Open-porous magnesium-based scaffolds withstand in vitro corrosion under cyclic loading: A mechanistic study.

The successful application of magnesium (Mg) alloys as biodegradable bone substitutes for critical-sized defects may be comprised by their high degradation rate resulting in a loss of mechanical integrity. This study investigates the degradation pattern of an open-porous fluoride-coated Mg-based scaffold immersed in circulating Hanks' Balanced Salt Solution (HBSS) with and without in situ cyclic compression (30 N/1 Hz). The changes in morphological and mechanical properties have been studied by combining in situ high-resolution X-ray computed tomography mechanics and digital volume correlation. Although in situ cyclic compression induced acceleration of the corrosion rate, probably due to local disruption of the coating layer where fatigue microcracks were formed, no critical failures in the overall scaffold were observed, indicating that the mechanical integrity of the Mg scaffolds was preserved. Structural changes, due to the accumulation of corrosion debris between the scaffold fibres, resulted in a significant increase (p < 0.05) in the material volume fraction from 0.52 ± 0.07 to 0.47 ± 0.03 after 14 days of corrosion. However, despite an increase in fibre material loss, the accumulated corrosion products appear to have led to an increase in Young's modulus after 14 days as well as lower third principal strain (εp3) accumulation (-91000 ± 6361 µÎµ and -60093 ± 2414 µÎµ after 2 and 14 days, respectively). Therefore, this innovative Mg scaffold design and composition provide a bone replacement, capable of sustaining mechanical loads in situ during the postoperative phase allowing new bone formation to be initially supported as the scaffold resorbs.

Multi-scale mechanical and morphological characterisation of sintered porous magnesium-based scaffolds for bone regeneration in critical-sized defects.

Magnesium (Mg) and its alloys are very promising degradable, osteoconductive and osteopromotive materials to be used as regenerative treatment for critical-sized bone defects. Under load-bearing conditions, Mg alloys must display sufficient morphological and mechanical resemblance to the native bone they are meant to replace to provide adequate support and enable initial bone bridging. In this study, unique highly open-porous Mg-based scaffolds were mechanically and morphologically characterised at different scales. In situ X-ray computed tomography (XCT) mechanics, digital volume correlation (DVC), electron microscopy and nanoindentation were combined to assess the influence of material properties on the apparent (macro) mechanics of the scaffold. The results showed that Mg exhibited a higher connected structure (38.4mm-3 and 6.2mm-3 for Mg and trabecular bone (Tb), respectively) and smaller spacing (245µm and 629µm for Mg and Tb, respectively) while keeping an overall appropriate porosity of 55% in the range of trabecular bone (30-80%). This fully connected and highly porous structure promoted lower local strain compared to the trabecular bone structure at material level (i.e. -22067 ± 8409µÎµ and -40120 ± 18364µÎµ at 6% compression for Mg and trabecular bone, respectively) and highly ductile mechanical behaviour at apparent level preventing premature scaffold failure. Furthermore, the Mg scaffolds exceeded the physiological strain of bone tissue generated in daily activities such as walking or running (500-2000µÎµ) by one order of magnitude. The yield stress was also found to be close to trabecular bone (2.06MPa and 6.67MPa for Mg and Tb, respectively). Based on this evidence, the study highlights the overall biomechanical suitability of an innovative Mg-based scaffold design to be used as a treatment for bone critical-sized defects. STATEMENT OF SIGNIFICANCE: Bone regeneration remains a challenging field of research where different materials and solutions are investigated. Among the variety of treatments, biodegradable magnesium-based implants represent a very promising possibility. The novelty of this study is based on the characterisation of innovative magnesium-based implants whose structure and manufacturing have been optimised to enable the preservation of mechanical integrity and resemble bone microarchitecture. It is also based on a multi-scale approach by coupling high-resolution X-ray computed tomography (XCT), with in situ mechanics, digital volume correlation (DVC) as well as nano-indentation and electron-based microscopy imaging to define how degradable porous Mg-based implants fulfil morphological and mechanical requirements to be used as critical bone defects regeneration treatment.

Total Absence of Dystrophin Expression Exacerbates Ectopic Myofiber Calcification and Fibrosis and Alters Macrophage Infiltration Patterns.

Duchenne muscular dystrophy (DMD) causes severe disability and death of young men because of progressive muscle degeneration aggravated by sterile inflammation. DMD is also associated with cognitive and bone-function impairments. This complex phenotype results from the cumulative loss of a spectrum of dystrophin isoforms expressed from the largest human gene. Although there is evidence for the loss of shorter isoforms having impact in the central nervous system, their role in muscle is unclear. We found that at 8 weeks, the active phase of pathology in dystrophic mice, dystrophin-null mice (mdxßgeo) presented with a mildly exacerbated phenotype but without an earlier onset, increased serum creatine kinase levels, or decreased muscle strength. However, at 12 months, mdxßgeo diaphragm strength was lower, whereas fibrosis increased, compared with mdx. The most striking features of the dystrophin-null phenotype were increased ectopic myofiber calcification and altered macrophage infiltration patterns, particularly the close association of macrophages with calcified fibers. Ectopic calcification had the same temporal pattern of presentation and resolution in mdxßgeo and mdx muscles, despite significant intensity differences across muscle groups. Comparison of the rare dystrophin-null patients against those with mutations affecting full-length dystrophins may provide mechanistic insights for developing more effective treatments for DMD.

fMRI study of neural sensitization to hedonic stimuli in long-term, daily cannabis users.

Although there is emergent evidence illustrating neural sensitivity to cannabis cues in cannabis users, the specificity of this effect to cannabis cues as opposed to a generalized hyper-sensitivity to hedonic stimuli has not yet been directly tested. Using fMRI, we presented 53 daily, long-term cannabis users and 68 non-using controls visual and tactile cues for cannabis, a natural reward, and, a sensory-perceptual control object to evaluate brain response to hedonic stimuli in cannabis users. The results showed an interaction between group and reward type such that the users had greater response during cannabis cues relative to natural reward cues (i.e., fruit) in the orbitofrontal cortex, striatum, anterior cingulate gyrus, and ventral tegmental area compared to non-users (cluster-threshold z = 2.3, P < 0.05). In the users, there were positive brain-behavior correlations between neural response to cannabis cues in fronto-striatal-temporal regions and subjective craving, marijuana-related problems, withdrawal symptoms, and levels of THC metabolites (cluster-threshold z = 2.3, P < 0.05). These findings demonstrate hyper-responsivity, and, specificity of brain response to cannabis cues in long-term cannabis users that are above that of response to natural reward cues. These observations are concordant with incentive sensitization models suggesting sensitization of mesocorticolimbic regions and disruption of natural reward processes following drug use. Although the cross-sectional nature of this study does not provide information on causality, the positive correlations between neural response and indicators of cannabis use (i.e., THC levels) suggest that alterations in the reward system are, in part, related to cannabis use. Hum Brain Mapp 37:3431-3443, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Partial least squares correspondence analysis: A framework to simultaneously analyze behavioral and genetic data.

For nearly a century, detecting the genetic contributions to cognitive and behavioral phenomena has been a core interest for psychological research. Recently, this interest has been reinvigorated by the availability of genotyping technologies (e.g., microarrays) that provide new genetic data, such as single nucleotide polymorphisms (SNPs). These SNPs-which represent pairs of nucleotide letters (e.g., AA, AG, or GG) found at specific positions on human chromosomes-are best considered as categorical variables, but this coding scheme can make difficult the multivariate analysis of their relationships with behavioral measurements, because most multivariate techniques developed for the analysis between sets of variables are designed for quantitative variables. To palliate this problem, we present a generalization of partial least squares-a technique used to extract the information common to 2 different data tables measured on the same observations-called partial least squares correspondence analysis-that is specifically tailored for the analysis of categorical and mixed ("heterogeneous") data types. Here, we formally define and illustrate-in a tutorial format-how partial least squares correspondence analysis extends to various types of data and design problems that are particularly relevant for psychological research that include genetic data. We illustrate partial least squares correspondence analysis with genetic, behavioral, and neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative. R code is available on the Comprehensive R Archive Network and via the authors' websites. (PsycINFO Database Record

The hyper-sentient addict: an exteroception model of addiction.

BACKGROUND: Exteroception involves processes related to the perception of environmental stimuli important for an organism's ability to adapt to its environment. As such, exteroception plays a critical role in conditioned response. In addiction, behavioral and neuroimaging studies show that the conditioned response to drug-related cues is often associated with alterations in brain regions including the precuneus/posterior cingulate cortex, an important node within the default mode network dedicated to processes such as self-monitoring. OBJECTIVE: This review aimed to summarize the growing, but largely fragmented, literature that supports a central role of exteroceptive processes in addiction. METHODS: We performed a systematic review of empirical research via PubMed and Google Scholar with keywords including 'addiction', 'exteroception', 'precuneus', and 'self-awareness', to identify human behavioral and neuroimaging studies that report mechanisms of self-awareness in healthy populations, and altered self-awareness processes, specifically exteroception, in addicted populations. RESULTS: Results demonstrate that exteroceptive processes play a critical role in conditioned cue response in addiction and serve as targets for interventions such as mindfulness training. Further, a hub of the default mode network, namely, the precuneus, is (i) consistently implicated in exteroceptive processes, and (ii) widely demonstrated to have increased activation and connectivity in addicted populations. CONCLUSION: Heightened exteroceptive processes may underlie cue-elicited craving, which in turn may lead to the maintenance and worsening of substance use disorders. An exteroception model of addiction provides a testable framework from which novel targets for interventions can be identified.

Differential reward network functional connectivity in cannabis dependent and non-dependent users.

BACKGROUND: Emergent studies show that similar to other substances of abuse, cue-reactivity to cannabis is also associated with neural response in the brain's reward pathway (Filbey et al., 2009). However, the inter-relatedness of brain regions during cue-reactivity in cannabis users remains unknown. METHODS: In this study, we conducted a series of investigations to determine functional connectivity during cue-reactivity in 71 cannabis users. First, we used psychophysiological interaction (PPI) analysis to examine coherent neural response to cannabis cues. Second, we evaluated whether these patterns of network functional connectivity differentiated dependent and non-dependent users. Finally, as an exploratory analysis, we determined the directionality of these connections via Granger connectivity analyses. RESULTS: PPI analyses showed reward network functional connectivity with the nucleus accumbens (NAc) seed region during cue exposure. Between-group contrasts found differential effects of dependence status. Dependent users (N=31) had greater functional connectivity with amygdala and anterior cingulate gyrus (ACG) seeds while the non-dependent users (N=24) had greater functional connectivity with the NAc, orbitofrontal cortex (OFC) and hippocampus seeds. Granger analyses showed that hippocampal and ACG activation preceded neural response in reward areas. CONCLUSIONS: Both PPI and Granger analyses demonstrated strong functional coherence in reward regions during exposure to cannabis cues in current cannabis users. Functional connectivity (but not regional activation) in the reward network differentiated dependent from non-dependent cannabis users. Our findings suggest that repeated cannabis exposure causes observable changes in functional connectivity in the reward network and should be considered in intervention strategies.

Neural effects of positive and negative incentives during marijuana withdrawal.

In spite of evidence suggesting two possible mechanisms related to drug-seeking behavior, namely reward-seeking and harm avoidance, much of the addiction literature has focused largely on positive incentivization mechanisms associated with addiction. In this study, we examined the contributing neural mechanisms of avoidance of an aversive state to drug-seeking behavior during marijuana withdrawal. To that end, marijuana users were scanned while performing the monetary incentive delay task in order to assess positive and negative incentive processes. The results showed a group x incentive interaction, such that marijuana users had greater response in areas that underlie reward processes during positive incentives while controls showed greater response in the same areas, but to negative incentives. Furthermore, a negative correlation between withdrawal symptoms and response in the amygdala during negative incentives was found in the marijuana users. These findings suggest that although marijuana users have greater reward sensitivity and less harm avoidance than controls, that attenuated amygdala response, an area that underlies fear and avoidance, was present in marijuana users with greater marijuana withdrawal symptoms. This is concordant with models of drug addiction that involve multiple sources of reinforcement in substance use disorders, and suggests the importance of strategies that focus on respective mechanisms.

Effect of age on variability in the production of text-based global inferences.

As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one's world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation--a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging.

Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): how to assign scans to categories without using spatial normalization.

We present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels) that are themselves grouped into regions of interest (ROIs). Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying observations and categories, and (3) optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs) on the factorial maps. Therefore MUSUBADA can analyze datasets with different voxel numbers per participant and, so does not require spatial normalization. MUSUBADA statistical inferences are implemented with cross-validation techniques (e.g., jackknife and bootstrap), its performance is evaluated with confusion matrices (for fixed and random models) and represented with prediction, tolerance, and confidence intervals. We present an example where we predict the image categories (houses, shoes, chairs, and human, monkey, dog, faces,) of images watched by participants whose brains were scanned. This example corresponds to a DA question in which the data table is made of subtables (one per subject) and with more variables than observations.

Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach.

The goal of this study was to evaluate human accuracy at identifying people from static and dynamic presentations of faces and bodies. Participants matched identity in pairs of videos depicting people in motion (walking or conversing) and in "best" static images extracted from the videos. The type of information presented to observers was varied to include the face and body, the face-only, and the body-only. Identification performance was best when people viewed the face and body in motion. There was an advantage for dynamic over static stimuli, but only for conditions that included the body. Control experiments with multiple-static images indicated that some of the motion advantages we obtained were due to seeing multiple images of the person, rather than to the motion, per se. To computationally assess the contribution of different types of information for identification, we fused the identity judgments from observers in different conditions using a statistical learning algorithm trained to optimize identification accuracy. This fusion achieved perfect performance. The condition weights that resulted suggest that static displays encourage reliance on the face for recognition, whereas dynamic displays seem to direct attention more equitably across the body and face.

How to compute reliability estimates and display confidence and tolerance intervals for pattern classifiers using the Bootstrap and 3-way multidimensional scaling (DISTATIS).

When used to analyze brain imaging data, pattern classifiers typically produce results that can be interpreted as a measure of discriminability or as a distance between some experimental categories. These results can be analyzed with techniques such as multidimensional scaling (MDS), which represent the experimental categories as points on a map. While such a map reveals the configuration of the categories, it does not provide a reliability estimate of the position of the experimental categories, and therefore cannot be used for inferential purposes. In this paper, we present a procedure that provides reliability estimates for pattern classifiers. This procedure combines bootstrap estimation (to estimate the variability of the experimental conditions) and a new 3-way extension of MDS, called DISTATIS, that can be used to integrate the distance matrices generated by the bootstrap procedure and to represent the results as MDS-like maps. Reliability estimates are expressed as (1) tolerance intervals which reflect the accuracy of the assignment of scans to experimental categories and as (2) confidence intervals which generalize standard hypothesis testing. When more than two categories are involved in the application of a pattern classifier, the use of confidence intervals for null hypothesis testing inflates Type I error. We address this problem with a Bonferonni-like correction. Our methodology is illustrated with the results of a pattern classifier described by O'Toole et al. (O'Toole, A., Jiang, F., Abdi, H., Haxby, J., 2005. Partially distributed representations of objects and faces in ventral temporal cortex. J. Cogn. Neurosci. 17, 580-590) who re-analyzed data originally collected by Haxby et al. (Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J., Pietrini, P., 2001. Distributed and overlapping representation of faces and objects in ventral temporal cortex. Science 293, 2425-2430).

Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data.

The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These "brain-reading" analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.