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Abstract
Brain tumour microstructure is potentially predictive of changes following treatment to cognitive functions subserved by the functional networks in which they are embedded. To test this hypothesis, intra-tumoural microstructure was quantified from diffusion-weighted MRI to identify which tumour subregions (if any) had a greater impact on participants’ cognitive recovery after surgical resection. Additionally, we studied the role of tumour microstructure in the functional interaction between the tumour and the rest of the brain. Sixteen patients (22–56 years, 7 females) with brain tumours located in or near speech-eloquent areas of the brain were included in the analyses. Two different approaches were adopted for tumour segmentation from a multishell diffusion MRI acquisition: the first used a two-dimensional four group partition of feature space, whilst the second used data-driven clustering with Gaussian mixture modelling. For each approach, we assessed the capability of tumour microstructure to predict participants’ cognitive outcomes after surgery and the strength of association between the BOLD signal of individual tumour subregions and the global BOLD signal. With both methodologies, the volumes of partially overlapped subregions within the tumour significantly predicted cognitive decline in verbal skills after surgery. We also found that these particular subregions were among those that showed greater functional interaction with the unaffected cortex. Our results indicate that tumour microstructure measured by MRI multishell diffusion is associated with cognitive recovery after surgery.
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1 University of Navarra, School of Education and Psychology, Pamplona, Spain (GRID:grid.5924.a) (ISNI:0000 0004 1937 0271)
2 HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS), Sevilla, Spain (GRID:grid.414816.e) (ISNI:0000 0004 1773 7922); University of Cambridge, Department of Psychiatry, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934)
3 University of Cambridge, Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934); The Centre for Mathematical Imaging in Healthcare, Department of Applied Mathematics and Theoretical Physics, Cambridge, UK (GRID:grid.5335.0); University of Dundee, School of Medicine & School of Science and Engineering, Dundee, UK (GRID:grid.8241.f) (ISNI:0000 0004 0397 2876)
4 University of Cambridge, Academic Neurosurgery Division, Department of Clinical Neurosciences, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934)
5 University of Cambridge, Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934)
6 University of Cambridge, MRC Cognition and Brain Sciences Unit, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934)
7 Bar-Ilan University, Faculty of Engineering, Ramat Gan, Israel (GRID:grid.22098.31) (ISNI:0000 0004 1937 0503); Bar-Ilan University, The Gonda Multidisciplinary Brain Research Center, Ramat Gan, Israel (GRID:grid.22098.31) (ISNI:0000 0004 1937 0503)
8 Neurosciences Research Centre, St George’s, University of London and St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, London, UK (GRID:grid.451349.e)
9 University of Cambridge, Department of Psychiatry, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934); Cambridge and Peterborough NHS Foundation Trust, Cambridge, UK (GRID:grid.450563.1) (ISNI:0000 0004 0412 9303)