Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfun...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/9/1521 |
| _version_ | 1797602856021262336 |
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| author | Venkateswaran Rajagopalan Krishna G. Chaitanya Erik P. Pioro |
| author_facet | Venkateswaran Rajagopalan Krishna G. Chaitanya Erik P. Pioro |
| author_sort | Venkateswaran Rajagopalan |
| collection | DOAJ |
| description | Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfunction. Magnetic resonance imaging (MRI) is primarily used to exclude conditions that mimic ALS. We have identified four different clinical/radiological phenotypes of ALS patients. We hypothesize that these ALS phenotypes arise from distinct pathologic processes that result in unique MRI signatures. To our knowledge, no machine learning (ML)-based data analyses have been performed to stratify different ALS phenotypes using MRI measures. During routine clinical evaluation, we obtained T1-, T2-, PD-weighted, diffusion tensor (DT) brain MRI of 15 neurological controls and 91 ALS patients (UMN-predominant ALS with corticospinal tract CST) hyperintensity, <i>n</i> = 21; UMN-predominant ALS without CST hyperintensity, <i>n</i> = 26; classic ALS, <i>n</i> = 23; and ALS patients with frontotemporal dementia, <i>n</i> = 21). From these images, we obtained 101 white matter (WM) attributes (including DT measures, graph theory measures from DT and fractal dimension (FD) measures using T1-weighted), 10 grey matter (GM) attributes (including FD based measures from T1-weighted), and 10 non-imaging attributes (2 demographic and 8 clinical measures of ALS). We employed classification and regression tree, Random Forest (RF) and also artificial neural network for the classifications. RF algorithm provided the best accuracy (70–94%) in classifying four different phenotypes of ALS patients. WM metrics played a dominant role in classifying different phenotypes when compared to GM or clinical measures. Although WM measures from both right and left hemispheres need to be considered to identify ALS phenotypes, they appear to be differentially affected by the degenerative process. Longitudinal studies can confirm and extend our findings. |
| first_indexed | 2024-03-11T04:21:20Z |
| format | Article |
| id | doaj.art-9fcc982a25fd4fefa3d4c3266e5ecf46 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T04:21:20Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-9fcc982a25fd4fefa3d4c3266e5ecf462023-11-17T22:44:49ZengMDPI AGDiagnostics2075-44182023-04-01139152110.3390/diagnostics13091521Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based StudyVenkateswaran Rajagopalan0Krishna G. Chaitanya1Erik P. Pioro2Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, IndiaDepartment of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, IndiaNeuromuscular Center, Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USAAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfunction. Magnetic resonance imaging (MRI) is primarily used to exclude conditions that mimic ALS. We have identified four different clinical/radiological phenotypes of ALS patients. We hypothesize that these ALS phenotypes arise from distinct pathologic processes that result in unique MRI signatures. To our knowledge, no machine learning (ML)-based data analyses have been performed to stratify different ALS phenotypes using MRI measures. During routine clinical evaluation, we obtained T1-, T2-, PD-weighted, diffusion tensor (DT) brain MRI of 15 neurological controls and 91 ALS patients (UMN-predominant ALS with corticospinal tract CST) hyperintensity, <i>n</i> = 21; UMN-predominant ALS without CST hyperintensity, <i>n</i> = 26; classic ALS, <i>n</i> = 23; and ALS patients with frontotemporal dementia, <i>n</i> = 21). From these images, we obtained 101 white matter (WM) attributes (including DT measures, graph theory measures from DT and fractal dimension (FD) measures using T1-weighted), 10 grey matter (GM) attributes (including FD based measures from T1-weighted), and 10 non-imaging attributes (2 demographic and 8 clinical measures of ALS). We employed classification and regression tree, Random Forest (RF) and also artificial neural network for the classifications. RF algorithm provided the best accuracy (70–94%) in classifying four different phenotypes of ALS patients. WM metrics played a dominant role in classifying different phenotypes when compared to GM or clinical measures. Although WM measures from both right and left hemispheres need to be considered to identify ALS phenotypes, they appear to be differentially affected by the degenerative process. Longitudinal studies can confirm and extend our findings.https://www.mdpi.com/2075-4418/13/9/1521ALS phenotypesMRImachine learningRandom Forestneural network |
| spellingShingle | Venkateswaran Rajagopalan Krishna G. Chaitanya Erik P. Pioro Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study Diagnostics ALS phenotypes MRI machine learning Random Forest neural network |
| title | Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study |
| title_full | Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study |
| title_fullStr | Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study |
| title_full_unstemmed | Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study |
| title_short | Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study |
| title_sort | quantitative brain mri metrics distinguish four different als phenotypes a machine learning based study |
| topic | ALS phenotypes MRI machine learning Random Forest neural network |
| url | https://www.mdpi.com/2075-4418/13/9/1521 |
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