Quantitative signal properties from standardized MRIs correlate with multiple sclerosis disability

OBJECTIVE: To enable use of clinical magnetic resonance images (MRIs) to quantify abnormalities in normal appearing (NA) white matter (WM) and gray matter (GM) in multiple sclerosis (MS) and to determine associations with MS-related disability. Identification of these abnormalities heretofore has required specialized scans not routinely available in clinical practice. METHODS: We developed an analytic technique which normalizes image intensities based on an intensity atlas for quantification of WM and GM abnormalities in standardized MRIs obtained with clinical sequences. Gaussian mixture modeling is applied to summarize image intensity distributions from T1-weighted and 3D-FLAIR (T2-weighted) images from 5010 participants enrolled in a multinational database of MS patients which collected imaging, neuroperformance and disability measures. RESULTS: Intensity distribution metrics distinguished MS patients from control participants based on normalized non-lesional signal differences. This analysis revealed non-lesional differences between relapsing MS versus progressive MS subtypes. Further, the correlation between our non-lesional measures and disability was approximately three times greater than that between total lesion volume and disability, measured using the patient derived disease steps. Multivariate modeling revealed that measures of extra-lesional tissue integrity and atrophy contribute uniquely, and approximately equally, to the prediction of MS-related disability. INTERPRETATION: These results support the notion that non-lesional abnormalities correlate more strongly with MS-related disability than lesion burden and provide new insight into the basis of abnormalities in NA WM. Non-lesional abnormalities distinguish relapsing from progressive MS but do not distinguish between progressive subtypes suggesting a common progressive pathophysiology. Image intensity parameters and existing biomarkers each independently correlate with MS-related disability

Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer\u27s disease

Amyloid-beta deposition is one of the hallmark pathologies in both sporadic Alzheimer\u27s disease and autosomal-dominant Alzheimer\u27s disease, the latter of which is caused by mutations in genes involved in amyloid-beta processing. Despite amyloid-beta deposition being a centrepiece to both sporadic Alzheimer\u27s disease and autosomal-dominant Alzheimer\u27s disease, some differences between these Alzheimer\u27s disease subtypes have been observed with respect to the spatial pattern of amyloid-beta. Previous work has shown that the spatial pattern of amyloid-beta in individuals spanning the sporadic Alzheimer\u27s disease spectrum can be reproduced with high accuracy using an epidemic spreading model which simulates the diffusion of amyloid-beta across neuronal connections and is constrained by individual rates of amyloid-beta production and clearance. However, it has not been investigated whether amyloid-beta deposition in the rarer autosomal-dominant Alzheimer\u27s disease can be modelled in the same way, and if so, how congruent the spreading patterns of amyloid-beta across sporadic Alzheimer\u27s disease and autosomal-dominant Alzheimer\u27s disease are. We leverage the epidemic spreading model as a data-driven approach to probe individual-level variation in the spreading patterns of amyloid-beta across three different large-scale imaging datasets (2 sporadic Alzheimer\u27s disease, 1 autosomal-dominant Alzheimer\u27s disease). We applied the epidemic spreading model separately to the Alzheimer\u27s Disease Neuroimaging initiative (n = 737), the Open Access Series of Imaging Studies (n = 510) and the Dominantly Inherited Alzheimer\u27s Network (n = 249), the latter two of which were processed using an identical pipeline. We assessed inter-and intra-individual model performance in each dataset separately and further identified the most likely subject-specific epicentre of amyloid-beta spread. Using epicentres defined in previous work in sporadic Alzheimer\u27s disease, the epidemic spreading model provided moderate prediction of the regional pattern of amyloid-beta deposition across all three datasets. We further find that, whilst the most likely epicentre for most amyloid-beta-positive subjects overlaps with the default mode network, 13% of autosomal-dominant Alzheimer\u27s disease individuals were best characterized by a striatal origin of amyloid-beta spread. These subjects were also distinguished by being younger than autosomal-dominant Alzheimer\u27s disease subjects with a default mode network amyloid-beta origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most autosomal-dominant Alzheimer\u27s disease patients express amyloid-beta spreading patterns similar to those of sporadic Alzheimer\u27s disease, but that there may be a subset of autosomal-dominant Alzheimer\u27s disease patients with a separate, striatal phenotype

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes

Preclinical Alzheimer's disease and longitudinal driving decline

Introduction: Links between preclinical Alzheimer's disease (AD) and driving difficulty onset would support the use of driving performance as an outcome in primary and secondary prevention trials among older adults (OAs). We examined whether AD biomarkers predicted the onset of driving difficulties among OAs. Methods: One hundred four OAs (65+ years) with normal cognition took part in biomarker measurements, a road test, clinical and psychometric batteries, and self-reported their driving habits. Results: Higher values of cerebrospinal fluid (CSF) tau/Aβ42 and phosphorylated tau (ptau181)/Aβ42 ratios, but not uptake on Pittsburgh compound B amyloid imaging (P = .12), predicted time to a rating of marginal or fail on the driving test using Cox proportional hazards models. Hazards ratios (95% confidence interval) were 5.75 (1.70–19.53), P = .005 for CSF tau/Aβ42; 6.19 (1.75–21.88), and P = .005 for CSF ptau181/Aβ42. Discussion Preclinical AD predicted time to receiving a marginal or fail rating on an on-road driving test. Driving performance shows promise as a functional outcome in AD prevention trials

Forensic Evidence Collection and DNA Identification in Acute Child Sexual Assault

OBJECTIVE: To describe forensic evidence findings and reevaluate previous recommendations with respect to timing of evidence collection in acute child sexual assault and to identify factors associated with yield of DNA. METHODS: This was a retrospective review of medical and legal records of patients aged 0 to 20 years who required forensic evidence collection. RESULTS: Ninety-seven of 388 (25%) processed evidence-collection kits were positive and 63 (65%) of them produced identifiable DNA. There were 20 positive samples obtained from children younger than 10 years; 17 of these samples were obtained from children seen within 24 hours of the assault. Three children had positive body samples beyond 24 hours after the assault, including 1 child positive for salivary amylase in the underwear and on the thighs 54 hours after the assault. DNA was found in 11 children aged younger than 10 years, including the child seen 54 hours after the assault. Collection of evidence within 24 hours of the assault was identified as an independent predictor of DNA detection. CONCLUSIONS: Identifiable DNA was collected from a child\u27s body despite cases in which: evidence collection was performed \u3e24 hours beyond the assault; the child had a normal/nonacute anogenital examination; there was no reported history of ejaculation; and the victim had bathed and/or changed clothes before evidence collection. Failure to conduct evidence collection on prepubertal children beyond 24 hours after the assault will result in rare missed opportunities to identify forensic evidence, including identification of DNA

Forensic Evidence Collection and DNA Identification in Acute Child Sexual Assault

OBJECTIVE: To describe forensic evidence findings and reevaluate previous recommendations with respect to timing of evidence collection in acute child sexual assault and to identify factors associated with yield of DNA. METHODS: This was a retrospective review of medical and legal records of patients aged 0 to 20 years who required forensic evidence collection. RESULTS: Ninety-seven of 388 (25%) processed evidence-collection kits were positive and 63 (65%) of them produced identifiable DNA. There were 20 positive samples obtained from children younger than 10 years; 17 of these samples were obtained from children seen within 24 hours of the assault. Three children had positive body samples beyond 24 hours after the assault, including 1 child positive for salivary amylase in the underwear and on the thighs 54 hours after the assault. DNA was found in 11 children aged younger than 10 years, including the child seen 54 hours after the assault. Collection of evidence within 24 hours of the assault was identified as an independent predictor of DNA detection. CONCLUSIONS: Identifiable DNA was collected from a child\u27s body despite cases in which: evidence collection was performed \u3e24 hours beyond the assault; the child had a normal/nonacute anogenital examination; there was no reported history of ejaculation; and the victim had bathed and/or changed clothes before evidence collection. Failure to conduct evidence collection on prepubertal children beyond 24 hours after the assault will result in rare missed opportunities to identify forensic evidence, including identification of DNA

Pediatric Sexual Assault Nurse Examiner Care: Trace Forensic Evidence, Ano-genital Injury, and Judicial Outcomes

Introduction: Although pediatric sexual assault nurse examiners (P-SANEs) have been providing care for over two decades there remain major gaps in the literature describing the quality of P-SANE care and legal outcomes associated with their cases. The purpose of this study was to compare quality indicators of care in a pediatric emergency department (PED) before and after the implementation of a P-SANE program described in terms of trace forensic evidence yield, identification of perpetrator DNA, and judicial outcomes in pediatric acute sexual assault. Method: A retrospective review of medical and legal records of all patients presenting to the PED at Nationwide Children\u27s Hospital with concerns of acute sexual abuse/assault requiring forensic evidence collection from 1/1/04 to 12/31/07 was conducted. Findings: Detection and documentation of ano-genital injury, evaluation and documentation of pregnancy status, and testing for N. gonorrhea and C. trachomatis was significantly improved since implementation of the P-SANE Program compared to the historical control. Discussion: The addition of a P-SANE to the emergency department (ED) provider team improved the quality of care to child/adolescent victims of acute sexual abuse/assault