AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors.

Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group.

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Is tDCS a potential first line treatment for major depression?

Transcranial direct current stimulation (tDCS) is a novel treatment option for major depression which could be provided as a first-line treatment. tDCS is a non-invasive form of transcranial stimulation which changes cortical tissue excitability by applying a weak (0.5-2 mA) direct current via scalp electrodes. Anodal and cathodal stimulation leads to depolarisation and hyperpolarisation, respectively, and cumulative effects are observed with repeated sessions. The montage in depression most often involves anodal stimulation to the left dorsolateral prefrontal cortex. Rates of clinical response, remission, and improvements in depressive symptoms following a course of active tDCS are greater in comparison to a course of placebo sham-controlled tDCS. In particular, the largest treatment effects are evident in first episode and recurrent major depression, while minimal effects have been observed in treatment-resistant depression. The proposed mechanism is neuroplasticity at the cellular and molecular level. Alterations in neural responses have been found at the stimulation site as well as subcortically in prefrontal-amygdala connectivity. A possible mediating effect could be cognitive control in emotion dysregulation. Additional beneficial effects on cognitive impairments have been reported, which would address an important unmet need. The tDCS device is portable and can be used at home. Clinical trials are required to establish the efficacy, feasibility and acceptability of home-based tDCS treatment and mechanisms.

Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort.

BACKGROUND: Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age. METHODS: Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer. RESULTS: CM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions. CONCLUSIONS: Severity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.

Modulatory effects of brain-derived neurotrophic factor Val66Met polymorphism on prefrontal regions in major depressive disorder.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism contributes to the development of depression (major depressive disorder, MDD), but it is unclear whether neural effects observed in healthy individuals are sustained in MDD. AIMS: To investigate BDNF Val66Met effects on key regions in MDD neurocircuitry: amygdala, anterior cingulate, middle frontal and orbitofrontal regions. METHOD: Magnetic resonance imaging scans were acquired in 79 persons with MDD (mean age 49 years) and 74 healthy volunteers (mean age 50 years). Effects on surface area and cortical thickness were examined with multiple comparison correction. RESULTS: People who were Met allele carriers showed reduced caudal middle frontal thickness in both study groups. Significant interaction effects were found in the anterior cingulate and rostral middle frontal regions, in which participants in the MDD group who were Met carriers showed the greatest reduction in surface area. CONCLUSIONS: Modulatory effects of the BDNF Val66Met polymorphism on distinct subregions in the prefrontal cortex in MDD support the neurotrophin model of depression.

Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine.

BACKGROUND: Longitudinal neuroimaging studies of major depressive disorder (MDD) have most commonly assessed the effects of antidepressants from the serotonin reuptake inhibitor class and usually reporting a single measure. Multimodal neuroimaging assessments were acquired from MDD patients during an acute depressive episode with serial measures during a 12-week treatment with the serotonin-norepinephrine reuptake inhibitor (SNRI) duloxetine. METHODS: Participants were medication-free MDD patients (n = 32; mean age 40.2 years) in an acute depressive episode and healthy controls matched for age, gender, and IQ (n = 25; mean age 38.8 years). MDD patients received treatment with duloxetine 60 mg daily for 12 weeks with an optional dose increase to 120 mg daily after 8 weeks. All participants had serial imaging at weeks 0, 1, 8, and 12 on a 3 Tesla magnetic resonance imaging (MRI) scanner. Neuroimaging tasks included emotional facial processing, negative attentional bias (emotional Stroop), resting state functional MRI and structural MRI. RESULTS: A significant group by time interaction was identified in the anterior default mode network in which MDD patients showed increased connectivity with treatment, while there were no significant changes in healthy participants. In the emotional Stroop task, increased posterior cingulate activation in MDD patients normalized following treatment. No significant group by time effects were observed for happy or sad facial processing, including in amygdala responsiveness, or in regional cerebral volumes. Reduced baseline resting state connectivity within the orbitofrontal component of the default mode network was predictive of clinical response. An early increase in hippocampal volume was predictive of clinical response. CONCLUSIONS: Baseline resting state functional connectivity was predictive of subsequent clinical response. Complementary effects of treatment were observed from the functional neuroimaging correlates of affective facial expressions, negative attentional bias, and resting state. No significant effects were observed in affective facial processing, while the interaction effect in negative attentional bias and individual group effects in resting state connectivity could be related to the SNRI class of antidepressant medication. The specificity of the observed effects to SNRI pharmacological treatments requires further investigation. TRIAL REGISTRATION: Registered at clinicaltrials.gov ( NCT01051466 ).

Home-based transcranial direct current stimulation in bipolar depression: an open-label treatment study of clinical outcomes, acceptability and adverse events.

BACKGROUND: Current treatments for bipolar depression have limited effectiveness, tolerability and acceptability. Transcranial direct current stimulation (tDCS) is a novel non-invasive brain stimulation method that has demonstrated treatment efficacy for major depressive episodes. tDCS is portable, safe, and individuals like having sessions at home. We developed a home-based protocol with real-time remote supervision. In the present study, we have examined the clinical outcomes, acceptability and feasibility of home-based tDCS treatment in bipolar depression. RESULTS: Participants were 44 individuals with bipolar disorder (31 women), mean age 47.27 ± 12.89 years, in current depressive episode of at least moderate severity (mean Montgomery Asberg Depression Rating Scale (MADRS) score 24.59 ± 2.64). tDCS was provided in bilateral frontal montage, F3 anode, F4 cathode, 2 mA, for 30 min, in a 6-week trial, for total 21 sessions, a follow up visit was conducted 5 months from baseline. Participants maintained their current treatment (psychotherapy, antidepressant or mood stabilising medication) or maintained being medication-free. A research team member was present by video conference at each session. 93.2% participants (n = 41) completed the 6-week treatment and 72.7% of participants (n = 32) completed the 5 month follow up. There was a significant improvement in depressive symptoms following treatment (mean MADRS 8.77 ± 5.37) which was maintained at the 5 month follow up (mean MADRS 10.86 ± 6.90), rate of clinical response was 77.3% (MADRS improvement of 50% or greater from baseline), and rate of clinical remission was 47.7% (MADRS rating of 9 or less). Acceptability was endorsed as "very acceptable" or "quite acceptable" by all participants. No participants developed mania or hypomania. CONCLUSIONS: In summary, home-based tDCS with real-time supervision was associated with significant clinical improvements and high acceptability in bipolar depression. Due to the open-label design, efficacy findings are preliminary. TRIAL REGISTRATION: ClinicalTrials.gov number NCT05436613 registered on 23 June 2022 https//www. CLINICALTRIALS: gov/study/NCT05436613.

Enhanced network synchronization connectivity following transcranial direct current stimulation (tDCS) in bipolar depression: Effects on EEG oscillations and deep learning-based predictors of clinical remission.

AIM: To investigate oscillatory networks in bipolar depression, effects of a home-based tDCS treatment protocol, and potential predictors of clinical response. METHODS: 20 participants (14 women) with bipolar disorder, mean age 50.75 ± 10.46 years, in a depressive episode of severe severity (mean Montgomery-Åsberg Rating Scale (MADRS) score 24.60 ± 2.87) received home-based transcranial direct current stimulation (tDCS) treatment for 6 weeks. Clinical remission defined as MADRS score < 10. Resting-state EEG data were acquired at baseline, prior to the start of treatment, and at the end of treatment, using a portable 4-channel EEG device (electrode positions: AF7, AF8, TP9, TP10). EEG band power was extracted for each electrode and phase locking value (PLV) was computed as a functional connectivity measure of phase synchronization. Deep learning was applied to pre-treatment PLV features to examine potential predictors of clinical remission. RESULTS: Following treatment, 11 participants (9 women) attained clinical remission. A significant positive correlation was observed with improvements in depressive symptoms and delta band PLV in frontal and temporoparietal regional channel pairs. An interaction effect in network synchronization was observed in beta band PLV in temporoparietal regions, in which participants who attained clinical remission showed increased synchronization following tDCS treatment, which was decreased in participants who did not achieve clinical remission. Main effects of clinical remission status were observed in several PLV bands: clinical remission following tDCS treatment was associated with increased PLV in frontal and temporal regions and in several frequency bands, including delta, theta, alpha and beta, as compared to participants who did not achieve clinical remission. The highest deep learning prediction accuracy 69.45 % (sensitivity 71.68 %, specificity 66.72 %) was obtained from PLV features combined from theta, beta, and gamma bands. CONCLUSIONS: tDCS treatment enhances network synchronization, potentially increasing inhibitory control, which underscores improvement in depressive symptoms. Baseline EEG-based measures might aid predicting clinical response.

Home-based transcranial direct current stimulation treatment for major depressive disorder: a fully remote phase 2 randomized sham-controlled trial.

Transcranial direct current stimulation (tDCS) has been proposed as a new treatment in major depressive disorder (MDD). This is a fully remote, multisite, double-blind, placebo-controlled, randomized superiority trial of 10-week home-based tDCS in MDD. Participants were 18 years or older, with MDD in current depressive episode of at least moderate severity as measured using the Hamilton Depression Rating Scale (mean = 19.07 ± 2.73). A total of 174 participants (120 women, 54 men) were randomized to active (n = 87, mean age = 37.09 ± 11.14 years) or sham (n = 87, mean age = 38.32 ± 10.92 years) treatment. tDCS consisted of five sessions per week for 3 weeks then three sessions per week for 7 weeks in a 10-week trial, followed by a 10-week open-label phase. Each session lasted 30 min; the anode was placed over the left dorsolateral prefrontal cortex and the cathode over the right dorsolateral prefrontal cortex (active tDCS 2 mA and sham tDCS 0 mA, with brief ramp up and down to mimic active stimulation). As the primary outcome, depressive symptoms showed significant improvement when measured using the Hamilton Depression Rating Scale: active 9.41 ± 6.25 point improvement (10-week mean = 9.58 ± 6.02) and sham 7.14 ± 6.10 point improvement (10-week mean = 11.66 ± 5.96) (95% confidence interval = 0.51-4.01, P = 0.012). There were no differences in discontinuation rates. In summary, a 10-week home-based tDCS treatment with remote supervision in MDD showed high efficacy, acceptability and safety. ClinicalTrials.gov registration: NCT05202119.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (ß = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Predictive neural biomarkers of clinical response in depression: a meta-analysis of functional and structural neuroimaging studies of pharmacological and psychological therapies.

We performed a systematic review and meta-analysis of neural predictors of response to the most commonly used, evidence based treatments in clinical practice, namely pharmacological and psychological therapies. Investigations of medication-free subjects suffering from a current major depressive episode who underwent positron emission tomography (PET) or functional or structural magnetic resonance imaging (MRI) scans prior to the initiation of treatment were reviewed. Results of 20 studies from 15 independent samples were included in the functional imaging meta-analysis and 9 studies from 6 independent samples in the structural neuroimaging meta-analysis. Regional activations with prognostic value include the well replicated finding that increased baseline activity in the anterior cingulate is predictive of a higher likelihood of improvement. As well, increased baseline activation in the insula and striatum is associated with higher likelihood of a poorer clinical response. Structural neuroimaging studies indicated that a decrease in right hippocampal volume is a statistically significant predictor of poorer treatment response. Overall, the predictive information that is measurable with brain imaging techniques is both multimodal and regionally distributed as it contains functional as well as structural correlates which encompass several brain regions within a frontostriatal-limbic network. To develop clinically relevant, prognostic markers will require high predictive accuracy at the level of the individual. Predicting clinical response will help to stratify patients and to identify at an early stage those patients who may require more intensive or combined therapies. We propose that structural and functional neuroimaging show significant potential for the development of prognostic markers of clinical response in the treatment of depression.

Interaction between effects of genes coding for dopamine and glutamate transmission on striatal and parahippocampal function.

The genes for the dopamine transporter (DAT) and the D-Amino acid oxidase activator (DAOA or G72) have been independently implicated in the risk for schizophrenia and in bipolar disorder and/or their related intermediate phenotypes. DAT and G72 respectively modulate central dopamine and glutamate transmission, the two systems most robustly implicated in these disorders. Contemporary studies have demonstrated that elevated dopamine function is associated with glutamatergic dysfunction in psychotic disorders. Using functional magnetic resonance imaging we examined whether there was an interaction between the effects of genes that influence dopamine and glutamate transmission (DAT and G72) on regional brain activation during verbal fluency, which is known to be abnormal in psychosis, in 80 healthy volunteers. Significant interactions between the effects of G72 and DAT polymorphisms on activation were evident in the striatum, parahippocampal gyrus, and supramarginal/angular gyri bilaterally, the right insula, in the right pre-/postcentral and the left posterior cingulate/retrosplenial gyri (P < 0.05, FDR-corrected across the whole brain). This provides evidence that interactions between the dopamine and the glutamate system, thought to be altered in psychosis, have an impact in executive processing which can be modulated by common genetic variation.

Effect of D-amino acid oxidase activator (DAOA; G72) on brain function during verbal fluency.

BACKGROUND: The D-Amino acid oxidase activator (G72 or DAOA) is believed to play a key role in the regulation of central glutamatergic transmission which is seen to be altered in psychosis. It is thought to regulate D-amino acid oxidase (DAO), which metabolizes D-serine, a co-agonist of NMDA-type glutamate receptors and to be involved in dendritic arborization. Linkage, genetic association and expression studies have implicated the G72 gene in both schizophrenia and bipolar disorder. AIMS: To examine the influence of G72 variation on brain function in the healthy population. METHOD: Fifty healthy volunteers were assessed using functional magnetic resonance imaging while performing a verbal fluency task. Regional brain activation and task-dependent functional connectivity during word generation was compared between different rs746187 genotypes. RESULTS: G72 rs746187 genotype had a significant effect on activation in the left postcentral and supramarginal gyri (FWE P < 0.05), and on the task-dependent functional coupling of this region with the retrosplenial cingulate gyrus (FWE P < 0.05). CONCLUSIONS: Our results may reflect an effect of G72 on glutamatergic transmission, mediated by an influence on D-amino acid oxidase activity, on brain areas particularly relevant to the hypoglutamatergic model of psychosis.

White matter abnormalities and illness severity in major depressive disorder.

BACKGROUND: White matter abnormalities have been implicated in the aetiology of major depressive disorder; however, the relationship between the severity of symptoms and white matter integrity is currently unclear. AIMS: To investigate white matter integrity in people with major depression and healthy controls, and to assess its relationship with depressive symptom severity. METHOD: Diffusion tensor imaging data were acquired from 66 patients with recurrent major depression and a control group of 66 healthy individuals matched for age, gender and IQ score, and analysed with tract-based spatial statistics. The relationship between white matter integrity and severity of depression as measured by the Beck Depression Inventory was examined. RESULTS: Depressive illness was associated with widespread regions of decreased white matter integrity, including regions in the corpus callosum, superior longitudinal fasciculus and anterior corona radiata, compared with the control group. Increasing symptom severity was negatively correlated with white matter integrity, predominantly in the corpus callosum. CONCLUSIONS: Widespread alterations in white matter integrity are evident in major depressive disorder. These abnormalities are heightened with increasing severity of depressive symptoms.

Epistasis between the DAT 3' UTR VNTR and the COMT Val158Met SNP on cortical function in healthy subjects and patients with schizophrenia.

Dopamine has a crucial role in the modulation of neurocognitive function, and synaptic dopamine activity is normally regulated by the dopamine transporter (DAT) and catechol-O-methyltransferase (COMT). Perturbed dopamine function is a key pathophysiological feature of schizophrenia. Our objectives were (i) to examine epistasis between the DAT 3' UTR variable number of tandem repeats (VNTR) and COMT Val158Met polymorphisms on brain activation during executive function, and (ii) to then determine the extent to which such interaction is altered in schizophrenia. Regional brain response was measured by using blood-oxygen-level-dependent fMRI during an overt verbal fluency task in 85 subjects (44 healthy volunteers and 41 patients with DSM-IV schizophrenia), and inferences were estimated by using an ANOVA in SPM5. There was a significant COMT x DAT nonadditive interaction effect on activation in the left supramarginal gyrus, irrespective of diagnostic group (Z-score = 4.3; family-wise error (FWE) p = 0.03), and in healthy volunteers alone (Z-score = 4.7; FWEp = 0.006). In this region, relatively increased activation was detected only when COMT Met-158/Met-158 subjects also carried the 9-repeat DAT allele, or when, reversely, Val-158/Val-158 subjects carried the 10/10-repeat genotype. Also, there was a significant diagnosis x COMT x DAT nonadditive interaction in the right orbital gyrus (Z-score = 4.3; FWEp = 0.04), where, only within patients, greater activation was only associated with a 9-repeat allele and Val-158 conjunction, and with a 10-repeat and Met-158 conjunction (Z-score = 4.3; FWE p = 0.04). These data demonstrate that COMT and DAT genes interact nonadditively to modulate cortical function during executive processing, and also, that this effect is significantly altered in schizophrenia, which may reflect abnormal dopamine function in the disorder.

Adjunctive home-based transcranial direct current stimulation treatment for major depression with real-time remote supervision: An open-label, single-arm feasibility study with long term outcomes.

Current treatments for major depressive disorder (MDD) have limited effectiveness and acceptability. Transcranial direct current stimulation (tDCS) is a novel non-invasive brain stimulation method that has demonstrated treatment efficacy in MDD. tDCS requires daily sessions, however clinical trials have been conducted in research centers requiring repeated visits. As tDCS is portable and safe, it could be provided at home. We developed a home-based protocol with real-time supervision, and we examined the clinical outcomes, acceptability and feasibility. Participants were 26 MDD (19 women), mean age 40.9 ± 14.2 years, in current depressive episode of moderate to severe severity (mean 17-item Hamilton Rating Scale for Depression (HAMD) score 19.12 ± 2.12). tDCS was provided in a bilateral frontal montage, F3 anode, F4 cathode, 2 mA, each session 30 min, in a 6-week trial, for a total 21 sessions. Participants maintained their current treatment (antidepressant medication, psychotherapy, or were enrolled in online CBT). Two tDCS device brands were used, and a research team member was present in person or by real-time video call at each session. 92.3% MDD participants (n = 24) completed the 6-week treatment. Attrition rate was 7.7%. There was a significant improvement in depressive symptoms following treatment (mean HAMD 5.33 ± 2.33), which was maintained at 6 months (mean HAMD 5.43 ± 2.73). Acceptability was endorsed as "very acceptable" or "quite acceptable" by all participants. Due to the open-label feasibility design, efficacy findings are preliminary. In summary, home-based tDCS with real-time supervision was associated with significant clinical improvements and high acceptability which were maintained in the long term.

Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression.

Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. Objective: To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles. Design, Setting, and Participants: The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021. Main Outcomes and Measures: Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics. Results: A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets). Conclusions and Relevance: This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction.