6,680 research outputs found
Dynamic reconfiguration of human brain networks during learning
Human learning is a complex phenomenon requiring flexibility to adapt
existing brain function and precision in selecting new neurophysiological
activities to drive desired behavior. These two attributes -- flexibility and
selection -- must operate over multiple temporal scales as performance of a
skill changes from being slow and challenging to being fast and automatic. Such
selective adaptability is naturally provided by modular structure, which plays
a critical role in evolution, development, and optimal network function. Using
functional connectivity measurements of brain activity acquired from initial
training through mastery of a simple motor skill, we explore the role of
modularity in human learning by identifying dynamic changes of modular
organization spanning multiple temporal scales. Our results indicate that
flexibility, which we measure by the allegiance of nodes to modules, in one
experimental session predicts the relative amount of learning in a future
session. We also develop a general statistical framework for the identification
of modular architectures in evolving systems, which is broadly applicable to
disciplines where network adaptability is crucial to the understanding of
system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4
figures, 3 table
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The role of HG in the analysis of temporal iteration and interaural correlation
Methods and Approaches for Characterizing Learning Related Changes Observed in functional MRI Data — A Review
Brain imaging data have so far revealed a wealth of information about neuronal circuits involved in higher mental functions like memory, attention, emotion, language etc. Our efforts are toward understanding the learning related effects in brain activity during the acquisition of visuo-motor sequential skills. The aim of this paper is to survey various methods and approaches of analysis that allow the characterization of learning related changes in fMRI data. Traditional imaging analysis using the Statistical Parametric Map (SPM) approach averages out temporal changes and presents overall differences between different stages of learning. We outline other potential approaches for revealing learning effects such as statistical time series analysis, modelling of haemodynamic response function and independent component analysis. We present example case studies from our visuo-motor sequence learning experiments to describe application of SPM and statistical time series analyses. Our review highlights that the problem of characterizing learning induced changes in fMRI data remains an interesting and challenging open research problem
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
Neuroplasticity Subserving Motor Skill Learning
Recent years have seen significant progress in our understanding of the neural substrates of motor skill learning. Advances in neuroimaging provide new insight into functional reorganization associated with the acquisition, consolidation, and retention of motor skills. Plastic changes involving structural reorganization in gray and white matter architecture that occur over shorter time periods than previously thought have been documented as well. Data from experimental animals provided crucial information on plausible cellular and molecular substrates contributing to brain reorganization underlying skill acquisition in humans. Here, we review findings demonstrating functional and structural plasticity across different spatial and temporal scales that mediate motor skill learning while identifying converging areas of interest and possible avenues for future research
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
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