Network Level Analysis Toolbox for connectome-wide association studies
Poster No:
1912
Submission Type:
Abstract Submission
Authors:
Muriah Wheelock1, Ari Segel1, Andrew Eck1, Donna Dierker1, Jim Pollaro1, Hong Chen1, Adam Eggebrecht2
Institutions:
1Washington University in St. Louis, St. Louis, MO, 2Washington University School of Medicine, St. Louis, MO
First Author:
Co-Author(s):
Introduction:
Determining the mechanisms by which the brain generates cognition, perception, and emotion hinges upon quantifying the relationships between coordinated brain activity and behavior. These brain-behavior association analyses typically consist of several thousand statistical tests which poses a challenge for controlling the false discovery rate. While contemporary connectome research views the brain as an extensive, complex network of non-adjacent, yet functionally and structurally connected brain regions, standard voxel extent cluster correction approaches do not utilize the spatial topology of brain networks when estimating cluster size significance (Friston et al., 1994). Similarly, an edge level Bonferroni or FDR correction on connectomes with thousands of potential connections is unlikely to yield significant findings (Greene et al., 2016). Limited prior work has leveraged the hierarchical network structure of the brain to probe connectome associations with behavior using a variety of statistical approaches (Noble et al., 2022; Sripada et al., 2020). However, a unified, standardized, extensible, and flexible software suite is lacking. Network Level Analysis (NLA) software fills this gap by offering a standardized toolkit that incorporates the hierarchical network structure of the brain to quantify connectome-wide associations with behavior.
Methods:
NLA is an extensible MATLAB based software package for the analysis of behavioral associations with brain connectivity data including functional, structural, or task connectivity. NLA utilizes a model-based statistical approach known variously as 'pathway analysis', 'over-representation analysis', or 'enrichment analysis', which was first used to describe behavioral or clinical associations in genome-wide association studies (Subramanian et al. 2005). In this way, NLA diverges from most contemporary tools either focusing on single connection associations (Marek et al., 2022) or cluster correction (Friston et al., 1994; Zalesky et al., 2010). By organizing connectivity-behavior associations according to an a priori model of underlying neurobiology (i.e., systems or networks), NLA leverages the structure of the human connectome and provides a framework for rational interpretation and replication of findings across research methodologies. Finally, the integration of connectome analysis and visualization techniques within a single, extensible MATLAB-based pipeline makes NLA a powerful tool for statistical testing and production of publication quality images all in one package (Figure 1A).
·Figure 1
Results:
To date, NLA has been used in several connectome-wide association studies spanning brain development to degeneration (Eggebrecht et al., 2017; Wheelock et al., 2019; Wheelock et al., 2023). Recently, we have developed a graphical user interface to increase accessibility (Figure 1B). We have significantly expanded the number of edge and network level statistical options, included additional quality control diagnostic plots, and incorporated many standard areal and system parcellation atlases (e.g., Schaefer, Gordon, Power, etc.).
Conclusions:
The NLA toolbox is a versatile analysis pipeline which leverages the structural and functional architecture of the brain in combination with rigorous statistical testing and validation procedures that can define brain-behavior relationships across species, across the lifespan, and in health and disease. Importantly, NLA uses data-driven permutation testing that respects the underlying covariance structure of connectivity data and leverages the fundamental topological structure of the connectome, affording whole-brain analyses while negating the need for punitive statistical thresholds.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Methods Development 1
Keywords:
Computational Neuroscience
Data analysis
Multivariate
Open-Source Software
Systems
Univariate
1|2Indicates the priority used for review
Provide references using author date format
Greene, D. J., Church, J. A., Dosenbach, N. U., Nielsen, A. N., Adeyemo, B., Nardos, B., . . . Schlaggar, B. L. (2016). Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI. Dev Sci, 19(4), 581-598. doi:10.1111/desc.12407
Noble, S., Mejia, A. F., Zalesky, A., & Scheinost, D. (2022). Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proc Natl Acad Sci U S A, 119(32), e2203020119. doi:10.1073/pnas.2203020119
Sripada, C., Rutherford, S., Angstadt, M., Thompson, W. K., Luciana, M., Weigard, A., . . . Heitzeg, M. (2020). Prediction of neurocognition in youth from resting state fMRI. Mol Psychiatry, 25(12), 3413-3421. doi:10.1038/s41380-019-0481-6
Subramanian, A. Gene set enrichment analysis: A knowledge-based approach for interpreting genomewide expression profiles. Proc Natl Acad Sci 102, 15545–15550 (2005).
Marek, S., Tervo-Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S., . . . Dosenbach, N. U. F. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature, 603(7902), 654-660. doi:10.1038/s41586-022-04492-9
Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: Identifying differences in brain networks. NeuroImage 53, 1197–1207 (2010).
Eggebrecht, A. T., Elison, J. T., Feczko, E., Todorov, A., Wolff, J. J., Kandala, S., . . . Pruett, J. R., Jr. (2017). Joint Attention and Brain Functional Connectivity in Infants and Toddlers. Cerebral Cortex, 27(3), 1709-1720. doi:10.1093/cercor/bhw403
Wheelock, M. D., Hect, J. L., Hernandez-Andrade, E., Hassan, S. S., Romero, R., Eggebrecht, A. T., & Thomason, M. E. (2019). Sex differences in functional connectivity during fetal brain development. Dev Cogn Neurosci, 36, 100632. doi:10.1016/j.dcn.2019.100632
Wheelock, M. D., Strain, J. F., Mansfield, P., Tu, J. C., Tanenbaum, A., Preische, O., . . . Dominantly Inherited Alzheimer, N. (2023). Brain network decoupling with increased serum neurofilament and reduced cognitive function in Alzheimer's disease. Brain, 146(7), 2928-2943. doi:10.1093/brain/awac498