The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A direct demonstration of functional specialization in human visual cortex
Open accessAbstract
We have used positron emission tomography (PET), which measures regional cerebral blood flow (rCBF), to demonstrate directly the specialization of function in the normal human visual cortex. A novel technique, statistical parametric mapping, was used to detect foci of significant change in cerebral blood flow within the prestriate cortex, in order to localize those parts involved in the perception of color and visual motion. For color, we stimulated the subjects with a multicolored abstract display containing no recognizable objects (Land color Mondrian) and contrasted the resulting blood flow maps with those obtained when subjects viewed an identical display consisting of equiluminous shades of gray. The comparison identified a unique area (area V4) located in the lingual and fusiform gyri of the prestriate cortex. For motion, blood flow maps when subjects viewed moving or stationary black and white random-square patterns were contrasted. The comparison identified a unique area located in the region of the temporo-parieto-occipital junction (area V5). We thus provide direct evidence to show that, just as in the macaque monkey, different areas of the human prestriate visual cortex are specialized for different attributes of vision. The striate cortex (V1) and the contiguous visual area (V2), which in the monkey brain feed both the homologous areas, were active in all 4 conditions. This pattern of activity allowed us to use an extension of the approach to assess the functional relationship between the 3 areas during color and motion stimulation. This is based on an hypothesis-led analysis of the covariance structure of the blood flow maps and promises to be a powerful tool for inferring anatomical pathways in the normal human brain.(ABSTRACT TRUNCATED AT 250 WORDS)
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The functional logic of cortical connections
Zeki, S., & Shipp, S. (1988) · Nature · doi:10.1038/335311a0 · Cited by 1,067 · cited 4×
“The importance of the demonstration of this similarity in the organization of monkey and human brain lies in the fact that, by analogy, one may be able to make predictions about other, as yet uncharted, human cortical areas, just as predictions about the internal functional organization of areas in monkeys can be made…”
BackgroundInfluentialSelf-citationMapping human visual cortex with positron emission tomography
Fox, P. T., Mintun, M. A., Raichle, M. E., Miezin, F. M., Allman, J. M., & Van Essen, D. C. (1986) · Nature · doi:10.1038/323806a0 · Cited by 422 · cited 2×
“The only comparable method of image analysis in the literature is that described by Fox et al. (1986) .”
MethodMass Action in Cerebral Function
Lashley, K. S. (1931) · Science · doi:10.1126/science.73.1888.245 · Cited by 164
Cited by
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S., & Turner, R. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.12.5675 · Cited by 4,212
Psychophysiological and Modulatory Interactions in Neuroimaging
Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997) · NeuroImage · doi:10.1006/nimg.1997.0291 · Cited by 3,097
Self-citationDevelopment and neurophysiology of mentalizing
Frith, U., & Frith, C. D. (2003) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.2002.1218 · Cited by 2,306
Mechanisms of visual attention in the human cortex
Ungerleider, S. K. A. L. G. (2000) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.23.1.315 · Cited by 2,159
Cohen, L., Dehaene, S., Naccache, L., Lehéricy, S., Dehaene-Lambertz, G., Hénaff, M.-A., & Michel, F. (2000) · Brain · doi:10.1093/brain/123.2.291 · Cited by 1,954
Metadata
- Authors
- Zeki, S., Watson, J., Lueck, C., Friston, K., Kennard, C., & Frackowiak, R.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 1991-03-01
- Volume
- 11
- Issue
- 3
- Pages
- 641-649
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W1578804663
- Cited by
- 1,716
- References
- 3
- FWCI
- 22.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.