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.

The Brain Networks Laboratory focuses on understanding the mechanisms of healthy and diseased brains by applying computational tools to neuroimaging data


Pablo Damasceno, PhD - Brain Network LabPablo Damasceno, PhD
Post Doc
[email protected]

As a physicist and data scientist, I look for simple models that reveal the basic mechanisms underlying natural processes while also attempting to gain insights from large collections of data. My goal is to integrate machine learning techniques and bottom-up theories to improve our models, our understanding of neurodegeneration and, ultimately, patient care. 

Xiao Gao, MBBS, MS - Brain Network LabGavin (Xiao) Gao, MBBS, MS
Clinical Research Coordinator
[email protected]

The heterogeneity across brain hemisphere is highly organized, following an underlying rule of both time (evolution and development) and space (connectome). A deeper deciphering of such a hierarchical heterogeneity could help us better understand how human brain works, in terms of feature extraction, consciousness, memory, and motivation, as well as help explain several stereotypical neural disease patterns, like AD and PD. I am currently keen on constructing a computational model integrating brain functional/constructional heterogeneity seen at different levels, including transcriptome, MEG data, and DTI data, meanwhile applying this model in predicting prognosis of clinical neurodegenerative patients. 

Pedro Maia, PhD - Brain Network LabPedro Maia, PhD
Postdoctoral Scholar
[email protected]

I am broadly interested in mathematical biology, especially in the emerging field of computational neurology. I undertake a modern approach to applied mathematics which includes modeling, computation, and data-driven analysis. Some of my main interests include mathematical models for focal axonal swellings arising in traumatic brain injuries and neurodegenerative diseases, using techniques such as spectral methods for nonlinear partial differential equations (active cable equations), information theory, neuronal coding, neuronal network dynamics, dimensionality reduction, statistical shape analysis and more. More recently, I’ve been broadening my research scope to apply machine learning to biomedical imaging, whole-brain network modeling, graph models for pathological spread of misfolded proteins in the brain, and data-driven techniques such as dynamical mode decomposition and deep learning. I am always excited to apply mathematics in creative ways to medicine, and, in particular, to brain disorders and cognitive deficits.

Christopher MeziasChristopher Mezias
PhD Candidate

My research in Dr. Raj’s lab focuses on two major topics. First, I attempt to predict tau and synuclein pathology spread patterns, over time, in mouse models of disease using frameworks that posit spread along the anatomical connectivity of the brain. I’m particularly interested in directional biases, relative to axon or fiber tract polarity, between the tau and synucelin pathologies of different degenerative conditions and am exploring these as a cause of the typified spatiotemporal disease progressions observed across different conditions. Second, I explore the relationship between anatomical brain. features, such as developmental groupings of regions, regional gene expression, and regional cell-type architecture and the brain’s connectivity network. I’m currently studying this in mice but will be expanding the project to monkey and human connectomes.

Sneha Pandya

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 - Brain Network LabJustin Torok
PhD Candidate
Visiting Graduate Student
[email protected]

My research in Dr. Raj’s lab 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. My current research focus is to extend our lab’s current mathematical framework of macroscale NDD dynamics using insights from modeling disease processes on a microscopic level. More broadly, I am 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.

Naomi Xia
PhD Candidate

I’m interested in understanding cognitive processes in healthy and diseased states using single cell resolution calcium imaging in awake behaving animals. She hopes to apply high and low dimensional encoding, graph theory and machine learning methods to describe how populations of cell activity interact in space and time to subserve memory and learning.

Xihe Xie - Brain Network Lab

Xihe Xie
PhD Candidate
Visiting Graduate Student
[email protected]

My research focuses on applying a combination of computational algorithms on mathematical models of brain networks. I believe the valuable data from human neuroimaging can reveal relationships between the brain’s structure and function, and we can utilize creative models to bridge the gap between empirical data and theories about the brain. 

Chang Cai, PhD
Postdoctoral Scholar
[email protected]

Chang is a Post Doc in the Biomagnetic Imaging Lab. He is an expert on source reconstruction algorithms for MEG and EEG. He is closely collaborating with the Brain Networks Lab on the efforts of modeling whole brain dynamics in both healthy and diseased brains.


January 8, 2019

Dr. Ashish Raj was recently featured as a research topic editor for Frontiers in Neurology, the ebook “Network Spread Models of Neurodegenerative Diseases” features articles contributed by leading experts proposing various network models of disease spread in the brain. The emerging field of network neuroscience visualizes the brain as a graph consisting of nodes representing regions and edges as connections between them. This complex network not only supports efficient communication along neural projects for brain activity, but also, the transmission and progression of neurodegenerative disorders like Alzheimer’s disease. If we could know the brain’s network organization, could we then predict how degenerative processes might develop on this network? The answer is, increasingly, yes.

The ebook with a collection of papers on various network models focusing on disease-specific mathematical modeling can be found here.

Raj, A., Medina, Y. I., eds. (2019). Network Spread Models of Neurodegenerative Diseases. Lausanne: Frontiers Media. Doi: 10.3389/978-2-88945-768-7

Neurohackademy 2018

August 10, 2018

Poster Presentation on "A Spectral Graph Model of Whole Brain Dynamics"

Graduate student from the Brain Networks Laboratory, Xihe Xie, attended Neurohackademy 2018 at University of Washington's eScience Institute in Seattle from July 30th to August 10th.

The two-week hackathon included daily lectures from leaders in the neuroimaging field, and more than 60 neuroscientists at various stages of their careers participated in discussions about neuro-ethics, reproducibility, and open source tools in neuroimaging. In the welcoming and collaborative spirit of the open science community, the course materials and lecture recordings are all shared via Neurohackademy's Github repository. The stimulating environment led to the creation of many collaborative projects within just about 4 days of hackathon, which are also shared via a Github repo.

Xihe worked alongside a group of talented brain hackers to decipher Nipype1 workflows and applied newly programmed processing steps to The Virtual Brain toolbox2. The project was inspired by The Virtual Brain's ability to simulate functional connectivity maps with neural mass models over a large range of parameters. Most researchers use The Virtual Brain for parameter space exploration over large number of subjects, but the computation time required for such a project grows exponentially as the number of simulations increases. Wrapping Nipype workflows around The Virtual Brain's python library not only parallelizes the simulations for all subjects and parameters, the creation of dependent python objects is also parallelized in the workflow. The group's hackathon project can be found in the Neurohackademy project's repository.

The hackademy also hosted a poster session where the Brain Networks Laboratory presented a poster on "A Spectral Graph Model of Whole Dynamics". It was one of the few posters focusing on network models of brain activity. The poster showcased "eigen-modes" as a powerful extract from diffusion-MRI data, and how well our parsimonious model captures the spatial distribution of whole brain dynamics. The manuscript is being constructed, so keep an eye out for the news in the near future!


[1] Gorgolewski, K., Burns, C.D., Madison, C., Clark, D., Halchenko, Y.O., Waskom, M.L., Ghosh, S.S. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5:13.

[2] Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide, Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013) The Virtual Brain: a simulator of primate brain network dynamics.Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)


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, , 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.

Nigra-seeded network diffusion shows spatial evolution of Parkinson’s

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.

Injured neurons spike trains distortions

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.

Structure-function modeling of brain networks

Pre-print paper:  Spectral Graph Theory of Brain Oscillations

 Brain Networks Lab Director