Abstract Background It’s crucial to quantify and compare the accuracies of plasma biomarkers in predicting amyloid PET status. Method The sample included a cross‐sectional sample of 212 participants from Baltimore Longitudinal Study of Aging. Amyloid PET status was determined from Pittsburgh compound B (PiB) PET using a Gaussian mixture model (140 PiB– and 72 PiB+). Using the Quanterix SIMOA Neuro‐4‐plex, three plasma biomarkers (amyloid‐beta 42 to 40 ratio [Aβ 42 /Aβ 40 ], glial fibrillary acidic protein [GFAP], and neurofilament light chain [NfL]) were evaluated. We used receiver operating characteristic (ROC) curve analysis to estimate area under the curve (AUC) for each of the three biomarkers in predicting amyloid PET status. AUC estimates were cross validated using leave‐one‐out method. Finally, from a set of potential features (i.e., the three plasma biomarkers, age and APOE ‐ε4 carrier status), we applied stepwise logistic regression to find the best subset of features to predict amyloid PET status. Result Participant characteristics are presented in Table 1. Each of the 3 biomarkers performed better than a random classifier, with GFAP having the highest validated AUC (0.692), followed by Aβ 42 /Aβ 40 (AUC=0.677), and NfL (AUC=0.625) (Figure 1). The only statistically significant AUC difference is between GFAP and NfL (p = 0.029) (Table 2). Based on stepwise logistic regression, the model with Aβ 42 /Aβ 40 , GFAP and APOE ‐ε4 status was selected as the final model with a moderate AUC of 0.732 (Figure 2). Odds ratios associated with each predictor are reported in Table 3. Conclusion Of the three Quanterix SIMOA plasma biomarkers, Aβ 42 /Aβ 40 and GFAP have similar AUCs and are better at predicting amyloid PET status compared with NfL, although all AUCs are modest. The final multivariable logistic regression model, which included Aβ 42 /Aβ 40 , GFAP and APOE ‐ε4 status, had a modest AUC of 0.732. Future studies with larger samples and more biomarkers, especially p‐tau measure, will be necessary to develop better prediction models.
As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.
To investigate the relationship between vestibular loss associated with aging and age-related decline in visuospatial function.Cross-sectional analysis within a prospective cohort study.Baltimore Longitudinal Study of Aging (BLSA).Community-dwelling BLSA participants with a mean age of 72 (range 26-91) (N = 183).Vestibular function was measured using vestibular-evoked myogenic potentials. Visuospatial cognitive tests included Card Rotations, Purdue Pegboard, Benton Visual Retention Test, and Trail-Making Test Parts A and B. Tests of executive function, memory, and attention were also considered.Participants underwent vestibular and cognitive function testing. In multiple linear regression analyses, poorer vestibular function was associated with poorer performance on Card Rotations (P = .001), Purdue Pegboard (P = .005), Benton Visual Retention Test (P = 0.008), and Trail-Making Test Part B (P = .04). Performance on tests of executive function and verbal memory were not significantly associated with vestibular function. Exploratory factor analyses in a subgroup of participants who underwent all cognitive tests identified three latent cognitive abilities: visuospatial ability, verbal memory, and working memory and attention. Vestibular loss was significantly associated with lower visuospatial and working memory and attention factor scores.Significant consistent associations between vestibular function and tests of visuospatial ability were observed in a sample of community-dwelling adults. Impairment in visuospatial skills is often one of the first signs of dementia and Alzheimer's disease. Further longitudinal studies are needed to evaluate whether the relationship between vestibular function and visuospatial ability is causal.
Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4–12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis.
The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these layers can aid in studying volume differences between patients with neurodegenerative diseases and normal aging subjects. In this work, we use convolutional neural networks (CNNs) to reconstruct surfaces representing meningeal layer boundaries from magnetic resonance (MR) images. We first use the CNNs to predict the signed distance functions (SDFs) representing these surfaces while preserving their anatomical ordering. The marching cubes algorithm is then used to generate continuous surface representations; both the subarachnoid space (SAS) and the intracranial volume (ICV) are computed from these surfaces. The proposed method is compared to a state-of-the-art deformable model-based reconstruction method, and we show that our method can reconstruct smoother and more accurate surfaces using less computation time. Finally, we conduct experiments with volumetric analysis on both subjects with multiple sclerosis and healthy controls. For healthy and MS subjects, ICVs and SAS volumes are found to be significantly correlated to sex (p<0.01) and age (p<0.03) changes, respectively.
To compare sleep and 24-hour rest/activity rhythms (RARs) between cognitively normal older adults who are β-amyloid-positive (Aβ+) or Aβ- and replicate a novel time-of-day-specific difference between these groups identified in a previous exploratory study.
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