| Literature DB >> 27920671 |
Wissam Deeb1, James J Giordano2, Peter J Rossi1, Alon Y Mogilner3, Aysegul Gunduz4, Jack W Judy4, Bryan T Klassen5, Christopher R Butson6, Craig Van Horne7, Damiaan Deny8, Darin D Dougherty9, David Rowell10, Greg A Gerhardt11, Gwenn S Smith12, Francisco A Ponce13, Harrison C Walker14, Helen M Bronte-Stewart15, Helen S Mayberg16, Howard J Chizeck17, Jean-Philippe Langevin18, Jens Volkmann19, Jill L Ostrem20, Jonathan B Shute21, Joohi Jimenez-Shahed22, Kelly D Foote23, Aparna Wagle Shukla1, Marvin A Rossi24, Michael Oh25, Michael Pourfar26, Paul B Rosenberg27, Peter A Silburn10, Coralie de Hemptine28, Philip A Starr28, Timothy Denison29, Umer Akbar30, Warren M Grill31, Michael S Okun1.
Abstract
This paper provides an overview of current progress in the technological advances and the use of deep brain stimulation (DBS) to treat neurological and neuropsychiatric disorders, as presented by participants of the Fourth Annual DBS Think Tank, which was convened in March 2016 in conjunction with the Center for Movement Disorders and Neurorestoration at the University of Florida, Gainesveille FL, USA. The Think Tank discussions first focused on policy and advocacy in DBS research and clinical practice, formation of registries, and issues involving the use of DBS in the treatment of Tourette Syndrome. Next, advances in the use of neuroimaging and electrochemical markers to enhance DBS specificity were addressed. Updates on ongoing use and developments of DBS for the treatment of Parkinson's disease, essential tremor, Alzheimer's disease, depression, post-traumatic stress disorder, obesity, addiction were presented, and progress toward innovation(s) in closed-loop applications were discussed. Each section of these proceedings provides updates and highlights of new information as presented at this year's international Think Tank, with a view toward current and near future advancement of the field.Entities:
Keywords: Alzheimer's disease; DARPA; Parkinson's disease; Tourette syndrome; closed-loop; deep brain stimulation; depression; post-traumatic stress disorder
Year: 2016 PMID: 27920671 PMCID: PMC5119052 DOI: 10.3389/fnint.2016.00038
Source DB: PubMed Journal: Front Integr Neurosci ISSN: 1662-5145
Figure 1Schematic (cartoon) representation of potential therapeutic targets of DBS for Tourette Syndrome. Figure is not drawn to scale. ALIC, anterior limb internal capsule (From Malaty and Akbar, 2014; with permission).
Figure 2Diagrammatic depiction of the University of Florida approach to implementing chronic responsive DBS therapy for Tourette Syndrome. Current experience with two patients with TS, who received bilateral centromedian (CM) thalamus depth leads and bilateral subdural grid implantation over their hand motor cortex (A), led to the discovery of tic specific features in CM thalamus (1–10 Hz) and motion detection features in hand motor cortex (15–30 Hz; beta rhythm) (B). A combination of these two features yielded highest detection of tics and differentiation from voluntary movements in linear discriminant analysis classifiers (C). These classifiers are embedded in PC+S and send control signals to Nexus- E stimulation engine (D). Once the detectors sense presence of tic related activity, stimulation will be activated to deliver stimulation to optimize therapeutic effects/outcomes.
Figure 3The graphs depict the results of analysis of the M1 signal in the frequency domain in PD patients with dyskinesia. It shows, in the graph on the left, that dyskinesia is associated with an increased neuronal synchronization in the gamma band (blue line) reflected as a narrow band peak. The graph on the right shows that this gamma-band signal is related to dyskinesia and independent of the functional state (rest, walking, or voluntary arm movement).
Figure 4The panes depict (from left to right) the evolution of surgical targeting of SCC in depression from, an anatomical “gray matter” target to identification of the “white matter” tracts activated, and finally tractographic data allowing identification of the involved pathways that elicit differing effects when targeted by DBS. This approach allows individualized target refinement and produces improved therapeutic outcomes. Genu, genus of the corpus callosum; Mid-SCC, mid subcallosal cingulate; Ac, anterior commissure; mF10, medial frontal Brodmann Area 10; ACC, anterior cingulate cortex; aTh, anterior thalamus; vSt, ventral striatum; Fr-st, frontal striatal fibers.
Figure 5(A) Example of adaptive stimulation voltage (top row) and tremor power with 25% (magenta) and 50% (cyan) thresholds of the control policy algorithm (bottom row). Black horizontal lines above upper panel indicate timing of calibration and closed loop DBS (aDBS). Dashed black line shows level of clinical stimulation voltage. (B) Comparison of mean tremor power at baseline and during aDBS across the group. (C) Comparison of average stimulation voltage during open loop continuous (cDBS) and aDBS for the group. (D) Insert to (A) showing the timing of the aDBS decision tracking. When tremor power exceeded the upper threshold (red triangles), the stimulation voltage increased. When tremor power fell below the lower threshold (blue triangles), stimulation voltage decreased. Stimulation voltage remained unchanged if the tremor power level remained between lower and upper thresholds.
Figure 6Vector diagram illustrating the difference between a categorical diagnosis (in this instance MDD or major depressive disorder) and a symptom based or behavioral based domain assessment. The limitation of the categorical diagnosis analysis is that it can average and thereby diffuse genuine subgroup (behavioral domain) therapeutic effects.
Figure 7Diagrammatic representation of a possible closed-loop DBS system comprised of sensors (e.g., ECoG, neurochemical sensors and local field potential sensing through the implanted electrodes) that influence the stimulator (actuator) signal. The sensed signal is classified, and with use of an implementation algorithm, can influence the stimulator output to induce therapeutic effects.
Figure 8Schematic results of an anonymous poll of ThinkTank participants to assess perceptions and attitudes about the current and near-term state of the DBS field. On the left is a representation of the stages of technological development, known as the “hype cycle” graph. Participants in the Think Tank were asked to rank different DBS applications and other neurotechnologies on the “hype cycle.” Their responses were averaged and categorized, as depicted in the table on the right. Categories were assigned by rounding to the nearest whole number. Details in text. Figure adapted from: Jackie Fenn, “When to leap on the hype cycle,” Decision Framework DF-08-6751, Research Note, GartnerGroup RAS Services, June 30, 1999.