20-year depressive symptoms, dementia, and structural neuropathology in older women.

INTRODUCTION: The course of depressive symptoms and dementia risk is unclear, as are potential structural neuropathological common causes. METHODS: Utilizing joint latent class mixture models, we identified longitudinal trajectories of annually assessed depressive symptoms and dementia risk over 21 years in 957 older women (baseline age 72.7 years old) from the Women's Health Initiative Memory Study. In a subsample of 569 women who underwent structural magnetic resonance imaging, we examined whether estimates of cerebrovascular disease and Alzheimer's disease (AD)-related neurodegeneration were associated with identified trajectories. RESULTS: Five trajectories of depressive symptoms and dementia risk were identified. Compared to women with minimal symptoms, women who reported mild and stable and emerging depressive symptoms were at the highest risk of developing dementia and had more cerebrovascular disease and AD-related neurodegeneration. DISCUSSION: There are heterogeneous profiles of depressive symptoms and dementia risk. Common neuropathological factors may contribute to both depression and dementia. Highlights The progression of depressive symptoms and concurrent dementia risk is heterogeneous. Emerging depressive symptoms may be a prodromal symptom of dementia. Cerebrovascular disease and AD are potentially shared neuropathological factors.

Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort.

Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults.

RATIONALE AND OBJECTIVES: Tools are needed for frailty screening of older adults. Opportunistic analysis of body composition could play a role. We aim to determine whether computed tomography (CT)-derived measurements of muscle and adipose tissue are associated with frailty. MATERIALS AND METHODS: Outpatients aged ≥ 55 years consecutively imaged with contrast-enhanced abdominopelvic CT over a 3-month interval were included. Frailty was determined from the electronic health record using a previously validated electronic frailty index (eFI). CT images at the level of the L3 vertebra were automatically segmented to derive muscle metrics (skeletal muscle area [SMA], skeletal muscle density [SMD], intermuscular adipose tissue [IMAT]) and adipose tissue metrics (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT]). Distributions of demographic and CT-derived variables were compared between sexes. Sex-specific associations of muscle and adipose tissue metrics with eFI were characterized by linear regressions adjusted for age, race, ethnicity, duration between imaging and eFI measurements, and imaging parameters. RESULTS: The cohort comprised 886 patients (449 women, 437 men, mean age 67.9 years), of whom 382 (43%) met the criteria for pre-frailty (ie, 0.10 < eFI ≤ 0.21) and 138 (16%) for frailty (eFI > 0.21). In men, 1 standard deviation changes in SMD (ß = -0.01, 95% confidence interval [CI], -0.02 to -0.001, P = .02) and VAT area (ß = 0.008, 95% CI, 0.0005-0.02, P = .04), but not SMA, IMAT, or SAT, were associated with higher frailty. In women, none of the CT-derived muscle or adipose tissue metrics were associated with frailty. CONCLUSION: We observed a positive association between frailty and CT-derived biomarkers of myosteatosis and visceral adiposity in a sex-dependent manner.

Is an MRI-derived anatomical measure of dementia risk also a measure of brain aging?

Machine learning methods have been applied to estimate measures of brain aging from neuroimages. However, only rarely have these measures been examined in the context of biologic age. Here, we investigated associations of an MRI-based measure of dementia risk, the Alzheimer's disease pattern similarity (AD-PS) scores, with measures used to calculate biological age. Participants were those from visit 5 of the Atherosclerosis Risk in Communities Study with cognitive status adjudication, proteomic data, and AD-PS scores available. The AD-PS score estimation is based on previously reported machine learning methods. We evaluated associations of the AD-PS score with all-cause mortality. Sensitivity analyses using only cognitively normal (CN) individuals were performed treating CNS-related causes of death as competing risk. AD-PS score was examined in association with 32 proteins measured, using a Somalogic platform, previously reported to be associated with age. Finally, associations with a deficit accumulation index (DAI) based on a count of 38 health conditions were investigated. All analyses were adjusted for age, race, sex, education, smoking, hypertension, and diabetes. The AD-PS score was significantly associated with all-cause mortality and with levels of 9 of the 32 proteins. Growth/differentiation factor 15 (GDF-15) and pleiotrophin remained significant after accounting for multiple-testing and when restricting the analysis to CN participants. A linear regression model showed a significant association between DAI and AD-PS scores overall. While the AD-PS scores were created as a measure of dementia risk, our analyses suggest that they could also be capturing brain aging.

Age-based differences in the disability of spine injuries in pediatric and adult motor vehicle crash occupants.

OBJECTIVE: The objective was to develop a disability-based metric for quantifying disability rates as a result of motor vehicle crash (MVC) spine injuries and compare functional outcomes between pediatric and adult subgroups. METHODS: Disability rate was quantified using Functional Independence Measure (FIM) scores within the National Trauma Data Bank-Research Data System for the top 95% most frequent Abbreviated Injury Scale (AIS) 3 spine injuries (14 unique injuries). Pediatric (7-18 years), young adult (19-45 years), middle-aged adult (46-65 years), and older adult (66+ years) MVC occupants with FIM scores available and at least one of the 14 spine injuries were included. FIM scores of 1 or 2 at time of discharge were used to define disability and correspond to full functional or modified dependence in self-feeding, locomotion, and/or verbal expression. Disability rate was evaluated on a per injury basis for each AIS 3 spine injury and calculated as the proportion of cases associated with disability (i.e. FIM of 1 or 2) out of the total cases of that particular injury. Disability rates were calculated with and without the exclusion of cases with severe co-injuries (AIS 4+) to minimize bias from additional non-spinal injuries that could have contributed to disability. Associations between adjusted disability rates and existing mortality rates were investigated. RESULTS: Locomotion impairment alone was the most frequent disability type for the top 14 AIS 3 spine injuries (7 cervical, 4 thoracic, and 3 lumbar) across all age groups and spine regions. Adjusted and unadjusted disability rates ranged from 0-69%. Adjusted disability rates increased with age: 14.8 ± 10% (mean ± SD) in pediatrics to 16.2 ± 6.6% (young adults), 29.2 ± 10.9% (middle-aged adults), and 45.0 ± 12.2% (older adults). Among all adult populations, adjusted mortality and disability rates were positively correlated (R2>0.24), with disability rates consistently greater than corresponding mortality rates. CONCLUSIONS: Older adults had significantly greater disability rates associated with MVC spine injuries across all spinal regions. MVC disability rates for pediatrics were considerably lower. Overall, rates of mortality were significantly lower than rates of disability. The adjusted disability rates developed can supplement existing injury metrics by accounting for age- and location-specific functional implications of MVC spine injuries.

Advanced Automatic Crash Notification Algorithm for Children.

BACKGROUND: Advanced automatic crash notification (AACN) can improve triage decision-making by using vehicle telemetry to alert first responders of a motor vehicle crash and estimate an occupant's likelihood of injury. The objective was to develop an AACN algorithm to predict the risk that a pediatric occupant is seriously injured and requires treatment at a Level I or II trauma center. METHODS: Based on 3 injury facets (severity; time sensitivity; predictability), a list of Target Injuries associated with a child's need for Level I/II trauma center treatment was determined. Multivariable logistic regression of motor vehicle crash occupants was performed creating the pediatric-specific AACN algorithm to predict risk of sustaining a Target Injury. Algorithm inputs included: delta-v, rollover quarter-turns, belt status, multiple impacts, airbag deployment, and age. The algorithm was optimized to achieve under-triage ≤5% and over-triage ≤50%. Societal benefits were assessed by comparing correctly triaged motor vehicle crash occupants using the AACN algorithm against real-world decisions. RESULTS: The pediatric AACN algorithm achieved 25% to 49% over-triage across crash modes, and under-triage rates of 2% for far-side, 3% for frontal and near-side, 8% for rear, and 14% for rollover crashes. Applied to real-world motor vehicle crashes, improvements of 59% in under-triage and 45% in over-triage are estimated: more appropriate triage of 32,320 pediatric occupants annually. CONCLUSIONS: This AACN algorithm accounts for pediatric developmental stage and will aid emergency personnel in correctly triaging pediatric occupants after a motor vehicle crash. Once incorporated into the trauma triage network, it will increase triage efficiency and improve patient outcomes.

Comparing data-driven and hypothesis-driven MRI-based predictors of cognitive impairment in individuals from the Atherosclerosis Risk in Communities (ARIC) study.

INTRODUCTION: A data-driven index of dementia risk based on magnetic resonance imaging (MRI), the Alzheimer's Disease Pattern Similarity (AD-PS) score, was estimated for participants in the Atherosclerosis Risk in Communities (ARIC) study. METHODS: AD-PS scores were generated for 839 cognitively non-impaired individuals with a mean follow-up of 4.86 years. The scores and a hypothesis-driven volumetric measure based on several brain regions susceptible to AD were compared as predictors of incident cognitive impairment in different settings. RESULTS: Logistic regression analyses suggest the data-driven AD-PS scores to be more predictive of incident cognitive impairment than its counterpart. Both biomarkers were more predictive of incident cognitive impairment in participants who were White, female, and apolipoprotein E gene (APOE) ε4 carriers. Random forest analyses including predictors from different domains ranked the AD-PS scores as the most relevant MRI predictor of cognitive impairment. CONCLUSIONS: Overall, the AD-PS scores were the stronger MRI-derived predictors of incident cognitive impairment in cognitively non-impaired individuals.

Comprehensive and Computable Molecular Diagnostic Panel (C2Dx) From Small Volume Specimens for Precision Oncology: Molecular Subtyping of Non-Small Cell Lung Cancer From Fine Needle Aspirates.

The Comprehensive, Computable NanoString Diagnostic gene panel (C2Dx) is a promising solution to address the need for a molecular pathological research and diagnostic tool for precision oncology utilizing small volume tumor specimens. We translate subtyping-related gene expression patterns of Non-Small Cell Lung Cancer (NSCLC) derived from public transcriptomic data which establish a highly robust and accurate subtyping system. The C2Dx demonstrates supreme performance on the NanoString platform using microgram-level FNA samples and has excellent portability to frozen tissues and RNA-Seq transcriptomic data. This workflow shows great potential for research and the clinical practice of cancer molecular diagnosis.

Automated Muscle Measurement on Chest CT Predicts All-Cause Mortality in Older Adults From the National Lung Screening Trial.

BACKGROUND: Muscle metrics derived from computed tomography (CT) are associated with adverse health events in older persons, but obtaining these metrics using current methods is not practical for large datasets. We developed a fully automated method for muscle measurement on CT images. This study aimed to determine the relationship between muscle measurements on CT with survival in a large multicenter trial of older adults. METHOD: The relationship between baseline paraspinous skeletal muscle area (SMA) and skeletal muscle density (SMD) and survival over 6 years was determined in 6,803 men and 4,558 women (baseline age: 60-69 years) in the National Lung Screening Trial (NLST). The automated machine learning pipeline selected appropriate CT series, chose a single image at T12, and segmented left paraspinous muscle, recording cross-sectional area and density. Associations between SMA and SMD with all-cause mortality were determined using sex-stratified Cox proportional hazards models, adjusted for age, race, height, weight, pack-years of smoking, and presence of diabetes, chronic lung disease, cardiovascular disease, and cancer at enrollment. RESULTS: After a mean 6.44 ± 1.06 years of follow-up, 635 (9.33%) men and 265 (5.81%) women died. In men, higher SMA and SMD were associated with a lower risk of all-cause mortality, in fully adjusted models. A one-unit standard deviation increase was associated with a hazard ratio (HR) = 0.85 (95% confidence interval [CI] = 0.79, 0.91; p < .001) for SMA and HR = 0.91 (95% CI = 0.84, 0.98; p = .012) for SMD. In women, the associations did not reach significance. CONCLUSION: Higher paraspinous SMA and SMD, automatically derived from CT exams, were associated with better survival in a large multicenter cohort of community-dwelling older men.

PM2.5 associated with gray matter atrophy reflecting increased Alzheimers risk in older women.

OBJECTIVE: To examine whether late-life exposure to PM2.5 (particulate matter with aerodynamic diameters <2.5-µm) contributes to progressive brain atrophy predictive of Alzheimer's disease (AD) using a community-dwelling cohort of women (aged 70-89) with up to two brain MRI scans (MRI-1: 2005-6; MRI-2: 2010-11). METHODS: AD pattern similarity (AD-PS) scores, developed by supervised machine learning and validated with MRI data from the AD Neuroimaging Initiative, was used to capture high-dimensional gray matter atrophy in brain areas vulnerable to AD (e.g., amygdala, hippocampus, parahippocampal gyrus, thalamus, inferior temporal lobe areas and midbrain). Based on participants' addresses and air monitoring data, we implemented a spatiotemporal model to estimate 3-year average exposure to PM2.5 preceding MRI-1. General linear models were used to examine the association between PM2.5 and AD-PS scores (baseline and 5-year standardized change), accounting for potential confounders and white matter lesion volumes. RESULTS: For 1365 women aged 77.9±3.7 years in 2005-6, there was no association between PM2.5 and baseline AD-PS score in cross-sectional analyses (ß=-0.004; 95% CI: -0.019, 0.011). Longitudinally, each interquartile range increase of PM2.5 (2.82-µg/m3) was associated with increased AD-PS scores during the follow-up, equivalent to a 24% (hazard ratio=1.24; 95% CI: 1.14, 1.34) increase in AD risk over 5-years (n=712; aged 77.4±3.5 years). This association remained after adjustment for socio-demographics, intracranial volume, lifestyle, clinical characteristics, and white matter lesions, and was present with levels below US regulatory standards (<12-µg/m3). CONCLUSIONS: Late-life exposure to PM2.5 is associated with increased neuroanatomical risk of AD, which may not be explained by available indicators of cerebrovascular damage.

Injury risk curves in far-side lateral motor vehicle crashes by AIS level, body region and injury code.

OBJECTIVE: The objective of this study was to develop injury risk curves as a function of change in vehicle velocity for occupants in far-side lateral motor vehicle crashes (MVCs) by AIS level, body region, and specific AIS codes that commonly occur in this crash mode. METHODS: The National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) years 2000-2015 database was queried, resulting in 4,495 non-weighted far-side crashes. For each case, occupant age, sex, and the following metadata were collected: vehicle model year, vehicle body type, lateral delta-v, normalized PDOF, multiple impacts, belt use, seat position, object contacted, striking vehicle body type, maximum crush extent and side airbag deployment. Multivariable logistic regression was used to develop risk curves for AIS 2+ through 5+ injuries, AIS 2+ injuries by body region (head, thorax, lower extremity), and for each of the 10 most frequent far-side AIS 2+ injuries. Significant covariates were determined by backwards elimination (p < 0.05). The full dataset and a subsampled dataset of only cases with side airbag deployment were used to develop risk curves. RESULTS: For AIS 2+ through 5+ injury, greater delta-V was associated with greater injury risk (OR's: 2.48-3.66 per 11.9 kph increase) and belt use was associated with lower risk (OR's: 0.04-0.36 compared to unbelted). Multiple impacts were significant predictors of increased AIS 3+, 4+ and 5+ injury risk (OR's: 2.56, 2.27 and 2.83 compared to single impact). For AIS 2+ body region injuries, lateral delta-V and maximum CDC extent were positively associated with increased head, thorax and lower extremity injury risk while belt use was associated with lower risk. Increased lateral delta-v, unbelted status, and greater maximum CDC extent frequently increased injury risk for the most common far-side injuries. Side airbag deployment was not a significant covariate for the injury risk models. CONCLUSIONS: The resulting risk models expand upon previous literature gaps to provide a more comprehensive view of contributors to injury risk for occupants in far-side MVCs. This study yields risk curves based on the latest available NASS-CDS data.

Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer's disease.

Evidence suggests exposure to particulate matter with aerodynamic diameter <2.5 µm (PM2.5) may increase the risk for Alzheimer's disease and related dementias. Whether PM2.5 alters brain structure and accelerates the preclinical neuropsychological processes remains unknown. Early decline of episodic memory is detectable in preclinical Alzheimer's disease. Therefore, we conducted a longitudinal study to examine whether PM2.5 affects the episodic memory decline, and also explored the potential mediating role of increased neuroanatomic risk of Alzheimer's disease associated with exposure. Participants included older females (n = 998; aged 73-87) enrolled in both the Women's Health Initiative Study of Cognitive Aging and the Women's Health Initiative Memory Study of Magnetic Resonance Imaging, with annual (1999-2010) episodic memory assessment by the California Verbal Learning Test, including measures of immediate free recall/new learning (List A Trials 1-3; List B) and delayed free recall (short- and long-delay), and up to two brain scans (MRI-1: 2005-06; MRI-2: 2009-10). Subjects were assigned Alzheimer's disease pattern similarity scores (a brain-MRI measured neuroanatomical risk for Alzheimer's disease), developed by supervised machine learning and validated with data from the Alzheimer's Disease Neuroimaging Initiative. Based on residential histories and environmental data on air monitoring and simulated atmospheric chemistry, we used a spatiotemporal model to estimate 3-year average PM2.5 exposure preceding MRI-1. In multilevel structural equation models, PM2.5 was associated with greater declines in immediate recall and new learning, but no association was found with decline in delayed-recall or composite scores. For each interquartile increment (2.81 µg/m3) of PM2.5, the annual decline rate was significantly accelerated by 19.3% [95% confidence interval (CI) = 1.9% to 36.2%] for Trials 1-3 and 14.8% (4.4% to 24.9%) for List B performance, adjusting for multiple potential confounders. Long-term PM2.5 exposure was associated with increased Alzheimer's disease pattern similarity scores, which accounted for 22.6% (95% CI: 1% to 68.9%) and 10.7% (95% CI: 1.0% to 30.3%) of the total adverse PM2.5 effects on Trials 1-3 and List B, respectively. The observed associations remained after excluding incident cases of dementia and stroke during the follow-up, or further adjusting for small-vessel ischaemic disease volumes. Our findings illustrate the continuum of PM2.5 neurotoxicity that contributes to early decline of immediate free recall/new learning at the preclinical stage, which is mediated by progressive atrophy of grey matter indicative of increased Alzheimer's disease risk, independent of cerebrovascular damage.

Age-based differences in the disability of extremity injuries in pediatric and adult occupants.

Objective: The objective was to develop a disability-based metric for motor vehicle crash (MVC) upper and lower extremity injuries and compare functional outcomes between children and adults.Methods: Disability risk (DR) was quantified using Functional Independence Measure (FIM) scores within the National Trauma Data Bank-Research Data System for the top 95% most frequently occurring Abbreviated Injury Scale (AIS) 3 extremity injuries (22 unique injuries). Pediatric (7-18 years), young adult (19-45 years), middle-aged (46-65 years), and older adult (66+ years) MVC occupants with an FIM score and at least one of the 22 extremity injuries were included. DR was calculated for each injury as the proportion of occupants who were disabled of those sustaining the injury. A maximum AIS-adjusted disability risk (DRMAIS) was also calculated for each injury, excluding occupants with AIS 4+ co-injuries.Results: Locomotion impairment was the most frequent disability type across all ages. DR and DRMAIS of the extremity injuries ranged from 0.06 to 1.00 (6%-100% disability risk). Disability risk increased with age, with DRMAIS increasing from 25.9% ± 8.6% (mean ± SD) in pediatric subjects to 30.4% ± 6.3% in young adults, 39.5% ± 6.6% in middle-aged adults, and 60.5 ± 13.3% in older adults. DRMAIS for upper extremity fractures differed significantly between age groups, with higher disability in older adults, followed by middle-aged adults. DRMAIS for pelvis, hip, shaft, knee, and other lower extremity fractures differed significantly between age groups, with older adult DRMAIS being significantly higher for each fracture type. DRMAIS for hip and lower extremity shaft fractures was also significantly higher in middle-aged occupants compared to pediatric and young adult occupants. The maximum AIS-adjusted mortality risk (MRMAIS, proportion of fatalities among occupants sustaining an MAIS 3 injury) was not correlated with DRMAIS for extremity injuries in pediatric, young adult, middle-aged, and older adult occupants (all R2 < 0.01). Disability associated with each extremity injury was higher than mortality risk.Conclusions: Older adults had significantly greater disability for MVC extremity injuries. Lower disability rates in children may stem from their increased physiological capacity for bone healing and relative lack of bone disease. The disability metrics developed can supplement AIS and other severity-based metrics by accounting for the age-specific functional implications of MVC extremity injuries.

Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.

RATIONALE AND OBJECTIVES: To develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) scans to evaluate for presence of sarcopenia. MATERIALS AND METHODS: A convolutional neural network based on the U-Net architecture was trained to perform muscle segmentation on a dataset of 1875 single slice CT images and was tested on 209 CT images of participants in the National Lung Screening Trial. Low-dose, noncontrast CT examinations were obtained at 33 clinical sites, using scanners from four manufacturers. The study participants had a mean age of 71.6 years (range, 70-74 years). Ground truth was obtained by manually segmenting the left paraspinous muscle at the level of the T12 vertebra. Muscle cross-sectional area (CSA) and muscle attenuation (MA) were recorded. Comparison between the ML algorithm and ground truth measures of muscle CSA and MA were obtained using Dice similarity coefficients and Pearson correlations. RESULTS: Compared to ground truth segmentation, the ML algorithm achieved median (standard deviation) Dice scores of 0.94 (0.04) in the test set. Mean (SD) muscle CSA was 14.3 (3.6) cm2 for ground truth and 13.7 (3.5) cm2 for ML segmentation. Mean (SD) MA was 41.6 (7.6) Hounsfield units (HU) for ground truth and 43.5 (7.9) HU for ML segmentation. There was high correlation between ML algorithm and ground truth for muscle CSA (r2 = 0.86; p < 0.0001) and MA (r2 = 0.95; p < 0.0001). CONCLUSION: The ML algorithm for measurement of paraspinous muscles compared favorably to manual ground truth measurements in the NLST. The algorithm generalized well to a heterogeneous set of low-dose CT images and may be capable of automated quantification of muscle metrics to screen for sarcopenia on routine chest CT examinations.

Development, Validation and Pilot Field Deployment of a Custom Mouthpiece for Head Impact Measurement.

The objective of this study was to develop a mouthpiece sensor with improved head kinematic measurement for use in non-helmeted and helmeted sports through laboratory validation and pilot field deployment in female youth soccer. For laboratory validation, data from the mouthpiece sensor was compared to standard sensors mounted in a headform at the center of gravity as the headform was struck with a swinging pendulum. Linear regression between peak kinematics measured from the mouthpiece and headform showed strong correlation, with r2 values of 0.95 (slope = 1.02) for linear acceleration, 1.00 (slope = 1.00) for angular velocity, and 0.97 (slope = 0.96) for angular acceleration. In field deployment, mouthpiece data were collected from four female youth soccer players and time-synchronized with film. Film-verified events (n = 915) were observed over 9 practices and 5 games, and 632 were matched to a corresponding mouthpiece event. This resulted in an overall sensitivity of 69.2% and a positive predictive value of 80.3%. This validation and pilot field deployment data demonstrates that the mouthpiece provides highly accurate measurement of on-field head impact data that can be used to further study the effects of impact exposure in both helmeted and non-helmeted sports.

General and domain-specific cognitive reserve, mild cognitive impairment, and dementia risk in older women.

INTRODUCTION: In a geographically diverse sample of women, we asked whether cognitive reserve (CR) is best viewed as a general or cognitive domain-specific construct and whether some cognitive reserve domains but not others exert protective effects on risk of developing mild cognitive impairment (MCI) or dementia. METHODS: Estimates of general and domain-specific CR were derived via variance decomposition in 972 cognitively intact women from the Women's Health Initiative Study of Cognitive Aging and Women's Health Memory Study Magnetic Resonance Imaging. Women were then followed up for 13 years. RESULTS: General CR was the strongest predictor of reduced risk for both MCI and dementia, compared to domain-specific CR measures. Verbal memory, figural memory, and spatial CR were independently protective of MCI, but only verbal memory was independently associated with reduced risk for dementia. DISCUSSION: Cognitive reserve is a heterogenous construct with valid quantitative measures identifiable across different neuropsychological processes associated with MCI and dementia.

Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases.

INTRODUCTION: The main goal of this work is to investigate the feasibility of estimating an anatomical index that can be used as an Alzheimer's disease (AD) risk factor in the Women's Health Initiative Magnetic Resonance Imaging Study (WHIMS-MRI) using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a well-characterized imaging database of AD patients and cognitively normal subjects. We called this index AD Pattern Similarity (AD-PS) scores. To demonstrate the construct validity of the scores, we investigated their associations with several AD risk factors. The ADNI and WHIMS imaging databases were collected with different goals, populations and data acquisition protocols: it is important to demonstrate that the approach to estimating AD-PS scores can bridge these differences. METHODS: MRI data from both studies were processed using high-dimensional warping methods. High-dimensional classifiers were then estimated using the ADNI MRI data. Next, the classifiers were applied to baseline and follow-up WHIMS-MRI GM data to generate the GM AD-PS scores. To study the validity of the scores we investigated associations between GM AD-PS scores at baseline (Scan 1) and their longitudinal changes (Scan 2 -Scan 1) with: 1) age, cognitive scores, white matter small vessel ischemic disease (WM SVID) volume at baseline and 2) age, cognitive scores, WM SVID volume longitudinal changes respectively. In addition, we investigated their associations with time until classification of independently adjudicated status in WHIMS-MRI. RESULTS: Higher GM AD-PS scores from WHIMS-MRI baseline data were associated with older age, lower cognitive scores, and higher WM SVID volume. Longitudinal changes in GM AD-PS scores (Scan 2 - Scan 1) were also associated with age and changes in WM SVID volumes and cognitive test scores. Increases in the GM AD-PS scores predicted decreases in cognitive scores and increases in WM SVID volume. GM AD-PS scores and their longitudinal changes also were associated with time until classification of cognitive impairment. Finally, receiver operating characteristic curves showed that baseline GM AD-PS scores of cognitively normal participants carried information about future cognitive status determined during follow-up. DISCUSSION: We applied a high-dimensional machine learning approach to estimate a novel AD risk factor for WHIMS-MRI study participants using ADNI data. The GM AD-PS scores showed strong associations with incident cognitive impairment and cross-sectional and longitudinal associations with age, cognitive function, cognitive status and WM SVID volume lending support to the ongoing validation of the GM AD-PS score.

Computer simulations for assessing cognitively intensive instrumental activities of daily living in older adults.

INTRODUCTION: Aim is to evaluate validity, reliability, diagnostic precision, and user acceptability of computer simulations of cognitively demanding tasks when administered to older adults with and without cognitive impairment. METHODS: Five simulation modules were administered to 161 individuals aged ≥60 years with no cognitive impairment (N = 81), mild cognitive impairment (N = 52), or dementia (N = 28). Groups were compared on total accuracy and time to complete the tasks (seconds). Receiver operating characteristics were evaluated. Reliability was assessed over one month. Participants rated face validity and acceptability. RESULTS: Total accuracy (P < .0001) and time (P = .0015) differed between groups. Test-retest correlations were excellent (0.79 and 0.88, respectively). Area under the curve ranged from good (0.77) to excellent (0.97). User ratings supported their face validity and acceptability. DISCUSSION: Brief computer simulations can be useful in assessing cognitive functional abilities of older adults and distinguishing varying degrees of impairment.

Functional outcomes of thoracic injuries in pediatric and adult occupants.

OBJECTIVE: To develop a disability metric for motor vehicle crash (MVC) thoracic injuries and compare functional outcomes between pediatric and adult populations. METHODS: Disability risk (DR) was quantified using Functional Independence Measure (FIM) scores within the National Trauma Data Bank (NTDB) for the top 95% most frequently occurring AIS 2, 3, 4, and 5 thoracic injuries in NASS-CDS 2000-2011. The NTDB contains a truncated form of the FIM score, including three items (self-feed, locomotion, and verbal expression), each graded from full functional dependence to full functional independence. Pediatric (ages 7-18 years), adult (19-45), middle-aged adult (46-65), and older adult (66+) MVC occupants were classified as disabled or not disabled based on the FIM scale. The DR was calculated for each injury within each age group by dividing the number of patients who were disabled that sustained the specific injury by the number of patients who sustained the specific injury. To account for the impact of more severe co-injuries, a maximum Abbreviated Injury Scale (MAIS) adjusted DR (DRMAIS) was also calculated. DR and DRMAIS could range from 0 (0% disability risk) to 1 (100% disability risk). RESULTS: The mean DRMAIS for MVC thoracic injuries was 20% for pediatric occupants, 22% for adults, 29% for middle-aged adults, and 43% for older adults. Older adults possessed higher DRMAIS values for diaphragm laceration/rupture, heart laceration, hemo/pneumothorax, lung contusion/laceration, rib fracture, and sternum fracture compared to the other age groups. The pediatric population possessed a higher DRMAIS value for flail chest compared to the other age groups. CONCLUSIONS: Older adults had significantly greater overall disability than each of the other age groups for thoracic injuries. The developed disability metrics are important in quantifying the significant burden of injuries and loss of quality life years. Such metrics can be used to better characterize severity of injury and further the understanding of age-related differences in injury outcomes, which can impact future age-specific modifications to AIS.

Characterization of the occult nature of frequently occurring pediatric motor vehicle crash injuries.

BACKGROUND: Occult injuries are those likely to be missed on initial assessment by first responders and, though initially asymptomatic, they may present suddenly and lead to rapid patient decompensation. No scoring systems to quantify the occultness of pediatric injuries have been established. Such a scoring system will be useful in the creation of an Advanced Automotive Crash Notification (AACN) system that assists first responders in making triage decisions following a motor vehicle crash (MVC). STUDY DESIGN: The most frequent MVC injuries were determined for 0-4, 5-9, 10-14 and 15-18 year olds. For each age-specific injury, experts with pediatric trauma expertise were asked to rate the likelihood that the injury may be missed by first responders. An occult score (ranging from 0-1) was calculated by averaging and normalizing the responses of the experts polled. RESULTS: Evaluation of all injuries across all age groups demonstrated greater occult scores for the younger age groups compared to older age groups (mean occult score 0-4yo: 0.61 ±â€¯0.23, 5-9yo: 0.53 ±â€¯0.25, 10-14yo: 0.48 ±â€¯0.23, and 15-18yo: 0.42 ±â€¯0.22, p < 0.01). Body-region specific occult scores revealed that experts judged abdominal, spine and thoracic injuries to be more occult than injuries to other body regions. CONCLUSIONS: The occult scores suggested that injuries are more difficult to detect in younger age groups, likely given their inability to express symptoms. An AACN algorithm that can predict the presence of clinically undetectable injuries at the scene can improve triage of children with these injuries to higher levels of care.