A Multi-Scale Functional Atlas Reveals Insights into the Integrative Nature of the Brain
Poster No:
1797
Submission Type:
Abstract Submission
Authors:
Kyle Jensen1, Jessica Turner2, Lucina Uddin3,4, Vince Calhoun5, Armin Iraji5
Institutions:
1Georgia State University, Atlanta, GA, 2Wexner Medical Center, The Ohio State University, Columbus, OH, 3Department of Psychology, University of California Los Angeles, Los Angeles, CA, 4Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, 5Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA
First Author:
Co-Author(s):
Lucina Uddin, Ph.D.
Department of Psychology, University of California Los Angeles|Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA|Los Angeles, CA
Department of Psychology, University of California Los Angeles|Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles
Los Angeles, CA|Los Angeles, CA
Vince Calhoun
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Armin Iraji
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Introduction:
Standardizing Terminology to Address Inconsistencies in Neuroscience. There is a great amount of inconsistency in the terminology used to describe functional networks in the field of neuroscience (Uddin, 2019). Inconsistent labels cause confusion and impede scientific progress. This is especially a problem for clinical research, where the development of biomarkers relies on consistent findings across studies.
Standardizing Functional Entities to Establish a Universal Reference Space. Another issue is that the same labels can be applied inconsistently to describe different entities. For example, different atlases have used the term "salience network" to describe different and non-overlapping brain regions (Kong, 2024). This issue is further exacerbated by differences in methodology, datasets, and individual subject variability.
A Replicable Multi-Scale Whole-Brain Atlas of Functional Networks. We tackle these challenges by presenting a highly replicable multi-scale functional brain atlas (Jensen, 2024) developed by pulling from a dataset consisting of more than 100,000 resting-state fMRI scans (Iraji, 2023). This atlas covers the whole brain, incorporates information from multiple spatial scales, and has demonstrated reliability across the lifespan (Bajracharya, 2024; Iraji, 2023), making it well equipped to capture the unique spatial attributes of the functional brain and explore its integrative nature.
Standardizing Functional Entities to Establish a Universal Reference Space. Another issue is that the same labels can be applied inconsistently to describe different entities. For example, different atlases have used the term "salience network" to describe different and non-overlapping brain regions (Kong, 2024). This issue is further exacerbated by differences in methodology, datasets, and individual subject variability.
A Replicable Multi-Scale Whole-Brain Atlas of Functional Networks. We tackle these challenges by presenting a highly replicable multi-scale functional brain atlas (Jensen, 2024) developed by pulling from a dataset consisting of more than 100,000 resting-state fMRI scans (Iraji, 2023). This atlas covers the whole brain, incorporates information from multiple spatial scales, and has demonstrated reliability across the lifespan (Bajracharya, 2024; Iraji, 2023), making it well equipped to capture the unique spatial attributes of the functional brain and explore its integrative nature.
Methods:
105 intrinsic connectivity networks (ICNs) derived using multi-model-order independent component analysis (ICA; Iraji, 2023) were labeled and organized using terminology familiar to the fields of cognitive and affective neuroscience. Labeling was based on both quantitative and qualitative methods, including visual inspection of each ICN's spatial map as well as a calculation of the number of significant (z-score > 1.96) voxels overlapping between each ICN and regions in the AAL and Brodmann Areas atlases. We described each of these ICNs with individual labels and ordered and grouped them into 7 domains and 14 subdomains based on functional and spatial similarity. Functional similarity was calculated as the mean of subject-specific functional network connectivity (FNC) across 39,342 subjects, which was then plotted in a 105×105 FNC matrix (Fig. 1a).
Results:
As shown in the FNC matrix (Fig. 1a), the domains and subdomains are highly modular, reflecting the integrative nature of the brain. For example, despite their incorporation of spatially distinct brain regions, the sensorimotor (SM) and insular-temporal (IT) domains are highly modular. This pattern illustrates the heterogeneity of function across brain regions as well as the recruitment of multiple structures to accomplish different functions. Another valuable insight gained is that brain networks may be more fluid than how they are traditionally conceptualized. For example, we observed the presence of a spatial gradient within the Triple Network (TN) domain (Fig. 2).
Conclusions:
We present a replicable multi-scale whole-brain atlas of functional networks, described in terms familiar to cognitive and affective neuroscience to improve harmonization and standardization in neuroimaging research. This atlas provides a platform for data-driven approaches to effectively capture individual subject variability and bestow valuable insights into the nature of large-scale brain networks. Specifically, we highlight the unique spatial attributes of ICNs in the human brain, with varying contributions from different brain regions at the individual ICN level (Fig. 1c), varying levels of overlap between ICNs at the domain level (Fig. 1b), and varying levels of overlap between domains across the whole brain (Fig. 1a). The spatial gradient in the TN (Fig. 2) is especially noteworthy as it suggests that the Central Executive, Default Mode, and Salience Networks frequently described in resting-state fMRI literature may be better described as a single continuum rather than a trichotomy.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Neuroinformatics and Data Sharing:
Brain Atlases 1
Keywords:
Atlasing
Cognition
FUNCTIONAL MRI
Open-Source Software
Other - taxonomy; nomenclature; labeling; brain networks; resting-state; functional connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
2. Iraji, A. (2023). Identifying canonical and replicable multi‐scale intrinsic connectivity networks in 100k+ RESTING‐STATE FMRI datasets. Human Brain Mapping, 44(17), 5729–5748. https://doi.org/10.1002/hbm.26472
3. Jensen, K. M. (2024). Addressing inconsistency in functional neuroimaging: A replicable data-driven multi-scale functional atlas for canonical brain networks (p. 2024.09.09.612129). bioRxiv. https://doi.org/10.1101/2024.09.09.612129
4. Kong, R. Q. (2024). A network correspondence toolbox for quantitative evaluation of novel neuroimaging results. https://doi.org/10.1101/2024.06.17.599426
5. Uddin, L. Q. (2019). Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks. Brain Topography, 32(6), 926–942. https://doi.org/10.1007/s10548-019-00744-6