Our Research
Our research group investigates the genetic and developmental mechanisms underlying neuropsychiatric disorders from the prenatal period through adolescence. We study the genetic architecture and developmental trajectories of childhood-onset conditions such as ADHD and autism, as well as the etiology and impact of severe perinatal psychiatric disorders, particularly those affecting maternal mental health and child outcomes.
Mental disorders are multifactorial, arising from complex interactions between biological and environmental factors. To fully understand these conditions, it is essential to study both the specific components of each disorder and how individuals interact with their parents, caregivers, and the broader environmental contexts in which they live. Advances in statistical and computational methods, coupled with the availability of large-scale longitudinal data, are providing the tools necessary to investigate these intricate interactions. These developments are driving a shift from a monocausal exposure-disease framework, which attributes a disorder to a single cause, to a multi-causal paradigm that recognizes the combined influence of genetic, biological, psychological, social, and environmental factors in their development.
Our lab directly contributes to this evolving understanding by investigating the factors that shape neuropsychiatric disorders across the lifespan. We focus on severe psychiatric conditions occurring during pregnancy and the postpartum period, exploring how physiological, hormonal, and genetic factors impact maternal mental health and the developmental trajectories of their children. Additionally, we study the genetic contributions to childhood conditions such as attention-deficit hyperactivity disorder and autism spectrum disorder, examining both rare and common genetic variations.
At the core of our work lies an unwavering commitment to excellence, innovation, and collaboration. These core values shape every aspect of our research, from design to execution. For us, excellence means adhering to the highest standards of scientific rigor, ensuring that our findings are robust, reliable, and contribute meaningfully to the understanding and treatment of mental health disorders. Innovation drives our approach, inspiring us to adopt cutting-edge methodologies, challenge established paradigms, and explore complex questions with fresh perspectives. Collaboration is equally essential, fostering an inclusive and interdisciplinary culture where diverse expertise and viewpoints come together to drive transformative discoveries.
We provide comprehensive supervision and didactic training for our team, offering unique opportunities to engage in groundbreaking research and contribute to understanding the complex origins of neuropsychiatric disorders. Aligned with our commitment to excellence, we actively recognize and cultivate the strengths of each team member, fostering a supportive environment where everyone feels empowered to make meaningful contributions.
Our lab is organized into three specialized working groups, each contributing unique expertise to our mission of advancing the understanding of neuropsychiatric disorders. The Biostatistics Working Group addresses phenotypic and registry data, emphasizing causal inference, time-to-event modeling, and electronic health record analysis. The Genomic Data Analysis Working Group explores genetic variation and structural genomics using rigorous statistical frameworks and machine learning algorithms. Lastly, the Artificial Intelligence for Mental Health Working Group integrates genetic, transcriptomic, and epidemiological data to build predictive models for psychiatric and medical comorbidities in neuropsychiatric disorders.
The Mahjani Lab is part of the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai. The Seaver Autism Center is a fully integrated, translational research center that leads progressive studies and provides personalized care to individuals with autism and related rare disorders. As a team, we are dedicated to discovering the biological causes of autism and developing breakthrough treatments.
Learn more about the Seaver Autism Center’s translational research.
Dr. Mahjani is also affiliated with the Women’s Mental Health Center and the Mindich Child Health and Development Institute at the Icahn School of Medicine at Mount Sinai.
Research Projects
Our research group investigates the genetic and developmental mechanisms underlying neuropsychiatric disorders, with a specific emphasis on autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Spanning prenatal development to adolescence, our research aims to elucidate the complex pathways contributing to these conditions through three interconnected research avenues:
1. Genetic Basis of Perinatal Psychiatric Disorders and Effects on Child Neuropsychiatric Risk
2. Genomic Underpinnings of Childhood Neuropsychiatric Disorders
3. Developmental Trajectories of Childhood Neuropsychiatric Disorders
1. Genetic Basis of Perinatal Psychiatric Disorders and Effects on Child Neuropsychiatric Risk:
We investigate the genetic architecture of severe psychiatric disorders occurring during pregnancy and the postpartum period, analyzing their physiological and developmental underpinnings. In parallel, we examine how these disorders contribute to the risk of ADHD and ASD in offspring through genetic and developmental mechanisms. Some of our active projects are:
Risk Architecture of Postpartum Psychosis
Postpartum psychosis is a rare yet profoundly severe psychiatric condition, affecting 0.1-0.2% of mothers following childbirth. Characterized by acute onset within days or weeks of delivery, postpartum psychosis manifests with a spectrum of symptoms, including mania, depression, psychosis, cognitive disorganization, irritability, and sleep disruptions. These symptoms pose significant risks of suicide and infanticide and necessitate immediate medical intervention. Furthermore, there is a 10-fold increase in the rate of first-onset psychiatric hospitalizations in the weeks following delivery, illustrating the condition’s severe impact on maternal health. Postpartum psychosis is a bipolar spectrum disorder, but the distinct timing, triggers, and severe risks associated with postpartum psychosis also highlight the need for a specialized classification and approach in both research and clinical practice. The goal of this project is to identify the distinct risk architecture of postpartum psychosis.
Prenatal Risk Factors for Neuropsychiatric Disorders in Children: The Role of Indirect Genetic Effects
Extensive research has established associations between various maternal factors during pregnancy and the onset of neuropsychiatric disorders in children. These maternal influences affect the child’s phenotype through two primary pathways: the direct transmission of genes and the maternal effect, where the mother’s phenotype influences her offspring via environmental pathways. Our findings indicate that maternal effects contribute to 5-15% of the risk for conditions such as obsessive-compulsive disorder, Tourette syndrome, and attention-deficit/hyperactivity disorder. Our ongoing research aims to pinpoint maternal and paternal factors that increase the risk of neuropsychiatric disorders in children through genetic and environmental pathways.
2. Genomic Underpinnings of Childhood Neuropsychiatric Disorders:
We analyze the genetic risk architecture of ADHD and ASD, with a primary focus on rare genetic variation, while also addressing the contributions of common genetic variation.
For ASD, some of our active projects are:
- Identifying genetic variants with moderate effect sizes associated with ASD.
- Exploring the role of tandem repeat expansions in ASD.
- Examining the interaction between genetic and environmental factors in contributing to ASD heterogeneity.
- Investigating the role of ancestral differences in shaping ASD heterogeneity.
For ADHD, some of our active projects are:
- Investigating the role of ultra-rare genetic variants in ADHD risk.
- Examining the interaction between genetic and environmental factors in contributing to ADHD heterogeneity.
3. Developmental Trajectories of Childhood Neuropsychiatric Disorders:
We employ advanced machine learning methods on electronic health record (EHR) data to explore temporal and causal relationships between neuropsychiatric and medical disorders. Our research seeks to map the progression of these disorders, identify critical developmental milestones, and clarify their interactions with comorbid conditions.
Email: behrang.mahjani@mssm.com
Behrang is an Assistant Professor at the Department of Psychiatry at ISMMS, the Department of Genetics and Genomic Sciences, and the Department of Artificial Intelligence and Human Health. Dr. Mahjani is also a Research Specialist for clinical and epidemiological research at the Department of Molecular Medicine and Surgery at Karolinska Institutet (KI) and is affiliated with the Department of Medical Epidemiology and Biostatistics at the same place.
Alongside his research, Behrang has dedicated significant effort to teaching and mentoring students, obtaining certificates in academic teaching and in supervising degree projects. Since 2003, he has taught a broad spectrum of subjects, from advanced programming to big data and machine learning. Behrang was also an invited lecturer for a national course on Statistical Decision Theory. He has guided students in learning statistical and computational methods and co-authored a textbook on Computer-Intensive Methods in Statistics, showcasing his commitment to education and mentorship.
Teaching:
- Big Data and Machine Learning (Postgraduate Course)
- Statistical Decision Theory and Bayesian Methods (Postgraduate Course)
- Advanced Statistical Computing (Postgraduate Course)
- Advanced R Programming (Graduate Course)
- Computer-Intensive Statistics and Data Mining (Graduate Course)
- Scientific Computing II (Undergraduate Course)
- Design and Analysis of Algorithms (Undergraduate Course)
- Data Structures and Algorithms (Undergraduate Course)
- Advanced Object-Oriented Programming with C++ (Undergraduate Course)
- Foundations of Programming (Undergraduate Course)
Certificates:
- Professional Certificate in Biostatistics, 2021
- Certificate in Academic Teacher Training, 2010
- Certificate in Supervising Students For Degree Projects, 2010
Education:
- PhD, Statistical and Scientific Computing, 2016, Uppsala University, Sweden. Thesis: Methods from statistical computing for genetic analysis of complex traits.
- MSc, Engineering Mathematics and Computational Science (specialization in Mathematical Statistics), 2011, Chalmers University of Technology, Sweden. Thesis: Exploring connectivity of random subgraphs of a graph.
- MSc, Complex Adaptive Systems (specialization in Population Genetics), 2009, Chalmers University of Technology, Sweden. Thesis: Sequential Markov Coalescent algorithm for populations with demographic structure.
- BSc, Applied Mathematics, 2004, K.N.T. University of Technology.
Email: seulgi.jung@mssm.edu
Seulgi is a postdoctoral fellow at Dr. Mahjani lab. He received his PhD in Biomedical Science from the University of Ulsan College of Medicine, Seoul, Korea. Currently, Dr. Jung seeks to understand how rare genetic variation influences the risk of developing neuropsychiatric disorders using whole exome sequencing (WES) data.
Email: madison.caballero@mssm.edu
Madison is a bioinformatician at Dr. Mahjani lab. She received her PhD in Genetics from the Department of Molecular Biology and Genetics, Cornell University. Her dissertation title was: “The Interplay Between DNA Replication Timing and Mutations”. Currently, Dr. Caballero seeks to understand how ultra-rare mutations influence the risk of autism.
Email: shelby.smout@mssm.edu
Shelby Smout is a postdoctoral fellow at Dr. Mahjani and Dr. Bergink labs. They received their PhD in Health Psychology from Virginia Commonwealth University in 2022 where they conducted research assessing the role of discrimination on depression, anxiety, and healthcare avoidance among gender diverse populations. In March 2023, they received a grant from the American Society of Transplantation to conduct a qualitative study on barriers and contributors to transplantation among transgender patients with end-stage kidney or liver disease. In the Mahjani and Bergink labs, Dr. Smout studies women’s mental health throughout the reproductive cycle and its impact on the neurodevelopment of their children.
Rachel Bercovith, MS, Bioinformatician II
Email: Rachel.Bercovith@mssm.edu
Rachel Bercovith is a Bioinformatician II at Dr. Mahjani labs. She received her MS in Medical Science, with a focus in Bioinformatics, from University of Toronto.
Email: christina.mahjani@mssm.edu
Christina is a research nurse at Dr. Mahjani lab. She received a Degree of Postgraduate Diploma in specialist nursing – mental health care and a Master of Medical Science in mental health care from Uppsala, Sweden. Christina has also done courses in the Master Programme in Public Health Science at Mid Sweden University. She has extensive experience in clinical epidemiology and register-based research in psychiatry.
Aishwaryaa is currently a research intern at Dr. Mahjani’s lab. Aishwaryaa is an undergraduate student at Princeton University, pursuing a B.S.E. in Computer Science. In the lab, Aishwaryaa is working on a project that explores sensory processing differences in individuals with autism who have SHANK3 deletions, compared to those with idiopathic autism.
Alumni
- Lily Cohen, BSc, Research Associate
- Audrey Paulin, BSc, Seavers Undergraduate Research Scholar
- Marina Natividad Avila, MSc, Bioinformatician
- Sahar Jaffer, Summer Volunteer
- Adrianna Kępińska, PhD, Postdoctoral Fellow
Zwanzig, S., Mahjani, B. (2019). Computer-Intensive Methods in Statistics, Chapman and Hall/CRC, ISBN: 978-0-367-19423-9.
This textbook gives an overview of statistical methods that have been developed during recent years due to increased computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment.
Fundings:
- 2025, Moderate effect size genes in autism spectrum disorder, NIMH, PI
- 2022, Risk architecture of postpartum psychosis, NIMH, PI
- 2021, Association between lower urinary tract symptoms and psychiatric comorbidities in children and adolescents, Swedish Research Council, Site PI
- 2020, Direct and maternal pleiotropic effects in autism spectrum disorder and comorbid psychiatric conditions, Seaver Autism Center for Research and Treatment Fellowship, PI
- 2020, Prenatal maternal depression and anxiety, and risk of neurodevelopmental disorders in children, Brain & Behavior Research Foundation Young Investigator Grants, PI
- 2018, Examining the role of maternal effect on the risk of autism spectrum disorder, Seaver Autism Center for Research and Treatment Fellowship, PI
- 2018, Distributed analysis of sensitive data in a precision medicine context, with applications in psychiatric genetics, Nordic strategic workshop grant, PI
Awards:
- 2021, The 2020 Eric Ziegel book review award from Technometrics
Open Positions:
We’re looking for motivated and curious researchers with strong backgrounds in computational science, statistics, or related fields to join our team. Our work spans statistical genetics, genomics, biostatistics, and bioinformatics, with an emphasis on interdisciplinary collaboration and innovative methods.
PhD students interested in doing a lab rotation are very welcome. We’ll work with you to design a rotation plan that fits your research interests and goals, and you’ll have the chance to get hands-on experience with real data and ongoing projects. Our aim is to make your time in the lab meaningful, productive, and a good opportunity to see if our research focus is a strong fit for you.
If you’re interested in our work and would like to learn more about opportunities in the lab, please reach out to Behrang Mahjani (behrang.mahjani@mssm.edu).