Optical imaging slit spectroscopy is a powerful method for estimating quantitative changes in cerebral haemodynamics, such as deoxyhaemoglobin, oxyhaemoglobin and blood volume (Hbr, HbO2 and Hbt, respectively). Its disadvantage is that there is a large loss of spatial data as one image dimension is used to encode spectral wavelength information. Single wavelength optical imaging, on the other hand, produces high-resolution spatiotemporal maps of brain activity, but yields only indirect measures of Hbr, HbO2 and Hbt. In this study we perform two-dimensional optical imaging spectroscopy (2D-OIS) in rat barrel cortex during contralateral whisker stimulation to obtain two-dimensional maps over time of Hbr, HbO2 and Hbt. The 2D-OIS was performed by illuminating the cortex with four wavelengths of light (575, 559, 495 and 587 nm), which were presented sequentially at a high frame rate (32 Hz). The contralateral whisker pad was stimulated using two different durations: 1 and 16 s (5 Hz, 1.2 mA). Control experiments used a hypercapnic (5% CO2) challenge to manipulate baseline blood flow and volume in the absence of corresponding neural activation. The 2D-OIS method allowed separation of artery, vein and parenchyma regions. The magnitude of the haemodynamic response elicited varied considerably between different vascular compartments; the largest responses in Hbt were in the arteries and the smallest in the veins. Phase lags in the HbO2 response between arteries and veins suggest that a process of upstream signalling maybe responsible for dilating the arteries. There was also a consistent increase in Hbr from arterial regions after whisker stimulation.

No abstract

In the primate visual system, areas V1 and V2 distribute information they receive from the retina to all higher cortical areas, sorting this information into dorsal and ventral streams. Therefore, knowledge of the organization of projections between V1 and V2 is crucial to understand how the cortex processes visual information. In primates, parallel output pathways from V1 project to distinct V2 stripes. The traditional tripartite division of V1-to-V2 projections was recently replaced by a bipartite scheme, in which thin stripes receive V1 inputs from blob columns, and thick and pale stripes receive common input from interblob columns. Here, we demonstrate that thick and pale stripes, instead, receive spatially segregated V1 inputs and that the interblob is partitioned into two compartments: the middle of the interblob projecting to pale stripes and the blob/interblob border region projecting to thick stripes. Double-labeling experiments further demonstrate that V1 cells project to either thick or pale stripes, but rarely to both. We also find laminar specialization of V1 outputs, with layer 4B contributing projections mainly to thick stripes, and no projections to one set of pale stripes. These laminar differences suggest different contribution of magno, parvo, and konio inputs to each V1 output pathway. These results provide a new foundation for parallel processing models of the visual system by demonstrating four V1-to-V2 pathways: blob columns-to-thin stripes, blob/interblob border columns-to-thick stripes, interblob columns-to-pale lateral stripes, layer 2/3-4A interblobs-to-pale medial stripes.

Dear Dr. SchriddeWe would like to thank the Editorial team at Neuron for con nuing to assist us in pilo ng NEURON-D--R through the review process with a "minor revision" decision.We were happy to note that reviewer stated that "This already strong paper is much improved", expressing that "I do not want to stand in the way of the dissemina on of this important work". They suggested a new regression model, although they "realized this is a lot to ask". We have nevertheless implemented this to allay their remaining concerns. Reviewer is fully sa sfied with our work, and in the previous round remarked that "This is an outstanding work -both in terms of the techniques combina on and their applica on in alerts monkeys, the high quality data obtained from several visual areas and the important ques ons".It was heartening to see that reviewer recognized that "The work they report is a tour-de-force of several state-of-the-art methods, and I am sure the results will be very useful to anybody inves ga ng the cor cal representa on of color". Of concern to us however, the third referee now provides a novel set of more extensive, purportedly 'fault-finding' commentary. Much of this could and should have been raised in the first review. Several points betray selec ve reading of the manuscript, and are refuted simply by ci ng the exis ng text (and/or exis ng literature). The first round of review judged the logic of our study as predicated on the assump on that color and form are separately encoded in V . We were obliged to add Figure S in the last revision to address this misconcep on. However, the follow-up review gave no jus fica on for this major cri cism. The second round focused on the broad nature of color processing, yet appear agnos c to the unique value of the current study comparing across three successive visual areas of the same prepara on (a point borne out by the strong support obtained from Reviewers and ). We have nonetheless taken all ac onable sugges ons construc vely (for example, adding a new CIELAB analysis into Figures CD and B), and ensured that we carefully disambiguate any text where improvements can be made (please see our reply to the Reviewers appended to this le er).We would like to re-emphasize that we believe there is no precedent in the literature for such a thorough inves ga on of cor cal func onal organiza on along the visual hierarchy of V , V , and V for color representa on. The color vision system progressively removes the V color bias along the visual pathway and achieves spectral uniformity be er reflec ng color percep on. We have implemented whatever improvements we can, to meet the reviews' expecta on for publica on of this manuscript. We would also like to thank you very much for your me and support and are looking forward to hearing from you.

Our ability to see the world in depth is a major accomplishment of the brain. Previous models of how positionally disparate cues to the two eyes are binocularly matched limit possible matches by invoking uniqueness and continuity constraints. These approaches cannot explain data wherein uniqueness fails and changes in contrast alter depth percepts, or where surface discontinuities cause surfaces to be seen in depth, although they are registered by only one eye (da Vinci stereopsis). A new stereopsis model explains these depth percepts by proposing how cortical complex cells binocularly filter their inputs and how monocular and binocular complex cells compete to determine the winning depth signals.