Structural brain measures among children with and without ADHD in the Adolescent Brain and Cognitive Development Study cohort: a cross-sectional US population-based study.

BACKGROUND: Structural neuroimaging research has identified a variety of abnormalities in cortical and subcortical structures in children with ADHD. However, studies to date have not employed large, non-referred samples, complete with data on potential confounding variables. Here, we tested for differences in structural MRI measures among children with and without ADHD using data from the Adolescent Brain and Cognitive Development (ABCD) Study, the largest paediatric brain imaging study in the USA. METHODS: In this cross-sectional study, we used baseline demographic, clinical, and neuroimaging data from the ABCD Study, which recruited children aged 9-10 years between Sept 1, 2016, and Aug 31, 2018, representative of the sociodemographic features of the US population. ADHD was diagnosed by parent report of symptoms. Neuroimaging data underwent centralised quality control and processing by the ABCD team. Linear mixed effects models were used to estimate Cohen's d values associated with ADHD for 79 brain measures of cortical thickness, cortical area, and subcortical volume. We used a novel simulation strategy to assess the ability to detect significant effects despite potential diagnostic misclassification. FINDINGS: Our sample included 10 736 participants (5592 boys, 5139 girls; 5692 White, 2165 Hispanic, 1543 Black, 221 Asian, and 1100 of other race or ethnicity), of whom, 949 met the criteria for ADHD and 9787 did not. In the full model, which included potential confounding variables selected a priori, we found only 11 significant differences across the 79 brain measures after false discovery rate correction, all indicating reductions in brain measures among participants with ADHD. Cohen's d values were small, ranging from -0·11 to -0·06, and were not meaningfully changed by using a more restrictive comparison group or alternative diagnostic methods. Simulations indicated adequate statistical power to detect differences even if there was substantial diagnostic misclassification. INTERPRETATION: In a sample representative of the general population, children aged 9-10 years with ADHD differed only modestly on structural brain measures from their unaffected peers. Future studies might need to incorporate other MRI modalities, novel statistical approaches, or alternative diagnostic classifications, particularly for research aimed at developing ADHD diagnostic biomarkers. FUNDING: Edwin S Webster Foundation and Duke University, NC, USA.

Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth.

Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held-out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr  = .012) and lower functioning on the Children's Global Assessment Scale (pcorr  = .012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr  = .008). Brain age prediction was more accurate using ComBat-harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.

The association between antidepressant treatment and brain connectivity in two double-blind, placebo-controlled clinical trials: a treatment mechanism study.

BACKGROUND: Antidepressant medications offer an effective treatment for depression, yet nearly 50% of patients either do not respond or have side-effects rendering them unable to continue the course of treatment. Mechanistic studies might help advance the pharmacology of depression by identifying pathways through which treatments exert their effects. Toward this goal, we aimed to identify the effects of antidepressant treatment on neural connectivity, the relationship with symptom improvement, and to test whether these effects were reproducible across two studies. METHODS: We completed two double-blind, placebo-controlled trials of SNRI antidepressant medications with MRI scans obtained before and after treatment. One was a 10-week trial of duloxetine (30-120 mg daily; mean 92·1 mg/day [SD 30·00]) and the other was a 12-week trial of desvenlafaxine (50-100 mg daily; 93·6 mg/day [16·47]). Participants consisted of adults with persistent depressive disorder. Adjusting for sex and age, we examined the effect of treatment on whole-brain functional connectivity. We also examined correlations between change in functional connectivity and improvement in symptoms of depression (24-item Hamilton Depression Rating Scale) and pain symptom severity (Symptom Checklist-90-Revised). FINDINGS: Participants were enrolled between Jan 26, 2006, and Nov 22, 2011, for the duloxetine RCT and Aug 5, 2012, and Jan 28, 2016, for the desvenlafaxine RCT. Before and after treatment MRI scans were collected in 32 participants for the duloxetine RCT and 34 participants for the desvenlafaxine RCT. In both studies, antidepressants decreased functional connectivity compared with placebo (duloxetine study: ß=-0·06; 95% CI -0·08 to -0·03; p<0·0001, ηp2=0·44; desvenlafaxine study: -0·06, -0·09 to -0·03; p<0·0001, ηp2=0·35) within a thalamo-cortico-periaqueductal network that has previously been associated with the experience of pain. Within the active drug groups, reductions in functional connectivity within this network correlated with improvements in depressive symptom severity in both studies (duloxetine study: r=0·38, 95% CI 0·01-0·65; p=0·0426; desvenlafaxine study: 0·44, 0·10-0·69; p=0·0138) and pain symptoms in the desvenlafaxine study (0·39, 0·04 to 0·65; p=0·0299). INTERPRETATION: The findings suggest the thalamo-cortico-periaqueductal network associated with the experience of pain is a new and potentially important target for novel antidepressant therapeutics. FUNDING: National Mental Health Institute, Eli Lilly and Company, Pfizer Pharmaceuticals, and the Edwin S Webster Foundation.

Schizotypal personality disorder in individuals with the Attenuated Psychosis Syndrome: Frequent co-occurrence without an increased risk for conversion to threshold psychosis.

The Attenuated Psychosis Syndrome (APS), proposed as a condition warranting further study in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), is a controversial diagnostic construct originally developed to identify individuals at clinical high-risk for psychosis. The relationship of APS and Schizotypal Personality Disorder (SPD) remains unclear with respect to their potential co-occurrence and the effect of SPD on risk for conversion to threshold psychosis. We examined the prevalence and effect on conversion of SPD in a cohort of 218 individuals whose symptoms met APS criteria. Results indicated that SPD was highly prevalent (68%), and that SPD did not influence risk for conversion. Rather, total positive symptom burden measured by the Structured Interview for Psychosis-Risk Syndromes (SIPS; OR 1.12, p = 0.02) emerged as the strongest predictor of conversion. These data suggest that when encountering a patient whose presentation meets SPD criteria, the clinician should assess whether APS criteria are also met and, for 1-2 years, carefully monitor positive symptoms for possible conversion to threshold psychosis.

Training in Good Psychiatric Management for Borderline Personality Disorder in Residency: An Aide to Learning Supportive Psychotherapy for Challenging-to-Treat Patients.

Given many competing demands, psychotherapy training to competency is difficult during psychiatric residency. Good Psychiatric Management for borderline personality disorder (GPM) offers an evidence-based, simplified, psychodynamically informed framework for the outpatient management of patients with borderline personality disorder, one of the most challenging disorders psychiatric residents must learn to treat. In this article, we provide an overview of GPM, and show that training in GPM meets a requirement for training in supportive psychotherapy; builds on psychodynamic psychotherapy training; and applies to other severe personality disorders, especially narcissistic personality disorder. We describe the interpersonal hypersensitivity model used in GPM as a straightforward way for clinicians to collaborate with patients in organizing approaches to psychoeducation, treatment goals, case management, use of multiple treatment modalities, and safety. A modification of the interpersonal hypersensitivity model that includes intra-personal hypersensitivity can be used to address narcissistic problems often present in borderline personality disorder. We argue that these features make GPM ideally suited for psychiatry residents in treating their most challenging patients, provide clinical examples to illustrate these points, and report the key lessons learned by a psychiatry resident after a year of GPM supervision.

Suicidal ideation and behavior in institutions of higher learning: A latent class analysis.

Suicide is the second leading cause of death among undergraduate students, with an annual rate of 7.5 per 100,000. Suicidal behavior (SB) is complex and heterogeneous, which might be explained by there being multiple etiologies of SB. Data-driven identification of distinct at-risk subgroups among undergraduates would bolster this argument. We conducted a latent class analysis (LCA) on survey data from a large convenience sample of undergraduates to identify subgroups, and validated the resulting latent class model on a sample of graduate students. Data were collected through the Interactive Screening Program deployed by the American Foundation for Suicide Prevention. LCA identified 6 subgroups from the undergraduate sample (N = 5654). In the group with the most students reporting current suicidal thoughts (N = 623, 66% suicidal), 22.5% reported a prior suicide attempt, and 97.6% endorsed moderately severe or worse depressive symptoms. Notably, LCA identified a second at-risk group (N = 662, 27% suicidal), in which only 1.5% of respondents noted moderately severe or worse depressive symptoms. When graduate students (N = 1138) were classified using the model, a similar frequency distribution of groups was found. Finding multiple replicable groups at-risk for suicidal behavior, each with a distinct prevalence of risk factors, including a group of students who would not be classified as high risk with depression-based screening, is consistent with previous studies that identified multiple potential etiologies of SB.

A computational approach for deciphering the organization of glycosaminoglycans.

BACKGROUND: Increasing evidence has revealed important roles for complex glycans as mediators of normal and pathological processes. Glycosaminoglycans are a class of glycans that bind and regulate the function of a wide array of proteins at the cell-extracellular matrix interface. The specific sequence and chemical organization of these polymers likely define function; however, identification of the structure-function relationships of glycosaminoglycans has been met with challenges associated with the unique level of complexity and the nontemplate-driven biosynthesis of these biopolymers. METHODOLOGY/PRINCIPAL FINDINGS: To address these challenges, we have devised a computational approach to predict fine structure and patterns of domain organization of the specific glycosaminoglycan, heparan sulfate (HS). Using chemical composition data obtained after complete and partial digestion of mixtures of HS chains with specific degradative enzymes, the computational analysis produces populations of theoretical HS chains with structures that meet both biosynthesis and enzyme degradation rules. The model performs these operations through a modular format consisting of input/output sections and three routines called chainmaker, chainbreaker, and chainsorter. We applied this methodology to analyze HS preparations isolated from pulmonary fibroblasts and epithelial cells. Significant differences in the general organization of these two HS preparations were observed, with HS from epithelial cells having a greater frequency of highly sulfated domains. Epithelial HS also showed a higher density of specific HS domains that have been associated with inhibition of neutrophil elastase. Experimental analysis of elastase inhibition was consistent with the model predictions and demonstrated that HS from epithelial cells had greater inhibitory activity than HS from fibroblasts. CONCLUSIONS/SIGNIFICANCE: This model establishes the conceptual framework for a new class of computational tools to use to assess patterns of domain organization within glycosaminoglycans. These tools will provide a means to consider high-level chain organization in deciphering the structure-function relationships of polysaccharides in biology.