Relating connectional architecture to grey matter function using diffusion imaging

Understanding brain function in terms of connectional architecture is a major goal of neuroimaging. However, direct investigation of the influence of brain circuitry on function has been hindered by the lack of a technique for exploring anatomical connectivity in the in vivo brain. Recent advances in magnetic resonance diffusion imaging have given scientists access to data relating to local white matter architecture and, for the first time, have raised the possibility of in vivo investigations into brain circuitry. This review investigates whether diffusion imaging may be used to identify regions of grey matter that are distinct in their connectional architecture, and whether these connectional differences are reflected either in local cytoarchitecture or in local grey matter function.Establishing a direct relationship between regional boundaries based on diffusion imaging and borders between regions that perform different functions would not only be of great significance when interpreting functional results, but would also provide a first step towards the validation of diffusion-based anatomical connectivity studies.     

Gene expression in the rodent brain is associated with its regional connectivity

The putative link between gene expression of brain regions and their neural connectivity patterns is a fundamental question in neuroscience. Here this question is addressed in the first large scale study of a prototypical mammalian rodent brain, using a combination of rat brain regional connectivity data with gene expression of the mouse brain. Remarkably, even though this study uses data from two different rodent species (due to the data limitations), we still find that the connectivity of the majority of brain regions is highly predictable from their gene expression levels-the outgoing (incoming) connectivity is successfully predicted for 73% (56%) of brain regions, with an overall fairly marked accuracy level of 0.79 (0.83). Many genes are found to play a part in predicting both the incoming and outgoing connectivity (241 out of the 500 top selected genes, p-value<1e-5). Reassuringly, the genes previously known from the literature to be involved in axon guidance do carry significant information about regional brain connectivity. Surveying the genes known to be associated with the pathogenesis of several brain disorders, we find that those associated with schizophrenia, autism and attention deficit disorder are the most highly enriched in the connectivity-related genes identified here. Finally, we find that the profile of functional annotation groups that are associated with regional connectivity in the rodent is significantly correlated with the annotation profile of genes previously found to determine neural connectivity in C. elegans (Pearson correlation of 0.24, p<1e-6 for the outgoing connections and 0.27, p<1e-5 for the incoming). Overall, the association between connectivity and gene expression in a specific extant rodent species' brain is likely to be even stronger than found here, given the limitations of current data.     

Associations between age and gray matter volume in anatomical brain networks in middle‐aged to older adults

Aging is associated with cognitive decline, diminished brain function, regional brain atrophy, and disrupted structural and functional brain connectivity. Understanding brain networks in aging is essential, as brain function depends on large‐scale distributed networks. Little is known of structural covariance networks to study inter‐regional gray matter anatomical associations in aging. Here, we investigate anatomical brain networks based on structural covariance of gray matter volume among 370 middle‐aged to older adults of 45–85 years. For each of 370 subjects, we acquired a T1‐weighted anatomical MRI scan. After segmentation of structural MRI scans, nine anatomical networks were defined based on structural covariance of gray matter volume among subjects. We analyzed associations between age and gray matter volume in anatomical networks using linear regression analyses. Age was negatively associated with gray matter volume in four anatomical networks (P < 0.001, corrected): a subcortical network, sensorimotor network, posterior cingulate network, and an anterior cingulate network. Age was not significantly associated with gray matter volume in five networks: temporal network, auditory network, and three cerebellar networks. These results were independent of gender and white matter hyperintensities. Gray matter volume decreases with age in networks containing subcortical structures, sensorimotor structures, posterior, and anterior cingulate cortices. Gray matter volume in temporal, auditory, and cerebellar networks remains relatively unaffected with advancing age.     

Multiplicity of control in the basal ganglia: computational roles of striatal subregions

The basal ganglia, in particular the striatum, are central to theories of behavioral control, and often identified as a seat of action selection. Reinforcement learning (RL) models--which have driven much recent experimental work on this region--cast striatum as a dynamic controller, integrating sensory and motivational information to construct efficient and enriching behavioral policies. Befitting this informationally central role, the BG sit at the nexus of multiple anatomical 'loops' of synaptic projections, connecting a wide range of cortical and subcortical structures. Numerous pioneering anatomical studies conducted over the past several decades have meticulously catalogued these loops, and labeled them according to the inferred functions of the connected regions. The specific cotermina of the projections are highly localized to several different subregions of the striatum, leading to the suggestion that these subregions perform complementary but distinct functions. However, until recently, the dominant computational framework outlined only a bipartite, dorsal/ventral, division of striatum. We review recent computational and experimental advances that argue for a more finely fractionated delineation. In particular, experimental data provide extensive insight into unique functions subserved by the dorsomedial striatum (DMS). These functions appear to correspond well with theories of a 'model-based' RL subunit, and may also shed light on the suborganization of ventral striatum. Finally, we discuss the limitations of these ideas and how they point the way toward future refinements of neurocomputational theories of striatal function, bringing them into contact with other areas of computational theory and other regions of the brain.     

Mathematical learning deficits originate in early childhood from atypical development of a frontoparietal brain network

Mathematical learning deficits are defined as a neurodevelopmental disorder (dyscalculia) in the International Classification of Diseases. It is not known, however, how such deficits emerge in the course of early brain development. Here, we conducted functional and structural magnetic resonance imaging (MRI) experiments in 3- to 6-year-old children without formal mathematical learning experience. We followed this sample until the age of 7 to 9 years, identified individuals who developed deficits, and matched them to a typically developing control group using comprehensive behavioral assessments. Multivariate pattern classification distinguished future cases from controls with up to 87% accuracy based on the regional functional activity of the right posterior parietal cortex (PPC), the network-level functional activity of the right dorsolateral prefrontal cortex (DLPFC), and the effective functional and structural connectivity of these regions. Our results indicate that mathematical learning deficits originate from atypical development of a frontoparietal network that is already detectable in early childhood.

Longitudinal neuroimaging of 3-6-year-old children reveals a predisposition for dyscalculia in early childhood originating from altered spontaneous activity, functional interaction and structural connectivity of a frontoparietal brain network.     

Functional Connectivity Alterations in Epilepsy from Resting-State Functional MRI

The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.     

Assessment of brain interactivity in the motor cortex from the concept of functional connectivity and spectral analysis of fMRI data

Functional magnetic resonance imaging (fMRI) was used to assess the contributions of movement preparation and execution of a visuomotor task in a cerebral motor network. The functional connectivity of the voxel time series between brain regions in the frequency space was investigated by performing spectral analysis of fMRI time series. The regional interactivities between the two portions of the supplementary motor area (pre-SMA and SMA-proper) and the primary motor cortex (M1), defined as a seed region, were evaluated. The spectral parameter of coherence was used to describe a correlation structure in the frequency domain between two voxel-based time series and to infer the strength of the functional interaction within our presumed motor network of connections. The results showed meaningful differences of the functional interactions between the two portions of the SMA and the M1 area depending on the task conditions. This approach demonstrated the existence of a functional dissociation between the pre-SMA and SMA-proper subregions. We therefore conclude that spectral analysis is useful for identifying functional interactions of brain regions and might provide a powerful tool to quantify changes in connectivity profiles associated with various components of an experimental task.     

Linking movement and reproductive history of brook trout to assess habitat connectivity in a heterogeneous stream network

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Beyond Cytoarchitectonics: The Internal and External Connectivity Structure of the Caudate Nucleus

While there is ample evidence on the functional and connectional differentiation of the caudate nucleus (CN), less is known about its potential microstructural subdivisions. However, this latter aspect is critical to the local information processing capabilities of the tissue. We applied diffusion MRI, a non-invasive in vivo method that has great potential for the exploration of the brain structure-behavior relationship, in order to characterize the local fiber structure in gray matter of the CN. We report novel evidence of a functionally meaningful structural tri-partition along the anterior-posterior axis of this region. The connectivity of the CN subregions is in line with connectivity evidence from earlier invasive studies in animal models. In addition, histological validation using polarized light imaging (PLI) confirms these results, corroborating the notion that cortico-subcortico-cortical loops involve microstructurally differentiated regions in the caudate nucleus. Methodologically speaking, the comparison with advanced analysis of diffusion MRI shows that diffusion tensor imaging (DTI) yields a simplified view of the CN fiber architecture which is refined by advanced high angular resolution imaging methods.     

Neural mechanisms underlying motivation of mental versus physical effort

Mental and physical efforts, such as paying attention and lifting weights, have been shown to involve different brain systems. These cognitive and motor systems, respectively, include cortical networks (prefronto-parietal and precentral regions) as well as subregions of the dorsal basal ganglia (caudate and putamen). Both systems appeared sensitive to incentive motivation: their activity increases when we work for higher rewards. Another brain system, including the ventral prefrontal cortex and the ventral basal ganglia, has been implicated in encoding expected rewards. How this motivational system drives the cognitive and motor systems remains poorly understood. More specifically, it is unclear whether cognitive and motor systems can be driven by a common motivational center or if they are driven by distinct, dedicated motivational modules. To address this issue, we used functional MRI to scan healthy participants while performing a task in which incentive motivation, cognitive, and motor demands were varied independently. We reasoned that a common motivational node should (1) represent the reward expected from effort exertion, (2) correlate with the performance attained, and (3) switch effective connectivity between cognitive and motor regions depending on task demand. The ventral striatum fulfilled all three criteria and therefore qualified as a common motivational node capable of driving both cognitive and motor regions of the dorsal striatum. Thus, we suggest that the interaction between a common motivational system and the different task-specific systems underpinning behavioral performance might occur within the basal ganglia.     

Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity

Background

Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.

Methodology/Principal Findings

In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.

Conclusions/Significance

The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty.     

Range‐wide population structure of European sea bass Dicentrarchus labrax

The euryhaline European sea bass Dicentrarchus labrax L., inhabiting the coasts of the eastern Atlantic Ocean and Mediterranean Sea, has had many opportunities for differentiation throughout its large natural range. However, evidence for this has been incompletely documented geographically and with an insufficient number of markers. Therefore, its full range was sampled at 22 sites and individuals were genotyped with a suite of mapped markers, including 14 microsatellite loci (N = 536) and 46 neutral or gene‐linked single nucleotide polymorphisms (SNPs; N = 644). We confirm that the Atlantic and Mediterranean basins harbour two distinct lineages. Within the Atlantic Ocean no pattern was obvious based on the microsatellite and SNP genotypes, except for a subtle difference between South‐eastern and North‐eastern Atlantic sea bass attributed to limited introgression of alleles of Mediterranean origin. SNP genotypes of the Mediterranean lineage differentiated into three groups, probably under the influence of geographical isolation. The Western Mediterranean group showed genetic homogeneity without evidence for outlier loci. The Adriatic group appeared as a distinct unit. The Eastern Mediterranean group showed a longitudinal gradient of genotypes and most interestingly an outlier locus linked to the somatolactin gene. Overall, the spatial pattern fits those observed with other taxa of between‐basin segregation and within‐basin connectivity, which concurs well with the swimming capabilities of European sea bass. Evidence from a few outlier loci in this and other studies encourages further exploration of its regional connectivity and adaptive evolution.     

Reconsidering connectivity in the sub‐Antarctic

Extreme and remote environments provide useful settings to test ideas about the ecological and evolutionary drivers of biological diversity. In the sub‐Antarctic, isolation by geographic, geological and glaciological processes has long been thought to underpin patterns in the region's terrestrial and marine diversity. Molecular studies using increasingly high‐resolution data are, however, challenging this perspective, demonstrating that many taxa disperse among distant sub‐Antarctic landmasses. Here, we reconsider connectivity in the sub‐Antarctic region, identifying which taxa are relatively isolated, which are well connected, and the scales across which this connectivity occurs in both terrestrial and marine systems. Although many organisms show evidence of occasional long‐distance, trans‐oceanic dispersal, these events are often insufficient to maintain gene flow across the region. Species that do show evidence of connectivity across large distances include both active dispersers and more sedentary species. Overall, connectivity patterns in the sub‐Antarctic at intra‐ and inter‐island scales are highly complex, influenced by life‐history traits and local dynamics such as relative dispersal capacity and propagule pressure, natal philopatry, feeding associations, the extent of human exploitation, past climate cycles, contemporary climate, and physical barriers to movement. An increasing use of molecular data – particularly genomic data sets that can reveal fine‐scale patterns – and more effective international collaboration and communication that facilitates integration of data from across the sub‐Antarctic, are providing fresh insights into the processes driving patterns of diversity in the region. These insights offer a platform for assessing the ways in which changing dispersal mechanisms, such as through increasing human activity and changes to wind and ocean circulation, may alter sub‐Antarctic biodiversity patterns in the future.     

Generalized reliability based on distances

The intraclass correlation coefficient (ICC) is a classical index of measurement reliability. With the advent of new and complex types of data for which the ICC is not defined, there is a need for new ways to assess reliability. To meet this need, we propose a new distance‐based ICC (dbICC), defined in terms of arbitrary distances among observations. We introduce a bias correction to improve the coverage of bootstrap confidence intervals for the dbICC, and demonstrate its efficacy via simulation. We illustrate the proposed method by analyzing the test‐retest reliability of brain connectivity matrices derived from a set of repeated functional magnetic resonance imaging scans. The Spearman‐Brown formula, which shows how more intensive measurement increases reliability, is extended to encompass the dbICC.     

Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal

The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal‐borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under‐acknowledged consideration in biologging is the trade‐off between sampling rate and sampling duration, created by battery‐ (or memory‐) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low‐frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free‐ranging red squirrels (200–300 g) that were fitted with accelerometers (2 g) recording tri‐axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long‐duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi‐month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior.     

Phylogeography of the sergeants Abudefduf sexfasciatus and A. vaigiensis reveals complex introgression patterns between two widespread and sympatric Indo‐West Pacific reef fishes

On evolutionary timescales, sea level oscillations lead to recurrent spatio‐temporal variation in species distribution and population connectivity. In this situation, applying classical concepts of biogeography is challenging yet necessary to understand the mechanisms underlying biodiversity in highly diverse marine ecosystems such as coral reefs. We aimed at studying the outcomes of such complex biogeographic dynamics on reproductive isolation by sampling populations across a wide spatial range of a species‐rich fish genus: the sergeants (Pomacentridae: Abudefduf). We generated a mutlilocus data set that included ten morpho‐species from 32 Indo‐West Pacific localities. We observed a pattern of mito‐nuclear discordance in two common and widely distributed species: Abudefduf sexfasciatus and Abudefduf vaigiensis. The results showed three regional sublineages (Indian Ocean, Coral Triangle region, western Pacific) in A. sexfasciatus (0.6–1.5% divergence at cytb). The other species, A. vaigiensis, is polyphyletic and consists of three distinct genetic lineages (A, B and C) (9% divergence at cytb) whose geographic ranges overlap. Although A. vaigiensis A and A. sexfasciatus were found to be distinct based on nuclear information, A. vaigiensis A was found to be nested within A. sexfasciatus in the mitochondrial gene tree. A. sexfasciatus from the Coral Triangle region and A. vaigiensis A were not differentiated from each other at the mitochondrial locus. We then used coalescent‐based simulation to characterize a spatially widespread but weak gene flow between the two species. We showed that these fishes are good candidates to investigate the evolutionary complexity of the discrepancies between phenotypic and genetic similarity in closely related species.     

Regional differences in the morphological and functional effects of aging on cerebral basement membranes and perivascular drainage of amyloid‐β from the mouse brain

Development of cerebral amyloid angiopathy (CAA) and Alzheimer's disease (AD) is associated with failure of elimination of amyloid‐β (Aβ) from the brain along perivascular basement membranes that form the pathways for drainage of interstitial fluid and solutes from the brain. In transgenic APP mouse models of AD, the severity of cerebral amyloid angiopathy is greater in the cerebral cortex and hippocampus, intermediate in the thalamus, and least in the striatum. In this study we test the hypothesis that age‐related regional variation in (1) vascular basement membranes and (2) perivascular drainage of Aβ contribute to the different regional patterns of CAA in the mouse brain. Quantitative electron microscopy of the brains of 2‐, 7‐, and 23‐month‐old mice revealed significant age‐related thickening of capillary basement membranes in cerebral cortex, hippocampus, and thalamus, but not in the striatum. Results from Western blotting and immunocytochemistry experiments showed a significant reduction in collagen IV in the cortex and hippocampus with age and a reduction in laminin and nidogen 2 in the cortex and striatum. Injection of soluble Aβ into the hippocampus or thalamus showed an age‐related reduction in perivascular drainage from the hippocampus but not from the thalamus. The results of the study suggest that changes in vascular basement membranes and perivascular drainage with age differ between brain regions, in the mouse, in a manner that may help to explain the differential deposition of Aβ in the brain in AD and may facilitate development of improved therapeutic strategies to remove Aβ from the brain in AD.