Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets Article Swipe
Mahsa Malekmohammadi
,
Richard Mustakos
,
Sameer A. Sheth
,
Nader Pouratian
,
Cameron C. McIntyre
,
Kelly R. Bijanki
,
Evangelia Tsolaki
,
Kevin Chiu
,
Meghan E. Robinson
,
Joshua A. Adkinson
,
Denise Oswalt
,
Stephen Carcieri
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1741-2552/ac7e6c
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1088/1741-2552/ac7e6c
Objective. Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians’ experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms. Approach. This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described. Main results. Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target ( P = 0.002). Significance . The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1741-2552/ac7e6c
- OA Status
- green
- Cited By
- 20
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283833870
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4283833870Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1741-2552/ac7e6cDigital Object Identifier
- Title
-
Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targetsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-05Full publication date if available
- Authors
-
Mahsa Malekmohammadi, Richard Mustakos, Sameer A. Sheth, Nader Pouratian, Cameron C. McIntyre, Kelly R. Bijanki, Evangelia Tsolaki, Kevin Chiu, Meghan E. Robinson, Joshua A. Adkinson, Denise Oswalt, Stephen CarcieriList of authors in order
- Landing page
-
https://doi.org/10.1088/1741-2552/ac7e6cPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11090244Direct OA link when available
- Concepts
-
Deep brain stimulation, Neuroimaging, Brain stimulation, Stimulation, Computer science, Tractography, Neuroscience, Electrical brain stimulation, Artificial intelligence, Medicine, Diffusion MRI, Psychology, Magnetic resonance imaging, Radiology, Pathology, Disease, Parkinson's diseaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 5, 2023: 7, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.placement | 21 |
| abstract_inverted_index.practice. | 50 |
| abstract_inverted_index.targeting | 192 |
| abstract_inverted_index.underwent | 176 |
| abstract_inverted_index.undesired | 133 |
| abstract_inverted_index.Objective. | 1 |
| abstract_inverted_index.activating | 114, 291 |
| abstract_inverted_index.amplifying | 70 |
| abstract_inverted_index.clinicians | 210, 262 |
| abstract_inverted_index.depression | 174 |
| abstract_inverted_index.described. | 199 |
| abstract_inverted_index.experience | 31 |
| abstract_inverted_index.integrated | 251 |
| abstract_inverted_index.introduces | 95 |
| abstract_inverted_index.minimizing | 119 |
| abstract_inverted_index.parameters | 39 |
| abstract_inverted_index.previously | 198 |
| abstract_inverted_index.programmer | 255 |
| abstract_inverted_index.seamlessly | 250 |
| abstract_inverted_index.technology | 55 |
| abstract_inverted_index.trajectory | 150 |
| abstract_inverted_index.Therapeutic | 2 |
| abstract_inverted_index.algorithms. | 91 |
| abstract_inverted_index.application | 165 |
| abstract_inverted_index.clinicians, | 69 |
| abstract_inverted_index.constraints | 47 |
| abstract_inverted_index.effectively | 84, 258 |
| abstract_inverted_index.established | 11 |
| abstract_inverted_index.hypothesize | 77 |
| abstract_inverted_index.innovations | 52 |
| abstract_inverted_index.parameters. | 161 |
| abstract_inverted_index.programming | 88 |
| abstract_inverted_index.stimulation | 7, 38, 110, 121, 155, 160, 205, 214, 231, 280, 287 |
| abstract_inverted_index.subcallosal | 182 |
| abstract_inverted_index.Significance | 242 |
| abstract_inverted_index.constraints. | 75 |
| abstract_inverted_index.implantation | 178 |
| abstract_inverted_index.indications, | 14 |
| abstract_inverted_index.neuroimaging | 81, 140 |
| abstract_inverted_index.preoperative | 139 |
| abstract_inverted_index.programming. | 25 |
| abstract_inverted_index.tractography | 190 |
| abstract_inverted_index.clinicians’ | 30 |
| abstract_inverted_index.postoperative | 145 |
| abstract_inverted_index.significantly | 229 |
| abstract_inverted_index.configurations | 63 |
| abstract_inverted_index.reconstruction | 146 |
| abstract_inverted_index.capsule/ventral | 187 |
| abstract_inverted_index.patient-specific | 106 |
| abstract_inverted_index.treatment-resistant | 173 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile.value | 0.91027136 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |