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Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional connectivity within cerebello-thalamo-cortical (CTC) pathways has been proposed as a trait-specific biomarker for CHR. However, it is unclear whether this functional neural signature remains specific when compared to a different risk group: youth with Distress syndromes. The purpose of the present work was to describe CTC alterations that distinguish between CHR and Distressed individuals. Using machine learning algorithms, we analyzed CTC connectivity features of CHR (n = 51), Distressed (n = 41), and healthy control (n = 36) participants. We found four cerebellar (lobes VII and left Crus II anterior/posterior) and two basal ganglia (right putamen and right thalamus) nodes containing a set of specific connectivity features that distinguished between CHR, Distressed and healthy control groups. Hyperconnectivity between medial lobule VIIb, somatomotor network and middle temporal gyrus was associated with CHR status and more severe symptoms. Detailed atlas parcellation suggested that CHR individuals may have dysfunction mainly within the associative (cognitive) pathways, particularly, between those brain areas responsible for the multi-sensory signal integration.

The pathophysiology of major depressive disorder (MDD) encompasses an array of changes at molecular and neurobiological levels. As chronic stress promotes neurotoxicity there are alterations in the expression of genes and gene-regulatory molecules. The hippocampus is particularly sensitive to the effects of stress and its posterior volumes can deliver clinically valuable information about the outcomes of antidepressant treatment. In the present work, we analyzed individuals with MDD (N = 201) and healthy controls (HC = 104), as part of the CAN-BIND-1 study. We used magnetic resonance imaging (MRI) to measure hippocampal volumes, evaluated gene expression with RNA sequencing, and assessed DNA methylation with the (Infinium MethylationEpic Beadchip), in order to investigate the association between hippocampal volume and both RNA expression and DNA methylation. We identified 60 RNAs which were differentially expressed between groups. Of these, 21 displayed differential methylation, and seven displayed a correlation between methylation and expression. We found a negative association between expression of Brain Abundant Membrane Attached Signal Protein 1 antisense 1 RNA (BASP1-AS1) and right hippocampal tail volume in the MDD group (β = −0.218, p = 0.021). There was a moderating effect of the duration of the current episode on the association between the expression of BASP1-AS1 and right hippocampal tail volume in the MDD group (β = −0.48, 95% C.I. [−0.80, −0.16]. t = −2.95 p = 0.004). In conclusion, we found that overexpression of BASP1-AS1 was correlated with DNA methylation, and was negatively associated with right tail hippocampal volume in MDD.

Task‐based functional neuroimaging methods are increasingly being used to identify biomarkers of treatment response in psychiatric disorders. To facilitate meaningful interpretation of neural correlates of tasks and their potential changes with treatment over time, understanding the reliability of the blood‐oxygen‐level dependent (BOLD) signal of such tasks is essential. We assessed test–retest reliability of an emotional conflict task in healthy participants collected as part of the Canadian Biomarker Integration Network in Depression. Data for 36 participants, scanned at three time points (weeks 0, 2, and 8) were analyzed, and intra‐class correlation coefficients (ICC) were used to quantify reliability. We observed moderate reliability (median ICC values between 0.5 and 0.6), within occipital, parietal, and temporal regions, specifically for conditions of lower cognitive complexity, that is, face, congruent or incongruent trials. For these conditions, activation was also observed within frontal and sub‐cortical regions, however, their reliability was poor (median ICC

Clinically oriented studies commonly acquire diffusion MRI (dMRI) data with a single non-zero b-value (i.e. single-shell) and diffusion weighting of b = 1000 s mm−2. To produce microstructural parameter maps, the tensor model is usually used, despite known limitations. Although compartment models have demonstrated improved fits in multi-shell dMRI data, they are rarely used for single-shell parameter maps, where their effectiveness is unclear from the literature. Here, various compartment models combining isotropic balls and symmetric tensors were fitted to single-shell dMRI data to investigate model fitting optimization and extract the most information possible. Full testing was performed in 5 subjects, and 3 subjects with multi-shell data were included for comparison. The results were tested and confirmed in a further 50 subjects. The Markov chain Monte Carlo (MCMC) model fitting technique outperformed non-linear least squares. Using MCMC, the 2-fibre-orientation mono-exponential ball and stick model ( BS ME 2 ) provided artifact-free, stable results, in little processing time. The analogous ball and zeppelin model ( BZ 2 ) also produced stable, low-noise parameter maps, though it required much greater computing resources (50 000 burn-in steps). In single-shell data, the gamma-distributed diffusivity ball and stick model ( BS GD 2 ) underperformed relative to other models, despite being an often-used software default. It produced artifacts in the diffusivity maps even with extremely long processing times. Neither increased diffusion weighting nor a greater number of gradient orientations improved BS GD 2 fits. In white matter (WM), the tensor produced the best fit as measured by Bayesian information criterion. This result contrasts with studies using multi-shell data. However, in crossing fibre regions the tensor confounded geometric effects with fractional anisotropy (FA): the planar/linear WM FA ratio was 49%, while BZ 2 and BS ME 2 retained 76% and 83% of restricted fraction, respectively. As a result, the BZ 2 and BS ME 2 models are strong candidates to optimize information extraction from single-shell dMRI studies.

There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT-awFC). The novel FATCAT-awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN-BIND-1) study. Large-scale resting-state networks were assessed. We found statistically significant anatomically-weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region-pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression.

Finding a clinically useful neuroimaging biomarker that can predict treatment response in patients with major depressive disorder (MDD) is challenging, in part because of poor reproducibility and generalizability of findings across studies. Previous work has suggested that posterior hippocampal volumes in depressed patients may be associated with antidepressant treatment outcomes. The primary purpose of this investigation was to examine further whether posterior hippocampal volumes predict remission following antidepressant treatment. Magnetic resonance imaging (MRI) scans from 196 patients with MDD and 110 healthy participants were obtained as part of the first study in the Canadian Biomarker Integration Network in Depression program (CAN-BIND 1) in which patients were treated for 16 weeks with open-label medication. Hippocampal volumes were measured using both a manual segmentation protocol and FreeSurfer 6.0. Baseline hippocampal tail (Ht) volumes were significantly smaller in patients with depression compared to healthy participants. Larger baseline Ht volumes were positively associated with remission status at weeks 8 and 16. Participants who achieved early sustained remission had significantly greater Ht volumes compared to those who did not achieve remission by week 16. Ht volume is a prognostic biomarker for antidepressant treatment outcomes in patients with MDD.

Methods We studied 48 MDD and 30 HC who performed an emotional conflict task in a functional magnetic resonance imaging (fMRI) scanner. Results On the emotional conflict task, MDD and HC demonstrated a robust emotional Stroop effect in reaction time and accuracy. Overall, accuracy was lower in MDD compared to HC with no significant reaction time differences. The fMRI data indicated lower BOLD activation in MDD compared to HC on comparisons of all trials, congruent, incongruent, and incongruent > congruent trials in regions including right inferior temporal gyrus, lateral occipital cortex, and occipital fusiform gyrus. Behavioural and neuroimaging data indicated no group differences in fearful versus happy face processing. Limitations Inclusion of a neutral condition may have provided a valuable contrast to how MDD and HC process stimuli without emotional valence compared to stimuli with a strong emotional valence. Conclusions MDD and HC demonstrated a robust emotional Stroop effect. Compared to HC, MDD demonstrated an overall reduced accuracy on the emotional conflict task and reduced BOLD activity in regions important for face perception and emotion information processing, with no differences in responding to fearful versus happy faces. These findings provide support for the theory of emotion context insensitivity in individuals with depression.

Background While response to antidepressants in major depressive disorder is difficult to predict, characterizing the organization and integrity of white matter in the brain with diffusion tensor imaging (DTI) may provide the means to distinguish between antidepressant responders and nonresponders. Methods DTI data were collected at 6 sites (Canadian Biomarker Integration Network in Depression-1 [CAN-BIND-1 study]) from 200 (127 women) depressed and 112 (71 women) healthy participants at 3 time points: at baseline, 2 weeks, and 8 weeks following initiation of selective serotonin reuptake inhibitor treatment. Therapeutic response was established by a 50% reduction of symptoms at 8 weeks. Analysis on responders, nonresponders, and control subjects yielded 4 scalar metrics: fractional anisotropy and mean, axial, and radial diffusivity. Region-of-interest analysis was carried out on 40 white matter regions using a skeletonization approach. Mixed-effects regression was incorporated to test temporal trends. Results The data acquired at baseline showed that axial diffusivity in the external capsule, which overlaps the superior longitudinal fasciculus, was significantly associated with medication response. Regression analysis revealed further baseline differences of responders compared with nonresponders in the cingulum regions, sagittal stratum, and corona radiata. Additional group differences relative to control subjects were seen in the internal capsule, posterior thalamic radiation, and uncinate fasciculus. Most effect sizes were moderate (near 0.5), with a maximum of 0.76 in the cingulum-hippocampus region. No temporal changes in DTI metrics were observed over the 8-week study period. Conclusions Several DTI measures of altered white matter specifically distinguished medication responders and nonresponders at baseline and show promise for predicting treatment response in depression.

Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convolutional neural networks (CNN) have emerged as a promising solution for large longitudinal neuroimaging studies. However, for these novel algorithms to be useful in clinical studies, the accuracy and reproducibility should be established on independent datasets. Here, we evaluate the performance of a CNN-based hippocampal segmentation algorithm that was developed by Thyreau and colleagues – Hippodeep. We compared its segmentation outputs to manual segmentation and FreeSurfer 6.0 in a sample of 200 healthy participants scanned repeatedly at seven sites across Canada, as part of the Canadian Biomarker Integration Network in Depression consortium. The algorithm demonstrated high levels of stability and reproducibility of volumetric measures across all time points compared to the other two techniques. Although more rigorous testing in clinical populations is necessary, this approach holds promise as a viable option for tracking volumetric changes in longitudinal neuroimaging studies.

Studies of clinical populations that combine MRI data generated at multiple sites are increasingly common. The Canadian Biomarker Integration Network in Depression (CAN-BIND; www.canbind.ca) is a national depression research program that includes multimodal neuroimaging collected at several sites across Canada. The purpose of the current paper is to provide detailed information on the imaging protocols used in a number of CAN-BIND studies. The CAN-BIND program implemented a series of platform-specific MRI protocols, including a suite of prescribed structural and functional MRI sequences supported by real-time monitoring for adherence and quality control. The imaging data are retained in an established informatics and databasing platform. Approximately 1300 participants are being recruited, including almost 1000 with depression. These include participants treated with antidepressant medications, transcranial magnetic stimulation, cognitive behavioural therapy and cognitive remediation therapy. Our ability to analyze the large number of imaging variables available may be limited by the sample size of the substudies. The CAN-BIND program includes a multimodal imaging database supported by extensive clinical, demographic, neuropsychological and biological data from people with major depression. It is a resource for Canadian investigators who are interested in understanding whether aspects of neuroimaging — alone or in combination with other variables — can predict the outcomes of various treatment modalities.

Understanding the neural underpinnings of major depressive disorder (MDD) and its treatment could improve treatment outcomes. So far, findings are variable and large sample replications scarce. We aimed to replicate and extend altered functional connectivity associated with MDD and pharmacotherapy outcomes in a large, multisite sample. Resting-state fMRI data were collected from 129 patients and 99 controls through the Canadian Biomarker Integration Network in Depression. Symptoms were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). Connectivity was measured as correlations between four seeds (anterior and posterior cingulate cortex, insula and dorsolateral prefrontal cortex) and all other brain voxels. Partial least squares was used to compare connectivity prior to treatment between patients and controls, and between patients reaching remission (MADRS ≤ 10) early (within 8 weeks), late (within 16 weeks), or not at all. We replicated previous findings of altered connectivity in patients. In addition, baseline connectivity of the anterior/posterior cingulate and insula seeds differentiated patients with different treatment outcomes. The stability of these differences was established in the largest single-site subsample. Our replication and extension of altered connectivity highlighted previously reported and new differences between patients and controls, and revealed features that might predict remission prior to pharmacotherapy. Trial registration:ClinicalTrials.gov: NCT01655706.

Quality control of morphometric neuroimaging data is essential to improve reproducibility. Owing to the complexity of neuroimaging data and subsequently the interpretation of their results, visual inspection by trained raters is the most reliable way to perform quality control. Here, we present a protocol for visual quality control of the anatomical accuracy of FreeSurfer parcellations, based on an easy-to-use open source tool called VisualQC. We comprehensively evaluate its utility in terms of error detection rate and inter-rater reliability on two large multi-site datasets, and discuss site differences in error patterns. This evaluation shows that VisualQC is a practically viable protocol for community adoption.

Background and methods Investigation of the insula may inform understanding of the etiopathogenesis of major depressive disorder (MDD). In the present study, we introduced a novel gray matter volume (GMV) based structural covariance technique, and applied it to a multi-centre study of insular subregions of 157 patients with MDD and 93 healthy controls from the Canadian Biomarker Integration Network in Depression (CAN-BIND, https://www.canbind.ca/ ). Specifically, we divided the unilateral insula into three subregions, and investigated their coupling with whole-brain GMV-based structural brain networks (SBNs). We compared between-group difference of the structural coupling patterns between the insular subregions and SBNs. Results The insula was divided into three subregions, including an anterior one, a superior-posterior one and an inferior-posterior one. In the comparison between MDD patients and controls we found that patients' right anterior insula showed increased inter-network coupling with the default mode network, and it showed decreased inter-network coupling with the central executive network; whereas patients' right ventral-posterior insula showed decreased inter-network coupling with the default mode network, and it showed increased inter-network coupling with the central executive network. We also demonstrated that patients' loading parameters of the right ventral-posterior insular structural covariance negatively correlated with their suicidal ideation scores; and controls' loading parameters of the right ventral-posterior insular structural covariance positively correlated with their motor and psychomotor speed scores, whereas these phenomena were not found in patients. Additionally, we did not find significant inter-network coupling between the whole-brain SBNs, including salience network, default mode network, and central executive network. Conclusions Our work proposed a novel technique to investigate the structural covariance coupling between large-scale structural covariance networks, and provided further evidence that MDD is a system-level disorder that shows disrupted structural coupling between brain networks.

Anhedonia is thought to reflect deficits in reward processing that are associated with abnormal activity in mesocorticolimbic brain regions. It is expressed clinically as a deficit in the interest or pleasure in daily activities. More severe anhedonia in major depressive disorder (MDD) is a negative predictor of antidepressant response. It is unknown, however, whether the pathophysiology of anhedonia represents a viable avenue for identifying biological markers of antidepressant treatment response. Therefore, this study aimed to examine the relationships between reward processing and response to antidepressant treatment using clinical, behavioral, and functional neuroimaging measures. Eighty-seven participants in the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) protocol received 8 weeks of open-label escitalopram. Clinical correlates of reward processing were assessed at baseline using validated scales to measure anhedonia, and a monetary incentive delay (MID) task during functional neuroimaging was completed at baseline and after 2 weeks of treatment. Response to escitalopram was associated with significantly lower self-reported deficits in reward processing at baseline. Activity during the reward anticipation, but not the reward consumption, phase of the MID task was correlated with clinical response to escitalopram at week 8. Early (baseline to week 2) increases in frontostriatal connectivity during reward anticipation significantly correlated with reduction in depressive symptoms after 8 weeks of treatment. Escitalopram response is associated with clinical and neuroimaging correlates of reward processing. These results represent an important contribution towards identifying and integrating biological, behavioral, and clinical correlates of treatment response. ClinicalTrials.gov: NCT01655706.

Highlights • Patients with major depression underwent two-stage 16-week antidepressant trial. • Whole-brain cortical thickness (CT) was measured in patients and healthy controls. • Site (N = 6) differences were corrected at each vertex using ComBat. • Patients exhibited thinner cortex in left rostral middle frontal cortex at baseline. • There were no baseline CT features associated with antidepressant response., Major depressive disorder (MDD) is considered a highly heterogeneous clinical and neurobiological mental disorder. We employed a novel layered treatment design to investigate whether cortical thickness features at baseline differentiated treatment responders from non-responders after 8 and 16 weeks of a standardized sequential antidepressant treatment. Secondary analyses examined baseline differences between MDD and controls as a replication analysis and longitudinal changes in thickness after 8 weeks of escitalopram treatment. 181 MDD and 95 healthy comparison (HC) participants were studied. After 8 weeks of escitalopram treatment (10–20 mg/d, flexible dosage), responders (>50% decrease in Montgomery-Åsberg Depression Scale score) were continued on escitalopram; non-responders received adjunctive aripiprazole (2–10 mg/d, flexible dosage). MDD participants were classified into subgroups according to their response profiles at weeks 8 and 16. Baseline group differences in cortical thickness were analyzed with FreeSurfer between HC and MDD groups as well as between response groups. Two-stage longitudinal processing was used to investigate 8-week escitalopram treatment-related changes in cortical thickness. Compared to HC, the MDD group exhibited thinner cortex in the left rostral middle frontal cortex [MNI(X,Y,Z=−29,9,54.5,−7.7); CWP=0.0002]. No baseline differences in cortical thickness were observed between responders and non-responders based on week-8 or week-16 response profile. No changes in cortical thickness was observed after 8 weeks of escitalopram monotherapy. In a two-step 16-week sequential clinical trial we found that baseline cortical thickness does not appear to be associated to clinical response to pharmacotherapy at 8 or 16 weeks.

Identifying biomarkers of serious mental illness, such as altered white matter microstructure, can aid in early diagnosis and treatment. White matter microstructure was assessed using constrained spherical deconvolution of diffusion imaging data in a sample of 219 youth (age 12–25 years, 64.84% female) across 8 sites. Participants were classified as healthy controls (HC; n = 47), familial risk for serious mental illness (n = 31), mild-symptoms (n = 37), attenuated syndromes (n = 66), or discrete disorder (n = 38) based on clinical assessments. Fractional anisotropy (FA) and mean diffusivity (MD) values were derived for the whole brain white matter, forceps minor, anterior cingulate, anterior thalamic radiations (ATR), inferior fronto-occipital fasciculus, superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF). Linear mixed effects models showed a significant effect of age on MD of the left ATR, left SLF, and left UF, and a significant effect of group on FA for all tracts examined. For most tracts, the discrete disorder group had significantly lower FA than other groups, and the attenuated syndromes group had higher FA compared to HC, with few differences between the remaining groups. White matter differences in MDD are most evident in individuals following illness onset, as few significant differences were observed in the risk phase.

Maternal folic acid supplementation is essential to reduce the risk of neural tube defects. We hypothesize that high levels of folic acid throughout gestation may produce neural networks more susceptible to seizure in offspring. We hence administered large doses of folic acid to rats before and during gestation and found their offspring had a 42% decrease in their seizure threshold. In vitro, acute application of folic acid or its metabolite 4Hfolate to neurons induced hyper-excitability and bursting. Cultured neuronal networks which develop in the presence of a low concentration (50 nM) of 4Hfolate had reduced capacity to stabilize their network dynamics after a burst of high-frequency activity, and an increase in the frequency of mEPSCs. Networks reared in the presence of the folic acid metabolite 5M4Hfolate developed a spontaneous, distinctive bursting pattern, and both metabolites produced an increase in synaptic density.