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Closed-loop enhancement and neural decoding of cognitive control in humans

Nature Biomedical Engineering volume 7, pages 576–588 (2023)Cite this article

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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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Fig. 1: Experimental paradigms.
Fig. 2: Effect of conflict and open-loop capsular stimulation on cognitive control.
Fig. 3: Effect of open-loop capsule stimulation on cognitive control.
Fig. 4: Closed-loop internal capsule stimulation efficiently enhances cognitive control.
Fig. 5: Neural decoding of cognitive states.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. Pre-processed and anonymized neural and behavioural data are available through Zenodo at https://zenodo.org/record/5083120#.YOhvWehKiUk and https://zenodo.org/record/5085197#.YOhtouhKiUk.

Code availability

Analysis code is available at https://github.com/tne-lab/MSIT-Nature-Biomedical-Engineering. The closed-loop neurostimulation system has been released as open-source code and documented46, and the neural decoding and state-space modelling engines have similarly been released for open download (https://github.com/TRANSFORM-DBS/Encoder-Decoder-Paper and https://github.com/Eden-Kramer-Lab/COMPASS).

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Acknowledgements

We thank A. Afzal, G. Belok, K. Farnes, J. Felicione, R. Franklin, A. Gilmour, A. Gosai, M. Moran, M. Robertson, C. Salthouse, D. Vallejo-Lopez and S. Zorowitz for technical assistance with data collection and the research participants, without whose generous help none of this would have been possible. This work was supported by grants from the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number W911NF-14-2-0045 issued by the Army Research Organization (ARO) contracting office in support of DARPA’s SUBNETS Program, the National Institutes of Health (UH3NS100548, R01MH111917, R01MH086400, R01DA026297, R01EY017658, K24NS088568), Ellison Foundation, Tiny Blue Dot Foundation, MGH Executive Council on Research, OneMind Institute, and the MnDRIVE and Medical Discovery Team-Addictions initiatives at the University of Minnesota. The views, opinions and findings expressed are those of the authors. They should not be interpreted as representing the official views or policies of the Department of Defense, Department of Health and Human Services, any other branch of the US Government, or any other funding entity.

Author information

Author notes

  1. Ali Yousefi

    Present address: Departments of Computer Science and Neuroscience, Worcester Polytechnic Institute, Worcester, MA, USA

  2. Emad N. Eskandar

    Present address: Department of Neurological Surgery, Albert Einstein College of Medicine, Bronx, NY, USA

  3. Alik S. Widge

    Present address: Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA

  4. These authors contributed equally: Sydney S. Cash, Alik S. Widge.

Authors and Affiliations

  1. Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Ishita Basu, Ali Yousefi, Kristen K. Ellard, Thilo Deckersbach, Darin D. Dougherty & Alik S. Widge

  2. Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

    Britni Crocker, Rina Zelmann, Angelique C. Paulk & Sydney S. Cash

  3. Department of Radiology, MGH/HST Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA

    Noam Peled

  4. Department of Neurology, Brigham & Womens Hospital, Boston, MA, USA

    Daniel S. Weisholtz

  5. Department of Neurological Surgery, Brigham & Womens Hospital, Boston, MA, USA

    G. Rees Cosgrove

  6. Department of Mathematics and Statistics, Boston University, Boston, MA, USA

    Uri T. Eden

  7. Department of Neurological Surgery, Massachusetts General Hospital, Boston, MA, USA

    Emad N. Eskandar

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Contributions

A.S.W., D.D.D., E.N.E. and S.S.C. designed the study. I.B., A.Y., B.C., R.Z. and U.T.E. designed key software and tools required for data collection. K.K.E. and T.D. selected the psychometric scales administered to participants and provided unpublished data related to norming of those questionnaires. E.N.E. and G.R.C. performed all surgical procedures. A.S.W., I.B., B.C., R.Z., A.C.P., S.S.C. and D.S.W. collected data with participants during acute seizure monitoring. A.S.W., I.B., A.Y., A.C.P. and N.P. analysed data. I.B. and A.S.W. wrote the paper, with substantial inputs from A.Y., R.Z., A.C.P. and S.S.C. All authors had opportunities for critical input into and revision of the submitted manuscript, and approved its submission.

Corresponding author

Correspondence to Alik S. Widge.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Biomedical Engineering thanks Edward Chang, Philip Star, Peter Tass and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 iEEG Recording montage.

Example recording montage from a single participant, with cortical parcellation overlaid. Electrode shanks represented by the grey dotted lines access a broad network covering multiple prefrontal structures, superficial and mesial temporal lobe, and striatum/internal capsule.

Extended Data Fig. 2 Accuracy results.

Accuracy during different stimulation experiments, for A) open-loop and B) closed-loop capsular stimulation. Boxes show the mean and confidence intervals for accuracy with stimulation at each site. Colours indicate stimulation sites as in the main text. The p-value above each bar represents a binomial exact test of accuracy compared to the non-stimulated baseline condition, with Benjamini-Hochberg false discovery rate correction. All accuracies are above 95%, with accuracy during stimulated blocks being very slightly higher in most cases. No results exceed chance significance. We did not have open- and closed-loop data from the same participants. To compare the CL and OL conditions, we therefore compared their accuracies across participants with a Fisher exact test for each of the three stimulation sites (L Dorsal, R Ventral, R Dorsal) that were used in both conditions. L Dorsal: p = 0.645. R Ventral: p = 0.440. R Dorsal: p = 0.655. These provide no evidence for a difference between OL and CL conditions. These results do not support a change in accuracy with any stimulation type. That is, the observed decrease in reaction times is a true performance improvement, not a shift along a speed-accuracy tradeoff. We were unable to analyse accuracy in the GLME framework because the differences between stimulation sites are so small as to make the models non-identifiable in all cases.

Extended Data Fig. 3 Cortical response to internal capsule stimulation.

Topographic structure of the internal capsule yields differential cortical effects from stimulation at different capsular sites. Before task-linked stimulation, we performed safety/perceptibility testing, where we repeatedly stimulated each potential site with brief 130 Hz pulse trains (see Methods). Each of those trains created an evoked response potential (ERP) in various cortical regions. For each participant, we collected all sEEG channels that were localized to grey matter of DLPFC or ACC. We then quantified the post-train ERP as the sum of the area under its polyphasic curve (AUC). We limited this analysis to channels ipsilateral to the site of stimulation. Each marker represents the mean log(AUC) in one participant. Boxes show the mean and confidence intervals for the ERP AUC from stimulation at each site. The stimulation sites that were more effective behaviorally produced the largest ERPs in these cognitive-control-associated regions, with right dorsal stimulation having the largest effects. (p-values represent t-test on the regression coefficients of a log-normal GLM, that is the same analysis used in main text Fig. 2). In the left hemisphere, dorsal stimulation produced larger responses than ventral stimulation, but this did not reach statistical significance given the small number of trials (5 test trains per participant). These results are consistent with the known topography of the internal capsule, where fibers that connect DLPFC and ACC to thalamus run in the dorsal-most part of the anterior limb, that is in close proximity to our chosen dorsal electrodes.

Extended Data Fig. 4 Open-loop and closed-loop effects in manifest data.

Effect of open-loop and closed-loop capsular stimulation on A) reaction time (RT) and B) Conflict related RT. Conflict related RT is calculated as the residual reaction time after subtracting the mean reaction time of the congruent trials in the same block, that is it has an expected value of 0 ms on non-conflict trials. We consider it as the closest raw/manifest data analogue of xconflict. We note, however, that both of these manifest RT variables include the Gaussian noise that is removed by the state-space filtering that produces xbase and xconflict. As such, the data in this figure are by definition noisier, and the analysis has lower statistical power. This leads to smaller effects in the open-loop results compared to main text Fig. 3. Closed-loop stimulation of the right dorsal internal capsule (our most effective open-loop intervention) was more effective than its open loop counterpart at reducing raw RT (the counterpart of xbase). Consistent with the specificity illustrated in main text Fig. 4 C, there was no advantage for closed-loop stimulation on the conflict-specific RT (the counterpart of xconflict). All formatting and graphical elements follow the conventions of main text Fig. 4.

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Basu, I., Yousefi, A., Crocker, B. et al. Closed-loop enhancement and neural decoding of cognitive control in humans. Nat. Biomed. Eng 7, 576–588 (2023). https://doi.org/10.1038/s41551-021-00804-y

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