Personalizing TMS with qEEG guidance: Two Ways to Stop Guessing Where to Stimulate
Most TMS treatments still place the coil based on scalp landmarks — a pragmatic approach that has served the field well but leaves significant individual variability unaddressed. We now have better tools. Two data sources, in particular, can tell you precisely where to aim and what to do when you get there: current source density (CSD) from qEEG and functional connectivity maps. Here's how each works, how to combine them and where to find the precision tools needed for personalized TMS.
First: what TMS actually does
Every rTMS protocol affects the cortex under the coil. High-frequency stimulation (10 Hz, or its fast equivalent intermittent theta burst / iTBS) is excitatory — it drives long-term potentiation plasticity, making the target more active and responsive. Low-frequency stimulation (1 Hz, or continuous theta burst / cTBS) is inhibitory — it suppresses local activity through LTD mechanisms. Location and mode are two separate decisions, and both matter.
Approach 1: CSD from qEEG — targeting the source of the problem
CSD (current source density ) localization (swLORETA) takes your EEG signal and estimates where in the brain the current generators are — and at what frequency they are operating. Compared against a normative databases, this tells you which region is dysregulated and in which direction.
With that information , the decision for TMS is simple:
Excess slow activity (delta/theta) at a source → the region is hypoactive. Apply excitatory TMS (10 Hz or iTBS) there.
Alpha deficit or excess alpha → also hypoactive or over-inhibited. Apply excitatory TMS.
Excess high beta or gamma → the region is hyperactive. Apply inhibitory TMS (1 Hz or cTBS).
Bilateral asymmetry (e.g., left frontal hypoactive + right frontal hyperactive) → consider a sequential bilateral protocol: inhibitory to the overactive side, then excitatory to the underactive side.
What CSD gives you that standard blind targeting doesn't: an individual, three-dimensional location or ‘address’ for the actual pathological generator in the brain. That coordinate, fed into neuronavigation, becomes your coil position.
Approach 2: Functional connectivity — targeting the network
Some therapeutic targets are too deep to stimulate directly. The subgenual anterior cingulate cortex (sgACC), for example, is hyperactive in depression but inaccessible to TMS. The solution: stimulate a cortical region that is functionally connected to it in a way that achieves the desired downstream effect.
The key insight, established by Fox et al. (2012, Biological Psychiatry) across 98 subjects and since validated prospectively, is that TMS effects propagate along functional connections. A DLPFC site that is anticorrelated with the sgACC at rest, when driven by excitatory TMS, tends to suppress sgACC activity — exactly what is needed in depression. Sites that are positively correlated with sgACC push it in the wrong direction.
The workflow in this case becomes:
Find the cortical voxel with the appropriate connectivity relationship — anticorrelated for downstream suppression, positively correlated for downstream excitation.
Apply the needed TMS type to that cortical voxel. The network does the rest.
Individual differences in DLPFC connectivity are large — median distances between individuals' optimal targets exceed 20 mm — which is why group-average coordinates systematically miss the mark for many patients and why personalized approaches are increasingly applied.
Using both together
CSD and connectivity are not competing methods — they answer different questions. CSD tells you where the pathology is generating and which direction to push it. Connectivity tells you which cortical spot is best positioned to reach the therapeutic downstream target.
The integrated logic then becomes as follows: let CSD identify your candidate cortical target and assign excitatory vs. inhibitory; then use connectivity to confirm that target has the right network relationship with the deeper region you want to modulate. When both agree, confidence is high. When they conflict — the CSD source is positively correlated with the very structure you want to suppress. You could then search nearby for a voxel that satisfies both criteria.
Klooster et al. (2021) described this as a three-step multimodal framework: symptom-based indirect target → connectivity-derived cortical location → electric field modeling for coil placement. The EEG adds a fourth opportunity: when to fire, since recent work (He et al., 2024; Sajda, 2025) suggests that delivering pulses in synchrony with the patient's prefrontal alpha phase further improves target engagement.
The bottom line
The era of placing a TMS coil "approximately over the left DLPFC" is ending. Between qEEG-guided source localization, connectivity-based network targeting, electric field modeling, and closed-loop EEG timing, we now have enough information to treat the right place, with the right protocol, at the right moment \ for each individual patient. These tools are all now integrated in LucerumCarto, which is partnering with TMS experts and top TMS providers such as Magstim to bring this precision targeting workflow into clinical practice. What the field still needs are the large prospective trials to confirm that this precision translates into better outcomes, and those are underway.
REFERENCES
1. Sun W, Billot A, Du J, Wei X, Lemley RA, Daneshzand M, et al. Precision Network Modeling of Transcranial Magnetic Stimulation Across Individuals Suggests Therapeutic Targets and Potential for Improvement. Human Brain Mapping. 2025 Aug;46(11):e70266. doi:10.1002/hbm.70266
2. Parmigiani S, Cline CC, Sarkar M, Forman L, Truong J, Ross JM, et al. Real-time optimization to enhance noninvasive cortical excitability assessment in the human dorsolateral prefrontal cortex. Clinical Neurophysiology. 2025 Jun;174:225–34. doi:10.1016/j.clinph.2025.02.261
3. Lioumis P, Roine T, Granö I, Aydogan DB, Ukharova E, Souza VH, et al. Optimization of TMS target engagement: current state and future perspectives. Front Neurosci. 2025 Jan 29;19:1517228. doi:10.3389/fnins.2025.1517228
4. Sajda P. The importance of being in-sync: closed-loop EEG-rTMS for personalizing target engagement for treatment of MDD. Brain Stimulation. 2025 Jan;18(1):231. doi:10.1016/j.brs.2024.12.052
5. Grosshagauer S, Vasileiadi M, Woletz M, Schuler AL, Williams N, Tik M. The importance of timing in TMS target engagement: Insights from chronometric TMS-fMRI. Brain Stimulation. 2025 Jan;18(1):223. doi:10.1016/j.brs.2024.12.029
6. He H, Sun X, Doose J, Faller J, McIntosh JR, Saber GT, et al. TMS-induced modulation of brain networks and its associations to rTMS treatment for depression: a concurrent fMRI-EEG-TMS study [Internet]. Psychiatry and Clinical Psychology; 2024 [cited 2026 Jun 26]. Available from: http://medrxiv.org/lookup/doi/10.1101/2024.12.24.24319609 doi:10.1101/2024.12.24.24319609
7. Eldaief MC, McMains S, Izquierdo-Garcia D, Daneshzand M, Nummenmaa A, Braga RM. Network-specific metabolic and haemodynamic effects elicited by non-invasive brain stimulation. Nat Mental Health. 2023 May 1;1(5):346–60. doi:10.1038/s44220-023-00046-8
8. Tik M, Woletz M, Schuler AL, Vasileiadi M, Cash RFH, Zalesky A, et al. Acute TMS/fMRI response explains offline TMS network effects – An interleaved TMS-fMRI study. NeuroImage. 2023 Feb;267:119833. doi:10.1016/j.neuroimage.2022.119833
9. Cao Z, Xiao X, Zhao Y, Jiang Y, Xie C, Paillère-Martinot ML, et al. Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders. Front Neurosci. 2023 Jan 4;16:1079078. doi:10.3389/fnins.2022.1079078
10. Klooster DCW, Ferguson MA, Boon PAJM, Baeken C. Personalizing Repetitive Transcranial Magnetic Stimulation Parameters for Depression Treatment Using Multimodal Neuroimaging. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2022 Jun;7(6):536–45. doi:10.1016/j.bpsc.2021.11.004
11. Dannhauer M, Huang Z, Beynel L, Wood E, Bukhari-Parlakturk N, Peterchev AV. TAP: targeting and analysis pipeline for optimization and verification of coil placement in transcranial magnetic stimulation. J Neural Eng. 2022 Apr 1;19(2):026050. doi:10.1088/1741-2552/ac63a4
12. Cash RFH, Weigand A, Zalesky A, Siddiqi SH, Downar J, Fitzgerald PB, et al. Using Brain Imaging to Improve Spatial Targeting of Transcranial Magnetic Stimulation for Depression. Biological Psychiatry. 2021 Nov;90(10):689–700. doi:10.1016/j.biopsych.2020.05.033
13. Fitzgerald PB. Targeting repetitive transcranial magnetic stimulation in depression: do we really know what we are stimulating and how best to do it? Brain Stimulation. 2021 May;14(3):730–6. doi:10.1016/j.brs.2021.04.018
14. Hawco C, Voineskos AN, Steeves JKE, Dickie EW, Viviano JD, Downar J, et al. Spread of activity following TMS is related to intrinsic resting connectivity to the salience network: A concurrent TMS-fMRI study. Cortex. 2018 Nov;108:160–72. doi:10.1016/j.cortex.2018.07.010
15. Fox MD, Liu H, Pascual-Leone A. Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity. NeuroImage. 2013 Feb;66:151–60. doi:10.1016/j.neuroimage.2012.10.082
16. González-Olvera JJ, Ricardo-Garcell J, García-Anaya MDL, Miranda-Terrés E, Reyes-Zamorano E, Armas-Castañeda G. Análisis de fuentes del EEG en pacientes tratados con estimulación magnética transcraneal a 5 Hz como tratamiento antidepresivo. Salud Ment. 2013 Jan 1;36(3):235. doi:10.17711/SM.0185-3325.2013.030
17. Fox MD, Halko MA, Eldaief MC, Pascual-Leone A. Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS). NeuroImage. 2012 Oct;62(4):2232–43. doi:10.1016/j.neuroimage.2012.03.035
18. Fox MD, Buckner RL, White MP, Greicius MD, Pascual-Leone A. Efficacy of Transcranial Magnetic Stimulation Targets for Depression Is Related to Intrinsic Functional Connectivity with the Subgenual Cingulate. Biological Psychiatry. 2012 Oct;72(7):595–603. doi:10.1016/j.biopsych.2012.04.028
19. Price GW, Lee JWY, Garvey CAL, Gibson N. The use of background EEG activity to determine stimulus timing as a means of improving rTMS efficacy in the treatment of depression: A controlled comparison with standard techniques. Brain Stimulation. 2010 Jul;3(3):140–52. doi:10.1016/j.brs.2009.08.004
20. Sack AT, Cohen Kadosh R, Schuhmann T, Moerel M, Walsh V, Goebel R. Optimizing Functional Accuracy of TMS in Cognitive Studies: A Comparison of Methods. Journal of Cognitive Neuroscience. 2009 Feb 1;21(2):207–21. doi:10.1162/jocn.2009.21126