A Fully Automatic Method for Comparing Cartridge Case Images.

When a gun is fired, it leaves marks on cartridge cases that are thought to be unique to the gun. In current practice, firearms examiners inspect cartridge cases for "sufficient agreement," in which case they conclude that they come from the same gun, testifying in courts as such. A 2016 President's Council of Advisors on Science and Technology report questioned the scientific validity of such analysis (President's Committee of Advisors on Science and Technology, Washington, DC, Executive Office of the President). One recommendation was to convert firearms analysis to an objective method. We propose a fully automated, open-source method for comparing breechface marks on cartridge cases using 2D optical images. We improve on existing methodology by automating the selection of marks, and removing the effects of circular symmetry. We propose an empirical computation of a "random match probability" given a known database, which can be used to quantify the weight of evidence. We demonstrate an improvement in accuracy on images from controlled test fires.

Voxel Level Survival Analysis of Grey Matter Volume and Incident Mild Cognitive Impairment or Alzheimer's Disease.

The purpose of this study was to identify, at the voxel level, brain regions associated with the time to develop mild cognitive impairment (MCI) or Alzheimer's disease (AD) from normal cognition. We analyzed incident MCI (n = 58) or AD (n = 151) in 292 cognitively normal participants in the Cardiovascular Health Study-Cognition Study (mean age = 79.2 ± 3.6 years). We used segmented, modulated grey matter maps from 3D (spoiled gradient echo) MRI scans obtained in 1998/99 (with clinical follow-up through 2012) that were smoothed with a 3-D 4 mm Gaussian filter. We fit approximately 1.92 million voxel-level Cox proportional hazard models to examine the grey matter volume effect on time to event, adjusting for age, sex, and diabetes. We used the significance threshold of p <  0.005 with contiguity threshold of at least 68 voxels (false detection probability <2.5×10 -8). Areas within the mesial temporal lobe (MTL), anterior temporal lobe, hippocampus, and posterior cingulate gyrus were associated with time to MCI or AD. The presence of white matter lesions (a marker of small vessel disease in the brain) was associated with the volumes of the MTL and precuneus; MRI-identified infarcts also predicted MTL volume. These findings are important because we identified critical brain regions that predict a person's increased likelihood of developing MCI or AD over a decade prior to the onset of clinical symptoms; these critical brain regions were themselves affected by the presence of vascular disease.

Bootstrapping GEE models for fMRI regional connectivity.

An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations during rest between groups. We apply generalized estimating equation (GEE) models to test for differences in regional correlations across groups. Both the GEE marginal model and GEE transition model are evaluated and compared to the standard pooling Fisher-z approach using simulation studies. Standard errors of all methods are estimated both theoretically (model-based) and empirically (bootstrap). Of all the methods, we find that the transition models have the best statistical properties. Overall, the model-based standard errors and bootstrap standard errors perform about the same. We also demonstrate the methods with a functional connectivity study in a healthy cognitively normal population of ApoE4+ participants and ApoE4- participants who are recruited from the Adult Children's Study conducted at the Washington University Knight Alzheimer's Disease Research Center.

A generalized estimating equations approach for resting-state functional MRI group analysis.

An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations between groups. The overall objective is to assess inter-regional correlations at a resting-state with no stimulus or task. We propose using a generalized estimating equation (GEE) transition model and a GEE marginal model to model the within-subject correlation for each region. Residuals calculated from the GEE models are used to correlate brain regions and assess between group differences. The standard pooling approach of group averages of the Fisher-z transformation assuming temporal independence is a typical approach used to compare group correlations. The GEE approaches and standard Fisher-z pooling approach are demonstrated with an Alzheimer's disease (AD) connectivity study in a population of AD subjects and healthy control subjects. We also compare these methods using simulation studies and show that the transition model may have better statistical properties.

Postconcussive symptoms are associated with compensatory cortical recruitment during a working memory task.

BACKGROUND: The severity of sports-related concussion is often characterized by the number and severity of postconcussive symptoms (eg, headache, dizziness, difficulty concentrating). Although the level of postconcussive symptoms after injury is believed to index the severity of the neurological insult sustained, studies examining the relationship between symptom severity and neural functioning in concussed athletes remain rare. OBJECTIVE: This exploratory study examined the association between self-reported symptom severity and functional activation on a working memory task in a group of 16 recently concussed student athletes. METHODS: Functional magnetic resonance imaging was used to examine the relationship of symptom severity to brain activation during a working memory task in 16 concussed subjects. RESULTS: Findings indicated that symptom severity was associated with regionally specific hyperactivation during a working memory task, even though symptom severity was not significantly related to task accuracy. CONCLUSION: The results add to a growing body of literature that demonstrates that functional neuroimaging may have the potential to serve as a sensitive biomarker of the severity of concussion and mild traumatic brain injury.

A novel algorithm for optimal image thresholding of biological data.

With the proliferation of both in vivo and in vitro microscopy techniques in the neurosciences, increased attention has been placed on the development of image analysis techniques. As experiments can produce large numbers of high bit depth images, automated processing methods have become necessary for handling these data sets. Thresholding, whereby a high bit depth image is converted into a binary image in order to identify a feature of interest, is one such standard automated technique; but the method of selecting an appropriate threshold value is far from standard. We present a novel algorithm, maximum correlation thresholding (MCT), that thresholds images accurately and efficiently without relying on any assumptions of the statistics of the image. As MCT produces thresholded images that preserve the most salient elements in the image, the algorithm performs as well as a trained user on a range of neurobiological data and in a variety of noisy conditions or when preprocessing steps preceded the thresholding operation. Our method will thus allow neuroscientists to automate image thresholding using a robust, computationally efficient algorithm, ultimately aiding in accurate image quantification and analysis.

Brain activation, response inhibition, and increased risk for substance use disorder.

BACKGROUND: Youth at high risk for developing substance use disorders (SUDs) often exhibit differences which suggest inhibitory impairments when compared to average risk youth. METHODS: To examine the underlying neural activity related to these impairments, functional MRI (fMRI) was employed in adolescents during an antisaccade task requiring inhibition of an eye movement response. Each subject's level of neurobehavioral disinhibition (ND) was assessed using a multi-informant, multi-method approach, which has been shown to be highly predictive of SUD onset. The fMRI data was categorized into neural regions of interest according to total frontal, parietal, occipital, and temporal lobe activation. RESULTS: Results demonstrated that ND score was negatively correlated with total amount of frontal activation, but was not significantly correlated with total activation in any other neural region. CONCLUSIONS: These results indicate deficits in frontal activation in youth with high amounts of ND, suggesting a possible developmental delay of executive processes in high-risk youth.

Lights, Camera, Action! systematic variation in 2-D difference gel electrophoresis images.

2-D Difference gel electrophoresis (DIGE) circumvents many of the problems associated with gel comparison via the traditional 2-DE approach. DIGE's accuracy and precision, however, is compromised by the existence of other significant sources of systematic variation, including that caused by the apparatus used for imaging proteins (location of the camera and lighting units, background material, imperfections within that material, etc.). Through a series of experiments, we estimate some of these factors, and account for their effect on the DIGE experimental data, thus providing improved estimates of the true relative protein intensities. The model presented here includes 2-DE images as a special case.

Functional brain abnormalities are related to clinical recovery and time to return-to-play in athletes.

OBJECTIVE: The relationship between athlete reports of symptoms, neurophysiological activation, and neuropsychological functioning is investigated in a sample of high school athletes. METHODS: All athletes were evaluated using functional magnetic resonance imaging (fMRI), a computer-based battery of neurocognitive tests, and a subjective symptom scale. Athletes were evaluated within approximately 1 week of injury and again after clinical recovery using all assessment modalities. RESULTS: This study found that abnormal fMRI results during the first week of recovery predicted clinical recovery. As a group, athletes who demonstrated hyperactivation on fMRI scans at the time of their first fMRI scan demonstrated a more prolonged clinical recovery than athletes who did not demonstrate hyperactivation at the time of their first fMRI scan. CONCLUSION: These results demonstrate the relationship between neurophysiological, neuropsychological, and subjective symptom data in a relatively large sample composed primarily of concussed high school athletes. fMRI represents an important evolving technology for the understanding of brain recovery after concussion and may help shape return-to-play guidelines in the future.

State-space models for optical imaging.

Measurement of stimulus-induced changes in activity in the brain is critical to the advancement of neuroscience. Scientists use a range of methods, including electrode implantation, surface (scalp) electrode placement, and optical imaging of intrinsic signals, to gather data capturing underlying signals of interest in the brain. These data are usually corrupted by artifacts, complicating interpretation of the signal; in the context of optical imaging, two primary sources of corruption are the heartbeat and respiration cycles. We introduce a new linear state-space framework that uses the Kalman filter to remove these artifacts from optical imaging data. The method relies on a likelihood-based analysis under the specification of a formal statistical model, and allows for corrections to the signal based on auxiliary measurements of quantities closely related to the sources of contamination, such as physiological processes. Furthermore, the likelihood-based modeling framework allows us to perform both goodness-of-fit testing and formal hypothesis testing on parameters of interest. Working with data collected by our collaborators, we demonstrate the method of data collection in an optical imaging study of a cat's brain.

Combining brains: a survey of methods for statistical pooling of information.

More than one subject is scanned in a typical functional brain imaging experiment. How can the scientist make best use of the acquired data to map the specific areas of the brain that become active during the performance of different tasks? It is clear that we can gain both scientific and statistical power by pooling the images from multiple subjects; furthermore, for the comparison of groups of subjects (clinical patients vs healthy controls, children of different ages, left-handed people vs right-handed people, as just some examples), it is essential to have a "group map" to represent each population and to form the basis of a statistical test. While the importance of combining images for these purposes has been recognized, there has not been an organized attempt on the part of neuroscientists to understand the different statistical approaches to this problem, which have various strengths and weaknesses. In this paper we review some popular methods for combining information, and demonstrate the surveyed techniques on a sample data set. Given a combination of brain images, the researcher needs to interpret the result and decide on areas of activation; the question of thresholding is critical here and is also explored.

Pursuit and saccadic eye movement subregions in human frontal eye field: a high-resolution fMRI investigation.

Recent positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies in humans have localized the frontal eye field (FEF) to the precentral sulcus (PCS). In macaque monkeys, low-threshold microstimulation and single unit recording studies have located a saccadic subregion of FEF in a restricted area along the anterior wall of the arcuate sulcus and a pursuit subregion located deeper in the sulcus close to the fundus. The functional organization and anatomical location of these two FEF subregions are still to be defined in humans. In the present study, we used fMRI with high spatial resolution image acquisition at 3.0 Tesla to map the saccade- and pursuit-related areas of FEF within the two walls of the PCS in 11 subjects. We localized the saccade-related area to the upper portion of the anterior wall of the precentral sulcus and the pursuit-related area to a deeper region along the anterior wall, extending in some subjects to the fundus or deep posterior wall. These findings localize distinct pursuit and saccadic subregions of FEF in humans and demonstrate a high degree of homology in the organization of these FEF subregions in the human and the macaque monkey.