Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

Development of insula connectivity between ages 12 and 30 revealed by high angular resolution diffusion imaging

The insula, hidden deep within the Sylvian fissures, has proven difficult to study from a connectivity perspective. Most of our current information on the anatomical connectivity of the insula comes from studies of nonhuman primates and post mortem human dissections. To date, only two neuroimaging studies have successfully examined the connectivity of the insula. Here we examine how the connectivity of the insula develops between ages 12 and 30, in 307 young adolescent and adult subjects scanned with 4-Tesla high angular resolution diffusion imaging (HARDI). The density of fiber connections between the insula and the frontal and parietal cortex decreased with age, but the connection density between the insula and the temporal cortex generally increased with age. This trajectory is in line with well-known patterns of cortical development in these regions. In addition, males and females showed different developmental trajectories for the connection between the left insula and the left precentral gyrus. The insula plays many different roles, some of them affected in neuropsychiatric disorders; this information on the insula's connectivity may help efforts to elucidate mechanisms of brain disorders in which it is implicated.

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.

L-dopa modulates functional connectivity in striatal cognitive and motor networks: A double-blind placebo-controlled study

Functional connectivity (FC) analyses of resting-state fMRI data allow for the mapping of large-scale functional networks, and provide a novel means of examining the impact of dopaminergic challenge. Here, using a double-blind, placebo-controlled design, we examined the effect of L-dopa, a dopamine precursor, on striatal resting-state FC in 19 healthy young adults.Weexamined the FC of 6 striatal regions of interest (ROIs) previously shown to elicit networks known to be associated with motivational, cognitive and motor subdivisions of the caudate and putamen (Di Martino et al., 2008). In addition to replicating the previously demonstrated patterns of striatal FC, we observed robust effects of L-dopa. Specifically, L-dopa increased FC in motor pathways connecting the putamen ROIs with the cerebellum and brainstem. Although L-dopa also increased FC between the inferior ventral striatum and ventrolateral prefrontal cortex, it disrupted ventral striatal and dorsal caudate FC with the default mode network. These alterations in FC are consistent with studies that have demonstrated dopaminergic modulation of cognitive and motor striatal networks in healthy participants. Recent studies have demonstrated altered resting state FC in several conditions believed to be characterized by abnormal dopaminergic neurotransmission. Our findings suggest that the application of similar experimental pharmacological manipulations in such populations may further our understanding of the role of dopaminergic neurotransmission in those conditions.

A functional MRI study of the relationship between naming treatment outcomes and resting state functional connectivity in post-stroke aphasia

Background: The majority of studies investigating the neural mechanisms underlying treatment in people with aphasia have examined task-based brain activity. However, the use of resting-state fMRI may provide another method of examining the brain mechanisms responsible for treatment-induced recovery, and allows for investigation into connectivity within complex functional networks Methods: Eight people with aphasia underwent 12 treatment sessions that aimed to improve object naming. Half the sessions employed a phonologically-based task, and half the sessions employed a semantic-based task, with resting-state fMRI conducted pre- and post-treatment. Brain regions in which the amplitude of low frequency fluctuations (ALFF) correlated with treatment outcomes were used as seeds for functional connectivity (FC) analysis. FC maps were compared from pre- to post-treatment, as well as with a group of 12 healthy older controls Results: Pre-treatment ALFF in the right middle temporal gyrus (MTG) correlated with greater outcomes for the phonological treatment, with a shift to the left MTG and supramarginal gyrus, as well as the right inferior frontal gyrus, post-treatment. When compared to controls, participants with aphasia showed both normalization and up-regulation of connectivity within language networks post-treatment, predominantly in the left hemisphere Conclusions: The results provide preliminary evidence that treatments for naming impairments affect the FC of language networks, and may aid in understanding the neural mechanisms underlying the rehabilitation of language post-stroke.

Using relationship mapping to understand sustainable housing stakeholders’ actions

The production of sustainable housing requires the cooperation of a variety of participants with different goals, needs, levels of commitment and cultures. To achieve mainstream net zero energy housing objectives, there is arguably a need for a non-linear network of collaboration between all the stakeholders. In order to create and improve such collaborative networks between stakeholders, we first need to map stakeholders’ relationships, processes, and practices. This paper discusses compares and contrasts maps of the sustainable housing production life-cycle in Australia, developed from different perspectives. The paper highlights the strengths and weaknesses of each visualization, clarifying where gaps in connectivity exist within existing industry networks. Understanding these gaps will help researchers and practitioners identify how to improve the collaboration between participants in the housing industry. This in turn may improve decision making across all stakeholder groups, leading to mainstream implementation of sustainability into the housing industry.

Understanding Connectivity between Groundwater Chemistry Data and Geological Stratigraphy via 3D Sub-surface Visualization and Analysis

This paper describes the 3D Water Chemistry Atlas - an open source, Web-based system that enables the three-dimensional (3D) sub-surface visualization of ground water monitoring data, overlaid on the local geological model. Following a review of existing technologies, the system adopts Cesium (an open source Web-based 3D mapping and visualization interface) together with a PostGreSQL/PostGIS database, for the technical architecture. In addition a range of the search, filtering, browse and analysis tools were developed that enable users to interactively explore the groundwater monitoring data and interpret it spatially and temporally relative to the local geological formations and aquifers via the Cesium interface. The result is an integrated 3D visualization system that enables environmental managers and regulators to assess groundwater conditions, identify inconsistencies in the data, manage impacts and risks and make more informed decisions about activities such as coal seam gas extraction, waste water extraction and re-use.

Towards a Comprehensive Approach to Quantifying and Mapping Ecosystem Services Flows

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Cross-hemispheric collaboration and segregation associated with task difficulty as revealed by structural and functional connectivity.

Although it is known that brain regions in one hemisphere may interact very closely with their corresponding contralateral regions (collaboration) or operate relatively independent of them (segregation), the specific brain regions (where) and conditions (how) associated with collaboration or segregation are largely unknown. We investigated these issues using a split field-matching task in which participants matched the meaning of words or the visual features of faces presented to the same (unilateral) or to different (bilateral) visual fields. Matching difficulty was manipulated by varying the semantic similarity of words or the visual similarity of faces. We assessed the white matter using the fractional anisotropy (FA) measure provided by diffusion tensor imaging (DTI) and cross-hemispheric communication in terms of fMRI-based connectivity between homotopic pairs of cortical regions. For both perceptual and semantic matching, bilateral trials became faster than unilateral trials as difficulty increased (bilateral processing advantage, BPA). The study yielded three novel findings. First, whereas FA in anterior corpus callosum (genu) correlated with word-matching BPA, FA in posterior corpus callosum (splenium-occipital) correlated with face-matching BPA. Second, as matching difficulty intensified, cross-hemispheric functional connectivity (CFC) increased in domain-general frontopolar cortex (for both word and face matching) but decreased in domain-specific ventral temporal lobe regions (temporal pole for word matching and fusiform gyrus for face matching). Last, a mediation analysis linking DTI and fMRI data showed that CFC mediated the effect of callosal FA on BPA. These findings clarify the mechanisms by which the hemispheres interact to perform complex cognitive tasks.

A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series.

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.

Nanometer-scale mapping of irreversible electrochemical nucleation processes on solid Li-ion electrolytes

Electrochemical processes associated with changes in structure, connectivity or composition typically proceed via new phase nucleation with subsequent growth of nuclei. Understanding and controlling reactions requires the elucidation and control of nucleation mechanisms. However, factors controlling nucleation kinetics, including the interplay between local mechanical conditions, microstructure and local ionic profile remain inaccessible. Furthermore, the tendency of current probing techniques to interfere with the original microstructure prevents a systematic evaluation of the correlation between the microstructure and local electrochemical reactivity. In this work, the spatial variability of irreversible nucleation processes of Li on a Li-ion conductive glass-ceramics surface is studied with ~30 nm resolution. An increased nucleation rate at the boundaries between the crystalline AlPO4 phase and amorphous matrix is observed and attributed to Li segregation. This study opens a pathway for probing mechanisms at the level of single structural defects and elucidation of electrochemical activities in nanoscale volumes.

Increased resting state functional connectivity in the default mode network in recovered anorexia nervosa.

Functional brain imaging studies have shown abnormal neural activity in individuals recovered from anorexia nervosa (AN) during both cognitive and emotional task paradigms. It has been suggested that this abnormal activity which persists into recovery might underpin the neurobiology of the disorder and constitute a neural biomarker for AN. However, no study to date has assessed functional changes in neural networks in the absence of task-induced activity in those recovered from AN. Therefore, the aim of this study was to investigate whole brain resting state functional connectivity in nonmedicated women recovered from anorexia nervosa. Functional magnetic resonance imaging scans were obtained from 16 nonmedicated participants recovered from anorexia nervosa and 15 healthy control participants. Independent component analysis revealed functionally relevant resting state networks. Dual regression analysis revealed increased temporal correlation (coherence) in the default mode network (DMN) which is thought to be involved in self-referential processing. Specifically, compared to healthy control participants the recovered anorexia nervosa participants showed increased temporal coherence between the DMN and the precuneus and the dorsolateral prefrontal cortex/inferior frontal gyrus. The findings support the view that dysfunction in resting state functional connectivity in regions involved in self-referential processing and cognitive control might be a vulnerability marker for the development of anorexia nervosa.

Neuropsychiatric symptoms in Alzheimer's disease are related to functional connectivity alterations in the salience network

Neuropsychiatric syndromes are highly prevalent in Alzheimer's disease (AD), but their neurobiology is not completely understood. New methods in functional magnetic resonance imaging, such as intrinsic functional connectivity or resting-state analysis, may help to clarify this issue. Using such approaches, alterations in the default-mode and salience networks (SNs) have been described in Alzheimer's, although their relationship with specific symptoms remains unclear. We therefore carried out resting-state functional connectivity analysis with 20 patients with mild to moderate AD, and correlated their scores on neuropsychiatric inventory syndromes (apathy, hyperactivity, affective syndrome, and psychosis) with maps of connectivity in the default mode network and SN. In addition, we compared network connectivity in these patients with that in 17 healthy elderly control subjects. All analyses were controlled for gray matter density and other potential confounds. Alzheimer's patients showed increased functional connectivity within the SN compared with controls (right anterior cingulate cortex and left medial frontal gyrus), along with reduced functional connectivity in the default-mode network (bilateral precuneus). A correlation between increased connectivity in anterior cingulate cortex and right insula areas of the SN and hyperactivity syndrome (agitation, irritability, aberrant motor behavior, euphoria, and disinhibition) was found. These findings demonstrate an association between specific network changes in AD and particular neuropsychiatric symptom types. This underlines the potential clinical significance of resting state alterations in future diagnosis and therapy. © 2013 Wiley Periodicals, Inc.

Connectivity processes and riparian vegetation of the upper Paraná River, Brazil

In fluvial systems, the relationship between a dominant variable (e.g. flood pulse) and its dependent ones (e.g. riparian vegetation) is called connectivity. This paper analyzes the connectivity elements and processes controlling riparian vegetation for a reach of the upper Paraná River (Brazil) and estimates the future changes in channel-vegetation relationship as a consequence of the managing of a large dam. The studied reach is situated 30km downstream from the Porto Primavera Dam (construction finished in 1999). Through aerial photography (1:25,000, 1996), RGB-CBERS satellite imagery and a previous field botany survey it was possible to elaborate a map with the five major morpho-vegetation units: 1) Tree-dominated natural levee, 2) Shrubby upper floodplain, 3) Shrub-herbaceous mid floodplain, 4) Grass-herbaceous lower floodplain and 5) Shrub-herbaceous flood runoff channel units. By use of a detailed topographic survey and statistical tools each morpho-vegetation type was analyzed according to its connectivity parameters (frequency, recurrence, permanence, seasonality, potamophase, limnophase and FCQ index) in the pre- and post-dam closure periods of the historical series. Data showed that most of the morpho-vegetation units were predicted to present changes in connectivity parameters values after dam closing and the new regime could affect, in different intensity, the river ecology and particularly the riparian vegetation. The methods used in this study can be useful for dam impact studies in other South American tropical rivers. © 2012 Elsevier Ltd.

Functional connectivity in BOLD and CBF data: similarity and reliability of resting brain networks

Resting-state functional connectivity (FC) fMRI (rs-fcMRI) offers an appealing approach to mapping the brain's intrinsic functional organization. Blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) are the two main rs-fcMRI approaches to assess alterations in brain networks associated with individual differences, behavior and psychopathology. While the BOLD signal is stronger with a higher temporal resolution, ASL provides quantitative, direct measures of the physiology and metabolism of specific networks. This study systematically investigated the similarity and reliability of resting brain networks (RBNs) in BOLD and ASL. A 2×2×2 factorial design was employed where each subject underwent repeated BOLD and ASL rs-fcMRI scans on two occasions on two MRI scanners respectively. Both independent and joint FC analyses revealed common RBNs in ASL and BOLD rs-fcMRI with a moderate to high level of spatial overlap, verified by Dice Similarity Coefficients. Test-retest analyses indicated more reliable spatial network patterns in BOLD (average modal Intraclass Correlation Coefficients: 0.905±0.033 between-sessions; 0.885±0.052 between-scanners) than ASL (0.545±0.048; 0.575±0.059). Nevertheless, ASL provided highly reproducible (0.955±0.021; 0.970±0.011) network-specific CBF measurements. Moreover, we observed positive correlations between regional CBF and FC in core areas of all RBNs indicating a relationship between network connectivity and its baseline metabolism. Taken together, the combination of ASL and BOLD rs-fcMRI provides a powerful tool for characterizing the spatiotemporal and quantitative properties of RBNs. These findings pave the way for future BOLD and ASL rs-fcMRI studies in clinical populations that are carried out across time and scanners.