Ryan Thorpe, Darcy Diesburg and Chad Williams named winner/runners-up of the 2022 Brainstorm Challenge

Depression is an epidemic in the United States. A mood disorder that causes a persistent feeling of sadness and loss of interest, some 21.0 million adults in the United States, or 8.4% of the population, had at least one major depressive episode in 2020.  

Year in Review: 2021 in stories

Amidst the many challenges of 2021, researchers in Brown University's Carney Institute for Brain Science contributed a whirlwind of scientific advances and news about the brain.

From announcements of major philanthropic gifts to support cutting-edge brain science at Brown, to groundbreaking research that demonstrated the first human use of high-bandwidth wireless brain-computer interface, below in reverse chronological order are Carney's top stories of 2021.



Autonomous Empirical Research

The integration of behavioral phenomena into computational models of brain function is a fundamental staple of empirical research in neuroscience and psychology. Yet, researchers are beginning to accumulate increasing amounts of data without having the temporal, computational or monetary resources to integrate these data into scientific theories, and/or to test resulting theories in follow-up experiments.

Improving mental health through computational neuroscience

Researchers at Brown University’s Robert J. and Nancy D. Carney Institute for Brain Science are continuously seeking a better understanding of the computational power of the human brain. But what happens to the insights they gain from each study? How can these discoveries be put to use more rapidly to help patients?


Precision TMS: Using EEG and machine learning to predict TMS response

Major Depressive Disorder (MDD) is a common and debilitating illness. One novel intervention is transcranial magnetic stimulation (TMS), which has been cleared by the Food and Drug Administration for pharmacoresistant MDD. TMS uses rapidly fluctuating magnetic fields to induce electrical activity and modify pathological neural networks in MDD. Despite its promise, TMS is effective in approximately 60% of patients, and requires daily treatments for up to six weeks.


PARRM: Period-based Artifact Reconstruction and Removal Method

Advances in neurotechnology have enabled concurrent electrical neuromodulation and recording of neural signals at adjacent locations in the central nervous system. However, stimulation artifacts obscure the sensed underlying neural activity. Researchers have developed a novel method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact.


NeuroDAC: An open-source arbitrary biosignal generator

Researchers are developing biomedical devices with embedded closed-loop algorithms for providing advanced adaptive therapies. As these devices become more capable and algorithms become more complex, tasked with integrating and interpreting multi-channel, multi-modal electrophysiological signals, there is a need for flexible bench-top testing and prototyping. NeuroDAC uses off-the-shelf audio equipment to construct a biosignal waveform generator capable of streaming pre-recorded biosignals from a host computer.


LANs: Likelihood approximation networks for fast and flexible inference in cognitive neuroscience

In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Candidate models are often defined as generative: models from which we can simulate data. However, typically cognitive neuroscience studies require going the other way around, from data to inferring models and their parameters. Common software tools for doing this inference typically depend on having access to a likelihood function which returns the likelihood of data observation for a model parameterization.


Honeycomb: A template for reproducible psychophysiological tasks for clinic, laboratory and home use

Psychophysiological tasks are a useful tool for studying human behavior driven by mental processes such as cognitive control, reward evaluation and learning. Neural mechanisms during behavioral tasks are often studied via simultaneous electrophysiological recordings. Popular online platforms such as Amazon Mechanical Turk (MTurk) and Prolific enable deployment of tasks to numerous participants simultaneously. However, there is currently no task-creation framework available for flexibly deploying tasks online and during simultaneous electrophysiology.


Human Neocortical Neurosolver for circuit-level interpretation of human MEG/EEG

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution, providing reliable markers of healthy and disease states. A downside of these rapidly advancing technologies is a lack of understanding the underlying neural generators, which limits their use in developing new theories of information processing or new methods for treating neuropathology. Human Neocortical Neurosolver (HNN) is a user-friendly software tool designed to bridge these macro-scale signals to the underlying cellular and circuit level activity.