Parkinson’s Disease Detection and Diagnosis from fMRI: A Literature Review

Vivar-Estudillo, Guillermina; Hajari, Nasim; Ibarra-Manzano, Mario Alberto; Cheng, Irene

doi:10.1007/978-3-030-78092-0_44

Cited by 6 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The initial hidden and cell states of the LSTM were set as all zeros. The hidden size M of the LSTM layer was tuned from the list of [16,32,64,128]. To prevent overfitting on the LSTM model, L2 regularization and the early-stopping mechanism were introduced to enhance the model's generalization ability.…”

Section: Model Training and Evaluationmentioning

confidence: 99%

“…A brain network graph analysis was also proposed on PD diagnosis using rs-fMRI, which achieved an average accuracy of 95% and identified disease associated brain network alterations [13]. However, most current work has mainly focused on distinguishing between PD patients and healthy control subjects [14]- [16], and thus investigating the onset of disease. Further characterization for the early stages of PD, e.g., understanding brain differences in stage 1 and 2, has not been explored, but is necessary to better understand the mechanism and progression of PD.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Early Disease Stage Characterization in Parkinson’s Disease from Resting-state fMRI Data Using a Long Short-term Memory Network

Guo¹,

Tinaz²,

Dvornek³

2022

Preprint

View full text Add to dashboard Cite

Parkinson’s disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it is challenging to classify early stages 1 and 2 and detect brain function alterations. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. Some machine learning approaches like support vector machine and logistic regression have been successfully applied in the early diagnosis of PD using fMRI data, which outperform classifiers based on manually selected morphological features. However, the early-stage characterization in FC changes has not been fully investigated. Given the complexity and non-linearity of fMRI data, we propose the use of a long short-term memory (LSTM) network to characterize the early stages of PD. The study included 84 subjects (56 in stage 2 and 28 in stage 1) from the Parkinson’s Progression Markers Initiative (PPMI), the largest-available public PD dataset. Under a repeated 10-fold stratified cross-validation, the LSTM model reached an accuracy of 71.63%, 13.52% higher than the best traditional machine learning method, indicating significantly better robustness and accuracy compared with other machine learning classifiers. We used the learned LSTM model weights to select the top brain regions that contributed to model prediction and performed FC analyses to characterize functional changes with disease stage and motor impairment to gain better insight into the brain mechanisms of PD.

show abstract

Section: Model Training and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Early Disease Stage Characterization in Parkinson’s Disease from Resting-state fMRI Data Using a Long Short-term Memory Network

Guo¹,

Tinaz²,

Dvornek³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A growing number of studies are attempting to proceed PD diagnosis at the level of brain functions by investigating brain activation features from functional magnetic resonance imaging, electroencephalographic, and diffusion tensor imaging in the resting state [14], [15], [16], [17]. In addition to these modalities, functional near-infrared spectroscopy (fNIRS), an emerging neuroimaging technology, is increasingly applied to explore the brain functional neurodegeneration of PD patients in clinical walking tests [18], [19], [20] for its portability for task-related measurement, convenience to transfer and use, flexibility for experiments and tolerance to movement noises, etc [21], [22].…”

Section: Introductionmentioning

confidence: 99%

An fNIRS-Based Dynamic Functional Connectivity Analysis Method to Signify Functional Neurodegeneration of Parkinson’s Disease

Zhang

Wang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Parkinson's disease (PD) is a prevalent brain disorder, and PD diagnosis is crucial for treatment. Existing methods for PD diagnosis are mainly focused on behavior analysis, while the functional neurodegeneration of PD has not been well investigated. This paper proposes a method to signify functional neurodegeneration of PD with dynamic functional connectivity analysis. A functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation from 50 PD patients and 41 age-matched healthy controls in clinical walking tests. Dynamic functional connectivity was constructed with sliding-window correlation analysis, and k-means clustering was applied to generate the key brain connectivity states. Dynamic state features including state occurrence probability, state transition percentage and state Manuscript

show abstract

“…Therefore, FCNs tend to capture the dependencies between BOLD signals of paired region-of-interests (ROIs) of the brain and have been used to identify potential neuroimaging biomarkers for the diagnosis of neurological diseases (El Gazzar et al, 2019 ; Wang et al, 2019a ). It can help us understand brain organization patterns and diagnose neurological diseases such as ASD (Bijsterbosch and Beckmann, 2017 ; Kazi et al, 2019 ), Alzheimer's disease and its prodromal stage (i.e., mild cognitive impairment) (Amini et al, 2021 ), Parkinson's disease (Vivar-Estudillo et al, 2021 ). However, previous studies often first extract handcrafted network features (such as node degree and clustering coefficient) from FCNs and then conduct prediction models for ASD diagnosis (Wang et al, 2019b ), where these two steps are treated separately and highly rely on expert knowledge.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI

Wang¹,

Cao²,

Qiao³

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

Resting-state functional MRI (rs-fMRI) has been widely used for the early diagnosis of autism spectrum disorder (ASD). With rs-fMRI, the functional connectivity networks (FCNs) are usually constructed for representing each subject, with each element representing the pairwise relationship between brain region-of-interests (ROIs). Previous studies often first extract handcrafted network features (such as node degree and clustering coefficient) from FCNs and then construct a prediction model for ASD diagnosis, which largely requires expert knowledge. Graph convolutional networks (GCNs) have recently been employed to jointly perform FCNs feature extraction and ASD identification in a data-driven manner. However, existing studies tend to focus on the single-scale topology of FCNs by using one single atlas for ROI partition, thus ignoring potential complementary topology information of FCNs at different spatial scales. In this paper, we develop a multi-scale graph representation learning (MGRL) framework for rs-fMRI based ASD diagnosis. The MGRL consists of three major components: (1) multi-scale FCNs construction using multiple brain atlases for ROI partition, (2) FCNs representation learning via multi-scale GCNs, and (3) multi-scale feature fusion and classification for ASD diagnosis. The proposed MGRL is evaluated on 184 subjects from the public Autism Brain Imaging Data Exchange (ABIDE) database with rs-fMRI scans. Experimental results suggest the efficacy of our MGRL in FCN feature extraction and ASD identification, compared with several state-of-the-art methods.

show abstract

Parkinson’s Disease Detection and Diagnosis from fMRI: A Literature Review

Cited by 6 publications

References 36 publications

Early Disease Stage Characterization in Parkinson’s Disease from Resting-state fMRI Data Using a Long Short-term Memory Network

Early Disease Stage Characterization in Parkinson’s Disease from Resting-state fMRI Data Using a Long Short-term Memory Network

An fNIRS-Based Dynamic Functional Connectivity Analysis Method to Signify Functional Neurodegeneration of Parkinson’s Disease

Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI

Contact Info

Product

Resources

About