Patellar and hamstring autografts are associated with different jump task loading asymmetries after ACL reconstruction

© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd After anterior cruciate ligament reconstruction (ACLR), there is a higher re-injury rate to the contralateral limb in athletes who undergo surgery using a bone-patellar-tendon-bone (BPTB) autograft than using a semitendinosus and gracilis hamstring tendon (HT) autograft. This may be influenced by differing lower-limb loading asymmetries present when athletes of each graft type return to play (RTP). The aim of this study was to compare bilateral countermovement jump (CMJ) phase-specific impulse asymmetries between athletes with BPTB and HT autografts 9 months post-ACLR, and to identify the relationship between impulse and isokinetic strength asymmetries. Male field sport athletes with a BPTB (n = 22) or HT (n = 22) autograft were tested approximately 9 months post-ACLR. An uninjured control group (n = 22) was also tested on a single occasion. Phase-specific bilateral absolute impulse asymmetries were calculated during the CMJ and compared between groups using Kruskal-Wallis and post-hoc testing. A linear regression model was used to assess the relationship between impulse asymmetries and isokinetic concentric knee extensor strength asymmetries. BPTB athletes demonstrated greater impulse asymmetries than HT athletes during the eccentric (P = 0.01) and concentric (P = 0.008) phases of the jump. Isokinetic strength asymmetry was a significant predictor of CMJ concentric impulse asymmetry in both BPTB (r2 = 0.39) and HT athletes (r2 = 0.18) but not eccentric impulse asymmetry in any group. The greater loading asymmetries demonstrated by BPTB than HT athletes 9 months after ACLR may contribute to the differing incidence rates of contralateral ACL injury. The findings suggest that graft-specific loading asymmetries should be targeted during rehabilitation prior to RTP

Fixation Prediction and Visual Priority Maps for Biped Locomotion

© 2017 IEEE. This paper presents an analysis of the low-level features and key spatial points used by humans during locomotion over diverse types of terrain. Although, a number of methods for creating saliency maps and task-dependent approaches have been proposed to estimate the areas of an image that attract human attention, none of these can straightforwardly be applied to sequences captured during locomotion, which contain dynamic content derived from a moving viewpoint. We used a novel learning-based method for creating a visual priority map informed by human eye tracking data. Our proposed priority map is created based on two fixation types: first exploiting the observation that humans search for safe foot placement and second that they observe the edges of a path as a guide to safe traversal of the terrain. Texture features and the difference between them, observed at the region around an eye position, are employed within a support vector machine to create a visual priority map for biped locomotion. The results show that our proposed method outperforms the state-of-the-art, particularly for more complex terrains, where achieving smooth locomotion needs more attention on the traversing path

The use of continuous spectral analysis for the assessment of postural stability changes after sports-related concussion

© 2019 Elsevier Ltd Impaired postural stability is associated with a variety of pathologies including sports-related concussion (SRC). Quantification of centre of pressure (COP) movement is the most common focus of instrumented assessment. Frequency-domain COP analyses have focused primarily on summary measures or pre-defined frequency bands but continuous analysis may provide novel and complementary insight into pathological control mechanisms. Our aims were (i) to compare post-SRC COP trajectory changes identified using clinician scores (Modified Balance Error Scoring System (M-BESS)), time-domain COP variables and continuous frequency spectral comparison; and (ii) to characterise frequency spectra changes. Male rugby players aged 15–19 years (n = 135) completed a pre-season baseline assessment comprising vision-obscured double-leg, single-leg and tandem stances on a force platform. Participants diagnosed with SRC during the season (n = 15) underwent repeat testing (median 4 days post-SRC; IQR 2.5–6.5). Baseline and post-SRC COP trajectories were compared using common time-domain COP variables, M-BESS scores and continuous frequency spectra. Post-SRC changes were identified using all three approaches. Spectral analysis revealed the largest effect size (Cliff's delta 0.39) and was the only method to identify differences in all three stances and in double-leg stance. All post-SRC increases in spectral content were in the anteroposterior direction; all decreases were in the mediolateral direction. Changes were localised to higher frequencies (1.7–8 Hz) except for double-leg stance anteroposterior direction, for which increases were observed throughout the analysed range. Our findings suggest that this method of spectral comparison may provide a more responsive and meaningful measure of postural stability changes after SRC than other commonly-used variables

Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum

Importance: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design. Objective: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates. Design, Setting, and Participants: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria. Exposures: Alzheimer disease biomarkers detected on PET or in CSF. Main Outcomes and Measures: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations. Results: Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18). Conclusions and Relevance: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies

Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum

Importance: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design. Objective: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates. Design, Setting, and Participants: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria. Exposures: Alzheimer disease biomarkers detected on PET or in CSF. Main Outcomes and Measures: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations. Results: Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18). Conclusions and Relevance: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies

Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum

Importance: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design. Objective: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates. Design, Setting, and Participants: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria. Exposures: Alzheimer disease biomarkers detected on PET or in CSF. Main Outcomes and Measures: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations. Results: Among the 19097 participants (mean [SD] age, 69.1 [9.8] years; 10148 women [53.1%]) included, 10139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P =.04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P =.004), subjective cognitive decline (9%; 95% CI, 3%-15%; P =.005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P =.004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P =.18). Conclusions and Relevance: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies