TY - JOUR
T1 - Micro-probing enables fine-grained mapping of neuronal populations using fMRI
AU - Carvalho, Joana
AU - Invernizzi, Azzurra
AU - Ahmadi, Khazar
AU - Hoffmann, Michael B
AU - Renken, Remco J
AU - Cornelissen, Frans W
N1 - Copyright © 2019. Published by Elsevier Inc.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - 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.
AB - 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.
KW - Visual field mapping
KW - Receptive field
KW - fMRI
KW - Computational modelling
KW - SUPERIOR TEMPORAL SULCUS
KW - PRIMARY VISUAL-CORTEX
KW - RECEPTIVE-FIELD
KW - ORIENTATION COLUMNS
KW - STABILITY
KW - SIZE
KW - REPRESENTATIONS
KW - ORGANIZATION
KW - PLASTICITY
KW - RESPONSES
U2 - 10.1016/j.neuroimage.2019.116423
DO - 10.1016/j.neuroimage.2019.116423
M3 - Article
C2 - 31811903
VL - 209
JO - Neuroimage
JF - Neuroimage
SN - 1053-8119
M1 - 116423
ER -