4.6 Article

Evaluation and Optimization of Methods for Generating High-Resolution Retinotopic Maps Using Visual Cortex Voltage-Sensitive Dye Imaging

期刊

FRONTIERS IN CELLULAR NEUROSCIENCE
卷 15, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fncel.2021.713538

关键词

retinotopic mapping; signal processing; cluster evaluation; visual cortex (V1); voltage sensitive dye imaging

资金

  1. Israeli Science Foundation (ISF)
  2. ERC [755748]
  3. Israeli Ministry of Defense
  4. Israeli Ministry of Science and Technology
  5. European Research Council (ERC) [755748] Funding Source: European Research Council (ERC)

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Neuronal activity magnitude localization and measurement are crucial for mapping and understanding neuronal systems. In this study, seven different methods for localizing cortical responses were evaluated, with Temporally Structured Component Analysis (TSCA) found to outperform others in generating high-resolution retinotopic maps.
The localization and measurement of neuronal activity magnitude at high spatial and temporal resolution are essential for mapping and better understanding neuronal systems and mechanisms. One such example is the generation of retinotopic maps, which correlates localized retinal stimulation with the corresponding specific visual cortex responses. Here we evaluated and compared seven different methods for extracting and localizing cortical responses from voltage-sensitive dye imaging recordings, elicited by visual stimuli projected directly on the rat retina by a customized projection system. The performance of these methods was evaluated both qualitatively and quantitatively by means of two cluster separation metrics, namely, the (adjusted) Silhouette Index (SI) and the (adjusted) Davies-Bouldin Index (DBI). These metrics were validated using simulated data, which showed that Temporally Structured Component Analysis (TSCA) outperformed all other analysis methods for localizing cortical responses and generating high-resolution retinotopic maps. The analysis methods, as well as the use of cluster separation metrics proposed here, can facilitate future research aiming to localize specific activity at high resolution in the visual cortex or other brain areas.

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