Does congenital deafness affect the structural and functional architecture of primary visual cortex?

Deafness results in greater reliance on the remaining senses. It is unknown whether the cortical architecture of the intact senses is optimized to compensate for lost input. Here we performed widefield population receptive field (pRF) mapping of primary visual cortex (V1) with functional magnetic resonance imaging (fMRI) in hearing and congenitally deaf participants, all of whom had learnt sign language after the age of 10 years. We found larger pRFs encoding the peripheral visual field of deaf compared to hearing participants. This was likely driven by larger facilitatory center zones of the pRF profile concentrated in the near and far periphery in the deaf group. pRF density was comparable between groups, indicating pRFs overlapped more in the deaf group. This could suggest that a coarse coding strategy underlies enhanced peripheral visual skills in deaf people. Cortical thickness was also decreased in V1 in the deaf group. These findings suggest deafness causes structural and functional plasticity at the earliest stages of visual cortex

Vision in context : from behavior to neurons

The visual system receives an enormous amount of information from the retina. For us to be able to make sense of our environment, the visual system needs to interpret the incoming information. This interpretation is not only based on the sensory information of the stimulus, but is also based on our knowledge of the world. Using this prior knowledge, the visual system derives the most likely cause of the retinal image. How the retinal image is processed in our brain and how this influences our behavior is a big question in neuroscience. The aim of this dissertation was to investigate how visual perception is affected by both the sensory-driven and knowledge-based information. We describe a number of studies where we investigated how these information sources affect the neural signal (Chapter 3, 4 and 5) and our behavior (Chapter 2). For this, we used a combination of behavioral methods and computational neuroimaging techniques. In our experiments, we used both synthetic and natural images. Where the synthetic images contained mainly sensory-driven information, the natural images contained both sensory-driven and knowledge-based information. We quantified both local contrast energy (sensory-driven information) and subjective importance (knowledge-based information) of the natural images. Using fMRI we can estimate the region of visual space to which each cortical location responds, i.e. the population receptive field (pRF) (Dumoulin & Wandell, 2008). In this thesis, we present three new analysis techniques that extend the analysis of the pRF-method, to measure contextual influences of both sensory-driven and knowledge-based information in visual cortex (Chapter 3, 4 and 5). In chapter 3 we present a method to reconstruct center-surround configurations in human visual cortex. We extended the pRF model by adding a suppressive surround to the pRF. In chapter 4 we present a method to identify (natural) images based on the inherent contrast of the images. In this study, we see that similar images were harder to identify than other images using the fMRI responses of visual area V1. We suggest that this is caused by the internal representation of V1 not only capturing sensory-driven information, but also knowledge-based information. This is supported by our results in chapter 5, where we show that the sensory-driven information (defined by the response to contrast) of early visual areas is enhanced according to the knowledge-based information (defined by subjective importance). In chapter 2 we show that the interaction we found in the neural responses of early visual areas is also reflected in our perceptual functioning, and more specifically in our ability to detect changes in a visual scene. The experiments in this thesis provide a quantitative link between physical sensory stimulation and our subjective perceptual experience on the neural signal and our behavior. Where these effects are usually studied using synthetic images, we extend these results to natural images

Frequency specific spatial interactions in human electrocorticography : V1 alpha oscillations reflect surround suppression

Electrical brain signals are often decomposed into frequency ranges that are implicated in different functions. Using subdural electrocorticography (ECoG, intracranial EEG) and functional magnetic resonance imaging (fMRI), we measured frequency spectra and BOLD responses in primary visual cortex (V1) and intraparietal sulcus (IPS). In V1 and IPS, 30-120. Hz (gamma, broadband) oscillations allowed population receptive field (pRF) reconstruction comparable to fMRI estimates. Lower frequencies, however, responded very differently in V1 and IPS. In V1, broadband activity extends down to 3. Hz. In the 4-7. Hz (theta) and 18-30. Hz (beta) ranges broadband activity increases power during stimulation within the pRF. However, V1 9-12. Hz (alpha) frequency oscillations showed a different time course. The broadband power here is exceeded by a frequency-specific power increase during stimulation of the area outside the pRF. As such, V1 alpha oscillations reflected surround suppression of the pRF, much like negative fMRI responses. They were consequently highly localized, depending on stimulus and pRF position, and independent between nearby electrodes. In IPS, all 3-25. Hz oscillations were strongest during baseline recording and correlated between nearby electrodes, consistent with large-scale disengagement. These findings demonstrate V1 alpha oscillations result from locally active functional processes and relate these alpha oscillations to negative fMRI signals. They highlight that similar oscillations in different areas reflect processes with different functional roles. However, both of these roles of alpha seem to reflect suppression of spiking activity

Frequency specific spatial interactions in human electrocorticography : V1 alpha oscillations reflect surround suppression

Electrical brain signals are often decomposed into frequency ranges that are implicated in different functions. Using subdural electrocorticography (ECoG, intracranial EEG) and functional magnetic resonance imaging (fMRI), we measured frequency spectra and BOLD responses in primary visual cortex (V1) and intraparietal sulcus (IPS). In V1 and IPS, 30-120. Hz (gamma, broadband) oscillations allowed population receptive field (pRF) reconstruction comparable to fMRI estimates. Lower frequencies, however, responded very differently in V1 and IPS. In V1, broadband activity extends down to 3. Hz. In the 4-7. Hz (theta) and 18-30. Hz (beta) ranges broadband activity increases power during stimulation within the pRF. However, V1 9-12. Hz (alpha) frequency oscillations showed a different time course. The broadband power here is exceeded by a frequency-specific power increase during stimulation of the area outside the pRF. As such, V1 alpha oscillations reflected surround suppression of the pRF, much like negative fMRI responses. They were consequently highly localized, depending on stimulus and pRF position, and independent between nearby electrodes. In IPS, all 3-25. Hz oscillations were strongest during baseline recording and correlated between nearby electrodes, consistent with large-scale disengagement. These findings demonstrate V1 alpha oscillations result from locally active functional processes and relate these alpha oscillations to negative fMRI signals. They highlight that similar oscillations in different areas reflect processes with different functional roles. However, both of these roles of alpha seem to reflect suppression of spiking activity

Connective field modeling

<p>The traditional way to study the properties of visual neurons is to measure their responses to visually presented stimuli. A second way to understand visual neurons is to characterize their responses in terms of activity elsewhere in the brain. Understanding the relationships between responses in distinct locations in the visual system is essential to clarify this network of cortical signaling pathways. Here, we describe and validate connective field modeling, a model-based analysis for estimating the dependence between signals in distinct cortical regions using functional magnetic resonance imaging (fMRI). Just as the receptive field of a visual neuron predicts its response as a function of stimulus position, the connective field of a neuron predicts its response as a function of activity in another part of the brain. Connective field modeling opens up a wide range of research opportunities to study information processing in the visual system and other topographically organized cortices. (C) 2012 Elsevier Inc. All rights reserved.</p>