Biology subjects

Caselli, R. J.

Publications and source records attributed to Caselli, R. J..

8 recordsLinked to original sources

bioRxiv2023

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons With and Without Significant Amyloid Plaques

BackgroundAmyloid-{beta} (A{beta}) plaques play a pivotal role in Alzheimers disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing A{beta} plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of A{beta}-plaques. ObjectiveTo develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma A{beta}42/40 ratio. MethodsWe extracted hippocampal multivariate morphometry statistics (MMS) from MR images and together with plasma A{beta}42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimers Institute (BAI), including prediction accuracy, precision, recall rate, F1 score and AUC score. ResultsResults from ADNI (mean age 72.6, A{beta}+ rate 49.5%) and BAI (mean age 66.2, A{beta}+ rate 36.9%) datasets revealed the integrated multimodal (IMM) models superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma A{beta}42/40 (0.73 and 0.81) predictors. ConclusionOur IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimers disease diagnostics, leveraging diverse pathologic features related to A{beta} plaques and structural MRI.

neuroscience↗

bioRxiv2021

Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model

Alzheimers disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics - the study of gene expression - also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a persons genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

bioinformatics↗

bioRxiv2021

FEDERATED MORPHOMETRY FEATURE SELECTION FOR HIPPOCAMPAL MORPHOMETRY ASSOCIATED BETA-AMYLOID AND TAU PATHOLOGY

Amyloid-{beta} (A{beta}) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimers disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of A{beta}/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with A{beta}/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two ADNI cohorts to understand hippocampal alterations that relate to A{beta}/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI) and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89x speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal A{beta}/tau. As potential biomarkers for A{beta}/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between A{beta}/tau burden and hippocampal morphometry.

neuroscience↗

bioRxiv2020

Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,125 Individuals from the ADNI and OASIS Databases

Biomarker-assisted preclinical/early detection and intervention in Alzheimers disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (A{beta}) plaques in the human brain. However, current methods to detect A{beta} pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that MRI-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain A{beta} burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each subject. Then we apply these individual representations and a binary random forest classifier to predict brain A{beta} positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate A{beta} positivity in people with mild cognitive impairment (MCI) (Accuracy (ACC)=0.89 (ADNI)) and in cognitively unimpaired (CU) individuals (ACC=0.79 (ADNI) and ACC=0.81 (OASIS)). These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM), and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

bioinformatics↗

bioRxiv2020

Unbalanced Sample Size Introduces Spurious Correlations to Genome-wide Heterozygosity Analyses

Excess of heterozygosity (H) is a widely used measure of genetic diversity of a population. As high-throughput sequencing and genotyping data become readily available, it has been applied to investigating the associations of genome-wide genetic diversity with human diseases and traits. However, these studies often report contradictory results. In this paper, we present a meta-analysis of five whole-exome studies to examine the association of H scores with Alzheimers disease. We show that the mean H score of a group is not associated with the disease status, but is associated with the sample size. Across all five studies, the group with more samples has a significantly lower H score than the group with fewer samples. To remove potential confounders in empirical data sets, we perform computer simulations to create artificial genomes controlled for the number of polymorphic loci, the sample size and the allele frequency. Analyses of these simulated data confirm the negative correlation between the sample size and the H score. Furthermore, we find that genomes with a large number of rare variants also have inflated H scores. These biases altogether can lead to spurious associations between genetic diversity and the phenotype of interest. Based on these findings, we advocate that studies shall balance the sample sizes when using genome-wide H scores to assess genetic diversities of different populations, which helps improve the reproducibility of future research.

bioinformatics↗

bioRxiv2019

Severe Hyposmia Distinguishes Neuropathologically Confirmed Dementia with Lewy Bodies from Alzheimer’s Disease Dementia

Due to the absence of core clinical features, many subjects with neuropathologically-confirmed dementia with Lewy bodies (DLB) are never diagnosed as such during life. Most of these are diagnosed with Alzheimers disease dementia (ADD) or unspecified dementia. Unrecognized DLB therefore is a critical impediment to clinical studies and treatment trials of both ADD and DLB. There are numerous published studies that suggest that olfactory function tests may be able to differentiate some neurodegenerative conditions from each other and from normal subjects, but there are very few studies with neuropathological confirmation of diagnosis. We compared University of Pennsylvania Smell Identification Test (UPSIT) results in 209 subjects: 1) 29 concurrently meeting intermediate or high consensus clinicopathological criteria for both DLB and ADD 2) 96 meeting criteria for ADD without DLB 3) 84 control subjects that were non-demented and without parkinsonism at death. The DLB subjects had significantly lower (one-way ANOVA p < 0.0001, pairwise Bonferroni p < 0.05) first and mean UPSIT scores (13.7 and 13.2) than ADD (23.3 and 22.2) or controls (29.6 and 28.9). For subjects with first and mean UPSIT scores less than 20 and 17, respectively, Firth logistic regression analysis, adjusted for age, gender and mean MMSE score, conferred statistically significant odds ratios of 17.5 and 18.0 for predicting a DLB vs ADD diagnosis, as compared to 3.3 for the presence or absence of visual hallucinations throughout the clinical observation period. To our knowledge, this is the largest study to date comparing olfactory function in subjects with neuropathologically confirmed DLB and ADD. Olfactory function testing may be a convenient and inexpensive strategy for enriching dementia studies or clinical trials with DLB subjects, or conversely, reducing the inclusion of DLB subjects in ADD studies or trials.

neuroscience↗

bioRxiv2019

Large-scale proteomic analysis of human prefrontal cortex identifies proteins associated with cognitive trajectory in advanced age

In advanced age, some individuals maintain a stable cognitive trajectory while others experience a rapid decline. Such variation in cognitive trajectory is only partially explained by traditional neurodegenerative pathologies. Hence, to identify new processes underlying variation in cognitive trajectory, we perform an unbiased proteome-wide association study of cognitive trajectory in a discovery (n=104) and replication cohort (n=39) of initially cognitively unimpaired, longitudinally assessed older-adult brain donors. We find 579 proteins associated with cognitive trajectory after meta-analysis. Notably, we present novel evidence for increased neuronal mitochondrial activities in cognitive stability regardless of the burden of traditional neuropathologies. Furthermore, we provide additional evidence for increased synaptic activities and decreased inflammation and apoptosis in cognitive stability. Importantly, we nominate proteins associated with cognitive trajectory, particularly the 38 proteins that act independently of neuropathologies and are also hub proteins of protein co-expression networks, as promising targets for future mechanistic studies of cognitive trajectory.

neuroscience↗

bioRxiv2019

Faster Cognitive Decline in Dementia due to Alzheimer Disease with Clinically Undiagnosed Lewy Body Disease

Neuropathology has demonstrated a high rate of comorbid pathology in dementia due to Alzheimers disease (ADD). The most common major comorbidity is Lewy body disease (LBD), either as dementia with Lewy bodies (AD-DLB) or Alzheimers disease with Lewy bodies (AD-LB), the latter representing subjects with ADD and LBD not meeting neuropathological distribution and density thresholds for DLB. Although it has been established that ADD subjects with undifferentiated LBD have a more rapid cognitive decline than those with ADD alone, it is still unknown whether AD-LB subjects, who represent the majority of LBD and approximately one-third of all those with ADD, have a different clinical course. Subjects with dementia included those with \"pure\" ADD (n = 137), AD-DLB (n = 64) and AD-LB (n = 114), all with two or more complete Mini Mental State Examinations (MMSE) and a full neuropathological examination. Linear mixed models assessing MMSE change showed that the AD-LB group had significantly greater decline compared to the ADD group ({beta} = -0.69, 95% CI: -1.05, -0.33, p<0.001) while the AD-DLB group did not ({beta} = -0.30, 95% CI: -0.73, 0.14, p = 0.18). Of those with AD-DLB and AD-LB, only 66% and 2.1%, respectively, had been diagnosed with LBD at any point during their clinical course. The probable cause of LBD clinical detection failure is the lack of a sufficient set of characteristic core clinical features. Core DLB clinical features were not more common in AD-LB as compared to ADD. Compared with clinically-diagnosed AD-DLB subjects, those that were clinically undetected had significantly lower prevalences of parkinsonism (p = 0.046), visual hallucinations (p = 0.0008) and dream enactment behavior (0.013). Clinical identification of ADD with LBD would allow stratified analyses of ADD clinical trials, potentially improving the probability of trial success.

neuroscience↗