Functional Evidence for Memory Stabilization in Sensorimotor Adaptation: A 24-h Resting-State fMRI Study.

Adaptation learning is crucial to maintain precise motor control in face of environmental perturbations. Although much progress has been made in understanding the psychophysics and neurophysiology of sensorimotor adaptation (SA), the time course of memory consolidation remains elusive. The lack of a reproducible gradient of memory resistance using protocols of retrograde interference has even led to the proposal that memories produced through SA do not consolidate. Here, we pursued an alternative approach using resting-state fMRI to track changes in functional connectivity (FC) induced by learning. Given that consolidation leads to long-term memory, we hypothesized that a change in FC that predicted long-term memory but not short-term memory would provide indirect evidence for memory stabilization. Six scans were acquired before, 15 min, 1, 3, 5.5, and 24 h after training on a center-out task under veridical or distorted visual feedback. The experimental group showed an increment in FC of a network including motor, premotor, posterior parietal cortex, cerebellum, and putamen that peaked at 5.5 h. Crucially, the strengthening of this network correlated positively with long-term retention but negatively with short-term retention. Our work provides evidence, suggesting that adaptation memories stabilize within a 6-h window, and points to different mechanisms subserving short- and long-term memory.

Aging Effects on Whole-Brain Functional Connectivity in Adults Free of Cognitive and Psychiatric Disorders.

Aging is associated with decreased resting-state functional connectivity (RSFC) within the default mode network (DMN), but most functional imaging studies have restricted the analysis to specific brain regions or networks, a strategy not appropriate to describe system-wide changes. Moreover, few investigations have employed operational psychiatric interviewing procedures to select participants; this is an important limitation since mental disorders are prevalent and underdiagnosed and can be associated with RSFC abnormalities. In this study, resting-state fMRI was acquired from 59 adults free of cognitive and psychiatric disorders according to standardized criteria and based on extensive neuropsychological and clinical assessments. We tested for associations between age and whole-brain RSFC using Partial Least Squares, a multivariate technique. We found that normal aging is not only characterized by decreased RSFC within the DMN but also by ubiquitous increases in internetwork positive correlations and focal internetwork losses of anticorrelations (involving mainly connections between the DMN and the attentional networks). Our results reinforce the notion that the aging brain undergoes a dedifferentiation processes with loss of functional diversity. These findings advance the characterization of healthy aging effects on RSFC and highlight the importance of adopting a broad, system-wide perspective to analyze brain connectivity.

Global genetic variations predict brain response to faces.

Face expressions are a rich source of social signals. Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼ 500,000 single nucleotide polymorphisms. Using genomic-relationship-matrix restricted maximum likelihood (GREML), we related this global genetic variance to that in the brain response to facial expressions, as assessed with functional magnetic resonance imaging (fMRI) in a community-based sample of adolescents (n = 1,620). Brain response to facial expressions was measured in 25 regions constituting a face network, as defined previously. In 9 out of these 25 regions, common genetic variance explained a significant proportion of phenotypic variance (40-50%) in their response to ambiguous facial expressions; this was not the case for angry facial expressions. Across the network, the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region (R(2) = 0.38, p<0.001). Furthermore, this variability showed an inverted U relationship with both the number of observed connections (R2 = 0.48, p<0.001) and the magnitude of brain response (R(2) = 0.32, p<0.001). Thus, a significant proportion of the brain response to facial expressions is predicted by common genetic variance in a subset of regions constituting the face network. These regions show the highest inter-individual variability in the number of connections with other network nodes, suggesting that the genetic model captures variations across the adolescent brains in co-opting these regions into the face network.

The association between pro-arrhythmic agents and aortic stenosis in young adults: is it sufficient to clarify the sudden unexpected deaths?

Most young patients with mild-to-moderate aortic stenosis show no symptoms, and sudden death appears only occasionally. We hypothesised that malignant ventricular arrhythmias could be responsible for the high incidence of sudden death in such patients. If multiple factors such as asymptomatic aortic stenosis in association with arrhythmia-provoking agents are involved, could it be sufficient to account for sudden unexpected death? In this study, eight cases of sudden death in young adults, with ages ranging from 22 to 36 years, who had never reported any symptoms that could be related to aortic stenosis, were investigated. Full autopsies were performed, and congenital aortic stenosis in all eight cases was confirmed. DNA testing for channelopathies was negative. Comprehensive toxicological analyses found an electrolyte imbalance, or non-toxic concentrations of amitriptyline, terfenadine, caffeine, and ethanol. Collectively, these results suggest that congenital asymptomatic aortic stenosis without cardiac hypertrophy in young adults is not sufficient to cause sudden death merely on its own; rather, an additional provoking factor is necessary. According to our findings, the provoking factor may be a state of physical or emotional stress, a state of electrolyte imbalance, or even taking a therapeutic dose of a particular drug.

Age-related Multiscale Changes in Brain Signal Variability in Pre-task versus Post-task Resting-state EEG.

Recent empirical work suggests that, during healthy aging, the variability of network dynamics changes during task performance. Such variability appears to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into resting-state dynamics. We recorded EEG in young, middle-aged, and older adults during a "rest-task-rest" design and investigated if aging modifies the interaction between resting-state activity and external stimulus-induced activity. Using multiscale entropy as our measure of variability, we found that, with increasing age, resting-state dynamics shifts from distributed to more local neural processing, especially at posterior sources. In the young group, resting-state dynamics also changed from pre- to post-task, where fine-scale entropy increased in task-positive regions and coarse-scale entropy increased in the posterior cingulate, a key region associated with the default mode network. Lastly, pre- and post-task resting-state dynamics were linked to performance on the intervening task for all age groups, but this relationship became weaker with increasing age. Our results suggest that age-related changes in resting-state dynamics occur across different spatial and temporal scales and have consequences for information processing capacity.

[MEG]PLS: A pipeline for MEG data analysis and partial least squares statistics.

The emphasis of modern neurobiological theories has recently shifted from the independent function of brain areas to their interactions in the context of whole-brain networks. As a result, neuroimaging methods and analyses have also increasingly focused on network discovery. Magnetoencephalography (MEG) is a neuroimaging modality that captures neural activity with a high degree of temporal specificity, providing detailed, time varying maps of neural activity. Partial least squares (PLS) analysis is a multivariate framework that can be used to isolate distributed spatiotemporal patterns of neural activity that differentiate groups or cognitive tasks, to relate neural activity to behavior, and to capture large-scale network interactions. Here we introduce [MEG]PLS, a MATLAB-based platform that streamlines MEG data preprocessing, source reconstruction and PLS analysis in a single unified framework. [MEG]PLS facilitates MRI preprocessing, including segmentation and coregistration, MEG preprocessing, including filtering, epoching, and artifact correction, MEG sensor analysis, in both time and frequency domains, MEG source analysis, including multiple head models and beamforming algorithms, and combines these with a suite of PLS analyses. The pipeline is open-source and modular, utilizing functions from FieldTrip (Donders, NL), AFNI (NIMH, USA), SPM8 (UCL, UK) and PLScmd (Baycrest, CAN), which are extensively supported and continually developed by their respective communities. [MEG]PLS is flexible, providing both a graphical user interface and command-line options, depending on the needs of the user. A visualization suite allows multiple types of data and analyses to be displayed and includes 4-D montage functionality. [MEG]PLS is freely available under the GNU public license (http://meg-pls.weebly.com).

Effect of Mercapto and Methyl Groups on the Efficiency of Imidazole and Benzimidazole-based Inhibitors of Iron Corrosion.

We report on the combined experimental and computational study of imidazole- and benzimidazole-based corrosion inhibitors containing methyl and/or mercapto groups. Electrochemical measurements and long-term immersion tests were performed on iron in NaCl solution, whilst computational study explicitly addresses the molecular level details of the bonding on iron surface by means of density functional theory calculations (DFT). Experimental data were the basis for the determination of inhibition efficiency and mechanism. Methyl group combined with mercapto group has a beneficial effect on corrosion inhibition at all inhibitor concentrations. The beneficial effect of mercapto group combined with benzene group is not so pronounced as when combined with methyl group. The latter is in stark contrast with the behaviour found previously on copper, where the effect of methyl group was detrimental and that of mercapto and benzene beneficial. Explicit DFT calculations reveal that methyl-group has a small effect on the inhibitor-surface interaction. In contrast, the presence of mercapto group involves the strong S-surface bonding and consequently the adsorption of inhibitors with mercapto group is found to be more exothermic.

Characterizing functional integrity: intraindividual brain signal variability predicts memory performance in patients with medial temporal lobe epilepsy.

Computational modeling suggests that variability in brain signals provides important information regarding the system's capacity to adopt different network configurations that may promote optimal responding to stimuli. Although there is limited empirical work on this construct, a recent study indicates that age-related decreases in variability across the adult lifespan correlate with less efficient and less accurate performance. Here, we extend this construct to the assessment of cerebral integrity by comparing fMRI BOLD variability and fMRI BOLD amplitude in their ability to account for differences in functional capacity in patients with focal unilateral medial temporal dysfunction. We were specifically interested in whether either of these BOLD measures could identify a link between the affected medial temporal region and memory performance (as measured by a clinical test of verbal memory retention). Using partial least-squares analyses, we found that variability in a set of regions including the left hippocampus predicted verbal retention and, furthermore, this relationship was similar across a range of cognitive tasks measured during scanning (i.e., the same pattern was seen in fixation, autobiographical recall, and word generation). In contrast, signal amplitude in the hippocampus did not predict memory performance, even for a task that reliably activates the medial temporal lobes (i.e., autobiographical recall). These findings provide a powerful validation of the concept that variability in brain signals reflects functional integrity. Furthermore, this measure can be characterized as a robust biomarker in this clinical setting because it reveals the same pattern regardless of cognitive challenge or task engagement during scanning.

Identification of a functional connectome for long-term fear memory in mice.

Long-term memories are thought to depend upon the coordinated activation of a broad network of cortical and subcortical brain regions. However, the distributed nature of this representation has made it challenging to define the neural elements of the memory trace, and lesion and electrophysiological approaches provide only a narrow window into what is appreciated a much more global network. Here we used a global mapping approach to identify networks of brain regions activated following recall of long-term fear memories in mice. Analysis of Fos expression across 84 brain regions allowed us to identify regions that were co-active following memory recall. These analyses revealed that the functional organization of long-term fear memories depends on memory age and is altered in mutant mice that exhibit premature forgetting. Most importantly, these analyses indicate that long-term memory recall engages a network that has a distinct thalamic-hippocampal-cortical signature. This network is concurrently integrated and segregated and therefore has small-world properties, and contains hub-like regions in the prefrontal cortex and thalamus that may play privileged roles in memory expression.

The modulation of BOLD variability between cognitive states varies by age and processing speed.

Increasing evidence suggests that brain variability plays a number of important functional roles for neural systems. However, the relationship between brain variability and changing cognitive demands remains understudied. In the current study, we demonstrate experimental condition-based modulation in brain variability using functional magnetic resonance imaging. Within a sample of healthy younger and older adults, we found that blood oxygen level-dependent signal variability was an effective discriminator between fixation and external cognitive demand. Across a number of regions, brain variability increased broadly on task compared with fixation, particularly in younger and faster performing adults. Conversely, older and slower performing adults exhibited fewer changes in brain variability within and across experimental conditions and brain regions, indicating a reduction in variability-based neural specificity. Increases in brain variability on task may represent a more complex neural system capable of greater dynamic range between brain states, as well as an enhanced ability to efficiently process varying and unexpected external stimuli. The current results help establish the developmental and performance correlates of state-to-state brain variability-based transitions and offer a new line of inquiry in the study of rest versus task modes in the human brain.

Oligodendrocyte genes, white matter tract integrity, and cognition in schizophrenia.

Oligodendrocyte genes and white matter tracts have been implicated in the pathophysiology of schizophrenia and may play an important etiopathogenic role in cognitive dysfunction in schizophrenia. The objective of the present study in 60 chronic schizophrenia patients individually matched to 60 healthy controls was to determine whether 1) white matter tract integrity influences cognitive performance, 2) oligodendrocyte gene variants influence white matter tract integrity and cognitive performance, and 3) effects of oligodendrocyte gene variants on cognitive performance are mediated via white matter tract integrity. We used the partial least-squares multivariate approach to ascertain relationships among oligodendrocyte gene variants, integrity of cortico-cortical and subcortico-cortical white matter tracts, and cognitive performance. Robust relationships among oligodendrocyte gene variants, white matter tract integrity, and cognitive performance were found in both patients and controls. We also showed that effects of gene variants on cognitive performance were mediated by the integrity of white matter tracts. Our results were strengthened by bioinformatic analyses of gene variant function. To our knowledge, this is the first study that has brought together these lines of investigation in the same population and highlights the importance of the oligodendrocyte/white matter pathway in schizophrenia, particularly as it pertains to cognitive function.

Blood pressure among rural Montenegrin children in relation to poverty and gender.

BACKGROUND: Health inequalities may begin during childhood. The aim of this study was to investigate the main effect of poverty and its interactive effect with gender on children's blood pressure. METHODS: The study was performed in two elementary schools from a rural region near Podgorica, the capital of Montenegro. A questionnaire including questions on family monthly income, children's physical activity and the consumption of junk food was self-administered by parents of 434 children (223 boys and 211 girls) aged 6-13 years. Children's poverty level was assessed using the recommendations from the National Study on Poverty in Montenegro. Children's body weight and height were measured and body mass index-for-gender-and-age percentile was calculated. An oscillometric monitor was used for measurement of children's resting blood pressure in school. RESULTS: A two-factorial analysis of variance with body mass index percentile, physical activity and junk food as covariates showed an interaction of gender and poverty on children's blood pressure, pointing to synergy between poverty and female gender, with statistical significance for raised diastolic pressure (F = 5.462; P = 0.021). Neither physical activity nor the consumption of junk food explained the interactive effect of poverty and gender on blood pressure. CONCLUSION: We show that poverty is linked to elevated blood pressure for girls but not boys, and this effect is statistically significant for diastolic pressure. The results are discussed in the light of gender differences in stress and coping that are endemic to poverty.

Enhanced mechanical properties and environmental stability of polymer-bonded magnets using three-step surface wet chemical modifications of Nd-Fe-B magnetic powder.

This research focuses on the surface modification of Nd-Fe-B magnetic powder to enhance its thermal and oxidation resistance without compromising magnetic properties and to improve adhesion to the polymer binder for enhanced mechanical properties. A three-step surface modification process involving phosphatization treatment, tetraethyl orthosilicate (TEOS) application, and 3-aminopropyltriethoxysilane (APTES) grafting, was applied to the powder, which was then compounded with polyamide 12 and injection-moulded into cylinders and dog-bone-shaped tubes. The resulting magnets exhibited remanence (Br) of 487.6 mT, coercivity (Hci) of 727.7 kA/m, and energy product (BHmax) of 39.3 kJ/m3. The modified magnets demonstrated exceptional corrosion resistance and thermal stability, with less than 5% irreversible flux loss after exposure to hot water, temperature shock, and pressurised steam. Furthermore, the modified magnets displayed significantly higher tensile strength, elongation at break, and elastic modulus with improvements of 62%, 16.7%, and 19.9%, respectively, compared to the non-modified batch. Additionally, the modified batch showed a notable 52% increase in flexural stress during flexural testing. These findings underscore the potential of silane surface modifications in producing injection-moulded permanent magnets based on Nd-Fe-B alloy, extending their shelf life and enhancing their overall performance.

The Impact of Plasma Surface Treatments on the Mechanical Properties and Magnetic Performance of FDM-Printed NdFeB/PA12 Magnets.

This study presents a novel approach for improving the interfacial adhesion between Nd-Fe-B spherical magnetic powders and polyamide 12 (PA12) in polymer-bonded magnets using plasma treatments. By applying radio frequency plasma to the magnetic powder and low-pressure microwave plasma to PA12, we achieved a notable enhancement in the mechanical and environmental stability of fused deposition modeling (FDM)-printed Nd-Fe-B/PA12 magnets. The densities of the FDM-printed materials ranged from 92% to 94% of their theoretical values, with magnetic remanence (Br) ranging from 85% to 89% of the theoretical values across all batches. The dual plasma-treated batch demonstrated an optimal mechanical profile with an elastic modulus of 578 MPa and the highest ductility at 21%, along with a tensile strength range of 6 to 7 MPa across all batches. Flexural testing indicated that this batch also achieved the highest flexural strength of 15 MPa with a strain of 5%. Environmental stability assessments confirmed that applied plasma treatments did not compromise resistance to corrosion, evidenced by negligible flux loss in both hygrothermal and bulk corrosion tests. These results highlight plasma treatment's potential to enhance mechanical strength, magnetic performance, and environmental stability.

ICA-based artifact correction improves spatial localization of adaptive spatial filters in MEG.

Beamformers are one of the most common inverse models currently used in the estimation of source activity from magnetoencephelography (MEG) data. They rely on a minimization of total power while constraining the gain in the voxel of interest, resulting in the suppression of background noise. Nonetheless, in cases where background noise is strong compared to the source of interest, or when many sources are present, the ability of the beamformer to detect and accurately localize weak sources is reduced. In visual paradigms, two main background sources can substantially impact an accurate estimation of weaker sources. Ocular artifacts are orders of magnitude higher than neural sources making it difficult for the beamformer to effectively suppress them. Primary visual activations also result in strong signals that can impede localization of weak sources. In this paper, we systematically evaluated how neural (visual) and non-neural (eye, heart) sources affect the localization accuracy of frontal and medial temporal sources in visual tasks. These sources are of tremendous interest in learning and memory studies as well as in clinical settings (Alzheimer's/epilepsy) and are typically difficult to localize robustly in MEG. Empirical data from two tasks - active learning and control - were used to evaluate our analysis techniques. Global field power calculations showed multiple time periods where active learning was significantly different from response selection with dominant sources converging to the eyes. Extensive leakage of eye activity into frontal and visual that evoked responses into parietal cortices was also observed. Contributions from ocular activity to the reconstructed time series were indiscernible from task-based recruitment of frontal sources in the original data. Removing artifacts (eye movements, cardiac, and muscular) by means of independent component analysis (ICA) led to a significant improvement in detection and localization of frontal and medial temporal sources. We verified our results by using simulations of sources placed in frontal and medial temporal regions with various types of background noise (eye, heart, and visual). We report that the detection and localization accuracy of frontal and medial temporal sources with beamformer techniques is highly dependent on the magnitude and location of background sources and that removing artifacts can substantially improve the beamformer's performance.

Influence of Magnet Particle Shape on Magnetic and Environmental Stability of FDM Polymer-Bonded Magnets.

In this research, the feasibility of additive manufacturing of permanent bonded magnets using fused deposition modelling (FDM) technology was investigated. The study employed polyamide 12 (PA12) as the polymer matrix and melt-spun and gas-atomized Nd-Fe-B powders as magnetic fillers. The effect of the magnetic particle shape and the filler fraction on the magnetic properties and environmental stability of polymer-bonded magnets (PBMs) was investigated. It was found that filaments for FDM made with gas-atomized magnetic particles were easier to print due to their superior flowability. As a result, the printed samples exhibited higher density and lower porosity when compared to those made with melt-spun powders. Magnets with gas-atomized powders and a filler loading of 93 wt.% showed a remanence (Br) of 426 mT, coercivity (Hci) of 721 kA/m, and energy product (BHmax) of 29 kJ/m3, while melt-spun magnets with the same filler loading had a remanence of 456 mT, coercivity of 713 kA/m, and energy product of 35 kJ/m3. The study further demonstrated the exceptional corrosion resistance and thermal stability of FDM-printed magnets, with less than 5% irreversible flux loss when exposed to hot water or air at 85 °C for over 1000 h. These findings highlight the potential of FDM printing for producing high-performance magnets and the versatility of this manufacturing method for various applications.

The importance of being variable.

New work suggests that blood oxygen level-dependent (BOLD) signal variability can be a much more powerful index of human age than mean activation, and that older brains are actually less variable than younger brains. However, little is known of how BOLD variability and task performance may relate. In the current study, we examined BOLD variability in relation to age, and reaction time speed and consistency in healthy younger (20-30 years) and older (56-85 years) adults on three cognitive tasks (perceptual matching, attentional cueing, and delayed match-to-sample). Results indicated that younger, faster, and more consistent performers exhibited significantly higher brain variability across tasks, and showed greater variability-based regional differentiation compared with older, poorer-performing adults. Also, when we compared brain variability- and typical mean-based effects, the respective spatial patterns were essentially orthogonal across brain measures, and any regions that did overlap were largely opposite in directionality of effect. These findings help establish the functional basis of BOLD variability, and further support the statistical and spatial differentiation between BOLD variability and BOLD mean. We thus argue that the precise nature of relations between aging, cognition, and brain function is underappreciated by using mean-based brain measures exclusively.

Brain signal variability relates to stability of behavior after recovery from diffuse brain injury.

Variability or noise is an unmistakable feature of neural signals; however such fluctuations have been regarded as not carrying meaningful information or as detrimental for neural processes. Recent empirical and computational work has shown that neural systems with a greater capacity for information processing are able to explore a more varied dynamic repertoire, and the hallmark of this is increased irregularity or variability in the neural signal. How this variability in neural dynamics affects behavior remains unclear. Here, we investigated the role of variability of magnetoencephalography signals in supporting healthy cognitive functioning, measured by performance on an attention task, in healthy adults and in patients with traumatic brain injury. As an index of variability, we calculated multiscale entropy, which quantifies the temporal predictability of a time series across progressively more coarse time scales. We found lower variability in traumatic brain injury patients compared to controls, arguing against the idea that greater variability reflects dysfunctional neural processing. Furthermore, higher brain signal variability indicated improved behavioral performance for all participants. This relationship was statistically stronger for people with brain injury, demonstrating that those with higher brain signal variability were also those who had recovered the most cognitive ability. Rather than impede neural processing, cortical signal variability within an optimal range enables the exploration of diverse functional configurations, and may therefore play a vital role in healthy brain function.

Extracting message inter-departure time distributions from the human electroencephalogram.

The complex connectivity of the cerebral cortex is a topic of much study, yet the link between structure and function is still unclear. The processing capacity and throughput of information at individual brain regions remains an open question and one that could potentially bridge these two aspects of neural organization. The rate at which information is emitted from different nodes in the network and how this output process changes under different external conditions are general questions that are not unique to neuroscience, but are of interest in multiple classes of telecommunication networks. In the present study we show how some of these questions may be addressed using tools from telecommunications research. An important system statistic for modeling and performance evaluation of distributed communication systems is the time between successive departures of units of information at each node in the network. We describe a method to extract and fully characterize the distribution of such inter-departure times from the resting-state electroencephalogram (EEG). We show that inter-departure times are well fitted by the two-parameter Gamma distribution. Moreover, they are not spatially or neurophysiologically trivial and instead are regionally specific and sensitive to the presence of sensory input. In both the eyes-closed and eyes-open conditions, inter-departure time distributions were more dispersed over posterior parietal channels, close to regions which are known to have the most dense structural connectivity. The biggest differences between the two conditions were observed at occipital sites, where inter-departure times were significantly more variable in the eyes-open condition. Together, these results suggest that message departure times are indicative of network traffic and capture a novel facet of neural activity.

The Effect of pH Value of a Simulated Physiological Solution on the Corrosion Resistance of Orthopaedic Alloys.

Metals and alloys used in orthopaedics and dentistry are exposed in vivo to various agents and environmental conditions. One of the important factors that determine the corrosion behaviour of metallic biomaterials is the pH of the environment. The corrosion resistance of stainless steel 316L (Fe/Cr18/Ni10/Mo3), titanium and titanium alloy Ti-6Al-4V (Ti90/Al6/V4) was studied in terms of their electrochemical properties and biodegradation in simulated physiological solutions of different pH values (4.5, 6.5, 7.5 and 8). The electrochemical characteristics of individual metal components were also investigated using cyclic voltammetry, linear polarization and potentiodynamic polarization methods. The concentration of dissolved metal ions released during 32 days immersion under simulated physiological conditions was determined by inductively coupled plasma mass spectroscopy. The corrosion behaviour of stainless steel 316L is strongly affected by the pH of the physiological solution in the range from 4.5 to 8.0. The corrosion resistance was enhanced at higher pH and the concentrations of released metal ions lower. The behaviour of titanium and its alloy however is almost independent of the pH.