Towards Precision Medicine: Robust Longitudinal Estimation of White Matter Connectivity
Poster No:
1307
Submission Type:
Abstract Submission
Authors:
Philip Pruckner1, Remika Mito2, David Vaughan3, Kurt Schilling4, Gregor Kasprian5, Silvia Bonelli6, Robert Smith3
Institutions:
1Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia, 2Department of Psychiatry, The University of Melbourne, Melbourne, Australia, 3The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, 4Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, Tennessee, United States, 5Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, 6Department of Neurology, Medical University of Vienna, Vienna, Austria
First Author:
Philip Pruckner
Florey Department of Neuroscience and Mental Health, The University of Melbourne
Melbourne, Australia
Florey Department of Neuroscience and Mental Health, The University of Melbourne
Melbourne, Australia
Co-Author(s):
Kurt Schilling, PhD
Vanderbilt Institute for Surgery and Engineering, Vanderbilt University
Nashville, Tennessee, United States
Vanderbilt Institute for Surgery and Engineering, Vanderbilt University
Nashville, Tennessee, United States
Gregor Kasprian
Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna
Vienna, Austria
Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna
Vienna, Austria
Introduction:
The prospect of mapping longitudinal white matter connectivity changes holds significant promise for early diagnosis, disease monitoring, and personalized treatments. Reconstructing white matter connectomes from diffusion magnetic resonance imaging (MRI) however relies on complex, iterative algorithms that are inherently sensitive to image noise and initialization parameters. This sensitivity introduces substantial reconstruction variance(Smith et al., 2015b), making it difficult to reliably distinguish subtle biological changes from methodological imprecision. Such limitations are particularly problematic for clinical studies, where analyses often depend on a limited number of cases. To address this problem, we here introduce an unbiased(Reuter et al., 2012) connectomics framework that harnesses shared information between timepoints to reduce unwanted methodological variance.
Methods:
For each subject, a template across sessions is constructed, incorporating T1-weighted and diffusion MRI information. For an unbiased streamlines tractogram within this template, per-streamline cross-sectional weights are determined using the SIFT2 algorithm(Smith et al., 2015a). These weights then serve as initialisation for optimizing the same tractogram to fit session-specific fiber densities. Finally, per-streamline weights are aggregated between cortical parcels(Desikan et al., 2006) to quantify edge-wise Fiber Bundle Capacity (a measure of information bandwidth)(Smith et al., 2022).
We demonstrate the benefits of this framework investigating longitudinal structural connectivity changes of 36 patients who underwent resective temporal lobe surgery for treatment of epilepsy(Pruckner et al., 2023). Streamlines intersecting the subject's resected tissue had an enforced null weight in the postoperative reconstruction. Outcomes are compared against independent connectome reconstruction of individual timepoints using the same software tools (Fig 1).
We demonstrate the benefits of this framework investigating longitudinal structural connectivity changes of 36 patients who underwent resective temporal lobe surgery for treatment of epilepsy(Pruckner et al., 2023). Streamlines intersecting the subject's resected tissue had an enforced null weight in the postoperative reconstruction. Outcomes are compared against independent connectome reconstruction of individual timepoints using the same software tools (Fig 1).
·Figure 1
Results:
Independent reconstruction of connectomes produced dubious increases and decreases of high prevalence and magnitude across both hemispheres. Unbiased connectome quantification on the other hand revealed predominantly connectivity decreases, mostly located within the hemisphere ipsilateral to the resection (Fig. 2A). Compared to cross-sectional reconstruction, connectomes estimated within our unbiased framework showed reduced edge-wise variance of connectivity differences (Fig. 2B). Projection of connectivity estimates from the contralateral hemispheres-where limited longitudinal change is expected biologically-into a common low-dimensional space confirmed greater similarity of timepoint pairs for each participant when using the unbiased framework (Euclidean distance 0.16±0.08 vs. 0.32±0.32, Fig. 2C).
·Figure 2
Conclusions:
Our proposed unbiased framework drastically reduces methodological imprecisions of longitudinal structural connectome mapping, enabling robust quantification of white matter connectivity changes over time. Compared to cross-sectional connectome reconstruction, it yielded brain-wide longitudinal connectivity estimates that better align with a priori expectation(McDonald et al., 2010). This improved precision will especially benefit clinical studies, where inferences must be drawn from small cohorts or even individual cases. The robust estimation of brain connectivity within an unbiased imaging framework also opens exciting development avenues for novel quantitative tractography methods with increased precision, bringing advanced diffusion imaging one step closer to clinical application.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Diffusion MRI Modeling and Analysis 1
Keywords:
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Precision Medicine
1|2Indicates the priority used for review
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2. McDonald (2010). Changes in fiber tract integrity and visual fields after anterior temporal lobectomy. Neurology, 75(18), 1631–1638.
3. Pruckner (2023). Visual outcomes after anterior temporal lobectomy and transsylvian selective amygdalohippocampectomy: A quantitative comparison of clinical and diffusion data. Epilepsia, 64(3), 705–717.
4. Reuter (2012). Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage, 61(4), 1402–1418.
5. Smith (2015a). SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage, 119, 338–351.
6. Smith (2015b). The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. NeuroImage, 104, 253–265.
7. Smith (2022). Quantitative streamlines tractography: Methods and inter-subject normalisation. Aperture Neuro, 1–25.