The total lymphocyte count, B cells, and CD8-positive T cells recovered to the normal range of controls (NR-CTLs) in 0 (0%), four (66.7%), and four (66.7%) of six samples at 0-3 months after the completion of chemotherapy, and in three (60%), four (80%), and three (60%) of five samples at 3-12 months after the completion of chemotherapy, respectively. Meanwhile, CD4-positive T cells remained lower than NR-CTLs in 0 (0%) of six samples, one (20%) of five samples, and seven (63.7%) of 11 samples at 0-3, 3-12 and 12-60 months after the completion of chemotherapy, respectively. CONCLUSIONS: Recovery to the NR-CTLs was rapidly achieved in B cells and CD8-positive T cells, while the recovery was slower and incomplete in CD4-positive T cells. Careful observation of infection in long-term follow-up clinics is needed.

Functional impairment-based segmentation of anterior cingulate cortex in depression and its relationship with treatment effects.

In major depressive disorder (MDD), the anterior cingulate cortex (ACC) is widely related to depression impairment and antidepressant treatment response. The multiplicity of ACC subdivisions calls for a fine-grained investigation of their functional impairment and recovery profiles. We recorded resting state fMRI signals from 59 MDD patients twice before and after 12-week antidepressant treatment, as well as 59 healthy controls (HCs). With functional connectivity (FC) between each ACC voxel and four regions of interests (bilateral dorsolateral prefrontal cortex [DLPFC] and amygdalae), subdivisions with variable impairment were identified based on groups' dissimilarity values between MDD patients before treatment and HC. The ACC was subdivided into three impairment subdivisions named as MedialACC, DistalACC, and LateralACC according to their dominant locations. Furthermore, the impairment pattern and the recovery pattern were measured based on group statistical analyses. DistalACC impaired more on its FC with left DLPFC, whereas LateralACC showed more serious impairment on its FC with bilateral amygdalae. After treatment, FCs between DistalACC and left DLPFC, and between LateralACC and right amygdala were normalized while impaired FC between LateralACC and left amygdala kept dysfunctional. Subsequently, FC between DistalACC and left DLPFC might contribute to clinical outcome prediction. Our approach could provide an insight into how the ACC was impaired in depression and partly restored after antidepressant treatment, from the perspective of the interaction between ACC subregions and critical frontal and subcortical regions.

Sub-second transient activated patterns to sad expressions in major depressive disorders discovered via hidden Markov model.

The pathological mechanisms of major depressive disorders (MDDs) is associated with the overexpression of negative emotions, and the fast transient-activated patterns underlying overrepresentation in depression still remain to be revealed to date. We hypothesized that the aberrant spatiotemporal attributes of the process of sad expressions are related to the neuropathology of MDD and help to detect the depression severity. We enrolled a total of 96 subjects including 47 patients with MDD and 49 healthy controls (HCs), and recorded their magnetoencephalography data under a sad expression recognition task. A hidden Markov model (HMM) was applied to separate the whole neural activity into several brain states, then to characterize the dynamics. To find the disrupted temporal-spatial characteristics, power estimations and fractional occupancy (FO) of each state were estimated and contrasted between MDDs and HCs. Three states were found over the period of emotional stimuli processing procedure. The early visual stage (0-270 ms) was mainly manifested by state 1, and the emotional information processing stage (270-600 ms) was manifested by state 2, while the state 3 remained a steady proportion across the whole period. MDDs activated statistically more in limbic system during state 2 (p = 0.0045) and less in frontoparietal control network during state 3 (p = 5.38 × 10-5 ) relative to HCs. Hamilton Depression Rating Scale scores were significantly correlated with the predicted disorder severity using FO values (p = 0.0062, r = 0.3933). Relative to HCs, MDDs perceived the sad contents quickly and spent more time overexpressing the negative emotions. These phenomena indicated MDD patients might easily indulge in negative emotion and neglect other things. Furthermore, temporal descriptors built by HMM could be potential biomarkers for identifying the severity of depression disorders.

Predicting Neuroimaging Biomarkers for Antidepressant Selection in Early Treatment of Depression.

BACKGROUND: Due to the biological heterogeneity, 60%-70% of patients with major depressive disorder (MDD) do not respond to or achieve remission from first-line antidepressants. Predicting neuroimaging biomarkers for early antidepressant treatment could guide initial antidepressant therapy. PURPOSE: To assess for neuroimaging biomarkers for antidepressant selection in early antidepressant treatment. STUDY TYPE: Prospective. SUBJECTS: A total of 85 MDD patients from the major site and 33 MDD patients from an out-of-sample test site. FIELD STRENGTH/SEQUENCE: A 3.0 T, T1-weighted imaging using a magnetization-prepared rapid acquisition gradient-echo sequence and diffusion tensor imaging (DTI) using an echo-planar sequence. ASSESSMENT: Baseline DTI data of patients who achieved early improvement after 2-weeks of antidepressant treatment (selective serotonin reuptake inhibitors [SSRI] or serotonin-norepinephrine reuptake inhibitors [SNRI]) were analyzed. An ensemble model was constructed using data from the major site and then applied to assess the early response of patients at the out-of-sample test site. STATISTICAL TESTS: Support vector machine combined with leave-one-out cross-validation were applied to construct the whole model from individual base models from different brain regions. Discriminative biomarkers were evaluated by calculating the changes in sensitivity and specificity obtained when removing a single base model from the whole model, the base model being removed changing in each run. RESULTS: Training performance over MDD patients at the major site achieved 75% accuracy while performance with accuracy of 70% was achieved in the out-of-sample test site. Assessing sensitivity and specificity changes following the removal of single base models from the prominent model highlighted the functions of two neural circuitries: SSRI-related emotion regulation circuitry, centered on the hippocampus (sensitivity changes: 10%) and amygdala (sensitivity changes: 11%); and SNRI-related emotion and reward circuitry, centered on the putamen (specificity changes: 8%) and orbital part of superior frontal gyrus (specificity changes: 12%). DATA CONCLUSION: These findings support future research on clinical antidepressant selection for MDD. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Global and reflective rumination are related to suicide attempts among patients experiencing major depressive episodes.

BACKGROUND: Recent attention has focused on the role of rumination in suicidality, with evidence indicating that rumination may be positively related to suicidal ideation. There remains disagreement on the nature of the relationship between rumination and suicide attempts, especially in major affective disorders. This study was designed to identify whether rumination is a risk factor for attempted suicide. METHODS: A total of 309 patients with major depressive episodes were recruited for this study, including 170 patients with major depression and 139 patients with bipolar disorder. All participants were categorized into two groups based on a series of clinical assessments: suicide attempters (n = 87) and non-suicide attempters (n = 222). Rumination was evaluated with the Ruminative Responses Scale. A binary logistic regression analysis was carried out to evaluate the relationship between rumination and suicide attempts. RESULTS: Both global ruminative levels and the two subtypes of rumination, brooding and reflection, were significantly higher in the suicide attempters than the non-suicide attempters. After controlling for age, current depression and anxiety symptoms, and episode frequency, it was found that global rumination and reflection (but not brooding) were positively associated with suicide attempts. CONCLUSION: These results suggest that rumination may be a risk factor for suicide attempts and highlight the maladaptive nature of reflection in patients with major depressive episodes.

Predicting escitalopram monotherapy response in depression: The role of anterior cingulate cortex.

Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in major depressive disorder (MDD). Escitalopram is recommended as first-line therapy for MDD and severe depression. An interesting hypothesis suggests that the reconfiguration of dynamic brain networks might provide important insights into antidepressant mechanisms. The present study assesses whether the spatiotemporal modulation across functional brain networks could serve as a predictor of effective antidepressant treatment with escitalopram. A total of 106 first-episode, drug-naïve patients and 109 healthy controls from three different multicenters underwent resting-state functional magnetic resonance imaging. Patients were considered as responders if they had a reduction of at least 50% in Hamilton Rating Scale for Depression scores at endpoint (>2 weeks). Multilayer modularity framework was applied on the whole brain to construct features in relation to network dynamic characters that were used for multivariate pattern analysis. Linear soft-threshold support vector machine models were used to separate responders from nonresponders. The permutation tests demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a significantly larger module allegiance of ACC was also found in treatment responders compared to nonresponders, suggesting high interactivities of ACC to other regions may be beneficial for the recovery after treatment. Consistent results across multicenters confirmed that ACC could serve as a predictor of escitalopram monotherapy treatment outcome, implying strong likelihood of replication in the future.

Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder.

BACKGROUND: In order to reduce unsuccessful treatment trials for depression, neuroimaging and genetic information can be considered as biomarkers. Together with machine-learning methods, prediction models have proved to be valuable for baseline prediction. PURPOSE: To propose an ensemble learning modeling framework that integrates imaging and genetic information for individualized baseline prediction of early-stage treatment response of antidepressants in major depressive disorder (MDD). STUDY TYPE: Prospective. SUBJECTS: In all, 98 inpatients with MDD. FIELD STRENGTH/SEQUENCE: 3.0T MRI and gradient-echo echo-planar imaging sequence. ASSESSMENT: Participants were divided into responders and nonresponders based on reducing rates of HDRS-6 after early-stage treatment of 2 weeks. Fourteen brain regions of interest were selected according to previous studies. An ensemble learning modeling framework was used to integrate imaging data and genetic data. STATISTICAL TESTS: Support vector machine (SVM) with linear kernel was utilized to integrate multimode information and then to construct the prediction model. Leave-one-out cross-validation (LOOCV) was used to evaluate the performance. The position characteristics obtained through SVM-RFE (recursive feature elimination) algorithm and LOOCV was considered to compare each feature's relative importance for the prediction model. RESULTS: Compared with the single-level prediction model, the ensemble learning prediction model showed improvement in prediction performance (accuracy from 0.61 to 0.86 with imaging data and genetic data). Integrated with 14 priori brain regions, the region of interest (ROI) map ensemble learning prediction model can achieve a performance that is analogous with the model with information from whole-brain regions (both with accuracy of 0.81). The integration of genetic features further improved the sensitivity of prediction (sensitivity from 0.78 to 0.87 under the ensemble learning framework). DATA CONCLUSION: Our ensemble learning prediction model demonstrated significant advantages in interpretability and information integration. The findings may provide more assistance for clinical treatment selection in MDD at the individual level. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:161-171.

Magnetoencephalography resting-state spectral fingerprints distinguish bipolar depression and unipolar depression.

OBJECTIVES: In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level. METHODS: Eyes closed resting-state magnetoencephalography (MEG) data were collected from 23 BD, 30 UD, and 31 healthy controls (HC). Individual power spectra were estimated by Fourier transform, and statistic spectral differences were assessed via a cluster permutation test. A support vector machine classifier was further applied to predict different mood disorder types based on discriminative oscillatory power. RESULTS: Both BD and UD showed decreased frontal-central gamma/beta ratios comparing to HC, in which gamma power (30-75 Hz) was decreased in BD while beta power (14-30 Hz) was increased in UD vs HC. The support vector machine model obtained significant high classification accuracies distinguishing three groups based on mean gamma and beta power (BD: 79.9%, UD: 81.1%, HC: 76.3%, P < .01). CONCLUSIONS: In combination with resting-state MEG data and machine learning technique, it is possible to make an individual and objective prediction for mode disorder types, which in turn has implications for diagnosis precision and treatment decision of mood disorder patients.

Variation in Brain Subcortical Network Topology Between Men With and Without PE: A Diffusion Tensor Imaging Study.

INTRODUCTION: Premature ejaculation (PE) is a highly prevalent male sexual dysfunction. Previous studies have found abnormal activity in the sympathetic nervous system and penile sensory pathway of PE. Few studies have investigated the neural mechanisms underlying PE. AIM: The aim of this study was to examine whether the altered cortico-subcortical network topological properties of the brain white matter structural network could be used to differentiate patients with PE from healthy control (HC) subjects. METHODS: Diffusion tensor images data were collected from 32 patients with PE and 35 HC participants. Then, brain white matter structural networks were reconstructed from image acquisition. MAIN OUTCOME MEASURE: Furthermore, nodal measures were calculated and hub regions were identified using the graph-theoretical methods. RESULTS: For cortical brain regions, increased strength, global efficiency, and decreased shortest path length were found in the right superior frontal gyrus (medial), and superior frontal gyrus (medial orbital) were found in patients with PE. In addition, patients with PE also showed decreased strength in the right rolandic operculum and decreased shortest path length, and increased global efficiency in the right inferior frontal gyrus (triangular part). For subcortical brain structures, patients with PE were associated with decreased shortest path length and increased global efficiency in the left insula and right caudate nucleus. Finally, the results showed that 9 PE-specific hub regions were identified in patients compared with HCs, including 7 cortical regions and 2 subcortical regions. CLINICAL IMPLICATIONS: Our results provide new understanding about the pathology of PE and enhances the understanding of PE pathology. STRENGTH & LIMITATIONS: Our results offer biological markers for understanding the physiopathology of PE. However, our study is a cross-sectional design, longitudinal design studies need to explore the causal relationships between aberrant topological characteristics and PE. CONCLUSION: Our results provide new insights into the neural mechanism of PE involving cortico-subcortical network changes, which could serve as a potential biomarker to differentiate individuals with PE from HCs. Chen J, Yang J, Huang X, et al. Variation in Brain Subcortical Network Topology Between Men With and Without PE: A Diffusion Tensor Imaging Study. J Sex Med 2020;17:48-59.

Caudothalamic dysfunction in drug-free suicidally depressed patients: an MEG study.

Major depressive disorder (MDD), characterized by low mood or anhedonia, is commonly associated with a greater suicidal susceptibility. There are numerous suicide-related findings pertaining to the dorsolateral prefrontal cortex (DLPFC), caudate nucleus and thalamus, which form a cortico-striato-thalamo-cortical (CSTC) circuit responsible for executive function and working memory. An aberrant CSTC circuitry is hypothesized to be implicated in depressed patients with a high suicidal risk. 27 MDD patients were assessed with the Nurses Global Assessment of Suicide Risk (NGASR), following which 14 patients were classified into a high suicide risk group (NGASR ≥ 12) and 13 patients were assigned to a low suicide risk group (NGASR < 6). All 27 patients were enrolled with 25 healthy controls for resting-state magnetoencephalography (MEG). Cross-frequency coupling (CFC) measured the phase of alpha-band (8-13 Hz) as it modulated to cortical gamma-band (30-48 Hz). There was a significantly lower alpha-to-gamma phase-amplitude coupling (PAC) between the right caudate and left thalamus in high-risk suicide group compared to both the low-risk suicide group and healthy controls. The presence of a weaker coupling between the right caudate and left thalamus is indicative of a caudothalamic abnormality in suicidally depressed patients. This implies that a disruption of CSTC loop could result in executive dysfunction and working memory impairment, leading to an increased suicidal risk in MDD patients. In the future, this preliminary study has the possibility of being replicated on a larger scale, and hence validates caudothalamic dysfunction as a reliable neuroimaging biomarker for suicide in depression.