Biology subjects

Provenzano, F. A.

Publications and source records attributed to Provenzano, F. A..

3 recordsLinked to original sources

bioRxiv2025

Structural Compression and Entorhinal Vulnerability: Linking Tentorial Adjacency to Tau Burden and Dementia Progression

Alzheimers disease (AD) is a growing public health crisis. The disease is defined neuropathologically by accumulation of amyloid-{beta} plaques and neurofibrillary tangles (NFTs) composed of abnormal tau protein in the brain. Early neurofibrillary degeneration in the entorhinal cortex (EC) is a hallmark of AD and a critical initiating event in the hierarchical pathoanatomical progression. However, the factors triggering initial tau deposition in the EC remain unclear. We propose a novel biomechanical cascade hypothesis, positing that the unique anatomical inferomedial positioning of the EC, including proximity to the tentorial incisura (TI) and other skull base structures, renders it susceptible to very mild yet persistent age-related mechanical stress, analogous to the effects of repetitive mild traumatic brain injury, triggering tau pathology. To test this hypothesis, we developed a method to quantify Entorhinal-Tentorial (EC-TI) proximity and applied it to multimodal imaging data from the Alzheimers Disease Neuroimaging Initiative (ADNI; n=47). Based on this neuroanatomical contact coefficient (NCC), participants were heuristically stratified into high (n=24) and low (n=23) adjacency groups. When controlling for other risk factors, tau PET signal in the EC predicted conversion from mild cognitive impairment to AD only in the high-adjacency group (LLR p=0.009, tau PET in EC p=0.036). These findings identify EC-TI proximity as a novel and anatomically grounded biomarker of AD progression risk. More broadly, they suggest a previously unrecognized biomechanical contribution to the initiation of tau pathology in aging and sporadic AD, opening new avenues for early detection, risk stratification, and mechanistically targeted prevention strategies.

neuroscience↗

bioRxiv2019

Detecting prodromal Alzheimer’s disease with MRI through deep learning

Deep learning applied to MRI for Alzheimers classification is hypothesized to improve if the deep learning model implicates diseases pathophysiology. The challenge in testing this hypothesis is that large-scale data are required to train this type of model. Here, we overcome this challenge by using a novel data augmentation strategy and show that our MRI-based deep learning model classifies Alzheimers dementia with high accuracy. Moreover, a class activation map was found dominated by signal from the hippocampal formation, a site where Alzheimers pathophysiology begins. Next, we tested the models performance in prodromal Alzheimers when patients present with mild cognitive impairment (MCI). We retroactively dichotomized a large cohort of MCI patients who were followed for up to 10 years into those with and without prodromal Alzheimers at baseline and used the dementia-derived model to generate individual deep learning MRI scores. We compared the two groups on these scores, and on other biomarkers of amyloid pathology, tau pathology, and neurodegeneration. The deep learning MRI scores outperformed nearly all other biomarkers, including--unexpectedly--biomarkers of amyloid or tau pathology, in classifying prodromal disease and in predicting clinical progression. Providing a mechanistic explanation, the deep learning MRI scores were found to be linked to regional tau pathology, through investigations using cross-sectional, longitudinal, premortem and postmortem data. Our findings validate that a diseases known pathophysiology can improve the design and performance of deep learning models. Moreover, by showing that deep learning can extract useful biomarker information from conventional MRIs, the advantages of this model extend practically, potentially reducing patient burden, risk, and cost.

neuroscience↗

bioRxiv2018

Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer’s Disease Classification

Deep learning techniques on MRI scans have demonstrated great potential to improve the diagnosis of neurological diseases. Here, we investigate the application of 3D deep convolutional neural networks (CNNs) for classifying Alzheimers disease (AD) based on structural MRI data. In particular, we take on two challenges that are under-explored in the literature on deep learning for neuroimaging. First deep neural networks typically require large-scale data that is not always available in medical studies. Therefore, we explore the use of including longitudinal scans in classification studies, greatly increasing the amount of data for training and improving the generalization performance of our classifiers. Moreover, previous studies applying deep learning to classifying Alzheimers disease from neuroimaging have typically addressed classification based on whole brain volumes but stopped short of performing in-depth regional analyses to localize the most predictive areas. Additionally, we show a deep net trained to distinguish between AD and cognitively normal subjects can be applied to classify mild cognitive impairment patients, with classification scores aligning empirically with the likelihood of progression to AD. Our initial results demonstrate both that we can classify AD with an area under the receiver operator characteristic curve (AUROC) of .990 and that we can predict conversion to AD among patients in the MCI subgroup with an AURUC of 0.787. We then localize the predictive regions, by performing both saliency-based interpretation and rigorous slice and lobar level ablation studies. Interestingly, our regional analyses identified the hippocampal formation, including the entorhinal cortex, to be the most predictive region for our models. This finding adds evidence that the hippocampal formation is an anatomical seat of AD and a prominent feature in its diagnosis. Together, the results of this study further demonstrate the potential of deep learning to impact AD classification and to identify ADs structural neuroimaging signatures. The proposed classification and regional analyses methods constitute a general framework that can easily be applied to other disorders and imaging modalities.\n\nAcronyms

neuroscience↗