Field potential 1/f activity in the subcallosal cingulate region as a candidate signal for monitoring deep brain stimulation for treatment-resistant depression
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Subcallosal cingulate cortex deep brain stimulation (SCC-DBS) is an experimental therapy for treatment-resistant depression (TRD). Refinement and optimization of SCC-DBS will benefit from increased study of SCC electrophysiology in context of ongoing high-frequency SCC-DBS therapy. The study objective was a 7-mo observation of frequency-domain 1/ f slope in off-stimulation local field potentials (SCC-LFPs) alongside standardized measurements of depression severity in 4 patients undergoing SCC-DBS. SCC was implanted bilaterally with a combined neurostimulation-LFP recording system. Following a 1-mo off-stimulation postoperative phase with multiple daily recordings, patients received bilateral SCC-DBS therapy (130 Hz, 90 μs) and weekly resting-state SCC-LFP recordings over a 6-mo treatment phase. 1/ f slopes for each time point were estimated via linear regression of log-transformed Welch periodograms. General linear mixed-effects models were constructed to estimate pretreatment sources of 1/ f slope variance, and 95% bootstrap confidence intervals were constructed to estimate treatment phase 1/ f slope association with treatment response (50% decrease in preimplantation symptom severity). Results show the time of recording was a prominent source of pretreatment 1/ f slope variance bilaterally, with increased 1/ f slope magnitude observed during night hours (2300–0659). Increase in right 1/ f slope was observed in the setting of treatment response, with bootstrap analysis supporting this observation in 3 of 4 subjects. We conclude that 1/ f slope can be measured longitudinally in a combined SCC-DBS/LFP recording system and likely conforms to known 1/ f circadian variability. The preliminary evidence of 1/ f slope increase during treatment response suggests a potential utility as a candidate biomarker for ongoing development of adaptive TRD-neuromodulation strategies. NEW & NOTEWORTHY In four patients with treatment-resistant depression undergoing therapeutic deep brain stimulation (DBS), we present the first longitudinal observations of local field potentials (LFP) from the subcallosal cingulate region outside the postoperative period. Specifically, our results demonstrate that frequency-domain 1/ f activity is measurable in a combined DBS-LFP recording system and that right hemisphere recordings appear sensitive to mood state, thus suggesting a potential readout suitable for consideration in ongoing efforts to develop adaptive DBS delivery systems.
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Investigators showed local field potential (LFP) spectral changes over time during SCC DBS for MDD, although it was difficult to demonstrate a clear and consistent correlation between any individual LFP feature and clinical outcome.
“In a recent psychiatric example, investigators showed local field potential (LFP) spectral changes over time during SCC DBS for MDD (Veerakumar et al. 2019), although it was difficult to demonstrate a clear and consistent correlation between any individual LFP feature and the clinical outcome.”
From Deep brain stimulation for psychiatric disorders: From focal brain targets to cognitive networks (2021) · cites this paper as Veerakumar et al. 2019
Recent psychiatric studies have shown network-level oscillatory changes from DBS at psychiatric targets.
“This has long been recognized in movement disorders (Little & Brown, 2012), but recent psychiatric studies have also shown network-level oscillatory changes from DBS at psychiatric targets (Veerakumar et al. 2019).”
From Deep brain stimulation for psychiatric disorders: From focal brain targets to cognitive networks (2021) · cites this paper as Veerakumar et al. 2019
Effective DBS modulates oscillations and their synchrony.
“Moreover, effective DBS modulates oscillations and their synchrony.”
From Deep brain stimulation for psychiatric disorders: From focal brain targets to cognitive networks (2021) · cites this paper as Veerakumar et al. 2019
In chronic SCC-DBS, local field potential 1/f slope changes recorded weekly at the right SCC lead during the first 6 months discriminated depressed from non-depressed states in three of four patients.
“local field potential changes in 1/f slope, a putative correlate of neuronal excitability, recorded weekly at the right SCC-lead during the first 6 months of SCC-DBS discriminated depressed from non-depressed states in three out of the four patients [85].”
From Deep brain stimulation for depression. (2022) · cites this paper as [85]
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Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369 · see citing works
A new depression scale designed to be sensitive to change
Montgomery, S. A., & Åsberg, M. (1979) · British Journal of Psychiatry · doi:10.1192/bjp.134.4.382 · Cited by 13,962 · see citing works
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526 · see citing works
Gabriel, S., Lau, R. W., & Gabriel, C. (1996) · Physics in Medicine and Biology · doi:10.1088/0031-9155/41/11/003 · Cited by 4,175 · see citing works
Deep Brain Stimulation for Treatment-Resistant Depression
Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., Schwalb, J. M., & Kennedy, S. H. (2005) · Neuron · doi:10.1016/j.neuron.2005.02.014 · Cited by 4,059 · see citing works
Self-citationFrequency Dependence of Signal Power and Spatial Reach of the Local Field Potential
Leski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H., & Einevoll, G. T. (2013) · PubMed Central · doi:10.18154/rwth-conv-084442 · Cited by 3,662 · see citing works
Neocortical excitation/inhibition balance in information processing and social dysfunction
Yizhar, O., Fenno, L. E., Prigge, M., Schneider, F., Davidson, T. J., O’Shea, D. J., Sohal, V. S., Goshen, I., Finkelstein, J., Paz, J. T., et al. (2011) · Nature · doi:10.1038/nature10360 · Cited by 2,454 · see citing works
Parameterizing neural power spectra into periodic and aperiodic components
Donoghue, T., Haller, M., Peterson, E. J., Varma, P., Sebastian, P., Gao, R., Noto, T., Lara, A. H., Wallis, J. D., Knight, R. T., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00744-x · Cited by 2,294 · see citing works
Self-citationLimbic-cortical dysregulation: a proposed model of depression
(1997) · The Journal of Neuropsychiatry and Clinical Neurosciences · doi:10.1176/jnp.9.3.471 · Cited by 1,585 · see citing works
Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant depression
Lozano, A. M., Mayberg, H. S., Giacobbe, P., Hamani, C., Craddock, R. C., & Kennedy, S. H. (2008) · Biological Psychiatry · doi:10.1016/j.biopsych.2008.05.034 · Cited by 1,024 · see citing works
Self-citationInferring synaptic excitation/inhibition balance from field potentials
Gao, R., Peterson, E. J., & Voytek, B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.078 · Cited by 1,010 · see citing works
Self-citationThe Temporal Structures and Functional Significance of Scale-free Brain Activity
He, B. J., Zempel, J. M., Snyder, A. Z., & Raichle, M. E. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.020 · Cited by 990 · see citing works
Age-Related Changes in 1/ f Neural Electrophysiological Noise
Voytek, B., Kramer, M., Case, J., Lepage, K. Q., Tempesta, Z. R., Knight, R. T., & Gazzaley, A. H. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2332-14.2015 · Cited by 744 · see citing works
Self-citationPower-Law Scaling in the Brain Surface Electric Potential
Miller, K. J., Sorensen, L. B., Ojemann, J. G., & den Nijs, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000609 · Cited by 742 · see citing works
Subcallosal Cingulate Deep Brain Stimulation for Treatment-Resistant Unipolar and Bipolar Depression
Holtzheimer, P. E. (2012) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.1456 · Cited by 608 · see citing works
Holtzheimer, P. E., Husain, M. M., Lisanby, S. H., Taylor, S. F., Whitworth, L. A., McClintock, S., Slavin, K. V., Berman, J., McKhann, G. M., Patil, P. G., et al. (2017) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30371-1 · Cited by 577 · see citing works
Self-citationVoytek, B., & Knight, R. T. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.04.016 · Cited by 556 · see citing works
Self-citationPrefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior
Ferenczi, E. A., Zalocusky, K. A., Liston, C., Grosenick, L., Warden, M. R., Amatya, D., Katovich, K., Mehta, H., Patenaude, B., Ramakrishnan, C., et al. (2016) · Science · doi:10.1126/science.aac9698 · Cited by 535 · see citing works
The Hamilton Rating Scale for Depression
Hamilton, M. (1986) · Springer eBooks · doi:10.1007/978-3-642-70486-4_14 · Cited by 463 · see citing works
Riva-Posse, P., Choi, K. S., Holtzheimer, P. E., McIntyre, C. C., Gross, R. E., Chaturvedi, A., Crowell, A. L., Garlow, S. J., Rajendra, J. K., & Mayberg, H. S. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.03.029 · Cited by 460 · see citing works
Self-citationRiva-Posse, P., Choi, K. S., Holtzheimer, P. E., Crowell, A. L., Garlow, S. J., Rajendra, J. K., McIntyre, C. C., Gross, R. E., & Mayberg, H. S. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.59 · Cited by 441 · see citing works
Self-citationAdaptive deep brain stimulation for Parkinson’s disease using motor cortex sensing
Swann, N. C., de Hemptinne, C., Thompson, M. C., Miocinovic, S., Miller, A. M., Gilron, R., Ostrem, J. L., Chizeck, H. J., & Starr, P. A. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aabc9b · Cited by 391 · see citing works
Stanslaski, S. R., Afshar, P., Cong, P., Giftakis, J., Stypulkowski, P. H., Carlson, D., Linde, D., Ullestad, D., Avestruz, A.-T., & Denison, T. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2183617 · Cited by 330 · see citing works
Bédard, C., Kröger, H., & Alain Destexhe, A. (2004) · Biophysical Journal · doi:10.1016/s0006-3495(04)74250-2 · Cited by 301 · see citing works
Reimann, M., Anastassiou, C. A., Perin, R., Hill, S., Markram, H., & Koch, C. (2013) · Neuron · doi:10.1016/j.neuron.2013.05.023 · Cited by 270 · see citing works
Bédard, C., Kröger, H., Alain Destexhe, A., & Alain Destexhe, A. (2004) · Cited by 249 · see citing works
The Montgomery Äsberg and the Hamilton ratings of depression: A comparison of measures
Carmody, T. J., Rush, A. J., Bernstein, I. H., Warden, D., Brannan, S. K., Burnham, D. B., Woo, A., & Trivedi, M. H. (2006) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2006.04.008 · Cited by 243 · see citing works
Beta oscillations in freely moving Parkinson's subjects are attenuated during deep brain stimulation
Quinn, E. J., Blumenfeld, Z., Velisar, A., Koop, M. M., Shreve, L., Trager, M. H., Hill, B. C., Kilbane, C., Henderson, J. M., & Bronte-Stewart, H. M. (2015) · Movement Disorders · doi:10.1002/mds.26376 · Cited by 227 · see citing works
Simulated power spectral density (PSD) of background electrocorticogram (ECoG)
Freeman, W. J., & Zhai, J. (2008) · Cognitive Neurodynamics · doi:10.1007/s11571-008-9064-y · Cited by 221 · see citing works
Podvalny, E., Noy, N., Harel, M., Bickel, S., Chechik, G., Schroeder, C. E., Mehta, A. D., Tsodyks, M. V., & Malach, R. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00943.2014 · Cited by 214 · see citing works
Individual differences in anterior EEG asymmetry: methodological problems and solutions
Hagemann, D. (2004) · Biological Psychology · doi:10.1016/j.biopsycho.2004.03.006 · Cited by 211 · see citing works
Bruder, G. E., Stewart, J. W., & McGrath, P. J. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.04.021 · Cited by 210 · see citing works
Macroscopic Models of Local Field Potentials and the Apparent 1/f Noise in Brain Activity
Bédard, C., & Alain Destexhe, A. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2008.12.3951 · Cited by 206 · see citing works
Rao, V. R., Sellers, K. K., Wallace, D. L., Lee, M. B., Bijanzadeh, M., Sani, O. G., Yang, Y., Shanechi, M. M., Dawes, H. E., & Chang, E. F. (2018) · Current Biology · doi:10.1016/j.cub.2018.10.026 · Cited by 181 · see citing works
An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood
Kirkby, L. A., Luongo, F. J., Lee, M. B., Nahum, M., Vleet, T. V., Rao, V. R., Dawes, H. E., Chang, E. F., & Sohal, V. S. (2018) · Cell · doi:10.1016/j.cell.2018.10.005 · Cited by 171 · see citing works
1/ f Neural Noise and Electrophysiological Indices of Contextual Prediction in Normative Aging
Dave, S., Brothers, T. A., & Swaab, T. Y. (2017) · Brain Research · doi:10.1016/j.brainres.2018.04.007 · Cited by 170 · see citing works
Widge, A. S., Zorowitz, S., Basu, I., Paulk, A. C., Cash, S. S., Eskandar, E. N., Deckersbach, T., Miller, E. K., & Dougherty, D. D. (2019) · Nature Communications · doi:10.1038/s41467-019-09557-4 · Cited by 152 · see citing works
Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential
Łęski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H., & Einevoll, G. T. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003137 · Cited by 145 · see citing works
Swann, N. C., de Hemptinne, C., Miocinovic, S., Qasim, S. E., Ostrem, J. L., Galifianakis, N. B., Luciano, M. S., Wang, S. S., Ziman, N., Taylor, R., et al. (2017) · Journal of neurosurgery · doi:10.3171/2016.11.jns161162 · Cited by 144 · see citing works
States and traits of neural irregularity in the age-varying human brain
Waschke, L., Wöstmann, M., & Obleser, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-17766-4 · Cited by 135 · see citing works
Choi, K. S., Riva‐Posse, P., Gross, R. E., & Mayberg, H. S. (2015) · JAMA Neurology · doi:10.1001/jamaneurol.2015.2564 · Cited by 133 · see citing works
Self-citationBroadway, J. M., Holtzheimer, P. E., Hilimire, M. R., Parks, N. A., DeVylder, J., Mayberg, H. S., & Corballis, P. M. (2012) · Neuropsychopharmacology · doi:10.1038/npp.2012.23 · Cited by 116 · see citing works
Self-citationNeumann, W., Huebl, J., Brücke, C., Gabriëls, L. A., Bajbouj, M., Merkl, A., Schneider, G.-H., Nuttin, B., Brown, P., & Kühn, A. A. (2014) · Molecular Psychiatry · doi:10.1038/mp.2014.2 · Cited by 115 · see citing works
Veerakumar, A., Challis, C., Gupta, P., Da, J., Upadhyay, A., Beck, S. G., & Berton, O. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.12.009 · Cited by 102 · see citing works
Self-citationTractography-activation models applied to subcallosal cingulate deep brain stimulation
Lujan, J. L., Chaturvedi, A., Choi, K. S., Holtzheimer, P. E., Gross, R. E., Mayberg, H. S., & McIntyre, C. C. (2013) · Brain stimulation · doi:10.1016/j.brs.2013.03.008 · Cited by 101 · see citing works
Theoretical Analysis of the Local Field Potential in Deep Brain Stimulation Applications
Lempka, S. F., & McIntyre, C. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059839 · Cited by 100 · see citing works
Rapid Eye Movement Sleep Density
FOSTER, F. G. (1976) · Archives of General Psychiatry · doi:10.1001/archpsyc.1976.01770090109011 · Cited by 98 · see citing works
Multimodal Approaches to Define Network Oscillations in Depression
Smart, O. L., Tiruvadi, V., & Mayberg, H. S. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.01.002 · Cited by 85 · see citing works
Self-citationNetwork-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons
El‐Boustani, S., Marre, O., Béhuret, S., Baudot, P., Yger, P., Bal, T., Alain Destexhe, A., & Frégnac, Y. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000519 · Cited by 75 · see citing works
Effect of Reference Scheme on Power and Phase of the Local Field Potential
Shirhatti, V., Borthakur, A., & Ray, S. (2016) · Neural Computation · doi:10.1162/neco_a_00827 · Cited by 74 · see citing works
Miceli, S., Ness, T. V., Einevoll, G. T., & Schubert, D. (2017) · eNeuro · doi:10.1523/eneuro.0291-16.2016 · Cited by 67 · see citing works
Sheehan, T. C., Sreekumar, V., Inati, S. K., & Zaghloul, K. A. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2389-17.2017 · Cited by 64 · see citing works
Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue
Halnes, G., Mäki‐Marttunen, T., Keller, D. X., Pettersen, K. H., Andreassen, O. A., & Einevoll, G. T. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005193 · Cited by 62 · see citing works
Chaudhuri, R., He, B. J., & Wang, X. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx233 · Cited by 59 · see citing works
Howell, B. L., Choi, K. S., Gunalan, K., Rajendra, J. K., Mayberg, H. S., & McIntyre, C. C. (2018) · Human Brain Mapping · doi:10.1002/hbm.24419 · Cited by 56 · see citing works
Self-citationPettersen, K. H., Lindén, H., Tetzlaff, T., & Einevoll, G. T. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003928 · Cited by 46 · see citing works
Zumsteg, D., Lozano, A. M., & Wennberg, R. A. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.06.707 · Cited by 45 · see citing works
Hilimire, M. R., Mayberg, H. S., Holtzheimer, P. E., Broadway, J. M., Parks, N. A., DeVylder, J. E., & Corballis, P. M. (2015) · Brain Stimulation · doi:10.1016/j.brs.2014.11.010 · Cited by 42 · see citing works
Self-citationWaters, A. C., Veerakumar, A., Choi, K. S., Howell, B. L., Tiruvadi, V., Bijanki, K. R., Crowell, A. L., Riva‐Posse, P., & Mayberg, H. S. (2018) · Human Brain Mapping · doi:10.1002/hbm.24327 · Cited by 41 · see citing works
Self-citationSmart, O., Choi, K. S., Riva-Posse, P., Tiruvadi, V., Rajendra, J., Waters, A. C., Crowell, A. L., Edwards, J., Gross, R. E., & Mayberg, H. S. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00043 · Cited by 41 · see citing works
Self-citationClark, D., Brown, E. C., Ramasubbu, R., & Kiss, Z. H. T. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.02.032 · Cited by 40 · see citing works
Targeting Cognition and Networks Through Neural Oscillations
Widge, A. S., & Miller, E. K. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.0740 · Cited by 39 · see citing works
Analysis of deep brain stimulation electrode characteristics for neural recording
Kent, A. R., & Grill, W. M. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/4/046010 · Cited by 37 · see citing works
Modulation of hippocampal activity with fornix Deep Brain Stimulation
Stypulkowski, P. H., Stanslaski, S. R., & Giftakis, J. E. (2017) · Brain stimulation · doi:10.1016/j.brs.2017.09.002 · Cited by 31 · see citing works
Martín‐Blanco, A., Serra-Blasco, M., Pérez‐Egea, R., de Diego-Adeliño, J., Carceller‐Sindreu, M., Puigdemont, D., Molet, J., Álvarez, E., Pérez, V., & Portella, M. J. (2014) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.10.035 · Cited by 24 · see citing works
On the reduction of thermal and flicker noise in ENG signal recording amplifiers
Gak, J., Miguez, M. R., Bremermann, M., & Arnaud, A. (2008) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-008-9187-4 · Cited by 21 · see citing works
A Neural Recording Amplifier with Low-Frequency Noise Suppression
Yoshida, T., Masui, Y., Eki, R., Iwata, A., Yoshida, M., & Uematsu, K. (2010) · IEICE Transactions on Electronics · doi:10.1587/transele.e93.c.849 · Cited by 8 · see citing works
Vallone, F., Cintio, A., Mainardi, M., Caleo, M., & Garbo, A. D. (2015) · Physical Review E · doi:10.1103/physreve.91.012702 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Haller M, Donoghue D, Peterson E, Varma P, Sebastian P, Gao R, Noto T, Knight RT, Shestyuk A, Voytek B. Parameterizing neural power spectra (Preprint). bioRxiv 299859, 2018. doi:10.1101/299859. · DOI
- Hamilton M. Assessment of Depression. Berlin: Springer, 1986, p. 143-152.
Cited by
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Deep brain stimulation for depression.
Figee, M., Riva-Posse, P., Choi, K. S., Bederson, L., Mayberg, H. S., & Kopell, B. H. (2022) · Neurotherapeutics · doi:10.1007/s13311-022-01270-3 · Cited by 175 · see citing works
Self-citationDeep brain stimulation for psychiatric disorders: From focal brain targets to cognitive networks
Sullivan, C. R. P., Olsen, S., & Widge, A. S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117515 · Cited by 112 · see citing works
From bed to bench side: Reverse translation to optimize neuromodulation for mood disorders
Rudebeck, P. H., Rich, E. L., & Mayberg, H. S. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902287116 · Cited by 42 · see citing works
Self-citationMcCall, M. V., Riva-Posse, P., Garlow, S. J., Mayberg, H. S., & Crowell, A. L. (2020) · Neurology Psychiatry and Brain Research · doi:10.1016/j.npbr.2020.05.002 · Cited by 13 · see citing works
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- Authors
- Veerakumar, A., Tiruvadi, V., Howell, B., Waters, A. C., Crowell, A. L., Voytek, B., Riva-Posse, P., Denison, L., Rajendra, J. K., Edwards, J. A., Bijanki, K. R., Choi, K. S., & Mayberg, H. S.
- Affiliations
- Emory University (US)
- Georgia Institute of Technology (US)
- University of California San Diego (US)
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
- National Institutes of Health (US)
- Corresponding author
- Mayberg, Helen S.
- ORCID
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2019-09-01
- Volume
- 122
- Issue
- 3
- Pages
- 1023-1035
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- PMC
- PMC6766743
- OpenAlex
- W2958634274
- Cited by
- 89
- References
- 68
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 12 · 2023: 21 · 2022: 10 · 2021: 9 · 2020: 9 · 2019: 2 · 2018: 1
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute (HHMI)
- HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (UH3NS103550)
- HHS | NIH | National Institute of Mental Health (NIMH) (R01 MH102238, R01 MH106173)
- NIH (F32 NS096839)
- Hope for Depression Research Foundation (Depression Research Foundation)
- Topics
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Life Sciences › Neuroscience › Neurology › Vagus Nerve Stimulation Research
- Keywords
- treatment-resistant depression, deep brain stimulation, local field potentials, circadian variation, biomarkers, adaptive neuromodulation, right hemisphere, treatment response
- MeSH terms
- Aged, Female, Gyrus Cinguli, Humans, Longitudinal Studies, Male, Middle Aged, Process Assessment, Health Care, Deep Brain Stimulation, Electrophysiological Phenomena, Depressive Disorder, Treatment-Resistant
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