Subgrouping and structural brain connectivity of Parkinson's disease – past studies and future directions
Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder associated with several motor and non-motor dysfunctions. The wide variety of clinical features often leads to divergent symptom progressions. Most PD studies have attempted subgrouping based on clinical features to help und...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Neuroscience Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772528622000620 |
| _version_ | 1811305062251626496 |
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| author | Tanmayee Samantaray Jitender Saini Cota Navin Gupta |
| author_facet | Tanmayee Samantaray Jitender Saini Cota Navin Gupta |
| author_sort | Tanmayee Samantaray |
| collection | DOAJ |
| description | Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder associated with several motor and non-motor dysfunctions. The wide variety of clinical features often leads to divergent symptom progressions. Most PD studies have attempted subgrouping based on clinical features to help understand the disease etiology and thereby contribute toward specific treatment. However, clinical symptoms have proven to be overlapping, arbitrary, and non-reliable in several cases, often biasing the deciphered subgroups. Moreover, the prodromal phase complicates diagnosis and subgrouping as it is characterized by limited clinical symptom expression. Hence, recent studies have used data-driven machine learning and deep learning methods to data-mine the heterogeneity and obtain subgroups. Structural Magnetic Resonance Imaging (sMRI) is a non-invasive approach for visualization and analysis of anatomical tissue properties of brain. It has enabled the detection of brain abnormalities and is a potential modality for subgrouping.This review article starts with a comprehensive discussion of clinical symptoms-based and data-driven structural neuroimaging-based subgrouping approaches in PD. Secondly, we summarize the work done in brain connectivity studies using structural MRI for PD. We give an overview of mathematical definitions, connectivity metrics, brain connectivity software, and widespread network atlases. Finally, we discuss the inherent challenges and give practical suggestions on selecting methods that could be attempted for subgrouping and connectivity analysis using structural MRI data for future Parkinson's research. |
| first_indexed | 2024-04-13T08:18:38Z |
| format | Article |
| id | doaj.art-0338ce5233f34f26be1b0136d454ff68 |
| institution | Directory Open Access Journal |
| issn | 2772-5286 |
| language | English |
| last_indexed | 2024-04-13T08:18:38Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroscience Informatics |
| spelling | doaj.art-0338ce5233f34f26be1b0136d454ff682022-12-22T02:54:42ZengElsevierNeuroscience Informatics2772-52862022-12-0124100100Subgrouping and structural brain connectivity of Parkinson's disease – past studies and future directionsTanmayee Samantaray0Jitender Saini1Cota Navin Gupta2Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, IndiaDepartment of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, IndiaNeural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, India; Corresponding author.Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder associated with several motor and non-motor dysfunctions. The wide variety of clinical features often leads to divergent symptom progressions. Most PD studies have attempted subgrouping based on clinical features to help understand the disease etiology and thereby contribute toward specific treatment. However, clinical symptoms have proven to be overlapping, arbitrary, and non-reliable in several cases, often biasing the deciphered subgroups. Moreover, the prodromal phase complicates diagnosis and subgrouping as it is characterized by limited clinical symptom expression. Hence, recent studies have used data-driven machine learning and deep learning methods to data-mine the heterogeneity and obtain subgroups. Structural Magnetic Resonance Imaging (sMRI) is a non-invasive approach for visualization and analysis of anatomical tissue properties of brain. It has enabled the detection of brain abnormalities and is a potential modality for subgrouping.This review article starts with a comprehensive discussion of clinical symptoms-based and data-driven structural neuroimaging-based subgrouping approaches in PD. Secondly, we summarize the work done in brain connectivity studies using structural MRI for PD. We give an overview of mathematical definitions, connectivity metrics, brain connectivity software, and widespread network atlases. Finally, we discuss the inherent challenges and give practical suggestions on selecting methods that could be attempted for subgrouping and connectivity analysis using structural MRI data for future Parkinson's research.http://www.sciencedirect.com/science/article/pii/S2772528622000620Parkinson's diseaseClinical symptomsData-driven methodsStructural neuroimaging-based subgroupingStructural brain network connectivity |
| spellingShingle | Tanmayee Samantaray Jitender Saini Cota Navin Gupta Subgrouping and structural brain connectivity of Parkinson's disease – past studies and future directions Neuroscience Informatics Parkinson's disease Clinical symptoms Data-driven methods Structural neuroimaging-based subgrouping Structural brain network connectivity |
| title | Subgrouping and structural brain connectivity of Parkinson's disease – past studies and future directions |
| title_full | Subgrouping and structural brain connectivity of Parkinson's disease – past studies and future directions |
| title_fullStr | Subgrouping and structural brain connectivity of Parkinson's disease – past studies and future directions |
| title_full_unstemmed | Subgrouping and structural brain connectivity of Parkinson's disease – past studies and future directions |
| title_short | Subgrouping and structural brain connectivity of Parkinson's disease – past studies and future directions |
| title_sort | subgrouping and structural brain connectivity of parkinson s disease past studies and future directions |
| topic | Parkinson's disease Clinical symptoms Data-driven methods Structural neuroimaging-based subgrouping Structural brain network connectivity |
| url | http://www.sciencedirect.com/science/article/pii/S2772528622000620 |
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