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William Drew

BWH Job Title:

Data Analyst II

Academic Rank:




Division: Center for Brain Circuit Therapeutics

Fox Lab; Center for Brain Circuit Therapeutics


William Drew, Alex Cohen, Amy Brodtmann, Maurizio Corbetta, Natalia Egorova, Sophia Gozzi, Jordan Grafman, Andrew M. Naidech, Joel L. Voss, Thomas Yeo, Michael D. Fox, Shan H. Siddiqi

Brain lesion parcellation using a precomputed human brain connectome improves symptom localization


Introduction: Lesion network mapping (LNM) uses brain lesions to link symptoms to functional brain networks causally. However, there are two main limitations of this method. First, LNM assumes a brain lesion is connected to a single functional brain network. However, lesions may span multiple functionally distinct brain regions, potentially introducing noise. Second, LNM is computationally inefficient, inhibiting the use of large connectome datasets like the 40,000+ subject UK Biobank. We attempt to resolve these limitations by developing a method to parcellate brain lesions using resting-state functional connectivity into regions connected to distinct networks and a method to compute lesion network maps rapidly and efficiently. We also apply these methods to improve lesion-symptom prediction of depression.

Methods: First, we generated a precomputed human brain connectome (PHBC) by computing each voxel’s mean whole-brain functional connectivity across 1,000 healthy individuals. Next, to address LNM’s first limitation, we developed the “precomputed” LNM method. Using the PHBC, a weighted average of the functional connectivity maps associated with the ROI’s voxels is computed, and a scaling factor is applied to account for differences between individual voxel BOLD signal strengths. Weights are computed as the standard deviation of a voxel’s BOLD signal amplitude. To address LNM’s second limitation, we used the PHBC to parcellate stroke lesions into distinct regions with common functional connectivity patterns. For each lesion, we extract a connectivity matrix comprising connectivity measures between every pair of voxels. Next, the connectivity matrix is thresholded to remove weak connections between voxels. The thresholded connectivity matrix is then clustered using Infomap. This modular community detection algorithm has been previously validated for resting-state functional network parcellation to group voxels into clusters with similar connectivity profiles. For each lesion, we used the largest parcel as a seed to generate functional connectivity maps. We compared these maps to depression outcomes in five lesion datasets (n=449), yielding a connectivity map of parcels associated with depression. The largest parcel was selected as a simple metric for the optimal component. We hypothesized that LNM using lesion parcels would explain more variance in post-lesion depression than whole lesions.

Results: Functional connectivity maps of whole lesions generated using both the “precomputed” and conventional LNM methods were similar (r=0.997) and were far more efficiently computed (~7X faster) when using a 1,000-subject normative functional connectome. In a leave-one-dataset-out cross-validation, lesion network maps derived from the largest parcel of each lesion from four datasets predicted depression outcomes in the fifth (r=0.155, p<0.001). This was significantly stronger (p=0.0011) than the predictive value of whole lesions. Conclusions: The PHBC and the “precomputed” LNM method accelerate LNM, enabling functional connectivity analyses of even single voxel lesions in a computationally efficient manner. The PHBC also enables functional parcellation of lesions, potentially removing noise from lesion analyses. This parcellation method significantly improved lesion-symptom localization when using each lesion’s largest parcel compared to whole lesions. We hypothesize that further LNM improvements can be achieved using a more specialized parcel selection method, such as selecting multiple relevant lesion parcels to consider their interacting network effects. References: 1. Fox, M. D. (2018), ‘Mapping Symptoms to Brain Networks with the Human Connectome’, The New England Journal of Medicine, 379(23), 2237–2245. 2. Alfaro-Almagro, F. et al. (2018), ‘Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank’, NeuroImage 166, 400–424 (2018). 3. Sanchez-Rodriguez, L. M., Iturria-Medina, Y., Mouches, P., et al. (2021), ‘Detecting brain network communities: Considering the role of information flow and its different temporal scales’, NeuroImage, 225, 117431. 4. Gordon, E. M., Laumann, T. O., Gilmore, A. W., et al. (2017), ‘Precision Functional Mapping of Individual Human Brains’, Neuron, 95(4), 791-807.e7. 5. Siddiqi, S. H., Schaper, F. L. W. V. J., Horn, A., et al. (2021), ‘Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease’, Nature Human Behaviour, 5(12), Article 12.