Abstract
Neuro-degenerative diseases such as Parkinson’s Disease (PD) are clinically found to cause alternations and failures in brain connectivity. In this work, a new classification framework using dynamic functional connectivity and topological features is proposed, and it is shown that such framework can give better insights over discriminative difference of the disease itself. After utilizing sparse group lasso with anatomically labeled resting-state fMRI signal, both discriminating brain regions and voxels within can be identified easily. To give an overview of the effectiveness of such framework, the classification performance with the network features extracted on dynamic functional network is quantitatively evaluated. Experimental results show that either single feature of clustering coefficient or combined feature group of characteristic path length, diameter, eccentricity and radius perform well in classifying PD, and as a result the identified feature can lead to better interpretation for clinical purposes.
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Notes
- 1.
1 TR represents the time between two successive data points in the time-series signal.
- 2.
Mathematically, \(t_i=\frac{1}{2}\sum _{j,h\in N}a_{ij}a_{ih}a_{jh}\), where \(a_{ij}\) is the connection status between i and j: \(a_{ij} = 1\) when link (i, j) exists (when i and j are neighbors); \(a_{ij} = 0\) otherwise (\(a_{ii} = 0\) for all i).
- 3.
From the formula, C is only defined when \(k_i\) is larger than 1.
- 4.
Mathematically, \(t_i^w = \frac{1}{2}\sum _{j,h\in N}(w_{ij}w_{ih}w_{jh})^{1/3}\), where \(w_{ij}\) is the connection weight between nodes i and j.
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Puk, K.M. et al. (2018). Uncovering Dynamic Functional Connectivity of Parkinson’s Disease Using Topological Features and Sparse Group Lasso. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_40
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