scholarly journals A machine-learning approach for detection of local brain networks and marginally weak signals identifies novel AD/MCI differentiating connectomic neuroimaging biomarkers

2021 ◽  
Author(s):  
Yanming Li ◽  
Jian Kang ◽  
Chong Wu ◽  
Ivo Dinov ◽  
jinxiang Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Accuracy ◽  
Brain Networks ◽  
P Value ◽  
Weak Signals ◽  
Whole Brain ◽  
Neuroimaging Biomarkers ◽  
Local Brain

Introduction: A computationally fast machine learning method is introduced for uncovering the whole-brain voxel-level connectomic spectra that differentiates different status of Alzheimer's disease (AD). The method is applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) Fluorine-fluorodeoxyglucose Positron Emission Tomography (FDG-PET) imaging and clinical data and identified novel AD/MCI differentiating connectomic neuroimaging biomarkers. Methods: A divide-and-conquer algorithm is introduced for detect informative local brain networks at voxel level and whole-brain scale. The connection information within the local networks is integrated into the node voxels, which makes detection of the marginally weak signals possible. Prediction accuracy is significantly improved by incorporating the local brain networks and marginally weak signals. Results: Brain connectomic structures differentiating AD and mild cognitive impairment (MCI), AD and healthy, and MIC and healthy were discovered. We identified novel AD/MCI-associated neuroimaging biomarkers by integrating local brain networks and marginally weak signals. For example, network-based signals in paracentral lobule (p-value=6.1e-5), olfactory cortex (p-value=4.6e-5), caudate nucleus (1.8e-3) and precentral gyrus (1.8e-3) are informative in differentiating AD and MCI. Connections between calcarine sulcus and lingual gyrus (p-value=0.049), between parahippocampal gyrus and Amygdala (p-value=0.025), between rolandic opercula and insula lobes (p-values=0.0028 and 0.0026). An overall prediction accuracy of 95.3% was achieved by integrating the selected local brain networks and marginally weak signals, compared to 84.0% by not considering the inter-voxel connections and using marginally strong signals only. Conclusion: (i) The connectomic structures differentiating AD and MCI are significantly different to that differentiating MCI and healthy, which may indicate different neuronal etiology for AD and MCI. (ii) Many neuroimaging biomarkers exert their effects on the outcome diseases through their connections to other markers. Integrating such connections can help identify novel neuroimaging biomarkers and improve disease prediction accuracy.

scholarly journals Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection

2020 ◽  
Vol 10 (3) ◽  
pp. 934 ◽  
Author(s):  
Eufemia Lella ◽  
Angela Lombardi ◽  
Nicola Amoroso ◽  
Domenico Diacono ◽  
Tommaso Maggipinto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Imaging Modality ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Whole Brain ◽  
Feature Importance ◽  
Importance Analysis ◽  
The Brain

Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer’s disease (AD) diagnosis through the use of diffusion weighted imaging (DWI) data. When combined with tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicability metric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions.

scholarly journals Pairwise Correlation Analysis of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1661
Author(s):  
Erik D. Huckvale ◽  
Matthew W. Hodgman ◽  
Brianna B. Greenwood ◽  
Devorah O. Stucki ◽  
Katrisa M. Ward ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Algorithms ◽  
Computational Time ◽  
Significant Feature ◽  
P Value ◽  
Mri Features ◽  
Feature Correlation ◽  
Highly Correlated

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10−13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.

Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization

2016 ◽  
Vol 13 (5) ◽  
pp. 498-508 ◽  
Author(s):  
V. Vigneron ◽  
A. Kodewitz ◽  
A. M. Tome ◽  
S. Lelandais ◽  
E. Lang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Support ◽  
Brain Areas

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-35 ◽  
Author(s):  
M. Tanveer ◽  
B. Richhariya ◽  
R. U. Khan ◽  
A. H. Rashid ◽  
P. Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques
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