| Literature DB >> 31572109 |
Adolfo Ramirez-Zamora1, James Giordano2, Edward S Boyden3,4,5, Viviana Gradinaru6, Aysegul Gunduz7, Philip A Starr8, Sameer A Sheth9, Cameron C McIntyre10, Michael D Fox11, Jerrold Vitek12, Vinata Vedam-Mai13, Umer Akbar14, Leonardo Almeida1, Helen M Bronte-Stewart15, Helen S Mayberg16, Nader Pouratian17, Aryn H Gittis18, Annabelle C Singer19, Meaghan C Creed20, Gabriel Lazaro-Munoz21, Mark Richardson22, Marvin A Rossi23, Leopoldo Cendejas-Zaragoza24, Pierre-Francois D'Haese25, Winston Chiong26, Ro'ee Gilron8, Howard Chizeck27, Andrew Ko28, Kenneth B Baker29, Joost Wagenaar30, Noam Harel31, Wissam Deeb1, Kelly D Foote13, Michael S Okun1.
Abstract
The annual deep brain stimulation (DBS) Think Tank aims to create an opportunity for a multidisciplinary discussion in the field of neuromodulation to examine developments, opportunities and challenges in the field. The proceedings of the Sixth Annual Think Tank recapitulate progress in applications of neurotechnology, neurophysiology, and emerging techniques for the treatment of a range of psychiatric and neurological conditions including Parkinson's disease, essential tremor, Tourette syndrome, epilepsy, cognitive disorders, and addiction. Each section of this overview provides insight about the understanding of neuromodulation for specific disease and discusses current challenges and future directions. This year's report addresses key issues in implementing advanced neurophysiological techniques, evolving use of novel modulation techniques to deliver DBS, ans improved neuroimaging techniques. The proceedings also offer insights into the new era of brain network neuromodulation and connectomic DBS to define and target dysfunctional brain networks. The proceedings also focused on innovations in applications and understanding of adaptive DBS (closed-loop systems), the use and applications of optogenetics in the field of neurostimulation and the need to develop databases for DBS indications. Finally, updates on neuroethical, legal, social, and policy issues relevant to DBS research are discussed.Entities:
Keywords: Parkinson’s disease; Tourette syndrome; deep brain stimulation; epilepsy; neuromodulation; optogenetics; temporal dispersion; tremor
Year: 2019 PMID: 31572109 PMCID: PMC6751331 DOI: 10.3389/fnins.2019.00936
Source DB: PubMed Journal: Front Neurosci ISSN: 1662-453X Impact factor: 4.677
FIGURE 1(A) SCC DBS evoked potentials in patients with treatment resistant depression may also be useful. It is less important to hit the visible target; it is more about hitting the correct portion of the connectome. (B) Use DTI: to target structures intraoperatively. (C) Record LFP at the DTI-optimized target. (D) Use high- density EEG: to detect evoked cortical responses for stimulation. This figure adapted from Cho et al. (2017).
FIGURE 2Network-based measures can be recorded during DBS surgery via the same burr hole utilized for DBS lead implantation. In these illustrations, the use of an electrocorticography strip is shown. Simultaneous signals are recorded from the motor cortices while also recording through the DBS lead.
FIGURE 3Intracranial neurophysiology opportunities for network assessment.
FIGURE 4(A) Using LFP’s and neurophysiology to define and address freezing of gait in Parkinson’s disease. (B) Temporal fluctuations and the extent of changeable behavior of STN band neural activity during movement differentiated PD freezers from non-freezers.
FIGURE 5Temporal interference as a non-invasive DBS technology.
FIGURE 6Bidirectional manipulation of cholinergic cells in the PPN exert opposing effects on locomotor behavior and reinforcement learning via specific projections to the ventral substantia nigra pars compacta (vSNc) and the ventral tegmental area (VTA). This figure adapted from Cho et al. (2017).
FIGURE 7Endogenous and optogenetically driven DRNDA firing. (A) TH-Cre mice were injected with AAV5-Syn-FLEX-GCaMP6f or AAV5-hSyn-DIO-EGFP and implanted with an optical fiber into the DRN for fiber photometry. (B) Confocal images of GCaMP6f+ (green) neurons show co-localization with TH+ neurons (red), but no overlap with 5-HT+ neurons (blue). Scale bar, 100 μm. (C) Social interaction between a male DRN resident mouse and a female intruder were associated with increased DRNDA activity; the trace is a representative recording with interaction bouts indicated. (D) Left: female interaction caused an increase in fluorescence at the onset (first interactions only). Right: quantification of the area under the curve per second (AUC) during the interaction (0–5 s) shows that social interaction caused significant increase in DRNDA activity from baseline (−5 to 0 s) (n = 7 DRN mice; paired t test, t6 = 11.97, ∗∗∗p < 0.001). (E) Chocolate consumption by a DRN mouse increased DRNDA activity; representative recording. (F) Left: DRNDA activity was increased upon chocolate consumption. Right: AUC quantification during consumption (0–5 s) compared with baseline (−5 to 0 s) shows that food consumption is associated with significant fluorescence increase (n = 7 DRN mice; paired t test, t6 = 4.273, ∗∗p < 0.01). (G) Electric footshocks (0.25 mA, 1 s) were delivered; representative DRNDA trace during two consecutive footshocks. (H) Left: footshock induced phasic DRNDA activation. Right: DRNDA activity after footshock (0–5 s) was significantly increased relative to baseline (−5 to 0 s) (n = 7 DRN mice; paired t test, t6 = 5.763, ∗∗p < 0.01). (I) Peak DRNDA fluorescence values during female interaction, chocolate consumption, and electric footshocks were significantly higher than those during novel and familiar object interaction (n = 7 DRNDA-GCaMP6f mice; one-way ANOVA, F4,30 = 22.77, p < 0.0001, Bonferroni post hoc analysis, ∗∗∗p < 0.001).
FIGURE 8Dorsal raphe nucleus dopaminergic (DRNDA) activity escalates in response to salient stimuli irrespective of valence; correlates with sleep–wake states, and can bidirectionally modulate arousal. (A) Experimental paradigm. (B) Auditory cues are associated with time-locked increases in DRNDA activity. (C) DRNDA activity increase, as indexed by the difference in the area under the curve between before and after tone presentation, was larger when auditory tone induced sleep-to-wake transitions than when it was turned on while awake or when it failed to cause sleep-to-wake transitions (n = 7 DRNDA–GCaMP6s; One-way ANOVA, F2,18 = 10.79, p < 0.001, Post hoc Bonferroni analysis, ∗∗p < 0.01). (D) Experimental paradigm. (E) Time-locked DRNDA inhibition decreased the probability of NREM-to-wake transitions upon auditory cues (n = 6 DRNDA–Arch, n = 4 DRNDA–eGFP; Two-tailed, unpaired t-test, ∗∗∗p < 0.001. (F) No significant change in the probability of REM-to-wake transitions (n = 6 DRNDA–Arch, n = 4 DRNDA–eGFP; Two-tailed, unpaired t-test, p > 0.1).
FIGURE 9Approaches to Addiction DBS.
FIGURE 10Patient specific modeling for DBS.
FIGURE 11Perspectives and Attitudes Toward DBS. An anonymous 40 question poll was sent online to assess participants’ perspectives and attitudes toward the current and near-term future developments and applications in the DBS field. Sixty six participants responded.