skip to main content
Explore Scholarly Publications and Datasets in the NSF-PAR

  • NSF Public Access
  • Search Results
  • Accepted Manuscript: A CONTRASTIVE LEARNING-BASED APPROACH TO MEASURE SPATIAL COUPLING AMONG BRAIN NETWORKS: A SCHIZOPHRENIA STUDY
Title: A CONTRASTIVE LEARNING-BASED APPROACH TO MEASURE SPATIAL COUPLING AMONG BRAIN NETWORKS: A SCHIZOPHRENIA STUDY
Resting-state functional magnetic resonance imaging (rsfMRI) has become a widely used approach for detecting subtle differences in functional brain fluctuations in various studies of the healthy and disordered brain. Such studies are often based on temporal functional connectivity (i.e., the correlation between time courses derived from regions or networks within the fMRI data). While being successful for a number of tasks, temporal connectivity does not fully leverage the available spatial information. In this research study, we present a new perspective on spatial functional connectivity, which involves learning patterns of spatial coupling among brain networks by utilizing recent advances in deep learning as well as the contrastive learning framework. We show that we can learn domain-specific mappings of brain networks that can, in turn, be used to characterize differences between schizophrenia patients and control. Furthermore, we show that the coupling of intradomain networks in the controls is stronger than in patients suffering from the disorder. We also evaluate the coupling among networks of different domains and find various patterns of stronger or weaker coupling among certain domains, which provide additional insights about the brain.
Authors:
;
Award ID(s):
2112455
Publication Date:
NSF-PAR ID:
10332735
Journal Name:
IEEE International Symposium on Biomedical Imaging (ISBI)
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , The Organization for Human Brain Mapping (OHBM))
    There is a growing research interest to extract the temporal dependency between brain networks. Among several existing methods, functional network connectivity (FNC) is one of the widely used approaches to capture the intrinsic functional relationships among brain networks. In this study, we introduced a novel approach that uses FNC matrices of Adolescent Brain and Cognitive Development (ABCD) data to evaluate multiple overlapping brain functional change patterns (FCPs). Results show several highly structured FCPs that have a significant change over a two-year period and become stronger with age including brain functional connectivity between visual (VS) and sensorimotor (SM) domains. Our approach is a powerful tool to visualize and evaluate patterns of whole brain functional changes in longitudinal data.
  2. ; ; ; ; ; ( , 2020 International Conference on Computational Science and Computational Intelligence (CSCI))
    Using electroencephalography (EEG) data from epileptic patients 1 , we investigated and compared functional connectivity networks of three various types of epileptiform discharges (ED; single, complex & repetitive spikes) in 4 regions of the brain. Our results showed different connectivity patterns among three ED types within-and between-brain regions. The one-way ANOVA test indicated significant differences between the mean of the average connectivity matrices (ACMs) of the single spike, which characterize focal epilepsy, and the other two ED types (complex & repetitive) which characterize generalized epilepsy. The interictal EEG segments, through the connectivity patterns they yield, could be considered as one of the key indicators for the diagnosis of focal or generalized epilepsy.
  3. ; ; ; ; ( , IEEE International Symposium on Biomedical Imaging (ISBI))
    Functional network connectivity (FNC) is a useful measure for evaluating the temporal dependency among brain networks. Longitudinal changes of intrinsic function are of great interest, but to date there has been little focus on multivariate patterns of FNC changes with development. In this paper, we proposed a novel approach that uses FNC matrices to estimate multiple overlapping brain functional change patterns (FCPs). We applied this approach to the large-scale Adolescent Brain and Cognitive Development (ABCD) data. Results reveal several highly structured FCPs showing a significant change over a two-year period including brain functional connectivity between visual (VS) and sensorimotor (SM) domains. This pattern of FNC expression becomes stronger with age. We also found a differential pattern of changes between male and female individuals. Our approach provides a powerful way to evaluate whole brain functional changes in longitudinal data.
  4. ; ; ; ( , Network Neuroscience)
    Author Summary Previous studies of local activity levels suggest that both shared and distinct neural mechanisms support the processing of symbolic (Arabic digits) and nonsymbolic (dot sets) number stimuli, involving regions distributed across frontal, temporal, and parietal cortices. Network-level characterizations of functional connectivity patterns underlying number processing have gone unexplored, however. In this study we examined the whole-brain functional architecture of symbolic and nonsymbolic number comparison. Stronger community membership was observed among auditory regions during symbolic processing, and among cingulo-opercular/salience and basal ganglia networks for nonsymbolic. A dual versus unified fronto-parietal/dorsal attention community organization was observed for symbolic and nonsymbolic formats, respectively. Finally, the inferior temporal gyrus and left intraparietal sulcus, both thought to be preferentially involved in processing number symbols, demonstrated robust differences in community membership between formats.
  5. ; ; ; ; ( , Frontiers in Neuroscience)
    Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using a tensor factorization-based approach called a PARAFAC2 model to reveal spatial dynamics. The PARAFAC2 model jointly analyzes data from multiple time windows revealing subject-mode patterns, evolving spatial regions (also referred to as networks) and temporal patterns. We compare the PARAFAC2 model with matrix factorization-based approaches relying on independent components, namely, joint independent component analysis (ICA) and independent vector analysis (IVA), commonly used in neuroimaging data analysis. We assess the performance of the methods in terms of capturing evolving networks throughmore »extensive numerical experiments demonstrating their modeling assumptions. In particular, we show that (i) PARAFAC2 provides a compact representation in all modes, i.e., subjects, time , and voxels , revealing temporal patterns as well as evolving spatial networks, (ii) joint ICA is as effective as PARAFAC2 in terms of revealing evolving networks but does not reveal temporal patterns, (iii) IVA's performance depends on sample size, data distribution and covariance structure of underlying networks. When these assumptions are satisfied, IVA is as accurate as the other methods, (iv) when subject-mode patterns differ from one time window to another, IVA is the most accurate. Furthermore, we analyze real fMRI data collected during a sensory motor task, and demonstrate that a component indicating statistically significant group difference between patients with schizophrenia and healthy controls is captured, which includes primary and secondary motor regions, cerebellum, and temporal lobe, revealing a meaningful spatial map and its temporal change.« less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email