Name
Abstract | ||
|---|---|---|
The characterization of receptive field (RF) properties is fundamental to understanding the neural basis of sensory and cognitive behaviour. The combination of non-invasive imaging, such as fMRI, with biologically inspired neural modelling has enabled the estimation of population RFs directly in humans. However, current approaches require making numerous a priori assumptions, so these cannot reveal unpredicted properties, such as fragmented RFs or subpopulations. This is a critical limitation in studies on adaptation, pathology or reorganization. Here, we introduce micro-probing (MP), a technique for fine-grained and largely assumption free characterization of multiple pRFs within a voxel. It overcomes many limitations of current approaches by enabling detection of unexpected RF shapes, properties and subpopulations, by enhancing the spatial detail with which we analyze the data. MP is based on tiny, fixed-size, Gaussian models that efficiently sample the entire visual space and create fine-grained probe maps. Subsequently, we derived population receptive fields (pRFs) from these maps. We demonstrate the scope of our method through simulations and by mapping the visual fields of healthy participants and of a patient group with highly abnormal RFs due to a congenital pathway disorder. Without using specific stimuli or adapted models, MP mapped the bilateral pRFs characteristic of observers with albinism. In healthy observers, MP revealed that voxels may capture the activity of multiple subpopulations RFs that sample distinct regions of the visual field. Thus, MP provides a versatile framework to visualize, analyze and model, without restrictions, the diverse RFs of cortical subpopulations in health and disease. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neuroimage.2019.116423 | NeuroImage |
Keywords | Field | DocType |
Visual field mapping,Receptive field,fMRI,Computational modelling | Receptive field,Voxel,Population,Visual space,Pattern recognition,A priori and a posteriori,Cognitive psychology,Psychology,Artificial intelligence,Meridian (perimetry, visual field),Sensory system,Visual field | Journal |
Volume | ISSN | Citations |
209 | 1053-8119 | 2 |
PageRank | References | Authors |
0.39 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joana Carvalho | 1 | 2 | 0.39 |
| Azzurra Invernizzi | 2 | 2 | 0.39 |
| Khazar Ahmadi | 3 | 4 | 1.13 |
| Michael B Hoffmann | 4 | 2 | 0.39 |
| Remco J. Renken | 5 | 9 | 2.58 |
| Frans W. Cornelissen | 6 | 34 | 7.35 |