Mapping human brain function is a long-standing goal of neuroscience that promises to inform the development of new treatments for brain disorders. Early maps of human brain function were based on locations of brain damage or brain stimulation that caused a functional change. Over time, this approach was largely replaced by technologies such as functional neuroimaging, which identify brain regions in which activity is correlated with behaviours or symptoms. Despite their advantages, these technologies reveal correlations, not causation. This creates challenges for interpreting the data generated from these tools and using them to develop treatments for brain disorders. A return to causal mapping of human brain function based on brain lesions and brain stimulation is underway. New approaches can combine these causal sources of information with modern neuroimaging and electrophysiology techniques to gain new insights into the functions of specific brain areas. In this Review, we provide a definition of causality for translational research, propose a continuum along which to assess the relative strength of causal information from human brain mapping studies and discuss recent advances in causal brain mapping and their relevance for developing treatments.
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Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).
Raichle, M. E. A brief history of human brain mapping. Trends Neurosci. 32, 118–126 (2009).
Genon, S., Reid, A., Langner, R., Amunts, K. & Eickhoff, S. B. How to characterize the function of a brain region. Trends Cogn. Sci. 22, 350–364 (2018).
Gusnard, D. A. & Raichle, M. E. Searching for a baseline: functional imaging and the resting human brain. Nat. Rev. Neurosci. 2, 685–694 (2001).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).
Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 13, 336–349 (2012).
Logothetis, N. K. What we can do and what we cannot do with fMRI. Nature 453, 869–878 (2008). This work presents an overview of common uses and pitfalls of correlative neuroimaging.
Jonas, E. & Kording, K. P. Could a neuroscientist understand a microprocessor? PLoS Comput. Biol. 13, e1005268 (2017). This work illustrates that conventional neuroscience tools are unable to explain the function of a microprocessor (a device that we understand completely), raising questions about whether they can explain anything meaningful about the brain.
Reid, A. T. et al. Advancing functional connectivity research from association to causation. Nat. Neurosci. 22, 1751–1760 (2019).
Laumann, T. O. & Snyder, A. Z. Brain activity is not only for thinking. Curr. Opin. Behav. Sci. 40, 130–136 (2021).
Etkin, A. A reckoning and research agenda for neuroimaging in psychiatry. Am. J. Psychiatry 176, 507–511 (2019).
Weichwald, S. & Peters, J. Causality in cognitive neuroscience: concepts, challenges, and distributional robustness. J. Cogn. Neurosci. 33, 226–247 (2021).
Etkin, A. Addressing the causality gap in human psychiatric neuroscience. JAMA Psychiatry 75, 3–4 (2018).
Angrist, J. D. & Pischke, J.-S. The credibility revolution in empirical economics: how better research design is taking the con out of econometrics. J. Econ. Perspect. 24, 3–30 (2010).
Leamer, E. E. Let’s take the con out of econometrics. Am. Econ. Rev. 73, 31–43 (1983).
Etkin, A. Mapping causal circuitry in human depression. Biol. Psychiatry 86, 732–733 (2019).
Bergmann, T. O. & Hartwigsen, G. Inferring causality from noninvasive brain stimulation in cognitive neuroscience. J. Cogn. Neurosci. 33, 195–225 (2021).
Pycroft, L., Stein, J. & Aziz, T. Deep brain stimulation: an overview of history, methods, and future developments. Brain Neurosci. Adv. 2, 2398212818816017 (2018).
Lozano, A. M. et al. Effect of GPi pallidotomy on motor function in Parkinson’s disease. Lancet 346, 1383–1387 (1995).
Groiss, S. J., Wojtecki, L., Sudmeyer, M. & Schnitzler, A. Deep brain stimulation in Parkinson’s disease. Ther. Adv. Neurol. Disord. 2, 20–28 (2009).
Benabid, A. L. et al. Long-term suppression of tremor by chronic stimulation of the ventral intermediate thalamic nucleus. Lancet 337, 403–406 (1991). This work is an early example of successful therapeutic use of DBS, using a target for tremor based partly on prior studies using brain lesions.
George, M. S. et al. A controlled trial of daily left prefrontal cortex TMS for treating depression. Biol. Psychiatry 48, 962–970 (2000).
Pascual-Leone, A., Rubio, B., Pallardó, F. & Catalá, M. D. Rapid-rate transcranial magnetic stimulation of left dorsolateral prefrontal cortex in drug-resistant depression. Lancet 348, 233–237 (1996). This example of successful therapeutic use of TMS uses a target for depression based, in part, on depression-causing lesions — later confirmed in larger trials, including.
Harlow, J. M. Passage of an iron rod through the head. Boston Med. Surg. J. 39, 389–393 (1848).
Scoville, W. B. & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11 (1957).
Penfield, W. & Jasper, H. Epilepsy and the Functional Anatomy of the Human Brain (Little, Brown and Co., 1954).
Penfield, W. & Perot, P. The brain’s record of auditory and visual experience: a final summary and discussion. Brain 86, 595–696 (1963).
Penfield, W. & Boldrey, E. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 60, 389–443 (1937).
Rorden, C. & Karnath, H.-O. Using human brain lesions to infer function: a relic from a past era in the fMRI age? Nat. Rev. Neurosci. 5, 812–819 (2004).
Michel, J.-B. et al. Quantitative analysis of culture using millions of digitized books. Science 331, 176–182 (2011).
Gaillard, R. et al. Direct intracranial, FMRI, and lesion evidence for the causal role of left inferotemporal cortex in reading. Neuron 50, 191–204 (2006).
Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M. & Raichle, M. E. Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature 331, 585–589 (1988). This classic study uses correlative neuroimaging to illustrate features of language processing in healthy individuals.
Dolan, R. J. & Fletcher, P. Dissociating prefrontal and hippocampal function in episodic memory encoding. Nature 388, 582–585 (1997).
Zalesky, A., Fornito, A. & Bullmore, E. On the use of correlation as a measure of network connectivity. Neuroimage 60, 2096–2106 (2012).
Uttal, W. R. The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain (MIT Press, 2001).
Pearl, J. Causality (Cambridge Univ. Press, 2009).
Angrist, J. D. & Pischke, J.-S. Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton Univ. Press, 2008).
Holland, P. W. Statistics and causal inference. J. Am. Stat. Assoc. 81, 945–960 (1986).
Pearl, J. & Mackenzie, D. The Book of Why: The New Science of Cause and Effect (Basic Books, 2018). This book details the basic principles of causal reasoning across disciplines.
Gotthelf, A. Aristotle’s Conception of Final Causality Thesis (Columbia Univ., 1975).
Black, D. L. Mental existence in Thomas Aquinas and Avicenna. Mediaev. Stud. 61, 45–79 (1999).
Taylor, R. in The Routledge Companion to Islamic Philosophy (eds Taylor, R. C. & Lopez-Farjeat, L. X.) 225–235 (Routledge, 2016).
Smith, N. K. The Philosophy of David Hume: A Critical Study of Its Origins and Central Doctrines (Macmillan, 1941).
Koch, R. An address on bacteriological research. BMJ 2, 380 (1890).
Bradford Hill, A. The environment and disease: association or causation? Proc. R. Soc. Med. 58, 295–300 (1965). This work introduces the Bradford Hill criteria, which have been used to understand causal relationships across multiple disciplines.
Fedak, K. M., Bernal, A., Capshaw, Z. A. & Gross, S. Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology. Emerg. Themes Epidemiol. 12, 14 (2015).
Araújo, L., Dalgalarrondo, P. & Banzato, C. On the notion of causality in medicine: addressing Austin Bradford Hill and John L. Mackie. Arch. Clin. Psychiatry 41, 56–61 (2014).
Sajadi, M. M., Mansouri, D. & Sajadi, M. R. Ibn Sina and the clinical trial. Ann. Intern. Med. 150, 640–643 (2009).
Bradford Hill, A. Memories of the British streptomycin trial in tuberculosis: the first randomized clinical trial. Controlled Clin. Trials 11, 77–79 (1990).
Gillies, D. Causality, Probability and Medicine (Taylor & Francis, 2018).
Kaplan, R. M. & Irvin, V. L. Likelihood of null effects of large NHLBI clinical trials has increased over time. PLoS ONE 10, e0132382 (2015).
Baluku, J. B. et al. Prevalence of malaria and TB coinfection at a national tuberculosis treatment centre in Uganda. J. Tropical Med. 2019, 3741294 (2019).
Koch, R. The etiology of tuberculosis. Mitth. aus dem Kaiserlichen Gesundheitsamte 2, 1–88 (1884).
Hernán, M. A. & Robins, J. M. Using big data to emulate a target trial when a randomized trial is not available. Am. J. Epidemiol. 183, 758–764 (2016).
Marinescu, I. E., Lawlor, P. N. & Kording, K. P. Quasi-experimental causality in neuroscience and behavioural research. Nat. Hum. Behav. 2, 891–898 (2018). This work presents an overview of different approaches to investigating causality when a prospective controlled experiment is not possible.
Cook, T. D., Shadish, W. R. & Wong, V. C. Three conditions under which experiments and observational studies produce comparable causal estimates: new findings from within-study comparisons. J. Policy Anal. Manag. 27, 724–750 (2008).
Fox, M. D. Mapping symptoms to brain networks with the human connectome. N. Engl. J. Med. 379, 2237–2245 (2018). This work is an introduction to LNM, which can be used to relate different lesion locations causing a symptom to common brain circuits.
Williamson, J. Establishing causal claims in medicine. Int. Stud. Philos. Sci. 32, 33–61 (2019).
Russo, F. & Williamson, J. Interpreting causality in the health sciences. Int. Stud. Philos. Sci. 21, 157–170 (2007).
Evans, A. S. Causation and disease: the Henle–Koch postulates revisited. Yale J. Biol. Med. 49, 175–195 (1976).
Cassidy, J. M., Mark, J. I. & Cramer, S. C. Functional connectivity drives stroke recovery: shifting the paradigm from correlation to causation. Brain https://doi.org/10.1093/brain/awab469 (2021).
Bates, E. et al. Voxel-based lesion–symptom mapping. Nat. Neurosci. 6, 448–450 (2003).
Wu, O. et al. Role of acute lesion topography in initial ischemic stroke severity and long-term functional outcomes. Stroke 46, 2438–2444 (2015).
Corbetta, M. et al. Common behavioral clusters and subcortical anatomy in stroke. Neuron 85, 927–941 (2015).
Parvizi, J. & Kastner, S. Promises and limitations of human intracranial electroencephalography. Nat. Neurosci. 21, 474–483 (2018).
Fox, K. C. R. et al. Intrinsic network architecture predicts the effects elicited by intracranial electrical stimulation of the human brain. Nat. Hum. Behav. 4, 1039–1052 (2020). This work illustrates how iES can be used to investigate the causal relevance of networks defined using task-free neuroimaging.
Koenigs, M. et al. Focal brain damage protects against post-traumatic stress disorder in combat veterans. Nat. Neurosci. 11, 232–237 (2008).
Knutson, K. M. et al. Injured brain regions associated with anxiety in Vietnam veterans. Neuropsychologia 51, 686–694 (2013).
Herbsman, T. et al. More lateral and anterior prefrontal coil location is associated with better repetitive transcranial magnetic stimulation antidepressant response. Biol. Psychiatry 66, 509–515 (2009).
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, 595–603 (2012).
Weigand, A. et al. Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites. Biol. Psychiatry 84, 28–37 (2018).
Gordon, E. M. et al. Individual-specific features of brain systems identified with resting state functional correlations. Neuroimage 146, 918–939 (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, 386–399 (2017).
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).
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 Psychiatry 78, 337–339 (2020).
Howell, B. et al. Quantifying the axonal pathways directly stimulated in therapeutic subcallosal cingulate deep brain stimulation. Hum. Brain Mapp. 40, 889–903 (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, 1361–1374 (2019).
Parvizi, J., Rangarajan, V., Shirer, W. R., Desai, N. & Greicius, M. D. The will to persevere induced by electrical stimulation of the human cingulate gyrus. Neuron 80, 1359–1367 (2013).
Schrouff, J. et al. Fast temporal dynamics and causal relevance of face processing in the human temporal cortex. Nat. Commun. 11, 656 (2020).
Yih, J., Beam, D. E., Fox, K. C. R. & Parvizi, J. Intensity of affective experience is modulated by magnitude of intracranial electrical stimulation in human orbitofrontal, cingulate and insular cortices. Soc. Cogn. Affect. Neurosci. 14, 339–351 (2019).
Downar, J. et al. Anhedonia and reward-circuit connectivity distinguish nonresponders from responders to dorsomedial prefrontal repetitive transcranial magnetic stimulation in major depression. Biol. Psychiatry 76, 176–185 (2014).
Drysdale, A. T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 12, 28–38 (2016).
Cash, R. F. H. et al. Using brain imaging to improve spatial targeting of transcranial magnetic stimulation for depression. Biol. Psychiatry 90, 689–700 (2020).
Ferguson, M. A. et al. A human memory circuit derived from brain lesions causing amnesia. Nat. Commun. 10, 3497 (2019).
Horn, A. et al. Connectivity predicts deep brain stimulation outcome in Parkinson disease. Ann. Neurol. 82, 67–78 (2017).
Joutsa, J., Horn, A., Hsu, J. & Fox, M. D. Localizing parkinsonism based on focal brain lesions. Brain 141, 2445–2456 (2018).
Corp, D. T. et al. Network localization of cervical dystonia based on causal brain lesions. Brain 142, 1660–1674 (2019).
Joutsa, J. et al. Identifying therapeutic targets from spontaneous beneficial brain lesions. Ann. Neurol. 84, 153–157 (2018).
Irmen, F. et al. Left prefrontal impact links subthalamic stimulation with depressive symptoms. Ann. Neurol. 87, 962–975 (2020).
Laganiere, S., Boes, A. D. & Fox, M. D. Network localization of hemichorea-hemiballismus. Neurology 86, 2187–2195 (2016).
Padmanabhan, J. L. et al. A human depression circuit derived from focal brain lesions. Biol. Psychiatry 86, 749–758 (2019).
Cotovio, G. et al. Mapping mania symptoms based on focal brain damage. J. Clin. Invest. 130, 5209–5222 (2020).
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, e5–e7 (2019).
Siddiqi, S. H. et al. Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease. Nat. Hum. Behav. 5, 1707–1716 (2021). This work is a recent test of whether different causal sources of information (lesion locations, non-invasive brain stimulation sites and invasive brain stimulation sites) map to a common brain circuit.
Baldermann, J. C. et al. Connectivity profile predictive of effective deep brain stimulation in obsessive-compulsive disorder. Biol. Psychiatry 85, 735–743 (2019).
Li, N. et al. A unified connectomic target for deep brain stimulation in obsessive-compulsive disorder. Nat. Commun. 11, 3364 (2020).
Siddiqi, S. H. et al. Distinct symptom-specific treatment targets for circuit-based neuromodulation. Am. J. Psychiatry 177, 435–446 (2020).
Kim, N. Y. et al. Lesions causing hallucinations localize to one common brain network. Mol. Psychiatry 26, 1299–1309 (2021).
Boes, A. D. et al. Network localization of neurological symptoms from focal brain lesions. Brain 138, 3061–3075 (2015).
Darby, R. R., Laganiere, S., Pascual-Leone, A., Prasad, S. & Fox, M. D. Finding the imposter: brain connectivity of lesions causing delusional misidentifications. Brain 140, 497–507 (2017).
Snider, S. B. et al. Cortical lesions causing loss of consciousness are anticorrelated with the dorsal brainstem. Hum. Brain Mapp. 41, 1520–1531 (2020).
Fischer, D. B. et al. A human brain network derived from coma-causing brainstem lesions. Neurology 87, 2427–2434 (2016).
Joutsa, J., Shih, L. C. & Fox, M. D. Mapping Holmes tremor circuit using the human brain connectome. Ann. Neurol. 86, 812–820 (2019).
Darby, R. R., Joutsa, J., Burke, M. J. & Fox, M. D. Lesion network localization of free will. Proc. Natl Acad. Sci. USA 115, 10792–10797 (2018).
Darby, R. R., Horn, A., Cushman, F. & Fox, M. D. Lesion network localization of criminal behavior. Proc. Natl Acad. Sci. USA 115, 601–606 (2018).
Cohen, A. L. et al. Looking beyond the face area: lesion network mapping of prosopagnosia. Brain 142, 3975–3990 (2019).
Cohen, A. L. et al. Tuber locations associated with infantile spasms map to a common brain network. Ann. Neurol. 89, 726–739 (2021).
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, E4367–E4375 (2014).
Horn, A. & Fox, M. D. Opportunities of connectomic neuromodulation. Neuroimage 221, 117180 (2020).
Bowren, M. et al. Post-stroke cognitive and motor outcomes predicted from lesion location and lesion network mapping. Brain https://doi.org/10.1093/brain/awac010 (2022).
Long, M. A. et al. Functional segregation of cortical regions underlying speech timing and articulation. Neuron 89, 1187–1193 (2016).
Ibayashi, K. et al. Focal cortical surface cooling is a novel and safe method for intraoperative functional brain mapping. World Neurosurg. 147, e118–e129 (2021).
Borchers, S., Himmelbach, M., Logothetis, N. & Karnath, H. O. Direct electrical stimulation of human cortex — the gold standard for mapping brain functions? Nat. Rev. Neurosci. 13, 63–70 (2011).
Winawer, J. & Parvizi, J. Linking electrical stimulation of human primary visual cortex, size of affected cortical area, neuronal responses, and subjective experience. Neuron 92, 1213–1219 (2016).
Parvizi, J. et al. Altered sense of self during seizures in the posteromedial cortex. Proc. Natl Acad. Sci. USA 118, e2100522118 (2021).
Vesuna, S. et al. Deep posteromedial cortical rhythm in dissociation. Nature 586, 87–94 (2020).
Knecht, S. et al. Degree of language lateralization determines susceptibility to unilateral brain lesions. Nat. Neurosci. 5, 695–699 (2002).
Pascual-Leone, A., Gates, J. R. & Dhuna, A. Induction of speech arrest and counting errors with rapid-rate transcranial magnetic stimulation. Neurology 41, 697–702 (1991).
Lefaucheur, J. P. & Picht, T. The value of preoperative functional cortical mapping using navigated TMS. Neurophysiol. Clin. 46, 125–133 (2016).
Riva-Posse, P. et al. Defining critical white matter pathways mediating successful subcallosal cingulate deep brain stimulation for treatment-resistant depression. Biol. Psychiatry 76, 963–969 (2014).
Riva-Posse, P. et al. A connectomic approach for subcallosal cingulate deep brain stimulation surgery: prospective targeting in treatment-resistant depression. Mol. Psychiatry 23, 843–849 (2018).
Li, N. et al. A unified functional network target for deep brain stimulation in obsessive-compulsive disorder. Biol. Psychiatry 90, 701–713 (2021).
Nestor, S. M. & Blumberger, D. M. Mapping symptom clusters to circuits: toward personalizing TMS targets to improve treatment outcomes in depression. Am. J. Psychiatry 177, 373–375 (2020).
Robinson, R. G., Kubos, K. L., Starr, L. B., Rao, K. & Price, T. R. Mood disorders in stroke patients. Importance of location of lesion. Brain 107, 81–93 (1984).
Robinson, R. G. & Szetela, B. Mood change following left hemispheric brain injury. Ann. Neurol. 9, 447–453 (1981).
Bergman, H., Wichmann, T. & DeLong, M. R. Reversal of experimental parkinsonism by lesions of the subthalamic nucleus. Science 249, 1436–1438 (1990).
Parvizi, J. et al. Electrical stimulation of human fusiform face-selective regions distorts face perception. J. Neurosci. 32, 14915–14920 (2012).
Rangarajan, V. et al. Electrical stimulation of the left and right human fusiform gyrus causes different effects in conscious face perception. J. Neurosci. 34, 12828–12836 (2014).
Ganos, C. et al. A neural network for tics: insights from causal brain lesions and deep brain stimulation. Brain https://doi.org/10.1093/brain/awac009 (2022).
Reich, M. M. et al. A brain network for deep brain stimulation induced cognitive decline in Parkinson’s disease. Brain https://doi.org/10.1093/brain/awac012 (2022).
Hamilton, L. S., Oganian, Y., Hall, J. & Chang, E. F. Parallel and distributed encoding of speech across human auditory cortex. Cell 184, 4626–4639.e13 (2021).
Hamilton, L. S., Edwards, E. & Chang, E. F. A spatial map of onset and sustained responses to speech in the human superior temporal gyrus. Curr. Biol. 28, 1860–1871.e4 (2018).
Zhi, D., King, M. & Diedrichsen, J. Evaluating brain parcellations using the distance controlled boundary coefficient. Preprint at bioRxiv https://doi.org/10.1101/2021.05.11.443151 (2021).
Warren, D. E. et al. Network measures predict neuropsychological outcome after brain injury. Proc. Natl Acad. Sci. USA 111, 14247–14252 (2014).
Eldaief, M. C., Halko, M. A., Buckner, R. L. & Pascual-Leone, A. Transcranial magnetic stimulation modulates the brain’s intrinsic activity in a frequency-dependent manner. Proc. Natl Acad. Sci. USA 108, 21229–21234 (2011).
Chen, A. C. et al. Causal interactions between fronto-parietal central executive and default-mode networks in humans. Proc. Natl Acad. Sci. USA 110, 19944–19949 (2013).
Ozdemir, R. A. et al. Individualized perturbation of the human connectome reveals reproducible biomarkers of network dynamics relevant to cognition. Proc. Natl Acad. Sci. USA 117, 8115–8125 (2020).
Gallen, C. L. & D’Esposito, M. Brain modularity: a biomarker of intervention-related plasticity. Trends Cogn. Sci. 23, 293–304 (2019).
Chiu, D. et al. Multifocal transcranial stimulation in chronic ischemic stroke: a phase 1/2a randomized trial. J. Stroke Cerebrovasc. Dis. 29, 104816 (2020).
Brys, M. et al. Multifocal repetitive TMS for motor and mood symptoms of Parkinson disease: a randomized trial. Neurology 87, 1907–1915 (2016).
Magsood, H., Syeda, F., Holloway, K., Carmona, I. C. & Hadimani, R. L. Safety study of combination treatment: deep brain stimulation and transcranial magnetic stimulation. Front. Hum. Neurosci. 14, 123 (2020).
Deng, Z., Lisanby, S. H. & Peterchev, A. V. in Annual Int. Conf. IEEE Engineering in Medicine and Biology 6821-6824 (IEEE, 2010).
Tetreault, A. M. et al. Network localization of clinical, cognitive, and neuropsychiatric symptoms in Alzheimer’s disease. Brain 143, 1249–1260 (2020).
Kapur, N. Paradoxical functional facilitation in brain-behaviour research. A critical review. Brain 119, 1775–1790 (1996).
Carrera, E. & Tononi, G. Diaschisis: past, present, future. Brain 137, 2408–2422 (2014).
Sperber, C. Rethinking causality and data complexity in brain lesion-behaviour inference and its implications for lesion-behaviour modelling. Cortex 126, 49–62 (2020).
Griffis, J. C., Metcalf, N. V., Corbetta, M. & Shulman, G. L. Structural disconnections explain brain network dysfunction after stroke. Cell Rep. 28, 2527–2540.e9 (2019).
Klomjai, W., Katz, R. & Lackmy-Vallee, A. Basic principles of transcranial magnetic stimulation (TMS) and repetitive TMS (rTMS). Ann. Phys. Rehabil. Med. 58, 208–213 (2015).
Su, D. et al. Frequency-dependent effects of subthalamic deep brain stimulation on motor symptoms in Parkinson’s disease: a meta-analysis of controlled trials. Sci. Rep. 8, 14456 (2018).
Radman, T., Ramos, R. L., Brumberg, J. C. & Bikson, M. Role of cortical cell type and morphology in subthreshold and suprathreshold uniform electric field stimulation in vitro. Brain Stimul. 2, 215–228 (2009).
Feredoes, E., Heinen, K., Weiskopf, N., Ruff, C. & Driver, J. Causal evidence for frontal involvement in memory target maintenance by posterior brain areas during distracter interference of visual working memory. Proc. Natl Acad. Sci. USA 108, 17510–17515 (2011).
Duffley, G., Anderson, D. N., Vorwerk, J., Dorval, A. D. & Butson, C. R. Evaluation of methodologies for computing the deep brain stimulation volume of tissue activated. J. Neural Eng. 16, 066024 (2019).
Thielscher, A., Antunes, A. & Saturnino, G. B. Field modeling for transcranial magnetic stimulation: a useful tool to understand the physiological effects of TMS? Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2015, 222–225 (2015).
Raij, T. et al. Prefrontal cortex stimulation enhances fear extinction memory in humans. Biol. Psychiatry 84, 129–137 (2018).
Curot, J. et al. Awake craniotomy and memory induction through electrical stimulation: why are Penfield’s findings not replicated in the modern era? Neurosurgery 87, E130–E137 (2020).
Cole, E. J. et al. Stanford accelerated intelligent neuromodulation therapy for treatment-resistant depression. Am. J. Psychiatry 177, 716–726 (2020).
Maeda, F., Keenan, J. P., Tormos, J. M., Topka, H. & Pascual-Leone, A. Interindividual variability of the modulatory effects of repetitive transcranial magnetic stimulation on cortical excitability. Exp. Brain Res. 133, 425–430 (2000).
Brown, J. C. et al. NMDA receptor partial agonist, d-cycloserine, enhances 10 Hz rTMS-induced motor plasticity, suggesting long-term potentiation (LTP) as underlying mechanism. Brain Stimul. 13, 530–532 (2020).
Minzenberg, M. J. & Leuchter, A. F. The effect of psychotropic drugs on cortical excitability and plasticity measured with transcranial magnetic stimulation: implications for psychiatric treatment. J. Affect. Disord. 253, 126–140 (2019).
Schaefer, A. et al. Local–global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).
Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2016).
Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53, 1–15 (2010).
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).
Laumann, T. O. et al. Functional system and areal organization of a highly sampled individual human brain. Neuron 87, 657–670 (2015).
Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807.e7 (2017).
Wang, D. et al. Parcellating cortical functional networks in individuals. Nat. Neurosci. 18, 1853–1860 (2015).
Hacker, C. D. et al. Resting state network estimation in individual subjects. Neuroimage 82, 616–633 (2013).
Cohen, A. L. & Fox, M. D. Reply: The influence of sample size and arbitrary statistical thresholds in lesion-network mapping. Brain 143, e41 (2020).
Botvinik-Nezer, R. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020).
Salvalaggio, A. et al. Reply: Lesion network mapping predicts post-stroke behavioural deficits and improves localization. Brain 144, e36 (2021).
Bergmann, T. O. et al. Concurrent TMS–fMRI for causal network perturbation and proof of target engagement. Neuroimage 237, 118093 (2021).
Golay, X., Hendrikse, J. & Lim, T. C. Perfusion imaging using arterial spin labeling. Top. Magn. Reson. Imaging 15, 10–27 (2004).
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).
MacInnes, J. J., Dickerson, K. C., Chen, N. K. & Adcock, R. A. Cognitive neurostimulation: learning to volitionally sustain ventral tegmental area activation. Neuron 89, 1331–1342 (2016).
Bauer, C. C. C. et al. Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: part 2: default mode network — preliminary evidence. Psychiatry Res. 284, 112770 (2020).
Tarakad, A. & Jankovic, J. Anosmia and ageusia in Parkinson’s disease. Int. Rev. Neurobiol. 133, 541–556 (2017).
Williams, Z. M., Bush, G., Rauch, S. L., Cosgrove, G. R. & Eskandar, E. N. Human anterior cingulate neurons and the integration of monetary reward with motor responses. Nat. Neurosci. 7, 1370–1375 (2004).
Sheth, S. A. et al. Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature 488, 218–221 (2012).
Shine, J. M. et al. Distinct patterns of temporal and directional connectivity among intrinsic networks in the human brain. J. Neurosci. 37, 9667–9674 (2017).
Rafiei, F., Safrin, M., Wokke, M. E., Lau, H. & Rahnev, D. Transcranial magnetic stimulation alters multivoxel patterns in the absence of overall activity changes. Hum. Brain Mapp. 42, 3804–3820 (2021).
Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420 (2012).
Granger, C. W. J. & Newbold, P. Forecasting Economic Time Series (Academic, 1977).
Friston, K. J., Harrison, L. & Penny, W. Dynamic causal modelling. Neuroimage 19, 1273–1302 (2003).
Barnett, L., Barrett, A. B. & Seth, A. K. Misunderstandings regarding the application of Granger causality in neuroscience. Proc. Natl Acad. Sci. USA 115, E6676–E6677 (2018).
Friston, K. Causal modelling and brain connectivity in functional magnetic resonance imaging. PLoS Biol. 7, e33 (2009).
Webb, J. T., Ferguson, M. A., Nielsen, J. A. & Anderson, J. S. BOLD granger causality reflects vascular anatomy. PLoS ONE 8, e84279 (2013).
Granger, C. W. Time series analysis, cointegration, and applications. Am. Econ. Rev. 94, 421–425 (2004).
Mehler, D. & Kording, K. The lure of causal statements: rampant mis-inference of causality in estimated connectivity. Preprint at https://arxiv.org/abs/1812.03363 (2018).
The authors thank the National Institute of Mental Health, the Brain and Behaviour Research Foundation, Brigham and Women’s Hospital, and Stanford University BioX for funding the present work.
S.H.S. serves as a consultant for Kaizen Brain Center, Acacia Mental Health and Magnus Medical, and owns stock in Brainsway Inc. (publicly traded) and Magnus Medical (not publicly traded). S.H.S. and M.D.F. have jointly received investigator-initiated research support from Neuronetics Inc. None of these organizations were involved in the present work. S.H.S. and M.D.F. each own independent intellectual property on the use of brain network mapping to target neuromodulation. The present work did not utilize any of this intellectual property. The authors report no other conflicts of interest related to the present work.
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- Causality gap
The missing logical link between correlation and causation, particularly as it relates to identifying treatment targets.
- Deep brain stimulation
(DBS). An invasive technique for modulating brain activity using surgically implanted depth electrodes, commonly used as a treatment for Parkinson disease.
- Transcranial magnetic stimulation
(TMS). A non-invasive technique for modulating brain activity via magnetic pulses applied over the scalp, commonly used as a treatment for major depressive disorder.
- Functional neuroimaging
A non-invasive technique for measuring changes in brain activity, commonly based on changes in blood oxygenation or metabolic activity.
- Causal inference
The process of assessing whether one event directly contributed to the occurrence of another event.
The hypothetical outcome if a causal event had not occurred.
- Causal chain
A sequence of events leading to a particular outcome.
- Bradford Hill criteria
A widely used framework for objectively appraising the strength of causal inference based on observed data.
- Randomized controlled trials
Research studies that randomly allocate clinical participants to active or control interventions.
- Natural experiments
Observed situations in which an intervention was incidentally assigned in a near-random manner.
- Symptom localization
The process by which a neurological or psychiatric problem is linked to a specific brain region.
- Effective connectivity
The influence that one neural unit exerts over another.
- Probabilistic causality
An approach that assesses whether an event modifies the probability of an outcome, even if it is not necessary or sufficient to induce the outcome.
- Voxel lesion symptom mapping
(VLSM). A probabilistic technique that analyses the relationship between tissue damage and behaviour on a voxel-by-voxel basis.
The phenomenon by which damage to one brain region can cause a change in function of distant regions.
- Brain circuit
Any interconnected network of units, potentially including monosynaptic structural connections and/or polysynaptic functional connections.
- Lesion network mapping
(LNM). A technique that identifies shared connectivity patterns among lesions associated with a specific symptom or behaviour.
- Intracranial electrical stimulation
(iES). Invasive technique for modulating brain activity using depth electrodes or subdural electrodes, commonly used for mapping brain function prior to brain surgery.
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Siddiqi, S.H., Kording, K.P., Parvizi, J. et al. Causal mapping of human brain function. Nat Rev Neurosci 23, 361–375 (2022). https://doi.org/10.1038/s41583-022-00583-8