Meta-analytic functional decoding of the cortical terminations of the 45 major white matter tracts
Poster No:
1775
Submission Type:
Abstract Submission
Authors:
Sohmee Kim1, Daniela Reyes1, Julio Peraza2, Sihang Guo1, Angela Laird2, Kimberly Ray1, Franco Pestilli1
Institutions:
1The University of Texas at Austin, Austin, TX, 2Florida International University, Miami, FL
First Author:
Co-Author(s):
Introduction:
White matter tracts (WMT) support brain function and behavior by connecting distal cortical regions. Currently, over 40 major WMT can be routinely mapped [1]. The tracts have been associated with some functional brain properties in health and disease [2]. However, the functional properties of the cortical termination of all the WMT remain poorly characterized. Moreover, the relationship between WMTs and cognitive functions have not been well characterized. Here, we leverage the Human Connectome Project (HCP) dataset [5], reproducible data processing platforms for white matter mapping [3], and meta-analytic methods [4] to characterize the relationship between the WMT cortical termination maps (CTMs) and functional activity reported in 13,459 functional MRI studies.
Methods:
Diffusion-weighted MRI data of 1,062 subjects from the HCP dataset [5] were processed with a series of web-based apps available on brainlife.io platform to create whole-brain tractograms [3]. Tractograms were then used to map white matter termination points on the cortical surface, generating CTMs for 45 major WMTs. CTMs were transformed to MNI space and averaged across subjects to create a density atlas of cortical terminations (Fig. 1A Structure).
Independent of WMT analyses, a Latent Dirichlet allocation (LDA)-based decoder trained on 13,459 functional MRI studies provided 104 functional topics and their associated brain activation coordinates [4] (Fig. 1A Function). CTMs were submitted to the LDA decoder to identify associations between white matter termination points and cognitive functions (Fig. 1A Results). Finally, hierarchical clustering analysis (HCA) clustered WMTs based on functional associations of the termination points.
Independent of WMT analyses, a Latent Dirichlet allocation (LDA)-based decoder trained on 13,459 functional MRI studies provided 104 functional topics and their associated brain activation coordinates [4] (Fig. 1A Function). CTMs were submitted to the LDA decoder to identify associations between white matter termination points and cognitive functions (Fig. 1A Results). Finally, hierarchical clustering analysis (HCA) clustered WMTs based on functional associations of the termination points.
Results:
Cognitive functions associated with the CTMs of 4 exemplary tracts used for validation are shown in Fig. 1B-E. First, termination points of the vertical occipital fasciculus (VOF) were strongly associated with visual processing, corroborating previous findings [1]. Posterior termination points of the Inferior Longitudinal Fasciculus (ILF), located in the occipital lobe, were strongly associated with visual functions, including motion detection, saccades, and object recognition. In contrast, anterior ILF termination points in the temporal lobe were linked to auditory processing, such as speech perception and music processing, as well as higher-order cognitive functions like sentence comprehension. This suggests that ILF connects the occipital and temporal lobes, facilitating the integration of visual, auditory, and semantic processing. The arcuate fasciculus, connecting Broca's and Wernicke's areas, was strongly related to language, sentence production, and comprehension. Consistent with the corticospinal tract's (CST) well-established role in motor control, its anterior termination points were closely associated with motor functions such as voluntary movement, motor execution, and fine motor control.
HCA identified 5 clusters of WMTs (Fig. 2). Cluster 1 included language-related tracts (e.g., arcuate fasciculus) and was linked to semantic and syntactic processing. Cluster 2 encompassed tracts associated with self-referential processing and the default mode network. Cluster 3 consisted of motor-related tracts, including the CST. Cluster 4 highlighted tracts linked to visual processing, predominantly in the occipital lobe. Cluster 5 involved tracts associated with task performance and motor control, spanning precentral and postcentral gyri. Validation with prior WM studies confirmed known functional roles while uncovering novel tract-function relationships.
HCA identified 5 clusters of WMTs (Fig. 2). Cluster 1 included language-related tracts (e.g., arcuate fasciculus) and was linked to semantic and syntactic processing. Cluster 2 encompassed tracts associated with self-referential processing and the default mode network. Cluster 3 consisted of motor-related tracts, including the CST. Cluster 4 highlighted tracts linked to visual processing, predominantly in the occipital lobe. Cluster 5 involved tracts associated with task performance and motor control, spanning precentral and postcentral gyri. Validation with prior WM studies confirmed known functional roles while uncovering novel tract-function relationships.
Conclusions:
This work provides an atlas of CTMs of healthy adults and their quantitative meta-analytic functional decoding. HCA revealed 5 clusters of WMT based on the similarity of their functional profiles. The findings advance our understanding of the diverse functional processes, and potentially networks, underpinned by WMTs, providing valuable insights into their roles in brain function.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Neuroinformatics and Data Sharing:
Informatics Other 2
Keywords:
Cognition
Data analysis
Informatics
Meta- Analysis
MRI
Open Data
Open-Source Code
STRUCTURAL MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
2. Fields, R. D. White matter in learning, cognition and psychiatric disorders. Trends in neurosciences 31, 361-370 (2008).
3. Hayashi, S. et al. brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nat Methods 21, 809–813 (2024).
4. Peraza. J. A. et al. Methods for decoding cortical gradients of functional connectivity. Imaging Neuroscience 2, 1-32 (2024).
5. Van Essen, D. C. et al. The Human Connectome Project: a data acquisition perspective. Neuroimage 62, 2222–2231 (2012).