Waz Sebastian, Wang Yalin, Lu Zhong-Lin
Center for Neural Science, New York University, 4 Washington Place, New York, 10003, NY, USA.
School of Computing and Augmented Intelligence, Arizona State University, 699 S. Mill Avenue, Tempe, 85281, AZ, USA.
bioRxiv. 2024 Aug 15:2024.08.13.607805. doi: 10.1101/2024.08.13.607805.
Patterns of BOLD response can be decoded using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). The time cost of evaluating the PRF model is high, often requiring days to decode BOLD signals for a small cohort of subjects. We introduce the qPRF, an efficient method for decoding that reduced the computation time by a factor of 1436 when compared to another widely available PRF decoder (Kay, Winawer, Mezer and Wandell, 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen, Smith, Barch, Behrens, Yacoub and Ugurbil, 2013). With a specially designed data structure and an efficient search algorithm, the qPRF optimizes the five PRF model parameters according to a least-squares criterion. To verify the accuracy of the qPRF solutions, we compared them to those provided by Benson, Jamison, Arcaro, Vu, Glasser, Coalson, Van Essen, Yacoub, Ugurbil, Winawer and Kay (2018). Both hemispheres of the 181 subjects in the HCP data set (a total of 10,753,572 vertices, each with a unique BOLD time series of 1800 frames) were decoded by qPRF in 15.2 hours on an ordinary CPU. The absolute difference in reported by Benson et al. and achieved by the qPRF was negligible, with a median of 0.39% ( units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater on 99.7% of vertices. The qPRF may facilitate the development and computation of more elaborate models based on the PRF framework, as well as the exploration of novel clinical applications.
可以使用群体感受野（PRF）模型来解码血氧水平依赖（BOLD）反应模式，以揭示视觉输入在皮层上的表征方式（杜穆林和万德尔，2008年）。评估PRF模型的时间成本很高，通常需要数天时间才能为一小群受试者解码BOLD信号。我们引入了qPRF，这是一种高效的解码方法，与另一种广泛使用的PRF解码器相比，在人类连接组计划（HCP；范埃森、史密斯、巴尔奇、贝伦斯、亚库布和乌古尔比尔，2013年）的数据基准上，其计算时间减少了1436倍（凯、维纳韦尔、梅泽尔和万德尔，2013年）。通过特殊设计的数据结构和高效的搜索算法，qPRF根据最小二乘准则优化五个PRF模型参数。为了验证qPRF解决方案的准确性，我们将其与本森、贾米森、阿卡罗、武、格拉瑟、科尔森、范埃森、亚库布、乌古尔比尔、维纳韦尔和凯（2018年）提供的解决方案进行了比较。HCP数据集中181名受试者的两个半球（总共10753572个顶点，每个顶点都有一个1800帧的独特BOLD时间序列）在普通CPU上用qPRF在15.2小时内进行了解码。本森等人报告的结果与qPRF实现的结果之间的绝对差异可以忽略不计，中位数为0.39%（单位在0%到100%之间）。一般来说，qPRF产生的拟合解决方案略好一些，在99.7%的顶点上实现了更高的[此处原文缺失相关内容]。qPRF可能有助于基于PRF框架开发和计算更精细的模型，以及探索新的临床应用。
