MR connectomics: Principles and challenges

Hagmann, Patric; Cammoun, Leila; Gigandet, Xavier; Gerhard, Stephan; Grant, P. Ellen; Wedeen, Van J.; Meuli, Reto; Thiran, Jean-Philippe; Honey, Christopher J.; Sporns, Olaf

doi:10.1016/j.jneumeth.2010.01.014

Cited by 256 publications

(213 citation statements)

References 106 publications

(126 reference statements)

Supporting

Mentioning

209

Contrasting

Unclassified

Order By: Relevance

“…Over the last years it has become clear that MR based connectomic techniques are of the highest interest for the neuroscience community (Bassett et al, 2010;Bullmore and Sporns, 2009;Fornito et al, 2010;Gross, 2008;Hagmann et al, 2010;Zalesky et al, 2010), but methodological issues remains. The presented method is a contribution to tackle these issues.…”

Section: Discussionmentioning

confidence: 99%

“…Diffusionbased connectivity has also been used in some studies (Behrens and Johansen-Berg, 2005;Klein et al, 2007;Tomassini et al, 2007) to parcellate gray matter. In order to emphasize the importance of whole brain connectivity, the term connectome has been coined by our group as early as 2005 (Hagmann, 2005;Hagmann et al, 2010;Sporns, 2008;Sporns et al, 2005). It refers to the complete description of the structural connectivity of the brain.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping the human connectome at multiple scales with diffusion spectrum MRI

Cammoun

Gigandet

Meskaldji

et al. 2012

Journal of Neuroscience Methods

Self Cite

459

525

View full text Add to dashboard Cite

a b s t r a c tThe global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated postprocessing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping the human connectome at multiple scales with diffusion spectrum MRI

Cammoun

Gigandet

Meskaldji

et al. 2012

Journal of Neuroscience Methods

Self Cite

459

525

View full text Add to dashboard Cite

show abstract

“…More generally, the current lack of empirical evidence for functional correlates of most network measures precludes neurobiologically grounded conclusions of functional network abnormalities and makes empirical validation of these interpretations a high priority. Such validation could, for instance, involve direct comparisons of structural and functional brain connectivity in the same individuals (Hagmann et al, 2010;Van Dijk et al, 2010) or the study of relationships between structural and functional connectivity in biologically realistic large-scale computational models of the brain (Deco et al, 2011).…”

Section: Review Of Van Den Heuvel Et Almentioning

confidence: 99%

Emerging Evidence of Connectomic Abnormalities in Schizophrenia

Rubinov¹,

Bassett²

2011

J. Neurosci.

View full text Add to dashboard Cite

Review of van den Heuvel et al.Schizophrenia is an etiologically and clinically heterogeneous psychiatric disorder, characterized by psychotic symptoms, motivational disturbances, and cognitive disorganization. Pioneers of neuropathology such as Meynert and Wernicke proposed in the late 19th century that schizophrenia is associated with abnormalities in anatomical connectivity between specialized brain regions. Over the last 30 years, neuroimaging studies have provided general evidence for the existence of such anatomical dysconnectivity. For instance, functional neuroimaging studies in schizophrenia show abnormal integration between multiple cortical and subcortical regions (Friston and Frith, 1995;Gur and Gur, 2010), implying the presence of underlying anatomical connection abnormalities. Structural neuroimaging studies show deep white-matter abnormalities in frontal and temporal regions and gray-matter reductions in frontal, temporal, limbic, and thalamic regions Bullmore, 2009, 2010). However, the precise location of affected anatomical connections in schizophrenia and the relationship between these connectivity abnormalities and emergent functional deficits remain unknown.Recent advances in diffusion magnetic resonance imaging (MRI) technology and white-matter fiber reconstruction algorithms make it increasingly possible to assemble high-resolution large-scale anatomical connectivity maps of the human brain. Such "connectomic" maps allow researchers to characterize the organization of anatomical brain networks and to identify the anatomical correlates of functional abnormalities in brain disorders (Sporns et al., 2005). One of the first diffusion MRI connectomic studies of schizophrenia was recently described by van den Heuvel et al. (2010). The authors characterized large-scale anatomical brain networks extracted from diffusion imaging data acquired from 40 healthy people and 40 people with schizophrenia. The location, direction, and length of white matter fiber tracts in these networks were inferred from diffusion of water molecules in brain tissue, using a white-matter fiber reconstruction algorithm. The connectivity strength of white matter tracts was inferred from the level of tract myelination, which was estimated using magnetization transfer imaging, a technique that quantifies the amount of macromolecules (including myelin) in the tracts. The tracts were combined with a parcellation of the brain into 108 regions of interest to construct whole-brain anatomical networks. The regional and global properties of these networks were then characterized as described below and compared between healthy and schizophrenia groups.van den Heuvel et al. (2010) characterized the properties of anatomical brain networks with statistical measures of network topology. The authors used four complementary network measures to assess the structural organization and infer the potential functional role of individual brain regions. First, the measure of regional connectivity, based on the total number of connections associate...

show abstract

“…Recently, the results reported in the literatures (Richiardi et al 2011;Shirer et al 2012) suggest us that functional connectivity is a new, alternative school of methodologies for quantitatively measuring functional brain response. Essentially, brain function is resulted from large-scale functional connectivities (Haynes and Rees 2006;Lynall et al 2010;Friston 2009;Hagmann et al 2010). The brain's comprehension of visual stimuli can be precisely represented by these functional connectivities and interactions among relevant brain networks (Friston 2009;Hagmann et al 2010;Lynall et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Essentially, brain function is resulted from large-scale functional connectivities (Haynes and Rees 2006;Lynall et al 2010;Friston 2009;Hagmann et al 2010). The brain's comprehension of visual stimuli can be precisely represented by these functional connectivities and interactions among relevant brain networks (Friston 2009;Hagmann et al 2010;Lynall et al 2010). Notably, a few recent studies (Hu et al 2012;Han et al 2013) have demonstrated that functional connectivity is an effective tool to model brain response to free viewing of videos.…”

Section: Introductionmentioning

confidence: 99%

Encoding brain network response to free viewing of videos

Han

Zhao

et al. 2014

Cogn Neurodyn

View full text Add to dashboard Cite

A challenging goal for cognitive neuroscience researchers is to determine how mental representations are mapped onto the patterns of neural activity. To address this problem, functional magnetic resonance imaging (fMRI) researchers have developed a large number of encoding and decoding methods. However, previous studies typically used rather limited stimuli representation, like semantic labels and Wavelet Gabor filters, and largely focused on voxel-based brain patterns. Here, we present a new fMRI encoding model to predict the human brain's responses to free viewing of video clips which aims to deal with this limitation. In this model, we represent the stimuli using a variety of representative visual features in the computer vision community, which can describe the global color distribution, local shape and spatial information and motion information contained in videos, and apply the functional connectivity to model the brain's activity pattern evoked by these video clips. Our experimental results demonstrate that brain network responses during free viewing of videos can be robustly and accurately predicted across subjects by using visual features. Our study suggests the feasibility of exploring cognitive neuroscience studies by computational image/video analysis and provides a novel concept of using the brain encoding as a test-bed for evaluating visual feature extraction.

show abstract

MR connectomics: Principles and challenges

Cited by 256 publications

References 106 publications

Mapping the human connectome at multiple scales with diffusion spectrum MRI

Mapping the human connectome at multiple scales with diffusion spectrum MRI

Emerging Evidence of Connectomic Abnormalities in Schizophrenia

Encoding brain network response to free viewing of videos

Contact Info

Product

Resources

About