Back

Evoked response signatures explain deep brain stimulation outcomes

Bahners, B. H.; Goede, L. L.; Meyer, G. M.; Poser, L.; Hart, L. A.; Pijar, J.; Rajamani, N.; Hollunder, B.; Madan, S.; Oxenford, S.; Waterstraat, G.; Curio, G.; Schnitzler, A.; Florin, E.; Kuhn, A. A.; Fox, M. D.; Horn, A.

2024-10-06 neurology
10.1101/2024.10.04.24314308 medRxiv
Show abstract

Deep brain stimulation (DBS) is an established treatment for Parkinsons disease. Still, DBS parameter programming currently follows a tedious trial-and-error process. DBS-evoked cortical potentials (EP) might guide parameter selection but this concept has not yet been tested. Further, mounting wet EEG systems is too time-consuming to scale in outpatient clinic settings. Here, we test the utility of a novel method that leverages the spatial pattern of EP using a dry EEG setup. We acquired EP in 58 hemispheres in patients with Parkinsons disease and compute a model which represents the optimal EP response pattern associated with maximal clinical improvements. Once defined, we use this pattern to estimate stimulation outcomes in unseen patients. Finally, we utilize it to identify optimal stimulation contacts in five unseen hemispheres where it selected the correct contact in all cases. The simple setup makes this novel method an attractive option to guide DBS programming in clinical practice.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 10%
18.2%
Similar papers in this journal
2
Brain
◐ 168 papers in training set
Top 0.2%
14.8%
Similar papers in this journal
3
Brain Stimulation
○ 125 papers in training set
Top 0.2%
9.5%
Similar papers in this journal
4
Annals of Neurology
○ 64 papers in training set
Top 0.1%
8.8%
Similar papers in this journal
50% of probability mass above
5
Nature Biomedical Engineering
○ 47 papers in training set
Top 0.1%
7.7%
Similar papers in this journal
6
Brain Communications
◐ 166 papers in training set
Top 0.8%
5.1%
7
Nature Neuroscience
○ 252 papers in training set
Top 3%
2.4%
8
npj Parkinson's Disease
○ 105 papers in training set
Top 0.7%
2.4%
9
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 22%
2.3%
10
Communications Biology
○ 993 papers in training set
Top 11%
2.1%
11
Scientific Reports
○ 3612 papers in training set
Top 49%
1.9%
12
eLife
● 5828 papers in training set
Top 55%
1.4%
13
Advanced Science
○ 286 papers in training set
Top 6%
1.3%
14
NeuroImage
○ 903 papers in training set
Top 5%
1.1%
15
Journal of Neurology, Neurosurgery & Psychiatry
● 30 papers in training set
Top 0.5%
1.1%
16
Science Advances
● 1243 papers in training set
Top 26%
1.1%
17
eBioMedicine
○ 183 papers in training set
Top 5%
1.0%
18
Journal of Neural Engineering
○ 221 papers in training set
Top 2%
1.0%
19
Neuron
○ 337 papers in training set
Top 5%
0.8%
20
Nature Medicine
○ 125 papers in training set
Top 4%
0.8%
21
Neurosurgery
○ 11 papers in training set
Top 0.4%
0.8%
22
iScience
○ 1154 papers in training set
Top 37%
0.8%
23
NeuroImage: Clinical
○ 144 papers in training set
Top 2%
0.8%
24
PLOS Computational Biology
● 1863 papers in training set
Top 20%
0.8%
25
Human Brain Mapping
○ 329 papers in training set
Top 4%
0.8%
26
Nature Genetics
○ 286 papers in training set
Top 6%
0.6%
27
Cell Reports
○ 1498 papers in training set
Top 29%
0.6%
28
Nature Computational Science
○ 55 papers in training set
Top 2%
0.6%
29
Movement Disorders
○ 71 papers in training set
Top 0.9%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.