C-ICT for Discovery of Multiple Associations in Multimodal Imaging Data: Application to Fusion of fMRI and DTI Data

Jia, Chunying; Mohammad, Ali Baig; Long, Qunfang; Calhoun, Vince D.; Waldstein, Shari R.

doi:10.1109/ciss.2019.8692878

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…CCA uses second-order statistics (that is, cross-covariance) to discover the relationships between two sets of multidimensional variables, by finding two sets of respective linear transformations (that is, canonical coefficients), such that the correlation between two projected variables (that is, canonical variables) is maximized. CCA has been used to align neural activities previously 8, 40,41 . The data was plotted for each problem across rats (a; n = 9 rats) or each rat across problems (b; n = 5 odour problems).…”

Section: Manifold Alignmentmentioning

confidence: 99%

Evolving schema representations in orbitofrontal ensembles during learning

Zhou

Jia

Montesinos-Cartagena

et al. 2020

Nature

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How do we learn about what to learn about? Specifically, how do the neural elements in our brain generalize what has been learned in one situation to recognize the common structure of – and speed learning in – other, similar situations? We know this happens; we and our mammalian kith become better at solving new problems – learning and deploying schemas 1 – 5 – as we go through life. However, we have little insight into this process. Here we show that using prior knowledge to facilitate learning is accompanied by the evolution of a neural schema in the orbitofrontal cortex (OFC). Single units were recorded from rats deploying a schema to learn a succession of odor sequence problems. With learning, OFC ensembles converged on a low-dimensional neural manifold across both problems and subjects; this manifold represented the common structure of the problems and its evolution accelerated across their learning. These results demonstrate the formation and use of a schema in a prefrontal brain region to support a complex cognitive operation. Our results not only reveal an important new role for the OFC in learning but also have significant implications for using ensemble analyses to tap into complex cognitive functions.

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Section: Manifold Alignmentmentioning

confidence: 99%

Evolving schema representations in orbitofrontal ensembles during learning

Zhou

Jia

Montesinos-Cartagena

et al. 2020

Nature

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“…Collectively, none of these methods are flexible in terms of dealing with different orders and finding "one-to-many associations" in real-world data. However, firstly, due to the disparate measurements in different modalities, the order of the signal subspace is very likely to be different between modalities [28,29]. Secondly, one component in one modality might be associated with multiple components in other modalities because of the intrinsic relationships between modalities.…”

Section: Introductionmentioning

confidence: 99%

Consecutive Independence and Correlation Transform for Multimodal Data Fusion: Discovery of One-to-Many Associations in Structural and Functional Imaging Data

et al. 2021

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Brain signals can be measured using multiple imaging modalities, such as magnetic resonance imaging (MRI)-based techniques. Different modalities convey distinct yet complementary information; thus, their joint analyses can provide valuable insight into how the brain functions in both healthy and diseased conditions. Data-driven approaches have proven most useful for multimodal fusion as they minimize assumptions imposed on the data, and there are a number of methods that have been developed to uncover relationships across modalities. However, none of these methods, to the best of our knowledge, can discover “one-to-many associations”, meaning one component from one modality is linked with more than one component from another modality. However, such “one-to-many associations” are likely to exist, since the same brain region can be involved in multiple neurological processes. Additionally, most existing data fusion methods require the signal subspace order to be identical for all modalities—a severe restriction for real-world data of different modalities. Here, we propose a new fusion technique—the consecutive independence and correlation transform (C-ICT) model—which successively performs independent component analysis and independent vector analysis and is uniquely flexible in terms of the number of datasets, signal subspace order, and the opportunity to find “one-to-many associations”. We apply C-ICT to fuse diffusion MRI, structural MRI, and functional MRI datasets collected from healthy controls (HCs) and patients with schizophrenia (SZs). We identify six interpretable triplets of components, each of which consists of three associated components from the three modalities. Besides, components from these triplets that show significant group differences between the HCs and SZs are identified, which could be seen as putative biomarkers in schizophrenia.

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