Kristin A. Linn

Structural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms

IMPORTANCE Structural brain abnormalities are prominent in psychotic disorders, including schizophrenia. However, it is unclear when aberrations emerge in the disease process and if such deficits are present in association with less severe psychosis spectrum (PS) symptoms in youth. OBJECTIVE To investigate the presence of structural brain abnormalities in youth with PS symptoms. DESIGN, SETTING, AND PARTICIPANTS The Philadelphia Neurodevelopmental Cohort is a prospectively accrued, community-based sample of 9498 youth who received a structured psychiatric evaluation. A subsample of 1601 individuals underwent neuroimaging, including structural magnetic resonance imaging, at an academic and childrens hospital health care network between November 1, 2009, and November 30, 2011. MAIN OUTCOMES AND MEASURES Measures of brain volume derived from T1-weighted structural neuroimaging at 3 T. Analyses were conducted at global, regional, and voxelwise levels. Regional volumes were estimated with an advanced multiatlas regional segmentation procedure, and voxelwise volumetric analyses were conducted as well. Nonlinear developmental patterns were examined using penalized splines within a general additive model. Psychosis spectrum (PS) symptom severity was summarized using factor analysis and evaluated dimensionally. RESULTS Following exclusions due to comorbidity and image quality assurance, the final sample included 791 participants aged youth 8 to 22 years. Fifty percent (n = 393) were female. After structured interviews, 391 participants were identified as having PS features (PS group) and 400 participants were identified as typically developing comparison individuals without significant psychopathology (TD group). Compared with the TD group, the PS group had diminished whole-brain gray matter volume (P = 1.8 × 10-10) and expanded white matter volume (P = 2.8 × 10-11). Voxelwise analyses revealed significantly lower gray matter volume in the medial temporal lobe (maximum z score = 5.2 and cluster size of 1225 for the right and maximum z score = 4.5 and cluster size of 310 for the left) as well as in frontal, temporal, and parietal cortex. Volumetric reduction in the medial temporal lobe was correlated with PS symptom severity. CONCLUSIONS AND RELEVANCE Structural brain abnormalities that have been commonly reported in adults with psychosis are present early in life in youth with PS symptoms and are not due to medication effects. Future longitudinal studies could use the presence of such abnormalities in conjunction with clinical presentation, cognitive profile, and genomics to predict risk and aid in stratification to guide early interventions.

Comparative effectiveness of less commonly used systemic monotherapies and common combination therapies for moderate to severe psoriasis in the clinical setting

BACKGROUND The effectiveness of psoriasis therapies in real-world settings remains relatively unknown. OBJECTIVE We sought to compare the effectiveness of less commonly used systemic therapies and commonly used combination therapies for psoriasis. METHODS This was a multicenter cross-sectional study of 203 patients with plaque psoriasis receiving less common systemic monotherapy (acitretin, cyclosporine, or infliximab) or common combination therapies (adalimumab, etanercept, or infliximab and methotrexate) compared with 168 patients receiving methotrexate evaluated at 1 of 10 US outpatient dermatology sites participating in the Dermatology Clinical Effectiveness Research Network. RESULTS In adjusted analyses, patients on acitretin (relative response rate 2.01; 95% confidence interval [CI] 1.18-3.41), infliximab (relative response rate 1.93; 95% CI 1.26-2.98), adalimumab and methotrexate (relative response rate 3.04; 95% CI 2.12-4.36), etanercept and methotrexate (relative response rate 2.22; 95% CI 1.25-3.94), and infliximab and methotrexate (relative response rate 1.72; 95% CI 1.10-2.70) were more likely to have clear or almost clear skin compared with patients on methotrexate. There were no differences among treatments when response rate was defined by health-related quality of life. LIMITATIONS Single time point assessment may result in overestimation of effectiveness. CONCLUSIONS The efficacy of therapies in clinical trials may overestimate their effectiveness as used in clinical practice. Although physician-reported relative response rates were different among therapies, absolute differences were small and did not correspond to differences in patient-reported outcomes.

Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis

The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. They assessed intersite variability in scan data, by imaging a volunteer with relapsing-remitting MS with a scan-rescan at each site. In multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses. BACKGROUND AND PURPOSE: MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. To assess intersite variability in scan data, we imaged a volunteer with relapsing-remitting MS with a scan-rescan at each site. MATERIALS AND METHODS: All imaging was acquired on Siemens scanners (4 Skyra, 2 Tim Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR) hyperintense lesions. Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related. RESULTS: Systematic biases due to site differences in expert-traced lesion measurements were significant (P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0–16.4 mL in T1 and 15.9–20.1 mL in T2) in lesion volumes. Site also explained >80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra versus the other 2 units. CONCLUSIONS: Even in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.

Traumatic brain injury in Africa in 2050: a modeling study

Our aim was to provide estimates of traumatic brain injury (TBI) in 2050 for the African population by region, sex and age strata.

Interactive Q-Learning for Quantiles

ABSTRACT A dynamic treatment regime is a sequence of decision rules, each of which recommends treatment based on features of patient medical history such as past treatments and outcomes. Existing methods for estimating optimal dynamic treatment regimes from data optimize the mean of a response variable. However, the mean may not always be the most appropriate summary of performance. We derive estimators of decision rules for optimizing probabilities and quantiles computed with respect to the response distribution for two-stage, binary treatment settings. This enables estimation of dynamic treatment regimes that optimize the cumulative distribution function of the response at a prespecified point or a prespecified quantile of the response distribution such as the median. The proposed methods perform favorably in simulation experiments. We illustrate our approach with data from a sequentially randomized trial where the primary outcome is remission of depression symptoms. Supplementary materials for this article are available online.

Addressing Confounding in Predictive Models with an Application to Neuroimaging

Abstract Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease efxcfects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In particular, we focus on the consequences of confounding by non-imaging variables such as age and sex on the results of MVPA. After reviewing current practice to address confounding in neuroimaging studies, we propose an alternative approach based on inverse probability weighting. Although the proposed method is motivated by neuroimaging applications, it is broadly applicable to many problems in machine learning and predictive modeling. We demonstrate the advantages of our approach on simulated and real data examples.

MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions: Method For Inter-Modal Segmentation Analysis

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions.

An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions

BACKGROUND AND PURPOSE: Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with clinical outcome. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (ie, spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions. MATERIALS AND METHODS: MR imaging was used to assess the probability of a lesion at each location. The texture of this map was quantified using a novel technique, and clusters resembling the center of a lesion were counted. Validity compared with a criterion standard count was demonstrated in 60 subjects observed longitudinally, and reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites. RESULTS: The proposed count and the criterion standard count were highly correlated (r = 0.97, P < .001) and not significantly different (t59 = −.83, P = .41), and the variability of the proposed count across repeat scans was equivalent to that of lesion load. After accounting for lesion load and age, lesion count was negatively associated (t58 = −2.73, P < .01) with the Expanded Disability Status Scale. Average lesion size had a higher association with the Expanded Disability Status Scale (r = 0.35, P < .01) than lesion load (r = 0.10, P = .44) or lesion count (r = −.12, P = .36) alone. CONCLUSIONS: This study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data and demonstrates its ability to recover obscured longitudinal information. The proposed count allows more accurate estimation of lesion size, which correlated more closely with disability scores than either lesion load or lesion count alone.

Cognitive Behavioral Therapy Is Associated With Enhanced Cognitive Control Network Activity in Major Depression and Posttraumatic Stress Disorder

BACKGROUND Both major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are characterized by depressive symptoms, abnormalities in brain regions important for cognitive control, and response to cognitive behavioral therapy (CBT). However, whether a common neural mechanism underlies CBT response across diagnoses is unknown. METHODS Brain activity during a cognitive control task was measured using functional magnetic resonance imaging in 104 participants: 28 patients with MDD, 53 patients with PTSD, and 23 healthy control subjects; depression and anxiety symptoms were determined on the same day. A patient subset (n = 31) entered manualized CBT and, along with controls (n = 19), was rescanned at 12 weeks. Linear mixed effects models assessed the relationship between depression and anxiety symptoms and brain activity before and after CBT. RESULTS At baseline, activation of the left dorsolateral prefrontal cortex was negatively correlated with Montgomery–Åsberg Depression Rating Scale scores across all participants; this brain–symptom association did not differ between MDD and PTSD. Following CBT treatment of patients, regions within the cognitive control network, including ventrolateral prefrontal cortex and dorsolateral prefrontal cortex, showed a significant increase in activity. CONCLUSIONS Our results suggest that dimensional abnormalities in the activation of cognitive control regions were associated primarily with symptoms of depression (with or without controlling for anxious arousal). Furthermore, following treatment with CBT, activation of cognitive control regions was similarly increased in both MDD and PTSD. These results accord with the Research Domain Criteria conceptualization of mental disorders and implicate improved cognitive control activation as a transdiagnostic mechanism for CBT treatment outcome.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases.