The bounds of education in the human brain connectome

Inter-individual heterogeneity is evident in aging; education level is known to contribute for this heterogeneity. Using a cross-sectional study design and network inference applied to resting-state fMRI data, we show that aging was associated with decreased functional connectivity in a large cortical network. On the other hand, education level, as measured by years of formal education, produced an opposite effect on the long-term. These results demonstrate the increased brain efficiency in individuals with higher education level that may mitigate the impact of age on brain functional connectivity.

De l'IRM de diffusion au connectome.

Objectifs: Comprendre les principes physique de la diffusion. Comprendre le principe de mesure de la diffusion par IRM. Ccomprendre la relation entre la diffusion de l'eau en milieu biologique et l'organisation de la matière blanche. Comprendre comment cartographier la connectivité cérébrale par irm de diffusion. Messages à retenir: Les propriétés de diffusion du tissu cérébral sont conditionnées par l'architecture axonale. La mesure de la diffusion par IRM permet de cartographier les trajectoires de fibres nerveuses et donc la connectivité cérébrale. La connectivité cérébrale peut être mesurés de manière non-invasive. Résumé: La "connectomique" est un domaine émergeant et prometteur des neurosciences qui utilise l'IRM de diffusion en combinaison avec des traitements algorithmiques avancés afin de mesurer les trajectoires de faisceaux de fibres et la connectivité cérébrale permettant d'étudier l'organisation de la structure du réseau neuronal cérébral dans son ensemble. Lors de ce cours nous reverrons les méthodes rendant cette cartographie possible et exposerons les techniques d'analyse utilisées pour obtenir de nouvelles informations sur l'architecture cérébrale. Nous reverrons également un certains nombre d'exemple d'applications où la connectomique offre une nouvelle manière d'analyser et de comprendre le fonctionnement du cerveau normal ou malade.

Connectome alterations in schizophrenia

Introduction : DTI has proven to be an exquisite biomarker of tissue microstructure integrity. This technique has been successfully applied to schizophrenia in showing that fractional anisotropy (FA, a marker of white matter integrity) is diminished in several areas of the brain (Kyriakopoulos M et al (2008)). New ways of representing diffusion data emerged recently and achieved to create structural connectivity maps in healthy brains (Hagmann P et al. (2008)). These maps have the capacity to study alterations over the entire brain at the connection and network level. This is of high interest in complex disconnection diseases like schizophrenia. We report on the specific network alterations of schizophrenic patients. Methods : 13 patients with chronic schizophrenia were recruited from in-patient, day treatment, out-patient clinics. Comparison subjects were recruited and group-matched to patients on age, sex, handedness, and parental social economic-status. This study was approved by the local IRB and subjects had to give informed written consent. They were scanned with a 3T clinical MRI scanner. DTI and high-resolution anatomical T1w imaging were performed during the same session. The path from diffusion MRI to a multi-resolution structural connection matrices of the entire brain is a five steps process that was performed in a similar way as described in Hagmann P et al. (2008). (1) DTI and T1w MRI of the brain, (2) segmentation of white and gray matter, (3) white matter tractography, (4) segmentation of the cortex into 242 ROIs of equal surface area covering the entire cortex (Fig 1), (5) the connection network was constructed by measuring for each ROI to ROI connection the related average FA along the corresponding tract. Results : For every connection between 2 ROIs of the network we tested the hypothesis H0: "average FA along fiber pathway is larger or equal in patients than in controls". H0 was rejected for connections where average FA in a connection was significantly lower in patients than in controls. Threshold p-value was 0.01 corrected for multiple comparisons with false discovery rate. We identified consistently that temporal, occipito-temporal, precuneo-temporal as well as frontal inferior and precuneo-cingulate connections were altered (Fig 2: significant connections in yellow). This is in agreement with the known literature, which showed across several studies that FA is diminished in several areas of the brain. More precisely, abnormalities were reported in the prefrontal and temporal white matter and to some extent also in the parietal and occipital regions. The alterations reported in the literature specifically included the corpus callosum, the arcuate fasciculus and the cingulum bundle, which was the case here as well. In addition small world indexes are significantly reduced in patients (p<0.01) (Fig. 3). Conclusions : Using connectome mapping to characterize differences in structural connectivity between healthy and diseased subjects we were able to show widespread connectional alterations in schizophrenia patients and systematic small worldness decrease, which is a marker of network desorganization. More generally, we described a method that has the capacity to sensitively identify structure alterations in complex disconnection syndromes where lesions are widespread throughout the connectional network.

The connectome mapper: an open-source processing pipeline to map connectomes with MRI.

Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses. Our pipeline is written in Python and is freely available as open-source at www.cmtk.org.

Global and local connectivity changes in schizophrenia investigated by diffusion connectome

Background: New ways of representing diffusion data emerged recently and achieved to create structural connectivitymaps in healthy brains (Hagmann P et al. (2008)). These maps have the capacity to study alterations over the entire brain at the connection and network level. This is of high interest in complex disconnection diseases like schizophrenia. In this Pathology where multiple lines of evidence suggest the association of the pathology with abnormalities in neural circuitry and impaired structural connectivity, the diffusion imaging has been widely applied. Despite the large findings, most of the research using the diffusion just uses some scalar map derived from diffusion to show that some markers of white matter integrity are diminished in several areas of the brain (Kyriakopoulos M et al (2008)). Thanks to the structural connectionmatrix constructed by the whole brain tractography, we report in this work the network connectivity alterations in the schizophrenic patients. Methods: We investigated 13 schizophrenic patients as assessed by the DIGS (Diagnostic Interview for genetic studies, DSM IV criteria) and 13 healthy controls. We have got from each volunteer a DT-MRI as well as Qball imaging dataset and a high resolution anatomic T1 performed during the same session; with a 3 T clinical MRI scanner. The controls were matched on age, gender, handedness, and parental social economic-status. For all the subjects, a low resolution connection matrix is obtained by dividing the cortex into 66 gyral based ROIs. A higher resolution matrix is constructed using 250 ROIs as described in Hagmann P et al. (2008). These ROIs are respectively used jointly with the diffusion tractography to construct the high and low resolution densities connection matrices for each subject. In a first step the matrices of the groups are compared in term of connectivity, and not in term of density to check if the pathological group shows a loss of global connectivity. In this context the density connection matrices were binarized. As some local connectivity changes were also suspected, especially in frontal and temporal areas, we have also looked for the areas where the connectivity showed significant changes. Results: The statistical analysis revealed a significant loss of global connectivity in the schizophrenic's brains at level 5%. Furthermore, by constructing specific statistics which represent local connectivity within the anatomical regions (66 ROIs) using the data obtained by the finest resolution (250 ROIs) to improve the robustness, we found the regions that cause this significant loss of connectivity. The significance is observed after multiple testing corrections by the False Discovery Rate. Discussion: The detected regions are almost the same as those reported in the literature as the involved regions in schizophrenia. Most of the connectivity decreases are noted in both hemispheres in the fronto-frontal and temporo-temporal regions as well as some temporal ROIs with their adjacent ROIs in parietal and occipital lobes.

Towards a connectome mapping pipeline for neonates using high-resolution MP2RAGE and DTI

Exploring the anatomical and functional connectivities between different regions of the brain (the "Connectome") is a core challenge in neuroscience. While robust methods are available for the adult brain, mapping the connectome in neonates is highly challenging. The purpose of this pilot study is to present a methodological approach for analyzing structural connectivity of a neonate brain and to exploit the MP2RAGE sequence with its advantageous contrast properties

Explorer le cerveau avec l'imagerie du connectome.

Objectifs: comprendre que les connections cérébrales forment un réseau structurel complexe de grande taille, comprendre que l'organisation architecturale de ce réseau définit les propriétés fonctionnelles de ce dernier, comprendre qu'il y a une interdépendance intime entre la structure et la fonction , le métabolisme, comprendre que le réseau change au cours du développement ou lors de lésions ou maladie cérébrale. Messages à retenir: le cerveau est un réseau neuronal complexe qui peut être mesurer avec l'IRM, la connectivité cérébrale est inhomogène, la connectivité structurelle détermine largement la fonction cérébrale, les réseau neuronaux se modifient au cours de la vie et dans certaines maladies cérébrales. Résumé: La "connectomique" est un domaine émergeant et prometteur des neurosciences qui utilise l'IRM de diffusion en combinaison avec des traitements algorithmiques avancés afin de mesurer les trajectoires de faisceaux de fibres et la connectivité cérébrale permettant d'étudier l'organisation de la structure du réseau neuronal cérébral dans son ensemble. Lors de ce cours nous reverrons les méthodes rendant cette cartographie possible et exposerons les techniques d'analyse utilisées pour obtenir de nouvelles informations sur l'architecture cérébrale. Nous reverrons également un certains nombre d'exemple d'applications où la connectomique offre une nouvelle manière d'analyser et de comprendre le fonctionnement du cerveau normal ou malade.

Mapping the development of the human connectome

From birth to early adulthood the brain undergoes dramatic modifications resulting in network development and optimization. In the present study we investigate the development of the human connectome but measuring myelination trajectories of individual connections over the entire brain structural network using high b-value diffusion imaging and tractography. We found significant changes in several network measures that support increased integration and efficiency. We also observe that the network doesn't myelinate at a uniform rate but with different myelination speeds dependant on the type of cortex.

The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes.

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit - a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/

Characterizing the connectome in schizophrenia with diffusion spectrum imaging.

Schizophrenia is a complex psychiatric disorder characterized by disabling symptoms and cognitive deficit. Recent neuroimaging findings suggest that large parts of the brain are affected by the disease, and that the capacity of functional integration between brain areas is decreased. In this study we questioned (i) which brain areas underlie the loss of network integration properties observed in the pathology, (ii) what is the topological role of the affected regions within the overall brain network and how this topological status might be altered in patients, and (iii) how white matter properties of tracts connecting affected regions may be disrupted. We acquired diffusion spectrum imaging (a technique sensitive to fiber crossing and slow diffusion compartment) data from 16 schizophrenia patients and 15 healthy controls, and investigated their weighted brain networks. The global connectivity analysis confirmed that patients present disrupted integration and segregation properties. The nodal analysis allowed identifying a distributed set of brain nodes affected in the pathology, including hubs and peripheral areas. To characterize the topological role of this affected core, we investigated the brain network shortest paths layout, and quantified the network damage after targeted attack toward the affected core. The centrality of the affected core was compromised in patients. Moreover the connectivity strength within the affected core, quantified with generalized fractional anisotropy and apparent diffusion coefficient, was altered in patients. Taken together, these findings suggest that the structural alterations and topological decentralization of the affected core might be major mechanisms underlying the schizophrenia dysconnectivity disorder. Hum Brain Mapp, 36:354-366, 2015. © 2014 Wiley Periodicals, Inc.