Dataset Information

NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity.

ABSTRACT: In Diffusion Weighted MR Imaging (DWI), the signal is affected by the biophysical properties of neuronal cells and their relative placement, as well as extra-cellular tissue compartments. Typically, microstructural indices, such as fractional anisotropy (FA) and mean diffusivity (MD), are based on a tensor model that cannot disentangle the influence of these parameters. Recently, Neurite Orientation Dispersion and Density Imaging (NODDI) has exploited multi-shell acquisition protocols to model the diffusion signal as the contribution of three tissue compartments. NODDI microstructural indices, such as intra-cellular volume fraction (ICVF) and orientation dispersion index (ODI) are directly related to neuronal density and orientation dispersion, respectively. One way of examining the neurophysiological role of these microstructural indices across neuronal fibres is to look into how they relate to brain function. Here we exploit a statistical framework based on sparse Canonical Correlation Analysis (sCCA) and randomised Lasso to identify structural connections that are highly correlated with resting-state functional connectivity measured with simultaneous EEG-fMRI. Our results reveal distinct structural fingerprints for each microstructural index that also reflect their inter-relationships.

SUBMITTER: Deligianni F

PROVIDER: S-EPMC4831788 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity.

Deligianni Fani F Carmichael David W DW Zhang Gary H GH Clark Chris A CA Clayden Jonathan D JD

PloS one 20160414 4

In Diffusion Weighted MR Imaging (DWI), the signal is affected by the biophysical properties of neuronal cells and their relative placement, as well as extra-cellular tissue compartments. Typically, microstructural indices, such as fractional anisotropy (FA) and mean diffusivity (MD), are based on a tensor model that cannot disentangle the influence of these parameters. Recently, Neurite Orientation Dispersion and Density Imaging (NODDI) has exploited multi-shell acquisition protocols to model t ...[more]

PMID: 27078862

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Project description:Functional magnetic resonance imaging (fMRI) is a powerful technique with the potential to estimate individual variations in behavioral and cognitive traits. Joint learning of multiple datasets can utilize their complementary information so as to improve learning performance, but it also gives rise to the challenge for data fusion to effectively integrate brain patterns elicited by multiple fMRI data. However, most of the current data fusion methods analyze each single dataset separately and further infer the relationship among them, which fail to utilize the multidimensional structure inherent across modalities and may ignore complex but important interactions. To address this issue, we propose a novel sparse tensor decomposition method to integrate multiple task-stimulus (paradigm) fMRI data. Seeing each paradigm fMRI as one modality, our proposed method considers the relationships across subjects and modalities simultaneously. In specific, a third-order tensor is first modeled by using the functional network connectivity (FNC) of subjects in multiple fMRI paradigms. A novel sparse tensor decomposition with the regularization terms is designed to factorize the tensor into a series of rank-one components, which can extract the shared components across modalities as the embedded features. The L2,1-norm regularizer (i.e., group sparsity) is enforced to select a few common features among multiple subjects. Validation of the proposed method is performed on realistic three paradigm fMRI datasets from the Philadelphia Neurodevelopmental Cohort (PNC) study, for the study of the relationship between the FNC and human cognitive abilities. Experimental results show our method outperforms several other competing methods in the prediction of individuals with different cognitive behaviors via the wide range achievement test (WRAT). Furthermore, our method discovers the FNC related to the cognitive behaviors, such as the connectivity associated with the default mode network (DMN) for three paradigms, and the connectivity between DMN and visual (VIS) domains within the emotion task.

Project description:The NODDI-DTI signal model is a modification of the NODDI signal model that formally allows interpretation of standard single-shell DTI data in terms of biophysical parameters in healthy human white matter (WM). The NODDI-DTI signal model contains no CSF compartment, restricting application to voxels without CSF partial-volume contamination. This modification allowed derivation of analytical relations between parameters representing axon density and dispersion, and DTI invariants (MD and FA) from the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. NODDI-DTI parameters were estimated by applying the proposed analytical relations to DTI parameters estimated from the first shell of data, and compared to parameters estimated by fitting the NODDI-DTI model to both shells of data (reference dataset) in the WM of 14 in vivo diffusion datasets recorded with two different protocols, and in simulated data. The first two datasets were also fit to the NODDI-DTI model using only the first shell (as for DTI) of data. NODDI-DTI parameters estimated from DTI, and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The simulations showed the NODDI-DTI method to be more noise-robust than the two-shell fitting procedure. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model, it was found that diffusional kurtosis strongly biased DTI-based MD values, and so, making assumptions based on healthy WM, a novel heuristic correction requiring only DTI data was derived and used to mitigate this bias. Since validations were only performed on healthy WM, application to grey matter or pathological WM would require further validation. Our results demonstrate NODDI-DTI to be a promising model and technique to interpret restricted datasets acquired for DTI analysis in healthy white matter with greater biophysical specificity, though its limitations must be borne in mind.

Project description:BACKGROUND:Brain microstructural maturation progresses rapidly in the third trimester of gestation and first weeks of life, but typical microstructural development may be influenced by the presence of critical congenital heart disease (CHD). OBJECTIVE:The aim of this study was to investigate the pattern of white matter (WM) microstructural development in neonates with different types of critical CHD. The secondary aim was to examine whether there is an association between WM microstructural maturity and neonatal ischemic brain injury. METHODS:For this prospective, longitudinal cohort study, 74 term born neonates underwent diffusion tensor imaging (DTI) before (N?=?56) and after (N?=?71) cardiac surgery performed <30?days of life for transposition of the great arteries (TGA), single ventricle physiology with aortic arch obstruction (SVP-AO), left- (LVOTO) or right ventricle outflow tract obstruction (RVOTO). Microstructural integrity was investigated by fractional anisotropy (FA) and by mean diffusivity (MD) in 16 white matter (WM) structures in three WM regions with correction for postmenstrual age. Ischemic brain injury was defined as moderate-severe white matter injury or stroke. RESULTS:Before cardiac surgery, the posterior parts of the corona radiata and internal capsule showed significantly higher FA and lower MD compared to the anterior parts. Centrally-located WM structures demonstrated higher FA compared to peripherally-located structures. Neonates with TGA had higher FA in projection-, association- and commissural WM before surgery, when compared to other CHD groups. Neonates with LVOTO showed lower preoperative MD in these regions, and neonates with SVP-AO higher MD. Differences in FA/MD between CHD groups were most clear in centrally located WM structures. Between CHD groups, no differences in postoperative FA/MD or in change from pre- to postoperative FA/MD were seen. Neonatal ischemic brain injury was not associated with pre- or postoperative FA/MD. CONCLUSIONS:Collectively, these findings revealed brain microstructural WM development to follow the same organized pattern in critical CHD as reported in healthy and preterm neonates, from posterior-to-anterior and central-to-peripheral. Neonates with TGA and LVOTO showed the most mature WM microstructure before surgery and SVP-AO the least mature. Degree of WM microstructural immaturity was not associated with ischemic brain injury.

Dataset Information

NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity.

Publications

NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity.

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