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  • Human connectomics.

    2 July 2018

    Recent advances in non-invasive neuroimaging have enabled the measurement of connections between distant regions in the living human brain, thus opening up a new field of research: Human connectomics. Different imaging modalities allow the mapping of structural connections (axonal fibre tracts) as well as functional connections (correlations in time series), and individual variations in these connections may be related to individual variations in behaviour and cognition. Connectivity analysis has already led to a number of new insights about brain organization. For example, segregated brain regions may be identified by their unique patterns of connectivity, structural and functional connectivity may be compared to elucidate how dynamic interactions arise from the anatomical substrate, and the architecture of large-scale networks connecting sets of brain regions may be analysed in detail. The combined analysis of structural and functional networks has begun to reveal components or modules with distinct patterns of connections that become engaged in different cognitive tasks. Collectively, advances in human connectomics open up the possibility of studying how brain connections mediate regional brain function and thence behaviour.

  • Addressing a systematic vibration artifact in diffusion-weighted MRI.

    2 July 2018

    We have identified and studied a pronounced artifact in diffusion-weighted MRI on a clinical system. The artifact results from vibrations of the patient table due to low-frequency mechanical resonances of the system which are stimulated by the low-frequency gradient switching associated with the diffusion-weighting. The artifact manifests as localized signal-loss in images acquired with partial Fourier coverage when there is a strong component of the diffusion-gradient vector in the left-right direction. This signal loss is caused by local phase ramps in the image domain which shift the apparent k-space center for a particular voxel outside the covered region. The local signal loss masquerades as signal attenuation due to diffusion, severely disrupting the quantitative measures associated with diffusion-tensor imaging (DTI). We suggest a way to improve the interpretation of affected DTI data by including a co-regressor which accounts for the empirical response of regions affected by the artifact. We also demonstrate that the artifact may be avoided by acquiring full k-space data, and that subsequent increases in TE can be avoided by employing parallel acceleration.

  • What is the most interesting part of the brain?

    2 July 2018

    Creative ideas and rigorous analysis are the hallmarks of much impactful science. However, there is an oft-aired suspicion in the neuroscience community that some scientists start with an advantage, simply because of the brain region or behaviour they study. We tested this unstated hypothesis by regressing the journal impact factor against both the pattern of brain activity and the experimental keywords across thousands of brain imaging studies. We found the results to be illuminating.

  • Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.

    3 July 2018

    Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.

  • The computation of social behavior.

    3 July 2018

    Neuroscientists are beginning to advance explanations of social behavior in terms of underlying brain mechanisms. Two distinct networks of brain regions have come to the fore. The first involves brain regions that are concerned with learning about reward and reinforcement. These same reward-related brain areas also mediate preferences that are social in nature even when no direct reward is expected. The second network focuses on regions active when a person must make estimates of another person's intentions. However, it has been difficult to determine the precise roles of individual brain regions within these networks or how activities in the two networks relate to one another. Some recent studies of reward-guided behavior have described brain activity in terms of formal mathematical models; these models can be extended to describe mechanisms that underlie complex social exchange. Such a mathematical formalism defines explicit mechanistic hypotheses about internal computations underlying regional brain activity, provides a framework in which to relate different types of activity and understand their contributions to behavior, and prescribes strategies for performing experiments under strong control.

  • How to perfect a chocolate soufflé and other important problems.

    2 July 2018

    When learning to achieve a goal through a complex series of actions, humans often group several actions into a subroutine and evaluate whether the subroutine achieved a specific subgoal. A new study reports brain responses consistent with such "hierarchical reinforcement learning."

  • Perceptual classification in a rapidly changing environment.

    3 July 2018

    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.

  • Separable learning systems in the macaque brain and the role of orbitofrontal cortex in contingent learning.

    3 July 2018

    Orbitofrontal cortex (OFC) is widely held to be critical for flexibility in decision-making when established choice values change. OFC's role in such decision making was investigated in macaques performing dynamically changing three-armed bandit tasks. After selective OFC lesions, animals were impaired at discovering the identity of the highest value stimulus following reversals. However, this was not caused either by diminished behavioral flexibility or by insensitivity to reinforcement changes, but instead by paradoxical increases in switching between all stimuli. This pattern of choice behavior could be explained by a causal role for OFC in appropriate contingent learning, the process by which causal responsibility for a particular reward is assigned to a particular choice. After OFC lesions, animals' choice behavior no longer reflected the history of precise conjoint relationships between particular choices and particular rewards. Nonetheless, OFC-lesioned animals could still approximate choice-outcome associations using a recency-weighted history of choices and rewards.

  • Separate value comparison and learning mechanisms in macaque medial and lateral orbitofrontal cortex.

    3 July 2018

    Uncertainty about the function of orbitofrontal cortex (OFC) in guiding decision-making may be a result of its medial (mOFC) and lateral (lOFC) divisions having distinct functions. Here we test the hypothesis that the mOFC is more concerned with reward-guided decision making, in contrast with the lOFC's role in reward-guided learning. Macaques performed three-armed bandit tasks and the effects of selective mOFC lesions were contrasted against lOFC lesions. First, we present analyses that make it possible to measure reward-credit assignment--a crucial component of reward-value learning--independently of the decisions animals make. The mOFC lesions do not lead to impairments in reward-credit assignment that are seen after lOFC lesions. Second, we examined how the reward values of choice options were compared. We present three analyses, one of which examines reward-guided decision making independently of reward-value learning. Lesions of the mOFC, but not the lOFC, disrupted reward-guided decision making. Impairments after mOFC lesions were a function of the multiple option contexts in which decisions were made. Contrary to axiomatic assumptions of decision theory, the mOFC-lesioned animals' value comparisons were no longer independent of irrelevant alternatives.