Connectivity-Informed fMRI Activation Detection

Ng, Bernard; Abugharbieh, Rafeef; Varoquaux, Gaël; Poline, Jean Baptiste; Thirion, Bertrand

doi:10.1007/978-3-642-23629-7_35

Cited by 15 publications

(21 citation statements)

References 16 publications

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“…It has been suggested that the much lower amplitude signal of resting state activity is a fraction of the task response amplitude Ng et al, 2011) and thus that connectivity analyses of the brain at rest may give insight into functional connectivity at a finer scale; in fact it is expected that functional connectivity based on resting-state data is reflective of anatomical connections (De Luca et al, 2006;Fox and Raichle, 2007;Koch et al, 2002;Quigley et al, 2003;van den Heuvel et al, 2009), although this reflection is not trivially identical (Honey et al 2010). …”

Section: Goals Of the Studymentioning

confidence: 99%

Evaluation and calibration of functional network modeling methods based on known anatomical connections

et al. 2013

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1-Abstract EnglishRecent studies have identified large scale brain networks based on the spatio-temporal structure of spontaneous fluctuations in resting-state fMRI data. It is expected that functional connectivity based on resting-state data is reflective of-but not identical to the underlying anatomical connectivity.However, which functional connectivity analysis methods reliably predict the network structure remains unclear. Here we tested and compared network connectivity analysis methods by applying them to fMRI restingstate time-series obtained from the human visual cortex. The methods evaluated here are those previously tested against simulated data in Smith et al. (Neuroimage, 2011).To this end, we defined regions within retinotopic visual areas V1, V2, and V3 according to their eccentricity in the visual field, delineating central, intermediate, and peripheral eccentricity regions of interest (ROIs). These ROIs served as nodes in the models we study. We based our evaluation -anatomical connectivity in the monkey visual cortex. For each evaluated method, we computed the fractional rate of detecting connections known to -th percentile of the 4 distribution of interaction magnitudes of those connections not expected to exist.Under optimal conditions, including session duration of 68 minutes, a relatively small network consisting of 9 nodes and artifact-free regression of the global effect, each of the top methods predicted the expected connections with 75%-83% c-sensitivity. Partial Correlation performed best (PCorr; 83%), followed by Regularized Inverse Covariance (ICOV; 79%), Bayesian Network methods (BayesNet; 77%), Correlation (75%), andGeneral Synchronization measures (75%). With decreased session duration, these top methods saw decreases in c-sensitivities, achieving 66%-78% and 60%-70% for 34 and 17 minute sessions, respectively. With a short resting-state fMRI scan of 8.5 minutes (TR = 2s), none of the methods predicted the real network well, with ICOV (53%) and PCorr (51%) performing best. With increased complexity of the network from 9 to 36 nodes, multivariate methods including PCorr and BayesNet saw a decrease in performance. However, this decrease became small when using data from a long (68 minutes) session. Artifact-free regression of the global effect significantly increased the c-sensitivity of all top-performing methods. In an overall evaluation across all tests we performed, PCorr, ICOV and BayesNet set themselves somewhat above all other methods. 5We propose that data-based calibration based on known anatomical connections be integrated into future network studies, in order to maximize sensitivity and reduce false positives. Studies have also demonstrated changes in fMRI-measured brain activity when the subjects are in a state of rest. Importantly, it has been shown that these spontaneous (i.e. without any external stimulation)fluctuations in fMRI signals demonstrate functional connectivity over large parts of the human brain (Xiong et al., 1999). These large parts of the brain correspo...

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Section: Goals Of the Studymentioning

confidence: 99%

Evaluation and calibration of functional network modeling methods based on known anatomical connections

et al. 2013

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“…On a large dataset of 60 subjects, we showed that applying our multimodal approach significantly increases subject consistency in the detected connection structure over analyzing fMRI data alone. Enhanced group activation detection was also obtained by incorporating a connectivity prior [9] learned with our proposed approach, thus demonstrating the gain of integrating anatomical and functional information in brain connectivity estimation.…”

Section: Introductionmentioning

confidence: 77%

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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“…The principle for three activity areas is the same as for two, but the drawback is that the number of possible area combinations increases exponentially. One solution to this could be to use connectivity information as a prior to which combinations to try [17]. While non-local fMRI analysis can be more sensitive, it will be harder to interpret the activity maps.…”

Section: Discussionmentioning

confidence: 99%

A functional connectivity inspired approach to non-local fMRI analysis

Eklund

Andersson

Knutsson

2012

2012 19th IEEE International Conference on Image Processing

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We propose non-local analysis of functional magnetic resonance imaging (fMRI) data in order to detect more brain activity. Our non-local approach combines the ideas of regular fMRI analysis with those of functional connectivity analysis, and was inspired by the non-local means algorithm that commonly is used for image denoising. We extend canonical correlation analysis (CCA) based fMRI analysis to handle more than one activity area, such that information from different parts of the brain can be combined. Our non-local approach is compared to fMRI analysis by the general linear model (GLM) and local CCA, by using simulated as well as real data.

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Connectivity-Informed fMRI Activation Detection

Cited by 15 publications

References 16 publications

Evaluation and calibration of functional network modeling methods based on known anatomical connections

Evaluation and calibration of functional network modeling methods based on known anatomical connections

A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference

A functional connectivity inspired approach to non-local fMRI analysis

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