155.09 Smith et al.,
from Fitzpatrick's lab, used optical image to test the development of cortical
maps of ferrets at different stages of development. They found that the on-off
map was developed first before the orientation map after eye opening.
155.18. Hermes et al
used Kay's model for BOLD response to model ECoG activation. Their idea was
that ECoG measured the field potential which is highly correlated with BOLD in
fMRI. Thus, they can use a model for BOLD for ECoG.
236.13-16 A series
of posters on T. Lee's lab used images with scene context to measure the RF
properties of V2 neurons and model their distributed representation.
236.18 Nandy et al.
showed that the sharpness of shape tuning is of a V4 cell is inversely
proportional to its location tuning. Both tuning function changes with time
after stimulus onset.
332.11 In this
curious study, Lafer-Sousa et al. measured BOLD activation on human and monkey
with either black-and-white or colored pictures of scene and faces. They showed
that in both species, color area is sandwiched between face area and scene
area. Hence, the ventral stream of both species has a similar organization.
333.03 Emmerling et
al. use 7T MRI to get 0.7x0.7 voxel size. They measured BOLD activation for
images with central square that is either far or near from the background.
Then, they did a MVPA on MT response for disparity tuning. They only see some
weak effect of disparity tuning.
Their technology is
impressive, but I think that they are on the wrong track. MVPA is a bad tool in
this case. MT should contain all sorts of disparity selectivity cell. They may
not favor one disparity over the other.
333.05 Zeater et al.
should that there are many binocular cells in LGN. A result that is going to
rewrite many textbooks.
333.07 Cooling
caudal intraparietal area reduced the activation in many 3D related areas in
the monkey brain and impair their 3D discrimination. It reveals a huge network
for 3D scene processing.
489.08 Nassi et al.
inject optogenetic virus into V1. They then stimulated V1 neurons either
directly with laser (optogenetic stimulation) or with visual stimuli. Both
drove excitatory and inhibitory responses. The interaction of the two effects
can be explained with a normalization model.
582.04 Meyer et al.
showed faces with different identity and viewpoints to monkeys. Then, measure
single cell responses in different face patches. They then used pattern
classification method to show what information is decoded in each patch.
674.03 Felleman et
al. used optical imaging to study color and orientation tuning, They showed a
pinwheel and quasi-linear organization for both hus and orientation in V4.
However, only 5% of V4 neuron show selectivity for both properties. Most V4
neurons are selective to either one of two.
674.04 There are two
types of activation for texture segregation: Feedfoward: Feature extraction,
boundary detection, feedback: region filling. The authors measured the V1
response while microstimulate the V4 neurons with the same receptive field. The
microstimulation causes a brief excitation followed by inhibition at V4, and
slightly drive V1 response.
674.08 Jonas et al.
from Rossion lab measured the intracranial responses to faces. They presented
nonface object in 1.2Hz and face in 6Hz. From the FFT analysis, they showed
that the energy ratio between face freq. and nonface Freq. increased gradually
from V1 to FFA.
674.09 The
excitatory and inhibitory features of V4 neurons have orthogonal
orientation. This produced curvature
selectivity in V4
674.10 Lateral
categorical regions biased toward lower visual field while ventral categorical
regions biased toward upper visual fields. The authors then estimated pRF of
voxels in several object areas used windowed scene images. They showed PPA pRF
is biased toward upper visual field, while TOS, lower visual field.
772.01 Dumoulin
group asked where visual cortex responding to retinal position of image
location. They measured the pRF for images either the left and the right eye
are of the same image or left and the right eye images has different location,
but not at the same time. They then fit pRF model for either eye. The brain is
able to respond to the offset. They then present images with small disparity.
They showed V1 respond to retinal position while V2, visual field location. V3
did not respond to offset for all voxel. But a subset of voxel showing
difference in left and right eye conditions, then, all areas from V2 to LO
responding to visual field position, only V1 to retinal position.
This study is
striking similar to the one carried out in my lab.
772.05 Allen et al.
asked whether amblyopia associated with anatomic abnormalities in white mater.
The did DTI with CSE (to resolve crossing fiber issue, the result is similar to
DSI). The ROIs are Pulvinar, V1, LGN and MT. They measure mean diffusivity and
fractional anisotropy. Amblyopes has a greater diffusivity than the normal
control in LGN-V1, V1-Mt and PLN-MT., ie., all pathways between
thalamo-cortical connection, but not cortico-cortical connections. Is that More
myelin degeneration for amblyopes?
Again, this study is
almost identical to the research going on in my lab.
772.06 They used functionally defined White matter,
which is functional MRI ROI diluted by 6mm . And then, used those area as seed
for DTI. Separate white matter tracts for face and scene regions. The normal and DP patients has the same
tracts, but differ in diffusitivity.
