Mitigating Mismatch Compression in Differential Local Field Potentials
Vineet Tiruvadi, Samuel James, Bryan Howell, Mosadoluwa Obatusin, Andrea Crowell, Patricio Riva-Posse, Robert E Gross, Cameron C McIntyre, Helen S Mayberg, Robert Butera, Vineet Tiruvadi, Samuel James, Bryan Howell, Mosadoluwa Obatusin, Andrea Crowell, Patricio Riva-Posse, Robert E Gross, Cameron C McIntyre, Helen S Mayberg, Robert Butera
Abstract
Deep brain stimulation (DBS) devices capable of measuring differential local field potentials ( ∂ LFP) enable neural recordings alongside clinical therapy. Efforts to identify oscillatory correlates of various brain disorders, or disease readouts, are growing but must proceed carefully to ensure readouts are not distorted by brain environment. In this report we identified, characterized, and mitigated a major source of distortion in ∂ LFP that we introduce as mismatch compression (MC). Using in vivo, in silico, and in vitro models of MC, we showed that impedance mismatches in the two recording electrodes can yield incomplete rejection of stimulation artifact and subsequent gain compression that distorts oscillatory power. We then developed and validated an opensource mitigation pipeline that mitigates the distortions arising from MC. This work enables more reliable oscillatory readouts for adaptive DBS applications.
Conflict of interest statement
Conflicts of Interest
Cameron C. McIntyre is a paid consultant for Boston Scientific Neuromodulation, receives royalties from Hologram Consultants, Neuros Medical, Qr8 Health, and is a shareholder in the following companies: Hologram Consultants, Surgical Information Sciences, CereGate, Autonomic Technologies, Cardionomic, Enspire DBS. Helen S. Mayberg has a consulting agreement with Abbott Labs (previously St Jude Medical, Neuromodulation), which has licensed her intellectual property to develop SCC DBS for the treatment of severe depression (US 2005/0033379A1). Robert E. Gross serves as a consultant to and receives research support from Medtronic, and serves as a consultant to Abbott Labs. The terms of these arrangements have been approved by Emory University, Icahn School of Medicine, and Duke University in accordance with policies to manage conflict of interest. All other authors have no COI to declare.
Figures
References
- Perlmutter JS and Mink JW, “Deep brain stimulation,” Annu. Rev. Neurosci, vol. 29, pp. 229–257, Jul. 2006.
- Mayberg HS et al., “Deep brain stimulation for treatment-resistant depression,” Neuron, vol. 45, no. 5, pp. 651–660, 2005.
- Holtzheimer PE and Mayberg HS, “Deep brain stimulation for psychiatric disorders,” Annu. Rev. Neurosci, vol. 34, pp. 289–307, 2011.
- Veerakumar A et al., “Field potential 1/f activity in the subcallosal cingulate region as a candidate signal for monitoring deep brain stimulation for treatment-resistant depression,” J. Neurophysiol, vol. 122, no. 3, pp. 1023–1035, Sep. 2019.
- Gilron R et al., “Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson’s disease,” Nature Biotechnol., vol. 39, no. 9, pp. 1078–1085, May 2021.
- Starr PA, “Totally implantable bidirectional neural prostheses: A flexible platform for innovation in neuromodulation,” Frontiers Neurosci., vol. 12, p. 619, Sep. 2018.
- Stanslaski S et al., “Design and validation of a fully implantable, chronic, closed-loop neuromodulation device with concurrent sensing and stimulation,” IEEE Trans. Neural Syst. Rehabil. Eng, vol. 20, no. 4, pp. 410–421, Jul. 2012.
- Stanslaski S et al., “A chronically implantable neural coprocessor for investigating the treatment of neurological disorders,” IEEE Trans. Biomed. Circuits Syst, vol. 12, no. 6, pp. 1230–1245, Dec. 2018.
- Swann NC et al., “Chronic multisite brain recordings from a totally implantable bidirectional neural interface: Experience in 5 patients with Parkinson’s disease,” J. Neurosurg, vol. 128, no. 2, pp. 605–616, Feb. 2018.
- Cummins DD et al., “Chronic sensing of subthalamic local field potentials: Comparison of first and second generation implantable bidirectional systems within a single subject,” Frontiers Neurosci., vol. 15, p. 987, Aug. 2021.
- Meyer G, Carponcy J, Salin PA, and Comte J-C, “Differential recordings of local field potential: A genuine tool to quantify functional connectivity,” PLoS ONE, vol. 13, no. 12, Dec. 2018, Art. no. e0209001.
- Schubert TF and Kim EM, Fundamentals of Electronics: Book 2: Amplifiers: Analysis and Design. San Rafael, CA, USA: Morgan Claypool, 2015.
- Buzsáki G, Anastassiou CA, and Koch C, “The origin of extracellular fields and currents—EEG, ECOG, LFP and spikes,” Nature Rev. Neurosci, vol. 13, no. 6, pp. 407–420, 2012.
- Butson CR and McIntyre CC, “Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation,” Clin. Neurophysiol, vol. 116, no. 10, pp. 2490–2500, Oct. 2005.
- Wong J et al., “Longitudinal follow-up of impedance drift in deep brain stimulation cases,” Tremor Other Hyperkinetic Movements, vol. 8, p. 542, Mar. 2018.
- Sillay KA, Chen JC, and Montgomery EB, “Long-term measurement of therapeutic electrode impedance in deep brain stimulation,” Neuromodulation, Technol. Neural Interface, vol. 13, no. 3, pp. 195–200, Jul. 2010, doi: 10.1111/j.1525-1403.2010.00275.x.
- Satzer D, Lanctin D, Eberly LE, and Abosch A, “Variation in deep brain stimulation electrode impedance over years following electrode implantation,” Stereotactic Funct. Neurosurg, vol. 92, no. 2, pp. 94–102, 2014.
- Carron R, “Commentary: Intraoperative high impedance levels during placement of deep brain stimulating electrode,” Operative Neurosurg., vol. 17, no. 6, pp. E267–E268, 2019.
- Satzer D, Lanctin D, Eberly LE, and Abosch A, “Variation in deep brain stimulation electrode impedance over years following electrode implantation,” Stereotactic Funct. Neurosurg, vol. 92, no. 2, pp. 94–102, 2014.
- Miocinovic S et al., “Experimental and theoretical characterization of the voltage distribution generated by deep brain stimulation,” Experim. Neurol, vol. 216, no. 1, pp. 166–176, Mar. 2009.
- Holtzheimer PE et al., “Subcallosal cingulate deep brain stimulation for treatment-resistant unipolar and bipolar depression,” Arch. Gen. Psychiatry, vol. 69, no. 2, pp. 150–158, 2012.
- Crowell AL, Garlow SJ, Riva-Posse P, and Mayberg HS, “Characterizing the therapeutic response to deep brain stimulation for treatment-resistant depression: A single center long-term perspective,” Frontiers Integrative Neurosci., vol. 9, p. 41, Jun. 2015.
- Howell B, Choi KS, Gunalan K, Rajendra J, Mayberg HS, and McIntyre CC, “Quantifying the axonal pathways directly stimulated in therapeutic subcallosal cingulate deep brain stimulation,” Hum. Brain Mapping, vol. 40, no. 3, pp. 889–903, Feb. 2019.
- Riva-Posse P et al., “A connectomic approach for subcallosal cingulate deep brain stimulation surgery: Prospective targeting in treatment-resistant depression,” Mol. Psychiatry, vol. 23, no. 4, pp. 843–849, Apr. 2018.
- Kent AR, Swan BD, Brocker DT, Turner DA, Gross RE, and Grill WM, “Measurement of evoked potentials during thalamic deep brain stimulation,” Brain Stimulation, vol. 8, no. 1, pp. 42–56, Jan. 2015.
- Kandadai MA, Raymond JL, and Shaw GJ, “Comparison of electrical conductivities of various brain phantom gels: Developing a ‘brain gel model,’” Mater. Sci. Eng., C, vol. 32, no. 8, pp. 2664–2667, Dec. 2012.
- Butson CR and McIntyre CC, “Differences among implanted pulse generator waveforms cause variations in the neural response to deep brain stimulation,” Clin. Neurophysiol, vol. 118, pp. 1889–1894, Aug. 2007.
- Holleman J, Zhang F, and Otis B, “Closed-loop bio-signal amplifiers: Experimental results,” in Ultra Low-Power Integrated Circuit Design for Wireless Neural Interfaces. Cham, Switzerland: Springer, 2011, pp. 37–44.
- Zhang F, Holleman J, and Otis BP, “Design of ultra-low power biopotential amplifiers for biosignal acquisition applications,” IEEE Trans. Biomed. Circuits Syst, vol. 6, no. 4, pp. 344–355, Aug. 2012.
- Wang DD et al., “Subthalamic local field potentials in Parkinson’s disease and isolated dystonia: An evaluation of potential biomarkers,” Neurobiol. Disease, vol. 89, pp. 213–222, May 2016.
- Donoghue T et al., “Parameterizing neural power spectra into periodic and aperiodic components,” Nature Neurosci., vol. 23, no. 12, pp. 1655–1665, Dec. 2020.
- Harris CR et al., “Array programming with numpy,” Nature, vol. 585, no. 7825, pp. 357–362, Sep. 2020.
- Virtanen P et al., “SciPy 1.0: Fundamental algorithms for scientific computing in Python,” Nature Methods, vol. 17, pp. 261–272, Feb. 2020.
- Wallin AEE, Price DC, Carson CG, and Meynadier F, Allantools: Allan Deviation Calculation, document p. ascl:1804.021, Apr. 2018.
- Tiruvadi V. (2022). Dbspace DBS Network Analyses. [Online]. Available:
- Northoff G, Magioncalda P, Martino M, Lee H-C, Tseng Y-C, and Lane T, “Too fast or too slow? Time and neuronal variability in bipolar disorder—A combined theoretical and empirical investigation,” Schizophrenia Bull., vol. 44, no. 1, pp. 54–64, Jan. 2018.
- Little S and Brown P, “The functional role of beta oscillations in Parkinson’s disease,” Parkinsonism Rel. Disorders, vol. 20, pp. S44–S48, Jan. 2014.
- Sendi MSE et al., “Intraoperative neural signals predict rapid antidepressant effects of deep brain stimulation,” Transl. Psychiatry, vol. 11, no. 1, pp. 1–7, Dec. 2021.
- Logothetis NK, Kayser C, and Oeltermann A, “In vivo measurement of cortical impedance spectrum in monkeys: Implications for signal propagation,” Neuron, vol. 55, no. 5, pp. 809–823, Sep. 2007.
- Latikka JA, Hyttinen JA, Kuurne TA, Eskola HJ, and Malmivuo JA, “The conductivity of brain tissues: Comparison of results in vivo and in vitro measurements,” in Proc. Conf. 23rd Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., Oct. 2001, pp. 910–912.
- Horn A and Fox MD, “Opportunities of connectomic neuromodulation,” NeuroImage, vol. 221, Nov. 2020, Art. no. 117180.
- Haber S, “Prefrontal cortical-cortical and subcortical circuits: White matter vs. grey matter stimulation targets,” Brain Stimulation, vol. 12, no. 2, p. 461, Mar. 2019.
- Holtzheimer PE et al., “Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial,” Lancet Psychiatry, vol. 4, no. 11, pp. 839–849, 2017.
- Little S et al., “Adaptive deep brain stimulation for Parkinson’s disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting,” J. Neurol., Neurosurg. Psychiatry, vol. 87, no. 12, pp. 1388–1389, Dec. 2016.
- Mamatkulov A, “Deep brain stimulation for Parkinson’s disease using motor cortex sensing,” Parkinsonism Rel. Disorders, vol. 79, pp. e15–e16, Oct. 2020.
- Ansó J et al., “Concurrent stimulation and sensing in bi-directional brain interfaces: A multi-site translational experience,” J. Neural Eng, vol. 19, no. 2, Apr. 2022, Art. no. 026025.
- Lempka SF, Miocinovic S, Johnson MD, Vitek JL, and McIntyre CC, “In vivo impedance spectroscopy of deep brain stimulation electrodes,” J. Neural Eng, vol. 6, no. 4, Aug. 2009, Art. no. 046001.
- Allen DP, Stegemoller EL, Zadikoff C, Rosenow JM, and MacKinnon CD, “Suppression of deep brain stimulation artifacts from the electroencephalogram by frequency-domain Hampel filtering,” Clin. Neurophysiol, vol. 121, no. 8, pp. 1227–1232, Aug. 2010.
- Tiruvadi V et al., “Decoding depression during subcallosal cingulate deep brain stimulation,” bioRxiv, to be published, doi: 10.1101/2022.07.27.501778.
Source: PubMed