A connectomic analysis of deep brain stimulation for treatment-resistant depression
Open accessAbstract
OBJECTIVE: Deep brain stimulation (DBS) has been used as a treatment of last resort for treatment-resistant depression (TRD) for more than a decade. Many DBS targets have been proposed and tested clinically, but the underlying circuit mechanisms remain unclear. Uncovering white matter tracts (WMT) activated by DBS targets may provide crucial information about the circuit substrates mediating DBS efficacy in ameliorating TRD. METHODS: We performed probabilistic tractography using diffusion magnetic resonance imaging datas from 100 healthy volunteers in Human Connectome Project datasets to analyze the structural connectivity patterns of stimulation targeting currently-used DBS target for TRD. We generated mean and binary fiber distribution maps and calculated the numbers of WMT streamlines in the dataset. RESULTS: Probabilistic tracking results revealed that activation of distinct DBS targets demonstrated modulation of overlapping but considerably distinct pathways. DBS targets were categorized into 4 groups: Cortical, Striatal, Thalamic, and Medial Forebrain Bundle according to their main modulated WMT and brain areas. Our data also revealed that Brodmann area 10 and amygdala are hub structures that are associated with all DBS targets. CONCLUSIONS: Our results together suggest that the distinct mechanism of DBS targets implies individualized target selection and formulation in the future of DBS treatment for TRD. The modulation of Brodmann area 10 and amygdala may be critical for the efficacy of DBS-mediated treatment of TRD.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DBS for MDD targets the cingulate cortex or striatum (including NAc) and increases activity there and in connected anhedonia/motivation areas, plus the PFC and amygdala.
“DBS for MDD targets the cingulate cortex or the striatum, which includes the NAc, and has been shown to enhance activity in these regions and connected areas involved in anhedonia and motivation [145, 146], as well as activity in the PFC and amygdala [139].”
From Beyond the serotonin deficit hypothesis: Communicating a neuroplasticity framework of major depressive disorder (2024) · cites this paper as [139]
Human imaging studies indicate neurostimulation increases activity in cortical and mesolimbic circuits tied to cognition, reward, and motivation.
“According to human neuroimaging studies, neurostimulation therapies enhance activity in cortical and mesolimbic circuits involved in cognition, reward, and motivation [139-141].”
From Beyond the serotonin deficit hypothesis: Communicating a neuroplasticity framework of major depressive disorder (2024) · cites this paper as [139]
VC/VS targets structurally and functionally connect to the MFB and partially to the SCC, but the SCC has selective fibers not directly connected to VC/VS or NAc.
“DBS targets in the VC/VS region structurally and functionally connect to the MFB and partially to the SCC [91], but the SCC has selective fibers that are not directly connected to VC/VS or NAc [92,93].”
From Deep brain stimulation for depression. (2022) · cites this paper as [93]
VC/VS stimulation may be very different from SCC stimulation, which involves monosynaptic white matter fibers to part of the NAc and thalamus that do not pass through the ALIC.
“Thus, VC/VS stimulation may be very different from SCC stimulation that involves monosynaptic white matter fibers to part of the NAc and thalamus that do not pass through the ALIC [93].”
From Deep brain stimulation for depression. (2022) · cites this paper as [93]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A robust and high-throughput Cre reporting and characterization system for the whole mouse brain
Madisen, L., Zwingman, T. A., Sunkin, S. M., Oh, S. W., Zariwala, H. A., Gu, H., Ng, L. L., Palmiter, R. D., Hawrylycz, M. J., Jones, A. R., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2467 · Cited by 6,907 · see citing works
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471 · see citing works
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284 · see citing works
The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture
Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Chu, C., Xie, S., Laird, A. R., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw157 · Cited by 2,957 · see citing works
Resting-state connectivity biomarkers define neurophysiological subtypes of depression
Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., et al. (2017) · Nature Medicine · doi:10.1038/nm.4246 · Cited by 2,170 · see citing works
Self-citationNetwork hubs in the human brain
van den Heuvel, M. P., & Sporns, O. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.012 · Cited by 2,123 · cited 1× · see citing works
“distributed information within a network [36,37].”
Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review
Assaf, Y., & Pasternak, O. (2008) · Journal of Molecular Neuroscience · doi:10.1007/s12031-007-0029-0 · Cited by 1,537 · see citing works
The Human Connectome Project's neuroimaging approach
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4361 · Cited by 1,090 · see citing works
Circuit architecture of VTA dopamine neurons revealed by systematic input-output mapping
Beier, K. T., Steinberg, E. E., DeLoach, K. E., Xie, S., Miyamichi, K., Schwarz, L., Gao, X. J., Kremer, E. J., Malenka, R. C., & Luo, L. (2015) · Cell · doi:10.1016/j.cell.2015.07.015 · Cited by 1,068 · see citing works
Self-citationBupropion-SR, Sertraline, or Venlafaxine-XR after Failure of SSRIs for Depression
Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Stewart, J. W., Nierenberg, A. A., Thase, M. E., Ritz, L., Biggs, M. M., Warden, D., Luther, J. F., et al. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa052963 · Cited by 1,033 · see citing works
Uncovering the mechanism(s) of action of deep brain stimulation: activation, inhibition, or both
McIntyre, C. C., Savasta, M., Goff, L. K., & Vitek, J. L. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2003.12.024 · Cited by 744 · see citing works
The orbital and medial prefrontal circuit through the primate basal ganglia
Haber, S., Kunishio, K., Mizobuchi, M., & Lynd-Balta, E. (1995) · The Journal of Neuroscience · doi:10.1523/jneurosci.15-07-04851.1995 · Cited by 605 · see citing works
The Central Amygdala as an Integrative Hub for Anxiety and Alcohol Use Disorders
Gilpin, N. W., Herman, M. A., & Roberto, M. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.09.008 · Cited by 457 · see citing works
Riva-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-citationNetwork perspectives on the mechanisms of deep brain stimulation
McIntyre, C. C., & Hahn, P. J. (2010) · Neurobiology of Disease · doi:10.1016/j.nbd.2009.09.022 · Cited by 439 · see citing works
Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis
Korgaonkar, M. S., Fornito, A., Williams, L. M., & Grieve, S. M. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.02.018 · Cited by 367 · see citing works
Coenen, V. A., Panksepp, J., Hurwitz, T. A., Urbach, H., & Mädler, B. (2012) · Journal of Neuropsychiatry · doi:10.1176/appi.neuropsych.11080180 · Cited by 342 · see citing works
Miyamichi, K., Shlomai, Y., Shu, M., Weissbourd, B., Luo, L., & Mizrahi, A. (2013) · Neuron · doi:10.1016/j.neuron.2013.08.027 · Cited by 310 · see citing works
Dandekar, M. P., Fenoy, A. J., Carvalho, A. F., Soares, J. C., & Quevedo, J. (2018) · Molecular Psychiatry · doi:10.1038/mp.2018.2 · Cited by 264 · see citing works
A tractography analysis of two deep brain stimulation white matter targets for depression
Gutman, D. A., Holtzheimer, P. E., Behrens, T. E. J., Johansen-Berg, H., & Mayberg, H. S. (2009) · Biological Psychiatry · doi:10.1016/j.biopsych.2008.09.021 · Cited by 220 · see citing works
Self-citationSafadi, Z., Grisot, G., Jbabdi, S., Behrens, T. E. J., Heilbronner, S. R., McLaughlin, N. C. R., Mandeville, J. B., Versace, A., Phillips, M. L., Lehman, J. F., et al. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2335-17.2017 · Cited by 179 · see citing works
Deep brain stimulation targets for treating depression
Drobisz, D., & Damborská, A. (2018) · Behavioural Brain Research · doi:10.1016/j.bbr.2018.11.004 · Cited by 159 · see citing works
Brodmann area 10: Collating, integrating and high level processing of nociception and pain
Peng, K., Steele, S. C., Becerra, L. R., & Borsook, D. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.11.004 · Cited by 159 · 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-citationMädler, B., & Coenen, V. A. (2012) · American Journal of Neuroradiology · doi:10.3174/ajnr.a2906 · Cited by 123 · see citing works
LeWinn, K. Z., Connolly, C. G., Wu, J., Drahos, M., Hoeft, F., Ho, T. C., Simmons, A. N., & Yang, T. T. (2014) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2014.04.021 · Cited by 117 · see citing works
Koelsch, S., & Skouras, S. (2013) · Human Brain Mapping · doi:10.1002/hbm.22416 · Cited by 114 · see citing works
Mackey, S., & Petrides, M. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07465.x · Cited by 105 · see citing works
A systematic review and meta-analysis of deep brain stimulation in treatment-resistant depression
Zhou, C., Zhang, H., Qin, Y., Tian, T., Xu, B., Chen, J., Zhou, X., Zeng, L., Fang, L., Qi, X., et al. (2018) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.11.012 · Cited by 100 · see citing works
Liebrand, L. C., Caan, M. W. A., Schuurman, P. R., van den Munckhof, P., Figee, M., Denys, D., & van Wingen, G. A. (2019) · Brain Stimulation · doi:10.1016/j.brs.2018.11.014 · Cited by 98 · see citing works
Cortical Input to the Frontal Pole of the Marmoset Monkey
Burman, K. J., Reser, D. H., Yu, H., & Rosa, M. G. P. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhq239 · Cited by 92 · see citing works
Coenen, V. A., Sajonz, B., Reisert, M., Boström, J. P., Bewernick, B. H., Urbach, H., Jenkner, C., Reinacher, P. C., Schlaepfer, T. E., & Mädler, B. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.08.020 · Cited by 90 · see citing works
Deng, F., Wang, Y., Huang, H., Niu, M., Zhong, S., Zhao, L., Qi, Z., Wu, X., Sun, Y., Niu, C., et al. (2017) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.09.006 · Cited by 82 · see citing works
Multimodal evaluation of the amygdala's functional connectivity
Kerestes, R., Chase, H. W., Phillips, M. L., Ladouceur, C. D., & Eickhoff, S. B. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.023 · Cited by 70 · see citing works
Circuits regulating pleasure and happiness in major depression
Loonen, A. J. M., & Ivanova, S. A. (2016) · Medical Hypotheses · doi:10.1016/j.mehy.2015.12.013 · Cited by 69 · see citing works
Reduced integrity of the uncinate fasciculus and cingulum in depression: A stem-by-stem analysis
Bhatia, K. D., Henderson, L. A., Hsu, E., & Yim, M. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.04.055 · Cited by 63 · see citing works
Sugimoto, K., Kakeda, S., Watanabe, K., Katsuki, A., Ueda, I., Igata, N., Igata, R., Abe, O., Yoshimura, R., & Korogi, Y. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0174-y · Cited by 62 · see citing works
Kamali, A., Sair, H. I., Blitz, A. M., Riascos, R., Mirbagheri, S., Keser, Z., & Hasan, K. M. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1119-3 · Cited by 61 · see citing works
Frontal white matter architecture predicts efficacy of deep brain stimulation in major depression
Coenen, V. A., Schlaepfer, T. E., Bewernick, B. H., Kilian, H. M., Kaller, C. P., Urbach, H., Li, M., & Reisert, M. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0540-4 · Cited by 47 · see citing works
Zhang, W.-H., Li, H., & Pan, X. (2015) · Human Brain Mapping · doi:10.1002/hbm.22636 · Cited by 39 · see citing works
Unraveling the Effects of Plasticity and Pain on Personality
Gustin, S. M., Peck, C. C., Macey, P. M., Murray, G. M., & Henderson, L. A. (2013) · Journal of Pain · doi:10.1016/j.jpain.2013.08.005 · Cited by 22 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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-citationPage, C. E., Epperson, C. N., Novick, A. M., Duffy, K. A., & Thompson, S. M. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02625-2 · Cited by 80 · see citing works
- Authors
- Zhu, Z., Hubbard, E., Guo, X., Barbosa, D. A. N., Popal, A. M., Cai, C., Jiang, H., Zheng, Z., Lin, J., Gao, W., Zhang, J., Bartas, K., Macchia, D., Derdeyn, P., Halpern, C. H., Mayberg, H. S., Beier, K. T., Zhu, J., & Wu, H.
- Affiliations
- Second Affiliated Hospital of Zhejiang University (CN)
- University of California, Irvine (US)
- Stanford Medicine (US)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Beier, Kevin T.
- Zhu, Junming
- Wu, Hemmings
- ORCID
- Venue
- Brain Stimulation
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 14
- Issue
- 5
- Pages
- 1226-1233
- ISSN
- 1876-4754 (ISSN-L)
- 1935-861X
- PMC
- PMC13541001
- OpenAlex
- W3195679819
- Cited by
- 37
- References
- 41
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 11 · 2023: 8 · 2022: 8
- FWCI
- 3.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Brain stimulation (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Tobacco-Related Disease Research Program
- American Parkinson Disease Association
- Zhejiang University
- National Institutes of Health
- Brain and Behavior Research Foundation
- Alzheimer's Association
- Topics
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Social Sciences › Psychology › Clinical Psychology › Obsessive-Compulsive Spectrum Disorders
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Keywords
- deep brain stimulation, treatment-resistant depression, white matter tracts, structural connectivity, DBS targets, probabilistic tractography, medial forebrain bundle, amygdala, Brodmann area 10
- MeSH terms
- Depression, Humans, Medial Forebrain Bundle, Deep Brain Stimulation, Depressive Disorder, Treatment-Resistant, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
