The purpose of all HBSF research is to develop novel paradigms and technological advances that will enable the study of the mind, and ultimately, find treatments for neurological disorders. We are working to achieve this by supporting basic and applied research and facilitating in a multidisciplinary exchange spanning both the natural and social sciences fields.
We are dedicated to achieving an all-encompassing understanding of human cognition and neurological disorders. Our research is focused on revolutionary, principle-driven solutions to cure intractable brain disorders.
What exactly do we mean when we say principle-driven?
HBSF advocates that effective and meaningful brain research must be 1) interdisciplinary and 2) must thrive under a theoretical paradigm. As an inside niche, we are constructing a complete framework that explains how the central nervous system and the brain are capable of meaningful action through read-and-write communication processes and neuromodulation devices.
This vision runs from the molecular level up to the human-environment interaction level. Since brain function research requires a comprehensive approach, we integrate studies from the single cell and local neural network level, the coding level, the systems level, the functional level, and the behavioral level.
A.I. & Machine Learning
HBSF is currently in collaboration with ni2o, a startup developing a revolutionary brain-computer interface called the Kiwi. The device is a wireless nanoscale brain implant used to electrically stimulate neurons in real time. We are working on increasing the KIWI’s channel density, resolution and cloud based analytics. KIWI’s initial application will be to treat neurodegenerative conditions and then neuropsychiatric conditions such as depression, schizophrenia and OCD. Ultimately the KIWI will advance our understanding of how the brain works.
The Fundamental Code Unit (FCU) is a new approach to cognition and a reevaluation of how brain processes are perceived and investigated. The FCU offers a new analytical model using sequence and coding to explain and predict structure. The FCU framework attempts to construct a complete framework that explains how the central nervous system and the brain are capable of meaningful action through read-and-write communication processes. This framework applies to the molecular level up to the level of human-environment interaction. As a theorem, it establishes a single mathematical model that can be used to describe the communication across different levels of brain function.
HBSF is working to develop nanotechnologies for both treatment solutions and to drive new brain science research. This research focuses on integrative analysis of cell types in the brain, via single-cell robotics. The goal is to derive a list of the cell types of the brain, classifying them by physiological, geometric, and molecular parameters. The approach is developing scalable nanoscale-resolution tags and nanoscopes to map brain circuits, leading to automated sectioning and staining for proteins and RNA for integrative molecular and anatomical mapping. The end goal is whole-brain-circuit neural recording and imaging by means of microfabricated, electronic, and computational methods for analyzing the neural dynamics of entire brain circuits from sensation to emotion to decision making to action.
HBSF is working with European partners to validate a non-invasive medical diagnostic methodology for early detection of neurodegenerative diseases. The wearable system uses multimodal sensors that combines Body Sensor Networks (BSN) and speech analysis to collect simultaneous data streams for early biomarkers of Parkinson’s Disease and Alzheimer’s Disease. The study measures Activities of Daily Living (ADL) with wearable voice and movement sensors to test the effectiveness of biomarker detection. The validation of this technology will enable future research in monitoring movement, speech, and possibly other biomarkers for effective early detection. Biomarker research also gives insight into the mechanisms and the progression of the disease as well.
We teamed up with Senior Fellow, Professor Tipu Aziz to support his efforts in establishing a working unit for functional neurosurgery in Dhaka, Bangladesh. Dr. Aziz will be working alongside colleagues to facilitate precise lesioning surgery for late stage Parkinson’s patients. This treatment is an effective alternative to DBS, which is a more costly option and not accessible to people in the region. We have donated $55,000 in medical equipment to ensure these patients receive treatment while training surgeons in the area to continue providing this option to patients in the city. All the doctors working on this project will be volunteering their time. This is part of our ongoing efforts to ensure everyone has equal access to treatments and resources regardless of economic status.
Language is a manifestation of complex systems of thought such as figurative metaphors. The ADAMA project seeks to answer questions such as; how does the human brain understand conceptual metaphors? So far, this is an unanswered phenomena yet to be replicated by artificial intelligence. Funded by IARPA and in collaboration with Georgetown University, Illinois Institute of Technology, Massachusetts Institute of Technology, and Ben Gurion University The ADAMA metaphor project aims to understand not only the neurocognitive mechanisms of language, but also how language can be utilized to gain greater insights into the mental processes of both healthy individuals and those with brain dysfunction.
MIT’s Mind Machine Project, Synthetic Intelligence lab and HBSF have joined forces to try to discover fundamental principles of brain operation that contribute to intelligence, in order to empower powerful new forms of artificial intelligence, and to enable the solving of brain disorders and the augmentation of human intelligence. Instead of trying to create Artificial Intelligence, our focus is on Artificial Consciousness, or characterizing and creating self-aware systems. The distinction may seem to be just in the name, but it is not: rather than creating a ‘thinking machine’ based on abstract principles, our goal is to grow it ground up to develop an experience of existence in connection with a working brain. Within this paradigm, Machine Learning approach would give way to creating Learning Machines, engineered systems that progress from lower to higher orders of cognition through building and enhancing their structure. Finally, Brain Modeling is being replaced Brain Augmentation: rather than trying to simulate a brain, our research agenda will focus on developing brain interfaces to assist with disability and enhance ability. In doing so, a much better practical understanding of the brain function will emerge, and ultimately humanity will achieve its goal of ‘understanding how we understand’.
Based out of Martinique, SOS KRIZ is a suicide helpline and psychiatric resources available 24/7. Run by volunteers trained to listen for signs of suicide and psychiatric crisis, SOS KRIZ builds on the SOS solitude project developed by Professor Seguin. Additionally, the program uses linguistic software to diagnose mood disorders and collects data for analysis.
According to the Institute of Medicine and National Institutes of Health, acute and chronic pain costs the US economy $635 billion every year as 40% of patients seek medical attention because of pain. HBSF has been working with academic and industry partners to identify an objective pain signature using noninvasive methods.
Funded by the DoD, this project seeks to identify biomarkers of pain using electroencephalography (EEG). The collaborative research team includes top experts in pain, neurosurgery, electrophysiology, computational neuroscience, military defense studies, and medical product commercialization partners in the field of neuroscience.
LXIO (language axiological input/output) is a psychiatric diagnostic system founded upon the Mood State Indicator (MSI) machine Learning algorithm. The LXIO system deciphers language in written formats (speech to text is also applicable) and passes its data through a parser, which breaks down each sentence into multiple words to create a data structure for particular phrases within specific time frames. Using discursive cues, we have found that it is possible to amass and analyze large amounts of human linguistic data to associate specific phenomena with physiological states. The LXIO project is in collaboration with MIT’s Synthetic Intelligence Laboratory.
HBSF is working with Hospital on Mobile to develop the Migraine.AI. app. to answer some of the biggest questions that could not be answered by traditional, retrospective approaches. The real-time, objectively-acquired data will enable us to validate (or challenge) suspected triggers, treatment regimens, and even migraine categorizations. Migraine.AI is running one of the largest prospective clinical trials to best optimize migraine therapies.
Bettering the Future of Brain Health
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