Estimated connectivity networks better predict disability than observed connectivity networks in Multiple Sclerosis

Journal Publications

Tozlu C, Olafson E, Jamison KW, Demmon E, Kaunzner U, Marcille M, Zinger N, Michaelson N, Safi N, Nguyen T, Gauthier S, Kuceyeski A. The sequence of regional structural disconnectivity due to multiple sclerosis lesions. Brain Commun. 2023

C Tozlu, S Card, K Jamison, S A Gauthier, A Kuceyeski. Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity. Network Neuroscience 2023.

K Buyukturkoglu, C Vergara, V Fuentealba, C Tozlu, Jacob B Dahan, B E Carroll, A Kuceyeski, C S Riley, J F Sumowski, C G Oliva, R Sitaram, P Guevara, V M Leavitt. Machine learning to investigate superficial white matter integrity in early multiple sclerosis. 2022. Journal of NeuroImaging.

H Rua SM, U W Kaunzner, S Pandya, E Sweeney, C Tozlu, A Kuceyeski, T D Nguyen, S A Gauthier. Lesion features on MRI discriminate multiple sclerosis patients. 2022. European Journal of Neuroimaging.

C Tozlu, K Jamison, Z Gu, S Gauthier, A Kuceyeski. Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups. 2021. NeuroImage: Clinical.

C Tozlu, K Jamison, S A Gauthier, A Kuceyeski. Dynamic functional connectivity better predicts disability than structural and static functional connectivity in people with multiple sclerosis. 2021. Frontiers in Neuroscience.

C Tozlu, Keith Jamison, Thanh Nguyen, Nicole Zinger, Ulrike Kaunzner, Sneha Pandya, Yi Wang, Susan Gauthier, Amy Kuceyeski. Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis. 2021. Brain and Behavior.

K Buyukturkoglu, D Zeng, S Bharadwaj, C Tozlu, E Mormina, K C Igwe, S Lee, C Habeck, A M Brickman, C S Riley, P L De Jager, J F Sumowski, V M Leavitt. Classifying multiple sclerosis patients on the basis of SDMT performance using machine learning. 2021. Multiple Sclerosis.

C Tozlu, D Edwards, A Boes, D Labar, K Z Tsagaris, J Silverstein, H P Lane, M R Sabuncu, C Liu, A Kuceyeski. Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke. 2020. Neurorehabilitation and Neuro Repair.

C Tozlu, B Ozenne, T-H Cho, N Nighoghossian, I Klærke Mikkelsen, L Derex, M Hermier, S Pedraza, J Fiehler, L Østergaard, Y Berthezène, J-C Baron, D Maucort-Boulch. Comparison of classification methods for tissue outcome after ischaemic stroke. 2019. European Journal of Neuroimaging.

Journal Publications - Under review

C Tozlu, E Olafson, K Jamison, Emily Demmon, Ulrike Kaunzner, Melanie Marcille, Nicole Zinger, Nara Michaelson, Neha Safi, Thanh Nguyen, Susan Gauthier, Amy Kuceyeski. The sequence of regional structural disconnectivity due to multiple sclerosis lesions. bioRxiv. 2023

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Selected conference presentations

C Tozlu, K Jamison, Z Gu, S Gauthier, A Kuceyeski. Functional connectivity networks estimated via deep learning outperform observed functional connectivity networks in classifying people with multiple sclerosis by disability level. Oral presentation. ECTRIMS 2021.

C Tozlu, K Jamison, Z Gu, S Gauthier, A Kuceyeski. Predicting disability from structural and functional coupling in multiple sclerosis. Oral presentation. ISMRM 2021.

K Buyukturkoglu, V Fuentealba, C Vergara, C Tozlu, JB Dahan, BE Carroll, C Guevara Oliva, A Kuceyeski, JF Sumowski, R Sitaram, P Guevara, VM Leavitt. Investigating Superficial White Matter Integrity in Early MS Using Machine Learning. Poster Presentation. ECTRIMS 2021.

K Buyukturkoglu, V Fuentealba, C Vergara, C Tozlu, JB Dahan, BE Carroll, C Guevara Oliva, A Kuceyeski, JF Sumowski, R Sitaram, P Guevara, VM Leavitt. Investigating Superficial White Matter Integrity in Early MS Using Machine Learning. Poster Presentation. AAN 2021.

UW Kaunzner, S Hurtado Rua, S Pandya, A Kuceyeski, C Tozlu, E Sweeney, N Nealon, J Perumal, T Vartanian, TD Nguyen, S Gauthier. Cluster analysis discriminates multiple sclerosis patients based on lesion size and myelin content. Poster Presentation. ECTRIMS 2020.

C Tozlu, K Jamison, A Simon, E Dhamala, S Gauthier, A Kuceyeski. Hurst exponent as an imaging biomarker of impairment in multiple sclerosis. Poster Presentation. ECTRIMS 2020.

K Buyukturkoglu, Y Li, C Tozlu, A Kuceyeski, J Sumowski, V Leavitt. Applying machine learning to multimodal neuroimaging data to predict visual episodic memory performance in multiple sclerosis. Poster Presentation. ECTRIMS 2020.

C Tozlu, K Jamison, S Gauthier, A Kuceyeski. Functional Connectivity Predicts MS Patients' Impairment Using An Ensemble Model Applied With A Machine Learning Approach. Oral Presentation. IMSVISUAL-ACTRIMS 2020.

C Tozlu, S Zhang, T Nguyen, N Nealon, J Perumal, T Vartanian, E Morris, UW Kaunzner, S Pandya, Y Wang, S Gauthier, A Kuceyeski. Classification Of MS Patients' Impairment Status Using Machine Learning Applied To Baseline Quantitative Susceptibility Mapping Imaging. Poster Presentation. ECTRIMS 2020.

K Buyukturkoglu, C Tozlu, S Bharadwaj, A Kuceyeski, J Sumowski, V Leavitt. Identifying The Role Of A Hippocampal-thalamic Memory Network In MS Using A Machine Learning Approach. Poster Presentation. ECTRIMS 2020.