FMRI mapping of a morphed continuum of 3D shapes within inferior temporal cortex

Roger B. H. Tootell, Kathryn J. Devaney, Jeremy C. Young, Gheorghe Postelnicu, Reza Rajimehr, Leslie G. Ungerleider
Proc. Natl. Acad. Sci. USA, 10.1073/pnas.0712274105

Here, we mapped fMRI responses to incrementally changing shapes along a continuous 3D morph, ranging from a head ("face") to a house ("place"). The response to each shape was mapped independently by using single-stimulus imaging, and stimulus shapes were equated for lower-level visual cues. We measured activity in 2-mm samples across human inferior temporal cortex from the fusiform face area (FFA) (apparently selective for faces) to the parahippocampal place area (PPA) (apparently selective for places), testing for (i) incremental changes in the topography of FFA and PPA (predicted by the continuous-mapping model) or (ii) little or no response to the intermediate morphed shapes (predicted by the category model). Neither result occurred; instead, we found approximately linearly graded changes in the response amplitudes to graded-shape changes, without changes in topography—similar to visual responses in different lower-tier cortical areas.

Fulltext: http://www.pnas.org/cgi/reprint/0712274105v1.pdf

Interpreting fMRI data: maps, modules and dimensions

Hans P. Op de Beeck*, Johannes Haushofer‡ and Nancy G. Kanwisher‡
Nature reviews neuroscience, volume 9, february 2008, 123

Neuroimaging research over the past decade has revealed a detailed picture
of the functional organization of the human brain. Here we focus on two fundamental
questions that are raised by the detailed mapping of sensory and cognitive functions and illustrate these questions with findings from the object-vision pathway. First, are functionally specific regions that are located close together best understood as distinct cortical modules or as parts of a larger-scale cortical map? Second, what functional properties define each cortical map or module? We propose a model in which overlapping continuous maps of simple features give rise to discrete modules that are selective for complex stimuli.

Fulltext: http://www.nature.com/nrn/journal/v9/n2/pdf/nrn2252.pdf

Map:
A clustering of neurons with similar functional properties that is characterized by a
gradual progression of preferred stimulus values across the cortical sheet.

Module:
A clustering of neurons with similar functional properties that is characterized by
discrete regions with clear boundaries across which there is no relation in preferred
stimulus values.

Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry

Wolfgang Einhäuser, James Stout, Christof Koch, Olivia Carter
PNAS | February 5, 2008 | vol. 105 | no. 5 | 1704-1709

During sustained viewing of an ambiguous stimulus, an individual's perceptual experience will generally switch between the different possible alternatives rather than stay fixed on one interpretation (perceptual rivalry). Here, we measured pupil diameter while subjects viewed different ambiguous visual and auditory stimuli. For all stimuli tested, pupil diameter increased just before the reported perceptual switch and the relative amount of dilation before this switch was a significant predictor of the subsequent duration of perceptual stability. These results could not be explained by blink or eye-movement effects, the motor response or stimulus driven changes in retinal input. Because pupil dilation reflects levels of norepinephrine (NE) released from the locus coeruleus (LC), we interpret these results as suggestive that the LC–NE complex may play the same role in perceptual selection as in behavioral decision making.

Fulltext: http://www.pnas.org/cgi/reprint/105/5/1704

Learning to Link Visual Contours

Wu Li, Valentin Piëch, Charles D. Gilbert
Neuron Volume 57, Issue 3, 7 February 2008, Pages 442-451

In complex visual scenes, linking related contour elements is important for object recognition. This process, thought to be stimulus driven and hard wired, has substrates in primary visual cortex (V1). Here, however, we find contour integration in V1 to depend strongly on perceptual learning and top-down influences that are specific to contour detection. In naive monkeys, the information about contours embedded in complex backgrounds is absent in V1 neuronal responses and is independent of the locus of spatial attention. Training animals to find embedded contours induces strong contour-related responses specific to the trained retinotopic region. These responses are most robust when animals perform the contour detection task but disappear under anesthesia. Our findings suggest that top-down influences dynamically adapt neural circuits according to specific perceptual tasks. This may serve as a general neuronal mechanism of perceptual learning and reflect top-down mediated changes in cortical states.

Fulltext: Science Direct

Cellular networks underlying human spatial navigation

Arne D. Ekstrom, Michael J. Kahana, Jeremy B. Caplan, Tony A. Fields, Eve A. Isham, Ehren L. Newman, Itzhak Fried
Nature 425, 184-188 (11 September 2003) | doi:10.1038/nature01964;

Place cells of the rodent hippocampus constitute one of the most striking examples of a correlation between neuronal activity and complex behaviour in mammals1, 2. These cells increase their firing rates when the animal traverses specific regions of its surroundings, providing a context-dependent map of the environment3, 4, 5. Neuroimaging studies implicate the hippocampus and the parahippocampal region in human navigation6, 7, 8. However, these regions also respond selectively to visual stimuli9, 10, 11, 12, 13. It thus remains unclear whether rodent place coding has a homologue in humans or whether human navigation is driven by a different, visually based neural mechanism. We directly recorded from 317 neurons in the human medial temporal and frontal lobes while subjects explored and navigated a virtual town. Here we present evidence for a neural code of human spatial navigation based on cells that respond at specific spatial locations and cells that respond to views of landmarks. The former are present primarily in the hippocampus, and the latter in the parahippocampal region. Cells throughout the frontal and temporal lobes responded to the subjects' navigational goals and to conjunctions of place, goal and view.

Fulltext: http://www.nature.com/nature/journal/v425/n6954/full/nature01964.html

Ultra-rapid categorisation in non-human primates.

Girard P, Jouffrais C, Kirchner CH.
Anim Cogn. 2008 Feb 8

The visual system of primates is remarkably efficient for analysing information about objects present in complex natural scenes. Recent work has demonstrated that they perform this at very high speeds. In a choice saccade task, human subjects can initiate a first reliable saccadic eye movement response to a target (the image containing an animal) in only 120 ms after image onset. Such fast responses impose severe time constraints if one considers neuronal responses latencies in high-level ventral areas of the macaque monkey. The question then arises: are non-human primates able to perform the task? Two rhesus macaque monkeys (Macaca mulatta) were trained to perform the same forced-choice categorization task as the one used in humans. Both animals performed the task with a high accuracy and generalized to new stimuli that were introduced everyday: accuracy levels were comparable both with new and well-known images (84% vs. 94%). More importantly, reaction times were extremely fast (minimum reaction time 100 ms and median reaction time 152 ms). Given that typical single units onset times in Inferotemporal cortex (IT) are about as long as the shortest behavioural responses measured here, we conclude that visual processing involved in ultra rapid categorisations might be based on rather simple shape cue analysis that can be achieved in areas such as extrastriate cortical area V4. The present paper demonstrates for the first time, that rhesus macaque monkeys (Macaca mulatta) are able to match human performance in a forced-choice saccadic categorisation task of animals in natural scenes.

PMID: 18259787
Fulltext: http://www.springerlink.com/content/wl565888n65w341h/

A voice region in the monkey brain.

Petkov CI, Kayser C, Steudel T, Whittingstall K, Augath M, Logothetis NK.
Nat Neurosci. 2008 Feb 10

For vocal animals, recognizing species-specific vocalizations is important for survival and social interactions. In humans, a voice region has been identified that is sensitive to human voices and vocalizations. As this region also strongly responds to speech, it is unclear whether it is tightly associated with linguistic processing and is thus unique to humans. Using functional magnetic resonance imaging of macaque monkeys (Old World primates, Macaca mulatta) we discovered a high-level auditory region that prefers species-specific vocalizations over other vocalizations and sounds. This region not only showed sensitivity to the 'voice' of the species, but also to the vocal identify of conspecific individuals. The monkey voice region is located on the superior-temporal plane and belongs to an anterior auditory 'what' pathway. These results establish functional relationships with the human voice region and support the notion that, for different primate species, the anterior temporal regions of the brain are adapted for recognizing communication signals from conspecifics.

PMID: 18264095

Fulltext: http://www.nature.com/neuro/journal/vaop/ncurrent/abs/nn2043.html