Joint Modeling of Anatomical and Functional Connectivity for Population Studies

Venkataraman, Archana; Rathi, Yogesh; Kubicki, Marek; Westin, Carl‐Fredrik; Golland, Polina

doi:10.1109/tmi.2011.2166083

Cited by 47 publications

(44 citation statements)

References 42 publications

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“…We divided the brain into P parcels (P set to 500) to enable finer brain partitioning than facilitated by standard brain atlases (typically P < 150). This choice of P provides a balance between functional localization and robustness to subject variability in tractography [7]. Parcellation was performed by concatenating RS-fMRI time courses across subjects and applying Ward clustering [15].…”

Section: Methodsmentioning

confidence: 99%

“…To exploit the joint information in dMRI and fMRI data, variants of ICA and canonical correlation analysis [6] have been proposed for identifying brain areas that display high correlations between anatomical and functional attributes, such as fractional anisotropy and activation effects. Recently, a probabilistic model has been put forth for combining dMRI and fMRI data in detecting group differences in brain connection structure [7].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference

Varoquaux

Poline

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Despite the clear potential benefits of combining fMRI and diffusion MRI in learning the neural pathways that underlie brain functions, little methodological progress has been made in this direction. In this paper, we propose a novel multimodal integration approach based on sparse Gaussian graphical model for estimating brain connectivity. Casting functional connectivity estimation as a sparse inverse covariance learning problem, we adapt the level of sparse penalization on each connection based on its anatomical capacity for functional interactions. Functional connections with little anatomical support are thus more heavily penalized. For validation, we showed on real data collected from a cohort of 60 subjects that additionally modeling anatomical capacity significantly increases subject consistency in the detected connection patterns. Moreover, we demonstrated that incorporating a connectivity prior learned with our multimodal connectivity estimation approach improves activation detection.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference

Varoquaux

Poline

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

View full text Add to dashboard Cite

show abstract

“…A number of past studies [1][2][3][4] reported a strong agreement between AC and FC, as estimated from tractography-based measures such as fiber counts and temporal correlations between RS-fMRI time courses, respectively. These findings incited new research directions, such as integrating dMRI and RS-fMRI data for multimodal connectivity estimation [5,6] and informing fMRI activation detection with AC priors [7].…”

Section: Introductionmentioning

confidence: 99%

Effects of tractography approach on consistency between anatomical and functional connectivity estimates

Yoldemir

Abugharbieh

2014

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

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Diffusion magnetic resonance imaging (dMRI) and resting state functional MRI (RS-fMRI) provide two complementary views of brain circuitry. dMRI facilitates the estimation of anatomical connectivity (AC) through fiber tractography, while RS-fMRI enables the estimation of functional connectivity (FC) based on temporal signal correlations between different brain areas. Recently, there is a methodological push in developing techniques to integrate dMRI and RS-fMRI for multimodal connectivity estimation, success of which highly depends on the consistency between the AC and FC estimates. Using the Human Connectome Project (HCP) data, we show increased AC-FC consistency with streamline tractography on orientation distribution functions (ODFs) compared to using conventional diffusion tensors. We also demonstrate a further, though smaller, improvement when global tractography on ODFs is deployed. Our results suggest that while accurate ODF estimation is important, more attention should be focused on improving tractography methods, which we believe could be highly beneficial.

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“…Most of the early work focused on direct comparisons of structural and functional connectivity information learned separately from dMRI and fMRI data [8,9]. More recently, merits of multi-modal integration for joint anatomical and functional connectivity inference have been explored [10][11][12]. Promising results in these studies indicate benefits of multi-modal integration though the scope of this strategy has mostly been limited to connectivity estimation.…”

Section: Introductionmentioning

confidence: 99%

Fiber Connectivity Integrated Brain Activation Detection

Yoldemir

Woodward

et al. 2013

Lecture Notes in Computer Science

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Abstract. Inference of brain activation through the analysis of functional magnetic resonance imaging (fMRI) data is seriously confounded by the high level of noise in the observations. To mitigate the effects of noise, we propose incorporating anatomical connectivity into brain activation detection as motivated by how the functional integration of distinct brain areas is facilitated via neural fiber pathways. In this work, we formulate activation detection as a probabilistic graph-based segmentation problem with fiber networks estimated from diffusion MRI (dMRI) data serving as a prior. Our approach is reinforced with a data-driven scheme for refining the connectivity prior to reflect the fact that not all fibers are necessarily deployed during a given cognitive task as well as to account for false fiber tracts arising from limitations of dMRI tractography. Validating on real clinical data collected from 7 schizophrenia patients and 13 matched healthy controls, we show that incorporating anatomical connectivity significantly increases sensitivity in detecting task activation in controls compared to existing univariate techniques. Further, we illustrate how our model enables the detection of significant group activation differences between controls and patients that are missed with standard methods.Keywords: activation detection, connectivity, dMRI, fMRI, random walker IntroductionFunctional magnetic resonance imaging (fMRI) has become the primary modality for studying human brain activity. To map brain regions to function, standard analysis models the fMRI observations at each voxel as a linear combination of expected temporal responses using the general linear model (GLM) [1]. This univariate approach does not model the integrative property of the brain, which is known to facilitate brain function [2]. To ameliorate this serious limitation, the use of local neighbourhood information has been proposed to regularize activation detection [3,4]. Although such methods help suppress false spatially-isolated activations by encouraging spatial continuity, they completely ignore long-range functional interactions. The incorporation of functional connectivity information into task activation detection has also been put forth [5], but the approach taken estimates both activation effects and functional connectivity from the same dataset, hence the information gain might be limited. Other works investigated the use of resting-state (RS) functional connectivity information to

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Joint Modeling of Anatomical and Functional Connectivity for Population Studies

Cited by 47 publications

References 42 publications

A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference

A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference

Effects of tractography approach on consistency between anatomical and functional connectivity estimates

Fiber Connectivity Integrated Brain Activation Detection

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