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. 2025 Feb 25:19:1544994.
doi: 10.3389/fnhum.2025.1544994. eCollection 2025.

Proceedings of the 12th annual deep brain stimulation think tank: cutting edge technology meets novel applications

Alfonso Enrique Martinez-Nunez  1 , Christopher J Rozell  2 , Simon Little  3 , Huiling Tan  4 , Stephen L Schmidt  5 , Warren M Grill  5   6 , Miroslav Pajic  5 , Dennis A Turner  5   6   7 , Coralie de Hemptinne  1 , Andre Machado  8   9 , Nicholas D Schiff  10 , Abbey S Holt-Becker  11 , Robert S Raike  11 , Mahsa Malekmohammadi  12   13 , Yagna J Pathak  14 , Lyndahl Himes  14 , David Greene  15 , Lothar Krinke  16   17 , Mattia Arlotti  16 , Lorenzo Rossi  16 , Jacob Robinson  18   19 , Bahne H Bahners  20   21   22 , Vladimir Litvak  23 , Luka Milosevic  24   25 , Saadi Ghatan  26   27 , Frederic L W V J Schaper  20 , Michael D Fox  20 , Nicholas M Gregg  28 , Cynthia Kubu  8 , James J Jordano  29   30   31 , Nicola G Cascella  32 , YoungHoon Nho  33 , Casey H Halpern  33   34 , Helen S Mayberg  35   36   37 , Ki Sueng Choi  35   36 , Haneul Song  35 , Jungho Cha  35 , Sankaraleengam Alagapan  2 , Nico U F Dosenbach  38   39   40   41   42   43 , Evan M Gordon  44 , Jianxun Ren  45 , Hesheng Liu  45   46 , Lorraine V Kalia  47   48 , Sarah-Anna Hescham  49   50   51 , Dorian M Kusyk  1 , Adolfo Ramirez-Zamora  1 , Kelly D Foote  1 , Michael S Okun  1 , Joshua K Wong  1
Affiliations

Proceedings of the 12th annual deep brain stimulation think tank: cutting edge technology meets novel applications

Alfonso Enrique Martinez-Nunez et al. Front Hum Neurosci. .

Erratum in

  • Corrigendum: Proceedings of the 12th annual deep brain stimulation think tank: cutting edge technology meets novel applications.
    Martinez-Nunez AE, Rozell CJ, Little S, Tan H, Schmidt SL, Grill WM, Pajic M, Turner DA, de Hemptinne C, Machado A, Schiff ND, Holt-Becker AS, Raike RS, Malekmohammadi M, Pathak YJ, Himes L, Greene D, Krinke L, Arlotti M, Rossi L, Robinson J, Bahners BH, Litvak V, Milosevic L, Ghatan S, Schaper FLWVJ, Fox MD, Gregg NM, Kubu C, Jordano JJ, Cascella NG, Nho Y, Halpern CH, Mayberg HS, Choi KS, Song H, Cha J, Alagapan S, Dosenbach NUF, Gordon EM, Ren J, Liu H, Kalia LV, Hescham SA, Kusyk DM, Ramirez-Zamora A, Foote KD, Okun MS, Wong JK. Martinez-Nunez AE, et al. Front Hum Neurosci. 2025 May 6;19:1612584. doi: 10.3389/fnhum.2025.1612584. eCollection 2025. Front Hum Neurosci. 2025. PMID: 40395686 Free PMC article.

Abstract

The Deep Brain Stimulation (DBS) Think Tank XII was held on August 21st to 23rd. This year we showcased groundbreaking advancements in neuromodulation technology, focusing heavily on the novel uses of existing technology as well as next-generation technology. Our keynote speaker shared the vision of using neuro artificial intelligence to predict depression using brain electrophysiology. Innovative applications are currently being explored in stroke, disorders of consciousness, and sleep, while established treatments for movement disorders like Parkinson's disease are being refined with adaptive stimulation. Neuromodulation is solidifying its role in treating psychiatric disorders such as depression and obsessive-compulsive disorder, particularly for patients with treatment-resistant symptoms. We estimate that 300,000 leads have been implanted to date for neurologic and neuropsychiatric indications. Magnetoencephalography has provided insights into the post-DBS physiological changes. The field is also critically examining the ethical implications of implants, considering the long-term impacts on clinicians, patients, and manufacturers.

Keywords: Parkinson’s disease; deep brain stimulation; depression; epilepsy; neuromodulation; obsessive-compulsive disorder; sleep; stroke.

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Conflict of interest statement

AH-B and RR were employed by Medtronic Inc. DG was employed by NeuroPace, Inc. LKr, MA, and LR were employed by Newronika SpA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Spectrogram of LFP in different frequency bands throughout the day. Different circadian patterns of increased activity within a specific frequency band are seen across different conditions and targets. Dyst, dystonia; ET, essential tremor; GPi, globus pallidus pars interna; PD, Parkinson’s disease; STN, subthalamic nucleus.
Figure 2
Figure 2
Illustration of a DBS lead in the right cerebellum.
Figure 3
Figure 3
Asymmetric sensing with stimulation on. (A) Stimulation and sensing configuration: Left STN stimulation contact 2b, sensing contacts 2a and 2a; Right STN stimulation contact 11b, sensing contacts 9 and 12. Sensing contacts 9 and 12 and not at equidistance to contact 11b. (B) time frequency plot and normalized amplitude spectrum calculated at time points indicated by the blue line.
Figure 4
Figure 4
Oscillatory coherent networks of several DBS targets studied to date.
Figure 5
Figure 5
Frontal X-ray depicting DBS electrodes implanted in the bilateral thalamus, and neocortical strip electrodes.
Figure 6
Figure 6
Lesion network mapping (top) identified that lesion locations associated with epilepsy were more negatively functionally connected (“anticorrelated,” cold colors) to the basal ganglia (globus pallidus internus, substantia nigra) and cerebellum. DBS network mapping (bottom) identified that DBS sites associated with better seizure control were more positively functionally connected (warm colors) to this same network (white outlines), converging on a common brain circuit target for epilepsy.
Figure 7
Figure 7
Single pulse and repetitive high-frequency stimulation of the thalamus during stereotactic electroencephalography (sEEG) are used to study the impact of thalamic neuromodulation on seizure network excitability.
Figure 8
Figure 8
Types of device abandonment.
Figure 9
Figure 9
Components and interactions of informed consent in clinical research.
Figure 10
Figure 10
Probabilistic tractography analysis of two subjects across different stimulation configurations.
Figure 11
Figure 11
Somato-cognitive action network (SCAN) functional connectivity to subcortex. Resting state functional connectivity (RSFC) analyses in Parkinson’s Disease (PD) patients and healthy controls has revealed that the subcortical circuitry previously mostly associated with classical effector-specific motor functions (foot, hand, mouth), is more strongly connected to the SCAN in cortex. The most important DBS (deep brain stimulation) targets in PD, namely the subthalamic nucleus (STN) and globus pallidus pars interna (GPi) are also part of SCAN, not motor networks.
See this image and copyright information in PMC

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