iBRAIN research laboratory – Bioinformatics Research in Artificial Intelligence and Neuroimaging
- Key terms | The group | Research goal and overview | Projects | Department collaborations | Project funding | Publications
- Brain tumours, Alzheimer’s Disease (AD), Traumatic Brain Injury (TBI), Ageing, Neuroimaging, Magnetic Resonance Imaging (MRI), Artificial Intelligence (AI)
2020 iBRAIN research unit weekly meeting over Zoom. L-R: Ian Harding (group head), Meng Law (group head), Adil Zia, Scott Kolbe (group head), Will Khan, Jo Wigglesworth, Andrei Irimia (US collaborator), Sahan Muthuhetti Gamage and Ben Sinclair, Louisa Selvadurai, Nurul Modh Shukur, Helen Kavnoudias, Brendan Major, Frederique Boonstra.
Meet the team View
To determine imaging, genomic, other biomarkers in early diagnosis, to apply artificial intelligence for more effective clinical management, and to test new therapeutic agents in clinical trials of both neurological and neurodegenerative diseases (e.g. Alzheimer’s Disease).
Our research is all about translational clinical imaging in neurological disease primarily, and also some non-neurological diseases. We utilise state of the art and ultra-high field MRI, PET, CT, magnetic particle imaging, and photon microscopy approaches towards imaging pathology. We also house a data warehouse/repository for imaging and other clinical data types to be used for machine learning, deep learning, artificial intelligence applications in diagnostics and therapeutics. We also perform pre-clinical and clinical trials on novel therapeutics in neurodegenerative diseases and non-neurological diseases.
We can facilitate access to, and use of imaging, storage and analytical infrastructure for all types of human and preclinical neuroscience applications. See our flier.
Current project funding
|Years||Title||Principal Investigators||Funding source||Amount|
|2015 - 2020||Alzheimer Disease Research Center (ADRC)||Chui||NIH/NIA P50-AG05142-31||$124,701|
|2016 - 2021||Vascular Contributions to Dementia and Genetic Risk Factors for Alzheimers Disease||Toga, Zlokovic||NIH/NIA P01-AG052350||$1,793,519|
|2017 – 2021||Precise DCE-MRI Assessment of Brain Tumours||Nayak, Law||NIH R33-CA22540-01||$262,513|
|2017 - 2022||Perimenopause in APOE4 Brain: Clinical Outcomes and Global Impact||Law||NIH P01-AG026572||$292,469|
|Montagne A, Barnes S, Sweeney M, ..., Law M, Zlokovic B. Blood-Brain Barrier Breakdown in the Aging Human Hippocampus. Neuron Jan 2015.||This paper was seminal in demonstrating the leakiness of the BBB with aging and in memory loss compared to controls, key in our hypothesis for the vascular contributions to Alzheimers Dementia. CI Law collected the human data and optimized the DCE MRI sequence for obtaining high resolution Ktrans data from the human hippocampus.|
|Barnes SR, Ng TS, Montagne A, Law M, Zlokovic BV, Jacobs RE. Optimal acquisition and modeling parameters for accurate assessment of low Ktrans BBB permeability using DCE MRI Magn Reson Med. 2016.||This paper provided the rationale for the parameters we employed for the acquisition and modeling of DCE MRI in applying to disease states with lower BBB permeability than brain tumors. These parameters have since been used in the Neuron paper and other publications in the investigation of the vascular contributions to Alzheimers Dementia.|
|Kammen A, Law M, Tjan B, Toga A, Shi Y. Automated Retinofugal Visual Pathway Reconstruction with Multi-shell HARD and FOD based analysis Neuroimage 2016||This paper describes a new approach in the modeling of DTI towards microstructural imaging with multi-shell DTI using a fiber orientation density to more accurately characterize the visual pathway. It was critical in the successful funding of an NIH grant the Low Vision Connectome which demonstrated the entire visual pathway from the retinotopic coordinates along the visual pathways to the cortical coordinates in the occipital visual cortex. lind patients with diseases such as macula degeneration and retinitis pigmentosa who have been treated with the Argus retinal bionic eye and stem cell therapies show neural regeneration along these pathways.|
|Gajawelli N, Lao Y, Apuzzo ML, ..., Law M. Neuroimaging changes in the brain in contact versus non-contact sport athletes using diffusion tensor imaging. World Neurosurg. 2013 Dec;80(6):824-8||The paper demonstrates differences on MRI and DTI in the brain between football players and volleyball players. It contributed to the changing of some of the rules with college and NFL football.|
|Barisano G, Sepehrband F, Ma S, ... Law M. Clinical 7T MRI: Are we there yet? A review on MRI at Ultra high field. Br J Radiol. 2018 Nov 1:2018049|
Montagne A, Nation DA, Sagare AP, et al. APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline. Nature. 2020;581(7806):71-76. doi:10.1038/s41586-020-2247-3
Sepehrband, F, Cabeen, R, Barisano, G, Sheikh-Bahaei, N, Choupan, J, Law, M, Toga, A, Alzheimer's Disease Neuroimaging Initiative. Nonparenchymal fluid is the source of increased mean diffusivity in preclinical Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring. 2019; 11: 348-354. 2.
Sepehrband, F, Cabeen, R, Choupan, J, Barisano, G, Law, M, Toga, A. Perivascular space fluid contributes to diffusion tensor imaging changes in white matter. NeuroImage. 2019; 197: 243-254.
Elshafeey, N, Kotrotsou, A, Hassan, A, Elshafei, N, Hassan, I, Ahmed, S, Abrol, S, Agarwal, A, El Salek, K, Bergamaschi, S, Acharya, J, Moron, F, Law, M, Fuller, G, Huse, J, Zinn, P, Colen, R. Multi-center demonstrates radiomic features derived from MRI perfusion images identify pseudorogression in glioblastoma. Nat Communication. 2019; 10 (1): 3170.
Nation DA, Sweeney MD, Montagne A, Law M et al. Blood–brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat Med. 2019. 25 (2): 270. Doi. 10.1038/s41591-018-0297
Ge Z, Xing Y, Zeng R, Mahapatra D, Seah J, Law M, Drummond T. Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation. Proc MICCAI 2019 6. Seah J, Tang J, Law M. Predicting Age from Non Contrast CT Brains using Deep Learning Algorithm. Proc. Annual Meeting of the ASNR May 2019
Seah J, Tang J, Law M. Predicting Age from Non Contrast CT Brains using Deep Learning Algorithm. Proc. Annual Meeting of the ASNR May 2019
Kim H, Irimia A, Crawford K, Liew SL, Law M, Mukherjee P, Manley G, Van Horn J, Toga AW et al. LONI QC system: a semi-automated, web-based deep learning approach for the comprehensive quality control of neuroimaging data. Frontiers in Neuroinformatics. 2019; 13: 60.
Sepehrband, F, Barisano, G, Sheikh-Bahaei, N, Cabeen, R, Choupan, J, Law, M, Toga, A. Image processing approaches to enhance perivascular space visibility and quantification using MRI. BioRxiv. 2019; 609362.
Barisano G, Sepehrband F, Ma S, Jann K, Cabeen R, Wang DJ, Toga AW, Law M. Clinical 7T MRI: Are we there yet? A review on MRI at Ultra high field. Br J Radiol. 2018; Nov 1:2018049.
Duncan D, Barisano G, Cabeen R, Sepehrband F, Garner R, Braimah A, Vespa P, Pitkänen A, Law M, Toga AW. Analytic tools for post-traumatic epileptogenesis biomarker search in multimodal dataset of an animal model and human patients. In: Frontiers in Neuroinformatics; 2018, Vol. 12.
Kammen A, Law M, Tjan B, Toga A, Shi Y. Automated Retinofugal Visual Pathway Reconstruction with Multi-shell HARD and FOD based analysis. Neuroimage. 2016; 125: 767-779.
Gajawelli N, Lao Y, Apuzzo ML, Romano R, Liu C, Tsao S, Hwang D, Wilkins B, Lepore N, Law M. Neuroimaging changes in the brain in contact versus non-contact sport athletes using diffusion tensor imaging. World Neurosurg. 2013; 80(6): 824-8.
Chervenak A, Van Erp T, Kesselman C, Law M, Hasso A, Ames J, MacCiardi F, Potkin S et al. A system architecture for sharing de-identified, research-ready brain scans and health information across clinical imaging centers. HealthGrid Applications and Technologies Meet Science Gateways for Life Sciences. IOS Press, 2012. pp. 19-28 (Studies in Health Technology and Informatics).