Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer’s disease imaging biomarker

2018 ◽  
Vol 60 (6) ◽  
pp. 769-776 ◽  
Author(s):  
Jill Abrigo ◽  
Lin Shi ◽  
Yishan Luo ◽  
Qianyun Chen ◽  
Winnie Chiu Wing Chu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Intraclass Correlation ◽  
Area Under The Curve ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Dice Similarity Coefficient ◽  
Reference Dataset ◽  
Magnetic Resonance Imaging Mri ◽  
Hippocampus Segmentation

Background One significant barrier to incorporate Alzheimer’s disease (AD) imaging biomarkers into diagnostic criteria is the lack of standardized methods for biomarker quantification. The European Alzheimer’s Disease Consortium-Alzheimer’s Disease Neuroimaging Initiative (EADC-ADNI) Harmonization Protocol project provides the most authoritative guideline for hippocampal definition and has produced a manually segmented reference dataset for validation of automated methods. Purpose To validate automated hippocampal volumetry using AccuBrain™, against the EADC-ADNI dataset, and assess its diagnostic performance for differentiating AD and normal aging in an independent cohort. Material and Methods The EADC-ADNI reference dataset comprise of manually segmented hippocampal labels from 135 volumetric T1-weighted scans from various scanners. Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and Pearson’s r were obtained for AccuBrain™ and FreeSurfer. The magnetic resonance imaging (MRI) of a separate cohort of 299 individuals (150 normal controls, 149 with AD) were obtained from the ADNI database and processed with AccuBrain™ to assess its diagnostic accuracy. Area under the curve (AUC) for total hippocampal volumes (HV) and hippocampal fraction (HF) were determined. Results Compared with EADC-ADNI dataset ground truths, AccuBrain™ had a mean DSC of 0.89/0.89/0.89, ICC of 0.94/0.96/0.95, and r of 0.95/0.96/0.95 for right/left/total HV. AccuBrain™ HV and HF had AUC of 0.76 and 0.80, respectively. Thresholds of ≤ 5.71 mL and ≤ 0.38% afforded 80% sensitivity for AD detection. Conclusion AccuBrain™ provides accurate automated hippocampus segmentation in accordance with the EADC-ADNI standard, with great potential value in assisting clinical diagnosis of AD.

AD Resemblance Atrophy Index as a Diagnostic Biomarker for Alzheimer’s Disease: A Retrospective Clinical and Biological Validation

2021 ◽  
Vol 79 (3) ◽  
pp. 1023-1032
Author(s):  
Yingren Mai ◽  
Qun Yu ◽  
Feiqi Zhu ◽  
Yishan Luo ◽  
Wang Liao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Structure ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Clinical Scenario ◽  
Clinical Criteria ◽  
Positron Emission ◽  
And Gender ◽  
Sensitivity Specificity

Background: Magnetic resonance imaging (MRI) provides objective information about brain structural atrophy in patients with Alzheimer’s disease (AD). This multi-structural atrophic information, when integrated as a single differential index, has the potential to further elevate the accuracy of AD identification from normal control (NC) compared to the conventional structure volumetric index. Objective: We herein investigated the performance of such an MRI-derived AD index, AD-Resemblance Atrophy Index (AD-RAI), as a neuroimaging biomarker in clinical scenario. Method: Fifty AD patients (19 with the Amyloid, Tau, Neurodegeneration (ATN) results assessed in cerebrospinal fluid) and 50 age- and gender-matched NC (19 with ATN results assessed using positron emission tomography) were recruited in this study. MRI-based imaging biomarkers, i.e., AD-RAI, were quantified using AccuBrain®. The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of these MRI-based imaging biomarkers were evaluated with the diagnosis result according to clinical criteria for all subjects and ATN biological markers for the subgroup. Results: In the whole groups of AD and NC subjects, the accuracy of AD-RAI was 91%, sensitivity and specificity were 88% and 96%, respectively, and the AUC was 92%. In the subgroup of 19 AD and 19 NC with ATN results, AD-RAI results matched completely with ATN classification. AD-RAI outperforms the volume of any single brain structure measured. Conclusion: The finding supports the hypothesis that MRI-derived composite AD-RAI is a more accurate imaging biomarker than individual brain structure volumetry in the identification of AD from NC in the clinical scenario.

scholarly journals Fifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer’s Disease

2021 ◽  
pp. 1-26
Author(s):  
Christopher Fowler ◽  
Stephanie R. Rainey-Smith ◽  
Sabine Bird ◽  
Julia Bomke ◽  
Pierrick Bourgeat ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Age At Onset ◽  
Cognitive Testing ◽  
Imaging Biomarkers ◽  
Positron Emission ◽  
Investigation Methods ◽  
Aβ Amyloid ◽  
Magnetic Resonance Imaging Mri ◽  
A Prospective Study

Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer’s disease dementia (AD)) as an ‘Inception cohort’ who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an ‘Enrichment cohort’ (as of 10 April 2019). Objective: Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation. Methods: Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations. Results: AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression. Conclusion: This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.

scholarly journals Diagnosis of Alzheimer’s Disease using Structural MRI and Convolution Neural Network

2020 ◽  
Vol 185 ◽  
pp. 03037
Author(s):  
Shuyang Bian
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Statistical Significance ◽  
Area Under The Curve ◽  
Structural Mri ◽  
3D Mri ◽  
Convolutional Network ◽  
Non Invasive ◽  
Magnetic Resonance Imaging Mri

Background: Alzheimer’s disease (AD) is a prevalent, neurological disease without effective treatment. However, if diagnosed early, the progression of the disease could be delayed through medication. Currently, one method to effectively diagnose AD early is to use Alternate Covering Neural Network (ACNN) network to discern various non-invasive Magnetic Resonance Imaging (MRI) images. This research aims to create an approach better than the current one and thus increase the accuracy of classifying MRI images, thereby diagnosing AD earlier and more perfectly. Methods: Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health Grant U19 AG024904) database provided 3013 different sets of 3D MRI images labeled as cognitively normal (CN), mild cognitive impairment (MCI), and AD. A newly-proposed, modified Residual Network (ResNet) and an ACNN network were then constructed. Their common goal was to learn how to classify these labeled MRI images. After training, the two models got unique parameters for using the updated network to diagnose new images. Finally, inference, or testing the diagnostic accuracy of the two models, were performed based on another 469 different 3D MRI image sets. The accuracy of classification for two separate models were compared. Results: Compared with the ACNN network with a weighted classification accuracy of 80.17%, the newly proposed ResNet network enhances the weighted accuracy to 85.07% and showed statistical significance (p<0.001). Through analyzing the occurrence of falsepositive cases by two models, a Receiver Operating Characteristic (ROC) curve was drawn. The area under the curve of the ROC confirms this enhancement as the area under the curve of ROC is greater than that of the ACNN model in two of the three cases (MCI 0.9293>0.9196; AD 0.9389>0.9146). Conclusions: The research proposed a new deep learning convolutional network to classify 3D structural MRI images. The new ResNet is better in that it showed increased accuracy with statistical significance and had fewer false-positive results compare with the traditional ACNN network, thereby promising to help doctors diagnose AD more quickly and more accurately.

scholarly journals Phosphorylated tau181 in plasma as a potential biomarker for Alzheimer’s disease in adults with Down syndrome

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alberto Lleó ◽  
Henrik Zetterberg ◽  
Jordi Pegueroles ◽  
Thomas K. Karikari ◽  
María Carmona-Iragui ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Down Syndrome ◽  
Single Molecule ◽  
Area Under The Curve ◽  
Imaging Biomarkers ◽  
Potential Biomarker ◽  
Prodromal Ad ◽  
Asymptomatic Individuals ◽  
The Relationship

AbstractPlasma tau phosphorylated at threonine 181 (p-tau181) predicts Alzheimer’s disease (AD) pathology with high accuracy in the general population. In this study, we investigated plasma p-tau181 as a biomarker of AD in individuals with Down syndrome (DS). We included 366 adults with DS (240 asymptomatic, 43 prodromal AD, 83 AD dementia) and 44 euploid cognitively normal controls. We measured plasma p-tau181 with a Single molecule array (Simoa) assay. We examined the diagnostic performance of p-tau181 for the detection of AD and the relationship with other fluid and imaging biomarkers. Plasma p-tau181 concentration showed an area under the curve of 0.80 [95% CI 0.73–0.87] and 0.92 [95% CI 0.89–0.95] for the discrimination between asymptomatic individuals versus those in the prodromal and dementia groups, respectively. Plasma p-tau181 correlated with atrophy and hypometabolism in temporoparietal regions. Our findings indicate that plasma p-tau181 concentration can be useful to detect AD in DS.

scholarly journals The differential diagnostic value of the callosal angle and Evans index in mild cognitive impairment and Alzheimer's disease

2020 ◽  
Vol 16 ◽  
Author(s):  
Mengqi Liu ◽  
Jing Zhang ◽  
Linxiong Zong ◽  
Wenping Fan ◽  
Botao Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diagnostic Value ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
P Value ◽  
Significant Difference ◽  
Evans Index

Background: Callosal angle (CA) and Evans index (EI) had been considered as imaging biomarkers to diagnosis normal-pressure hydrocephalus as traditional MR measurement methods. Objective: The current study was aimed to evaluate the differential diagnostic value of CA and EI in the mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Methods: Five-hundred and two subjects were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, which included 168 normal controls (NC), 233 MCI and 101 AD patients. The structural MR images were interactively applied with multiplanar reconstruction to measure the CA and EI. Results: CA presented no significant difference among NC, MCI and AD groups (H value = 3.848, P value = 0.146), and EI was demonstrated the higher in MCI and AD groups than that in NC groups (P = 0.000 and 0.001, respectively). MCI group had significant larger EI (0.29±0.04) than that (0.27±0.03) in NC group in 70-75 years old sub-groups. ROC showed that the area under the curve was 0.704±0.045 for NC-MCI in 70-75 years old groups. The correlation analysis indicated that EI was significantly negatively related with MMSE scores of MCI patients (r = -0.131, P = 0.046). Conclusion: EI might serve as a screening imaging biomarker for MCI in 70-75 years old, and show limited differential value for the diagnosis of AD. CA could present no diagnostic value for MCI and AD in the current study.

Amyloid Beta-Weighted Cortical Thickness: A New Imaging Biomarker in Alzheimer's Disease

2015 ◽  
Vol 12 (6) ◽  
pp. 563-571 ◽  
Author(s):  
Chan Kim ◽  
Jihye Hwang ◽  
Jong-Min Lee ◽  
Jee Hoon Roh ◽  
Jae-Hong Lee ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cortical Thickness ◽  
Amyloid Beta ◽  
Imaging Biomarker

scholarly journals Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06226
Author(s):  
Diedre Carmo ◽  
Bruna Silva ◽  
Clarissa Yasuda ◽  
Letícia Rittner ◽  
Roberto Lotufo
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Convolutional Neural Networks ◽  
Hippocampus Segmentation

scholarly journals Plasma contact factors as novel biomarkers for diagnosing Alzheimer’s disease

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jung Eun Park ◽  
Do Sung Lim ◽  
Yeong Hee Cho ◽  
Kyu Yeong Choi ◽  
Jang Jae Lee ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Clinical Stage ◽  
Area Under The Curve ◽  
Contact System ◽  
Plasma Diagnostic ◽  
Vascular Abnormalities ◽  
Prodromal Ad ◽  
Novel Biomarkers

Abstract Background Alzheimer’s disease (AD) is the most common cause of dementia and most of AD patients suffer from vascular abnormalities and neuroinflammation. There is an urgent need to develop novel blood biomarkers capable of diagnosing Alzheimer’s disease (AD) at very early stage. This study was performed to find out new accurate plasma diagnostic biomarkers for AD by investigating a direct relationship between plasma contact system and AD. Methods A total 101 of human CSF and plasma samples from normal and AD patients were analyzed. The contact factor activities in plasma were measured with the corresponding specific peptide substrates. Results The activities of contact factors (FXIIa, FXIa, plasma kallikrein) and FXa clearly increased and statistically correlated as AD progresses. We present here, for the first time, the FXIIa cut-off scores to as: > 26.3 U/ml for prodromal AD [area under the curve (AUC) = 0.783, p < 0.001] and > 27.2 U/ml for AD dementia (AUC = 0.906, p < 0.001). We also describe the cut-off scores from the ratios of CSF Aβ1–42 versus the contact factors. Of these, the representative ratio cut-off scores of Aβ1–42/FXIIa were to be: < 33.8 for prodromal AD (AUC = 0.965, p < 0.001) and < 27.44 for AD dementia (AUC = 1.0, p < 0.001). Conclusion The activation of plasma contact system is closely associated with clinical stage of AD, and FXIIa activity as well as the cut-off scores of CSF Aβ1–42/FXIIa can be used as novel accurate diagnostic AD biomarkers.

scholarly journals Classifying Non-Dementia and Alzheimer’s Disease/Vascular Dementia Patients Using Kinematic, Time-Based, and Visuospatial Parameters: The Digital Clock Drawing Test

2021 ◽  
Vol 82 (1) ◽  
pp. 47-57 ◽  
Author(s):  
Anis Davoudi ◽  
Catherine Dion ◽  
Shawna Amini ◽  
Patrick J. Tighe ◽  
Catherine C. Price ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Vascular Dementia ◽  
Area Under The Curve ◽  
Clock Drawing Test ◽  
Digital Clock ◽  
Clock Drawing ◽  
Drawing Test ◽  
Dementia Patients ◽  
Optimal Area

Background: Advantages of digital clock drawing metrics for dementia subtype classification needs examination. Objective: To assess how well kinematic, time-based, and visuospatial features extracted from the digital Clock Drawing Test (dCDT) can classify a combined group of Alzheimer’s disease/Vascular Dementia patients versus healthy controls (HC), and classify dementia patients with Alzheimer’s disease (AD) versus vascular dementia (VaD). Methods: Healthy, community-dwelling control participants (n = 175), patients diagnosed clinically with Alzheimer’s disease (n = 29), and vascular dementia (n = 27) completed the dCDT to command and copy clock drawing conditions. Thirty-seven dCDT command and 37 copy dCDT features were extracted and used with Random Forest classification models. Results: When HC participants were compared to participants with dementia, optimal area under the curve was achieved using models that combined both command and copy dCDT features (AUC = 91.52%). Similarly, when AD versus VaD participants were compared, optimal area under the curve was, achieved with models that combined both command and copy features (AUC = 76.94%). Subsequent follow-up analyses of a corpus of 10 variables of interest determined using a Gini Index found that groups could be dissociated based on kinematic, time-based, and visuospatial features. Conclusion: The dCDT is able to operationally define graphomotor output that cannot be measured using traditional paper and pencil test administration in older health controls and participants with dementia. These data suggest that kinematic, time-based, and visuospatial behavior obtained using the dCDT may provide additional neurocognitive biomarkers that may be able to identify and tract dementia syndromes.

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