Jiayu Chen Research Assistant Professor, TReNDS Center & Georgia State University
My research interests center on imaging genomics, aiming to characterize the genetic underpinnings of neurobiological traits that further relate to phenotypic manifestations of various mental disorders. My research involves developing computational methods to mine high-dimensional complex data for better understanding of genetic effects on brain at network level, as well as data fusion to integrate different modalities, spanning omic (genetic, epigenetic, transcriptomic), neuroimaging (sMRI, fMRI, DTI) and phenotypic levels, to better characterize mental illnesses and facilitate improved patient stratification and more precise treatment planning.
Zening Fu Research Scientist, TReNDS Center & Georgia State University
I am interested in developing novel algorithms for tracking the dynamic brain connectivity and exploring the underlying physiological meaning of brain dynamics. Human brain is a highly dynamic system characterized by non-stationary neural activities and represented by changing psycho-physical states and mental functions. Identifying brain dynamics from complex neuroimaging signals and exploring the functional relevance of brain dynamics has gained increasing popularity for it is essential towards understanding how brain is organized to support cognitive and affective processes as well as utilization of brain dynamics in neural engineering and clinical applications. My current work could be divided into two parts: the first part focuses on developing new methods for tracking the dynamic brain connectivity; the second part focuses on identifying the brain dynamics and exploring their potential physiological mechanism.
Jeremy Bockholt Manager & Analyst Programmer, TReNDS Center [GSU|GATech|Emory]
H. Jeremy Bockholt works as the manager of application development at TReNDS. Mr. Bockholt is an enterprise data management professional and senior full stack software engineer working on commercialization of technology developed at TReNDS, including COINS, COINSTAC and other projects. Mr. Bockholt helps to lead software development activities at TReNDS and assists faculty and senior staff to develop proposals for external funding of various biomedical informatics research topics.
Anees Abrol Research Scientist, TReNDS Center & Georgia State University
Dr. Abrol investigates the development of advanced machine learning and signal processing based deep data fusion frameworks to understand complex interactions in multimodal brain imaging data. Development of such end-to-end trained deep learning models is targeted to facilitate the discovery of crucial non-linear interrelationships between the data modalities, otherwise inaccessible to standard machine learning models. Leveraging this additional wealth of information can enable breakthrough advances in our pursuit of significant neuroimaging objectives such as identifying disease biomarkers at early stages, predicting progression to brain abnormalities and evaluating treatment effects of drugs on individuals with cognitive impairments. Motivated by this, his ongoing research includes assessing the effectiveness of engaging deep learning models to explain vital neuroimaging tasks, with a particular focus on sourcing superior lower-dimensional representations and finer methodical interpretations. Other research interests include exploring complex spatiotemporal associations in brain dynamics wherein his significant contributions include corroborating robustness and disease characterization/prediction utility of time-varying functional connectivity state profiles of the human brain at rest.
Jeffrey Malins Assistant Professor, Department of Psychology, Georgia State University
Nice to meet you! I am an assistant professor in the Department of Psychology at Georgia State University, and I am also affiliated faculty with the GSU Center for Research on the Challenges of Acquiring Language and Literacy and the GSU Neuroscience Institute. Prior to joining the faculty at GSU, I was an Associate Research Scientist in Pediatrics at Yale University. I also completed a postdoctoral fellowship at Haskins Laboratories, where I remain a Research Affiliate.
My research focuses on the brain networks that support reading, spoken language processing, and attentional control. I use neuroimaging to study how these networks overlap, diverge, and change over the course of learning. I also examine how different biological, cognitive, and environmental factors shape the connectivity of these networks. In my research, I work with numerous populations of learners, including school-age children, adolescents, and adults; individuals with reading, language, and/or attention deficits; and individuals who speak or read more than one language.
Over the past few years, I have had the pleasure of working with several collaborators in the GSU community to study reading development in children. Using fMRI, we are currently looking at the intersection between the brain networks underlying reading and attentional control (Arrington, Malins, et al., 2019, Developmental Cognitive Neuroscience). We are also following up on a recent study suggesting that a certain amount of variability in brain activity may be beneficial for reading growth (Malins et al., 2018, Journal of Neuroscience). In the future, I am particularly interested in examining how diverse experiences with language – such as bilingual language experience in children – help to shape the brain networks that support literacy skills.
I look forward to continuing to build connections with the neuroimaging community in Atlanta and beyond. Together, I hope we can find ways to connect brain research with current practices in education in order to help individuals reach their learning potential.
Enrico Premi , Spedali Civili Hospital
I attended medical school at the University of Brescia (Italy), completing my residency program in Neurology at University of Brescia (Prof. Alessandro Padovani) in 2012. During this period, I had had the opportunity to increase my expertise in the field of neurodegenerative diseases (Prof. Barbara Borroni, Prof. Alessandro Padovani) from a clinical and research point of view. I focused my research interests on neuroimaging (2007: San Raffaele University, Milan, Italy: Prof. Perani; 2009: SPM course, UCL, London, UK; 2012: Neuroimaging Lab, Fondazione “S.Lucia”, Rome, Italy, Prof. Bozzali). I currently have a permanent contract as clinical neurologist in the Stroke Unit, ASST “Spedali Civili” (Brescia, Italy): my everyday clinical practise is focused on the clinical evaluation and treatment of patients with cerebrovascular diseases (ischemic and haemorragic stroke). In parallel, I continued my research activity in collaboration with the Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia. Since my residency period my research interests was focused on the application of advanced neuroimaging techniques to neurodegenerative diseases, in particular Frontotemporal Dementia (FTD) to explore structural and functional correlates of the disease. In the last years I explored in particular: i) the preclinical phase of FTD (studying presymptomatic carriers of FTD causative mutations), ii) the behavioral/neuroimaging correlates in FTD, iii) the neuroanatomical correlates of Cognitive Reserve in FTD, iv) the utilization of advanced statistical approaches to clinical neuroimaging in neurodegenerative diseases.
Rogers Ferreira Da Silva Research Scientist, TReNDS Center & Georgia State University
Dr. Rogers F. Silva is a multidisciplinary scientist with extensive experience developing algorithms for statistical and machine learning, image analysis, numerical optimization, memory-efficient large-scale data reduction, and distributed analyses of big multimodal, multi-subject neuroimaging data.
His research interests include · heterogeneous multimodal data fusion · deep statistical and machine learning · image, video and data analysis · multiobjective, combinatorial and constrained optimization · image and signal processing · multimodal neuroimaging
As a modeling-oriented scientist actively conducting research on novel multimodal multidimensional learning (MML) methods for brain research, Dr. Silva leverages his multidisciplinary background to create new algorithms for deep unsupervised learning that can fully leverage the joint information and shared variability contained in heterogeneous multimodal (or multiview) datasets. He also develops new algorithms for federated learning of private multi-site data, seeking to enable collaborative research that can take advantage of decentralized datasets without requiring direct access to data stored in remote data centers.
He received the B.Sc. degree in Electrical Engineering in 2003 from the Catholic University (PUCRS), Porto Alegre, Brazil, the M.S. degree in Computer Engineering (with minors in Statistics and in Mathematics) in 2011, and the Ph.D. degree (with distinction) in Computer Engineering in 2017, both from The University of New Mexico, Albuquerque, NM, USA.
He also received training in diffusion spectrum imaging at CMU and University of Pittsburgh in 2016, multimodal brain imaging at the MGH Martinos Center and Harvard/MIT in 2016, and deep learning at MILA, University of Montreal in 2017.
Before joining the TReNDS Center, Dr. Silva developed novel approaches for multimodal medical image analysis as a Postdoctoral Fellow at the Mind Research Network, in addition to client-centric tools and processes using machine learning and statistical analysis as a Data Scientist with Datalytic Solutions. Previously, he also had experience as an engineer, lecturer, and consultant.
Vince Calhoun Founding Director & Distinguished University Professor, TReNDS Center
Vince D. Calhoun Dr. Calhoun is founding director of the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) and a Georgia Research Alliance eminent scholar in brain health and image analysis where he holds appointments at Georgia State University, Georgia Institution of Technology and Emory University. He was previously the President of the Mind Research Network and Distinguished Professor of Electrical and Computer Engineering at the University of New Mexico. He is the author of more than 800 full journal articles and over 850 technical reports, abstracts and conference proceedings. His work includes the development of flexible methods to analyze functional magnetic resonance imaging data such as independent component analysis (ICA), deep learning for neuroimaging, data fusion of multimodal imaging and genetics data, neuroinformatics tools, and the identification of biomarkers for disease. His research is funded by the NIH and NSF among other funding agencies. Dr. Calhoun is a fellow of the Institute of Electrical and Electronic Engineers, The American Association for the Advancement of Science, The American Institute of Biomedical and Medical Engineers, The American College of Neuropsychopharmacology, and the International Society of Magnetic Resonance in Medicine. He served at the chair for the Organization for Human Brain Mapping from 2018-2019 is a past chair of the IEEE Machine Learning for Signal Processing Technical Committee. He currently serves on the IEEE BISP Technical Committee and is also a member of IEEE Data Science Initiative Steering Committee.
Jingyu Liu, PhD. Associate Professor of Computer Science, TReNDS Center & Georgia State University
Dr. Liu’s research focuses on exploring and identifying genetic, epigenetic, transcriptomic effects on or association with brain anomalies related to various mental illnesses and disorders, as well as understanding normal brain development. Her research involves developing methods to unveil hidden relations between various phenotypes and genetic/epigenetic features across the whole genome. The brain-based phenotypes can be extracted from MRI images, EEG and MEG signals, while the genetic and epigenetic features are from single nucleotide polymorphisms (SNP), copy number variations (CNV), DNA methylation, and gene expression. Due to complexity of data, various types of methods/algorithms are being developed and extended, including multimodal feature extraction, multivariate association, multilayer networks, and statistical models. The disorders being studied include, but not limited to, schizophrenia, bipolar disorder, substance abuse, ADHD, mood disorders. “Everything should be made as simple as possible, but not simpler.” – Albert Einstein
Armin Iraji Research Scientist, TReNDS Center & Georgia State University
Armin’s background is data science and signal processing including machine learning, data mining, image processing, and time-series analysis. With over 10 years of experience in medical imaging analyses such as MRI, CT, EEG, and photoacoustic imaging, Armin has devoted most of his research to developing analytical techniques to interpret brain signals with the goal of improving patient care and directly translating brain signals into actions.
Maria Misiura Social Media Strategist & Designer, TReNDS Center & Georgia State University
Maria is currently pursuing a PhD in Psychology, and is a graduate research assistant in Jess Turner's Imaging Genetics and Informatics lab whose primary research focus is the application of neuroimaging techniques to aging and neurodegenerative diseases. She is excited to get the word out about the TReNDS center and its various achievements and research projects by assisting with web development, social media strategy, and graphic design.
Sergey Plis Associate Professor of Computer Science, TReNDS Center & Georgia State University
Dr. Plis’s educational background is in engineering (MS), artificial intelligence (MS) and computer science (PhD). His research goals lie in developing computational instruments that enable knowledge extraction from observational multimodal data collected at different temporal and spatial scales. His focus is on understanding systems and processes formed by interactions of multiple “agents”. The human brain, his main application area, is an example of such system: neurons (or measured voxels) are the agents that interact and form networks that themselves are entities of interest with influence structure indicative of mental state, disorder and differences between individuals. Understanding the patterns, networks and interactions can improve our understanding of how the brain works but the data are complex, multidimensional, and neither modality alone carries enough information. The situation typical in many domains with complex incomplete observational measurements including climatology, social sciences, and others. The chosen methodology mainly draws from the fields of machine learning and data science. Specific developments are focused on multimodal pattern recognition, inference, predictive modeling, tracking, and causal learning. Ongoing work is focused on inferring multimodal probabilistic and causal descriptions of function-induced networks based on fusion of fast and slow imaging modalities. This includes feature estimation via deep learning-based pattern recognition and learning causal graphical models.
Lucina Uddin Associate Professor, Department of Psychology, University of Miami
After receiving a Ph.D. in cognitive neuroscience from the psychology department at UCLA in 2006, Dr. Uddin completed a postdoctoral fellowship at the Child Study Center at NYU. For several years she worked as a faculty member in Psychiatry & Behavioral Science at the Stanford School of Medicine. She joined the psychology department at the University of Miami in 2014. Within a cognitive neuroscience framework, Dr. Uddin’s research combines analyses of resting-state fMRI and diffusion weighted imaging data to examine the organization of large-scale brain networks supporting executive functions. Her current projects focus on understanding dynamic network interactions underlying cognitive inflexibility in neurodevelopmental disorders such as autism. Dr. Uddin’s work (over 125 publications) has been published in the Journal of Neuroscience, Cerebral Cortex, JAMA Psychiatry, Biological Psychiatry, PNAS, and Nature Reviews Neuroscience. She was awarded the Young Investigator award by the Organization for Human Brain Mapping in 2017.
Jessica Turner Associate Professor, Neuroscience, Psychology, TReNDS Center & Georgia State University
Dr. Turner received her PhD in Psychology (Cognitive Sciences) from the University of California, Irvine, followed by a post-doctoral position at Rutgers the State University of New Jersey, learning single-cell recording and optical imaging techniques. Having determined that invasive measures were not her preferred techniques, she moved into functional and structural neuroimaging and was fascinated by the ability to measure brain function non-invasively. Since then, her research program uses neuroimaging of clinical populations to improve understanding of the structural and functional circuitry underlying mental illness and health, and integrates several approaches: The combination of imaging with genetics, to identify genotypes which might help individualize treatment and prognosis; structural and functional imaging across multiple institutions to develop robust clinical neuroimaging studies; use of these neuroimaging methods in schizophrenia and other disorders to determine the relationship between brain volume and functional characteristics with disease status and symptom profiles; and large-scale neuroimaging data sharing to support the international collaborations needed to perform imaging genetics analyses. Since 2013 she has been at Georgia State University as faculty in psychology and neuroscience, and the head of the Imaging Genetics and Informatics Laboratory.Website: https://psychology.gsu.edu/profile/jessica-turner/
Peter Bandettini Chief, Section on Functional Imaging Methods, National Institute of Mental Health
Dr. Bandettini received his undergraduate degree in Physics from Marquette University in 1989, and his Ph.D. in Biophysics in 1994 at the Medical College of Wisconsin where he led the effort to carry out one of the first successful experiments in functional MRI. He completed his post doc at the Massachusetts General Hospital NMR Center in 1996. After spending three years as an Assistant Professor at the Medical College of Wisconsin he was recruited in 1999 to become Director of the Functional MRI Facility at and Chief of the Section on Functional Imaging Methods the National Institutes of Health. Recently, he has become the founding Director of the Center for Multimodal Neuroimaging at the National Institute of Mental Health and has started a Machine Learning group and a Data Sharing group. He also recently completed a 6 year tenure as Editor In Chief of the Journal, NeuroImage. He is the recipient of the 2001 OHBM Wiley Young Investigator Award, and in 2020 was awarded the ISMRM Gold Medal. His research focus over the past 29 years has been on advancing functional MRI in all ways, including novel fMRI methods in acquisition, processing, and paradigm design. He current research focus is high resolution layer fMRI, dynamic connectivity, understanding and mitigating physiologic noise in fMRI time series, and deriving individual specific information using fMRI. He has published over 175 papers and has presented over 390 invited lectures.
Shella Keilholz , Georgia Institution of Technology
Dr. Shella D. Keilholz received her B.S. degree in physics from the University of Missouri Rolla (now Missouri University of Science and Technology) and her Ph.D. degree in engineering physics at the University in Virginia. Her thesis focused on quantitative measurements of perfusion with arterial spin labeling MRI. After graduation, she went to Dr. Alan Koretsky’s lab at the NIH as a Postdoctoral Researcher to learn functional neuroimaging. She is currently a Professor in the joint Emory/Georgia Tech Biomedical Engineering Department, Atlanta, GA, USA and Program Director for the 9.4 T MRI. Her research seeks to elucidate the neurophysiological processes that underlie the BOLD signal and develop analytical techniques that leverage spatial and temporal information to separate contributions from different sources.
Dante Chialvo Head, Center for Complex Systems & Brain Sciences (CEMSC3), Universidad Nacional de San Martin
Dr. Dante R. Chialvo received his diploma in 1982 from the National University of Rosario, in Argentina. In 1985 was appointed Professor of the Department of Physiology of the University of Rosario. From 1987 to 1992 was Associate Professor in the State University of New York (Syracuse, NY) in the Department of Pharmacology and latter in the Computational Neuroscience Program. Between 1992 and 1995 was associated with the Santa Fe Institute for the Sciences of Complexity, in Santa Fe, New México. Until 2010, he was Full Professor at Northwestern University (Chicago), and UCLA, when he returned to Argentina as Principal Investigator of Conicet (Argentina).
Currently he is Full Professor and head of the Center for Complex Systems and Brain Sciences (Cemsc3) at the UNSAM (Universidad Nacional de San Martin) in Buenos Aires, Argentina.
Throughout these years, he has been Visiting Professor at numerous universities including Wuerzburg University (Germany), University of Copenhagen (Denmark), The Rockefeller University (U.S.A.), University of the Balearic Islands, University of Barcelona, University Complutense of Madrid, (Spain), Naples (Italy) and University of Rosario, University of Cordoba (Argentina), Universidad Mayor de San Andres, La Paz, (Bolivia) , Jagellonian Univ. (Krakow) among others.
Dr. Chialvo has published more than 100 scientific papers, all dedicated to understand natural phenomena from the point of view of Nonlinear Dynamics of Complex Systems. His work covers a wide range of topics, including the mathematical modeling of cardiac arrhythmias, the study of molecular motors as stochastic ratchets, neural coding, and self-organization and collective phenomena in ants swarms, brain and communities, among others. In 2005 he was the recipient of a Fulbright US Scholar Award (2005), in 2006 the Distinguished Visiting Professor of the University Complutense, (Psychology Department), Madrid, Spain, Visiting Professor Award of the Seconda Università degli Studi di Napoli, Aversa Italy and elected Fellow of the American Physical Society in 2007.
Juan (Helen) Zhou Principal Investigator, Center for Sleep and Cognition, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore
Daniele Marinazzo Professor of Neuroimaging Data Analysis, Ghent University
I am a statistical physicist (MSc 2001, PhD 2007, University of Bari) who has always worked towards the characterization of the dynamics of complex systems, mainly the brain. From 2008 to 2011 I was a postdoc at CNRS, University Paris 5, performing in vivo electrophysiology and dynamic clamp experiments. Since 2011 I am Research Professor of Data Analysis at Ghent University, Belgium. I teach techniques of neuroimaging data analysis; I am member of the Belgian node of the International Neuroinformatics Coordinating Facility (INCF) and a mentor for Google Summer of Code on their behalf. I am co-editor in chief of Neurons, Behavior, Data Analysis, and Theory, deputy editor of PLOS Computational Biology, editor of NeuroImage, Network Neuroscience, Brain Topography, and PLOS One, editor of the PLOS complexity channel, and referee for many journals in the field of neuroscience and applied physics.
Tonya White Associate Professor and Principal Investigator of Pediatric Population Neuroimaging, Erasmus University Medical Centre
Adeel Razi Associate Professor & ARC DECRA Fellow, Turner Institute for Brain and Mental Health, Monash University
Adeel Razi is an Associate Professor at the Turner Institute for Brain and Mental Health, Monash University, Australia, where he is the Head of the Computational Neuroscience Laboratory. His research is cross-disciplinary, combining engineering, physics, and machine-learning approaches, to model complex, multi-scale, network dynamics of brain structure and function using neuroimaging. He is currently Australian Research Council DECRA Fellow (2017-2020) and has also been awarded NHMRC Investigator (Emerging Leader) Fellowship (2021-2025). He is an `Honorary’ Senior Research Fellow at the Wellcome Centre for Human Neuroimaging of University College London, where he also worked from 2012 to 2018. He received the B.E. degree in Electrical Engineering from the N.E.D. University of Engineering & Technology, Pakistan, the M.Sc. degree in Communications Engineering from the University of Technology Aachen (RWTH), Germany, and the Ph.D. degree in Electrical Engineering from the University of New South Wales, Australia in 2012.
Dimitri Van De Ville Professor of Bioengineering, EPFL and University of Geneva
Dimitri Van De Ville received the Ph.D. degree in computer science engineering from Ghent University, Belgium, in 2002. He was a post-doctoral fellow (2002-2005) at the lab of Prof. Michael Unser at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, before becoming responsible for the Signal Processing Unit at the University Hospital of Geneva, Switzerland, as part of the Centre d’Imagerie Biomédicale (CIBM). In 2009, he received a Swiss National Science Foundation professorship and since 2015 became Professor of Bioengineering at the EPFL, jointly affiliated with the University of Geneva, Switzerland. His main research interest is in computational neuroimaging to advance cognitive and clinical neurosciences. His methods toolbox includes wavelets, sparsity, deconvolution, graph signal processing. He was a recipient of the Pfizer Research Award 2012, the NARSAD Independent Investigator Award 2014, the Leenaards Foundation Award 2016, and was elected Fellow of the IEEE in 2020.
Dr. Van De Ville serves as an Editor for the new journal NEUROIMAGE: REPORTS since 2020, as a Senior Editor for the IEEE TRANSACTIONS ON SIGNAL PROCESSING since 2019 and as an Editor for the SIAM Journal on Imaging Science from 2018 on. He served as an Associate Editor for the IEEE TRANSACTIONS ON IMAGE PROCESSING from 2006 to 2009, the IEEE SIGNAL PROCESSING LETTERS from 2004 to 2006. He was the Chair of the Bio Imaging and Signal Processing (BISP) TC of the IEEE Signal Processing Society (2012-2013) and the Founding Chair of the EURASIP Biomedical Image & Signal Analytics SAT (2016-2018). He is Co-Chair of the biennial Wavelets & Sparsity series conferences, together with Y. Lu and M. Papadakis.
Emmanuelle Tognoli Research Professor, Complex Systems and Brain Sciences, Florida Atlantic University
Dr. Tognoli is a Research Professor in Complex Systems and Brain Sciences at Florida Atlantic University. Her overarching scientific motivation is to understand brain function and dysfunction using the concepts and tools of complexity science. Her main research areas are spatiotemporal brain metastability, the neurophysiological basis of social behavior and the development of complex experimental systems for human-machine and neuro-technological interfaces. Her thinking has been enriched by numerous collaborations with psychiatrists, neurologists, neuropsychologists, ophthalmologists, physicists, mathematicians, behavioral and biological scientists, psychologists and engineers.