Motor response suppression and the prepotent tendency to respond: A parametric fMRI study

In the present study we utilised functional magnetic resonance imaging (fMRI) to examine cerebral activation during performance of a classic motor task in which response suppression load was parametrically varied. Linear increases in activity were observed in a distributed network of regions across both cerebral hemispheres, although with more extensive involvement of the right prefrontal cortex. Activated regions included prefrontal, parietal and occipitotemporal cortices. Decreasing activation was similarly observed in a distributed network of regions. These response forms are discussed in terms of an increasing requirement for visual cue discrimination and suppression/selection of motor responses, and a decreasing probability of the occurrence of non-target stimuli and attenuation of a prepotent tendency to respond. The results support recent proposals for a dominant role for the right-hemisphere in performance of motor response suppression tasks that emphasise the importance of the right prefrontal cortex.

Cerebral regions associated with verbal response initiation, suppression and strategy use

Cerebral activation associated with performance on a novel task involving two conditions was investigated with functional magnetic resonance imaging (fMRI). In the response initiation condition, subjects nominated the general superordinate category to which each of a series of exemplars (concrete nouns) belonged. In the response suppression condition, subjects were required to nominate a general superordinate category to which each exemplar did not belong, with the instruction that they were not to nominate the same category response twice in a row. Both conditions produced distinct patterns of activation relative to an articulation control condition employing identical stimuli. When initiation and suppression conditions were directly compared, response suppression produced activation in the right frontal pole, orbital frontal cortex and anterior cingulate, left dorsolateral prefrontal cortex and posterior cingulate, and bilaterally in the precuneus, visual association cortex and cerebellum. Response latencies were significantly longer in the suppression condition. Two broadly-defined strategies associated with the correct production of words during the suppression condition were a self-ordered selection from among the superordinate categories identified during the first section of the task and the generation of novel category responses. The neuroanatomical correlates of response initiation, suppression and strategy use are discussed, as are the respective roles of response suppression and strategy generation.

Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

A genetic analysis of cortical thickness in 372 twins

Imaging genetics is a new field of neuroscience that blends methods from computational anatomy and quantitative genetics to identify genetic influences on brain structure and function. Here we analyzed brain MRI data from 372 young adult twins to identify cortical regions in which gray matter volume is influenced by genetic differences across subjects. Thickness maps, reconstructed from surface models of the cortical gray/white and gray/CSF interfaces, were smoothed with a 25 mm FWHM kernel and automatically parcellated into 34 regions of interest per hemisphere. In structural equation models fitted to volume values at each surface vertex, we computed components of variance due to additive genetic (A), shared (C) and unique (E) environmental factors, and tested their significance. Cortical regions in the vicinity of the perisylvian language cortex, and at the frontal and temporal poles, showed significant additive genetic variance, suggesting that volume measures from these regions may provide quantitative phenotypes to narrow the search for quantitative trait loci that influence brain structure.

The contribution of genes to cortical thickness and volume

We analyzed brain MRI data from 372 young adult twins toidentify cortical regions in which gray matter thickness and volume are influenced by genetics. This was achieved using an A/C/E structural equation model that divides the variance of these traits, at each point on the cortex, into additive genetic (A), shared (C), and unique environmental (E) components. A strong genetic influencewas found in frontal and parietal regions. Inaddition, we correlated cortical thickness with full-scale intelligence quotient for comparison with the A/C/E maps, and several regions where cortical structure was correlated with intelligence quotient are under genetic control. These cortical measures may be useful phenotypes to narrow the searchfor quantitative trait lociinfluencing brain structure.

A new combined surface and volume registration

3D registration of brain MRI data is vital for many medical imaging applications. However, purely intensitybased approaches for inter-subject matching of brain structure are generally inaccurate in cortical regions, due to the highly complex network of sulci and gyri, which vary widely across subjects. Here we combine a surfacebased cortical registration with a 3D fluid one for the first time, enabling precise matching of cortical folds, but allowing large deformations in the enclosed brain volume, which guarantee diffeomorphisms. This greatly improves the matching of anatomy in cortical areas. The cortices are segmented and registered with the software Freesurfer. The deformation field is initially extended to the full 3D brain volume using a 3D harmonic mapping that preserves the matching between cortical surfaces. Finally, these deformation fields are used to initialize a 3D Riemannian fluid registration algorithm, that improves the alignment of subcortical brain regions. We validate this method on an MRI dataset from 92 healthy adult twins. Results are compared to those based on volumetric registration without surface constraints; the resulting mean templates resolve consistent anatomical features both subcortically and at the cortex, suggesting that the approach is well-suited for cross-subject integration of functional and anatomic data.

Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture

Recent advances in diffusion-weighted MRI (DWI) have enabled studies of complex white matter tissue architecture in vivo. To date, the underlying influence of genetic and environmental factors in determining central nervous system connectivity has not been widely studied. In this work, we introduce new scalar connectivity measures based on a computationally-efficient fast-marching algorithm for quantitative tractography. We then calculate connectivity maps for a DTI dataset from 92 healthy adult twins and decompose the genetic and environmental contributions to the variance in these metrics using structural equation models. By combining these techniques, we generate the first maps to directly examine genetic and environmental contributions to brain connectivity in humans. Our approach is capable of extracting statistically significant measures of genetic and environmental contributions to neural connectivity.

A reproducible method for automated extraction of brain volumes from 3D human head MR images

An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D) MR images (proton density, T1 weighted, and T2-weighted) of the human head is described. The procedure is divided into four levels: preprocessing, segmentation, scalp removal, and postprocessing. A user-provided reference point is the sole operator-dependent input required. The method's parameters were first optimized and then fixed and applied to 30 repeat data sets from 15 normal older adult subjects to investigate its reproducibility. Percent differences between total brain volumes (TBVs) for the subjects' repeated data sets ranged from .5% to 2.2%. We conclude that the method is both robust and reproducible and has the potential for wide application.

The tensor distribution function

Diffusion weighted magnetic resonance imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of six directions, second-order tensors (represented by three-by-three positive definite matrices) can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve more complicated white matter configurations, e.g., crossing fiber tracts. Recently, a number of high-angular resolution schemes with more than six gradient directions have been employed to address this issue. In this article, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Using the calculus of variations, we solve the TDF that optimally describes the observed data. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function. Moreover, a tensor orientation distribution function (TOD) may also be derived from the TDF, allowing for the estimation of principal fiber directions and their corresponding eigenvalues.

Efficacy and safety of adalimumab in patients with non-radiographic axial spondyloarthritis: Results of a randomised placebo-controlled trial (ABILITY-1)

Purpose: To evaluate the efficacy and safety of adalimumab in patients with non-radiographic axial spondyloarthritis (nr-axSpA). Methods: Patients fulfilled Assessment of Spondyloarthritis international Society (ASAS) criteria for axial spondyloarthritis, had a Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score of ≥ 4, total back pain score of ≥ 4 (10 cm visual analogue scale) and inadequate response, intolerance or contraindication to non-steroidal anti-inflammatory drugs (NSAIDs); patients fulfilling modified New York criteria for ankylosing spondylitis were excluded. Patients were randomised to adalimumab (N=91) or placebo (N=94). The primary endpoint was the percentage of patients achieving ASAS40 at week 12. Efficacy assessments included BASDAI and Ankylosing Spondylitis Disease Activity Score (ASDAS). MRI was performed at baseline and week 12 and scored using the Spondyloarthritis Research Consortium of Canada (SPARCC) index. Results: Significantly more patients in the adalimumab group achieved ASAS40 at week 12 compared with patients in the placebo group (36% vs 15%, p<0.001). Significant clinical improvements based on other ASAS responses, ASDAS and BASDAI were also detected at week 12 with adalimumab treatment, as were improvements in quality of life measures. Inflammation in the spine and sacroiliac joints on MRI significantly decreased after 12 weeks of adalimumab treatment. Shorter disease duration, younger age, elevated baseline C-reactive protein or higher SPARCC MRI sacroiliac joint scores were associated with better week 12 responses to adalimumab. The safety profile was consistent with what is known for adalimumab in ankylosing spondylitis and other diseases. Conclusions: In patients with nr-axSpA, adalimumab treatment resulted in effective control of disease activity, decreased inflammation and improved quality of life compared with placebo. Results from ABILITY-1 suggest that adalimumab has a positive benefit-risk profile in active nr-axSpA patients with inadequate response to NSAIDs.

Functional localization of the human color center by decreased water displacement using diffusion-weighted fMRI

Introduction Decreased water displacement following increased neural activity has been observed using diffusion-weighted functional MRI (DfMRI) at high b-values. The physiological mechanisms underlying the diffusion signal change may be unique from the standard blood oxygenation level-dependent (BOLD) contrast and closer to the source of neural activity. Whether DfMRI reflects neural activity more directly than BOLD outside the primary cerebral regions remains unclear. Methods Colored and achromatic Mondrian visual stimuli were statistically contrasted to functionally localize the human color center Area V4 in neurologically intact adults. Spatial and temporal properties of DfMRI and BOLD activation were examined across regions of the visual cortex. Results At the individual level, DfMRI activation patterns showed greater spatial specificity to V4 than BOLD. The BOLD activation patterns were more prominent in the primary visual cortex than DfMRI, where activation was localized to the ventral temporal lobe. Temporally, the diffusion signal change in V4 and V1 both preceded the corresponding hemodynamic response, however the early diffusion signal change was more evident in V1. Conclusions DfMRI may be of use in imaging applications implementing cognitive subtraction paradigms, and where highly precise individual functional localization is required.

Magnetization, magnetotransport and electron magnetic resonance studies of nanoparticles and nanowires of Pr0.5Sr0.5MnO3

In this paper, we present the preparation and characterization of nanoparticles and nanowires of Pr0.5Sr0.5MnO3 (PSMO). The main results of this investigation are as follows: (a) a comparison with the properties of the bulk material shows that the ferromagnetic (FM) transition at 270 K remains unaffected but the anti-ferromagnetic (AFM) transition at TN = 150 K disappears in the nanoparticles, (b) the size induced ground state magnetic phase (below 150 K) is predominantly FM, coexisting with a residual AFM phase, and (c) the temperature dependence of magnetic anisotropy shows complex behaviour, being higher in the nanoparticles at high temperatures and lower at moderately lower temperatures in comparison with the bulk. The results obtained from the extensive magnetization, magnetotransport and electron magnetic resonance studies made on various samples are presented and discussed in detail.

Invasive and non-invasive modalities of imaging carotid stenosis

Despite recent therapeutic advances, acute ischemic complications of atherosclerosis remain the primary cause of morbidity and mortality in Western countries, with carotid atherosclerotic disease one of the major preventable causes of stroke. As the impact of this disease challenges our healthcare systems, we are becoming aware that factors influencing this disease are more complex than previously realized. In current clinical practice, risk stratification relies primarily on evaluation of the degree of luminal stenosis and patient symptomatology. Adequate investigation and optimal imaging are important factors that affect the quality of a carotid endarterectomy (CEA) service and are fundamental to patient selection. Digital subtraction angiography is still perceived as the most accurate imaging modality for carotid stenosis and historically has been the cornerstone of most of the major CEA trials but concerns regarding potential neurological complications have generated substantial interest in non-invasive modalities, such as contrast-enhanced magnetic resonance angiography. The purpose of this review is to give an overview to the vascular specialist of the current imaging modalities in clinical practice to identify patients with carotid stenosis. Advantages and disadvantages of each technique are outlined. Finally, limitations of assessing luminal stenosis in general are discussed. This article will not cover imaging of carotid atheroma morphology, function and other emerging imaging modalities of assessing plaque risk, which look beyond simple luminal measurements.