Metric validation for detection of delayed and directed coupling
Kate Dembny, Hafsa Farooqi, Alexander B Herman, Theoden I Netoff and David P Darrow
June 3, 2026 — A team of University of Minnesota researchers have shed new light on the metrics scientists use to map the human brain, which could change the way we understand and treat neurological and psychiatric disorders, such as epilepsy, depression, and Parkinson’s disease.
Traveling-wave transcranial alternating current stimulation (twtACS) causally links neural timing to cognitive function
Sangjun Lee, Jimin Park, Ivan Alekseichuk, Taylor A Berger, Ana M G Manea, Harry Tran, Gabriela Delgado Salazar, Seth D König, Alexander B Herman, David P Darrow, Jan Zimmermann and Alexander Opitz
May 12, 2026. Led by the Opitz Lab at the University of Minnesota, this study introduces a noninvasive stimulation method that controls the direction in which brain waves travel across the cortex. Intracranial recordings from our patients confirmed the effect in the human brain, and the direction of stimulation influenced cognitive performance.
Social aloofness is associated with non-social explore-exploit decisions
Evan Knep, Xinyuan Yan, Cathy S Chen, Suma Jacob, David P Darrow, R Becket Ebitz, Nicola Grissom and Alexander B Herman
In a sample of 1,001 people, those reporting less social interest explored less and were more sensitive to negative outcomes when making decisions. Computational modeling showed this reflected less random choice behavior rather than cognitive rigidity, linking social traits to how people balance exploring and exploiting.
Distinct computational mechanisms of uncertainty processing explain opposing exploratory behaviors in anxiety and apathy
Xinyuan Yan, R Becket Ebitz, Nicola Grissom, David P Darrow and Alexander B Herman
Anxiety and apathy shape how people interpret uncertainty. People with more anxiety treated uncertainty as a sign that the world was changing and explored more, while those with more apathy treated it as random noise and explored less. These findings offer a computational framework for understanding decision making in neuropsychiatric disorders.
A foraging-theory-based model captures the spectrum of human behavioral diversity in sequential decision making
Dameon C Harrell, Meriam Zid, Veldon-James Laurie, Cathy S Chen, Nicola M Grissom, David P Darrow, R Becket Ebitz and Alexander B Herman
Preprint. Most models of choice assume people directly compare the values of their options. Across 1,000 participants, we found that people instead compare each option to an internal threshold, much like animals deciding when to leave a food patch, and this model better captures the full range of human decision strategies.
Dynamic prefrontal coupling coordinates adaptive decision-making
Xinyuan Yan, Seth D König, R Becket Ebitz, Benjamin Y Hayden, David P Darrow and Alexander B Herman
Preprint. Using intracranial recordings from patients performing a reward task, we found that two prefrontal regions track uncertainty, value and prediction error in distinct frequency bands. Their coordination through theta and gamma coupling predicted when people would stick with a choice or switch.
Feature extraction and prediction of spinal cord stimulation evoked compound action potentials in humans
Seth König, Ahmed Ramadan, Disa Sullivan, Vasudha Goel, R Scott Stayner, David Schultz, Alexander B Herman, Theoden I Netoff and David P Darrow*
The Herman-Darrow lab recently had a paper published in the Journal of Neural Engineering involving our work on evoked compound action potentials (ECAPs) during spinal cord stimulation (SCS) may be useful in the treatment of chronic pain as a control signal for closed-loop neuromodulation.
Click here for the story from the University of Minnesota Medical School
Or you can find the paper here
Assessing expert reliability in determining intracranial EEG channel quality and introducing the automated bad channel detection algorithm
Tariq Hattab, Seth D König, Danielle C Carlson, Rebecca F Hayes, Zhiyi Sha, Michael C Park, Lora Kahn, Sima Patel, Robert A McGovern, Thomas Henry, Fawad Khan, Alexander B Herman and David P Darrow (July 26th, 2024)
Objective. To evaluate the inter- and intra-rater reliability for the identification of bad channels among neurologists, EEG Technologists, and naïve research personnel, and to compare their performance with the automated bad channel detection (ABCD) algorithm for detecting bad channels. Approach. Six Neurologists, ten EEG Technologists, and six naïve research personnel (22 raters in total) were asked to rate 1440 real intracranial EEG channels as good or bad. Intra- and interrater kappa statistics were calculated for each group. We then compared each group to the ABCD algorithm which uses spectral and temporal domain features to classify channels as good or bad. Main results. Analysis of channel ratings from our participants revealed variable intra-rater reliability within each group, with no significant differences across groups. Inter-rater reliability was moderate among neurologists and EEG Technologists but minimal among naïve participants. Neurologists demonstrated a slightly higher consistency in ratings than EEG Technologists. Both groups occasionally misclassified flat channels as good, and participants generally focused on low-frequency content for their assessments. The ABCD algorithm, in contrast, relied more on high-frequency content. A logistic regression model showed a linear relationship between the algorithm's ratings and user responses for predominantly good channels, but less so for channels rated as bad. Sensitivity and specificity analyses further highlighted differences in rating patterns among the groups, with neurologists showing higher sensitivity and naïve personnel higher specificity. Significance. Our study reveals the bias in human assessments of intracranial electroencephalography (iEEG) data quality and the tendency of even experienced professionals to overlook certain bad channels, highlighting the need for standardized, unbiased methods. The ABCD algorithm, outperforming human raters, suggests the potential of automated solutions for more reliable iEEG interpretation and seizure characterization, offering a reliable approach free from human biases.
Flexible multi-step hypothesis testing of human ECoG data using cluster-based permutation tests with GLMEs
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Combining CBPT with GLMEs allows statistical analysis to match experimental design.
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CBPT with GLMEs accounts for subject variability and hierarchical random effects.
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The proposed method maintains control of type I error, type II error, and FWER.
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CBPT with GLMEs can be applied to individual channels and pseudo-population data.
Reliability of visual review of intracranial electroencephalogram in identifying the seizure onset zone: A systematic review and implications for the accuracy of automated methods
Our new paper was published on October 27th, 2022, in the Official Journal of the International League Against Epilepsy after much collaboration between multiple departments at the University of Minnesota and UMMC - East Bank.
Injuries from Less-Lethal Weapons During the George Floyd Protests in Minneapolis Paper
Our new paper "Injuries from Less-Lethal Weapons During the George Floyd Protests in Minneapolis" was just published on The New England Journal of Medicine. It details several instances when protesters sustained severe injuries from non-lethal weapons, revealing that projectiles are not appropriate for crowd control.
Protective Effect of Stem Cells from Toxicity Induced by Gulf War Illness Serum in N2A Neuroblastoma Cells
Danielle Carlson's Paper entitled "Protective Effect of Stem Cells from Toxicity Induced by Gulf War Illness (GWI) Serum in N2A Neuroblastoma Cells" was recently published in the Journal of Neurology and Neuromedicine.