Age-dependent modulation of motor network connectivity for skill acquisition, consolidation and interlimb transfer after motor practice

Objective: Age-related differences in neural strategies for motor learning are not fully understood. We determined the effects of age on the relationship between motor network connectivity and motor skill acquisition, consolidation, and interlimb transfer using dynamic imaging of coherent sources. Methods: Healthy younger (n = 24, 18-24 y) and older (n = 24, 65-87 y) adults unilaterally practiced a visuomotor task and resting-state electroencephalographic data was acquired before and after practice as well as at retention. Results: The results showed that right-hand skill acquisition and consolidation did not differ between age groups. However, age affected the ability to transfer the newly acquired motor skill to the non-practiced limb. Moreover, strengthened left- and right-primary motor cortex-related beta conectivity was negatively and positively associated with right-hand skill acquisition and left-hand skill consolidation in older adults, respectively. Conclusion: Age-dependent modulations of bilateral resting-state motor network connectivity indicate age-specific strategies for the acquisition, consolidation, and interlimb transfer of novel motor tasks. Significance: The present results provide insights into the mechanisms underlying motor learning that are important for the development of interventions for patients with unilateral injuries. (C) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved

Disentangling the effects of age and mild traumatic brain injury on brain network connectivity:A resting state fMRI study

INTRODUCTION: Cognitive complaints are common shortly after mild traumatic brain injury (mTBI) but may persist up to years. Age-related cognitive decline can worsen these symptoms. However, effects of age on mTBI sequelae have scarcely been investigated. METHODS: Fifty-four mTBI patients (median age: 35 years, range 19-64 years, 67% male) and twenty age- and sex-matched healthy controls were studied using resting state functional magnetic resonance imaging in the sub-acute phase. Independent component analysis was used to identify intrinsic connectivity networks (ICNs). A multivariate approach was adopted to evaluate the effects of age and group on the ICNs in terms of (static) functional network connectivity (FNC), intensities of spatial maps (SMs) and time-course spectral power (TC). RESULTS: We observed significant age-related changes for a) FNC: changes between 10 pairs of ICNs, mostly involving the default mode (DM) and/or the cognitive-control (CC) domains; b) SMs: intensity decrease in clusters across three domains and intensity increase in clusters across two domains, including the CC but not the DM and c) TC: spectral power decrease within the 0-0.15 Hz range and increase within the 0.20-0.25 Hz range for increasing age within networks located in frontal areas, including the anterior DM. Groups only differed for TC within the 0.065-0.10 Hz range in the cerebellar ICN and no age × group interaction effect was found. CONCLUSIONS: We showed robust effects of age on connectivity between and within ICNs that are associated with cognitive functioning. Differences between mTBI patients and controls were only found for activity in the cerebellar network, increasingly recognized to participate in cognition. Our results suggest that to allow for capturing the true effects related to mTBI and its effects on cognitive functioning, age should be included as a covariate in mTBI studies, in addition to age-matching groups

Block copolymers confined in a nanopore: Pathfinding in a curving and frustrating flatland

We have studied structure formation in a confined block copolymer melt by means of dynamic density functional theory (DDFT). The confinement is two-dimensional, and the confined geometry is that of a cylindrical nanopore. Although the results of this study are general, our coarse-grained molecular model is inspired by an experimental lamellae-forming PS-PBD diblock copolymer system (Shin et al, Science, 306, 76 (2004)), in which an exotic toroidal structure was observed upon confinement in alumina nanopores. Our computational study shows that a zoo of exotic structures can be formed, although the majority, including the catenoid, helix and double helix that were also found in Monte Carlo (MC) nanopore studies, are metastable states. We introduce a general classification scheme and consider the role of kinetics and elongational pressure on stability and formation pathway of both equilibrium and metastable structures in detail. We find that helicity and three-fold connections mediate structural transitions on a larger scale. Moreover, by matching the remaining parameter in our mesoscopic method, the Flory-Huggins parameter, to the experimental system, we obtain a structure that resembles the experimental toroidal structure in great detail. Here, the most important factor seems to be the roughness of the pore, i.e. small variations of the pore radius on a scale that is larger than the characteristic size in the system.Comment: The following article has been accepted by JCP. After it is published, it will be found at http://jcp.aip.org

Visual Exploration of Dynamic Multichannel EEG Coherence Networks

Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline-based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time-annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks

Application of free energy expansions to mesoscopic dynamics of copolymer melts using a Gaussian chain molecular model

The present paper deals with some mathematical aspects of generalized time-dependent Ginzburg-Landau theories for the numerical simulation of mesoscale phase separation kinetics of copolymer melts. We shortly discuss the underlying theory and introduce an expansion of the external potential, to be used in the dynamics algorithm, which is similar to free-energy expansions. This expansion is valid for both compressible and incompressible multicomponent copolymer melts using a Gaussian chain model. The expansion is similar to the well-known random phase approximation (RPA) but differs in some important aspects. Also, the application of RPA like free energy expansions to dynamics is new. Our derivation leads to simple expressions for the vertex coefficients, which enables us to numerically calculate their full wave Vector dependence, without assuming an ordered morphology. We find that our fourth-order vertex is negative for some wave vectors which has important consequences for the simulation of mesoscopic dynamics. We propose a fitting procedure for the vertex coefficients to overcome the computationally expensive calculation of the linear and bilinear expansion terms in the expansion, This procedure provides analytically derived parameters for a gradient free energy expansion, which allows for a whole new class of phase-separation models to be defined. (C) 1997 American Institute of Physics

Muscle co-activity tuning in Parkinsonian hand movement : disease-specific changes at behavioral and cerebral level

We investigated different degrees of muscle co-activity in simple hand movement at behavioral and cerebral level in healthy subjects and Parkinson’s disease (PD) patients. We compared 'singular' movements, dominated by the activity of one agonist muscle, to 'composite' movements, requiring conjoint activity of multiple muscles, in a center-out (right hand) step-tracking task. Behavioral parameters were obtained by EMG and kinematic recordings. fMRI was used to investigate differences in underlying brain activations between PD patients (N= 12) and healthy (age-matched) subjects (N= 18). In healthy subjects, composite movements recruited the striatum and cortical areas comprising bilaterally the supplementary motor area and premotor cortex, contralateral medial prefrontal cortex, primary motor cortex, primary visual cortex, and ipsilateral superior parietal cortex. Contrarily, the ipsilateral cerebellum was more involved in singular movements. This striking dichotomy between striatal and cortical recruitment versus cerebellar involvement may reflect the complementary roles of these areas in motor control, in which the basal ganglia are involved in movement selection and the cerebellum in movement optimization. Compared to healthy subjects, PD patients showed decreased activation of the striatum and cortical areas in composite movement, while performing worse at behavioral level. This implies that PD patients are especially impaired on tasks requiring highly tuned muscle co-activity. Singular movement, on the other hand, was characterized by a combination of increased activation of the ipsilateral parietal cortex and left cerebellum. As singular movement performance was only slightly compromised, we interpret this as a reflection of increased visuospatial processing, possibly as a compensational mechanism

BMI Not WHR Modulates BOLD fMRI Responses in a Sub-Cortical Reward Network When Participants Judge the Attractiveness of Human Female Bodies

In perceptual terms, the human body is a complex 3d shape which has to be interpreted by the observer to judge its attractiveness. Both body mass and shape have been suggested as strong predictors of female attractiveness. Normally body mass and shape co-vary, and it is difficult to differentiate their separate effects. A recent study suggested that altering body mass does not modulate activity in the reward mechanisms of the brain, but shape does. However, using computer generated female body-shaped greyscale images, based on a Principal Component Analysis of female bodies, we were able to construct images which covary with real female body mass (indexed with BMI) and not with body shape (indexed with WHR), and vice versa. Twelve observers (6 male and 6 female) rated these images for attractiveness during an fMRI study. The attractiveness ratings were correlated with changes in BMI and not WHR. Our primary fMRI results demonstrated that in addition to activation in higher visual areas (such as the extrastriate body area), changing BMI also modulated activity in the caudate nucleus, and other parts of the brain reward system. This shows that BMI, not WHR, modulates reward mechanisms in the brain and we infer that this may have important implications for judgements of ideal body size in eating disordered individuals

Cerebellar Atrophy in Cortical Myoclonic Tremor and Not in Hereditary Essential Tremor-a Voxel-Based Morphometry Study

Essential tremor (ET) presumably has a cerebellar origin. Imaging studies showed various cerebellar and also cortical structural changes. A number of pathology studies indicated cerebellar Purkinje cell pathology. ET is a heterogeneous disorder, possibly indicating different underlying disease mechanisms. Familial cortical myoclonic tremor with epilepsy (FCMTE), with evident Purkinje cell degeneration, can be an ET mimic. Here, we investigate whole brain and, more specifically, cerebellar morphological changes in hereditary ET, FCMTE, and healthy controls. Anatomical magnetic resonance images were preprocessed using voxel-based morphometry. Study 1 included voxel-wise comparisons of 36 familial, propranolol-sensitive ET patients, with subgroup analysis on age at onset and head tremor, and 30 healthy controls. Study 2 included voxel-wise comparisons in another nine ET patients, eight FCMTE patients, and nine healthy controls. Study 3 compared total cerebellar volume between 45 ET patients, 8 FCTME patients, and 39 controls. In our large sample of selected hereditary ET patients and ET subgroups, no local atrophy was observed compared to healthy controls or FCMTE. In ET patients with head tremor, a volume increase in cortical motor regions was observed. In FCMTE, a decrease in total cerebellar volume and in local cerebellar gray matter was observed compared to healthy controls and ET patients. The current study did not find local atrophy, specifically not in the cerebellum in hereditary ET, contrary to FCMTE. Volume increase of cortical motor areas in ET patients with head tremor might suggest cortical plasticity changes due to continuous involuntary head movements

Spinodal Decomposition in a Binary Polymer Mixture: Dynamic Self Consistent Field Theory and Monte Carlo Simulations

We investigate how the dynamics of a single chain influences the kinetics of early stage phase separation in a symmetric binary polymer mixture. We consider quenches from the disordered phase into the region of spinodal instability. On a mean field level we approach this problem with two methods: a dynamical extension of the self consistent field theory for Gaussian chains, with the density variables evolving in time, and the method of the external potential dynamics where the effective external fields are propagated in time. Different wave vector dependencies of the kinetic coefficient are taken into account. These early stages of spinodal decomposition are also studied through Monte Carlo simulations employing the bond fluctuation model that maps the chains -- in our case with 64 effective segments -- on a coarse grained lattice. The results obtained through self consistent field calculations and Monte Carlo simulations can be compared because the time, length, and temperature scales are mapped onto each other through the diffusion constant, the chain extension, and the energy of mixing. The quantitative comparison of the relaxation rate of the global structure factor shows that a kinetic coefficient according to the Rouse model gives a much better agreement than a local, i.e. wave vector independent, kinetic factor. Including fluctuations in the self consistent field calculations leads to a shorter time span of spinodal behaviour and a reduction of the relaxation rate for smaller wave vectors and prevents the relaxation rate from becoming negative for larger values of the wave vector. This is also in agreement with the simulation results.Comment: Phys.Rev.E in prin