Brain Networks Laboratory (Raj Lab)
The Brain Networks Laboratory focuses on understanding the mechanisms of healthy and diseased brains by applying computational tools to neuroimaging data.
People
Ashish Raj, PhD
Professor
[email protected]
I have more than 15 years experience in computer vision, signal processing, graph theory, medical imaging and informatics. I have had continuous and successive experiences in forming and leading research teams comprising of basic and clinical scientists in executing NIH-funded research projects. I have published more than 100 peer reviewed papers ranging from microwave engineering, superconductivity, image/signal processing, vision, graph theory and neuroscience, and two US patents. I have attracted several NIH grants, on graph algorithms for accelerated MRI, theoretical neuroscience and network modeling of dementia and Parkinson’s. These include the prestigious EUREKA and BRAIN Initiative grants by the NIH.
The defining characteristic of my work has been inter-disciplinarity: finding innovative ways to apply computation and algorithms to biomedical applications. My group was an early adopter of mathematical models of brain connectivity networks, a subject that marries computer science with neuroradiology. I have deep interest in the graph properties of brain networks, and how they are altered in neurological disorders like epilepsy, dementia, movement disorders, traumatic brain injury and stroke. My team has developed novel image reconstruction algorithms for fast MRI, motion correction for MR angiography, and new methods in tractography, Q-ball imaging, brain connectivity networks and computational neurology.
My lab now focuses on computational and graph theoretical modeling of the brain, using neuroimaging technologies combined with mathematical modeling. My research program is perfectly poised to help bring the fields of neurology and radiology into the era of personalized, precision medicine, using mathematical modeling and data science.
Chaitali Anand, PhD
Postdoctoral Scholar
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Chaitali is a post-doc in the Raj Lab. She is interested in understanding the factors affecting Alzheimer’s disease (AD) pathology progression, and uses the Network Diffusion Model as well as its extension - the Nexopathy in silico (Nexis) model to achieve that. Specifically, she is investigating AD pathology progression by combining the effects of network (connectome-mediated) spread, modulators of connectome-mediated spread (such as microglia), and cell-type-based regional vulnerability to the disease. In addition, Chaitali is highly interested in the translational aspects of understanding AD pathology and employs mathematical modeling on data acquired from mouse models of AD and human AD patients. She also has a joint appointment with the Chaumeil Lab where she applies MR spectroscopy techniques to assess cerebral metabolism in mouse models of AD.
Outside of lab, Chaitali enjoys going for runs by the Piers in SF, acrylic painting, and playing badminton.
Daren Ma
Specialist
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Daren is a Machine Learning Specialist in the Raj Lab. His research interests include the modeling of Alzheimer’s Disease, Brain Segmentation, Parcellation, and other applied Deep Learning methods in Neuroscience. His recent project is concentrated on predicting the cognitive ability using subjects’ T1-weighted MRI scans from the ADNI dataset. Daren is also responsible for handling the technical issues such as server maintenance in the lab.
Benjamin Sipes, MS
Specialist
[email protected]
Ben is a BioEngineering PhD student in the RajLab. His interests are varied, but they center around ways to predict brain function from structure. We use linear mathematical models to understand the brain’s structure-function problem, and Ben seeks to understand how different model parameters relate to health, development, and consciousness. Ben also does much of the RajLab data processing, which includes structural and diffusion MRI processing through pipetography, functional MRI with fMRIPrep, and XCPEngine. Outside of the lab, Ben enjoys reading fantasy, science fiction and philosophy, playing competitive games, painting, and hanging out with cats.
Sneha Pandya
Specialist
I am a research specialist in the field of biomedical engineering and am currently working for Weill Cornell Medicine, NY. Over the past 6+ years I have worked closely alongside Dr. Raj serving radiology and neurology departments by applying problem-solving techniques and engineering principles to current clinical problems in the imaging, diagnosis and treatment of major brain diseases. The predominant drive of my academic career has been applying automated techniques and performing statistical analysis to current issues in neuroimaging. As I continue my research career at Weill Cornell Medicine, I plan to extend my work by expanding my research pursuits and collaborations while concurrently maintaining my interest in the development and application of brain imaging.
Justin Torok
Postdoctoral Scholar
[email protected]
Justin is a postdoc in the Raj lab. His research has centered around utilizing mathematical models to explain the progression of neurodegenerative disease (NDD) in individual patients. Past work includes the development of a tool for inferring the regional origins of atrophy patterns in Alzheimer’s disease and Mild Cognitive Impairment clinical cohorts. He has also modeled axonal transport of pathological tau and co-led the development of a pipeline for mapping the distributions of cell types in the brain. His current research focus is to further extend existing models used by the lab to combine effects from network spread, interactions between amyloid-β and tau, and cell-type-based regional vulnerability. More broadly, he is interested in utilizing mathematical tools to advance our understanding of the underlying organizing principles of biology, and in particular how these are disrupted in human disease.
Parul Verma, PhD
Postdoctoral Scholar
[email protected]
Parul is a postdoc in the RajLab. Her work is on mathematical modeling of the relationship between the brain structure and function. She is using this modeling approach to investigate biophysics of different brain states and diseases. She is also currently funded by the Alzheimer’s Association postdoc fellowship to work on structure-function mechanisms of neural dynamics in Alzheimer's disease. Outside of the lab, Parul enjoys practicing karate and cooking.
Research
Predictive model of disease spread using network diffusion
Growing evidence suggests that a “prion-like” mechanism underlies the pathogenesis of many neurodegenerative disorders. Recently multiple reports suggest that misfolded αS can spread via a direct trans-neuronal “prion-like” mechanism, like other protein species involved in neurodegenerative disorders: tau, Aβ, TDP-43 and Huntingtin. Misfolded proteins appear to undergo a corruptive templating process, whereby it can trigger misfolding of adjacent same-species proteins, which in turn is thought to cascade along neuronal pathways. While these qualitative findings are becoming entrenched, we had proposed a connectivity-based graph-theoretic network-diffusion model (NDM) to convert these findings into quantitatively testable models. This model was successful in capturing the network-wide ramification of trans-neuronal transmission in Alzheimer’s and other dementias and in predicting future longitudinal progression.
A) Glassbrains of network diffusion model seeded at the bilateral substantia nigra shows spatial evolution of Parkinson’s from substantia nigra to connected striatal areas. B) Evolution of substantia nigra-seeded network diffusion exhibit the classic striatal and limbic areas as early affected regions.
Effects of neurodegeneration and injury to neuron spike trains
Injured neurons distort, confuse or block the information encoded in spike trains. Whether injury occurs through de- myelinating effects or focal axonal swellings, spike trains are compromised in a similar fashion in traumatic brain injuries as well as a number of leading neurodegenerative diseases such as Alzheimers and Multiple Sclerosis. We show in a simple phenomenological model of single cells that neural-response frequencies in the slow-gamma range of 38–41 Hz statistically emerge as the most insulated against common spike-train distortions caused by injury.
Structure-function modeling of brain networks
The relationship between the brain’s structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. Brain structure and function at the scale of macroscopic networks, i.e. amongst identifiable GM regions and their long-range connections through WM fiber bundles, can be adequately measured using current non-invasive measurement techniques. Similarly, brain function manifested in neural oscillations can be measured non-invasively and reconstructed across whole-brain networks. We address the open question of how does structure constrain functional activity patterns that arise on the macroscopic network with a linear, hierarchical graph spectral model of brain activity. This novel model yields an elegant closed-form solution of the structure-function problem with simple, universal rules of dynamics specified by few unknown parameters. This parsimony stands in contrast to conventional complex numerical simulations of coupled non-linear lumped neural mass models. The model was highly successful in reproducing empirical spatial and spectral patterns of activity measured by scalp magneto-encephalography (MEG) after source localization. The model may represent an important step towards understanding the fundamental relationship between network topology and the macroscopic whole-brain dynamics.
Pre-print paper: Spectral Graph Theory of Brain Oscillations
Brain Networks Lab Director
Brain Networks Raj Lab
185 Berry Street Bldg B, 370
San Francisco, CA 94158
China Basin campus
Ph: (415) 353-3442
Email: [email protected]
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