Fusing DTI and fMRI data: A survey of methods and applications

Zhu, Dajiang; Zhang, Tuo; Jiang, Xi; Hu, Xintao; Chen, Hanbo; Yang, Ning; Lv, Jinglei; Han, Junwei; Guo, Lei; Liu, Tianming

doi:10.1016/j.neuroimage.2013.09.071

Cited by 113 publications

(87 citation statements)

References 113 publications

(171 reference statements)

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“…Hyper-connectivity network, either based on structural or functional interactions among the brain regions, has been used for brain disease diagnosis [1]. Functional interactions and structural interactions can be extracted from functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), respectively [2]. However, the conventional hyper-network, which is constructed solely based on single modality data, ignores the potential complementary information conveyed by other modalities.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Hyper-connectivity Networks for MCI Classification

Gao

Jie

et al. 2017

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

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Hyper-connectivity network is a network where every edge is connected to more than two nodes, and can be naturally denoted using a hyper-graph. Hyper-connectivity brain network, either based on structural or functional interactions among the brain regions, has been used for brain disease diagnosis. However, the conventional hyper-connectivity network is constructed solely based on single modality data, ignoring potential complementary information conveyed by other modalities. The integration of complementary information from multiple modalities has been shown to provide a more comprehensive representation about the brain disruptions. In this paper, a novel multimodal hyper-network modelling method was proposed for improving the diagnostic accuracy of mild cognitive impairment (MCI). Specifically, we first constructed a multimodal hyper-connectivity network by simultaneously considering information from diffusion tensor imaging and resting-state functional magnetic resonance imaging data. We then extracted different types of network features from the hyper-connectivity network, and further exploited a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Our proposed multimodal hyper-connectivity network demonstrated a better MCI classification performance than the conventional single modality based hyper-connectivity networks.

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Section: Introductionmentioning

confidence: 99%

Multimodal Hyper-connectivity Networks for MCI Classification

Gao

Jie

et al. 2017

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017

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“…Schulz et al (2004), por exemplo, realiza uma análise de conectividade com imagens tanto de fMRI como de magnetoencefalograma (MEG); Rykhlevskaia et al (2008) apresenta uma revisão dos métodos integrando fMRI com ressonância magnética estrutural, e Zhu et al (2014) uma revisão dos métodos integrando fMRI com imagens de difusão de tensor (diffusion tensor imaging ou DTI). No caso de redes Bayesianas, Iyer et al (2013), por exemplo, utiliza o DTI como uma matriz de base para reduzir o espaço de busca de redes Bayesinas.…”

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Identicação de sistemas neurais com redes bayesianas dinâmicas e transferência de entropia

Santos¹

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“…This fusion of information can avoid some pitfalls of overanalysing changes in activation patterns, while considerably improving the sensitivity and interpretability of other modalities. 174,175 One fusion method which is being progressively adopted is the use of t-fMRI activation patterns to guide diffusion tractography, allowing this method to focus microstructural and structural-connectivity measurements on functionally relevant areas.…”

Section: T-fmri Fusionmentioning

confidence: 99%

“…103,175 In such a method, areas of significant fMRI activation are used as seeding regions for diffusion tractography.…”

Section: Introductionmentioning

confidence: 99%

“…This method has been used in several studies of healthy subjects, 175 by utilising standard voxel-based software packages such as SPM (http://www.fil.ion.ucl.ac.uk/spm/software) and MRTrix. 180 Standard fMRI analyses include an appreciable amount of implicit and explicit smoothing, which can artificially increase the size of the detected activation beyond its true boundaries.…”

Section: Introductionmentioning

confidence: 99%

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Measuring motor-training induced neuroplasticity using diffusion and functional MRI: a sensitive, pathology-robust approach

Reid¹

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Brain injury constitutes a significant health burden worldwide, and outcomes are generally poor. To improve treatment, we must better understand how injury affects brain function, how the brain responds to learning pressures (neuroplasticity), and the interaction between the two. This is difficult, however, because of current technological shortcomings.This thesis focusses on the measurement of neuroplasticity in response to motor training in situations such as rehabilitation following brain injury. Although the principles and methods discussed generalise to many forms of brain pathology, discussion is generally focussed on unilateral cerebral palsy (UCP) because altered brain-development in UCP makes analyses particularly difficult. This begins with a literature review (Chapter 2) that outlines UCP, rehabilitative approaches, and the relationship between atypical brain organisation and early-life brain injury. Also discussed is how novel rehabilitation strategies may stem from studying brain changes induced by rehabilitation, and why the most informative findings are likely to come from studies utilising multiple imaging modalities.Blood Oxygen-Level Dependent Functional Magnetic Resonance Imaging (fMRI) measures brain activity indirectly and has been used to study neuroplasticity in both healthy participants and those with brain pathology. Although fMRI confounds are well understood, no published work has explored their cumulative effect when brain injury is present. Resultantly, confounds have largely been ignored, leading to heterogeneous findings that have provided little toward understanding brain changes, biologically speaking. Chapter 3 examines the cumulative effect of fMRI confounds, assumptions, and other issues, when attempting to measure neuroplasticity in participants with brain injury. This work concludes that, although fMRI can be expected to locate approximate regions of brain activation in an individual scan, numerous confounding factors make its interpretation, in terms of neuroplasticity, difficult without additional information acquired through alternative means.Diffusion MRI (dMRI) is an imaging modality that is typically used to identify the brain's white-matter pathways and calculate metrics that are influenced by factors such as myelination or axonal density. Standard dMRI analyses are ill suited to studying brain injury due to their reliance on brain 'atlases' which indicate relationships between brain regions and brain functions. Brain pathology can prevent reliable atlas registration, and alter relationships between structure and function. Chapter 4 details a novel dMRI analysis designed to measure neuroplasticity, addressing the need for a sensitive and reliable alternative to standalone-fMRI. This method utilises surface-based fMRI analyses, rather than brain atlases, to locate regions involved in motor execution. Corticospinal (CST) and thalamocortical tracts emanating from these regions are delineated using dMRI tractography and iii machine learning. This method was app...

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Fusing DTI and fMRI data: A survey of methods and applications

Cited by 113 publications

References 113 publications

Multimodal Hyper-connectivity Networks for MCI Classification

Multimodal Hyper-connectivity Networks for MCI Classification

Identicação de sistemas neurais com redes bayesianas dinâmicas e transferência de entropia

Measuring motor-training induced neuroplasticity using diffusion and functional MRI: a sensitive, pathology-robust approach

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