Adaptive Strategy for the Statistical Analysis of Connectomes

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores

A two-year participatory intervention project with owners to reduce lameness and limb abnormalities in working horses in Jaipur, India

Participatory methods are increasingly used in international human development, but scientific evaluation of their efficacy versus a control group is rare. Working horses support families in impoverished communities. Lameness and limb abnormalities are highly prevalent in these animals and a cause for welfare concern. We aimed to stimulate and evaluate improvements in lameness and limb abnormalities in horses whose owners took part in a 2-year participatory intervention project to reduce lameness (PI) versus a control group (C) in Jaipur, India.In total, 439 owners of 862 horses participated in the study. PI group owners from 21 communities were encouraged to meet regularly to discuss management and work practices influencing lameness and poor welfare and to track their own progress in improving these. Lameness examinations (41 parameters) were conducted at the start of the study (Baseline), and after 1 year and 2 years. Results were compared with control horses from a further 21 communities outside the intervention. Of the 149 horses assessed on all three occasions, PI horses showed significantly (P<0.05) greater improvement than C horses in 20 parameters, most notably overall lameness score, measures of sole pain and range of movement on limb flexion. Control horses showed slight but significantly greater improvements in four parameters, including frog quality in fore and hindlimbs.This participatory intervention succeeded in improving lameness and some limb abnormalities in working horses, by encouraging changes in management and work practices which were feasible within owners’ socioeconomic and environmental constraints. Demonstration of the potentially sustainable improvements achieved here should encourage further development of participatory intervention approaches to benefit humans and animals in other contexts

Neural development features: Spatio-temporal development of the Caenorhabditis elegans neuronal network

The nematode Caenorhabditis elegans, with information on neural connectivity, three-dimensional position and cell linage provides a unique system for understanding the development of neural networks. Although C. elegans has been widely studied in the past, we present the first statistical study from a developmental perspective, with findings that raise interesting suggestions on the establishment of long-distance connections and network hubs. Here, we analyze the neuro-development for temporal and spatial features, using birth times of neurons and their three-dimensional positions. Comparisons of growth in C. elegans with random spatial network growth highlight two findings relevant to neural network development. First, most neurons which are linked by long-distance connections are born around the same time and early on, suggesting the possibility of early contact or interaction between connected neurons during development. Second, early-born neurons are more highly connected (tendency to form hubs) than later born neurons. This indicates that the longer time frame available to them might underlie high connectivity. Both outcomes are not observed for random connection formation. The study finds that around one-third of electrically coupled long-range connections are late forming, raising the question of what mechanisms are involved in ensuring their accuracy, particularly in light of the extremely invariant connectivity observed in C. elegans. In conclusion, the sequence of neural network development highlights the possibility of early contact or interaction in securing long-distance and high-degree connectivity

Do brain networks evolve by maximizing their information flow capacity?

We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization

P2Y2 and P2Y6 receptor activation elicits intracellular calcium responses in human adipose-derived mesenchymal stromal cells

Adipose tissue contains self-renewing multipotent cells termed mesenchymal stromal cells. In situ, these cells serve to expand adipose tissue by adipogenesis, but their multipotency has gained interest for use in tissue regeneration. Little is known regarding the repertoire of receptors expressed by adipose-derived mesenchymal stromal cells (AD-MSCs). The purpose of this study was to undertake a comprehensive analysis of purinergic receptor expression. Mesenchymal stromal cells were isolated from human subcutaneous adipose tissue and confirmed by flow cytometry. The expression profile of purinergic receptors was determined by quantitative real-time PCR and immunocytochemistry. The molecular basis for adenine and uracil nucleotide-evoked intracellular calcium responses was determined using Fura-2 measurements. All the known subtypes of P2X and P2Y receptors, excluding P2X2, P2X3 and P2Y12 receptors, were detected at the mRNA and protein level. ATP, ADP and UTP elicited concentration-dependent calcium responses in mesenchymal cells (N = 7–9 donors), with a potency ranking ADP (EC50 1.3 ± 1.0 μM) > ATP (EC50 2.2 ± 1.1 μM) = UTP (3.2 ± 2.8 μM). Cells were unresponsive to UDP (< 30 μM) and UDP-glucose (< 30 μM). ATP responses were attenuated by selective P2Y2 receptor antagonism (AR-C118925XX; IC50 1.1 ± 0.8 μM, 73.0 ± 8.5% max inhibition; N = 7 donors), and UTP responses were abolished. ADP responses were attenuated by the selective P2Y6 receptor antagonist, MRS2587 (IC50 437 ± 133nM, 81.0 ± 8.4% max inhibition; N = 6 donors). These data demonstrate that adenine and uracil nucleotides elicit intracellular calcium responses in human AD-MSCs with a predominant role for P2Y2 and P2Y6 receptor activation. This study furthers understanding about how human adipose-derived mesenchymal stromal cells can respond to external signalling cues

Impaired Small-World Network Efficiency and Dynamic Functional Distribution in Patients with Cirrhosis

Hepatic encephalopathy (HE) is a complex neuropsychiatric syndrome and a major complication of liver cirrhosis. Dysmetabolism of the brain, related to elevated ammonia levels, interferes with intercortical connectivity and cognitive function. For evaluation of network efficiency, a ‘small-world’ network model can quantify the effectiveness of information transfer within brain networks. This study aimed to use small-world topology to investigate abnormalities of neuronal connectivity among widely distributed brain regions in patients with liver cirrhosis using resting-state functional magnetic resonance imaging (rs-fMRI). Seventeen cirrhotic patients without HE, 9 with minimal HE, 9 with overt HE, and 35 healthy controls were compared. The interregional correlation matrix was obtained by averaging the rs-fMRI time series over all voxels in each of the 90 regions using the automated anatomical labeling model. Cost and correlation threshold values were then applied to construct the functional brain network. The absolute and relative network efficiencies were calculated; quantifying distinct aspects of the local and global topological network organization. Correlations between network topology parameters, ammonia levels, and the severity of HE were determined using linear regression and ANOVA. The local and global topological efficiencies of the functional connectivity network were significantly disrupted in HE patients; showing abnormal small-world properties. Alterations in regional characteristics, including nodal efficiency and nodal strength, occurred predominantly in the association, primary, and limbic/paralimbic regions. The degree of network organization disruption depended on the severity of HE. Ammonia levels were also significantly associated with the alterations in local network properties. Results indicated that alterations in the rs-fMRI network topology of the brain were associated with HE grade; and that focal or diffuse lesions disturbed the functional network to further alter the global topology and efficiency of the whole brain network. These findings provide insights into the functional changes in the human brain in HE

Integrating Functional and Diffusion Magnetic Resonance Imaging for Analysis of Structure-Function Relationship in the Human Language Network

The capabilities of magnetic resonance imaging (MRI) to measure structural and functional connectivity in the human brain have motivated growing interest in characterizing the relationship between these measures in the distributed neural networks of the brain. In this study, we attempted an integration of structural and functional analyses of the human language circuits, including Wernicke's (WA), Broca's (BA) and supplementary motor area (SMA), using a combination of blood oxygen level dependent (BOLD) and diffusion tensor MRI.Functional connectivity was measured by low frequency inter-regional correlations of BOLD MRI signals acquired in a resting steady-state, and structural connectivity was measured by using adaptive fiber tracking with diffusion tensor MRI data. The results showed that different language pathways exhibited different structural and functional connectivity, indicating varying levels of inter-dependence in processing across regions. Along the path between BA and SMA, the fibers tracked generally formed a single bundle and the mean radius of the bundle was positively correlated with functional connectivity. However, fractional anisotropy was found not to be correlated with functional connectivity along paths connecting either BA and SMA or BA and WA. for use in diagnosing and determining disease progression and recovery

The Overlapping Community Structure of Structural Brain Network in Young Healthy Individuals

Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas) in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions) can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of informa- tion through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain