The Visual System

Donkelaar, Hans J. ten; Cruysberg, Johannes R. M.

doi:10.1007/978-3-030-41878-6_8

Cited by 2 publications

(1 citation statement)

References 226 publications

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“… Agcaoglu et al (2015) showed the visual network was the most dominantly right lateralized functional network, including lingual gyrus, talus gyrus, cerebellum, suboccipital gyrus and inferior temporal gyrus. Ten Donkelaar and Cruysberg (2020) showed that lesions of the cuneus and posterior parietal lobe cause visuospatial disorders such as impaired motion perception, spatial disorientation and defects in attention. In addition, many researches have proved that the visual cortex evolved a region in the middle-temporal cortex that is highly specialized to process visual motion ( Zeki, 1974 ; Zeki and Whitteridge, 1980 ; Baker et al, 1981 ).…”

Section: Resultsmentioning

confidence: 99%

Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data

et al. 2022

Front. Neurosci.

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Resting-state functional connectivity hypernetworks, in which multiple nodes can be connected, are an effective technique for diagnosing brain disease and performing classification research. Conventional functional hypernetworks can characterize the complex interactions within the human brain in a static form. However, an increasing body of evidence demonstrates that even in a resting state, neural activity in the brain still exhibits transient and subtle dynamics. These dynamic changes are essential for understanding the basic characteristics underlying brain organization and may correlate significantly with the pathological mechanisms of brain diseases. Therefore, considering the dynamic changes of functional connections in the resting state, we proposed methodology to construct resting state high-order functional hyper-networks (rs-HOFHNs) for patients with depression and normal subjects. Meanwhile, we also introduce a novel property (the shortest path) to extract local features with traditional local properties (cluster coefficients). A subgraph feature-based method was introduced to characterize information relating to global topology. Two features, local features and subgraph features that showed significant differences after feature selection were subjected to multi-kernel learning for feature fusion and classification. Compared with conventional hyper network models, the high-order hyper network obtained the best classification performance, 92.18%, which indicated that better classification performance can be achieved if we needed to consider multivariate interactions and the time-varying characteristics of neural interaction simultaneously when constructing a network.

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