2011年8月31日水曜日

Perceptual classification in a rapidly changing environment.


Summerfield C, Behrens TE, Koechlin E.
Neuron. 2011 Aug 25;71(4):725-36.

大「脳」洋航海記より。
http://viking-neurosci.sakura.ne.jp/blog-wp/?p=6134

Humans and monkeys can learn to classify perceptual information in a statistically optimal fashion if the functional groupings remain stable over many hundreds of trials, but little is known about categorization when the environment changes rapidly. Here, we used a combination of computational modeling and functional neuroimaging to understand how humans classify visual stimuli drawn from categories whose mean and variance jumped unpredictably. Models based on optimal learning (Bayesian model) and a cognitive strategy (working memory model) both explained unique variance in choice, reaction time, and brain activity. However, the working memory model was the best predictor of performance in volatile environments, whereas statistically optimal performance emerged in periods of relative stability. Bayesian and working memory models predicted decision-related activity in distinct regions of the prefrontal cortex and midbrain. These findings suggest that perceptual category judgments, like value-guided choices, may be guided by multiple controllers.

2011年8月24日水曜日

Anatomical Evidence for the Involvement of the Macaque Ventrolateral Prefrontal Area 12r in Controlling Goal-Directed Actions


Elena Borra, Marzio Gerbella, Stefano Rozzi, and Giuseppe Luppino
J. Neurosci. 2011;31 12351-12363
http://www.jneurosci.org/cgi/content/abstract/31/34/12351?etoc

The macaque ventrolateral prefrontal (VLPF) area 12r is thought to be involved in higher-order nonspatial information processing. We found that this area is connectionally heterogeneous, and the intermediate part is fully integrated in a cortical network involved in selecting and controlling object-oriented hand and mouth actions. Specifically, intermediate area 12r displayed dense connections with the caudal half of area 46v and orbitofrontal areas and relatively strong extraprefrontal connections involving the following: (1) the hand- and mouth-related ventral premotor area F5 and the anterior intraparietal (AIP) area, jointly involved in visuomotor transformations for grasping; (2) the SII sector that is connected to AIP and F5; (3) a sector of the inferotemporal area TEa/m, primarily corresponding to the sector densely connected to AIP; and (4) the insular and opercular frontal sectors, which are connected to AIP and F5. This connectivity pattern differed markedly from those of the caudal and rostral parts of area 12r. Caudal area 12r displayed dense connections with the caudal part of the VLPF, including oculomotor areas 8/FEF and 45B, relatively weak orbitofrontal connections and extraprefrontal connections limited to the inferotemporal cortex. Rostral area 12r displayed connections mostly with rostral prefrontal and orbitofrontal areas and relatively weaker connections with the fundus and the upper bank of the superior temporal sulcus. The present data suggest that the intermediate part of area 12r is involved in nonspatial information processing related to object properties and identity, for selecting and controlling goal-directed hand and mouth actions.

2011年8月21日日曜日

The Control of Mimicry by Eye Contact Is Mediated by Medial Prefrontal Cortex


Yin Wang, Richard Ramsey, and Antonia F. de C. Hamilton
The Journal of Neuroscience, 17 August 2011, 31(33): 12001-12010.

Twitterで1hc0mさんが紹介してた。
http://twitter.com/#!/1hc0m/status/105468633362341888

Spontaneous mimicry of other people's actions serves an important social function, enhancing affiliation and social interaction. This mimicry can be subtly modulated by different social contexts. We recently found behavioral evidence that direct eye gaze rapidly and specifically enhances mimicry of intransitive hand movements (Wang et al., 2011). Based on past findings linking medial prefrontal cortex (mPFC) to both eye contact and the control of mimicry, we hypothesized that mPFC might be the neural origin of this behavioral effect. The present study aimed to test this hypothesis. During functional magnetic resonance imaging (fMRI) scanning, 20 human participants performed a simple mimicry or no-mimicry task, as previously described (Wang et al., 2011), with direct gaze present on half of the trials. As predicted, fMRI results showed that performing the task activated mirror systems, while direct gaze and inhibition of the natural tendency to mimic both engaged mPFC. Critically, we found an interaction between mimicry and eye contact in mPFC, superior temporal sulcus (STS) and inferior frontal gyrus. We then used dynamic causal modeling to contrast 12 possible models of information processing in this network. Results supported a model in which eye contact controls mimicry by modulating the connection strength from mPFC to STS. This suggests that mPFC is the originator of the gaze–mimicry interaction and that it modulates sensory input to the mirror system. Thus, our results demonstrate how different components of the social brain work together to on-line control mimicry according to the social context.

2011年8月18日木曜日

Associative Learning Increases Trial-by-Trial Similarity of BOLD-MRI Patterns


Renee M. Visser, H. Steven Scholte, and Merel Kindt
J. Neurosci. 2011;31 12021-12028 Open Access
http://www.jneurosci.org/cgi/content/abstract/31/33/12021?etoc

Associative learning is a dynamic process that allows us to incorporate new knowledge within existing semantic networks. Even after years, a seemingly stable association can be altered by a single significant experience. Here, we investigate whether the acquisition of new associations affects the neural representation of stimuli and how the brain categorizes stimuli according to preexisting and emerging associations. Functional MRI data were collected during a differential fear conditioning procedure and at test (4–5 weeks later). Two pictures of faces and two pictures of houses served as stimuli. One of each pair coterminated with a shock in half of the trials (partial reinforcement). Applying Multivoxel Pattern Analysis (MVPA) in a trial-by-trial manner, we quantified changes in the similarity of neural representations of stimuli over the course of conditioning. Our findings show an increase in similarity of neural patterns throughout the cortex on consecutive trials of the reinforced stimuli. Furthermore, neural pattern similarity reveals a shift from original categories (faces/houses) toward new categories (reinforced/unreinforced) over the course of conditioning. This effect was differentially represented in the cortex, with visual areas primarily reflecting similarity of low-level stimulus properties (original categories) and frontal areas reflecting similarity of stimulus significance (new categories). Effects were not dependent on overall response amplitude and were still present during follow-up. We conclude that trial-by-trial MVPA is a useful tool for examining how the human brain encodes relevant associations and forms new associative networks.

2011年8月10日水曜日

Negative Reward Signals from the Lateral Habenula to Dopamine Neurons Are Mediated by Rostromedial Tegmental Nucleus in Primates


Simon Hong, Thomas C. Jhou, Mitchell Smith, Kadharbatcha S. Saleem, and Okihide Hikosaka
The Journal of Neuroscience, 10 August 2011, 31(32):11457-11471.

Lateral habenula (LHb) neurons signal negative “reward-prediction errors” and inhibit midbrain dopamine (DA) neurons. Yet LHb neurons are largely glutamatergic, indicating that this inhibition may occur through an intermediate structure. Recent studies in rats have suggested a candidate for this role, the GABAergic rostromedial tegmental nucleus (RMTg), but this neural pathway has not yet been tested directly. We now show using electrophysiology and anatomic tracing that (1) the monkey has an inhibitory structure similar to the rat RMTg; (2) RMTg neurons receive excitatory input from the LHb, exhibit negative reward-prediction errors, and send axonal projections near DA soma; and (3) stimulating this structure inhibits DA neurons. Surprisingly, some RMTg neurons responded to reward cues earlier than the LHb, and carry “state-value” signals not found in DA neurons. Thus, our data suggest that the RMTg translates LHb reward-prediction errors (negative) into DA reward-prediction errors (positive), while transmitting additional motivational signals to non-DA networks.