Thousands of Americans are killed by gunfire each year, and hundreds of thousands more are injured or threatened with guns in robberies and assaults. The burden of gun violence in urban areas is particularly high. Critics suggest that the results of firearm trace data and gun trafficking investigation studies cannot be used to understand the illegal supply of guns to criminals and, therefore, that regulatory and enforcement efforts designed to disrupt illegal firearms markets are futile in addressing criminal access to firearms. In this paper, we present new data to address three key arguments used by skeptics to undermine research on illegal gun market dynamics. We find that criminals rely upon a diverse set of illegal diversion pathways to acquire guns, gun traffickers usually divert small numbers of guns, newer guns are diverted through close-to-retail diversions from legal firearms commerce, and that a diverse set of gun trafficking indicators are needed to identify and shut down gun trafficking pathways.
Gun violence; Gun policy; Gun trafficking; Injury prevention
Frontotemporal lobar degeneration (FTLD) is most commonly associated with TAR-DNA binding protein (TDP-43) or tau pathology at autopsy, but there are no in vivo biomarkers reliably discriminating between sporadic cases. As disease-modifying treatments emerge, it is critical to accurately identify underlying pathology in living patients so that they can be entered into appropriate etiology-directed clinical trials. Patients with tau inclusions (FTLD-TAU) appear to have relatively greater white matter (WM) disease at autopsy than those patients with TDP-43 (FTLD-TDP). In this paper, we investigate the ability of white matter (WM) imaging to help discriminate between FTLD-TAU and FTLD-TDP during life using diffusion tensor imaging (DTI).
Patients with autopsy-confirmed disease or a genetic mutation consistent with FTLD-TDP or FTLD-TAU underwent multimodal T1 volumetric MRI and diffusion weighted imaging scans. We quantified cortical thickness in GM and fractional anisotropy (FA) in WM. We performed Eigenanatomy, a statistically robust dimensionality reduction algorithm, and used leave-one-out cross-validation to predict underlying pathology. Neuropathological assessment of GM and WM disease burden was performed in the autopsy-cases to confirm our findings of an ante-mortem GM and WM dissociation in the neuroimaging cohort.
ROC curve analyses evaluated classification accuracy in individual patients and revealed 96% sensitivity and 100% specificity for WM analyses. FTLD-TAU had significantly more WM degeneration and inclusion severity at autopsy relative to FTLD-TDP.
These neuroimaging and neuropathological investigations provide converging evidence for greater WM burden associated with FTLD-TAU, and emphasize the role of WM neuroimaging for in vivo discrimination between FTLD-TAU and FTLD-TDP.
We contribute a novel and interpretable dimensionality reduction strategy, eigenanatomy, that is tuned for neuroimaging data. The method approximates the eigendecomposition of an image set with basis functions (the eigenanatomy vectors) that are sparse, unsigned and are anatomically clustered. We employ the eigenanatomy vectors as anatomical predictors to improve detection power in morphometry. Standard voxel-based morphometry (VBM) analyzes imaging data voxel-by-voxel—and follows this with cluster-based or voxel-wise multiple comparisons correction methods to determine significance. Eigenanatomy reverses the standard order of operations by first clustering the voxel data and then using standard linear regression in this reduced dimensionality space. As with traditional region-of-interest (ROI) analysis, this strategy can greatly improve detection power. Our results show that eigenanatomy provides a principled objective function that leads to localized, data-driven regions of interest. These regions improve our ability to quantify biologically plausible rates of cortical change in two distinct forms of neurodegeneration. We detail the algorithm and show experimental evidence of its efficacy.
Prior work has related sentence processing to executive deficits in non-demented patients with Parkinson’s disease (PD). We extended this investigation to patients with dementia with Lewy bodies (DLB) and PD dementia (PDD) by examining grammatical and working memory components of sentence processing in the full range of patients with Lewy body spectrum disorder (LBSD). Thirty-three patients with LBSD were given a two-alternative, forced-choice sentence-picture matching task. Sentence type, working memory, and grammatical structure were systematically manipulated in the sentences. We found that patients with PDD and DLB were significantly impaired relative to non-demented PD patients and healthy controls. The deficit in PDD/DLB was most pronounced for sentences lengthened by the strategic placement of an additional prepositional phrase and for sentences with an additional proposition due to a center-embedded clause. However, there was no effect for subject-relative versus object-relative grammatical structure. An MRI voxel-based morphometry analysis in a subset of patients showed significant gray matter thinning in the frontal lobe bilaterally, and this extended to temporal, parietal and occipital regions. A regression analysis related sentence processing difficulty in LBSD to frontal neocortex, including inferiorprefrontal, premotor, and dorsolateral prefrontal regions, as well as right superior temporal cortex. These findings are consistent with the hypothesis that patients with PDD and DLB have difficulty processing sentences with increased working memory demands and that this deficit is related in part to their frontal disease.
Lewy body; Parkinson’s; sentence processing; working memory; MRI; prefrontal
Few studies have examined connected speech in demented and non-demented patients with Parkinson’s disease (PD). We assessed the speech production of 35 patients with Lewy body spectrum disorder (LBSD), including non-demented PD patients, patients with PD dementia (PDD), and patients with dementia with Lewy bodies (DLB), in a semi-structured narrative speech sample in order to characterize impairments of speech fluency and to determine the factors contributing to reduced speech fluency in these patients. Both demented and non-demented PD patients exhibited reduced speech fluency, characterized by reduced overall speech rate and long pauses between sentences. Reduced speech rate in LBSD correlated with measures of between-utterance pauses, executive functioning, and grammatical comprehension. Regression analyses related non-fluent speech, grammatical difficulty, and executive difficulty to atrophy in frontal brain regions. These findings indicate that multiple factors contribute to slowed speech in LBSD, and this is mediated in part by disease in frontal brain regions.
Parkinson’s disease; speech; language; fluency; dementia with Lewy bodies
While grammatical aspects of language are preserved, executive deficits are prominent in Lewy body spectrum disorder (LBSD), including Parkinson’s disease (PD), Parkinson’s dementia (PDD) and dementia with Lewy bodies (DLB). We examined executive control during sentence processing in LBSD by assessing temporary structural ambiguities. Using an on-line word detection procedure, patients heard sentences with a syntactic structure that has high-compatibility or low-compatibility with the main verb’s statistically preferred syntactic structure, and half of the sentences were lengthened strategically between the onset of the ambiguity and its resolution. We found selectively slowed processing of lengthened ambiguous sentences in the PDD/DLB subgroup. This correlated with impairments on measures of executive control. Regression analyses related the working memory deficit during ambiguous sentence processing to significant cortical thinning in frontal and parietal regions. These findings emphasize the role of prefrontal disease in the executive limitations that interfere with processing ambiguous sentences in LBSD.
Parkinson’s; Lewy body; syntactic ambiguity; working memory; frontal
Narrative discourse is an essential component of day-to-day communication, but little is known about narrative in Lewy Body spectrum disorder (LBSD), including Parkinson's disease (PD), Parkinson's disease with dementia (PDD), and dementia with Lewy bodies (DLB). We performed a detailed analysis of a semi-structured speech sample in 32 non-aphasic patients with LBSD, and we related their narrative impairments to gray matter (GM) atrophy using voxel-based morphometry. We found that patients with PDD and DLB have significant difficulty organizing their narrative speech. This was correlated with deficits on measures of executive functioning and speech fluency. Regression analyses associated this deficit with reduced cortical volume in inferior frontal and anterior cingulate regions. These findings are consistent with a model of narrative discourse that includes executive as well as language components and with an impairment of the organizational component of narrative discourse in patients with PDD and DLB.
Parkinson's disease; discourse; speech; language; Dementia with Lewy bodies
Converging lines of evidence suggest an adverse effect of heavy cannabis use on adolescent brain development, particularly on the hippocampus. In this preliminary study, we compared hippocampal morphology in 14 “treatment-seeking” adolescents (aged 18-20) with a history of prior heavy-cannabis use (5.8 joints/day) after an average of 6.7 months of drug abstinence, and 14 demographically matched normal controls. Participants underwent a high-resolution 3D MRI as well as cognitive testing including the California Verbal Learning Test (CVLT). Heavy-cannabis users showed significantly smaller volumes of the right (p< .04) and left (p< .02) hippocampus, but no significant differences in the amygdala region compared to controls. In controls, larger hippocampus volumes were observed to be significantly correlated with higher CVLT verbal learning and memory scores, but these relationships were not observed in cannabis users. In cannabis users, a smaller right hippocampus volume was correlated with a higher amount of cannabis use (r= - .57, p< .03). These data support a hypothesis that heavy-cannabis use may have an adverse effect on hippocampus development. These findings, after an average 6.7 month of supervised abstinence, lend support to a theory that cannabis use may impart long-term structural and functional damage. Alternatively, the observed hippocampal volumetric abnormalities may represent a risk factor for cannabis dependence. These data have potential significance for understanding the observed relationship between early cannabis exposure during adolescence and subsequent development of adult psychopathology reported in the literature for schizophrenia and related psychotic disorders.
hippocampus; cannabis; adolescence; magnetic resonance imaging; CVLT; learning and memory
We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs (http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool.
Image segmentation; Open source; Multivariate; Cortical parcellation; Evaluation; BrainWeb; ITK
Much of our understanding regarding the mechanisms for induction of disease following inhalation of respirable elongated mineral particles (REMP) is based on studies involving the biological effects of asbestos fibers. The factors governing the disease potential of an exposure include duration and frequency of exposures; tissue-specific dose over time; impacts on dose persistence from in vivo REMP dissolution, comminution, and clearance; individual susceptibility; and the mineral type and surface characteristics. The mechanisms associated with asbestos particle toxicity involve two facets for each particle's contribution: (1) the physical features of the inhaled REMP, which include width, length, aspect ratio, and effective surface area available for cell contact; and (2) the surface chemical composition and reactivity of the individual fiber/elongated particle. Studies in cell-free systems and with cultured cells suggest an important way in which REMP from asbestos damage cellular molecules or influence cellular processes. This may involve an unfortunate combination of the ability of REMP to chemically generate potentially damaging reactive oxygen species, through surface iron, and the interaction of the unique surfaces with cell membranes to trigger membrane receptor activation. Together these events appear to lead to a cascade of cellular events, including the production of damaging reactive nitrogen species, which may contribute to the disease process. Thus, there is a need to be more cognizant of the potential impact that the total surface area of REMP contributes to the generation of events resulting in pathological changes in biological systems. The information presented has applicability to inhaled dusts, in general, and specifically to respirable elongated mineral particles.
We use a new, unsupervised multivariate imaging and analysis strategy to identify related patterns of reduced white matter integrity, measured with the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI), and decreases in cortical thickness, measured by high resolution T1-weighted imaging, in Alzheimer's disease (AD) and frontotemporal dementia (FTD). This process is based on a novel computational model derived from sparse canonical correlation analysis (SCCA) that allows us to automatically identify mutually predictive, distributed neuroanatomical regions from different imaging modalities. We apply the SCCA model to a dataset that includes 23 control subjects that are demographically-matched to 49 subjects with autopsy or CSF-biomarker-diagnosed AD (n=24) and FTD (n=25) with both DTI and T1-weighted structural imaging. SCCA shows that the FTD-related frontal and temporal degeneration pattern is correlated across modalities with permutation corrected p < 0.0005. In AD, we find significant association between cortical thinning and reduction in white matter integrity within a distributed parietal and temporal network (p < 0.0005). Furthermore, we show that—within SCCA identified regions—significant differences exist between FTD and AD cortical-connective degeneration patterns. We validate these distinct, multimodal imaging patterns by showing unique relationships with cognitive measures in AD and FTD. We conclude that SCCA is a potentially valuable approach in image analysis that can be applied productively to distinguishing between neurodegenerative conditions.
dementia; multivariate; correlation; diffusion tensor; cortical thickness; AD; FTD; canonical correlation
A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as “N4ITK,” available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized 3 He lung image data, and 9.4T postmortem hippocampus data.
B-spline approximation; bias field; inhomogeneity; N3