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Rezaei MR, Jeoung H, Gharamani A, Saha U, Bhat V, Popovic MR, Yousefi A, Chen R, Lankarany M. Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model. J Neural Eng 2023; 20:056016. [PMID: 37473753 DOI: 10.1088/1741-2552/ace932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/20/2023] [Indexed: 07/22/2023]
Abstract
Objective. The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g. the response time), remains to be identified.Approach. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals' response time) recorded in ten Parkinson's disease (PD) patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy populations.Main results. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies.Significance. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.
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Affiliation(s)
- Mohammad R Rezaei
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Haseul Jeoung
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ayda Gharamani
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- Worcester Polytechnic Institute, MA, United States of America
| | - Utpal Saha
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Venkat Bhat
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ali Yousefi
- Worcester Polytechnic Institute, MA, United States of America
| | - Robert Chen
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Milad Lankarany
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
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2
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Nagrale SS, Yousefi A, Netoff TI, Widge AS. In silicodevelopment and validation of Bayesian methods for optimizing deep brain stimulation to enhance cognitive control. J Neural Eng 2023; 20:036015. [PMID: 37105164 PMCID: PMC10193041 DOI: 10.1088/1741-2552/acd0d5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 03/18/2023] [Accepted: 04/27/2023] [Indexed: 04/29/2023]
Abstract
Objective.deep brain stimulation (DBS) of the ventral internal capsule/striatum (VCVS) is a potentially effective treatment for several mental health disorders when conventional therapeutics fail. Its effectiveness, however, depends on correct programming to engage VCVS sub-circuits. VCVS programming is currently an iterative, time-consuming process, with weeks between setting changes and reliance on noisy, subjective self-reports. An objective measure of circuit engagement might allow individual settings to be tested in seconds to minutes, reducing the time to response and increasing patient and clinician confidence in the chosen settings. Here, we present an approach to measuring and optimizing that circuit engagement.Approach.we leverage prior results showing that effective VCVS DBS engages cognitive control circuitry and improves performance on the multi-source interference task, that this engagement depends primarily on which contact(s) are activated, and that circuit engagement can be tracked through a state space modeling framework. We develop a simulation framework based on those empirical results, then combine this framework with an adaptive optimizer to simulate a principled exploration of electrode contacts and identify the contacts that maximally improve cognitive control. We explore multiple optimization options (algorithms, number of inputs, speed of stimulation parameter changes) and compare them on problems of varying difficulty.Main results.we show that an upper confidence bound algorithm outperforms other optimizers, with roughly 80% probability of convergence to a global optimum when used in a majority-vote ensemble.Significance.we show that the optimization can converge even with lag between stimulation and effect, and that a complete optimization can be done in a clinically feasible timespan (a few hours). Further, the approach requires no specialized recording or imaging hardware, and thus could be a scalable path to expand the use of DBS in psychiatric and other non-motor applications.
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Affiliation(s)
- Sumedh S Nagrale
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States of America
| | - Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, United States of America
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States of America
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3
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Widge AS. Closed-Loop Deep Brain Stimulation for Psychiatric Disorders. Harv Rev Psychiatry 2023; 31:162-171. [PMID: 37171475 PMCID: PMC10188203 DOI: 10.1097/hrp.0000000000000367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
ABSTRACT Deep brain stimulation (DBS) is a well-established approach to treating medication-refractory neurological disorders and holds promise for treating psychiatric disorders. Despite strong open-label results in extremely refractory patients, DBS has struggled to meet endpoints in randomized controlled trials. A major challenge is stimulation "dosing"-DBS systems have many adjustable parameters, and clinicians receive little feedback on whether they have chosen the correct parameters for an individual patient. Multiple groups have proposed closed loop technologies as a solution. These systems sense electrical activity, identify markers of an (un)desired state, then automatically deliver or adjust stimulation to alter that electrical state. Closed loop DBS has been successfully deployed in movement disorders and epilepsy. The availability of that technology, as well as advances in opportunities for invasive research with neurosurgical patients, has yielded multiple pilot demonstrations in psychiatric illness. Those demonstrations split into two schools of thought, one rooted in well-established diagnoses and symptom scales, the other in the more experimental Research Domain Criteria (RDoC) framework. Both are promising, and both are limited by the boundaries of current stimulation technology. They are in turn driving advances in implantable recording hardware, signal processing, and stimulation paradigms. The combination of these advances is likely to change both our understanding of psychiatric neurobiology and our treatment toolbox, though the timeframe may be limited by the realities of implantable device development.
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Affiliation(s)
- Alik S Widge
- From the Department of Psychiatry & Behavioral Sciences and Medical Discovery Team on Addictions, University of Minnesota
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4
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Basu I, Yousefi A, Crocker B, Zelmann R, Paulk AC, Peled N, Ellard KK, Weisholtz DS, Cosgrove GR, Deckersbach T, Eden UT, Eskandar EN, Dougherty DD, Cash SS, Widge AS. Closed-loop enhancement and neural decoding of cognitive control in humans. Nat Biomed Eng 2023; 7:576-588. [PMID: 34725508 PMCID: PMC9056584 DOI: 10.1038/s41551-021-00804-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/02/2021] [Indexed: 12/20/2022]
Abstract
Deficits in cognitive control-that is, in the ability to withhold a default pre-potent response in favour of a more adaptive choice-are common in depression, anxiety, addiction and other mental disorders. Here we report proof-of-concept evidence that, in participants undergoing intracranial epilepsy monitoring, closed-loop direct stimulation of the internal capsule or striatum, especially the dorsal sites, enhances the participants' cognitive control during a conflict task. We also show that closed-loop stimulation upon the detection of lapses in cognitive control produced larger behavioural changes than open-loop stimulation, and that task performance for single trials can be directly decoded from the activity of a small number of electrodes via neural features that are compatible with existing closed-loop brain implants. Closed-loop enhancement of cognitive control might remediate underlying cognitive deficits and aid the treatment of severe mental disorders.
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Affiliation(s)
- Ishita Basu
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ali Yousefi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Departments of Computer Science and Neuroscience, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Noam Peled
- Department of Radiology, MGH/HST Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Kristen K Ellard
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - G Rees Cosgrove
- Department of Neurological Surgery, Brigham & Womens Hospital, Boston, MA, USA
| | - Thilo Deckersbach
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Emad N Eskandar
- Department of Neurological Surgery, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurological Surgery, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA.
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5
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Webler RD, Oathes DJ, van Rooij SJH, Gewirtz JC, Nahas Z, Lissek SM, Widge AS. Causally mapping human threat extinction relevant circuits with depolarizing brain stimulation methods. Neurosci Biobehav Rev 2023; 144:105005. [PMID: 36549377 PMCID: PMC10210253 DOI: 10.1016/j.neubiorev.2022.105005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/17/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
Laboratory threat extinction paradigms and exposure-based therapy both involve repeated, safe confrontation with stimuli previously experienced as threatening. This fundamental procedural overlap supports laboratory threat extinction as a compelling analogue of exposure-based therapy. Threat extinction impairments have been detected in clinical anxiety and may contribute to exposure-based therapy non-response and relapse. However, efforts to improve exposure outcomes using techniques that boost extinction - primarily rodent extinction - have largely failed to date, potentially due to fundamental differences between rodent and human neurobiology. In this review, we articulate a comprehensive pre-clinical human research agenda designed to overcome these failures. We describe how connectivity guided depolarizing brain stimulation methods (i.e., TMS and DBS) can be applied concurrently with threat extinction and dual threat reconsolidation-extinction paradigms to causally map human extinction relevant circuits and inform the optimal integration of these methods with exposure-based therapy. We highlight candidate targets including the amygdala, hippocampus, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, and mesolimbic structures, and propose hypotheses about how stimulation delivered at specific learning phases could strengthen threat extinction.
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Affiliation(s)
- Ryan D Webler
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
| | - Desmond J Oathes
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Jonathan C Gewirtz
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA; Department of Psychology, Arizona State University, AZ, USA
| | - Ziad Nahas
- Department of Psychology, Arizona State University, AZ, USA
| | - Shmuel M Lissek
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Alik S Widge
- Department of Psychiatry and Medical Discovery Team on Addictions, University of Minnesota Medical School, MN, USA
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6
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Saalmann YB, Mofakham S, Mikell CB, Djuric PM. Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications. CURRENT RESEARCH IN NEUROBIOLOGY 2022; 4:100071. [PMID: 36619175 PMCID: PMC9816916 DOI: 10.1016/j.crneur.2022.100071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 11/30/2022] [Accepted: 12/19/2022] [Indexed: 12/30/2022] Open
Abstract
Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits - each requiring fine, tailored modulation - that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition.
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Affiliation(s)
- Yuri B. Saalmann
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Sima Mofakham
- Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, USA
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Charles B. Mikell
- Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, USA
| | - Petar M. Djuric
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA
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7
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Rezaei MR, Hadjinicolaou AE, Cash SS, Eden UT, Yousefi A. Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data. Neural Comput 2022; 34:1100-1135. [PMID: 35344988 DOI: 10.1162/neco_a_01491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/08/2022] [Indexed: 11/04/2022]
Abstract
With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period. While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.
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Affiliation(s)
- Mohammad R Rezaei
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9.,Krembil Research Institute, University Health Network, Toronto, ON M5T 2S8.,KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON M5G 2A2, Canada
| | - Alex E Hadjinicolaou
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114.,Harvard Medical School, Boston, MA 02115, U.S.A.
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114.,Harvard Medical School, Boston, MA 02115, U.S.A.
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, U.S.A.
| | - Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, U.S.A.
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8
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Dzianok P, Antonova I, Wojciechowski J, Dreszer J, Kublik E. The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults. Gigascience 2022; 11:6543635. [PMID: 35254424 PMCID: PMC8900497 DOI: 10.1093/gigascience/giac015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/22/2021] [Accepted: 01/27/2022] [Indexed: 12/28/2022] Open
Abstract
Background One of the goals of neuropsychology is to understand the brain mechanisms underlying aspects of attention and cognitive control. Several tasks have been developed as a part of this body of research, however their results are not always consistent. A reliable comparison of the data and a synthesis of study conclusions has been precluded by multiple methodological differences. Here, we describe a publicly available, high-density electroencephalography (EEG) dataset obtained from 42 healthy young adults while they performed 3 cognitive tasks: (i) an extended multi-source interference task; (ii) a 3-stimuli oddball task; (iii) a control, simple reaction task; and (iv) a resting-state protocol. Demographic and psychometric information are included within the dataset. Dataset Validation First, data validation confirmed acceptable quality of the obtained EEG signals. Typical event-related potential (ERP) waveforms were obtained, as expected for attention and cognitive control tasks (i.e., N200, P300, N450). Behavioral results showed the expected progression of reaction times and error rates, which confirmed the effectiveness of the applied paradigms. Conclusions This dataset is well suited for neuropsychological research regarding common and distinct mechanisms involved in different cognitive tasks. Using this dataset, researchers can compare a wide range of classical EEG/ERP features across tasks for any selected subset of electrodes. At the same time, 128-channel EEG recording allows for source localization and detailed connectivity studies. Neurophysiological measures can be correlated with additional psychometric data obtained from the same participants. This dataset can also be used to develop and verify novel analytical and classification approaches that can advance the field of deep/machine learning algorithms, recognition of single-trial ERP responses to different task conditions, and detection of EEG/ERP features for use in brain-computer interface applications.
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Affiliation(s)
- Patrycja Dzianok
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology PAS, 02-093, Warsaw, Poland
| | - Ingrida Antonova
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology PAS, 02-093, Warsaw, Poland
| | - Jakub Wojciechowski
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology PAS, 02-093, Warsaw, Poland.,Bioimaging Research Center, Institute of Physiology and Pathology of Hearing, 02-042, Warsaw, Poland
| | - Joanna Dreszer
- Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Toruń, 87-100, Toruń, Poland
| | - Ewa Kublik
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology PAS, 02-093, Warsaw, Poland
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9
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Arefnezhad S, Hamet J, Eichberger A, Frühwirth M, Ischebeck A, Koglbauer IV, Moser M, Yousefi A. Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework. Sci Rep 2022; 12:2650. [PMID: 35173189 PMCID: PMC8850607 DOI: 10.1038/s41598-022-05810-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 01/14/2022] [Indexed: 01/22/2023] Open
Abstract
Drowsiness is a leading cause of accidents on the road as it negatively affects the driver’s ability to safely operate a vehicle. Neural activity recorded by EEG electrodes is a widely used physiological correlate of driver drowsiness. This paper presents a novel dynamical modeling solution to estimate the instantaneous level of the driver drowsiness using EEG signals, where the PERcentage of eyelid CLOSure (PERCLOS) is employed as the ground truth of driver drowsiness. Applying our proposed modeling framework, we find neural features present in EEG data that encode PERCLOS. In the decoding phase, we use a Bayesian filtering solution to estimate the PERCLOS level over time. A data set that comprises 18 driving tests, conducted by 13 drivers, has been used to investigate the performance of the proposed framework. The modeling performance in estimation of PERCLOS provides robust and repeatable results in tests with manual and automated driving modes by an average RMSE of 0.117 (at a PERCLOS range of 0 to 1) and average High Probability Density percentage of 62.5%. We further hypothesized that there are biomarkers that encode the PERCLOS across different driving tests and participants. Using this solution, we identified possible biomarkers such as Theta and Delta powers. Results show that about 73% and 66% of the Theta and Delta powers which are selected as biomarkers are increasing as PERCLOS grows during the driving test. We argue that the proposed method is a robust and reliable solution to estimate drowsiness in real-time which opens the door in utilizing EEG-based measures in driver drowsiness detection systems.
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Affiliation(s)
- Sadegh Arefnezhad
- Institute of Automotive Engineering, Graz University of Technology, 8010, Graz, Austria.
| | - James Hamet
- Neurable Company, Boston, MA, 02108, USA.,Vistim Labs Company, Salt Lake City, UT, 84103, USA
| | - Arno Eichberger
- Institute of Automotive Engineering, Graz University of Technology, 8010, Graz, Austria
| | | | - Anja Ischebeck
- Institute of Psychology, University of Graz, 8010, Graz, Austria
| | - Ioana Victoria Koglbauer
- Institute of Engineering and Business Informatics, Graz University of Technology, Graz, 8010, Austria
| | - Maximilian Moser
- Human Research Institute, Weiz, 8160, Austria.,Chair of Department of Physiology, Medical University of Graz, 8036, Graz, Austria
| | - Ali Yousefi
- Neurable Company, Boston, MA, 02108, USA.,Department of Computer Science Worcester Polytechnic Institute, 100 Institute Road, MA, 01609, Worcester, USA
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10
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Wendt K, Denison T, Foster G, Krinke L, Thomson A, Wilson S, Widge AS. Physiologically informed neuromodulation. J Neurol Sci 2021; 434:120121. [PMID: 34998239 PMCID: PMC8976285 DOI: 10.1016/j.jns.2021.120121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 01/09/2023]
Abstract
The rapid evolution of neuromodulation techniques includes an increasing amount of research into stimulation paradigms that are guided by patients' neurophysiology, to increase efficacy and responder rates. Treatment personalisation and target engagement have shown to be effective in fields such as Parkinson's disease, and closed-loop paradigms have been successfully implemented in cardiac defibrillators. Promising avenues are being explored for physiologically informed neuromodulation in psychiatry. Matching the stimulation frequency to individual brain rhythms has shown some promise in transcranial magnetic stimulation (TMS). Matching the phase of those rhythms may further enhance neuroplasticity, for instance when combining TMS with electroencephalographic (EEG) recordings. Resting-state EEG and event-related potentials may be useful to demonstrate connectivity between stimulation sites and connected areas. These techniques are available today to the psychiatrist to diagnose underlying sleep disorders, epilepsy, or lesions as contributing factors to the cause of depression. These technologies may also be useful in assessing the patient's brain network status prior to deciding on treatment options. Ongoing research using invasive recordings may allow for future identification of mood biomarkers and network structure. A core limitation is that biomarker research may currently be limited by the internal heterogeneity of psychiatric disorders according to the current DSM-based classifications. New approaches are being developed and may soon be validated. Finally, care must be taken when incorporating closed-loop capabilities into neuromodulation systems, by ensuring the safe operation of the system and understanding the physiological dynamics. Neurophysiological tools are rapidly evolving and will likely define the next generation of neuromodulation therapies.
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Affiliation(s)
- Karen Wendt
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
| | - Timothy Denison
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Gaynor Foster
- Welcony Inc., Plymouth, MN, United States of America
| | - Lothar Krinke
- Welcony Inc., Plymouth, MN, United States of America; Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, WV, United States of America
| | - Alix Thomson
- Welcony Inc., Plymouth, MN, United States of America
| | - Saydra Wilson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America; Medical Discovery Team on Additions, University of Minnesota, Minneapolis, MN, United States of America
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11
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Chang YC, Wang YK, Pal NR, Lin CT. Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2464-2473. [PMID: 34748496 DOI: 10.1109/tnsre.2021.3126264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between every pair of states. We compute the transition probability between every pair of states to represent the relationships between the states. This state transition diagram is named as the Fuzzy Covert State Transition Diagram (FCOSTD), which helps the understanding of human states and human performance. We then apply FCOSTD on distracted driving experiments. FCOSTD successfully discovers the external and covert states, faithfully reveals the transition of the brain between states, and the route of the state change when humans are distracted during a driving task. The experimental results demonstrate that different subjects have similar states and inter-state transition behaviour (establishing the consistency of the system) but different ways to allocate brain resources as different actions are being taken.
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Wodeyar A, Schatza M, Widge AS, Eden UT, Kramer MA. A state space modeling approach to real-time phase estimation. eLife 2021; 10:e68803. [PMID: 34569936 PMCID: PMC8536256 DOI: 10.7554/elife.68803] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 09/24/2021] [Indexed: 11/14/2022] Open
Abstract
Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the Open Ephys acquisition system, making it widely available for use in experiments.
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Affiliation(s)
- Anirudh Wodeyar
- Mathematics and Statistics, Boston UniversityBostonUnited States
| | - Mark Schatza
- Department of Psychiatry, University of MinnesotaMinneapolisUnited States
| | - Alik S Widge
- Department of Psychiatry, University of MinnesotaMinneapolisUnited States
| | - Uri T Eden
- Mathematics and Statistics, Boston UniversityBostonUnited States
- Center for Systems Neuroscience, Boston UniversityBostonUnited States
| | - Mark A Kramer
- Mathematics and Statistics, Boston UniversityBostonUnited States
- Center for Systems Neuroscience, Boston UniversityBostonUnited States
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Zelmann R, Paulk AC, Basu I, Sarma A, Yousefi A, Crocker B, Eskandar E, Williams Z, Cosgrove GR, Weisholtz DS, Dougherty DD, Truccolo W, Widge AS, Cash SS. CLoSES: A platform for closed-loop intracranial stimulation in humans. Neuroimage 2020; 223:117314. [PMID: 32882382 PMCID: PMC7805582 DOI: 10.1016/j.neuroimage.2020.117314] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 07/15/2020] [Accepted: 08/25/2020] [Indexed: 01/02/2023] Open
Abstract
Targeted interrogation of brain networks through invasive brain stimulation has become an increasingly important research tool as well as therapeutic modality. The majority of work with this emerging capability has been focused on open-loop approaches. Closed-loop techniques, however, could improve neuromodulatory therapies and research investigations by optimizing stimulation approaches using neurally informed, personalized targets. Implementing closed-loop systems is challenging particularly with regard to applying consistent strategies considering inter-individual variability. In particular, during intracranial epilepsy monitoring, where much of this research is currently progressing, electrodes are implanted exclusively for clinical reasons. Thus, detection and stimulation sites must be participant- and task-specific. The system must run in parallel with clinical systems, integrate seamlessly with existing setups, and ensure safety features are in place. In other words, a robust, yet flexible platform is required to perform different tests with a single participant and to comply with clinical requirements. In order to investigate closed-loop stimulation for research and therapeutic use, we developed a Closed-Loop System for Electrical Stimulation (CLoSES) that computes neural features which are then used in a decision algorithm to trigger stimulation in near real-time. To summarize CLoSES, intracranial electroencephalography (iEEG) signals are acquired, band-pass filtered, and local and network features are continuously computed. If target features are detected (e.g. above a preset threshold for a certain duration), stimulation is triggered. Not only could the system trigger stimulation while detecting real-time neural features, but we incorporated a pipeline wherein we used an encoder/decoder model to estimate a hidden cognitive state from the neural features. CLoSES provides a flexible platform to implement a variety of closed-loop experimental paradigms in humans. CLoSES has been successfully used with twelve patients implanted with depth electrodes in the epilepsy monitoring unit. During cognitive tasks (N=5), stimulation in closed loop modified a cognitive hidden state on a trial by trial basis. Sleep spindle oscillations (N=6) and sharp transient epileptic activity (N=9) were detected in near real-time, and stimulation was applied during the event or at specified delays (N=3). In addition, we measured the capabilities of the CLoSES system. Total latency was related to the characteristics of the event being detected, with tens of milliseconds for epileptic activity and hundreds of milliseconds for spindle detection. Stepwise latency, the actual duration of each continuous step, was within the specified fixed-step duration and increased linearly with the number of channels and features. We anticipate that probing neural dynamics and interaction between brain states and stimulation responses with CLoSES will lead to novel insights into the mechanism of normal and pathological brain activity, the discovery and evaluation of potential electrographic biomarkers of neurological and psychiatric disorders, and the development and testing of patient-specific stimulation targets and control signals before implanting a therapeutic device.
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Affiliation(s)
- Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Ishita Basu
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, University of Cincinnati, OH, USA
| | - Anish Sarma
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Ali Yousefi
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Emad Eskandar
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Albert Einstein College of Medicine, NY, USA
| | - Ziv Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Wilson Truccolo
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, University of Minnesota, MI, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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Nadalin JK, Martinet LE, Blackwood EB, Lo MC, Widge AS, Cash SS, Eden UT, Kramer MA. A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects. eLife 2019; 8:44287. [PMID: 31617848 PMCID: PMC6821458 DOI: 10.7554/elife.44287] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 10/06/2019] [Indexed: 01/14/2023] Open
Abstract
Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.
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Affiliation(s)
- Jessica K Nadalin
- Department of Mathematics and Statistics, Boston University, Boston, United States
| | | | - Ethan B Blackwood
- Department of Psychiatry, University of Minnesota, Minneapolis, United States
| | - Meng-Chen Lo
- Department of Psychiatry, University of Minnesota, Minneapolis, United States
| | - Alik S Widge
- Department of Psychiatry, University of Minnesota, Minneapolis, United States
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, United States
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, United States
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, United States
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Provenza NR, Paulk AC, Peled N, Restrepo MI, Cash SS, Dougherty DD, Eskandar EN, Borton DA, Widge AS. Decoding task engagement from distributed network electrophysiology in humans. J Neural Eng 2019; 16:056015. [PMID: 31419211 PMCID: PMC6765221 DOI: 10.1088/1741-2552/ab2c58] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Here, our objective was to develop a binary decoder to detect task engagement in humans during two distinct, conflict-based behavioral tasks. Effortful, goal-directed decision-making requires the coordinated action of multiple cognitive processes, including attention, working memory and action selection. That type of mental effort is often dysfunctional in mental disorders, e.g. when a patient attempts to overcome a depression or anxiety-driven habit but feels unable. If the onset of engagement in this type of focused mental activity could be reliably detected, decisional function might be augmented, e.g. through neurostimulation. However, there are no known algorithms for detecting task engagement with rapid time resolution. APPROACH We defined a new network measure, fixed canonical correlation (FCCA), specifically suited for neural decoding applications. We extracted FCCA features from local field potential recordings in human volunteers to give a temporally continuous estimate of mental effort, defined by engagement in experimental conflict tasks. MAIN RESULTS Using a small number of features per participant, we accurately decoded and distinguished task engagement from other mental activities. Further, the decoder distinguished between engagement in two different conflict-based tasks within seconds of their onset. SIGNIFICANCE These results demonstrate that network-level brain activity can detect specific types of mental efforts. This could form the basis of a responsive intervention strategy for decision-making deficits.
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Affiliation(s)
- Nicole R Provenza
- Brown University School of Engineering, Providence, RI, United States of America
- Charles Stark Draper Laboratory, Cambridge, MA, United States of America
| | - Angelique C Paulk
- Massachusetts General Hospital Neurosurgery Research, Boston, MA, United States of America
- Massachusetts General Hospital Neurology, Boston, MA, United States of America
| | - Noam Peled
- MGH/HST Martinos Center for Biomedical Imaging, Charlestown, MA, United States of America
| | - Maria I Restrepo
- Center for Computation and Visualization, Brown University, Providence, RI 02912, United States of America
| | - Sydney S Cash
- Massachusetts General Hospital Neurology, Boston, MA, United States of America
| | - Darin D Dougherty
- Massachusetts General Hospital Psychiatry, Boston, MA, United States of America
| | - Emad N Eskandar
- Massachusetts General Hospital Neurosurgery Research, Boston, MA, United States of America
- Present affiliation: Chair, Department of Neurological Surgery, Montefiore Medical Center, New York, NY, United States of America
| | - David A Borton
- Brown University School of Engineering, Providence, RI, United States of America
- Carney Institute for Brain Science, Providence, RI, United States of America
- Department of Veterans Affairs, Providence Medical Center, Center for Neurorestoration and Neurotechnology, Providence, RI, United States of America
| | - Alik S Widge
- Massachusetts General Hospital Psychiatry, Boston, MA, United States of America
- Present affiliation: Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States of America
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