Functional and Structural Brain Correlates of Socioeconomic Status.

Socioeconomic status (SES) is a multidimensional construct that includes not only measures of material wealth, but also education, social prestige, and neighborhood quality. Socioeconomic correlates between wealth and cognitive functions have been well established in behavioral studies. However, functional and structural brain correlates of SES remain unclear. Here, we sought to uncover the most likely neural regions to be affected by low SES, specifically associated with age. Using effect size-seed-based d Mapping, we compiled studies that examined individuals with low SES and performed functional magnetic resonance imaging and voxel-based morphometry meta-analyses. The results revealed that as from early to late age, individuals exposed to low SES are less likely to have sustained executive network activity yet a greater likelihood to enhanced activity within reward-related regions. A similar activity was shown for gray matter volume across early to older age. These findings provide the first quantitative integration of neuroimaging results pertaining to the neural basis of SES. Hypoactivation of the executive network and hyperactivation of the reward network in low SES individuals may support the scarcity hypothesis and animal models of the effects of early adversity.

White-Matter Pathways for Statistical Learning of Temporal Structures.

Extracting the statistics of event streams in natural environments is critical for interpreting current events and predicting future ones. The brain is known to rapidly find structure and meaning in unfamiliar streams of sensory experience, often by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the brain pathways that support this type of statistical learning. Here, we test whether changes in white-matter (WM) connectivity due to training relate to our ability to extract temporal regularities. By combining behavioral training and diffusion tensor imaging (DTI), we demonstrate that humans adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. In particular, we show that learning relates to the decision strategy that individuals adopt when extracting temporal statistics. We next test for learning-dependent changes in WM connectivity and ask whether they relate to individual variability in decision strategy. Our DTI results provide evidence for dissociable WM pathways that relate to individual strategy: extracting the exact sequence statistics (i.e., matching) relates to connectivity changes between caudate and hippocampus, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to connectivity changes between prefrontal, cingulate and basal ganglia (caudate, putamen) regions. Thus, our findings provide evidence for distinct cortico-striatal circuits that show learning-dependent changes of WM connectivity and support individual ability to learn behaviorally-relevant statistics.

Spatial Embedding and Wiring Cost Constrain the Functional Layout of the Cortical Network of Rodents and Primates.

Mammals show a wide range of brain sizes, reflecting adaptation to diverse habitats. Comparing interareal cortical networks across brains of different sizes and mammalian orders provides robust information on evolutionarily preserved features and species-specific processing modalities. However, these networks are spatially embedded, directed, and weighted, making comparisons challenging. Using tract tracing data from macaque and mouse, we show the existence of a general organizational principle based on an exponential distance rule (EDR) and cortical geometry, enabling network comparisons within the same model framework. These comparisons reveal the existence of network invariants between mouse and macaque, exemplified in graph motif profiles and connection similarity indices, but also significant differences, such as fractionally smaller and much weaker long-distance connections in the macaque than in mouse. The latter lends credence to the prediction that long-distance cortico-cortical connections could be very weak in the much-expanded human cortex, implying an increased susceptibility to disconnection syndromes such as Alzheimer disease and schizophrenia. Finally, our data from tracer experiments involving only gray matter connections in the primary visual areas of both species show that an EDR holds at local scales as well (within 1.5 mm), supporting the hypothesis that it is a universally valid property across all scales and, possibly, across the mammalian class.

Review on Cowan WM, Gottlieb DI, Hendrickson AE, Price JL, Woolsey TA. 1972. The autoradiographic demonstration of axonal connections in the central nervous system. Brain Res 37: 21-51.

UNLABELLED: Axoplasmically transported proteins synthesized in neuronal somata labeled by radioactively labeled amino acids (tritium), following local targeted injections for tracing of pathways in the central nervous system using autoradiography. Results from a number of neuronal systems, including: the rat olfactory bulb; cortico-thalamic projections in the mouse; commissural connections of the rat hippocampus; and retinal projections in the monkey and chick are documented. Pathway origins are clear, as the number and distribution of the labeled cells and the normal structure of the injection site is preserved. Light and electron microscopic autoradiography shows that proteins are transported, at two rates: rapid transport (>100mm/day) of fewer proteins accumulating in axon terminals; and, slow transport (1-5mm/day) of the bulk of labeled proteins distributed along the length of axons. Different survival times can be selected to evaluate terminal projection field(s) or pathways from origin to termination. The clarity of autoradiographic labeling of pathways and their terminations is comparable to other techniques (such as the Nauta-Gygax and the Fink-Heimer methods and the electron microscopy of terminal degeneration). Labeled amino acids do not label molecules in fibers of passage and there is no retrograde transport of labeled material from the axon terminals. The functional polarity of fiber pathways can be easily established. We summarize the merits of this technique is based upon an established physiological properties of neurons that are summarized in contrast to currently used techniques dependent upon pathological changes in neurons, axons, or axonal terminals. ARTICLE ABSTRACT: This article considers a heavily cited Brain Research article that reported an extremely important turning point in the ability to demonstrate neuroanatomical pathways in the central nervous system. Using radioactive leucine microinjections into the brain, neurons synthesized proteins from this amino acid that were transported down their axons to the terminal synapses on the target neurons. Tracing the transport of the labeled protein by autoradiography permitted quantitative analysis of projections and pathways. As a result, pathway analysis was transformed from studying the degenerating processes of lesioned neurons to the study of intact pathways in non-manipulated brains. The classical protocol has since been widely applied and used to investigate countless brain circuits. This article is part of a Special Issue entitled SI:50th Anniversary Issue.

Neuromarkers of the common angiotensinogen polymorphism in healthy older adults: A comprehensive assessment of white matter integrity and cognition.

The common angiotensinogen (AGT) M268T polymorphism (rs699; historically referred to as M235T) has been identified as a significant risk factor for cerebrovascular pathologies, yet it is unclear if healthy older adults carrying the threonine amino acid variant have a greater risk for white matter damage in specific fiber tracts. Further, the impact of the threonine variant on cognitive function remains unknown. The present study utilized multiple indices of diffusion tensor imaging (DTI) and neuropsychological assessment to examine the integrity of specific white matter tracts and cognition between individuals with homozygous genotypes of M268T (MetMet n=27, ThrThr n=27). Differences in subcortical hyperintensity (SH) volume were also examined between groups. Results indicated that the threonine variant was associated with significantly reduced integrity in the superior longitudinal fasciculus (SLF) and the cingulate gyrus segment of the cingulum bundle (cingulum CG) compared to those with the methionine variant, and poorer cognitive performance on tests of attention/processing speed and language. Despite these associations, integrity of these tracts did not significantly mediate relationships between cognition and genetic status, and SH did not differ significantly between groups. Collectively our results suggest that the threonine variant of M268T is a significant risk factor for abnormalities in specific white matter tracts and cognitive domains in healthy older adults, independent of SH burden.

Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences.

Using diffusion MRI, a number of studies have investigated the properties of whole-brain white matter (WM) networks with differing network construction methods (node/edge definition). However, how the construction methods affect individual differences of WM networks and, particularly, if distinct methods can provide convergent or divergent patterns of individual differences remain largely unknown. Here, we applied 10 frequently used methods to construct whole-brain WM networks in a healthy young adult population (57 subjects), which involves two node definitions (low-resolution and high-resolution) and five edge definitions (binary, FA weighted, fiber-density weighted, length-corrected fiber-density weighted, and connectivity-probability weighted). For these WM networks, individual differences were systematically analyzed in three network aspects: (1) a spatial pattern of WM connections, (2) a spatial pattern of nodal efficiency, and (3) network global and local efficiencies. Intriguingly, we found that some of the network construction methods converged in terms of individual difference patterns, but diverged with other methods. Furthermore, the convergence/divergence between methods differed among network properties that were adopted to assess individual differences. Particularly, high-resolution WM networks with differing edge definitions showed convergent individual differences in the spatial pattern of both WM connections and nodal efficiency. For the network global and local efficiencies, low-resolution and high-resolution WM networks for most edge definitions consistently exhibited a highly convergent pattern in individual differences. Finally, the test-retest analysis revealed a decent temporal reproducibility for the patterns of between-method convergence/divergence. Together, the results of the present study demonstrated a measure-dependent effect of network construction methods on the individual difference of WM network properties.

Individual diversity of functional brain network economy.

On average, brain network economy represents a trade-off between communication efficiency, robustness, and connection cost, although an analogous understanding on an individual level is largely missing. Evaluating resting-state networks of 42 healthy participants with seven Tesla functional magnetic resonance imaging and graph theory revealed that not even half of all possible connections were common across subjects. The strongest similarities among individuals were observed for interhemispheric and/or short-range connections, which may relate to the essential feature of the human brain to develop specialized systems within each hemisphere. Despite this marked variability in individual network architecture, all subjects exhibited equal small-world properties. Furthermore, interdependency between four major network economy metrics was observed across healthy individuals. The characteristic path length was associated with the clustering coefficient (peak correlation r=0.93), the response to network attacks (r=-0.97), and the physical connection cost in three-dimensional space (r=-0.62). On the other hand, clustering was negatively related to attack response (r=-0.75) and connection cost (r=-0.59). Finally, increased connection cost was associated with better response to attacks (r=0.65). This indicates that functional brain networks with high global information transfer also exhibit strong network resilience. However, it seems that these advantages come at the cost of decreased local communication efficiency and increased physical connection cost. Except for wiring length, the results were replicated on a subsample at three Tesla (n=20). These findings highlight the finely tuned interrelationships between different parameters of brain network economy. Moreover, the understanding of the individual diversity of functional brain network economy may provide further insights in the vulnerability to mental and neurological disorders.

NSF workshop report: discovering general principles of nervous system organization by comparing brain maps across species.

Efforts to understand nervous system structure and function have received new impetus from the federal Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. Comparative analyses can contribute to this effort by leading to the discovery of general principles of neural circuit design, information processing, and gene-structure-function relationships that are not apparent from studies on single species. We here propose to extend the comparative approach to nervous system 'maps' comprising molecular, anatomical, and physiological data. This research will identify which neural features are likely to generalize across species, and which are unlikely to be broadly conserved. It will also suggest causal relationships between genes, development, adult anatomy, physiology, and, ultimately, behavior. These causal hypotheses can then be tested experimentally. Finally, insights from comparative research can inspire and guide technological development. To promote this research agenda, we recommend that teams of investigators coalesce around specific research questions and select a set of 'reference species' to anchor their comparative analyses. These reference species should be chosen not just for practical advantages, but also with regard for their phylogenetic position, behavioral repertoire, well-annotated genome, or other strategic reasons. We envision that the nervous systems of these reference species will be mapped in more detail than those of other species. The collected data may range from the molecular to the behavioral, depending on the research question. To integrate across levels of analysis and across species, standards for data collection, annotation, archiving, and distribution must be developed and respected. To that end, it will help to form networks or consortia of researchers and centers for science, technology, and education that focus on organized data collection, distribution, and training. These activities could be supported, at least in part, through existing mechanisms at NSF, NIH, and other agencies. It will also be important to develop new integrated software and database systems for cross-species data analyses. Multidisciplinary efforts to develop such analytical tools should be supported financially. Finally, training opportunities should be created to stimulate multidisciplinary, integrative research into brain structure, function, and evolution.

Structural and functional aspects relating to cost and benefit of rich club organization in the human cerebral cortex.

Recent findings have demonstrated that a small set of highly connected brain regions may play a central role in enabling efficient communication between cortical regions, together forming a densely interconnected "rich club." However, the density and spatial layout of the rich club also suggest that it constitutes a costly feature of brain architecture. Here, combining anatomical T1, diffusion tensor imaging, magnetic transfer imaging, and functional MRI, several aspects of structural and functional connectivity of the brain's rich club were examined. Our findings suggest that rich club regions and rich club connections exhibit high levels of wiring volume, high levels of white matter organization, high levels of metabolic energy usage, long maturational trajectories, more variable regional time series, and more inter-regional functional couplings. Taken together, these structural and functional measures extend the notion that rich club organization represents a high-cost feature of brain architecture that puts a significant strain on brain resources. The high cost of the rich club may, however, be offset by significant functional benefits that the rich club confers to the brain network as a whole.

Quantitative assessment of a framework for creating anatomical brain networks via global tractography.

Interregional connections of the brain measured with diffusion tractography can be used to infer valuable information regarding both brain structure and function. However, different tractography algorithms can generate networks that exhibit different characteristics, resulting in poor reproducibility across studies. Therefore, it is important to benchmark different tractography algorithms to quantitatively assess their performance. Here we systematically evaluated a newly introduced tracking algorithm, global tractography, to derive anatomical brain networks in a fiber phantom, 2 post-mortem macaque brains, and 20 living humans, and compared the results with an established local tracking algorithm. Our results demonstrated that global tractography accurately characterized the phantom network in terms of graph-theoretic measures, and significantly outperformed the local tracking approach. Results in brain tissues (post-mortem macaques and in vivo humans), however, showed that although the performance of global tractography demonstrated a trend of improvement, the results were not vastly different than that of local tractography, possibly resulting from the increased fiber complexity of real tissues. When using macaque tracer-derived connections as the ground truth, we found that both global and local algorithms generated non-random patterns of false negative and false positive connections that were probably related to specific fiber systems and largely independent of the tractography algorithm or tissue type (post-mortem vs. in vivo) used in the current study. Moreover, a close examination of the transcallosal motor connections, reconstructed via either global or local tractography, demonstrated that the lateral transcallosal fibers in humans and macaques did not exhibit the denser homotopic connections found in primate tracer studies, indicating the need for more robust brain mapping techniques based on diffusion MRI data.

Direct segmentation of the major white matter tracts in diffusion tensor images.

Diffusion-weighted images of the human brain are acquired more and more routinely in clinical research settings, yet segmenting and labeling white matter tracts in these images is still challenging. We present in this paper a fully automated method to extract many anatomical tracts at once on diffusion tensor images, based on a Markov random field model and anatomical priors. The approach provides a direct voxel labeling, models explicitly fiber crossings and can handle white matter lesions. Experiments on simulations and repeatability studies show robustness to noise and reproducibility of the algorithm, which has been made publicly available.

Improved assessment of ex vivo brainstem neuroanatomy with high-resolution MRI and DTI at 7 Tesla.

The aim of the present work was to provide the topography of the main gray nuclei and white matter tracts of the human brainstem at 7 Tesla (7 T) high-field magnetic resonance imaging (MRI) using structural imaging (T1) and diffusion tensor imaging (DTI). Both imaging techniques represent a new field of increasing interest for its potential neuroanatomic and neuropathologic value. Brainstems were obtained postmortem from human donors, fixated by intracarotid perfusion of 10% neutral buffered formalin, and scanned in a Bruker BioSpec 7 T horizontal scanner. 3D-data sets were acquired using the modified driven equilibrium Fourier transform (MDEFT) sequence and Spin Echo-DTI (SE-DTI) sequence was used for DTI acquisition. High-resolution structural MRI and DTI of the human brainstem acquired postmortem reveals its basic cyto- and myeloar-chitectonic organization, only visualized to this moment by histological techniques and higher magnetic field strengths. Brainstem structures that are usually not observed with lower magnetic fields were now topographically identified at midbrain, pons, and medullar levels. The application of high-resolution structural MRI will contribute to precisely determine the extension and topography of brain lesions. Indeed, the current findings will be useful to interpret future high-resolution in vivo MRI studies in living humans.

Effective connectivity: influence, causality and biophysical modeling.

This is the final paper in a Comments and Controversies series dedicated to "The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution". We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener-Akaike-Granger-Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments.

FADTTS: functional analysis of diffusion tensor tract statistics.

The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying associations between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate the understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modeling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands. However, FADTTS is not crucial for estimation and reduces to the functional analysis method for the single measure.

LoAd: a locally adaptive cortical segmentation algorithm.

Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of diseases such as Alzheimer's, Huntington's, and schizophrenia. Methods that measure the thickness of the cerebral cortex from in-vivo magnetic resonance (MR) images rely on an accurate segmentation of the MR data. However, segmenting the cortex in a robust and accurate way still poses a challenge due to the presence of noise, intensity non-uniformity, partial volume effects, the limited resolution of MRI and the highly convoluted shape of the cortical folds. Beginning with a well-established probabilistic segmentation model with anatomical tissue priors, we propose three post-processing refinements: a novel modification of the prior information to reduce segmentation bias; introduction of explicit partial volume classes; and a locally varying MRF-based model for enhancement of sulci and gyri. Experiments performed on a new digital phantom, on BrainWeb data and on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) show statistically significant improvements in Dice scores and PV estimation (p<10(-3)) and also increased thickness estimation accuracy when compared to three well established techniques.

Statistical analysis of minimum cost path based structural brain connectivity.

Diffusion MRI can be used to study the structural connectivity within the brain. Brain connectivity is often represented by a binary network whose topology can be studied using graph theory. We present a framework for the construction of weighted structural brain networks, containing information about connectivity, which can be effectively analyzed using statistical methods. Network nodes are defined by segmentation of subcortical structures and by cortical parcellation. Connectivity is established using a minimum cost path (mcp) method with an anisotropic local cost function based directly on diffusion weighted images. We refer to this framework as Statistical Analysis of Minimum cost path based Structural Connectivity (SAMSCo) and the weighted structural connectivity networks as mcp-networks. In a proof of principle study we investigated the information contained in mcp-networks by predicting subject age based on the mcp-networks of a group of 974 middle-aged and elderly subjects. Using SAMSCo, age was predicted with an average error of 3.7 years. This was significantly better than predictions based on fractional anisotropy or mean diffusivity averaged over the whole white matter or over the corpus callosum, which showed average prediction errors of at least 4.8 years. Additionally, we classified subjects, based on the mcp-networks, into groups with low and high white matter lesion load, while correcting for age, sex and white matter atrophy. The SAMSCo classification outperformed the classification based on the diffusion measures with a classification accuracy of 76.0% versus 63.2%. We also performed a classification in groups with mild and severe atrophy, correcting for age, sex and white matter lesion load. In this case, mcp-networks and diffusion measures yielded similar classification accuracies of 68.3% and 67.8% respectively. The SAMSCo prediction and classification experiments indicate that the mcp-networks contain information regarding age, white matter lesion load and white matter atrophy, and that in case of age and white matter lesion load the mcp-network based models outperformed the predictions based on diffusion measures.

Effective connectivity of fMRI data using ancestral graph theory: dealing with missing regions.

Most of the current methods to assess effective connectivity from functional magnetic resonance imaging (fMRI) rely on the assumption that all relevant brain regions are entered into the analysis. If this assumption is untenable, which we believe is most often the case, then spurious connections between brain regions can appear. In this paper we propose to use an ancestral graph to model connectivity, which provides a way to avoid spurious connections. The ancestral graph is determined from trial-by-trial variation and not from the time series. A random effects model is defined for ancestral graphs which allows for individual differences in terms of graph parameters (e.g., connection strength). Procedures for model selection, model fit, and hypothesis testing of ancestral graphs are proposed. The hypothesis test can be used to find differences in connection strength between, for example, conditions. Monte Carlo simulations show that the ancestral graph is appropriate to model connectivity from fMRI condition specific trial data. To assess the accuracy further, the proposed method is applied to real fMRI data to determine how brain regions interact during speech monitoring.

Assessment of functional and structural connectivity between motor cortex and thalamus using fMRI and DWI.

Connectivity evaluations have been performed in a noninvasive manner by examining resting state fMRI alongside diffusion-weighted images (DWI). The spatial structures of coherent spontaneous BOLD fluctuations provided the most convincing preliminary evidence that the BOLD signal was predominantly of neuronal origin rather than non-neuronal, artifactual noise. In this study we have shown that in thalamocortical network, the results of functional connectivity analysis and DWI correspond well with each other, thereby providing cross-validation of the two techniques. We have used the resting state fMRI data of 3 subjects with 10 minute resting state functional images via a 3T Siemens scanner. we used cross correlation for functional analysis and reported thalamocortical results with p value=0.01 and cluster size=100, Then showed corresponding tracts connecting premotor cortex and thalamus. In addition, both techniques correspond well to histological delineation and invasive tract tracing, which provides a 'gold standard' validation of the two techniques. The degree of structural connectivity has been shown to correlate with the strength of functional connectivity, thereby providing a potentially straightforward structural explanation for many of the changes in functional connectivity in disease states.

Independent component analysis-based multifiber streamline tractography of the human brain.

An independent component analysis-based approach has been developed to estimate the orientations of two or three crossing fibers in a voxel to conduct human brain streamline tractography from diffusion data acquired along 25 gradient directions at a b-value of 1000 sec/mm(2) . The approach relies on unmixing signals from fibers mixed within, and spread over, a small cluster of 11 voxels. Simulation studies of diffusion data for two or three crossing fibers at signal-to-noise ratios of 15 and 30 suggest the accuracy to determine interfiber angles with independent component analysis is similar to that attained by a gaussian mixture and other multicompartmental models but at two orders of magnitude faster computational speed. Compared to previous multicompartmental models, independent component analysis visually shows good recovery of fiber orientations and tracts in the crossing region of commonly available orthogonal and 60° phantom diffusion datasets. A 3T MRI human studies show that in contrast to conventional streamline tractography and a multicompartment model, independent component analysis shows better recovery of the continuity of fronto-occipital tracts and cingulum from regions where these tracts are mixed with corpus callosum and other pathways.

Opportunities and pitfalls in the quantification of fiber integrity: what can we gain from Q-ball imaging?

The quantification of fiber integrity is central to the clinical application of diffusion imaging. Compared to diffusion tensor imaging (DTI), Q-ball imaging (QBI) allows for the depiction of multiple fiber directions within a voxel. However, this advantage has not yet been shown to translate directly to superior quantification of fiber integrity. Furthermore, recent developments in QBI reconstruction with solid angle consideration have led to sharper and intrinsically normalized orientation distribution functions. The implications of this technique on quantification are also unknown. To investigate this, the generalized fractional anisotropy (GFA) from the original and the more recent QBI reconstruction scheme and the DTI derived fractional anisotropy (FA) were evaluated comparatively using Monte Carlo simulations and real MRI measurements of crossing fiber phantoms. Contrast-to-noise ratio, accuracy, independence of the acquisition setup and the relation of single fiber anisotropies to measured anisotropy in crossings were assessed. In homogeneous single-fiber regions at b-values around 1000 s/mm2, the FA performed best. While the original QBI reconstruction does not show a clear advantage even at higher b-values and in crossing regions, the new reconstruction scheme yields superior properties and is recommended for quantification at higher b-values and especially in regions of heterogeneous fiber configuration.