/cloudfront-us-east-1.images.arcpublishing.com/gray/YPAEOJGSYFFKVCA7BLM3SBK6L4.png)
Luber, B. M. et al. Using neuroimaging to individualize TMS treatment for depression: Toward a new paradigm for imaging-guided intervention. Neuroimage 148, 1–7. https://doi.org/10.1016/j.neuroimage.2016.12.0831 (2017).
Siddiqi, S. H., Kording, K. P., Parvizi, J. & Fox, M. D. Causal mapping of human brain function. Nat. Rev. Neurosci. 23, 361–375 (2022).
Fitzgerald, P. B. et al. A randomized trial of rTMS targeted with MRI based neuro-navigation in treatment-resistant depression. Neuropsychopharmacology 34(5), 1255–1262. https://doi.org/10.1038/npp.2008.233 (2009).
Fox, M. D. et al. Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proc. Natl. Acad. Sci. USA. 111(41), E4367–E4375. https://doi.org/10.1073/pnas.1405003111 (2014).
Fox, M. D., Buckner, R. L., White, M. P., Greicius, M. D. & Pascual-Leone, A. Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol. Psychiatry. 72(7), 595–603. https://doi.org/10.1016/j.biopsych.2012.04.028 (2012).
Weigand, A. et al. Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites. Biol. Psychiatry. 84(1), 28–37. https://doi.org/10.1016/j.biopsych.2017.10.028 (2018).
Cash, R. F. H. et al. Subgenual functional connectivity predicts antidepressant treatment response to transcranial magnetic stimulation: Independent validation and evaluation of personalization. Biol. Psychiatry. 86(2), e5–e7. https://doi.org/10.1016/j.biopsych.2018.12.002 (2019).
Cash, R. F. H. et al. Using brain imaging to improve spatial targeting of transcranial magnetic stimulation for depression. Biol. Psychiatry https://doi.org/10.1016/j.biopsych.2020.05.033 (2020).
Fox, M. D., Liu, H. & Pascual-Leone, A. Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity. Neuroimage 66, 151–160. https://doi.org/10.1016/j.neuroimage.2012.10.082 (2013).
Williams, N. R. et al. High-dose spaced theta-burst TMS as a rapid-acting antidepressant in highly refractory depression. Brain 141(3), e18. https://doi.org/10.1093/brain/awx379 (2018).
Ning, L., Makris, N., Camprodon, J. A. & Rathi, Y. Limits and reproducibility of resting-state functional MRI definition of DLPFC targets for neuromodulation. Brain Stimul. 12(1), 129–138. https://doi.org/10.1016/j.brs.2018.10.004 (2019).
Siddiqi, S. H. et al. Individualized connectome-targeted transcranial magnetic stimulation for neuropsychiatric sequelae of repetitive traumatic brain injury in a retired NFL player. J. Neuropsychiatry Clin. Neurosci. 31(3), 254–263. https://doi.org/10.1176/appi.neuropsych.18100230 (2019).
Siddiqi, S. H. et al. Repetitive transcranial magnetic stimulation with resting-state network targeting for treatment-resistant depression in traumatic brain injury: A randomized, controlled double-blinded pilot study. J. Neurotrauma. 36(8), 1361–1374. https://doi.org/10.1089/neu.2018.5889 (2019).
Cash, R. F. H., Cocchi, L., Lv, J., Fitzgerald, P. B. & Zalesky, A. Functional magnetic resonance imaging-guided personalization of transcranial magnetic stimulation treatment for depression. JAMA Psychiat. https://doi.org/10.1001/jamapsychiatry.2020.3794 (2020).
Siddiqi, S. H., Weigand, A., Pascual-Leone, A. & Fox, M. D. Identification of personalized TMS targets based on subgenual cingulate connectivity: An independent replication. Biol. Psychiatry. 90, e55–e56 (2021).
Cole, E. J. et al. Stanford accelerated intelligent neuromodulation therapy for treatment-resistant depression. Am. J. Psychiatry 177(8), 716–726. https://doi.org/10.1176/appi.ajp.2019.19070720 (2020).
Cole, E. J. et al. Stanford neuromodulation therapy (SNT): A double-blind randomized controlled trial. Am. J. Psychiatry 179(2), 132–141. https://doi.org/10.1176/appi.ajp.2021.20101429 (2022).
Mueller, S. et al. Reliability correction for functional connectivity: Theory and implementation. Hum. Brain Mapp. 36(11), 4664–4680. https://doi.org/10.1002/hbm.22947 (2015).
Laumann, T. O. et al. Functional system and areal organization of a highly sampled individual human brain. Neuron 87(3), 657–670. https://doi.org/10.1016/j.neuron.2015.06.037 (2015).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178. https://doi.org/10.1038/nature18933 (2016).
Hacker, C. D. et al. Resting state network estimation in individual subjects. Neuroimage 82, 616–633. https://doi.org/10.1016/j.neuroimage.2013.05.108 (2013).
Wang, D. et al. Parcellating cortical functional networks in individuals. Nat. Neurosci. 18(12), 1853–1860. https://doi.org/10.1038/nn.4164 (2015).
Birn, R. M. et al. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage 83, 550–558. https://doi.org/10.1016/j.neuroimage.2013.05.099 (2013).
Lee, M. H. et al. Clinical resting-state fMRI in the preoperative setting: Are we ready for prime time?. Top. Magn. Reson. Imaging. 25(1), 11–18. https://doi.org/10.1097/RMR.0000000000000075 (2016).
Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95(4), 791–807. https://doi.org/10.1016/j.neuron.2017.07.011 (2017).
Greicius, M. D. et al. Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry. 62(5), 429–437. https://doi.org/10.1016/j.biopsych.2006.09.020 (2007).
Liston, C. et al. Default mode network mechanisms of transcranial magnetic stimulation in depression. Biol. Psychiatry. 76(7), 517–526. https://doi.org/10.1016/j.biopsych.2014.01.023 (2014).
Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. USA. 102(27), 9673–9678. https://doi.org/10.1073/pnas.0504136102 (2005).
Chai, X. J., Castanon, A. N., Ongur, D. & Whitfield-Gabrieli, S. Anticorrelations in resting state networks without global signal regression. Neuroimage 59(2), 1420–1428. https://doi.org/10.1016/j.neuroimage.2011.08.048 (2012).
Wong, C. W., Olafsson, V., Tal, O. & Liu, T. T. Anti-correlated networks, global signal regression, and the effects of caffeine in resting-state functional MRI. Neuroimage 63(1), 356–364. https://doi.org/10.1016/j.neuroimage.2012.06.035 (2012).
Gordon, E. M. et al. Individual-specific features of brain systems identified with resting state functional correlations. Neuroimage 146(918–939), 918–939. https://doi.org/10.1016/j.neuroimage.2016.08.032 (2017).
Gordon, E. M., Laumann, T. O., Adeyemo, B. & Petersen, S. E. Individual variability of the system-level organization of the human brain. Cereb. Cortex. 27(1), 386–399. https://doi.org/10.1093/cercor/bhv239 (2017).
Siddiqi, S. H. et al. Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease. Nat. Hum. Behav. 5, 1707 (2021).
Han, K. et al. Disrupted modular organization of resting-state cortical functional connectivity in U.S. military personnel following concussive “mild” blast-related traumatic brain injury. Neuroimage 84, 76–96. https://doi.org/10.1016/j.neuroimage.2013.08.017 (2014).
Han, K., Chapman, S. B. & Krawczyk, D. C. Disrupted intrinsic connectivity among default, dorsal attention, and frontoparietal control networks in individuals with chronic traumatic brain injury. J. Int. Neuropsychol. Soc. 22(2), 263–279. https://doi.org/10.1017/S1355617715001393 (2016).
van der Horn, H. J. et al. Graph analysis of functional brain networks in patients with mild traumatic brain injury. PLoS ONE 12(1), e0171031. https://doi.org/10.1371/journal.pone.0171031 (2017).
Caeyenberghs, K., Verhelst, H., Clemente, A. & Wilson, P. H. Mapping the functional connectome in traumatic brain injury: What can graph metrics tell us?. Neuroimage 160, 113–123. https://doi.org/10.1016/j.neuroimage.2016.12.003 (2017).
Siddiqi, S. H. rTMS for major depression associated with TBI. Open Science Framework. osf.io/vjddq
Van Essen, D. C. et al. The human connectome project: A data acquisition perspective. Neuroimage 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018 (2012).
Power, J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320–341. https://doi.org/10.1016/j.neuroimage.2013.08.048 (2014).
Blumberger, D. M. et al. Unilateral and bilateral MRI-targeted repetitive transcranial magnetic stimulation for treatment-resistant depression: A randomized controlled study. J. Psychiatry Neurosci. 41(4), E58-66. https://doi.org/10.1503/jpn.150265 (2016).
Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.201110.1152/jn.00338.2011 (2011).
Cole, M. W., Yang, G. J., Murray, J. D., Repovš, G. & Anticevic, A. Functional connectivity change as shared signal dynamics. J. Neurosci. Methods 259, 22–39. https://doi.org/10.1016/j.jneumeth.2015.11.011 (2016).
Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex. 28(9), 3095–3114. https://doi.org/10.1093/cercor/bhx179 (2018).
Hamilton, J. P. et al. Default-mode and task-positive network activity in major depressive disorder: Implications for adaptive and maladaptive rumination. Biol. Psychiatry. 70(4), 327–333. https://doi.org/10.1016/j.biopsych.2011.02.003 (2011).
Kaiser, R. H., Andrews-Hanna, J. R., Wager, T. D. & Pizzagalli, D. A. Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity. JAMA Psychiat. 72(6), 603–611. https://doi.org/10.1001/jamapsychiatry.2015.0071 (2015).
Choi, K. S., Riva-Posse, P., Gross, R. E. & Mayberg, H. S. Mapping the “depression switch” during intraoperative testing of subcallosal cingulate deep brain stimulation. JAMA Neurol. 72(11), 1252–1260. https://doi.org/10.1001/jamaneurol.2015.2564 (2015).
Murphy, K. & Fox, M. D. Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage 154, 169–173. https://doi.org/10.1016/j.neuroimage.2016.11.052 (2017).
Cash, R. F. H. et al. Personalized connectivity-guided DLPFC-TMS for depression: Advancing computational feasibility, precision and reproducibility. Hum. Brain Mapp. 42(13), 4155–4172. https://doi.org/10.1002/hbm.25330 (2021).
Chen, J. et al. Left versus right repetitive transcranial magnetic stimulation in treating major depression: A meta-analysis of randomised controlled trials. Psychiatry Res. 210(3), 1260–1264. https://doi.org/10.1016/j.psychres.2013.09.007 (2013).
Sale, M. V., Mattingley, J. B., Zalesky, A. & Cocchi, L. Imaging human brain networks to improve the clinical efficacy of non-invasive brain stimulation. Neurosci. Biobehav. Rev. 57, 187–198. https://doi.org/10.1016/j.neubiorev.2015.09.010 (2015).
Opitz, A., Fox, M. D., Craddock, R. C., Colcombe, S. & Milham, M. P. An integrated framework for targeting functional networks via transcranial magnetic stimulation. Neuroimage 127, 86–96. https://doi.org/10.1016/j.neuroimage.2015.11.040 (2016).
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