Functional Organisation of Human Visual Cortex Revealed by fMRI

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 9-9 ◽  
Author(s):  
R B H Tootell ◽  
A M Dale ◽  
N Hadjikhani ◽  
A K Liu ◽  
S Marrett ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Visual Information ◽  
Visual Stimulation ◽  
Frequency Tuning ◽  
Human Subjects ◽  
Planar Imaging ◽  
Posterior Cortex ◽  
Human Visual Cortex ◽  
Wide Range ◽  
Functional Organisation

Until recently, comparatively little was known about the functional organisation of human visual cortex. Functional magnetic resonance imaging (fMRI), in conjunction with cortical flattening techniques and psychophysically relevant visual stimulation, has greatly clarified human visual-information processing. To date, we have completed cortical surface reconstructions (flattening), coupled with a wide range of visual stimulus testing, on 28 normal human subjects. Visual activation was acquired on a 1.5 T GE MR scanner with ANMR echo-planar imaging, with the use of a custom, bilateral, quadrature surface coil covering posterior cortex. Approximately ten visual cortical areas can now be functionally localised each with unique functional and topographical properties. The most well-defined areas are: V1, V2, V3, VP, V3A, V4v, MT, SPO, and perhaps MSTd. Most of the properties in these human areas are similar to those reported in presumably homologous areas of macaque, but distinctive species differences also appear to exist, notably in V3/VP, V4v, and V3A. Human areas showing prominant motion-selectivity include V3A, MT/MSTd, SPO, and a small area near the superior sylvian fissure. Retinotopic areas include V1, V2, V3, VP, V4v, and V3A. The human cortical magnification factor appears higher towards the fovea than in macaque, but, like macaque, preferred spatial frequency tuning varies inversely with eccentricity in all retinotopic areas in which sinusoidal gratings are effective stimuli.

Adult Cortical Dynamics

1998 ◽  
Vol 78 (2) ◽  
pp. 467-485 ◽  
Author(s):  
CHARLES D. GILBERT
Keyword(s):  
Visual Cortex ◽  
Functional Properties ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Visual Space ◽  
Visual Pathway ◽  
Local Features ◽  
Spatial Integration ◽  
Cortical Dynamics ◽  
Wide Range

Gilbert, Charles D. Adult Cortical Dynamics. Physiol. Rev. 78: 467–485, 1998. — There are many influences on our perception of local features. What we see is not strictly a reflection of the physical characteristics of a scene but instead is highly dependent on the processes by which our brain attempts to interpret the scene. As a result, our percepts are shaped by the context within which local features are presented, by our previous visual experiences, operating over a wide range of time scales, and by our expectation of what is before us. The substrate for these influences is likely to be found in the lateral interactions operating within individual areas of the cerebral cortex and in the feedback from higher to lower order cortical areas. Even at early stages in the visual pathway, cells are far more flexible in their functional properties than previously thought. It had long been assumed that cells in primary visual cortex had fixed properties, passing along the product of a stereotyped operation to the next stage in the visual pathway. Any plasticity dependent on visual experience was thought to be restricted to a period early in the life of the animal, the critical period. Furthermore, the assembly of contours and surfaces into unified percepts was assumed to take place at high levels in the visual pathway, whereas the receptive fields of cells in primary visual cortex represented very small windows on the visual scene. These concepts of spatial integration and plasticity have been radically modified in the past few years. The emerging view is that even at the earliest stages in the cortical processing of visual information, cells are highly mutable in their functional properties and are capable of integrating information over a much larger part of visual space than originally believed.

scholarly journals Novel neural circuit mechanism for visual edge detection

2015 ◽  
Vol 112 (3) ◽  
pp. 875-880 ◽  
Author(s):  
Charles F. Stevens
Keyword(s):  
Visual Cortex ◽  
Edge Detection ◽  
Visual Information ◽  
Neural Circuit ◽  
Frequency Tuning ◽  
Orientation Tuning ◽  
Image Patch ◽  
Small Parts ◽  
The Brain ◽  
Slope Orientation

The primary visual cortex is organized in a way that assigns a specific collection of neurons the job of providing the rest of the brain with all of the information it needs about each small part of the image present on the retina: Neighboring patches of the visual cortex provide the information about neighboring patches of the visual world. Each one of these cortical patches—often identified as a “pinwheel”—contains thousands of neurons, and its corresponding image patch is centered on a particular location in the retina. For stimuli within their image patch, neurons respond selectively to lines or edges with a particular slope (orientation tuning) and to regions of the patch of different sizes (known as spatial frequency tuning). The same number of neurons is devoted to reporting each possible slope (orientation). For the cells that cover different-sized regions of their image patch, however, the number of neurons assigned depends strongly on their preferred region size. Only a few neurons report on large and small parts of the image patch, but many neurons report visual information from medium-sized areas. I show here that having different numbers of neurons responsible for image regions of different sizes actually carries out a computation: Edges in the image patch are extracted. I also explain how this edge-detection computation is done.

Increased oxygen consumption in human visual cortex: response to visual stimulation

2009 ◽  
Vol 98 (2) ◽  
pp. 85-89 ◽  
Author(s):  
M. S. Vafaee ◽  
S. Marrett ◽  
E. Meyer ◽  
A. C. Evans ◽  
A. Gjedde
Keyword(s):  
Oxygen Consumption ◽  
Visual Cortex ◽  
Visual Stimulation ◽  
Human Visual Cortex

scholarly journals Radial frequency tuning in human visual cortex

2017 ◽  
Vol 17 (10) ◽  
pp. 293
Author(s):  
Antony Morland ◽  
Samuel Lawrence ◽  
Richard Vernon ◽  
Bruce Keefe ◽  
Andre Gouws ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Frequency Tuning ◽  
Human Visual Cortex ◽  
Radial Frequency

scholarly journals Dynamics of spatial frequency tuning in mouse visual cortex

2012 ◽  
Vol 107 (11) ◽  
pp. 2937-2949 ◽  
Author(s):  
Samme Vreysen ◽  
Bin Zhang ◽  
Yuzo M. Chino ◽  
Lutgarde Arckens ◽  
Gert Van den Bergh
Keyword(s):  
Visual Cortex ◽  
Spatial Frequency ◽  
Visual Information ◽  
Frequency Tuning ◽  
Neuronal Response ◽  
Correlation Technique ◽  
Low Frequencies ◽  
Temporal Shift ◽  
Spatial Frequency Tuning ◽  
Spatial Frequencies

Neuronal spatial frequency tuning in primary visual cortex (V1) substantially changes over time. In both primates and cats, a shift of the neuron's preferred spatial frequency has been observed from low frequencies early in the response to higher frequencies later in the response. In most cases, this shift is accompanied by a decreased tuning bandwidth. Recently, the mouse has gained attention as a suitable animal model to study the basic mechanisms of visual information processing, demonstrating similarities in basic neuronal response properties between rodents and highly visual mammals. Here we report the results of extracellular single-unit recordings in the anesthetized mouse where we analyzed the dynamics of spatial frequency tuning in V1 and the lateromedial area LM within the lateral extrastriate area V2L. We used a reverse-correlation technique to demonstrate that, as in monkeys and cats, the preferred spatial frequency of mouse V1 neurons shifted from low to higher frequencies later in the response. However, this was not correlated with a clear selectivity increase or enhanced suppression of responses to low spatial frequencies. These results suggest that the neuronal connections responsible for the temporal shift in spatial frequency tuning may considerably differ between mice and monkeys.

scholarly journals Microsaccade generation requires a foveal anchor

2020 ◽  
Vol 12 (6) ◽  
Author(s):  
Jorge Otero-Millan ◽  
Rachel E Langston ◽  
Francisco Costela ◽  
Stephen L Macknik ◽  
Susana Martinez-Conde
Keyword(s):  
Visual Information ◽  
Visual Stimulation ◽  
Human Subjects ◽  
Covert Attention ◽  
Natural Scenes ◽  
Peripheral Stimulation ◽  
Free Viewing ◽  
Passive Viewing ◽  
Foveal Stimulation ◽  
Activation Map

Visual scene characteristics have the ability to affect various aspects of saccade and microsaccade dynamics. For example, blank visual scenes are known to elicit diminished saccade and microsaccade production, compared to natural scenes. Similarly, microsaccades are less frequent in the dark. Yet, the extent to which foveal and peripheral visual information contribute to microsaccade production remains unclear: because microsaccade are directed to covert attention locations as per the superior colliculus activation map, it follows that peripheral stimulation could suffice to produce regular microsaccade dynamics, even without foveal stimulation being present. Here we compared the characteristics of microsaccades generated in the presence or absence of foveal and/or peripheral visual stimulation, while human subjects conducted four types of oculomotor tasks (fixation, free-viewing, guided-viewing and fixation during passive viewing). Foveal information was either available, or made unavailable by the presentation of both solid and blurred scotomas. We found foveal stimulation to be critical for microsaccade production, and peripheral stimulation, by itself, to be insufficient to yield microsaccades. Our results indicate that a foveal visual anchor is necessary for microsaccade generation.   

scholarly journals GABAergic inhibition in the human visual cortex relates to eye dominance

2020 ◽  
Author(s):  
I. Betina Ip ◽  
Claudia Lunghi ◽  
Uzay E. Emir ◽  
Andrew J. Parker ◽  
Holly Bridge
Keyword(s):  
Visual Cortex ◽  
Magnetic Resonance ◽  
Visual System ◽  
Sensory Input ◽  
Visual Stimulation ◽  
The Other ◽  
7 Tesla ◽  
Gabaergic Inhibition ◽  
Human Visual Cortex ◽  
Eye Dominance

ABSTRACTOur binocular world is seamlessly assembled from two retinal images that remain segregated until the cerebral cortex. Despite the coherence of this input, there is often an imbalance between the strength of these connections in the brain. ‘Eye dominance’ provides a measure of the perceptual dominance of one eye over the other. Theoretical models suggest that eye dominance is related to reciprocal inhibition between monocular units in the primary visual cortex, the first location where the binocular input is combined. As the specific inhibitory interactions in the binocular visual system critically depend on the presence of visual input, we sought to test the role of inhibition by measuring the concentrations of inhibitory (GABA) neurotransmitters during monocular visual stimulation of the dominant and the non-dominant eye. GABA-levels were acquired in V1 using a combined functional magnetic resonance imaging (fMRI) and magnetic resonance spectroscopy (MRS) sequence on a 7-Tesla MRI scanner. Individuals with stronger eye dominance had a greater difference in GABAergic inhibition between the eyes. This relationship was present only when the visual system was actively processing sensory input and was not present at rest. We provide the first evidence that imbalances in GABA levels during ongoing sensory processing are related to eye dominance in the human visual cortex. This provides strong support to the view that intracortical inhibition underlies normal eye dominance.SIGNIFICANCE STATEMENTWhat we see is shaped by excitation and inhibition in our brain. We investigated how eye dominance, the perceptual preference of one eye’s input over the other, is related to levels of inhibitory neurotransmitter GABA during monocular visual stimulation. GABAergic inhibition is related to eye dominance, but only when the visual system is actively processing sensory input. This provides key support for the view that imbalances in visual competition that are observed in the normal visual system arise from an inability of GABA signalling to suppress the stronger sensory representation.

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