Computational neuroscience employs mathematical models, theoretical analysis, and abstractions of the brain to understand the principles that govern its development, physiology, cognitive abilities, and contributions to behavior. Theoretical models aim to capture the essential features of the nervous system at multiple spatial and temporal scales to enable the development of hypotheses that can be directly tested by biological, clinical, or psychological experiments. Likewise, computational researchers at the Friedman Brain Institute work closely with experimental data on various scales to uncover novel insights and generate new experimental predictions. Our computational team leverages several different approaches to understand the brain, and includes the Nash Family Center for Advanced Circuit Therapeutics and the Center for Computational Psychiatry.
Areas of Research
Center for Computational Psychiatry
Computational psychiatry is a nascent research area seeking to characterize mental disorders in terms of aberrant computations at multiple scales. Recent progress in human neuroscience has also highlighted the need for computational models that can bridge the explanatory gap between pathophysiology and psychopathology. The computational expertise and tools required to address this gap exist only across disciplines, combining skills and knowledge from investigators and clinicians that are jointly interested in solving problems of mental health.
Leveraging on the rich clinical resources and computational expertise across departments, Mount Sinai’s new Center for Computational Psychiatry is dedicated to deepening our understanding of how both algorithms and biology of the brain contribute to dysfunction like addiction, eating disorder, autism, and personality disorders. The Center is especially interested in a transdiagnostic approach towards mental dysfunction; for example, by examining how aberrant social cognition might manifest itself similarly or differently across a range of distinct diagnostic labels.
Brain Research and Artificial Intelligence in New York (BRAINY)
Bringing together the fields of brain research and artificial intelligence/machine learning to figure out how the brain works using mathematical and computational models. When models are combined with data collected from neuroscience experiments, artificial systems can be designed that are capable of performing realistic behaviors using only the machinery the biological nervous system has access to (i.e., neurons and synapses operating at a fast timescale). Building such systems enables researchers to ‘reverse engineer’ them to reveal the operating principles of the real brain. The resulting integrative theories and models have the potential to transform the study the brain, by making specific, quantifiable predictions that lead to new experiments and drive new hypotheses about how the brain works— in health and disease.
Core scientists: Kanaka Rajan
Nash Family Center for Advanced Therapeutics (C-ACT)
The Nash Family Center for Advanced Circuit Therapeutics (C-ACT) is an interdisciplinary program that develops and tests new brain-tuning strategies to accelerate the delivery of state-of-the art individualized surgical treatments for patients with advanced neuropsychiatric disorders. Whether it is those where brain stimulation therapies are already clinically available (e.g. Deep Brain Stimulation (DBS) for Parkinson’s disease, epilepsy, Obsessive Compulsive Disorder (OCD), pain) or more experimental DBS applications, including depression, dementia, eating disorders and addiction. The Center further catalyzes integrative research activities involving surgical patients using multimodal imaging, invasive and noninvasive electrophysiology, quantitative performance and behavioral metrics (wearable sensors, motion capture, face/speech analyses), and computational models of behavior and disease mechanisms, all supported by a centralized clinical and research bioinformatics infrastructure. The genesis and evaluation of next-generation implantable devices, computation-based algorithms for treatment delivery, and clinical piloting of novel applications complement ongoing personalized, evidence-based, multidisciplinary care.
Subcortical shape abnormalities in bulimia nervosa
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2019.
Xiaosi Gu - Addiction beyond pharmacological effects: The role of environment complexity and bounded rationality
Several decision-making vulnerabilities have been identified as underlying causes for addictive behaviours, or the repeated execution of stereotyped actions despite their adverse consequences.
Modeling Therapeutic Alliance in the Age of Telepsychiatry
With the growth in computational psychiatry, provides scientists with new opportunities to quantify how patient-therapist relationships relate to treatment outcomes.
“Rethinking brain-wide interactions through multi-region ‘network of networks’ models”
The neural control of behavior is distributed across many functionally and anatomically distinct brain regions even in small nervous systems.
“Neuronal dynamics regulating brain and behavioral state transitions”
Prolonged behavioral challenges can cause animals to switch from active to passive coping strategies to manage effort-expenditure during stress.