Abstract There are growing concerns about potential delayed, neuropsychiatric consequences (e.g, cognitive decline, mood or anxiety disorders) of sports-related traumatic brain injury (TBI). Autopsy ...studies of brains from a limited number of former athletes have described characteristic, pathologic changes of chronic traumatic encephalopathy (CTE) leading to questions about the relationship between these pathologic and the neuropsychiatric disturbances seen in former athletes. Research in this area will depend on in vivo methods that characterize molecular changes in the brain, linking CTE and other sports-related pathologies with delayed emergence of neuropsychiatric symptoms. In this pilot project we studied former National Football League (NFL) players using new neuroimaging techniques and clinical measures of cognitive functioning. We hypothesized that former NFL players would show molecular and structural changes in medial temporal and parietal lobe structures as well as specific cognitive deficits, namely those of verbal learning and memory. We observed a significant increase in binding of 11 CDPA-713 to the translocator protein (TSPO), a marker of brain injury and repair, in several brain regions, such as the supramarginal gyrus and right amygdala, in 9 former NFL players compared to 9 age-matched, healthy controls. We also observed significant atrophy of the right hippocampus. Finally, we report that these same former players had varied performance on a test of verbal learning and memory, suggesting that these molecular and pathologic changes may play a role in cognitive decline. These results suggest that localized brain injury and repair, indicated by increased 11 CDPA-713 binding to TSPO, may be linked to history of NFL play. 11 CDPA-713 PET is a promising new tool that can be used in future study design to examine further the relationship between TSPO expression in brain injury and repair, selective regional brain atrophy, and the potential link to deficits in verbal learning and memory after NFL play.
Summary
An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying ...new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a “common” analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased‐donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures gradient boosting (GB) and random forests (RF) to predict delayed graft function, one‐year acute rejection, death‐censored graft failure C, all‐cause graft failure, and death. Their performances were compared on the validation set using ‐statistics. In predicting rejection, regression (C = 0.6010.6110.621) actually outperformed GB (C = 0.5810.5910.601) and RF (C = 0.5690.5790.589). For all other outcomes, the C‐statistics were nearly identical across methods (delayed graft function, 0.717–0.723; death‐censored graft failure, 0.637–0.642; all‐cause graft failure, 0.633–0.635; and death, 0.705–0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful.
IMPORTANCE: Microglia, the resident immune cells of the central nervous system, play an important role in the brain’s response to injury and neurodegenerative processes. It has been proposed that ...prolonged microglial activation occurs after single and repeated traumatic brain injury, possibly through sports-related concussive and subconcussive injuries. Limited in vivo brain imaging studies months to years after individuals experience a single moderate to severe traumatic brain injury suggest widespread persistent microglial activation, but there has been little study of persistent glial cell activity in brains of athletes with sports-related traumatic brain injury. OBJECTIVE: To measure translocator protein 18 kDa (TSPO), a marker of activated glial cell response, in a cohort of National Football League (NFL) players and control participants, and to report measures of white matter integrity. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional, case-control study included young active (n = 4) or former (n = 10) NFL players recruited from across the United States, and 16 age-, sex-, highest educational level–, and body mass index–matched control participants. This study was conducted at an academic research institution in Baltimore, Maryland, from January 29, 2015, to February 18, 2016. MAIN OUTCOMES AND MEASURES: Positron emission tomography–based regional measures of TSPO using 11CDPA-713, diffusion tensor imaging measures of regional white matter integrity, regional volumes on structural magnetic resonance imaging, and neuropsychological performance. RESULTS: The mean (SD) ages of the 14 NFL participants and 16 control participants were 31.3 (6.1) years and 27.6 (4.9) years, respectively. Players reported a mean (SD) of 7.0 (6.4) years (range, 1-21 years) since the last self-reported concussion. Using 11CDPA-713 positron emission tomographic data from 12 active or former NFL players and 11 matched control participants, the NFL players showed higher total distribution volume in 8 of the 12 brain regions examined (P < .004). We also observed limited change in white matter fractional anisotropy and mean diffusivity in 13 players compared with 15 control participants. In contrast, these young players did not differ from control participants in regional brain volumes or in neuropsychological performance. CONCLUSIONS AND RELEVANCE: The results suggest that localized brain injury and repair, indicated by higher TSPO signal and white matter changes, may be associated with NFL play. Further study is needed to confirm these findings and to determine whether TSPO signal and white matter changes in young NFL athletes are related to later onset of neuropsychiatric symptoms.
Over the last three decades, data have become ubiquitous and cheap. This transition has accelerated over the last five years and training in statistics, machine learning, and data analysis has ...struggled to keep up. In April 2014, we launched a program of nine courses, the Johns Hopkins Data Science Specialization, which has now had more than 4 million enrollments over the past five years. Here, the program is described and compared to standard data science curricula as they were organized in 2014 and 2015. We show that novel pedagogical and administrative decisions introduced in our program are now standard in online data science programs. The impact of the Data Science Specialization on data science education in the U.S. is also discussed. Finally, we conclude with some thoughts about the future of data science education in a data democratized world.
We used a large convenience sample (
= 22,223) from the Simons Powering Autism Research (SPARK) dataset to evaluate causal, explanatory theories of core autism symptoms. In particular, the ...data-items collected supported the testing of theories that posited altered language abilities as cause of social withdrawal, as well as alternative theories that competed with these language theories. Our results using this large dataset converge with the evolution of the field in the decades since these theories were first proposed, namely supporting primary social withdrawal (in some cases of autism) as a cause of altered language development, rather than vice versa. To accomplish the above empiric goals, we used a highly theory-constrained approach, one which differs from current data-driven modeling trends but is coherent with a very recent resurgence in theory-driven psychology. In addition to careful explication and formalization of theoretical accounts, we propose three principles for future work of this type: specification, quantification, and integration. Specification refers to constraining models with pre-existing data, from both outside and within autism research, with more elaborate models and more veridical measures, and with longitudinal data collection. Quantification refers to using continuous measures of both psychological causes and effects, as well as weighted graphs. This approach avoids "universality and uniqueness" tests that hold that a single cognitive difference could be responsible for a heterogeneous and complex behavioral phenotype. Integration of multiple explanatory paths within a single model helps the field examine for multiple contributors to a single behavioral feature or to multiple behavioral features. It also allows integration of explanatory theories across multiple current-day diagnoses and as well as typical development.
•Intra-hippocampal connectivity was age and sex-invariant within ɛ3/ɛ3 homozygotes.•ɛ3/ɛ3 participants had age and sex-dependent relationships linking connectivity to cognition.•APOE ɛ4 males showed ...sustained or higher connectivity with age.•APOE ɛ4 carriers with lower DMN connectivity had poorer cognitive performance.
The default mode network (DMN) overlaps with regions showing early Alzheimer's Disease (AD) pathology. Age, sex, and apolipoprotein E ɛ4 are the predominant risk factors for developing AD. How these risk factors interact to influence DMN connectivity and connectivity-cognition relationships before the onset of impairment remains unknown. Here, we examined these issues in 475 cognitively normal adults, targeting total DMN connectivity, its anticorrelated network (acDMN), and the DMN-hippocampal component. There were four main findings. First, in the ɛ3 homozygous group, lower DMN and acDMN connectivity was observed with age. Second, sex and ɛ4 modified the relationship between age and connectivity for the DMN and hippocampus with ɛ4 vs. ɛ3 males showing sustained or higher connectivity with age. Third, in the ɛ3 group, age and sex modified connectivity-cognition relationships with the oldest participants having the most differential patterns due to sex. Fourth, ɛ4 carriers with lower connectivity had poorer cognitive performance. Taken together, our results show the three predominant risk factors for AD interact to influence brain function and function-cognition relationships.
Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease ...outcomes. In this study, we propose an ℓ
-type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ
-norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.
Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA ...produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates. However, this and other hierarchical ICA models assume unrealistically that subject effects are spatially independent. Here, we propose spatial template ICA (stICA), which incorporates spatial priors into the template ICA framework for greater estimation efficiency. Additionally, the joint posterior distribution can be used to identify brain regions engaged in each network using an excursions set approach. By leveraging spatial dependencies and avoiding massive multiple comparisons, stICA has high power to detect true effects. We derive an efficient expectation-maximization algorithm to obtain maximum likelihood estimates of the model parameters and posterior moments of the latent fields. Based on analysis of simulated data and fMRI data from the Human Connectome Project, we find that stICA produces estimates that are more accurate and reliable than benchmark approaches, and identifies larger and more reliable areas of engagement. The algorithm is computationally tractable, achieving convergence within 12 hr for whole-cortex fMRI analysis.
Supplementary materials
for this article are available online.
Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage ...in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects ...to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICCMSE) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully when using partial correlations.
•Empirical Bayes shrinkage methods for functional connectivity are proposed.•A novel reliability measure analogous to intraclass correlation is proposed.•Shrinkage significantly improves reliability of full and partial correlations.•Partial correlation reliability is highly sensitive to ridge regression penalty.•Partial correlation reliability is worse overall but better for some connections.
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