scholarly journals Impairment of CSF Egress through the Cribriform Plate plays an Apical Role in Alzheimer's disease Etiology

2021 ◽  
Author(s):  
Ricardo Zaragoza ◽  
Daniel Miulli ◽  
Samir Kashyap ◽  
Tyler A Carson ◽  
Andre Obenaus ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Clinical Study ◽  
Olfactory Bulb ◽  
Cribriform Plate ◽  
Post Mortem ◽  
Csf Flow ◽  
Disease Etiology

Cerebrospinal fluid (CSF) clears the brain's interstitial spaces, and disruptions in CSF flow or egress impact homeostasis, contributing to various neurological conditions. Here, we recast the human cribriform plate from innocuous bony structure to complex regulator of CSF egress with an apical role in Alzheimer's disease etiology. It includes the pathological evaluation of 70 post-mortem samples using high-resolution contrast-enhanced micro-CT and cutting-edge machine learning, a novel ferret model of neurodegeneration, and a clinical study with 560 volunteers, to provide conclusive evidence of a relationship between cribriform plate aging/pathology and cognitive impairment. Interstitial spaces within the medial temporal lobe and basal forebrain are cleared by CSF flow that drains through olfactory structures to the olfactory bulb, directly above the cribriform plate. We characterized CSF flow channels from subarachnoid spaces under the olfactory bulb to the nasal mucosa through subarachnoid evaginations that subdivide into tiny tubules that connect to an elaborate conduit system within the cribriform plate. These conduits form an internal watershed that runs from the crista galli's vault to a bony manifold within the olfactory fossa's back wall, connecting with large apertures in between. We found that the cross-sectional area of apertures limits CSF flux through the cribriform plate, which declines with increasing age. Subjects with a confirmed post-mortem diagnosis of Alzheimer's disease had the smallest CSF flux capacity, which reduces CSF-mediated clearance in upstream areas and leads to the accumulation of toxic macromolecules that seen AD pathology. We surgically occluded apertures in adult ferrets and found that this manipulation induced progressive deficits in spatiotemporal memory and significant atrophy of the temporal lobe, olfactory bulbs, and lateral olfactory stria. Finally, we explored human cribriform plate aging/pathology and cognition in a clinical study with 560 participants (20-95y). We evaluated cribriform plate morphology with CT and Deep Learning, assessed memory with a novel touch screen platform, tested olfactory discrimination, and asked questions about family history and relevant life events, like broken noses. Deep learning algorithms effectively parsed subjects and established the feasibility of predicting Alzheimer's disease years before a clinical presentation of cognitive impairment. This study provides a new perspective of the cribriform plate as a critical regulator of CSF egress and brain homeostasis.

Detection of prospective memory deficits in mild cognitive impairment of suspected Alzheimer’s disease etiology using a novel event-based prospective memory task

2009 ◽  
Vol 15 (1) ◽  
pp. 154-159 ◽  
Author(s):  
ALBERTO BLANCO-CAMPAL ◽  
ROBERT F. COEN ◽  
BRIAN A. LAWLOR ◽  
JOSEPH B. WALSH ◽  
TERESA E. BURKE
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Prospective Memory ◽  
Small Sample ◽  
Cutoff Score ◽  
Convenience Sample ◽  
Disease Etiology ◽  
Event Based

AbstractWe investigated the relative discriminatory efficacy of an event-based prospective memory (PM) task, in which specificity of the instructions and perceptual salience of the PM cue were manipulated, compared with two widely used retrospective memory (RM) tests (Rivermead Paragraph Recall Test and CERAD-Word List Test), when detecting mild cognitive impairment of suspected Alzheimer’s disease etiology (MCI-AD) (N = 19) from normal controls (NC) (N = 21). Statistical analyses showed high discriminatory capacity of the PM task for detecting MCI-AD. The Non-Specific-Non-Salient condition proved particularly useful in detecting MCI-AD, possibly reflecting the difficulty of the task, requiring more strategic attentional resources to monitor for the PM cue. With a cutoff score of <4/10, the Non-Specific-Non-Salient condition achieved a sensitivity = 84%, and a specificity = 95%, superior to the most discriminative RM test used (CERAD-Total Learning: sensitivity = 83%; specificity = 76%). Results suggest that PM is an early sign of memory failure in MCI-AD and may be a more pronounced deficit than retrospective failure, probably reflecting the greater self-initiated retrieval demands involved in the PM task used. Limitations include the relatively small sample size, and the use of a convenience sample (i.e. memory clinic attenders and healthy active volunteers), reducing the generalizability of the results, which should be regarded as preliminary. (JINS, 2009, 15, 154–159.)

scholarly journals Small RNA modifications in Alzheimer’s disease

2020 ◽  
Author(s):  
Xudong Zhang ◽  
Fatima Trebak ◽  
Lucas AC Souza ◽  
Junchao Shi ◽  
Tong Zhou ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Prefrontal Cortex ◽  
Small Rna ◽  
Small Rnas ◽  
Post Mortem ◽  
Rna Modifications ◽  
Non Coding Rnas ◽  
And Control

AbstractBackgroundWhile significant advances have been made in uncovering the aetiology of Alzheimer’s disease and related dementias at the genetic level, molecular events at the epigenetic level remain largely undefined. Emerging evidence indicates that small non-coding RNAs (sncRNAs) and their associated RNA modifications are important regulators of complex physiological and pathological processes, including aging, stress responses, and epigenetic inheritance. However, whether small RNAs and their modifications are altered in dementia is not known.MethodsWe performed LC-MS/MS–based, high-throughput assays of small RNA modifications in post-mortem samples of the prefrontal lobe cortices of Alzheimer’s disease (AD) and control individuals. We noted that some of the AD patients has co-occurring vascular cognitive impairment-related pathology (VaD).FindingsWe report altered small RNA modifications in AD samples compared with normal controls. The 15–25-nucleotide (nt) RNA fraction of these samples was enriched for microRNAs, whereas the 30–40-nt RNA fraction was enriched for tRNA-derived small RNAs (tsRNAs), rRNA-derived small RNAs (rsRNAs), and YRNA-derived small RNAs (ysRNAs). Interestingly, most of these altered RNA modifications were detected both in the AD and AD with co-occurring vascular dementia subjects. In addition, sequencing of small RNA in the 30–40-nt fraction from AD cortices revealed reductions in rsRNA-5S, tsRNA-Tyr, and tsRNA-Arg.InterpretationThese data suggest that sncRNAs and their associated modifications are novel signals that may be linked to the pathogenesis and development of Alzheimer’s disease.FundingNIH grants (R01HL122770, R01HL091905, 1P20GM130459, R01HD092431, P50HD098593, GM103440), AHA grant (17IRG33370128), Sigmund Gestetner Foundation Fellowship to P Kehoe.Research in ContextEvidence before this studyAlzheimer’s disease (AD) and vascular dementia (VaD) are marked by cognitive impairment and neuropathologies caused by significant neuronal death. Associated gene mutations are rare in subjects with dementia, and the aetiology of these diseases is still not completely understood. Recent emerging evidence suggests that epigenetic changes are risk factors for the development of dementia. However, studies assessing small RNA modifications—one of the features of epigenetics—in dementia are lacking.Added value of this studyWe used high-throughput liquid chromatography-tandem mass spectrometry and small RNA sequencing to profile small RNA modifications and the composition of small RNAs in post-mortem samples of the prefrontal cortex of AD and control subjects. We detected and quantified 16 types of RNA modifications and identified distinct small non-coding RNAs and modification signatures in AD subjects compared with controls.Implications of all the available evidenceThis study identified novel types and compositions of small RNA modifications in the prefrontal cortex of AD patients compared with control subjects in post-mortem samples. The cellular locations of these RNA modifications and whether they are drivers or outcomes of AD are still not known. However, results from the present study may open new possibilities for dissecting the dementia pathology.

scholarly journals HAVAs: Alzheimer’s Disease Detection using Normative and Pathological Lifespan Models

2021 ◽  
Author(s):  
Pierrick Coupé ◽  
José V. Manjón ◽  
Boris Mansencal ◽  
Thomas Tourdias ◽  
Gwenaëlle Catheline ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Clinical Practice ◽  
State Of The Art ◽  
Brain Structures ◽  
Volumetric Analysis ◽  
Classification Performance

AbstractIn this paper, we present an innovative MRI-based method for Alzheimer’s Disease (AD) detection and mild cognitive impairment (MCI) prognostic, using lifespan trajectories of brain structures. After a full screening of the most discriminant structures between AD and normal aging based on MRI volumetric analysis of 3032 subjects, we propose a novel Hippocampal-Amygdalo-Ventricular Alzheimer score (HAVAs) based on normative lifespan models and AD lifespan models. During a validation on three external datasets on 1039 subjects, our approach showed very accurate detection (AUC ≥ 94%) of patients with AD compared to control subjects and accurate discrimination (AUC=78%) between progressive MCI and stable MCI (during a 3 years follow-up). Compared to normative modelling and recent state-of-the-art deep learning methods, our method demonstrated better classification performance. Moreover, HAVAs simplicity makes it fully understandable and thus well-suited for clinical practice or future pharmaceutical trials.

scholarly journals A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease

NeuroImage ◽  
2019 ◽  
Vol 189 ◽  
pp. 276-287 ◽  
Author(s):  
Simeon Spasov ◽  
Luca Passamonti ◽  
Andrea Duggento ◽  
Pietro Liò ◽  
Nicola Toschi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Approach

Diagnosis of Alzheimer's disease based on Deeply-Fused Nets

2020 ◽  
Vol 23 ◽  
Author(s):  
ChangZu Chen ◽  
Qi Wu ◽  
ZuoYong Li ◽  
Lei Xiao ◽  
Zhong Yi Hu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Disease Diagnosis ◽  
Automatic Diagnosis ◽  
Single Base ◽  
Base Network ◽  
Normal Controls

Aim and Objective: Fast and accurate diagnosis of Alzheimer's disease is very important for the care and further treatment of patients. Along with the development of deep learning, impressive progress has also been made in the automatic diagnosis of AD. Most existing studies on automatic diagnosis are concerned with a single base network, whose accuracy for disease diagnosis still needs to be improved. This study was undertaken to propose a method to improve the accuracy of automatic diagnosis of AD. Materials and Methods: MRI image data from the Alzheimer’s Disease Neuroimaging Initiative were used to train a deep learning model to achieve computer-aided diagnosis of Alzheimer's disease. The data consisted of 138 with AD, 280 with mild cognitive impairment, and 138 normal controls. Here, a new deeply-fused net is proposed, which combines several deep convolutional neural networks, thereby avoiding the error of a single base network and increasing the classification accuracy and generalization capacity. Results: Experiments show that when differentiating between patients with AD, mild cognitive impairment, and normal controls on a subset of the ADNI database without data leakage, the new architecture improves the accuracy by about 4 percentage points as compared to a single standard base network. Conclusion: This new approach exhibits better performance, but there is still much to be done before its clinical application. In the future, greater research effort will be devoted to improving the performance of the deeply-fused net.

MRI-Derived Atrophy of the Olfactory Bulb and Tract in Mild Cognitive Impairment and Alzheimer's Disease

2009 ◽  
Vol 17 (1) ◽  
pp. 213-221 ◽  
Author(s):  
Philipp A. Thomann ◽  
Vasco Dos Santos ◽  
Ulrich Seidl ◽  
Pablo Toro ◽  
Marco Essig ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Olfactory Bulb

scholarly journals Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer’s Disease

2021 ◽  
Vol 80 (3) ◽  
pp. 1079-1090
Author(s):  
Sanjay Nagaraj ◽  
Tim Q. Duong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithm ◽  
Selection Methods ◽  
Deep Learning Algorithm ◽  
Neurocognitive Tests ◽  
Risk Stratification Score

Background: Many neurocognitive and neuropsychological tests are used to classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD) from cognitive normal (CN). This can make it challenging for clinicians to make efficient and objective clinical diagnoses. It is possible to reduce the number of variables needed to make a reasonably accurate classification using machine learning. Objective: The goal of this study was to develop a deep learning algorithm to identify a few significant neurocognitive tests that can accurately classify these four groups. We also derived a simplified risk-stratification score model for diagnosis. Methods: Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from 383 EMCI, 644 LMCI, 394 AD patients, and 516 cognitive normal from the Alzheimer’s Disease Neuroimaging Initiative database. A neural network algorithm was trained on data split 90% for training and 10% testing using 10-fold cross-validation. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis. We also evaluated five different feature selection methods. Results: The five feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes, Delayed total recall, Modified Preclinical Alzheimer Cognitive Composite with Trails test, Modified Preclinical Alzheimer Cognitive Composite with Digit test, and Mini-Mental State Examination. The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.963 on the test dataset. Conclusion: The deep-learning algorithm and simplified risk score accurately classifies EMCI, LMCI, AD and CN patients using a few common neurocognitive tests.

scholarly journals Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging

2021 ◽  
Vol 13 ◽  
Author(s):  
Ping Zhou ◽  
Rong Zeng ◽  
Lun Yu ◽  
Yabo Feng ◽  
Chuxin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Positron Emission Tomography ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Emission Tomography ◽  
Fluorodeoxyglucose Positron Emission Tomography ◽  
Clinical Method ◽  
Positron Emission ◽  
Fluorodeoxyglucose Positron Emission

Objectives: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the older people. Some types of mild cognitive impairment (MCI) are the clinical precursors of AD, while other MCI forms tend to remain stable over time and do not progress to AD. To discriminate MCI patients at risk of AD from stable MCI, we propose a novel deep-learning radiomics (DLR) model based on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and combine DLR features with clinical parameters (DLR+C) to improve diagnostic performance.Methods:18F-fluorodeoxyglucose positron emission tomography (PET) data from the Alzheimer's disease Neuroimaging Initiative database (ADNI) were collected, including 168 patients with MCI who converted to AD within 3 years and 187 patients with MCI without conversion within 3 years. These subjects were randomly partitioned into 90 % for the training/validation group and 10 % for the independent test group. The proposed DLR approach consists of three steps: base DL model pre-training, network features extraction, and integration of DLR+C, where a convolution network serves as a feature encoder, and a support vector machine (SVM) operated as the classifier. In comparative experiments, we compared our DLR+C method with four other methods: the standard uptake value ratio (SUVR) method, Radiomics-ROI method, Clinical method, and SUVR + Clinical method. To guarantee the robustness, 10-fold cross-validation was processed 100 times.Results: Under the DLR model, our proposed DLR+C was advantageous and yielded the best classification performance in the diagnosis of conversion with the accuracy, sensitivity, and specificity of 90.62 ± 1.16, 87.50 ± 0.00, and 93.39 ± 2.19%, respectively. In contrast, the respective accuracy of the other four methods reached 68.38 ± 1.27, 73.31 ± 6.93, 81.09 ± 1.97, and 85.35 ± 0.72 %. These results suggested the DLR approach could be used successfully in the prediction of conversion to AD, and that our proposed DLR-combined clinical information was effective.Conclusions: This study showed DLR+C could provide a novel and valuable method for the computer-assisted diagnosis of conversion to AD from MCI. This DLR+C method provided a quantitative biomarker which could predict conversion to AD in MCI patients.

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