CiteULike: Group: Glimcher_Lab - with tag cortex

Differences in Cortical Serotonergic Innervation among Humans, Chimpanzees, and Macaque Monkeys: A Comparative Study

Mary Raghanti — 2007-11-16T13:58:07-00:00

Cereb. Cortex (22 June 2007), bhm089.

In this study, we assess the possibility that the evolution of human intellectual capacities was supported by changes in the supply of serotonin to the frontal cortex. To this end, quantitative comparative analyses were performed among humans, chimpanzees, and macaques. Immunohistochemical methods were used to visualize serotonin transporter-immunoreactive (SERT-ir) axons within the cerebral cortex. Areas 9 and 32 were chosen for evaluation due to their roles in working memory and theory of mind, respectively. Primary motor cortex was also evaluated because it is not associated with higher cognitive functions. The findings revealed that humans do not display a quantitative increase in serotonin innervation. However, the results indicated region- and layer-specific differences among species in serotonergic innervation pattern. Compared with macaques, humans and chimpanzees together displayed a greater density of SERT-ir axons relative to neuron density in layers V/VI. This change was detected in cortical areas 9 and 32, but not in primary motor cortex. Further, morphological specializations, coils of axons, were observed in humans and chimpanzees that were absent in macaques. These features may represent a greater capacity for cortical plasticity exclusive to hominoids. Taken together, these results indicate a significant reorganization of cortical serotonergic transmission in humans and chimpanzees. 10.1093/cercor/bhm089

Comparison of hemodynamic response nonlinearity across primary cortical areas

David Soltysik — 2007-07-18T21:25:21-00:00

NeuroImage, Vol. 22, No. 3. (July 2004), pp. 1117-1127.

Hemodynamic responses to auditory and visual stimuli and motor tasks were assessed for the nonlinearity of response in each of the respective primary cortices. Five stimulus or task durations were used (1, 2, 4, 8, and 16 s), and five male subjects (aged 19 +/- 1.9 years) were imaged. Two tests of linearity were conducted. The first test consisted of using BOLD responses to short stimuli to predict responses to longer stimuli. The second test consisted of fitting ideal impulse response functions to the observed responses for each event duration. Both methods show that the extent of the nonlinearity varies across cortices. Results for the second method indicate that the hemodynamic response is nonlinear for stimuli less than 10 s in the primary auditory cortex, nonlinear for tasks less than 7 s in the primary motor cortex, and nonlinear for stimuli less than 3 s in the primary visual cortex. In addition, neural adaptation functions were characterized that could model the observed nonlinearities.

Turning on and off recurrent balanced cortical activity.

Y Shu — 2006-11-30T21:35:21-00:00

Nature, Vol. 423, No. 6937. (15 May 2003), pp. 288-293.

The vast majority of synaptic connections onto neurons in the cerebral cortex arise from other cortical neurons, both excitatory and inhibitory, forming local and distant 'recurrent' networks. Although this is a basic theme of cortical organization, its study has been limited largely to theoretical investigations, which predict that local recurrent networks show a proportionality or balance between recurrent excitation and inhibition, allowing the generation of stable periods of activity. This recurrent activity might underlie such diverse operations as short-term memory, the modulation of neuronal excitability with attention, and the generation of spontaneous activity during sleep. Here we show that local cortical circuits do indeed operate through a proportional balance of excitation and inhibition generated through local recurrent connections, and that the operation of such circuits can generate self-sustaining activity that can be turned on and off by synaptic inputs. These results confirm the long-hypothesized role of recurrent activity as a basic operation of the cerebral cortex.

Connection from cortical area V2 to V3 A in macaque monkey.

JC Anderson — 2006-10-01T20:07:18-00:00

J Comp Neurol, Vol. 488, No. 3. (1 August 2005), pp. 320-330.

The V2 projection to V3 A was labeled by pressure microinjecting biotinylated dextran amine (BDA) and Phaseolus vulgaris lectin (PHA-L) into V2 just posterior to the lunate sulcus. Dense terminal labeling in clusters was found in layer 4, with a weaker terminal projection in layer 3. About 3.5--4.1% of the synapses in the densest bouton clusters in layer 4 were made by labeled boutons. All were asymmetric (Gray's type 1) synapses, made by spiny, excitatory neurons. The most frequently encountered synaptic targets were spines (76% in layer 4, 98% in layer 2/3). The remainder of the synaptic targets were dendritic shafts, of which just less than half (44%) had the characteristic ultrastructure of smooth (inhibitory) cells. Multisynaptic boutons were rare (mean synapses per bouton for layer 4 1.2, for layer 2/3 1.1). The mean size of the postsynaptic densities found on spines (0.11 microm(2)) was not significantly different from that for dendrites (0.09 microm(2)). In terms of their type, laminar location, number, and targets, the synapses that formed the V2 projection to V3 A are typical of a major, excitatory, feedforward projection of macaque visual cortex.

Connection from cortical area V2 to MT in macaque monkey.

JC Anderson — 2006-10-01T20:06:49-00:00

J Comp Neurol, Vol. 443, No. 1. (28 January 2002), pp. 56-70.

The extrastriate visual area of the macaque monkey called MT or V5, receives its input from multiple sources. We have previously examined the synaptic connections made by V1 cells that project to MT (Anderson et al., 1998). Here, we provide a similar analysis of the projection from V2 to MT. The major target of the V2 projection in MT is layer 4, where it forms clusters of asymmetric (excitatory) synapses. Unlike the V1 projection, it also forms synapses in layers 1 and 2 and does not form synapses in layer 6. The most frequently encountered targets of boutons labeled from V2 were spines (67% in layer 4; 82% in layer 2/3). Unusually, only 5/12 boutons examined in layer 1 actually formed synapses. Unlike the V1 projection, multisynaptic boutons were rare (mean, 1.1 synapses per bouton vs. 1.7 for the V1 projection). Like the V1 projection, the input to MT from any point in V2 is sparse (contributing approximately 4-6% of the asymmetric synapses in the densest clusters in layer 4). The synapses of the V2 projection were similar in size to those of the V1 projection (0.1 microm(2) vs. 0.09 microm(2)) and both formed more complex postsynaptic densities on spines than on dendritic shafts. The clear differences between the V1 and V2 projection to MT indicate that their functions are complementary rather than completely overlapping.

Microcircuits in visual cortex.

KA Martin — 2006-10-01T20:05:39-00:00

Curr Opin Neurobiol, Vol. 12, No. 4. (August 2002), pp. 418-425.

The microcircuitry of the neocortex is bewildering in its anatomical detail, but seen through the filters of physiology, some simple circuits have been suggested. Intensive investigations of the cortical representation of orientation, however, show how difficult it is to achieve any consensus on what the circuits are, how they develop, and how they work. New developments in modeling allied with powerful experimental tools are changing this. Experimental work combining optical imaging with anatomy and physiology has revealed a rich local cortical circuitry. Whereas older models of cortical circuits have concentrated on simple 'feedforward' circuits, newer theoretical work has explored more the role of the recurrent cortical circuits, which are more realistic representations of the actual circuits and are computationally richer.

Synaptic connection from cortical area V4 to V2 in macaque monkey.

JC Anderson — 2006-10-01T20:03:53-00:00

J Comp Neurol, Vol. 495, No. 6. (20 April 2006), pp. 709-721.

The major target of the V4 projection in V2 is layer 1, where it forms a tangential spread of asymmetric (excitatory) synapses. This is characteristic of a "feedback" projection. Some axons formed discrete clusters of bouton terminaux between lengths of myelinated axon, while others were unbranched and formed a continuous distribution of en passant boutons with no intercalated myelin. Minor projections were found in layers 2/3 and 6. Dendritic spines were the most frequently encountered targets of the V4 projection (80% in layer 1 and layer 2/3, 94% in layer 6). The remaining targets were dendritic shafts. In layer 1, 69% of target dendrites (12% of all targets) had characteristics identifying them as smooth (GABAergic) cells. In layer 2/3 and layer 6 virtually all the shaft synapses were on smooth dendrites (86% and 100%, respectively). Multisynaptic boutons were rare (mean 1.1 synapses per bouton). Synapses formed in layer 6 were smaller than those of layer 1 (mean area 0.073 microm(2) vs. 0.117 microm(2)). Synapses formed with spines had a more complex postsynaptic density than those formed with dendritic shafts. With respect to targets and synaptic type and size and morphology of synapses, the feedback projection from V4 to V2 resembles those of feedforward projections. The principal difference between the feedforward and feedback projection is in the lamina location of their terminal boutons. The concentration of the V4 projection on layer 1, where it forms asymmetric synapses mainly with spines, suggests that it excites the distal apical dendrites of pyramidal cells.

Towards a theory of the laminar architecture of cerebral cortex: computational clues from the visual system.

RD Raizada — 2005-12-07T17:02:32-00:00

Cereb Cortex, Vol. 13, No. 1. (January 2003), pp. 100-113.

One of the most exciting and open research frontiers in neuroscience is that of seeking to understand the functional roles of the layers of cerebral cortex. New experimental techniques for probing the laminar circuitry of cortex have recently been developed, opening up novel opportunities for investigating how its six-layered architecture contributes to perception and cognition. The task of trying to interpret this complex structure can be facilitated by theoretical analyses of the types of computations that cortex is carrying out, and of how these might be implemented in specific cortical circuits. We have recently developed a detailed neural model of how the parvocellular stream of the visual cortex utilizes its feedforward, feedback and horizontal interactions for purposes of visual filtering, attention and perceptual grouping. This model, called LAMINART, shows how these perceptual processes relate to the mechanisms that ensure the stable development of cortical circuits in the infant, and to the continued stability of learning in the adult. The present article reviews this laminar theory of visual cortex, considers how it may be generalized towards a more comprehensive theory that encompasses other cortical areas and cognitive processes, and shows how its laminar framework generates a variety of testable predictions.

Computational models of cortical visual processing.

DJ Heeger — 2005-11-07T13:48:09-00:00

Proc Natl Acad Sci U S A, Vol. 93, No. 2. (23 January 1996), pp. 623-627.

The visual responses of neurons in the cerebral cortex were first adequately characterized in the 1960s by D. H. Hubel and T. N. Wiesel [(1962) J. Physiol. (London) 160, 106-154; (1968) J. Physiol. (London) 195, 215-243] using qualitative analyses based on simple geometric visual targets. Over the past 30 years, it has become common to consider the properties of these neurons by attempting to make formal descriptions of these transformations they execute on the visual image. Most such models have their roots in linear-systems approaches pioneered in the retina by C. Enroth-Cugell and J. R. Robson [(1966) J. Physiol. (London) 187, 517-552], but it is clear that purely linear models of cortical neurons are inadequate. We present two related models: one designed to account for the responses of simple cells in primary visual cortex (V1) and one designed to account for the responses of pattern direction selective cells in MT (or V5), an extrastriate visual area thought to be involved in the analysis of visual motion. These models share a common structure that operates in the same way on different kinds of input, and instantiate the widely held view that computational strategies are similar throughout the cerebral cortex. Implementations of these models for Macintosh microcomputers are available and can be used to explore the models' properties.