Dissociating object directed and non-object directed action in the human mirror system; implications for theories of motor simulation.

Mirror neurons are single cells found in macaque premotor and parietal cortices that are active during action execution and observation. In non-human primates, mirror neurons have only been found in relation to object-directed movements or communicative gestures, as non-object directed actions of the upper limb are not well characterized in non-human primates. Mirror neurons provide important evidence for motor simulation theories of cognition, sometimes referred to as the direct matching hypothesis, which propose that observed actions are mapped onto associated motor schemata in a direct and automatic manner. This study, for the first time, directly compares mirror responses, defined as the overlap between action execution and observation, during object directed and meaningless non-object directed actions. We present functional MRI data that demonstrate a clear dissociation between object directed and non-object directed actions within the human mirror system. A premotor and parietal network was preferentially active during object directed actions, whether observed or executed. Moreover, we report spatially correlated activity across multiple voxels for observation and execution of an object directed action. In contrast to predictions made by motor simulation theory, no similar activity was observed for non-object directed actions. These data demonstrate that object directed and meaningless non-object directed actions are subserved by different neuronal networks and that the human mirror response is significantly greater for object directed actions. These data have important implications for understanding the human mirror system and for simulation theories of motor cognition. Subsequent theories of motor simulation must account for these differences, possibly by acknowledging the role of experience in modulating the mirror response

Expertise with non-speech 'auditory Greebles' recruits speech-sensitive cortical regions

Regions of the human temporal lobe show greater activation for speech than for other sounds. These differences may reflect intrinsically specialized domain-specific adaptations for processing speech, or they may be driven by the significant expertise we have in listening to the speech signal. To test the expertise hypothesis, we used a video-game-based paradigm that tacitly trained listeners to categorize acoustically complex, artificial nonlinguistic sounds. Before and after training, we used functional MRI to measure how expertise with these sounds modulated temporal lobe activation. Participants’ ability to explicitly categorize the nonspeech sounds predicted the change in pretraining to posttraining activation in speech-sensitive regions of the left posterior superior temporal sulcus, suggesting that emergent auditory expertise may help drive this functional regionalization. Thus, seemingly domain-specific patterns of neural activation in higher cortical regions may be driven in part by experience-based restructuring of high-dimensional perceptual space

Novel modeling of task versus rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics

Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a measure with which to cluster or compare similarity between features but in a conceptually different way. We have used DTW to provide a more interpretable spectral description of the data, compared to standard approaches such as the Fourier and related transforms. The DTW approach and standard discrete Fourier transform (DFT) are assessed against benchmark measures of neural dynamics. These include EEG microstates, EEG avalanches, and the sum squared error (SSE) from a multilayer perceptron (MLP) prediction of the EEG time series, and simultaneously acquired FMRI BOLD signal. We explored the relationships between these variables of interest in an EEG-FMRI dataset acquired during a standard cognitive task, which allowed us to explore how DTW differentially performs in different task settings. We found that despite strong correlations between DTW and DFT-spectra, DTW was a better predictor for almost every measure of brain dynamics. Using these DTW measures, we show that predictability is almost always higher in task than in rest states, which is consistent to other theoretical and empirical findings, providing additional evidence for the utility of the DTW approach

Disconnection of network hubs and cognitive impairment after traumatic brain injury.

Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed (R(2) = 0.64), executive function (R(2) = 0.56) and associative memory (R(2) = 0.25). These results were then replicated in a separate group of patients without microbleeds. The most influential graph metrics were betweenness centrality and eigenvector centrality, which provide measures of the extent to which a given brain region connects other regions in the network. Reductions in betweenness centrality and eigenvector centrality were particularly evident within hub regions including the cingulate cortex and caudate. Our results demonstrate that betweenness centrality and eigenvector centrality are reduced within network hubs, due to the impact of traumatic axonal injury on network connections. The dominance of betweenness centrality and eigenvector centrality suggests that cognitive impairment after traumatic brain injury results from the disconnection of network hubs by traumatic axonal injury

Dimensional analysis considerations in the engine rotor fragment containment/deflection problem

Dimensional analysis techniques are described and applied to the containment/deflection problem of bursting high-rpm rotating parts of turbojet engines. The use of dimensional analysis to select a feasible set of experiments and to determine the important parameters to be varied is presented. The determination of a containment coefficient based on the nondimensionalized parameters is developed for the reduction of experimental data and as an assist to designers of containment/deflection devices

Experimental and data analysis techniques for deducing collision-induced forces from photographic histories of engine rotor fragment impact/interaction with a containment ring

An analysis method termed TEJ-JET is described whereby measured transient elastic and inelastic deformations of an engine-rotor fragment-impacted structural ring are analyzed to deduce the transient external forces experienced by that ring as a result of fragment impact and interaction with the ring. Although the theoretical feasibility of the TEJ-JET concept was established, its practical feasibility when utilizing experimental measurements of limited precision and accuracy remains to be established. The experimental equipment and the techniques (high-speed motion photography) employed to measure the transient deformations of fragment-impacted rings are described. Sources of error and data uncertainties are identified. Techniques employed to reduce data reading uncertainties and to correct the data for optical-distortion effects are discussed. These procedures, including spatial smoothing of the deformed ring shape by Fourier series and timewise smoothing by Gram polynomials, are applied illustratively to recent measurements involving the impact of a single T58 turbine rotor blade against an aluminum containment ring. Plausible predictions of the fragment-ring impact/interaction forces are obtained by one branch of this TEJ-JET method; however, a second branch of this method, which provides an independent estimate of these forces, remains to be evaluated

The control of global brain dynamics: opposing actions of frontoparietal control and default mode networks on attention

Understanding how dynamic changes in brain activity control behavior is a major challenge of cognitive neuroscience. Here, we consider the brain as a complex dynamic system and define two measures of brain dynamics: the synchrony of brain activity, measured by the spatial coherence of the BOLD signal across regions of the brain; and metastability, which we define as the extent to which synchrony varies over time. We investigate the relationship among brain network activity, metastability, and cognitive state in humans, testing the hypothesis that global metastability is “tuned” by network interactions. We study the following two conditions: (1) an attentionally demanding choice reaction time task (CRT); and (2) an unconstrained “rest” state. Functional MRI demonstrated increased synchrony, and decreased metastability was associated with increased activity within the frontoparietal control/dorsal attention network (FPCN/DAN) activity and decreased default mode network (DMN) activity during the CRT compared with rest. Using a computational model of neural dynamics that is constrained by white matter structure to test whether simulated changes in FPCN/DAN and DMN activity produce similar effects, we demonstate that activation of the FPCN/DAN increases global synchrony and decreases metastability. DMN activation had the opposite effects. These results suggest that the balance of activity in the FPCN/DAN and DMN might control global metastability, providing a mechanistic explanation of how attentional state is shifted between an unfocused/exploratory mode characterized by high metastability, and a focused/constrained mode characterized by low metastability