22 papers · ranked by Valyu relevance
Kimberly B. Hoang, Isaac R. Cassar, Warren M. Grill, Dennis A. Turner
'Dennis A. Turner'] The goal of this review is to describe in what ways feedback or adaptive stimulation may be delivered and adjusted based on relevant biomarkers. Specific treatment mechanisms underlying therapeutic brain stimulation remain unclear, in spite of the demonstrated efficacy in a number of nervous system…
M. Beudel, P. Brown
Although Deep Brain Stimulation (DBS) is an established treatment for Parkinson's disease (PD), there are still limitations in terms of effectivity, side-effects and battery consumption. One of the reasons for this may be that not only pathological but also physiological neural activity can be suppressed whilst…
Sina Tafazoli, Camden J. MacDowell, Zongda Che, Katherine C. Letai + 2 more
Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed an…
Kenneth H. Louie, Jannine P. Balakid, Jessica E. Bath, Seongmi Song + 5 more
'Hamid Fekri Azgomi' 'Jacob H. Marks' 'Julia T. Choi' 'Philip A. Starr' 'Doris D. Wang'] Gait dysfunction in Parkinson’s disease (PD) is a major source of disability and is often resistant to traditional deep brain stimulation (DBS). Here, we report a novel neuromodulation paradigm, gait-phase-synchronized adaptive DBS…
Roberto Rodriguez-Zurrunero, Alvaro Araujo, Madeleine M. Lowery, Carlos Gómez
'Carlos Gómez'] The identification of a new generation of adaptive strategies for deep brain stimulation (DBS) will require the development of mixed hardware-software systems for testing and implementing such controllers clinically. Towards this aim, introducing an operating system (OS) that provides high-level…
Oleksandr V. Popovych, Peter A. Tass
Adaptive deep brain stimulation (aDBS) is a closed-loop method, where high-frequency DBS is turned on and off according to a feedback signal, whereas conventional high-frequency DBS (cDBS) is delivered permanently. Using a computational model of subthalamic nucleus and external globus pallidus, we extend the concept of…
Jeroen G.V. Habets, Margot Heijmans, Mark L. Kuijf, Marcus L.F. Janssen + 2 more
'Marcus L.F. Janssen' 'Yasin Temel' 'Pieter L. Kubben'] Title: ABSTRACT Advancing conventional open-loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment.…
Dan Piña-Fuentes, J. Marc C. van Dijk, Jonathan C. van Zijl, Harmen R. Moes + 4 more
Beta-based adaptive Deep Brain Stimulation (aDBS) is effective in Parkinson’s disease (PD), when assessed in the immediate post-operative phase. However, potential benefits of aDBS on stimulation-induced side effects in chronically implanted patients are yet to be assessed. To determine the effectiveness and…
Mayela Zamora, Robert Toth, Francesca Morgante, Jon Ottaway + 14 more
There is growing interest in using adaptive neuro-modulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed…
Simon Little, Alek Pogosyan, Spencer Neal, Ludvic Zrinzo + 4 more
'Marwan Hariz' 'Thomas Foltynie' 'Patricia Limousin' 'Peter Brown'] Adaptive deep brain stimulation (aDBS) has the potential to improve the treatment of Parkinson's disease by optimizing stimulation in real time according to fluctuating disease and medication state. In the present realization of adaptive DBS we record…
Michelle Pan, Mariah Schrum, Vivek Myers, Erdem Bıyık + 1 more
For Adaptive Brain Stimulation Authors: ['Michelle Pan' 'Mariah Schrum' 'Vivek Myers' 'Erdem Bıyık' 'Anca D. Dragan'] Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and poststroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each…
Md Abu Bakr Siddique, Jakub Orłowski, Yan Zhang, Hongyu An
Parkinson's disease (PD) affects millions worldwide and causes severe motor symptoms. Adaptive deep brain stimulation (aDBS) delivers physiologically informed stimulation that can track fluctuations in PD motor symptoms, enabling more intelligent DBS control. However, most existing aDBS approaches are primarily…
Christian Schmidt, Ursula van Rienen
—Objective: The aim of this study is to propose an adaptive scheme embedded into an open-source environment for the estimation of the neural activation extent during deep brain stimulation and to investigate the feasibility of approximating the neural activation extent by thresholds of the field solution. Methods…
Harsh Ravivarapu, Gaurav Bagwe, Xiaoyong Yuan, Chunxiu Yu + 1 more
—Deep brain stimulation (DBS) is an established intervention for Parkinson's disease (PD), but conventional open-loop systems lack adaptability, are energy-inefficient due to continuous stimulation, and provide limited personalization to individual neural dynamics. Adaptive DBS (aDBS) offers a closed-loop alternative…
Tzu-Chi Liu, Po-Lin Chen, Yi-Chieh Chen, Po-Hsun Tu + 4 more
Objective. Adaptive deep brain stimulation (aDBS) leverages symptomrelated biomarkers to deliver personalized neuromodulation therapy, with the potential to improve treatment efficacy and reduce power consumption compared to conventional DBS. However, stimulation-induced signal contamination remains a major technical…
Giacomo Severini, Alexander Koenig, Iahn Cajigas, Nicholas Lesniewski-Laas + 2 more
Subsensory noise stimulation targeting sensory receptors has been shown to improve balance control in healthy and impaired individuals. However, the potential for application of this technique in other contexts is still unknown. Gait control and adaptation rely heavily on the input from proprioceptive organs in the…
R Hoskin, D Talmi
An understanding of the mechanisms behind the influence of expectation and context on pain perception is crucial for improving analgesic treatments. Prediction error (PE) signalling is thought to contribute to the influence of expectation on pain perception. It is also thought that the brain engages in ‘adaptive…
Jelena Mladenović, Jérémie Mattout, Fabien Lotte
Thanks to the technological advancements, the interest in Brain Computer Interfaces (BCI) has grown immensely during the last decades. BCIs are mainly used to facilitate the interaction between people with different disabilities and their environment (Millán 2010, Perrin 2012). However, there have been outreach in…
Vishal P. Patil, Ian Ho, Manu Prakash
Computation, mechanics and materials merge in biological systems [1], which can continually self-optimize through internal adaptivity across length scales, from cytoplasm [2] and biofilms [3, 4] to animal herds [5]. Recent interest in such material-based computation [6–11] uses the principles of energy minimization…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
For applications in gas sensing, purification, and capture, we often wish to search a large set of metal-organic frameworks (MOFs) for the top-K in terms of their Henry coefficient of an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample…
Finlay Clark, Graeme Robb, Daniel Cole, Julien Michel
Alchemical absolute binding free energy (ABFE) calculations have substantial potential in drug discovery, but are often prohibitively computationally expensive. To unlock their potential, efficient automated ABFE workflows are required to reduce both computational cost and human intervention. We present a…