Brain tests when watching a movie
People sometimes understand the world in a common way, and sometimes there is remarkable personality. Studies have shown that when we have the same perception, feelings, and mental states in real life, our brain exhibits similar activity modes. Conversely, our brain’s response to the same incident may help to decode our individual differences in cognition and feelings. In this talk, I will introduce our efforts to develop ecological brain tests following the above rationales. By measuring the brain response to custom movies, we have established machine learning models to assess the emotional maturity of children and adolescents and identify mental disorders. Zhi Yang, Shanghai Jiao Tong University Professor, Shanghai Mental Health Center; Professor, Institute of Psychological and Behavioral Sciences, Shanghai Jiao Tong University; TCCI Investigator – digital medicine

“A critical approach to understanding a system is to know its origin. My hope is that my research on the brain and its growth process can help us to understand human behavior and mental health issues.”

Research Areas: Human Brain Development, Child and Adolescent Mental Health, Neuroimaging Methodology

Expressions of emotion and their neuronal correlates in mice
Emotions serve essential survival functions and gate individual and social behavior. Despite their important roles, emotions remain one of the least understood products of brain function. In my talk, I will discuss recent work from my lab demonstrating that facial expressions provide a sensitive readout of emotion states in mice. Furthermore, I will present ongoing work aimed at elucidating the role of the insular cortex in emotion processes. Nadine Gogalla, Max Planck Institute of Neurobiology – Munich
For her doctoral thesis, Nadine Gogolla worked in the laboratory of Pico Caroni at the Friedrich Miescher Institute of Biomedical Research and obtained her PhD in Neurobiology from University of Basel in in 2007. She then was a postdoctoral fellow in the laboratory of Takao Hensch at Harvard University in Cambridge, MA. Since 2014, Nadine Gogolla is a Max Planck Research Group Leader at the MPI of Neurobiology in Martinsried, Germany. In 2017, she was awarded the prestigious ERC Starting grant.

Gogolla’s research program is designed to investigate the neuronal underpinnings of emotion. Towards this goal, her lab develops novel readouts and behavioural essays to measure emotional states in animals. Her work combines these readouts with state-of-the art technologies in systems neurosciences, such as two-photon calcium imaging, single-unit and in-vivo patch-clamp electrophysiology, fibre-photometry, viral tracings and optogenetics.

What can we learn about brain health from a wristband and smartphone?
Years ago, I led our team at MIT creating the first wearable and non-contact imaging machine learning algorithms to characterize human emotional states digitally, using physiology. We redesigned wearable sensors enabling comfortable 24/7 data collection, and then discovered several surprising findings. For example, measuring what we thought initially was a simple sweat-stress response, turned out to contain specific patterns even more interesting than those studied for 100 years in labs examining “electrodermal activity” (EDA). We further learned that EDA also can have its highest peak during certain phases of sleep, and when activated by specific kinds of neurological stimulation. This talk will highlight some of the most surprising findings along the journey of measuring emotion “in the wild” with implications for anxiety, depression, sleep-memory consolidation, epilepsy, autism, and more. With these new insights, and a smartphone, what important future challenges might we be able to solve? Rosalind Picard, MIT and Empatica
Picard is a professor, inventor, engineer, and scientist. She wrote the book Affective Computing that outlines how to give machines the skills of emotional intelligence, which inspired the growing field of Affective Computing. Her inventions have led to AI-based advances in neurology and psychiatry, and she has co-founded two successful businesses Affectiva and Empatica: Affectiva provides software for Emotion AI, while Empatica provides the first FDA-cleared smartwatch alerting to potentially life-threatening seizures, as well as a new AI wearable, now in studies by the US Health and Human Services, warning of a possible viral infection before symptoms appear. At MIT, she is a full professor teaching and directing research at the Media Lab, and serves as founding faculty chair for MindHandHeart, MIT’s campus-wide wellbeing initiative. Picard has received many recognitions including one of the highest honors for an engineer, election to the National Academy.

Machine-Learning in Clinical Neurosurgery and Applied Neuroscience
Ying Mao, Fudan University
Director of the Tianqiao and Chrissy Chen Institute for Translational Research, President of Huashan Hospital

When I discovered that we, as neurosurgeons, had the privilege of touching the brain, I realized that it is our responsibility to engage in brain science research and to study the benefits of treating, restoring and reshaping the brain.”

Professor Ying Mao is President of Huashan Hospital at Fudan University. Concurrently, he is Designate President of the Chinese Neurosurgical Society and President of the Congress of Neurological Surgeons of Shanghai Medical Doctor Association. Professor Mao received his Medical Doctor’s Degree at Shanghai Medical University and was a postdoctoral researcher at the Crosby Neurosurgical Laboratories, University of Michigan. In 2017, he was conferred the “Shanghai Medical Excellence Contribution Award.”

Research Areas: Cerebrovascular Disease, Minimally Invasive Brain Tumor Microsurgery for brain tumors, Functional Neurosurgery

The neural representation of speech
Human speech perception results from neural computations that transform external acoustic speech signals into internal representations of words. The superior temporal gyrus (STG) contains the non-primary auditory cortex and is a critical locus for phonological processing. I will describe how speech sound representation in the STG relies on fundamentally nonlinear and dynamical processes, such as categorization, normalization, contextual restoration, and the extraction of temporal structure. A spatial mosaic of local cortical sites on the STG exhibit complex auditory encoding for distinct acoustic-phonetic and prosodic features. We propose that as a population ensemble, these distributed patterns of neural activity give rise to abstract, higher-order phonemic and syllabic representations that support speech perception. I will present a multi-scale, recurrent model of phonological processing in the STG, highlighting the critical interface between auditory and language systems. Eddie Chang, UCSF
Edward Chang is a neurosurgeon-scientist and Weill Chair of Neurosurgery at the University of California, San Francisco. He specializes in advanced brain mapping to preserve crucial areas for language and cognitive functions in the brain. Chang is a member of the UCSF Weill Institute for Neuroscience and co-director of the Center for Neural Engineering and Prostheses, a collaboration between UCSF and UC Berkeley.

Chang’s research focuses on the brain mechanisms for human behaviors such as speech and mood. For example, by studying the brain activity associated with the physical movements of speaking, his team was able to teach a computer to decode and transform these brain signals into synthetic speech. This technology has the potential to eventually lead to speech prosthetics for paralyzed people who have lost the ability to speak.

Chang is the inaugural Bowes Biomedical Investigator at UCSF, an HHMI Faculty Scholar, and a recipient of the NIH Director’s New Innovator Award and the Blavatnik National Award for Life Sciences.

Artificial neural networks as models of language comprehension in the human brain
The goal of my research program is to understand the computations and representations that enable us to share complex thoughts with one another via language, and their neural implementation. A decade ago, I developed a robust new approach to the study of language in the brain based on identifying language-responsive cortex functionally in individual participants. Using this approach, I identified and characterized a set of frontal and temporal brain areas that i) support language comprehension and production (spoken and written); ii) are robustly separable from the lower-level perceptual (e.g., speech processing) and motor (e.g., articulation) brain areas; iii) are spatially and functionally similar across diverse languages (>40 languages from 11 language families); and iv) form a functionally integrated system with substantial redundancy across different components. I will highlight three key findings from our work.

First, I will show that the language brain regions are highly selective for language over diverse non-linguistic processes—from math and music, to executive processes, to non-verbal semantic cognition, and even processing computer code—while also showing a deep and intriguing link with a system that supports social cognition.

Second, I will show that, contra many leading accounts, the language regions support both understanding of word meanings and sentence-structure building, with no part of the language network being selective for combinatorial/syntactic processing. This strong integration between word meanings and structure building aligns with general arguments agains spatial separation in the brain between memory and computation.

Finally, I will present recent evidence of predictive coding in the language network during naturalistic comprehension, and show that state-of-the-art artificial neural network language models—optimized for predictive processing—accurately capture neural responses during language comprehension. The latter line of work is a critical first step to developing mechanistic accounts of language comprehension. Evelina Fedorenko, MIT
Dr. Fedorenko is a cognitive neuroscientist who studies the human language system. She received her bachelor’s degree from Harvard University in 2002, and her Ph.D. from the Massachusetts Institute of Technology in 2007. She was then awarded a K99R00 career development award from the National Institute for Child Health and Human Development at the U.S. National Institutes of Health. In 2014, she joined the faculty at Harvard Medical School/Massachusetts General Hospital in Boston, and in 2019 she returned to MIT where she is currently the Frederick A. (1971) and Carole J. Middleton Career Development Associate Professor of Neuroscience in the Brain and Cognitive Sciences Department and the McGovern Institute for Brain Research. Dr. Fedorenko uses fMRI, intracranial recordings and stimulation, EEG/ERPs, MEG, as well as computational modeling, to study adults and children, including those with developmental and acquired brain disorders.