Sairanen, V. | Search bioRxiv

bioRxiv2024

Combining function and structure in a single macro-scale connectivity model of the human brain

Understanding how functional relationships relate to the brains structural architecture remains a central challenge in network neuroscience. Many Laplacian-based approaches describe function-structure coupling at the node level, which can make it difficult to identify the specific anatomical pathways that support observed functional relationships. This work introduces a constrained Laplacian formulation that incorporates externally specified pairwise functional relationships and yields a nodal field whose graph-gradient representation produces edge-level quantities describing how the structural network accommodates these relationships. The method is implemented using Modified Nodal Analysis, enabling efficient computation on large connectomes. Given an observed pattern of functional associations and a structural connectivity graph, the proposed framework estimates which structural edges are most consistent with supporting the imposed pattern. The framework is demonstrated in multiple settings, including a single-subject example, a controlled diffusion phantom, an in-silico function-structure simulation, and analyses of Human Connectome Project data at both group level (207 subjects) and test-retest conditions (three subjects). Across these applications, the method produces an edge-level representation of function-structure coupling, enabling pathway-specific analysis of brain connectivity.

neuroscience↗

bioRxiv2021

Incorporating outlier information into diffusion MR tractogram filtering for robust structural brain connectivity and microstructural analyses

The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) filtering algorithm to adjust for these signal drop-out motion artifacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable in properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effect in the human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available. HighlightsO_LIWe present a novel augmentation to tractogram filtering method that accounts for subject motion related signal dropout artefacts in diffusion weighted images. C_LIO_LIOur method is validated with realistic Monte-Carlo whole brain simulations and evaluated with in vivo infant data. C_LIO_LIWe show that even if data has 10% of motion corrupted slices our method is capable to mitigate their effect in structural brain connectivity analyses and microstructural mapping. C_LI

neuroscience↗