Neuroimaging biomarkers as predictors of treatment outcome in Major Depressive Disorder

The association of sex and age of treatment-resistant tendency to antidepressants: A cohort study of 325,615 patients with major depressive disorder

BACKGROUND

Treatment resistance to antidepressants can impose a significant burden on patients with major depressive disorder (MDD). This study aimed to evaluate the effects of age, sex, and psychiatric and physical comorbidities on the tendency toward treatment resistance to antidepressants (TRT).

METHODS

We utilized data from the Taiwan National Health Insurance Research Database. Patients diagnosed with MDD were included in the study. Physical comorbidities were assessed using the Charlson Comorbidity Index. TRT was defined as receiving antidepressant treatment at an adequate defined daily dose, followed by a subsequent switch to another antidepressant within one year after the initial diagnosis of depression. Logistic regression was used to estimate the odds ratios (ORs) of various potential factors associated with TRT.

RESULTS

A total of 325,615 patients with MDD were included in the study. After adjusting for key confounders, patients aged 20 to 29 years had the highest OR (1.60; 95 % confidence interval [CI]: 1.50-1.70) for TRT compared to the oldest age group (≥80 years). The ORs gradually decreased with increasing age. Males had a significantly lower OR (0.89; 95 % CI: 0.88-0.91) for TRT than females. TRT was also associated with the presence of physical and psychiatric comorbidities, except for autism spectrum disorder (ASD).

LIMITATIONS

As a naturalistic observational study, our findings are subject to potential confounding factors that cannot be fully controlled for, as would be possible in a formal randomized controlled trial.

CONCLUSIONS

Our study highlights the impact of age and sex on TRT. Clinicians should consider these risk factors when managing patients with MDD.

Deep graph learning of multimodal brain networks defines treatment-predictive signatures in major depression

Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD's complex and varied neuropathology. Identifying biomarkers for antidepressant treatment requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined with advanced data-driven methods, can enhance our understanding of the neurobiological processes influencing treatment outcomes. To address this, we analyzed resting-state fMRI and EEG connectivity data from 130 patients treated with sertraline and 135 patients with placebo from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks to integrate data-augmented connectivity and cross-modality correlation, aiming to predict individual symptom changes by revealing multimodal brain network signatures. The results showed that our model demonstrated promising prediction accuracy, with an R2 value of 0.24 for sertraline and 0.20 for placebo. It also exhibited potential in transferring predictions using only EEG. Key brain regions identified for predicting sertraline response included the inferior temporal gyrus (fMRI) and posterior cingulate cortex (EEG), while for placebo response, the precuneus (fMRI) and supplementary motor area (EEG) were critical. Additionally, both modalities identified the superior temporal gyrus and posterior cingulate cortex as significant for sertraline response, while the anterior cingulate cortex and postcentral gyrus were common predictors in the placebo arm. Additionally, variations in the frontoparietal control, ventral attention, dorsal attention, and limbic networks were notably associated with MDD treatment. By integrating fMRI and EEG, our study established novel multimodal brain network signatures to predict individual responses to sertraline and placebo in MDD, providing interpretable neural circuit patterns that may guide future targeted interventions. Trial Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) ClinicalTrials.gov Identifier: NCT#01407094.

Meta analysis of resting frontal alpha asymmetry as a biomarker of depression

This meta-analysis investigated resting frontal alpha asymmetry (FAA) as a potential biomarker for major depressive disorder (MDD). Studies included articles utilizing FAA measure involving EEG electrodes (F3/F4, F7/F8, or Fp1/Fp2) and covering both MDD and controls. Hedges' d was calculated from FAA means and standard deviations (SDs). A systematic search of PubMed through July 2023 identified 23 studies involving 1928 MDD participants and 2604 controls. The analysis revealed a small but significant grand mean effect size (ES) for FAA (F4 - F3), suggesting limited diagnostic value of FAA in MDD. Despite the presence of high heterogeneity across studies, subgroup analyses did not identify significant differences based on calculation formula, reference montage, age, or depression severity. The findings indicate that FAA may have limited standalone diagnostic utility but could complement existing clinical assessments for MDD, highlighting the need for a multifaceted approach to depression diagnosis and prognosis.

From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium and ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence of mental health disorders like depression and schizophrenia, which necessitate precise, innovative approaches. Emerging technologies like artificial intelligence, induced pluripotent stem cells, and multi-omics have the potential to transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies such as animal models and single-variable analyses continue to be used, frequently failing to capture the complexities of human neuropsychiatric conditions. Summary: This review critically evaluates the transition from serendipity to precision-based methodologies in neuropsychiatric research. It focuses on key innovations such as dynamic systems modeling and network-based approaches that use genetic, molecular, and environmental data to identify new therapeutic targets. Furthermore, it emphasizes the importance of interdisciplinary collaboration and human-specific models in overcoming the limitations of traditional approaches. Conclusions: We highlight precision psychiatry's transformative potential for revolutionizing mental health care. This paradigm shift, which combines cutting-edge technologies with systematic frameworks, promises increased diagnostic accuracy, reproducibility, and efficiency, paving the way for tailored treatments and better patient outcomes in neuropsychiatric care.

Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

Neural signatures of emotional biases predict clinical outcomes in difficult-to-treat depression

Background

Neural predictors underlying variability in depression outcomes are poorly understood. Functional MRI measures of subgenual cortex connectivity, self-blaming and negative perceptual biases have shown prognostic potential in treatment-naïve, medication-free and fully remitting forms of major depressive disorder (MDD). However, their role in more chronic, difficult-to-treat forms of MDD is unknown.

Methods

Forty-five participants (n = 38 meeting minimum data quality thresholds) fulfilled criteria for difficult-to-treat MDD. Clinical outcome was determined by computing percentage change at follow-up from baseline (four months) on the self-reported Quick Inventory of Depressive Symptomatology (16-item). Baseline measures included self-blame-selective connectivity of the right superior anterior temporal lobe with an a priori Brodmann Area 25 region-of-interest, blood-oxygen-level-dependent a priori bilateral amygdala activation for subliminal sad vs happy faces, and resting-state connectivity of the subgenual cortex with an a priori defined ventrolateral prefrontal cortex/insula region-of-interest.

Findings

A linear regression model showed that baseline severity of depressive symptoms explained 3% of the variance in outcomes at follow-up (F[3,34] = .33, p = .81). In contrast, our three pre-registered neural measures combined, explained 32% of the variance in clinical outcomes (F[4,33] = 3.86, p = .01).

Conclusion

These findings corroborate the pathophysiological relevance of neural signatures of emotional biases and their potential as predictors of outcomes in difficult-to-treat depression.

Neural responses to facial emotions and subsequent clinical outcomes in difficult-to-treat depression

BACKGROUND

Amygdala and dorsal anterior cingulate cortex responses to facial emotions have shown promise in predicting treatment response in medication-free major depressive disorder (MDD). Here, we examined their role in the pathophysiology of clinical outcomes in more chronic, difficult-to-treat forms of MDD.

METHODS

Forty-five people with current MDD who had not responded to ⩾2 serotonergic antidepressants (n = 42, meeting pre-defined fMRI minimum quality thresholds) were enrolled and followed up over four months of standard primary care. Prior to medication review, subliminal facial emotion fMRI was used to extract blood-oxygen level-dependent effects for sad v. happy faces from two pre-registered a priori defined regions: bilateral amygdala and dorsal/pregenual anterior cingulate cortex. Clinical outcome was the percentage change on the self-reported Quick Inventory of Depressive Symptomatology (16-item).

RESULTS

We corroborated our pre-registered hypothesis (NCT04342299) that lower bilateral amygdala activation for sad v. happy faces predicted favorable clinical outcomes (rs[38] = 0.40, p = 0.01). In contrast, there was no effect for dorsal/pregenual anterior cingulate cortex activation (rs[38] = 0.18, p = 0.29), nor when using voxel-based whole-brain analyses (voxel-based Family-Wise Error-corrected p < 0.05). Predictive effects were mainly driven by the right amygdala whose response to happy faces was reduced in patients with higher anxiety levels.

CONCLUSIONS

We confirmed the prediction that a lower amygdala response to negative v. positive facial expressions might be an adaptive neural signature, which predicts subsequent symptom improvement also in difficult-to-treat MDD. Anxiety reduced adaptive amygdala responses.

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.

Leptin's Inverse Association With Brain Morphology and Depressive Symptoms: A Discovery and Confirmatory Study Across 2 Independent Samples

BACKGROUND

Major depressive disorder has a complex, bidirectional relationship with metabolic dysfunction, but the neural correlates of this association are not well understood.

METHODS

In this cross-sectional investigation, we used a 2-step discovery and confirmatory strategy utilizing 2 independent samples (sample 1: 288 participants, sample 2: 196 participants) to examine the association between circulating indicators of metabolic health (leptin and adiponectin) and brain structures in individuals with major depressive disorder.

RESULTS

We found a replicable inverse correlation between leptin levels and cortical surface area within essential brain areas responsible for emotion regulation, such as the left posterior cingulate cortex, right pars orbitalis, right superior temporal gyrus, and right insula (standardized beta coefficient range: -0.27 to -0.49, puncorrected < .05). Notably, this relationship was independent of C-reactive protein levels. We also identified a significant interaction effect of leptin levels and diagnosis on the cortical surface area of the right superior temporal gyrus (standardized beta coefficient = 0.26 in sample 1, standardized beta coefficient = 0.30 in sample 2, puncorrected < .05). We also observed a positive correlation between leptin levels and atypical depressive symptoms in both major depressive disorder groups (r = 0.14 in sample 1, r = 0.29 in sample 2, puncorrected < .05).

CONCLUSIONS

The inverse association between leptin and cortical surface area in brain regions that are important for emotion processing and leptin's association with atypical depressive symptoms support the hypothesis that metabolic processes may be related to emotion regulation. However, the molecular mechanisms through which leptin may exert these effects should be explored further.

Large Individual Differences in Functional Connectivity in the Context of Major Depression and Antidepressant Pharmacotherapy

Clinical studies of major depression (MD) generally focus on group effects, yet interindividual differences in brain function are increasingly recognized as important and may even impact effect sizes related to group effects. Here, we examine the magnitude of individual differences in relation to group differences that are commonly investigated (e.g., related to MD diagnosis and treatment response). Functional MRI data from 107 participants (63 female, 44 male) were collected at baseline, 2, and 8 weeks during which patients received pharmacotherapy (escitalopram, N = 68) and controls (N = 39) received no intervention. The unique contributions of different sources of variation were examined by calculating how much variance in functional connectivity was shared across all participants and sessions, within/across groups (patients vs controls, responders vs nonresponders, female vs male participants), recording sessions, and individuals. Individual differences and common connectivity across groups, sessions, and participants contributed most to the explained variance (>95% across analyses). Group differences related to MD diagnosis, treatment response, and biological sex made significant but small contributions (0.3-1.2%). High individual variation was present in cognitive control and attention areas, while low individual variation characterized primary sensorimotor regions. Group differences were much smaller than individual differences in the context of MD and its treatment. These results could be linked to the variable findings and difficulty translating research on MD to clinical practice. Future research should examine brain features with low and high individual variation in relation to psychiatric symptoms and treatment trajectories to explore the clinical relevance of the individual differences identified here.

Amygdala real-time fMRI neurofeedback upregulation in treatment resistant depression: Proof of concept and dose determination

Previous work has shown that adults suffering from major depressive disorder (MDD) can increase their amygdala reactivity while recalling positive memories via real-time neurofeedback (rt-fMRI-nf) training, which is associated with reduction in depressive symptoms. This study investigated if this intervention could also be considered for patients suffering from MDD who do not respond to standard psychological and pharmacological interventions, i.e., treatment resistant (TR-MDD). 15 participants received 5 neurofeedback sessions. Outcome measures were depressive symptoms assessed by BDI scores up to 12 weeks following acute intervention, and amygdala activity changes from initial baseline to final transfer run during neurofeedback sessions (neurofeedback success). Participants succeeded in increasing their amygdala activity. A main effect of visit on BDI scores indicated a significant reduction in depressive symptomatology. Percent signal change in the amygdala showed a learning curve during the first session only. Neurofeedback success computed by session was significantly positive only during the second session. When examining the baseline amygdala response, baseline activity stabilized/asymptoted by session 3. This proof-of-concept study suggests that only two neurofeedback sessions are necessary to enable those patients to upregulate their amygdala activity, warranting a future RCT. Over the course of the rtfMRI-nf intervention, participants also reported reduced depressive symptomatology. Clinical trial registration number: NCT03428828 on ClinicalTrials.gov.

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.

Comparable Efficacy of Repeated Transcranial Direct Current Stimulation, Cognitive Behavioral Therapy, and Their Combination in Improvement of Cold and Hot Cognitive Functions and Amelioration of Depressive Symptoms

ABSTRACT

This study aimed to evaluate the effectiveness of repeated transcranial direct current stimulation (rtDCS), cognitive behavioral therapy (CBT), and their combination (rtDCS-CBT) in the treatment of cognitive dysfunction, social cognition, and depressive symptoms in women diagnosed with major depressive disorder (MDD). A total of 40 female participants with MDD were randomly assigned to one of four groups: rtDCS, CBT, rtDCS-CBT, and a control group. The participants' depressive symptoms, executive functions, and social cognition were assessed at baseline, preintervention, postintervention, and during a 1-month follow-up. The rtDCS group received 10 sessions of anodal dorsolateral and cathodal ventromedial prefrontal cortex (2 mA for 20 minutes). The CBT group received 10 sessions of traditional CBT, whereas the combined group received CBT after the tDCS sessions. The results of the analysis of variance indicated that all intervention groups demonstrated significant improvements in depressive symptoms, cognitive dysfunction, and social cognition compared with the control group (all p < 0.001). Furthermore, the rtDCS-CBT group exhibited significantly greater reductions in depressive symptoms when compared with each intervention alone (all p < 0.001). Notably, working memory improvements were observed only in the rtDCS group ( p < 0.001). In conclusion, this study suggests that both CBT and tDCS, either individually or in combination, have a positive therapeutic impact on enhancing executive functions, theory of mind, and depressive symptoms in women with MDD.

Neural correlates of episodic memory decline following electroconvulsive therapy: An exploratory functional magnetic resonance imaging study

BACKGROUND

Electroconvulsive therapy (ECT) is an efficient and rapid-acting treatment indicated for severe depressive disorders. While ECT is commonly accompanied by transient memory decline, the brain mechanisms underlying these side effects remain unclear.

AIMS

In this exploratory functional magnetic resonance (fMRI) study, we aimed to compare effects of ECT versus pharmacological treatment on neural response during episodic memory encoding in patients with affective disorders.

METHODS

This study included 32 ECT-treated patients (major depressive disorder (MDD), n = 23; bipolar depression, n = 9) and 40 partially remitted patients in pharmacological treatment (MDD, n = 24; bipolar disorder, n = 16). Participants underwent neuropsychological assessment, a strategic picture encoding fMRI scan paradigm, and mood rating. The ECT group was assessed before ECT (pre-ECT) and 3 days after their eighth ECT session (post-ECT).

RESULTS

Groups were comparable on age, gender, and educational years (ps ⩾ 0.05). Within-group analyses revealed a selective reduction in verbal learning and episodic memory pre- to post-ECT (p = 0.012) but no decline in global cognitive performance (p = 0.3). Functional magnetic resonance imaging analyses adjusted for mood symptoms revealed greater activity in ECT-treated patients than pharmacologically treated No-ECT patients across left precentral gyrus (PCG), right dorsomedial prefrontal cortex (dmPFC), and left middle frontal gyrus (MFG). In ECT-treated patients, greater decline in verbal learning and memory performance from pre- to post-ECT correlated with higher PCG response (r = -0.46, p = 0.008), but not with dmPFC or MFG activity (ps ⩾ 0.1), post-ECT.

CONCLUSIONS

Episodic memory decline was related to greater neural activity in the left PCG, but unrelated to increased dmPFC and MFG activity, immediately after ECT.

Biomarker-Guided Tailored Therapy in Major Depression

This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.

Frontoamygdalar Effective Connectivity in Youth Depression and Treatment Response

BACKGROUND

Emotion regulation deficits are characteristic of youth depression and are underpinned by altered frontoamygdalar function. However, the causal dynamics of frontoamygdalar pathways in depression and how these dynamics relate to treatment prognosis remain unexplored. This study aimed to assess frontoamygdalar effective connectivity during cognitive reappraisal in youths with depression and to test whether pathway dynamics are predictive of individual response to combined cognitive behavioral therapy plus treatment with fluoxetine or placebo.

METHODS

One hundred seven young people with moderate to severe depression and 94 healthy control participants completed a functional magnetic resonance imaging cognitive reappraisal task. After the task, 87 participants with depression were randomized and received 12 weeks of cognitive behavioral therapy plus either fluoxetine or placebo. Dynamic causal modeling was used to map frontoamygdalar effective connectivity during reappraisal and to assess the predictive capacity of baseline frontoamygdalar effective connectivity on depression diagnosis and posttreatment depression remission.

RESULTS

Young people with depression showed weaker inhibitory modulation of ventrolateral prefrontal cortex to amygdala connectivity during reappraisal (0.29 Hz, posterior probability = 1.00). Leave-one-out cross-validation demonstrated that this effect was sufficiently large to predict individual diagnostic status (r = 0.20, p = .003). Posttreatment depression remission was associated with weaker excitatory ventromedial prefrontal cortex to amygdala connectivity (-0.56 Hz, posterior probability = 1.00) during reappraisal at baseline, though this effect did not predict individual remission status (r = -0.02, p = .561).

CONCLUSIONS

Frontoamygdalar effective connectivity shows promise in identifying youth depression diagnosis, and circuits responsible for negative affect regulation are implicated in responsiveness to first-line depression treatments.

Machine learning prediction will be part of future treatment of depression

Machine learning (ML) is changing the way that medicine is practiced. While already clinically utilised in diagnostic radiology and outcome prediction in intensive care unit, ML approaches in psychiatry remain nascent. Implementing ML algorithms in psychiatry, particularly in the treatment of depression, is significantly more challenging than other areas of medicine in part because of the less demarcated disease nosology and greater variability in practice. Given the current exiguous capacity of clinicians to predict patient and treatment outcomes in depression, there is a significantly greater need for better predictive capability. Early studies have shown promising results. ML predictions were significantly better than chance within the sequenced treatment alternatives to relieve depression (STAR*D) trial (accuracy 64.6%, p < 0.0001) and combining medications to enhance depression outcomes (COMED) randomised Controlled Trial (RCT) (accuracy 59.6%, p = 0.043), with similar results found in larger scale, retrospective studies. The greater flexibility and dimensionality of ML approaches has been demonstrated in studies incorporating diverse input variables including electroencephalography scans, achieving 88% accuracy for treatment response, and cognitive test scores, achieving up to 72% accuracy for treatment response. The predicting response to depression treatment (PReDicT) trial tested ML informed prescribing of antidepressants against standard therapy and found there was both better outcomes for anxiety and functional endpoints despite the algorithm only having a balanced accuracy of 57.5%. Impeding the progress of ML algorithms in psychiatry are pragmatic hurdles, including accuracy, expense, acceptability and comprehensibility, and ethical hurdles, including medicolegal liability, clinical autonomy and data privacy. Notwithstanding impediments, it is clear that ML prediction algorithms will be part of depression treatment in the future and clinicians should be prepared for their arrival.

Subgenual Anterior Cingulate Cortex Reactivity to Rejection Vs. Acceptance Predicts Depressive Symptoms among Adolescents with an Anxiety History

OBJECTIVE

The goal of this study was to examine whether neural sensitivity to negative peer evaluation conveys risk for depression among youth with a history of anxiety. We hypothesized that brain activation in regions that process affective salience in response to rejection, relative to acceptance, from virtual peers would predict depressive symptoms 1 year later and would be associated with ecological momentary assessment (EMA) reports of peer connectedness.

METHOD

Participants were 38 adolescents ages 11-16 (50% female) with a history of anxiety, recruited from a previous clinical trial. The study was a prospective naturalistic follow-up of depressive symptoms assessed 2 years (Wave 2) and 3 years (Wave 3) following treatment. At Wave 2, participants completed the Chatroom Interact Task during neuroimaging and 16 days of EMA.

RESULTS

Controlling for depressive and anxiety symptoms at Wave 2, subgenual anterior cingulate (sgACC; β = .39, p = .010) activation to peer rejection (vs. acceptance) predicted depressive symptoms at Wave 3. SgACC activation to rejection (vs. acceptance) was highly negatively correlated with EMA reports of connectedness with peers in daily life (r = - .71, p < .001).

CONCLUSION

Findings suggest that elevated sgACC activation to negative, relative to positive, peer evaluation may serve as a risk factor for depressive symptoms among youth with a history of anxiety, perhaps by promoting vigilance or reactivity to social evaluative threats. SgACC activation to simulated peer evaluation appears to have implications for understanding how adolescents experience their daily social environments in ways that could contribute to depressive symptoms.

Cingulate prediction of response to antidepressant and cognitive behavioral therapies for depression: Meta-analysis and empirical application

We sought to identify baseline (pre-treatment) neural markers associated with treatment response in major depressive disorder (MDD), specific to treatment type, Cognitive Behavioral Therapy (CBT) or pharmacotherapy (selective serotonin reuptake inhibitors; SSRI). We conducted a meta-analysis of functional magnetic resonance imaging (fMRI) studies to identify neural prognostic indicators of response to CBT or SSRI. To verify the regions derived from literature, the meta-analytic regions were used to predict clinical change in a verification sample of participants with MDD who received either CBT (n = 60) or an SSRI (n = 19) as part of prior clinical trials. The meta-analysis consisted of 21 fMRI studies that used emotion-related tasks. It yielded prognostic regions of the perigenual (meta pgACC) and subgenual anterior cingulate cortex (meta sgACC), associated with SSRI and CBT response, respectively. When applying the meta-analytic regions to predict treatment response in the verification sample, reactivity of the meta pgACC was prognostic for SSRI response, yet the effect direction was opposite of most prior studies. Meta sgACC reactivity failed to be prognostic for CBT response. Results confirm the prognostic potential of neural reactivity of ACC subregions in MDD but further research is necessary for clinical translation.

Brain Features of Treatment-Resistant Depression: A Review of Structural and Functional Connectivity Magnetic Resonance Imaging Studies

Increased awareness of the growing disease burden of treatment resistant depression (TRD), in combination with technological advances in MRI, affords the unique opportunity to research biomarkers that characterize TRD. We provide a narrative review of MRI studies investigating brain features associated with treatment-resistance and treatment outcome in those with TRD. Despite heterogeneity in methods and outcomes, relatively consistent findings include reduced gray matter volume in cortical regions and reduced white matter structural integrity in those with TRD. Alterations in resting state functional connectivity of the default mode network were also found. Larger studies with prospective designs are warranted.

Structural and functional improvement of amygdala sub-regions in postpartum depression after acupuncture

Objective

This study aimed to analyze the changes in structure and function in amygdala sub-regions in patients with postpartum depression (PPD) before and after acupuncture.

Methods

A total of 52 patients with PPD (All-PPD group) were included in this trial, 22 of which completed 8 weeks of acupuncture treatment (Acu-PPD group). An age-matched control group of 24 healthy postpartum women (HPW) from the hospital and community were also included. Results from the 17-Hamilton Depression Scale (17-HAMD) and the Edinburgh Postnatal Depression Scale (EPDS) were evaluated, and resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed at baseline and after the acupuncture treatment. Sub-regions of the amygdala were used as seed regions to measure gray matter volume (GMV) and analyzed for resting-state functional connectivity (RSFC) values separately. Finally, correlation analyses were performed on all patients with PPD to evaluate association values between the clinical scale scores, GMV, and RSFC values, while controlling for age and education. Pearson's correlation analyses were conducted to investigate the relevance between GMV and RSFC values of brain regions that differed before and after acupuncture treatment and clinical scale scores in Acu-PPD patients.

Results

The HAMD scores for Acu-PPD were reduced after acupuncture treatment (P < 0.05), suggesting the positive effects of acupuncture on depression symptoms. Structurally, the All-PPD group showed significantly decreased GMV in the left lateral part of the amygdala (lAMG.L) and the right lateral part of the amygdala (lAMG.R) compared to the HPW group (P < 0.05). In addition, the GMV of lAMG.R was marginally increased in the Acu-PPD group after acupuncture (P < 0.05). Functionally, the Acu-PPD group showed a significantly enhanced RSFC between the left medial part of the amygdala (mAMG.L) and the left vermis_6, an increased RSFC between the right medial part of the amygdala (mAMG.R) and left vermis_6, and an increased RSFC between the lAMG.R and left cerebelum_crus1 (P < 0.05). Moreover, correlation studies revealed that the GMV in the lAMG.R was significantly related to the EPDS scores in the All-PPD group (P < 0.05).

Conclusion

Our findings demonstrated that the structure of amygdala sub-regions is impaired in patients with PPD. Acupuncture may improve depressive symptoms in patients with PPD, and the mechanism may be attributed to changes in the amygdala sub-region structure and the functional connections of brain areas linked to the processing of negative emotions. The fMRI-based technique can provide comprehensive neuroimaging evidence to visualize the central mechanism of action of acupuncture in PPD.

Unbalanced amygdala communication in major depressive disorder

BACKGROUND

Previous studies suggested an association between functional alteration of the amygdala and typical major depressive disorder (MDD) symptoms. Examining whether and how the interaction between the amygdala and regions/functional networks is altered in patients with MDD is important for understanding its neural basis.

METHODS

Resting-state functional magnetic resonance imaging data were recorded from 67 patients with MDD and 74 age- and sex-matched healthy controls (HCs). A framework for large-scale network analysis based on seed mappings of amygdala sub-regions, using a multi-connectivity-indicator strategy (cross-correlation, total interdependencies (TI), Granger causality (GC), and machine learning), was employed. Multiple indicators were compared between the two groups. The altered indicators were ranked in a supporting-vector machine-based procedure and associated with the Hamilton Rating Scale for Depression scores.

RESULTS

The amygdala connectivity with the default mode network and ventral attention network regions was enhanced and that with the somatomotor network, dorsal frontoparietal network, and putamen regions in patients with MDD was reduced. The machine learning analysis highlighted altered indicators that were most conducive to the classification between the two groups.

LIMITATIONS

Most patients with MDD received different pharmacological treatments. It is difficult to illustrate the medication state's effect on the alteration model because of its complex situation.

CONCLUSION

The results indicate an unbalanced interaction model between the amygdala and functional networks and regions essential for various emotional and cognitive functions. The model can help explain potential aberrancy in the neural mechanisms that underlie the functional impairments observed across various domains in patients with MDD.

Aberrant Resting-State Functional Connectivity in MDD and the Antidepressant Treatment Effect-A 6-Month Follow-Up Study

BACKGROUND

The mechanism by which antidepressants normalizing aberrant resting-state functional connectivity (rsFC) in patients with major depressive disorder (MDD) is still a matter of debate. The current study aimed to investigate aberrant rsFC and whether antidepressants would restore the aberrant rsFC in patients with MDD.

METHODS

A total of 196 patients with MDD and 143 healthy controls (HCs) received the resting-state functional magnetic resonance imaging and clinical assessments at baseline. Patients with MDD received antidepressant treatment after baseline assessment and were re-scanned at the 6-month follow-up. Network-based statistics were employed to identify aberrant rsFC and rsFC changes in patients with MDD and to compare the rsFC differences between remitters and non-remitters.

RESULTS

We identified a significantly decreased sub-network and a significantly increased sub-network in MDD at baseline. Approximately half of the aberrant rsFC remained significantly different from HCs after 6-month treatment. Significant overlaps were found between baseline reduced sub-network and follow-up increased sub-network, and between baseline increased sub-network and follow-up decreased sub-network. Besides, rsFC at baseline and rsFC changes between baseline and follow-up in remitters were not different from non-remitters.

CONCLUSIONS

Most aberrant rsFC in patients with MDD showed state-independence. Although antidepressants may modulate aberrant rsFC, they may not specifically target these aberrations to achieve therapeutic effects, with only a few having been directly linked to treatment efficacy.

Molecular imaging findings for treatment resistant depression

Approximately 40% of patients with major depressive disorder (MDD) had limited response to conventional antidepressant treatments, resulting in treatment-resistant depression (TRD), a debilitating subtype that yielded a significant disease burden worldwide. Molecular imaging techniques, such as positron emission tomography (PET) and single photon emission tomography (SPECT), can measure targeted macromolecules or biological processes in vivo. These imaging tools provide a unique possibility to explore the pathophysiology and treatment mechanisms underlying TRD. This work reviewed and summarized prior PET and SPECT studies to examine the neurobiology and treatment-induced changes of TRD. A total of 51 articles were included with supplementary information from studies for MDD and healthy controls (HC). We found that there were altered regional blood flow or metabolic activity in several brain regions, such as the anterior cingulate cortex, prefrontal cortex, insula, hippocampus, amygdala, parahippocampus, and striatum. These regions have been suggested to engage in the pathophysiology or treatment resistance of depression. There was also limited data to demonstrate the changes in the markers of serotonin, dopamine, amyloid, and microglia over some regions in TRD. Moreover, several observed abnormal imaging indices were linked to treatment outcomes, supporting their specificity and clinical relevance. To address the limitations of the included studies, we proposed that future studies needed longitudinal designs, multimodal approaches, and radioligands targeting specific neural substrates for TRD to evaluate their baseline and treatment-related alterations in TRD. Adequate data sharing and reproducible data analysis can facilitate advances in this field.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans

BACKGROUND

Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA).

METHODS

A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample.

RESULTS

30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics.

LIMITATIONS

Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results.

CONCLUSIONS

A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3

Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.

Plasticity of synapses and reward circuit function in the genesis and treatment of depression

What changes in brain function cause the debilitating symptoms of depression? Can we use the answers to this question to invent more effective, faster acting antidepressant drug therapies? This review provides an overview and update of the converging human and preclinical evidence supporting the hypothesis that changes in the function of excitatory synapses impair the function of the circuits they are embedded in to give rise to the pathological changes in mood, hedonic state, and thought processes that characterize depression. The review also highlights complementary human and preclinical findings that classical and novel antidepressant drugs relieve the symptoms of depression by restoring the functions of these same synapses and circuits. These findings offer a useful path forward for designing better antidepressant compounds.

Real-Time fMRI Neurofeedback Training as a Neurorehabilitation Approach on Depressive Disorders: A Systematic Review of Randomized Control Trials

Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-nf) training is an emerging intervention for neurorehabilitation. However, its translation into clinical use on participants with clinical depression is unclear, the effect estimates from randomized control trials and the certainty of the supporting evidence on the effect estimates are unknown. As the number of studies on neurofeedback increases every year, and better quality evidence becomes available, we evaluate the evidence of all randomized control trials available on the clinical application of rt-fMRI-nf training on participants with clinical depression. We performed electronic searches in Pubmed, Embase, CENTRAL, rtFIN database, Epistemonikos, trial registers, reference lists, other systematic reviews, conference abstracts, and cross-citation in Google Scholar. Reviewers independently selected studies, extracted data and evaluated the risk of bias. The certainty of the evidence was judged using the GRADE framework. This review complies with PRISMA guidelines and was submitted to PROSPERO registration. We found 435 results. After the selection process, we included 11 reports corresponding to four RCTs. The effect of rt-fMRI-nf on improving the severity of clinical depression scores demonstrated a tendency to favor the intervention; however, the general effect was not significant. At end of treatment, SMD (standardized mean difference): -0.32 (95% CI -0.73 to 0.10). At follow-up, SMD: -0.33 (95% CI -0.91, 1.25). All the studies showed changes in BOLD fMRI activation after training; however, only one study confirmed regulation success during a transfer run. Whole-brain analyses suggests that rt-fMRI nf may alter activity patterns in brain networks. More studies are needed to evaluate quality of life, acceptability, adverse effects, cognitive tasks, and physiology measures. We conclude that the current evidence on the effect of rt-fMRI-nf training for decision-making outcomes in patients with clinical depression is still based on low certainty of the evidence.

Evaluation of depression status and its influencing factors in convalescent elderly patients with first-episode stroke

OBJECTIVE

To investigate the occurrence and the influencing factors of post-stroke depression (PSD) in first-episode stroke.

METHODS

A total of 350 elderly stroke patients who were admitted to Wuxi Central Rehabilitation Hospital for the first time from January 2020 to December 2020 were enrolled in this study. The Hamilton Depression Scale (HAMD) was used to evaluate the depression status of stroke patients. The sociodemographic data, clinical symptoms, social environment and behavioral patterns of the patients were collected to analyze the related factors of depression after stroke through SPSS 20.0 software.

RESULTS

The incidence of PSD was 45.71%. There were statistical differences among different gender, lesion nature, lesion location, smoking, hypertension, diabetes, hospitalization enpenses, season of onset, BMI index, NIHSS score, barthel index score, blood pressure variation coefficient and other factors (p = 0.000). Post-stroke depression score was positively correlated with NIHSS score and coefficient of variation of systolic blood pressure (r = 0.935, p = 0.000; r = 0.921, p = 0.000), and negatively correlated with barthel index score (r = -0.964, p = 0.000). Through multivariate Logistic regression analysis, it was found that male (OR=8.624, 95%CI: 5.672-11.715), cerebral infarction (OR=2.561, 95%CI: 1.256-3.567), and the right side lesion (OR=1.933, 95%CI: 1.024-3.026), smoking (OR=2.457, 95%CI: 1.611-3.625), onset in autumn and winter (OR=2.049, 95%CI: 1.201-2.919), high BMI (OR=2.461, 95%CI): 1.426-3.432) were risk factors for depression after stroke, and low SBPV (OR=0.567, 95%CI: 0.352-0.758) and low NISHH score (OR=0.256, 95%CI: 0.105-0.486) were the protective factor for subsequent depression of stroke.

CONCLUSION

Males, smoking, patients with onset in autumn and winter, lesions on the right side, high BMI, high NISHH score and high systolic blood pressure variation were closely related to PSD, which should be paid for attention for such patients to prevent the occurrence of PSD and take intervention measures.

Altered functional connectivity in common resting-state networks in patients with major depressive disorder: A resting-state functional connectivity study

The neural correlates of major depressive disorder (MDD) remain disputed. In the absence of reliable biological markers, the dysfunction and interaction of neural networks have been proposed as pathophysiological neural mechanisms in depression. Here, we examined the functional connectivity (FC) of brain networks. 51 healthy volunteers (mean age 33.57 ± 7.80) and 55 individuals diagnosed with MDD (mean age 33.89 ± 11.00) participated by performing a resting-state (rs) fMRI scan. Seed to voxel FC analyses were performed. Compared to healthy control (HC), MDD patients showed higher connectivity between the hippocampus and the anterior cingulate cortex (ACC) and lower connectivity between the insula and the ACC. The MDD group displayed lower connectivity between the inferior parietal lobule (IPL) and the superior frontal gyrus (SFG). The current data replicate previous findings regarding the cortico-limbic network (hippocampus - ACC connection) and the salience network (insula - ACC connection) and provide novel insight into altered rsFC in MDD, in particular involving the hippocampus - ACC and the insula - ACC connection. Furthermore, altered connectivity between the IPL and SFG indicates that the processing in higher cognitive processes such as attention and working memory is affected in MDD. These data further support dysfunctional neuronal networks as an interesting pathophysiological marker in depression.

Generalizability of treatment outcome prediction in major depressive disorder using structural MRI: A NeuroPharm study

Brain morphology has been suggested to be predictive of drug treatment outcome in major depressive disorders (MDD). The current study aims at evaluating the performance of pretreatment structural brain magnetic resonance imaging (MRI) measures in predicting the outcome of a drug treatment of MDD in a large single-site cohort, and, importantly, to assess the generalizability of these findings in an independent cohort. The random forest, boosted trees, support vector machines and elastic net classifiers were evaluated in predicting treatment response and remission following an eight week drug treatment of MDD using structural brain measures derived with FastSurfer (FreeSurfer). Models were trained and tested within a nested cross-validation framework using the NeuroPharm dataset (n = 79, treatment: escitalopram); their generalizability was assessed using an independent clinical dataset, EMBARC (n = 64, treatment: sertraline). Prediction of antidepressant treatment response in the Neuropharm cohort was statistically significant for the random forest (p = 0.048), whereas none of the models could significantly predict remission. Furthermore, none of the models trained using the entire NeuroPharm dataset could significantly predict treatment outcome in the EMBARC dataset. Although our primary findings in the NeuroPharm cohort support some, but limited value in using pretreatment structural brain MRI to predict drug treatment outcome in MDD, the models did not generalize to an independent cohort suggesting limited clinical applicability. This study emphasizes the importance of assessing model generalizability for establishing clinical utility.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

Cortical thickness and related depressive symptoms in early abstinence from chronic methamphetamine use

Methamphetamine use is surging globally as a cause of morbidity and mortality. Treatment is typically sought in early abstinence, when craving and depressive symptoms are intense, contributing to relapse and poor outcomes. To advance an understanding of this problem and identify therapeutic targets, we conducted a retrospective analysis of brain structure in 89 adults with Methamphetamine Use Disorder who were in early abstinence and 89 healthy controls. Unlike most prior research, the participants did not significantly differ in age, sex and recent use of alcohol and tobacco (p-values ≥ 0.400). We analysed thickness across the entire cerebral cortex by fitting a general linear model to identify differences between groups. Follow-up regressions were performed to determine whether cortical thickness in regions showing group differences was related to craving, measured on a visual analogue scale, or to the Beck Depression Inventory score. Participants in early methamphetamine abstinence (M ± SD = 22.1 ± 25.6 days) exhibited thinner cortex in clusters within bilateral frontal, parietal, temporal, insular, and right cingulate cortices relative to controls (p-values < 0.001, corrected for multiple comparisons). Unlike craving (β = 0.007, p = 0.947), depressive symptoms were positively correlated with cortical thickness across clusters (β = 0.239, p = 0.030) and with thickness in the anterior cingulate cluster (β = 0.246, p = 0.027) in the methamphetamine-dependent group. Inasmuch as anterior cingulate pathology predicts response to antidepressants for Major Depressive Disorder, cingulate structure may also identify patients with Methamphetamine Use Disorder who can benefit from antidepressant medication.

A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study

The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.

MRI predictors of pharmacotherapy response in major depressive disorder

Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.

Static and treatment-responsive brain biomarkers of depression relapse vulnerability following prophylactic psychotherapy: Evidence from a randomized control trial

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A prospective study of neural biomarkers of relapse in remitted depressed patients.

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Assessed neural response to dysphoric mood-induction before and after psychotherapy.

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Relapse over a 2-year follow-up linked to dysphoria-evoked sensory inhibition.

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Relapse risk was lower when dorsolateral prefrontal reactivity decreased over time.

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Depression prophylaxis may involve reducing dysphoria-evoked sensory inhibition.

Background

Neural reactivity to dysphoric mood induction indexes the tendency for distress to promote cognitive reactivity and sensory avoidance. Linking these responses to illness prognosis following recovery from Major Depressive Disorder informs our understanding of depression vulnerability and provides engagement targets for prophylactic interventions.

Methods

A prospective fMRI neuroimaging design investigated the relationship between dysphoric reactivity and relapse following prophylactic intervention. Remitted depressed outpatients (N = 85) were randomized to 8 weeks of Cognitive Therapy with a Well-Being focus or Mindfulness Based Cognitive Therapy. Participants were assessed before and after therapy and followed for 2 years to assess relapse status. Neural reactivity common to both assessment points identified static biomarkers of relapse, whereas reactivity change identified dynamic biomarkers.

Results

Dysphoric mood induction evoked prefrontal activation and sensory deactivation. Controlling for past episodes, concurrent symptoms and medication status, somatosensory deactivation was associated with depression recurrence in a static pattern that was unaffected by prophylactic treatment, HR 0.04, 95% CI [0.01, 0.14], p < .001. Treatment-related prophylaxis was linked to reduced activation of the left lateral prefrontal cortex (LPFC), HR 3.73, 95% CI [1.33, 10.46], p = .013. Contralaterally, the right LPFC showed dysphoria-evoked inhibitory connectivity with the right somatosensory biomarker

Conclusions

These findings support a two-factor model of depression relapse vulnerability, in which: enduring patterns of dysphoria-evoked sensory deactivation contribute to episode return, but vulnerability may be mitigated by targeting prefrontal regions responsive to clinical intervention. Emotion regulation during illness remission may be enhanced by reducing prefrontal cognitive processes in favor of sensory representation and integration.

Four-Week Mentalizing Imagery Therapy for Family Dementia Caregivers: A Randomized Controlled Trial with Neural Circuit Changes

INTRODUCTION

Family caregivers of patients with dementia suffer a high burden of depression and reduced positive emotions. Mentalizing imagery therapy (MIT) provides mindfulness and guided imagery skills training to improve balanced mentalizing and emotion regulation.

OBJECTIVE

Our aims were to test the hypotheses that MIT for family caregivers would reduce depression symptoms and improve positive psychological traits more than a support group (SG), and would increase dorsolateral prefrontal cortex (DLPFC) connectivity and reduce subgenual anterior cingulate cortex (sgACC) connectivity.

METHODS

Forty-six caregivers participated in a randomized controlled trial comparing a 4-week MIT group (n = 24) versus an SG (n = 22). Resting state neuroimaging was obtained at baseline and post-group in 28 caregivers, and questionnaires completed by all participants. The primary outcome was change in depression; secondary measures included anxiety, mindfulness, self-compassion, and well-being. Brain networks with participation of DLPFC and sgACC were identified. Connectivity strengths of DLPFC and sgACC with respective networks were determined with dual regression. DLPFC connectivity was correlated with mindfulness and depression outcomes.

RESULTS

MIT significantly outperformed SG in improving depression, anxiety, mindfulness, self-compassion, and well-being, with moderate to large effect sizes. Relative to SG, participants in MIT showed significant increases in DLPFC connectivity - exactly replicating pilot study results - but no change in sgACC. DLPFC connectivity change correlated positively with mindfulness and negatively with depression change.

CONCLUSIONS

In this trial, MIT was superior to SG for reducing depression and anxiety symptoms and improving positive psychological traits. Neuroimaging results suggested that strengthening DLPFC connectivity with an emotion regulation network might be mechanistically related to MIT effects.

A Computational Neural Model for Mapping Degenerate Neural Architectures

Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFA's utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI data and the implications of applying NTFA to do so.

Predicting treatment selections for individuals with major depressive disorder according to functional connectivity subgroups

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. Here, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations.

METHODS

All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery dataset (n=228), we firstly identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic, symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups' preferred interventions.

RESULTS

Three subgroups with specific treatment recommendations were emerged including: (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities. (2) a stimulation-oriented subgroup with more alleviation in suicide. (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing respectively conducted on three testing datasets, results showed an overall accuracy of 72.83%.

CONCLUSIONS

Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes.

The neurobiology of treatment-resistant depression: A systematic review of neuroimaging studies

Treatment-resistant depression (TRD) is a debilitating condition associated with higher medical costs, increased illness burden, and reduced quality of life compared to non-treatment-resistant major depressive disorder (MDD). The question arises whether TRD can be considered a distinct MDD sub-type based on neurobiological features. To answer this question we conducted a systematic review of neuroimaging studies investigating the neurobiological differences between TRD and non-TRD. Our main findings are that patients with TRD show 1) reduced functional connectivity (FC) within the default mode network (DMN), 2) reduced FC between components of the DMN and other brain areas, and 3) hyperactivity of DMN regions. In addition, aberrant activity and FC in the occipital lobe may play a role in TRD. The main limitations of most studies were related to inherent confounding factors for comparing TRD with non-TRD, such as differences in disease chronicity/severity and medication history. Future studies may use prospective longitudinal neuroimaging designs to delineate which effects are present in treatment-naive patients and which effects are the result of disease progression.

Altered effective connectivity of the extended face processing system in depression and its association with treatment response: findings from the YoDA-C randomized controlled trial

BACKGROUND

Depression is commonly associated with fronto-amygdala dysfunction during the processing of emotional face expressions. Interactions between these regions are hypothesized to contribute to negative emotional processing biases and as such have been highlighted as potential biomarkers of treatment response. This study aimed to investigate depression associated alterations to directional connectivity and assess the utility of these parameters as predictors of treatment response.

METHODS

Ninety-two unmedicated adolescents and young adults (mean age 20.1; 56.5% female) with moderate-to-severe major depressive disorder and 88 healthy controls (mean age 19.8; 61.4% female) completed an implicit emotional face processing fMRI task. Patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or placebo. Using dynamic causal modelling, we examined functional relationships between six brain regions implicated in emotional face processing, comparing both patients and controls and treatment responders and non-responders.

RESULTS

Depressed patients demonstrated reduced inhibition from the dlPFC to vmPFC and reduced excitation from the dlPFC to amygdala during sad expression processing. During fearful expression processing patients showed reduced inhibition from the vmPFC to amygdala and reduced excitation from the amygdala to dlPFC. Response was associated with connectivity from the amygdala to dlPFC during sad expression processing and amygdala to vmPFC connectivity during fearful expression processing.

CONCLUSIONS

Our study clarifies the nature of face processing network alterations in adolescents and young adults with depression, highlighting key interactions between the amygdala and prefrontal cortex. Moreover, these findings highlight the potential utility of these interactions in predicting treatment response.

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Trait and state alterations in excitatory connectivity between subgenual anterior cingulate cortex and cerebellum in patients with current and remitted depression

Neuroimaging studies have indicated that the altered functional connectivity (FC) of the subgenual anterior cingulate cortex (sgACC) might be potential pathophysiology of major depressive disorder (MDD). However, directed connectivity is proven to be more closely to neurophysiological processes underlying brain activity than FC. This study aimed to identify the alterations underlying directed connectivity of the sgACC in patients with current and remitted MDD. We conducted a cross-sectional neuroimaging study by recruiting 36 patients with current MDD, 20 patients with remitted MDD, and 36 matched healthy controls. Multiple linear regression was employed to estimate bidirectional connectivity between bilateral sgACC and 115 brain regions over 230 time points. Besides, graph theory was applied to further investigate the information transfer across bilateral sgACC and abnormal brain regions. We found that both patients with current and remitted MDD showed a similar abnormality in bidirectional excitatory connectivity between the left sgACC and the right cerebellum. Patients with current MDD exhibited an increase in excitatory connectivity from the left cerebellum to the right sgACC, which was positively correlated with the HAMD score. Meanwhile, significantly decreased betweenness of the left sgACC was detected in all depressive patients. Our findings suggest that the changed bidirectional excitatory connectivity between the left sgACC and the right cerebellum might be a trait alteration and the abnormal increased excitatory connectivity from the left cerebellum to the right sgACC might be a state alteration of MDD. This work may provide a valuable contribution to identify trait and state alterations in the brain for depression.

Effective Connectivity of Right Amygdala Subregions Predicts Symptom Improvement Following 12-Week Pharmacological Therapy in Major Depressive Disorder

The low rates of treatment response still exist in the pharmacological therapy of major depressive disorder (MDD). Exploring an optimal neurological predictor of symptom improvement caused by pharmacotherapy is urgently needed for improving response to treatment. The amygdala is closely related to the pathological mechanism of MDD and is expected to be a predictor of the treatment. However, previous studies ignored the heterogeneousness and lateralization of amygdala. Therefore, this study mainly aimed to explore whether the right amygdala subregion function at baseline can predict symptom improvement after 12-week pharmacotherapy in MDD patients. We performed granger causality analysis (GCA) to identify abnormal effective connectivity (EC) of right amygdala subregions in MDD and compared the EC strength before and after 12-week pharmacological therapy. The results show that the abnormal EC mainly concentrated on the frontolimbic circuitry and default mode network (DMN). With relief of the clinical symptom, these abnormal ECs also change toward normalization. In addition, the EC strength of right amygdala subregions at baseline showed significant predictive ability for symptom improvement using a regularized least-squares regression predict model. These findings indicated that the EC of right amygdala subregions may be functionally related in symptom improvement of MDD. It may aid us to understand the neurological mechanism of pharmacotherapy and can be used as a promising predictor for symptom improvement in MDD.

Functional connectivity of the hippocampus in predicting early antidepressant efficacy in patients with major depressive disorder

BAKGROUD

The hippocampus is involved in the pathophysiology of major depressive disorder (MDD), and its structure and function have been reported to be related to the antidepressant response. This study aimed to identify relationships between hippocampal functional connectivity (FC) and early improvement in patients with MDD and to further explore the ability of hippocampal FC to predict early efficacy.

METHODS

Thirty-six patients with nonpsychotic MDD were recruited and underwent resting-state functional magnetic resonance imaging scans at baseline. After two weeks of treatment with escitalopram, patients were divided into subgroups with early improved depression (EID, n= 19) and nonimproved depression (NID, n=17) . A voxelwise FC analysis was performed with the bilateral hippocampus as seeds, two-sample t-tests were used to compare hippocampal FC between groups. Receiver operating characteristic (ROC) curves were constructed to determine the best FC measures and optimal threshold for differentiating EID from END.

RESULTS

The EID group showed significantly higher FC between the left hippocampus and left inferior frontal gyrus and precuneus than the END group. And the left hippocampal FC of these two regions were positively correlated with the reduction ratio of the depressive symptom scores. The ROC curve analysis revealed that summed FC scores for these two regions exhibited the highest area under the curve, with a sensitivity of 0.947 and specificity of 0.882 at a summed score of 0.14.

LIMITATIONS

The sample used in this study was relatively small.

CONCLUSIONS

These findings demonstrated that FC of the left hippocampus can predict early efficacy of antidepressant.

Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Importance of test-retest reliability for promoting fMRI based screening and interventions in major depressive disorder

Proponents of personalized medicine have promoted neuroimaging in three areas of clinical application for major depression: clinical prediction, outcome evaluation, and treatment, via neurofeedback. Whereas psychometric considerations such as test-retest reliability are basic precursors to clinical adoption for most clinical instruments, we show, in this article, that basic psychometrics have not been regularly attended to in fMRI of depression. For instance, no fMRI neurofeedback study has included measures of test-retest reliability, despite the implicit assumption that brain signals are stable enough to train. We consider several factors that could be useful to aid clinical translation, including (1) attending to how the BOLD response is parameterized, (2) identifying and promoting regions or voxels with stronger psychometric properties, (3) accounting for within-individual changes (e.g., in symptomatology) across time, and (4) focusing on tasks and clinical populations that are relevant for the intended clinical application. We apply these principles to published prognostic and neurofeedback data sets. The broad implication of this work is that attention to psychometrics is important for clinical adoption of mechanistic assessment, is feasible, and may improve the underlying science.