Consistent Run Selection for Independent Component Analysis: Application to Fmri Analysis

Long, Qunfang; Jia, Chunying; Boukouvalas, Zois; Gabrielson, Ben; Emge, Darren K.

doi:10.1109/icassp.2018.8461646

Cited by 29 publications

(20 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In parameter-tuned cIVA, we constrain one source at a time, truebolds^1[m], to one of the exemplar components, d n , whereas the rest of the sources, truebolds^l[m],l=2,…,L, are unconstrained. For each d n , we obtain 10 solutions using parameter-tuned cIVA with γ n = 3, n = 1, …, N , using the IVA-L-SOS algorithm for different random initializations and select the most consistent run using the method described in Long et al (2018b). IVA-L-SOS is a type of IVA algorithm that assumes the sources are multivariate Laplacian distributed and exploits second order statistics (SOS) (Bhinge et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia

2019

Self Cite

View full text Add to dashboard Cite

Dynamic functional network connectivity (dFNC) analysis is a widely-used to study associations between dynamic functional correlations and cognitive abilities. Traditional methods analyze time-varying association of different spatial networks while assuming that the spatial network itself is stationary. However, there has been very little work focused on voxelwise spatial variability. Exploiting the variability across both the temporal and spatial domains provide a more promising direction to obtain reliable dynamic functional patterns. However, methods for extracting time-varying spatio-temporal patterns from large-scale functional magnetic resonance imaging (fMRI) data present some challenges, such as degradation in performance with respect to increase in size of the data, estimation of the number of dynamic components, and the potential sensitivity of the resulting dFNCs to selection of the networks. In this work, we implement subsequent extraction of exemplars and dynamics using a constrained independent vector analysis, a data-driven method that efficiently estimates spatial and temporal dynamics from large-scale resting-state fMRI data. We explore the benefits of analyzing spatial dFNC (sdFNC) patterns over temporal dFNC (tdFNC) patterns in the context of differentiating healthy controls and patients with schizophrenia. Our results indicate that for resting-state fMRI data, sdFNC patterns were able to better classify patients and controls, and yield more distinguishing features compared with tdFNC patterns. We also estimate structured patterns of connectivity/states using sdFNC patterns, an area that has not been studied so far, and observe that sdFNC was able to successfully capture distinct information from healthy controls and patients with schizophrenia. In addition, sdFNC patterns were also able to identify functional patterns that associate with signs of paranoia and abnormalities in the patients group. We also observe that patients with schizophrenia tend to switch to or stay in a state corresponding to a hyperconnected brain network.

show abstract

Section: Methodsmentioning

confidence: 99%

Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Owing to the variability in the solution space of the ICA algorithms, we ran the ICA on each dataset 20 times and selected the most consistent run by using an algorithmic consistency metric based on the cross-inter-symbol interference (cross-ISI) 39 . The ISI is a global metric for performance evaluation but only when the ground-truth is known.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Evolving schema representations in orbitofrontal ensembles during learning

Zhou

Jia

Montesinos-Cartagena

et al. 2020

Nature

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Since ICA is an iterative algorithm, the optimization of ICA yields different solutions depending on the initialization. Therefore, we performed ICA for 30 independent runs with different random initializations and selected the most consistent run using a metric called cross inter-symbol interference (cross-ISI) [65]. Similarly, IVA-G is also an iterative algorithm, so we performed IVA-G 50 times independently and used cross-ISI to select the most consistent run.…”

Section: Algorithm Choicementioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Consistent Run Selection for Independent Component Analysis: Application to Fmri Analysis

Cited by 29 publications

References 22 publications

Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia

Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia

Evolving schema representations in orbitofrontal ensembles during learning

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

Contact Info

Product

Resources

About