Connectivity-Based Brain Parcellation for Parkinson's Disease

Liu, Yu; Liu, Aiping; Li, Liangyong; Yun-xia, WU; McKeown, Martin J.; Chen, Xun; Wu, Feng

doi:10.1109/tbme.2022.3222072

Cited by 6 publications

(16 citation statements)

References 133 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Macroscale networks are functionally integrated through connections, while distinct regions are functionally separated through local differentiation [4,7]. Based on this fundamental principle of brain organization, researchers have developed several prevalent traditional methods of fMRI brain parcellation, including clustering, decomposition, and boundary mapping [4,26].…”

Section: Traditional Parcellation Methodsmentioning

confidence: 99%

“…The boundary mapping approach usually is used to fMRI connected similarity to delineate the brain boundaries by detecting abrupt changes in local functional properties [45]. Additionally, other traditional methods include connectivitybased labeling, region growing, and community detection, see extensive reviews [4,26].…”

Section: Traditional Parcellation Methodsmentioning

confidence: 99%

“…Another widely used functional connectivity measures the synchronization between the neuronal activation patterns of different brain regions [38], reflecting the spatial organization of brain networks. According to an assumption of homogeneous connectivity patterns, the researchers further calculate the similarity between the functional connectivity for subsequent parcellation [26], i.e. connectional similarity.…”

Section: Fmri Feature Selectionmentioning

confidence: 99%

“…By contrast, each image can contain over 100 000 spatial voxels, which yields high-dimensional features of functional connectivity or connectional similarity, especially at the voxel level. To reduce the computational cost and redundant information, several strategies have been proposed for connectivity construction and dimensionality reduction, including downsampling, prior atlases, and decomposition algorithms, see an in-depth review by Li [26]. Additionally, some other factors (e.g.…”

Section: Fmri Feature Selectionmentioning

confidence: 99%

“…Compared to the traditional linear methods (e.g. K-means clustering, independent component analysis (ICA)), flexible and nonlinear deep learning models seem to be preferable for describing dynamic nonlinear brain systems [16,25,26]. In addition to accurately mapping a brain parcellation to new data, deep learning has achieved better performance in reproducibility, homogeneity, etc [16,[27][28][29].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Application of deep learning in fMRI-based human brain parcellation: a review

Li,

Chen,

Ling

et al. 2023

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Functional magnetic resonance imaging (fMRI)-based human brain parcellation reveals brain fundamental organizational principles noninvasively, providing prior guidance for functional analysis and physiological measurement of the brain. Recently, the profound success of deep learning in natural and medical images has attracted an increasing number of deep learning-based investigations for brain parcellation which have accomplished encouraging achievements. This review aims to provide researchers with a comprehensive overview of deep learning-based fMRI brain parcellation and promote the development of this emerging frontier. To begin, we present the history of brain parcellation, emphasizing the current advantages of deep learning in this field. Following a summary of the input modalities, traditional methods, and evaluation criteria for fMRI-based brain parcellation, we comprehensively review the current deep-learning approaches and categorize them based on network architectures. The datasets, models, performance, advantages and limitations are described in detail. Finally, we discuss the current challenges and future directions in methodology, neuroscience, and application, to inspire future investigation in deep learning-based brain parcellation.

show abstract

Section: Traditional Parcellation Methodsmentioning

confidence: 99%

Section: Traditional Parcellation Methodsmentioning

confidence: 99%

Section: Fmri Feature Selectionmentioning

confidence: 99%

Section: Fmri Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application of deep learning in fMRI-based human brain parcellation: a review

Li,

Chen,

Ling

et al. 2023

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

Technological developments in electric-based DBS

Tonge

2024

Deep Brain Stimulation

View full text Add to dashboard Cite

fMRI-based spatio-temporal parcellations of the human brain

Ling,

Liu,

et al. 2024

Current Opinion in Neurology

View full text Add to dashboard Cite

Purpose of review Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research. Recent findings Traditional brain parcellation techniques, which often rely on cytoarchitectonic criteria, overlook the functional and temporal information accessible through fMRI. The adoption of machine learning techniques, notably deep learning, offers the potential to harness both spatial and temporal information for more nuanced brain segmentation. However, the search for a one-size-fits-all solution to brain segmentation is impractical, with the choice between group-level or individual-level models and the intended downstream analysis influencing the optimal parcellation strategy. Additionally, evaluating these models is complicated by our incomplete understanding of brain function and the absence of a definitive “ground truth”. Summary While recent methodological advancements have significantly enhanced our grasp of the brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio-temporal representations. Future efforts will likely focus on refining model evaluation and selection as well as developing methods that offer clear interpretability for clinical usage, thereby facilitating further breakthroughs in our comprehension of the brain.

show abstract

Connectivity-Based Brain Parcellation for Parkinson's Disease

Cited by 6 publications

References 133 publications

Application of deep learning in fMRI-based human brain parcellation: a review

Application of deep learning in fMRI-based human brain parcellation: a review

Technological developments in electric-based DBS

fMRI-based spatio-temporal parcellations of the human brain

Contact Info

Product

Resources

About