Three Carney faculty named as MIT Faculty Founder Initiative finalists

Carney professors Haass-Koffler, Jones and Petzschner were announced as finalists in the second annual 2023-24 MIT-Royalty Pharma Prize Competition. The competition supports female faculty entrepreneurs in biotechnology and provides them with resources to help take their ideas to commercialization. 


Neuro-Informed Hierarchical Models of Cognition

Which neural and cognitive processes drive alterations in decision making and learning? This project offers an easy-to-use python-based software toolbox, which allows scientists from cognitive neuroscientists to clinical researchers to rigorously characterize interpretable mechanisms that can explain changes in observable behaviors and their neural correlates in healthy and clinical populations.

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.


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.