Author
Andrew S. Nencka
Bio: Andrew S. Nencka is an academic researcher from Medical College of Wisconsin. The author has contributed to research in topics: Medicine & Concussion. The author has an hindex of 17, co-authored 88 publications receiving 1042 citations.
Topics: Medicine, Concussion, Resting state fMRI, Temporal lobe, Connectome
Papers published on a yearly basis
Papers
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University of California, San Diego1, McGill University2, Oregon Health & Science University3, Florida International University4, Yale University5, Washington University in St. Louis6, Virginia Commonwealth University7, University of Vermont8, University of Michigan9, Medical University of South Carolina10, National Institutes of Health11, SRI International12, University of Southern California13, McGovern Institute for Brain Research14, Harvard University15, Medical College of Wisconsin16, University of California, Irvine17, University of California, Los Angeles18, University of California, San Francisco19, University of Colorado Boulder20, University of Florida21, University of Maryland, Baltimore22, University of Massachusetts Boston23, University of Minnesota24, University of Pittsburgh25, University of Rochester26, University of Tennessee27, University of Utah28, University of Wisconsin–Milwaukee29, United States Department of Veterans Affairs30, Boston University31
TL;DR: The baseline neuroimaging processing and subject-level analysis methods used by the Adolescent Brain Cognitive Development Study are described to be a resource of unprecedented scale and depth for studying typical and atypical development.
431 citations
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University of California1, McGill University2, Oregon Health & Science University3, Florida International University4, Yale University5, University of Washington6, Virginia Commonwealth University7, University of Vermont8, University of Michigan9, Medical University of South Carolina10, National Institute on Drug Abuse11, SRI International12, Children's Hospital Los Angeles13, National Institutes of Health14, McGovern Institute for Brain Research15, Harvard University16, Medical College of Wisconsin17, University of Colorado Boulder18, University of Florida19, University of Maryland, Baltimore20, University of Massachusetts Amherst21, University of Minnesota22, University of Pittsburgh23, University of Rochester24, University of Tennessee25, University of Utah26, University of Wisconsin–Milwaukee27, Boston University28
TL;DR: The baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes are described.
Abstract: The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The ABCD Study is a collaborative effort, including a Coordinating Center, 21 data acquisition sites across the United States, and a Data Analysis and Informatics Center (DAIC). The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data will provide a resource of unprecedented scale and depth for studying typical and atypical development. Here, we describe the baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes. Neuroimaging processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI.
276 citations
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TL;DR: The hypothesis that physiological changes persist beyond the point of clinical recovery after SRC is supported, and advanced ASL MRI methods might be useful for detecting and tracking the longitudinal course of underlying neurophysiological recovery from concussion.
Abstract: Sport-related concussion (SRC) is a major health problem, affecting millions of athletes each year. While the clinical effects of SRC (e.g., symptoms and functional impairments) typically resolve within several days, increasing evidence suggests persistent neurophysiological abnormalities beyond the point of clinical recovery after injury. This study aimed to evaluate cerebral blood flow (CBF) changes in acute SRC, as measured using advanced arterial spin labeling (ASL) magnetic resonance imaging (MRI). We compared CBF maps assessed in 18 concussed football players (age, 17.8 ± 1.5 years) obtained within 24 h and at 8 days after injury with a control group of 19 matched non-concussed football players. While the control group did not show any changes in CBF between the two time-points, concussed athletes demonstrated a significant decrease in CBF at 8 days relative to within 24 h. Scores on the clinical symptom (Sport Concussion Assessment Tool 3, SCAT3) and cognitive measures (Standardized Assess...
148 citations
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TL;DR: Overall, the results of this study are consistent with the hypothesis that SRC is associated with changes in white matter tracts shortly after injury, and these differences are correlated clinically with acute symptoms and functional impairments.
Abstract: Sport-related concussion (SRC) is an important public health issue. While standardized assessment tools are useful in the clinical management of acute concussion, the underlying pathophysiology of SRC and the time course of physiological recovery after injury remain unclear. In this study, we used diffusion tensor imaging (DTI) to detect white-matter alterations in football players within 48 hours after SRC. As part of the NCAA-DoD CARE Consortium study of SRC, 30 American football players diagnosed with acute concussion, and 28 matched controls received clinical assessments and underwent advanced MRI scans. To avoid selection bias and partial voluming effects, whole-brain skeletonized white matter was examined via tract-based spatial statistics (TBSS) to investigate between group differences in DTI metrics and their associations with clinical outcome measures. Mean diffusivity was significantly higher in the brain white matter of the concussed athletes, particularly in frontal and sub-frontal long white-...
60 citations
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TL;DR: Changes in white matter were evident after SRC at 6 months after injury but were not observed in contact-sport exposure, and the persistent white-matter abnormalities were associated with clinical outcomes and delayed recovery time.
Abstract: Objective To study longitudinal recovery trajectories of white matter after sports-related concussion (SRC) by performing diffusion tensor imaging (DTI) on collegiate athletes who sustained SRC. Methods Collegiate athletes (n = 219, 82 concussed athletes, 68 contact-sport controls, and 69 non–contact-sport controls) were included from the Concussion Assessment, Research and Education Consortium. The participants completed clinical assessments and DTI at 4 time points: 24 to 48 hours after injury, asymptomatic state, 7 days after return-to-play, and 6 months after injury. Tract-based spatial statistics was used to investigate group differences in DTI metrics and to identify white-matter areas with persistent abnormalities. Generalized linear mixed models were used to study longitudinal changes and associations between outcome measures and DTI metrics. Cox proportional hazards model was used to study effects of white-matter abnormalities on recovery time. Results In the white matter of concussed athletes, DTI-derived mean diffusivity was significantly higher than in the controls at 24 to 48 hours after injury and beyond the point when the concussed athletes became asymptomatic. While the extent of affected white matter decreased over time, part of the corpus callosum had persistent group differences across all the time points. Furthermore, greater elevation of mean diffusivity at acute concussion was associated with worse clinical outcome measures (i.e., Brief Symptom Inventory scores and symptom severity scores) and prolonged recovery time. No significant differences in DTI metrics were observed between the contact-sport and non–contact-sport controls. Conclusions Changes in white matter were evident after SRC at 6 months after injury but were not observed in contact-sport exposure. Furthermore, the persistent white-matter abnormalities were associated with clinical outcomes and delayed recovery time.
48 citations
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9,362 citations
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01 May 1981TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.
4,948 citations
01 Jan 2016
TL;DR: As you may know, people have search numerous times for their chosen novels like this statistical parametric mapping the analysis of functional brain images, but end up in malicious downloads.
Abstract: Thank you very much for reading statistical parametric mapping the analysis of functional brain images. As you may know, people have search numerous times for their chosen novels like this statistical parametric mapping the analysis of functional brain images, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some infectious bugs inside their desktop computer.
1,719 citations
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TL;DR: fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data that dispenses of manual intervention, thereby ensuring the reproducibility of the results.
Abstract: Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.
1,465 citations
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TL;DR: FMRIPrep has the potential to transform fMRI research by equipping neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow which can help ensure the validity of inference and the interpretability of their results.
Abstract: Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available for each step. The complexity of these workflows has snowballed with rapid advances in MR data acquisition and image processing techniques. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for task-based and resting fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection comprising participants from 54 different studies in the OpenfMRI repository. We review the distinctive features of fMRIPrep in a qualitative comparison to other preprocessing workflows. We demonstrate that fMRIPrep achieves higher spatial accuracy as it introduces less uncontrolled spatial smoothness than one commonly used preprocessing tool. FMRIPrep has the potential to transform fMRI research by equipping neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow which can help ensure the validity of inference and the interpretability of their results.
684 citations