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.

Assessing remission in major depressive disorder using a functional-structural data fusion pipeline: A CAN-BIND-1 study.

Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity (awFC)). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the 'full picture' of connectivity changes for REMs and NREMs while making for an easy intuitive approach.

The Development and Evaluation of a Cross-Context Employment Program for Autistic Adolescents.

Vocational programs typically focus on building the skills of autistic youth. However, there is growing recognition that the supportive environment (or ecosystem) around an individual plays an important role in finding and maintaining work. Programs at the ecosystem-level can be established by coordinating support before high school ends. Cocreation of a vocational program by support providers can facilitate an integrated effort to prepare autistic youth for employment. In this study, we describe and evaluate the Job-Train Program (JTP), a vocational program for autistic high school students codesigned with educators and a community-based social services agency. A school board, community-based social services agency, and academics partnered to cocreate JTP. JTP combined skill teaching and paid supported employment on a university campus. This pilot study evaluated JTP using qualitative and quantitative data. Twelve autistic youth were recruited, aged 15-18 years (10 males, 2 females) with an average intelligence quotient of 101.9 (standard deviation = 14.4), from the Wechsler Abbreviated Scale of Intelligence-2. Youth and parents completed self-report measures (pre-post), including the primary outcome, Canadian Occupational Performance Measure (COPM). Post-JTP, interviews, focus groups, and surveys collected additional information from youth (n = 11), parents (n = 10), job coaches (n = 5), and employers (n = 8). Youth COPM scores indicated significant improvements in self-perceived ratings of skill performance (z = -2.5, p = 0.01) and satisfaction (z = -2.6, p = 0.01). Qualitative data corroborated COPM results noting youth skill improvements in self-esteem, independence, communication, and understanding work. Findings demonstrated a promising vocational training model for autistic high school students informing the development of integrated service pathways to support preparation for employment.

Why was this program developed?: When autistic young people leave school, they can experience difficulties in getting a job. We need to test whether job training might be helpful for autistic young people when they are leaving school. Current support focuses mostly on developing educational skills, but it is important that we think about the strengths and abilities of the individual within their environment. In this study, we worked with educators from schools and a community service agency (who support autistic adults) to develop a job training support program for autistic youth. What does this program do?: We designed the 13-week Job-Train Program (JTP) to provide training and paid work experience, develop work abilities, and increase support around the autistic youth. Participants took part in weekly group sessions about work skills, and they did 8 weeks of paid work, supported by a job coach on a university campus. How did researchers evaluate the program?: Twelve autistic high school students (age 15­18) took part, and eight university departments hosted work experiences. We used several approaches to see if the program was helping and to identify areas where we could improve the program in the future. Ten parents and 11 autistic youth completed the Canadian Occupational Performance Measure (COPM) before and after the program, so we could see if there were any changes in work-related skills. We also completed interviews with youth, focus groups with parents, and surveys with job coaches to gather feedback. What were the early findings?: Scores on the COPM questionnaire showed that the young people rated themselves as more skilled and they were more satisfied with their skills after the program. Parent ratings showed a similar pattern. When we spoke to youth, parents, and job coaches, they mentioned improvements in responsibility and independence. Eight employers in university departments gained awareness of autistic youth as employees and all were willing to be part of the program again. Parents suggested that having more training of advocacy skills would help youth with gaining work in the future. What were the weaknesses of this project?: We did not assess how well the job coaches did in delivering the program or exactly how they made accommodations within the work experience jobs. Autistic individuals and their parents were not included in program development. What are the next steps?: We now plan to include autistic youth and their parents in further refining the program. We also plan to follow up with the youth who took part, to see how they are doing in the long term. We also will improve the support provided by job coaches. How will this work help autistic adults now or in future?: The JTP approach may help autistic youth as they go into employment and could provide high-quality support for the transition to adulthood. We also show that university campuses could be great places for autistic youth to gain experience, so in the future hope that universities and schools work together more to help support autistic youth.

Brain structure and function in the fourth decade of life after extremely low birth weight: An MRI and EEG study.

OBJECTIVE: To examine potential long-term effects of extremely low birth weight (ELBW; ≤ 1000 g) on adult brain structure, brain function, and cognitive-behavioral performance. METHODS: A subset of survivors from the prospectively-followed McMaster ELBW Cohort (n = 23, MBW = 816 g) and their peers born at normal birth weight (NBW; ≥ 2500 g; n = 14, MBW = 3361 g) provided T1-weighted magnetic resonance imaging (MRI) brain scans, resting electroencephalographic (EEG) recordings, and behavioral responses to a face-processing task in their early thirties. RESULTS: Visual discrimination accuracy for human faces, resting EEG alpha power, and long-distance alpha coherence were lower in ELBW survivors than NBW adults, and volumes of white matter hypointensities (WMH) were higher. Across groups, face-processing performance was correlated positively with posterior EEG spectral power and long-distance alpha and theta coherence, and negatively with WMH. The associations between face-processing scores and parietal alpha power and theta coherence were reduced after adjustment for WMH. CONCLUSIONS: Electrocortical activity, brain functional connectivity, and higher-order processing ability may be negatively affected by WMH burden, which is greater in adults born extremely preterm. SIGNIFICANCE: Decrements in electrocortical activity and behavioral performance in adult ELBW survivors may be partly explained by increased WMH volumes in this vulnerable population.

Serotonin transporter (5-HTT) gene network moderates the impact of prenatal maternal adversity on orbitofrontal cortical thickness in middle childhood.

In utero, the developing brain is highly susceptible to the environment. For example, adverse maternal experiences during the prenatal period are associated with outcomes such as altered neurodevelopment and emotion dysregulation. Yet, the underlying biological mechanisms remain unclear. Here, we investigate whether the function of a network of genes co-expressed with the serotonin transporter in the amygdala moderates the impact of prenatal maternal adversity on the structure of the orbitofrontal cortex (OFC) in middle childhood and/or the degree of temperamental inhibition exhibited in toddlerhood. T1-weighted structural MRI scans were acquired from children aged 6-12 years. A cumulative maternal adversity score was used to conceptualize prenatal adversity and a co-expression based polygenic risk score (ePRS) was generated. Behavioural inhibition at 18 months was assessed using the Early Childhood Behaviour Questionnaire (ECBQ). Our results indicate that in the presence of a low functioning serotonin transporter gene network in the amygdala, higher levels of prenatal adversity are associated with greater right OFC thickness at 6-12 years old. The interaction also predicts temperamental inhibition at 18 months. Ultimately, we identified important biological processes and structural modifications that may underlie the link between early adversity and future deviations in cognitive, behavioural, and emotional development.

Examining resting-state network connectivity in children exposed to perinatal maternal adversity using anatomically weighted functional connectivity (awFC) analyses; A preliminary report.

Introduction: Environmental perturbations during critical periods can have pervasive, organizational effects on neurodevelopment. To date, the literature examining the long-term impact of early life adversity has largely investigated structural and functional imaging data outcomes independently. However, emerging research points to a relationship between functional connectivity and the brain's underlying structural architecture. For instance, functional connectivity can be mediated by the presence of direct or indirect anatomical pathways. Such evidence warrants the use of structural and functional imaging in tandem to study network maturation. Accordingly, this study examines the impact of poor maternal mental health and socioeconomic context during the perinatal period on network connectivity in middle childhood using an anatomically weighted functional connectivity (awFC) approach. awFC is a statistical model that identifies neural networks by incorporating information from both structural and functional imaging data. Methods: Resting-state fMRI and DTI scans were acquired from children aged 7-9 years old. Results: Our results indicate that maternal adversity during the perinatal period can affect offspring's resting-state network connectivity during middle childhood. Specifically, in comparison to controls, children of mothers who had poor perinatal maternal mental health and/or low socioeconomic status exhibited greater awFC in the ventral attention network. Discussion: These group differences were discussed in terms of the role this network plays in attention processing and maturational changes that may accompany the consolidation of a more adult-like functional cortical organization. Furthermore, our results suggest that there is value in using an awFC approach as it may be more sensitive in highlighting connectivity differences in developmental networks associated with higher-order cognitive and emotional processing, as compared to stand-alone FC or SC analyses.

Frontal EEG asymmetry moderates the relation between borderline personality disorder features and feelings of social rejection in adolescents.

Although associations among borderline personality disorder (BPD), social rejection, and frontal EEG alpha asymmetry scores (FAA, a neural correlate of emotion regulation and approach-withdrawal motivations) have been explored in different studies, relatively little work has examined these relations during adolescence in the same study. We examined whether FAA moderated the relation between BPD features and rejection sensitivity following a validated social exclusion paradigm, Cyberball. A mixed, clinical-community sample of 64 adolescents (females = 62.5%; Mage = 14.45 years; SD = 1.6; range = 11-17 years) completed psychodiagnostic interviews and a self-report measure of BPD (Time 1). Approximately two weeks later (Time 2), participants completed a resting EEG recording followed by Cyberball. FAA moderated the relation between BPD features and overall feelings of rejection following Cyberball: individuals with greater relative left FAA had the highest and lowest feelings of social rejection depending on whether they had high and low BPD feature scores, respectively. Results remained after controlling for age, sex, gender, depression, and BPD diagnosis. These results suggest that FAA may moderate the relation between BPD features and social rejection, and that left frontal brain activity at rest may be differentially associated with those feelings in BPD. Findings are discussed in terms of the link between left frontal brain activity in the regulation and dysregulation of social approach behaviors, characteristic of BPD.

Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report.

Many previous intervention studies have used functional magnetic resonance imaging (fMRI) data to predict the antidepressant response of patients with major depressive disorder (MDD); however, practical constraints have limited many of those attempts to small, single centre studies which may not adequately reflect how these models will generalize when used in clinical practice. Not only does the act of collecting data at multiple sites generally increase sample sizes (a critical point in machine learning development) it also generates a more heterogeneous dataset due to systematic differences in scanners at different sites, and geographical differences in patient populations. As part of the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, 144 MDD patients from six sites underwent resting state fMRI prior to starting escitalopram treatment, and again two weeks after the start. Here, we consider ways to use machine learning techniques to produce models that can predict response (measured at eight weeks after initiation), based on various parcellations, functional connectivity (FC) metrics, dimensionality reduction algorithms, and base learners, and also whether to use scans from one or both time points. Models that use only baseline (pre-treatment) or only week 2 (early-response) whole-brain FC features consistently failed to perform significantly better than default models. Utilizing the change in FC between these two time points, however, yielded significant results, with the best performing analytical pipeline achieving 69.6% (SD 10.8) accuracy. These results appear contrary to findings from many smaller single-site studies, which report substantially higher predictive accuracies from models trained on only baseline resting state FC features, suggesting these models may not generalize well beyond data used for development. Further, these results indicate the potential value of collecting data both before and shortly after treatment initiation.

Biophysical compartment models for single-shell diffusion MRI in the human brain: a model fitting comparison.

Clinically oriented studies commonly acquire diffusion MRI (dMRI) data with a single non-zerob-value (i.e. single-shell) and diffusion weighting ofb= 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 (BSME2) provided artifact-free, stable results, in little processing time. The analogous ball and zeppelin model (BZ2) 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 (BSGD2) 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 improvedBSGD2fits. 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%, whileBZ2andBSME2retained 76% and 83% of restricted fraction, respectively. As a result, theBZ2andBSME2models are strong candidates to optimize information extraction from single-shell dMRI studies.

Baseline Functional Connectivity in Resting State Networks Associated with Depression and Remission Status after 16 Weeks of Pharmacotherapy: A CAN-BIND Report.

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.

Cognitive Development and Brain Gray Matter Susceptibility to Prenatal Adversities: Moderation by the Prefrontal Cortex Brain-Derived Neurotrophic Factor Gene Co-expression Network.

Background: Previous studies focused on the relationship between prenatal conditions and neurodevelopmental outcomes later in life, but few have explored the interplay between gene co-expression networks and prenatal adversity conditions on cognitive development trajectories and gray matter density. Methods: We analyzed the moderation effects of an expression polygenic score (ePRS) for the Brain-derived Neurotrophic Factor gene network (BDNF ePRS) on the association between prenatal adversity and child cognitive development. A score based on genes co-expressed with the prefrontal cortex (PFC) BDNF was created, using the effect size of the association between the individual single nucleotide polymorphisms (SNP) and the BDNF expression in the PFC. Cognitive development trajectories of 157 young children from the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) cohort were assessed longitudinally in 4-time points (6, 12, 18, and 36 months) using the Bayley-II mental scales. Results: Linear mixed-effects modeling indicated that BDNF ePRS moderates the effects of prenatal adversity on cognitive growth. In children with high BDNF ePRS, higher prenatal adversity was associated with slower cognitive development in comparison with those exposed to lower prenatal adversity. Parallel-Independent Component Analysis (pICA) suggested that associations of expression-based SNPs and gray matter density significantly differed between low and high prenatal adversity groups. The brain IC included areas involved in visual association processes (Brodmann area 19 and 18), reallocation of attention, and integration of information across the supramodal cortex (Brodmann area 10). Conclusion: Cognitive development trajectories and brain gray matter seem to be influenced by the interplay of prenatal environmental conditions and the expression of an important BDNF gene network that guides the growth and plasticity of neurons and synapses.

Investigating executive functions in youth with OCD and hoarding symptoms.

Executive functions (EF) deficits are hypothesized to be a core contributor to hoarding symptoms. EF have been studied in adult hoarding populations, but studies in youth are lacking. The current study compared multiple EF subdomains between youth with obsessive-compulsive disorder (OCD) and youth with OCD and hoarding symptoms. Forty youth (8-18 years old) with a primary diagnosis of OCD were recruited. Participants were divided by hoarding severity on the Child Saving Inventory (CSI) into either the "hoarding group" (upper 33.3%) or the "low-hoarding group" (lower 66.7%). Groups were compared on EF tasks of cognitive flexibility, decision-making, and inhibitory control. Youth in the hoarding group exhibited significantly higher cognitive flexibility and lowered perseveration than the low-hoarding group. Hoarding and low-hoarding groups did not differ in any other EF subdomain. Hoarding symptoms in youth with OCD were not associated with deficits in EF subdomains; instead, youth who hoard exhibited higher cognitive flexibility compared to youth with low hoarding symptoms.

Prefrontal cortex VAMP1 gene network moderates the effect of the early environment on cognitive flexibility in children.

During development, genetic and environmental factors interact to modify specific phenotypes. Both in humans and in animal models, early adversities influence cognitive flexibility, an important brain function related to behavioral adaptation to variations in the environment. Abnormalities in cognitive functions are related to changes in synaptic connectivity in the prefrontal cortex (PFC), and altered levels of synaptic proteins. We investigated if individual variations in the expression of a network of genes co-expressed with the synaptic protein VAMP1 in the prefrontal cortex moderate the effect of early environmental quality on the performance of children in cognitive flexibility tasks. Genes overexpressed in early childhood and co-expressed with the VAMP1 gene in the PFC were selected for study. SNPs from these genes (post-clumping) were compiled in an expression-based polygenic score (PFC-ePRS-VAMP1). We evaluated cognitive performance of the 4 years-old children in two cohorts using similar cognitive flexibility tasks. In the first cohort (MAVAN) we utilized two CANTAB tasks: (a) the Intra-/Extra-dimensional Set Shift (IED) task, and (b) the Spatial Working Memory (SWM) task. In the second cohort, GUSTO, we used the Dimensional Change Card Sort (DCCS) task. The results show that in 4 years-old children, the PFC-ePRS-VAMP1 network moderates responsiveness to the effects of early adversities on the performance in attentional flexibility tests. The same result was observed for a spatial working memory task. Compared to attentional flexibility, reversal learning showed opposite effects of the environment, as moderated by the ePRS. A parallel ICA analysis was performed to identify relationships between whole-brain voxel based gray matter density and SNPs that comprise the PFC-ePRS-VAMP1. The early environment predicts differences in gray matter content in regions such as prefrontal and temporal cortices, significantly associated with a genetic component related to Wnt signaling pathways. Our data suggest that a network of genes co-expressed with VAMP1 in the PFC moderates the influence of early environment on cognitive function in children.

Exploring brain connectivity changes in major depressive disorder using functional-structural data fusion: A CAN-BIND-1 study.

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.

Localization and Identification of Brain Microstructural Abnormalities in Paediatric Concussion.

In the United States, approximately 2.53 million people sustain a concussion each year. Relative to adults, youth show greater cognitive deficits following concussion and a longer recovery. An accurate and reliable imaging method is needed to determine injury severity and symptom resolution. The primary objective of this study was to characterize concussions with diffusion tensor imaging (DTI). This was performed through a normative Z-scoring analysis of DTI metrics, fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD), to quantify patient-specific injuries and identify commonly damaged brain regions in paediatric concussion patients relative to healthy controls. It was hypothesized that personalizing the detection analysis through normative Z-scoring would provide an understanding of trauma-induced microstructural damage. Concussion patients were volunteers recruited from the Emergency Department of the McMaster Children's Hospital with a recent concussion (n = 26), 9 males and 17 females, mean age 14.22 ± 2.64, while healthy paediatric brain DTI datasets (25 males and 24 females, mean age 13.52 ± 1.03) were obtained from an MRI data repository. Significant abnormalities were commonly found in the longitudinal fasciculus, fronto-occipital fasciculus, and corticospinal tract, while unique abnormalities were localized in a number of other areas reflecting the individuality of each child's injury. Total injury burden, determined by the number of regions containing outliers per DTI metric per patient, was used as the metric to quantify the overall injury severity of each patient. The primary outcome of this analysis found that younger patients experienced a significantly greater injury burden when measured using fractional anisotropy (p < 0.001). These results show that DTI was able to detect microstructural changes caused by concussion, on a per-person basis, and has the potential to be a useful tool for improving diagnostic accuracy and prognosis of a concussion.

Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study.

Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network's (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies.

White matter microstructure in youth at risk for serious mental illness: A comparative analysis.

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.

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.

Evaluating Adherence to Return to School and Activity Protocols in Children After Concussion.

OBJECTIVE: The need to have a pediatric-specific concussion management protocol on Return to School (RTS) and Return to Activity (RTA) after concussion has been recognized internationally. The first step to evaluate the protocol effectiveness is to establish whether children and youth are adhering to these recommendations. The objective of this study was to explore the prevalence and predictors of adherence to RTS and RTA concussion management protocols for children/youth. DESIGN: A prospective cohort of children/youth with concussion. SETTING: Childhood Disability Research Centre. PARTICIPANTS: One hundred thirty-nine children/youth aged 5 to 18 years, diagnosed with concussion and symptomatic upon enrollment, were followed for up to 6 months. Primary recruitment occurred from a Children's Hospital Emergency Department. INTERVENTION: Provision of RTS/RTA guidelines. MAIN OUTCOME MEASURES: Measurement of adherence came from multiple sources, including the child's and parent's knowledge of protocols, research personnel evaluations, and self-reported stages of RTS/RTA and Post-Concussion Symptom Scale (PCSS) scores. RESULTS: Spearman correlations and logistic regression were used, investigating the relationship between PCSS and progression of protocols and determining predictors of adherence. Significant negative associations between total PCSS score and stage of RTS/RTA protocols were found. Fifty-three percent and 56% of the participants adhered to the RTS and RTA protocols, respectively. CONCLUSIONS: Children's knowledge of protocols and total PCSS scores significantly predicted adherence to RTS/RTA and may be the most important factors in predicting adherence during recovery from concussion.

DCC gene network in the prefrontal cortex is associated with total brain volume in childhood.

BACKGROUND: Genetic variation in the guidance cue DCC gene is linked to psychopathologies involving dysfunction in the prefrontal cortex. We created an expression-based polygenic risk score (ePRS) based on the DCC coexpression gene network in the prefrontal cortex, hypothesizing that it would be associated with individual differences in total brain volume. METHODS: We filtered single nucleotide polymorphisms (SNPs) from genes coexpressed with DCC in the prefrontal cortex obtained from an adult postmortem donors database (BrainEAC) for genes enriched in children 1.5 to 11 years old (BrainSpan). The SNPs were weighted by their effect size in predicting gene expression in the prefrontal cortex, multiplied by their allele number based on an individual's genotype data, and then summarized into an ePRS. We evaluated associations between the DCC ePRS and total brain volume in children in 2 community-based cohorts: the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) and University of California, Irvine (UCI) projects. For comparison, we calculated a conventional PRS based on a genome-wide association study of total brain volume. RESULTS: Higher ePRS was associated with higher total brain volume in children 8 to 10 years old (ß = 0.212, p = 0.043; n = 88). The conventional PRS at several different thresholds did not predict total brain volume in this cohort. A replication analysis in an independent cohort of newborns from the UCI study showed an association between the ePRS and newborn total brain volume (ß = 0.101, p = 0.048; n = 80). The genes included in the ePRS demonstrated high levels of coexpression throughout the lifespan and are primarily involved in regulating cellular function. LIMITATIONS: The relatively small sample size and age differences between the main and replication cohorts were limitations. CONCLUSION: Our findings suggest that the DCC coexpression network in the prefrontal cortex is critically involved in whole brain development during the first decade of life. Genes comprising the ePRS are involved in gene translation control and cell adhesion, and their expression in the prefrontal cortex at different stages of life provides a snapshot of their dynamic recruitment.