Changes in anatomical brain connectivity between ages 12 and 30: A HARDI study of 467 adolescents and adults

Graph theory can be applied to matrices that represent the brain's anatomical connections, to better understand global properties of anatomical networks, such as their clustering, efficiency and "small-world" topology. Network analysis is popular in adult studies of connectivity, but only one study - in just 30 subjects - has examined how network measures change as the brain develops over this period. Here we assessed the developmental trajectory of graph theory metrics of structural brain connectivity in a cross-sectional study of 467 subjects, aged 12 to 30. We computed network measures from 70×70 connectivity matrices of fiber density generated using whole-brain tractography in 4-Tesla 105-gradient high angular resolution diffusion images (HARDI). We assessed global efficiency and modularity, and both age and age 2 effects were identified. HARDI-based connectivity maps are sensitive to the remodeling and refinement of structural brain connections as the human brain develops.

Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, highangular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

White matter abnormalities associated with Alzheimer's disease and mild cognitive impairment: A critical review of MRI studies

In this article, the authors aim to present a critical review of recent MRI studies addressing white matter (WM) abnormalities in Alzheimer's disease (AD) and mild cognitive impairment (MCI), by searching PubMed and reviewing MRI studies evaluating subjects with AD or MCI using WM volumetric methods, diffusion tensor imaging and assessment of WM hyperintensities. Studies have found that, compared with healthy controls, AD and MCI samples display WM volumetric reductions and diffusion tensor imaging findings suggestive of reduced WM integrity. These changes affect complex networks relevant to episodic memory and other cognitive processes, including fiber connections that directly link medial temporal structures and the corpus callosum. Abnormalities in cortico-cortical and cortico-subcortical WM interconnections are associated with an increased risk of progression from MCI to dementia. It can be concluded that WM abnormalities are detectable in early stages of AD and MCI. Degeneration of WM networks causes disconnection among neural cells and the degree of such changes is related to cognitive decline. © 2013 2013 Expert Reviews Ltd.

Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults

Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain's anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12 year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16 year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4 T. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant's co-registered anatomical scans. The proportion of fiber connections between all pairs of cortical regions, or nodes, was found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70 × 70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.

Discovery of genes that affect human brain connectivity: A genome-wide analysis of the connectome

Human brain connectivity is disrupted in a wide range of disorders from Alzheimer's disease to autism but little is known about which specific genes affect it. Here we conducted a genome-wide association for connectivity matrices that capture information on the density of fiber connections between 70 brain regions. We scanned a large twin cohort (N=366) with 4-Tesla high angular resolution diffusion imaging (105-gradient HARDI). Using whole brain HARDI tractography, we extracted a relatively sparse 70×70 matrix representing fiber density between all pairs of cortical regions automatically labeled in co-registered anatomical scans. Additive genetic factors accounted for 1-58% of the variance in connectivity between 90 (of 122) tested nodes. We discovered genome-wide significant associations between variants and connectivity. GWAS permutations at various levels of heritability, and split-sample replication, validated our genetic findings. The resulting genes may offer new leads for mechanisms influencing aberrant connectivity and neurodegeneration. © 2012 IEEE.

Genetics of path lengths in brain connectivity networks: HARDI-based maps in 457 adults

Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra's algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer's risk gene, CLU.

Investigating brain connectivity heritability in a twin study using diffusion imaging data

Heritability of brain anatomical connectivity has been studied with diffusion-weighted imaging (DWI) mainly by modeling each voxel's diffusion pattern as a tensor (e.g., to compute fractional anisotropy), but this method cannot accurately represent the many crossing connections present in the brain. We hypothesized that different brain networks (i.e., their component fibers) might have different heritability and we investigated brain connectivity using High Angular Resolution Diffusion Imaging (HARDI) in a cohort of twins comprising 328 subjects that included 70 pairs of monozygotic and 91 pairs of dizygotic twins. Water diffusion was modeled in each voxel with a Fiber Orientation Distribution (FOD) function to study heritability for multiple fiber orientations in each voxel. Precision was estimated in a test-retest experiment on a sub-cohort of 39 subjects. This was taken into account when computing heritability of FOD peaks using an ACE model on the monozygotic and dizygotic twins. Our results confirmed the overall heritability of the major white matter tracts but also identified differences in heritability between connectivity networks. Inter-hemispheric connections tended to be more heritable than intra-hemispheric and cortico-spinal connections. The highly heritable tracts were found to connect particular cortical regions, such as medial frontal cortices, postcentral, paracentral gyri, and the right hippocampus.

N-syndecan and HB-GAM in neural migration and differentiation : Modulation of growth factor activity in brain

The juvenile sea squirt wanders through the sea searching for a suitable rock or hunk of coral to cling to and make its home for life. For this task it has a rudimentary nervous system. When it finds its spot and takes root, it doesn't need its brain any more so it eats it. It's rather like getting tenure. Daniel C. Dennett (from Consciousness Explained, 1991) The little sea squirt needs its brain for a task that is very simple and short. When the task is completed, the sea squirt starts a new life in a vegetative state, after having a nourishing meal. The little brain is more tightly structured than our massive primate brains. The number of neurons is exact, no leeway in neural proliferation is tolerated. Each neuroblast migrates exactly to the correct position, and only a certain number of connections with the right companions is allowed. In comparison, growth of a mammalian brain is a merry mess. The reason is obvious: Squirt brain needs to perform only a few, predictable functions, before becoming waste. The more mobile and complex mammals engage their brains in tasks requiring quick adaptation and plasticity in a constantly changing environment. Although the regulation of nervous system development varies between species, many regulatory elements remain the same. For example, all multicellular animals possess a collection of proteoglycans (PG); proteins with attached, complex sugar chains called glycosaminoglycans (GAG). In development, PGs participate in the organization of the animal body, like in the construction of parts of the nervous system. The PGs capture water with their GAG chains, forming a biochemically active gel at the surface of the cell, and in the extracellular matrix (ECM). In the nervous system, this gel traps inside it different molecules: growth factors and ECM-associated proteins. They regulate the proliferation of neural stem cells (NSC), guide the migration of neurons, and coordinate the formation of neuronal connections. In this work I have followed the role of two molecules contributing to the complexity of mammalian brain development. N-syndecan is a transmembrane heparan sulfate proteoglycan (HSPG) with cell signaling functions. Heparin-binding growth-associated molecule (HB-GAM) is an ECM-associated protein with high expression in the perinatal nervous system, and high affinity to HS and heparin. N-syndecan is a receptor for several growth factors and for HB-GAM. HB-GAM induces specific signaling via N-syndecan, activating c-Src, calcium/calmodulin-dependent serine protein kinase (CASK) and cortactin. By studying the gene knockouts of HB-GAM and N-syndecan in mice, I have found that HB-GAM and N-syndecan are involved as a receptor-ligand-pair in neural migration and differentiation. HB-GAM competes with the growth factors fibriblast growth factor (FGF)-2 and heparin-binding epidermal growth factor (HB-EGF) in HS-binding, causing NSCs to stop proliferation and to differentiate, and affects HB-EGF-induced EGF receptor (EGFR) signaling in neural cells during migration. N-syndecan signaling affects the motility of young neurons, by boosting EGFR-mediated cell migration. In addition, these two receptors form a complex at the surface of the neurons, probably creating a motility-regulating structure.

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.

A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data.

Interest in structural brain connectivity has grown with the understanding that abnormal neural connections may play a role in neurologic and psychiatric diseases. Small animal connectivity mapping techniques are particularly important for identifying aberrant connectivity in disease models. Diffusion magnetic resonance imaging tractography can provide nondestructive, 3D, brain-wide connectivity maps, but has historically been limited by low spatial resolution, low signal-to-noise ratio, and the difficulty in estimating multiple fiber orientations within a single image voxel. Small animal diffusion tractography can be substantially improved through the combination of ex vivo MRI with exogenous contrast agents, advanced diffusion acquisition and reconstruction techniques, and probabilistic fiber tracking. Here, we present a comprehensive, probabilistic tractography connectome of the mouse brain at microscopic resolution, and a comparison of these data with a neuronal tracer-based connectivity data from the Allen Brain Atlas. This work serves as a reference database for future tractography studies in the mouse brain, and demonstrates the fundamental differences between tractography and neuronal tracer data.

Neuronal Expression of Cd36, Cd44, and Cd83 Antigen Transcripts Maps to Distinct and Specific Murine Brain Circuits

Cells recruited by the innate immune response rely on surface-expressed molecules in order to receive signals from the local environment and to perform phagocytosis, cell adhesion, and others processes linked to host defense. Hundreds of surface antigens designated through a cluster of differentiation (CD) number have been used to identify particular populations of leukocytes. Surprisingly, we verified that the genes that encode Cd36 and Cd83 are constitutively expressed in specific neuronal cells. For instance, Cd36 mRNA is expressed in some regions related to circuitry involved in pheromone responses and reproductive behavior. Cd44 expression, reanalyzed and detailed here, is associated with the laminar formation and midline thalamic nuclei in addition to striatum, extended amygdala, and a few hypothalamic, cortical, and hippocampal regions. A systemic immune challenge was able to increase Cd44 expression quickly in the area postrema and motor nucleus of the vagus but not in regions presenting expressive constitutive expression. In contrast to Cd36 and Cd44, Cd83 message was widely distributed from the olfactory bulb to the brain stem reticular formation, sparing the striatopallidum, olivary region, and cerebellum. Its pattern of expression nevertheless remained strongly associated with hypothalamic, thalamic, and hindbrain nuclei. Unlike the other transcripts, Cd83 mRNA was rapidly modulated by restraint stress. Our results indicate that these molecules might play a role in specific neural circuits and present functions other than those attributed to leukocyte biology. The data also suggest that these surface proteins, or their associated mRNA, could be used to label neurons in specific circuits/regions. J. Comp. Neurol. 517:906-924, 2009. (C) 2009 Wiley-Liss, Inc.

The anatomical connections of the macaque monkey orbitofrontal cortex. A review

The orbitofrontal cortex (OfC) is a heterogeneous prefrontal sector selectively connected with a wide constellation of other prefrontal, limbic, sensory and premotor areas. Among the limbic cortical connections, the ones with the bippocampus and parabippocampal cortex are particularly salient. Sensory cortices connected with the OfC include areas involved in olfactory, gustatory, somatosensory, auditory and visual processing. Subcortical structures with prominent OfC connections include the amygdala, numerous thalamic nuclei, the striatum, hypothalamus, periaqueductal gray matter, and biochemically specific cell groups in the basal forebrain and brainstem. Architectonic and connectional evidence supports parcellation of the OfC. The rostrally placed isocortical sector is mainly connected with isocortical areas, including sensory areas of the auditory, somatic and visual modalities, whereas the caudal non-isocortical sector is principally connected with non-isocortical areas, and, in the sensory domain, with olfactory and gustatory areas. The connections of the isocortical and non- isocortical orbital sectors with the amygdala, thalamus, striatum, hypotbalamus and periaqueductal gray matter are also specific. The medial sector of the OfC is selectively connected with the bippocampus, posterior parabippocampal cortex, posterior cingulate and retrosplenial areas, and area prostriata, while the lateral orbitofrontal sector is the most heavily connected with sensory areas of the gustatory, somatic and visual modalities, with premotor regions, and with the amygdala.

Brain Pathways Involved in the Modulatory Effects of Noradrenaline in Lateral Septal Area on Cardiovascular Responses

We have previously reported that stimulation of alpha-1 adrenoceptors by noradrenaline (NA) injected into the lateral septal area (LSA) of anaesthetized rats causes pressor and bradycardic responses that are mediated by acute vasopressin release into the circulation through activation of the paraventricular nucleus (PVN). Although the PVN is the final structure of this pathway, the LSA has no direct connections with the PVN, suggesting that other structures may connect these areas. To address this issue, the present study employed c-Fos immunohistochemistry to investigate changes caused by NA microinjection into the LSA in neuronal activation in brain structures related to systemic vasopressin release. NA microinjected in the LSA caused pressor and bradycardic responses, which were blocked by intraseptal administration of alpha-1 adrenoceptor antagonist (WB4101, 10 nmol/200 nL) or systemic V-1 receptor antagonist (dTyr(CH2)5(Me)AVP, 50 mu g/kg). NA also increased c-Fos immunoreactivity in the prelimbic cortex (PL), infralimbic cortex (IL), dorsomedial periaqueductal gray (dmPAG), bed nucleus of the stria terminalis (BNST), PVN, and medial amygdala (MeA). No differences in the diagonal band of Broca, cingulate cortex, and dorsolateral periaqueductal gray (dlPAG) were found. Systemic administration of the vasopressin receptor antagonist dTyr AVP (CH2)5(Me) did not change the increase in c-Fos expression induced by intra-septal NA. This latter effect, however, was prevented by local injection of the alpha-1 adrenoceptor antagonist WB4101. These results suggest that areas such as the PL, IL, dmPAG, BNST, MeA, and PVN could be part of a circuit responsible for vasopressin release after activation of alpha-1 adrenoceptors in the LSA.

A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Abstract Background Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ‘‘hubs’’ for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.

Role of BDNF secretion and signaling in the activity-dependent refinement of synaptic connections

Neuronal networks exhibit diverse types of plasticity, including the activity-dependent regulation of synaptic functions and refinement of synaptic connections. In addition, continuous generation of new neurons in the “adult” brain (adult neurogenesis) represents a powerful form of structural plasticity establishing new connections and possibly implementing pre-existing neuronal circuits (Kempermann et al, 2000; Ming and Song, 2005). Neurotrophins, a family of neuronal growth factors, are crucially involved in the modulation of activity-dependent neuronal plasticity. The first evidence for the physiological importance of this role evolved from the observations that the local administration of neurotrophins has dramatic effects on the activity-dependent refinement of synaptic connections in the visual cortex (McAllister et al, 1999; Berardi et al, 2000; Thoenen, 1995). Moreover, the local availability of critical amounts of neurotrophins appears to be relevant for the ability of hippocampal neurons to undergo long-term potentiation (LTP) of the synaptic transmission (Lu, 2004; Aicardi et al, 2004). To achieve a comprehensive understanding of the modulatory role of neurotrophins in integrated neuronal systems, informations on the mechanisms about local neurotrophins synthesis and secretion as well as ditribution of their cognate receptors are of crucial importance. In the first part of this doctoral thesis I have used electrophysiological approaches and real-time imaging tecniques to investigate additional features about the regulation of neurotrophins secretion, namely the capability of the neurotrophin brain-derived neurotrophic factor (BDNF) to undergo synaptic recycling. In cortical and hippocampal slices as well as in dissociated cell cultures, neuronal activity rapidly enhances the neuronal expression and secretion of BDNF which is subsequently taken up by neurons themselves but also by perineuronal astrocytes, through the selective activation of BDNF receptors. Moreover, internalized BDNF becomes part of the releasable source of the neurotrophin, which is promptly recruited for activity-dependent recycling. Thus, we described for the first time that neurons and astrocytes contain an endocytic compartment competent for BDNF recycling, suggesting a specialized form of bidirectional communication between neurons and glia. The mechanism of BDNF recycling is reminiscent of that for neurotransmitters and identifies BDNF as a new modulator implicated in neuro- and glio-transmission. In the second part of this doctoral thesis I addressed the role of BDNF signaling in adult hippocampal neurogenesis. I have generated a transgenic mouse model to specifically investigate the influence of BDNF signaling on the generation, differentiation, survival and connectivity of newborn neurons into the adult hippocampal network. I demonstrated that the survival of newborn neurons critically depends on the activation of the BDNF receptor TrkB. The TrkB-dependent decision regarding life or death in these newborn neurons takes place right at the transition point of their morphological and functional maturation Before newborn neurons start to die, they exhibit a drastic reduction in dendritic complexity and spine density compared to wild-type newborn neurons, indicating that this receptor is required for the connectivity of newborn neurons. Both the failure to become integrated and subsequent dying lead to impaired LTP. Finally, mice lacking a functional TrkB in the restricted population of newborn neurons show behavioral deficits, namely increased anxiety-like behavior. These data suggest that the integration and establishment of proper connections by newly generated neurons into the pre-existing network are relevant features for regulating the emotional state of the animal.