Multimodal Magnetic Resonance Imaging for Brain Disorders: Advances and Perspectives

Jiang, Tianzi; Liu, Yong; Shi, Feng; Shu, Ni; Liu, Bing; Jiang, Jiefeng; Zhou, Yuan

doi:10.1007/s11682-008-9038-z

Cited by 9 publications

(8 citation statements)

References 82 publications

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“…It has been proven that information extracted from different modalities and different brain tissues might complement each other and provides more comprehensive characterization of brain abnormalities [9, 11, 12, 18, 36]. To integrate complementary information from different modalities and different brain tissue types, we proposed in this paper an approach that combine structural connectivity information from white matter (WM) regions and functional connectivity information from gray matter (GM) regions to distinguish individuals with MCI from normal controls.…”

Section: Methodsmentioning

confidence: 99%

Identification of MCI individuals using structural and functional connectivity networks

et al. 2012

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Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer’s disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.

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Section: Methodsmentioning

confidence: 99%

Identification of MCI individuals using structural and functional connectivity networks

et al. 2012

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“…The demand for multiscale data in the investigation of fundamental disease processes has been recognized for several years (Jiang, Liu et al 2008), as well as the need for cooperation across centers and even disciplines to integrate and interpret the data (Van Horn and Toga 2009b). Examples of multisite neuroimaging efforts can be found in the ubiquitous application of neuroimaging in health but also in devestating illnesses such as Parkinson’s (Evangelou, Maraganore et al 2009; see also http://ppmi.loni.ucla.edu/), psychiatric disorders (Schumann, Loth et al 2010), and also the mapping of human brain connectivity (Toga, Clark et al 2012).…”

Section: Multisite Consortia and Data Sharingmentioning

confidence: 99%

Human neuroimaging as a “Big Data” science

Horn

Toga

2013

Brain Imaging and Behavior

128

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The maturation of in vivo neuroimaging has lead to incredible quantities of digital information about the human brain. While much is made of the data deluge in science, neuroimaging represents the leading edge of this onslaught of “big data”. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Yet few, if any, common solutions exist to support the science of neuroimaging. In this article, we discuss how modern neuroimaging research represents a mutifactorial and broad ranging data challenge, involving the growing size of the data being acquired; sociologial and logistical sharing issues; infrastructural challenges for multi-site, multi-datatype archiving; and the means by which to explore and mine these data. As neuroimaging advances further, e.g. aging, genetics, and age-related disease, new vision is needed to manage and process this information while marshalling of these resources into novel results. Thus, “big data” can become “big” brain science.

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“…Furthermore, there are inconsistencies between independent structural and functional connectivity studies, which may result from differences in patient groups, imaging and analysis methods [4]. Multi-modal integrative approaches of connectivity are potentially useful tools in understanding brain function and for investigating the impact of pathology on brain networks [62], [63].…”

Section: Discussionmentioning

confidence: 99%

A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks

Deligianni¹,

Varoquaux²,

Thirion³

et al. 2013

IEEE Trans. Med. Imaging

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Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work [1], our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state functional magnetic resonance imaging in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation.

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Multimodal Magnetic Resonance Imaging for Brain Disorders: Advances and Perspectives

Cited by 9 publications

References 82 publications

Identification of MCI individuals using structural and functional connectivity networks

Identification of MCI individuals using structural and functional connectivity networks

Human neuroimaging as a “Big Data” science

A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks

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