Personalizing rTMS parameters for depression treatment using multimodal neuroimaging

  • D.C.W. Klooster
    Correspondence
    Corresponding author: D.C.W. Klooster Ghent University Hospital Ghent Experimental Psychiatry Laboratory Corneel Heymanslaan 10 9000 Ghent Belgium
    Affiliations
    Eindhoven University of Technology, Department of Electrical Engineering, Flux Building, De Groene Loper 19, 5612 AP, Eindhoven, The Netherlands

    4Brain, Department of Head and Skin, Ghent University, Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium

    Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Ghent University, Corneel Heymanslaan 10, 9000, Ghent, Belgium
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  • M.A. Ferguson
    Affiliations
    Center for Brain Circuit Therapeutics, Brigham & Women’s Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, United States
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  • P.A.J.M. Boon
    Affiliations
    Eindhoven University of Technology, Department of Electrical Engineering, Flux Building, De Groene Loper 19, 5612 AP, Eindhoven, The Netherlands

    4Brain, Department of Head and Skin, Ghent University, Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
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  • C. Baeken
    Affiliations
    Eindhoven University of Technology, Department of Electrical Engineering, Flux Building, De Groene Loper 19, 5612 AP, Eindhoven, The Netherlands

    Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Ghent University, Corneel Heymanslaan 10, 9000, Ghent, Belgium

    University Hospital Brussels, Department of Psychiatry, Laarbeeklaan 101, 1090 Jette, Belgium
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Open AccessPublished:November 17, 2021DOI:https://doi.org/10.1016/j.bpsc.2021.11.004

      Abstract

      Repetitive transcranial magnetic stimulation (rTMS) is a tool that can be used to administer treatment to neuropsychiatric disorders such as major depressive disorder (MDD). Though, the clinical efficacy is still rather modest. Overly general stimulation protocols that neither consider patient-specific depression symptomology nor individualized brain characteristics, such as anatomy, or structural and functional connections, may be the cause of the high inter- and intra-individual variability in rTMS clinical responses.
      Multimodal neuroimaging can provide the necessary insights into individual brain characteristics and can therefore be used to personalize rTMS parameters. Optimal coil positioning should include a three-step process: 1) identify the optimal (indirect) target area based on the exact symptom pattern of the patient; 2) derive the cortical (direct) target location based on functional and/or structural connectomes derived from functional and diffusion MRI data; 3) determine the ideal coil position by computational modeling, such that the electric field distribution overlaps with the cortical target. These TMS-induced electric field simulations, derived from anatomical and diffusion MRI data, can be further applied to compute optimal stimulation intensities. Besides MRI, EEG can provide complementary information regarding the ongoing brain oscillations. This information can be used to determine the optimal timing and frequency of the stimuli.
      The heightened benefits of these personalized stimulation approaches are logically reasoned, but speculative. Randomized clinical trials will be required to compare clinical responses from standard rTMS protocols to personalized protocols. Ultimately, an optimized clinical response may result from precision protocols derived from combinations of personalized stimulation parameters.

      Keywords

      Introduction

      Major depressive disorder (MDD) is one of the earliest, most well-recognized mental disorders, and is a major contributor to the overall global disease burden (
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      Major depressive disorder.
      ). Symptoms include mood disturbances, anhedonia, weight changes, abnormal sleep patterns, psychomotor alterations, tiredness, persistent feelings of worthlessness, loss of focus, and potential suicidal thoughts (
      • Fava M.
      • Kendler K.S.
      Major Depressive Disorder Review.
      ). Since diagnosis entails patient reports of at least five out of nine symptoms, there are several hundred unique combinations of clinical symptoms. This makes depression a highly heterogeneous disorder (
      • Fava M.
      • Kendler K.S.
      Major Depressive Disorder Review.
      ). Current available treatment options, such as psycho- and pharmacotherapy, leave approximately one out of three patients unresponsive (
      • Fava M.
      • Kendler K.S.
      Major Depressive Disorder Review.
      ). Alternative treatment options are needed to improve the response and remission rates.
      Transcranial magnetic stimulation (TMS) is a biomechanical tool that can be used to non-invasively interfere with ongoing neuronal activity in the brain (
      • Wagner T.
      • Valero-Cabre A.
      • Pascual-Leone A.
      Noninvasive Human Brain Stimulation.
      ). The duration of the effects of repetitive application of stimuli outlasts the actual period of stimulation, making repetitive (r)TMS a potential treatment option for a broad variety of neurological and psychiatric disorders (
      • Klooster D.C.W.
      • de Louw A.J.A.
      • Aldenkamp A.P.
      • Besseling R.M.H.
      • Mestrom R.M.C.
      • Carrette S.
      • et al.
      Technical aspects of neurostimulation: Focus on equipment, electric field modeling, and stimulation protocols.
      ). High frequency (HF) rTMS applied to the left dorsolateral prefrontal cortex (DLPFC) using a figure-of-eight or an H1-coil has level A (i.e., definite efficacy) for the treatment of depression (
      • Lefaucheur J.P.
      • Aleman A.
      • Baeken C.
      • Benninger D.H.
      • Brunelin J.
      • di Lazzaro V.
      • et al.
      Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014–2018).
      ). Moreover, level B evidence (probable efficacy) was obtained for low-frequency (LF) rTMS of the right DLPFC and bihemispheric stimulation; combining right sided LF- rTMS (or continuous theta burst stimulation, cTBS) and left sided HF-rTMS (or intermittent theta burst stimulation, iTBS) (
      • Lefaucheur J.P.
      • Aleman A.
      • Baeken C.
      • Benninger D.H.
      • Brunelin J.
      • di Lazzaro V.
      • et al.
      Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014–2018).
      ). The overall clinical response rates remain rather modest, irrespective to the chosen rTMS protocol. According to a meta-analysis by Berlim et al., 29·3% and 18·6% of depression subjects who received rTMS responded (i.e., ≥50% reduction in Hamilton Depression Rating Scale (HDRS)) or remitted (i.e.,HDRS ≤8) respectively (
      • Berlim M.T.
      • van den Eynde F.
      • Tovar-Perdomo S.
      • Daskalakis Z.J.
      Response, remission and drop-out rates following high-frequency repetitive transcranial magnetic stimulation (rTMS) for treating major depression: A systematic review and meta-analysis of randomized, double-blind and sham-controlled trials.
      ). Heterogeneous effects across individuals are very common. This heterogeneity could potentially be attributed to the application of too general 'one-fits-all' rTMS protocols for a broad patient population.
      Due to the large parameter space - including coil position and orientation, stimulation intensity, stimulation frequency, pulse frequency, number of pulses, number of sessions, and intersession intervals - there are innumerable ways in which rTMS can be administered. Presently, stimulation protocols are derived from knowledge about the neuropathology that needs to be treated. These ‘one-fits-all’ stimulation protocols do not take interindividual variations in brain characteristics into account that can be extracted from multimodal neuroimaging. For example, anatomical magnetic resonance imaging (MRI) can provide detailed information about individual anatomy. Furthermore, advanced MRI data can be used to derive connectivity maps between brain regions on the individual level, further referred to as individualized connectomes (
      • Cash R.F.H.
      • Weigand A.
      • Zalesky A.
      • Siddiqi S.H.
      • Downar J.
      • Fitzgerald P.B.
      • Fox M.D.
      Using brain imaging to improve spatial targeting of TMS for depression.
      ,

      Horn A, Fox MD (2020): Opportunities of Connectomic Neuromodulation. NeuroImage 117180.

      ). For example, resting-state functional MRI (rs-fMRI) can reveal information about brain function and functional networks, and diffusion weighted MRI (DW-MRI) can provide information about the white matter networks with high spatial resolution. Since psychiatric disorders such as MDD are being newly conceptualized as disorders of brain networks instead of single brain regions, network imaging is important for the understanding of both pathology and treatment response (
      • 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.
      ,
      • Fornito A.
      • Bullmore E.T.
      • Zalesky A.
      Opportunities and Challenges for Psychiatry in the Connectomic Era.
      ). Besides MRI, electro-encephalography (EEG) provides complementary information by recording electrical activity within the brain with a high temporal resolution. Previous work shows that the brain’s excitability state, derived from EEG data, can be used to administer stimulation at the optimal timing (
      • Zrenner C.
      • Desideri D.
      • Belardinelli P.
      • Ziemann U.
      Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex.
      ). Furthermore, stimulation frequencies can be adapted to individual firing frequencies (
      • Leuchter A.F.
      • Wilson A.C.
      • Vince-Cruz N.
      • Corlier J.
      Novel method for identification of individualized resonant frequencies for treatment of Major Depressive Disorder (MDD) using repetitive Transcranial Magnetic Stimulation (rTMS): A proof-of-concept study.
      ,
      • Corlier J.
      • Carpenter L.L.
      • Wilson A.C.
      • Tirrell E.
      • Gobin A.P.
      • Kavanaugh B.
      • Leuchter A.F.
      The relationship between individual alpha peak frequency and clinical outcome with repetitive Transcranial Magnetic Stimulation (rTMS) treatment of Major Depressive Disorder (MDD).
      ).
      Here, we describe ways how multimodal imaging may be used to determine patient-specific stimulation parameters. These personalized rTMS protocols could reduce variability in clinical outcomes and thereby potentially increase the overall clinical efficacy (

      Fitzgerald PB (2021, May 1): Targeting repetitive transcranial magnetic stimulation in depression: do we really know what we are stimulating and how best to do it? Brain Stimulation, vol. 14. Elsevier Inc., pp 730–736.

      ,

      Vila-Rodriguez F, Frangou S (2021): Individualized functional targeting for rTMS: A powerful idea whose time has come? Human Brain Mapping. John Wiley and Sons Inc. https://doi.org/10.1002/hbm.25543

      ,

      Cash RFH, Cocchi L, Lv J, Fitzgerald PB, Zalesky A (2021, March 1): Functional Magnetic Resonance Imaging-Guided Personalization of Transcranial Magnetic Stimulation Treatment for Depression. JAMA Psychiatry, vol. 78. American Medical Association, pp 337–339.

      ). Information on the search criteria can be found in the supplementary material. Recently, Modak and Fitzgerald published a systematic review on the added value of personalizing TMS protocols for depression treatment (

      Modak A, Fitzgerald PB (2021): Personalising transcranial magnetic stimulation for depression using neuroimaging: A systematic review. World Journal of Biological Psychiatry. Taylor and Francis Ltd. https://doi.org/10.1080/15622975.2021.1907710

      ). Indeed, it was suggested that personalized protocols, derived from various neuroimaging techniques, tend to be more effective compared to standard TMS. The ‘Stanford Accelerated Intelligent Neuromodulation Therapy’ trial is a recent example of an evidence-based indication that personalized rTMS may play a role in enhanced treatment efficacy (
      • Cole E.
      • Gulser M.
      • Stimpson K.
      • Bentzley B.
      • Hawkins J.
      • Xiao X.
      • et al.
      Stanford accelerated intelligent neuromodulation therapy for treatment-resistant depression (SAINT-TRD).
      ). A remission rate of 86.4% was reported, which is significantly higher than earlier reported open-label remission rates.
      In the current paper, new developments are discussed and recommendations for future personalized rTMS protocols are offered. To do this, we focus on the stimulation parameters that can be personalized using multimodal imaging techniques; coil position, stimulation intensity, timing, and frequency.

       TMS targeting

      The brain area that is affected by TMS depends on the position of the TMS-coil on the scalp. Because of physical properties, the TMS induced electric field is strong enough in the superficial layers of the cortex to interfere with neuronal activity. Deeper brain structures can be reached, but because of the depth-focality trade-off it is impossible to directly target deep brain structures without stimulating more superficial structures that are closer to the stimulation coil (
      • Bergmann T.O.
      • Varatheeswaran R.
      • Hanlon C.A.
      • Madsen K.H.
      • Thielscher A.
      • Siebner H.R.
      Concurrent TMS-fMRI for causal network perturbation and proof of target engagement.
      ). However, even though stimulation is commonly administered to a single brain region using a figure-of-eight coil, the effects propagate via distributed functional and/or structural networks (

      Klooster DCW, Vos IN, Caeyenberghs K, Leemans A, David S, Besseling RMH, et al. (2020): Indirect frontocingulate structural connectivity predicts clinical response to accelerated rTMS in major depressive disorder. Journal of psychiatry & neuroscience : JPN 45. https://doi.org/10/1503/jpn.190088

      ,
      • Fox M.D.
      • Buckner R.L.
      • Liu H.
      • Mallar Chakravarty M.
      • Lozano A.M.
      • Pascual-Leone A.
      Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases.
      ,
      • Momi D.
      • Ozdemir R.A.
      • Tadayon E.
      • Boucher P.
      • Shafi M.M.
      • Pascual-Leone A.
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      Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation.
      ,
      • Momi D.
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      • Boucher P.
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      • Fasolo M.
      • et al.
      Perturbation of resting-state network nodes preferentially propagates to structurally rather than functionally connected regions.
      ,
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      • et al.
      Tracking Dynamic Interactions Between Structural and Functional Connectivity: A TMS/EEG-dMRI Study.
      ,
      • Tik M.
      • Hoffmann A.
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      • de Lara L.N.
      • et al.
      Towards understanding rTMS mechanism of action: Stimulation of the DLPFC causes network-specific increases in functional connectivity.
      ,

      Harita S, Momi D, Mazza F, Griffiths JD, Corresponding Author * = (n.d.): Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder. https://doi.org/10.1101/2021.07.15.452518

      ). As such, rTMS is conceptualized as a brain circuit therapy (

      Horn A, Fox MD (2020): Opportunities of Connectomic Neuromodulation. NeuroImage 117180.

      ).
      The left DLPFC is the most used stimulation target for antidepressant treatment, although other cortical targets have been suggested (
      • Cash R.F.H.
      • Weigand A.
      • Zalesky A.
      • Siddiqi S.H.
      • Downar J.
      • Fitzgerald P.B.
      • Fox M.D.
      Using brain imaging to improve spatial targeting of TMS for depression.
      ,
      • Downar J.
      • Daskalakis Z.J.
      New targets for rTMS in depression: A review of convergent evidence.
      ,
      • Fitzgerald P.B.
      • Laird A.R.
      • Maller J.
      • Daskalakis Z.J.
      A meta-analytic study of changes in brain activation in depression.
      ). The 5-cm rule – which requires identification of the motor cortex and moving the coil 5 cm in the rostral direction - is still often used to position the stimulation coil over the DLPFC. However, this 5-cm rule does not consider interindividual differences, such as head size and shape, making this method suboptimal (
      • Herbsman T.
      • Avery D.
      • Ramsey D.
      • Holtzheimer P.
      • Wadjik C.
      • Hardaway F.
      • et al.
      More Lateral and Anterior Prefrontal Coil Location Is Associated with Better Repetitive Transcranial Magnetic Stimulation Antidepressant Response.
      ,
      • Herwig U.
      • Padberg F.
      • Unger J.
      • Spitzer M.
      • Schönfeldt-Lecuona C.
      Transcranial magnetic stimulation in therapy studies: Examination of the reliability of “standard” coil positioning by neuronavigation.
      ). Together with the findings that more anterior stimulation sites led to a better clinical response, a 5.5 or even the 6-cm rule are nowadays used (
      • Johnson K.A.
      • Baig M.
      • Ramsey D.
      • Lisanby S.H.
      • Avery D.
      • McDonald W.M.
      • et al.
      Prefrontal rTMS for treating depression: Location and intensity results from the OPT-TMS multi-site clinical trial.
      ,
      • Weigand A.
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      • Caballero R.
      • Cooke D.
      • Stern A.P.
      • Taylor S.F.
      • et al.
      Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites.
      ). Alternative methods have been proposed to localize the DLPFC based on 10-20 EEG system (
      • Herwig U.
      • Satrapi P.
      • Schönfeldt-lecuona C.
      Using the International 10-20 EEG System for Positioning of Transcranial Magnetic Stimulation Using the International 10-20 EEG System for Positioning of Transcranial Magnetic Stimulation.
      ,
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      • Fried P.
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      • Lee K.
      • Giacobbe P.
      • et al.
      Concordance between BeamF3 and MRI-neuronavigated target sites for repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex.
      ,
      • Beam W.
      • Borckardt J.J.
      • Reeves S.T.
      • George M.S.
      An efficient and accurate new method for locating the F3 position for prefrontal TMS applications.
      ,

      Trapp NT, Bruss J, King Johnson M, Uitermarkt BD, Garrett L, Heinzerling A, et al. (2020, May 1): Reliability of targeting methods in TMS for depression: Beam F3 vs. 5.5 cm. Brain Stimulation, vol. 13. Elsevier Inc., pp 578–581.

      ) or anatomical MRI in combination with neuronavigation (
      • Peleman K.
      • van Schuerbeek P.
      • Luypaert R.
      • Stadnik T.
      • de Raedt R.
      • de Mey J.
      • et al.
      Using 3D-MRI to localize the dorsolateral prefrontal cortex in TMS research.
      ,
      • Hebel T.
      • Göllnitz A.
      • Schoisswohl S.
      • Weber F.C.
      • Abdelnaim M.
      • Wetter T.C.
      • et al.
      A direct comparison of neuronavigated and non-neuronavigated intermittent theta burst stimulation in the treatment of depression.
      ). None of these methods consider differences in individual brain connectivity.
      In this section, we propose a pipeline for optimal coil positioning at the scalp based on neuroimaging that involves three steps. A distinction is made between the coil position at the scalp, the direct targets (also referred to as cortical targets), and the indirect targets, i.e., deeper brain regions that are likely to contribute to clinical response.

       Optimal indirect targets for depression treatment

      The subgenual aspect of the anterior cingulate cortex (sgACC) has received much attention as an indirect target for rTMS treatment (Figure 1, a1) (
      • Fox M.D.
      • Buckner R.L.
      • Liu H.
      • Mallar Chakravarty M.
      • Lozano A.M.
      • Pascual-Leone A.
      Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases.
      ,
      • Weigand A.
      • Horn A.
      • Caballero R.
      • Cooke D.
      • Stern A.P.
      • Taylor S.F.
      • et al.
      Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites.
      ,
      • Fox M.D.
      • Buckner R.L.
      • White M.P.
      • Greicius M.D.
      • Pascual-Leone A.
      Efficacy of TMS targets for depression is related to intrinsic functional connectivity with the subgenual cingulate.
      ,
      • Oathes D.J.
      • Zimmerman J.
      • Duprat R.
      • Cavdaroglu S.
      • Scully M.
      • Rosenberg B.
      • et al.
      Individualized non-invasive brain stimulation engages the subgenual anterior cingulate and amydala.
      ). It has been linked to depression and clinical response across diverse antidepressant treatment modalities (
      • Hamani C.
      • Mayberg H.
      • Stone S.
      • Laxton A.
      • Haber S.
      • Lozano A.M.
      The subcallosal cingulate gyrus in the context of major depression.
      ). An important recent study showed further evidence that the sgACC is part of the general depression circuit (
      • Siddiqi S.H.
      • Schaper F.L.W.V.J.
      • Horn A.
      • Hsu J.
      • Padmanabhan J.L.
      • Brodtmann A.
      • et al.
      Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease.
      ). It was shown that the depression circuits, derived by correlating the depression scores with the functional connectivity maps from lesion data, and TMS- and DBS- stimulation sites, converge (
      • Siddiqi S.H.
      • Schaper F.L.W.V.J.
      • Horn A.
      • Hsu J.
      • Padmanabhan J.L.
      • Brodtmann A.
      • et al.
      Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease.
      ,
      • Fox M.D.
      Mapping Symptoms to Brain Networks with the Human Connectome.
      ). Up till now, it remains unclear whether the sgACC is the only indirect target for depression treatment or if it is even the most important indirect target.
      Figure thumbnail gr1
      Figure 1Contribution of various MR neuroimaging modalities to personalize rTMS parameters for depression treatment. Figure a shows how anatomical, functional, and diffusion MRI can be used to derive personalized coil positioning and orientation and intensity. The proposed three-step approach to determine the optimal coil position is shown in Figure A,C-D. A priori knowledge about the pathophysiology of MDD can be used, also including the subjects’ symptom profile, to determine the optimal indirect stimulation target. This can be a deep brain structure such as for example the sgACC (a). Subsequently, functional and structural connectome information, derived from rs-fMRI and DW-MRI (B and E respectively), can be used to define the cortical target. TMS-induced electric field simulation (F) can help to determine the coil position and orientation at the scalp such that there is maximal overlap between the TMS-induced electric field and the cortical target. Instead of simulating a TMS-induced electric field based on a coil position and a head model, an inverse method can be used to derive the coil position and orientation given a cortical target area. A comparable inverse method can provide information about the stimulation intensity if the preferred electric field strength in the cortex is set a priori. These individual computational models require anatomical MRI data for the generation of an individual head model, including segmentation (E). These models benefit from incorporation of DW-MRI (F) data since the direction-dependent conductivity values can be derived from fiber tracts. Anatomical data can furthermore be used to correct the stimulation intensity for the coil-cortex distance (C).

       Connectome based cortical target derivation

       Functional connectivity

      Using a priori knowledge regarding the role of the sgACC in the clinical effectiveness of rTMS studies, investigations have focused on the link between clinical response and sgACC connectivity with the direct cortical target in the left DLPFC. Rs-fMRI showed that the functional anticorrelation between DLPFC and sgACC is associated with clinical outcomes of rTMS in depressed patients, suggesting that the spot with the highest functional anticorrelation may be an optimal cortical stimulation target for MDD treatment (Figure 1a,b) (
      • Weigand A.
      • Horn A.
      • Caballero R.
      • Cooke D.
      • Stern A.P.
      • Taylor S.F.
      • et al.
      Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites.
      ,
      • Fox M.D.
      • Liu H.
      • Pascual-Leone A.
      Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity.
      ,
      • Cash R.F.H.
      • Zalesky A.
      • Thomson R.H.
      • Tian Y.
      • Cocchi L.
      • Fitzgerald P.B.
      Subgenual Functional Connectivity Predicts Antidepressant Treatment Response to Transcranial Magnetic Stimulation: Independent Validation and Evaluation of Personalization.
      ,
      • Baeken C.
      • Marinazzo D.
      • Wu G.-R.
      • van Schuerbeek P.
      • de Mey J.
      • Marchetti I.
      • et al.
      Accelerated HF-rTMS in treatment-resistant unipolar depression : insights from subgenual anterior cingulate functional connectivity.
      ). It should be noted that a recent study could not replicate these findings (
      • Hopman H.J.
      • Chan S.M.S.
      • Chu W.C.W.
      • Lu H.
      • Tse C.Y.
      • Chau S.W.H.
      • et al.
      Personalized prediction of transcranial magnetic stimulation clinical response in patients with treatment-refractory depression using neuroimaging biomarkers and machine learning.
      ). More recently, a relationship between the distance between the actual stimulation site and the individual’s optimal stimulation site, defined based on sgACC anti-correlations, was shown in two independent studies using individual rs-fMRI data (

      Cash RFH, Cocchi L, Lv J, Fitzgerald PB, Zalesky A (2021, March 1): Functional Magnetic Resonance Imaging-Guided Personalization of Transcranial Magnetic Stimulation Treatment for Depression. JAMA Psychiatry, vol. 78. American Medical Association, pp 337–339.

      ,

      Siddiqi SH, Weigand A, Pascual-Leone A, Fox MD (2021, November 15): Identification of Personalized Transcranial Magnetic Stimulation Targets Based on Subgenual Cingulate Connectivity: An Independent Replication. Biological Psychiatry, vol. 90. Elsevier Inc., pp e55–e56.

      ) (see Figure 2).
      Figure thumbnail gr2
      Figure 2Relationship between the proximity to personalized stimulation sites and clinical responses to rTMS for depression treatment was shown by two independent retrospective studies (Cash et al. (

      Cash RFH, Cocchi L, Lv J, Fitzgerald PB, Zalesky A (2021, March 1): Functional Magnetic Resonance Imaging-Guided Personalization of Transcranial Magnetic Stimulation Treatment for Depression. JAMA Psychiatry, vol. 78. American Medical Association, pp 337–339.

      ), Siddiqi et al. (

      Siddiqi SH, Weigand A, Pascual-Leone A, Fox MD (2021, November 15): Identification of Personalized Transcranial Magnetic Stimulation Targets Based on Subgenual Cingulate Connectivity: An Independent Replication. Biological Psychiatry, vol. 90. Elsevier Inc., pp e55–e56.

      )). Figure A. shows the distance between the actual stimulation position that was used for treatment (derived from beam F3 method in Cash et al. and 5.5 cm rule in Siddiqi et al.) and the presumed optimal stimulation target, defined as the mean of the cluster with the highest anti-correlation with the sgACC. Figure b. and c. show the relationship between these distances and clinical responses. In figure B., clinical improvement was defined as the change in Montgomery-Åsberg Depression Rating Scale (MADRS) measured after three weeks rTMS, with respect to baseline. For the derivation of the individualized optimal stimulation targets, 13 minutes (2 sessions of 6 min 40 seconds concatenated) rs-fMRI data were used. This study included 26 subjects. In figure C., the Beck Depression Inventory (BDI) was used to define clinical response after 6 weeks rTMS treatment with respect to baseline and 28-minutes rs-fMRI data were collected to define the individual’s optimal stimulation site. In this study, 25 depression patients were included. In both studies, significant (p<0.001 and p<0.005) and negative correlations (R=-0.6) were found, indicating that the clinical response increases when the stimulation was administered to a position closer to one’s individual optimal stimulation site. (Figure replicated from Cash et al. and Siddiqi et al. with permission from publishers).

       Normative versus individualized functional connectome data

      It is challenging to obtain robust individualized connectomes. Limbic regions are prone to low signal-to-noise ratios given their physical proximity to artifact-generating anatomy (e.g., sinus cavities). This is unfortunate for neuropsychiatric treatment planning since functional localization within the limbic network is crucial for indirect target determination and subsequent connectivity analyses. To compensate for the poor signal-to-noise ratio of individualized connectomes, relatively long scanning sessions are required that might be hard for patients to tolerate and logistically unfeasible (

      Gordon EM, Laumann TO, Adeyemo B, Petersen SE (2017): Individual Variability of the System-Level Organization of the Human Brain. Cerebral cortex (New York, NY : 1991) 27: 386–399.

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      • Poldrack R.A.
      • Laumann T.O.
      • Koyejo O.
      • Gregory B.
      • Hover A.
      • Chen M.Y.
      • et al.
      Long-term neural and physiological phenotyping of a single human.
      ).
      Given these limitations, average connectomes (also referred to as the normative connectome) were developed (
      • Yeo B.T.T.T.
      • Krienen F.M.
      • Sepulcre J.
      • Sabuncu M.R.
      • Lashkari D.
      • Hollinshead M.
      • et al.
      The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
      ). These publicly available normative connectomes are more robust and have also shown their potential value in a large range of clinical applications (
      • Weigand A.
      • Horn A.
      • Caballero R.
      • Cooke D.
      • Stern A.P.
      • Taylor S.F.
      • et al.
      Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites.
      ,
      • Fox M.D.
      • Liu H.
      • Pascual-Leone A.
      Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity.
      ,
      • Cash R.F.H.
      • Zalesky A.
      • Thomson R.H.
      • Tian Y.
      • Cocchi L.
      • Fitzgerald P.B.
      Subgenual Functional Connectivity Predicts Antidepressant Treatment Response to Transcranial Magnetic Stimulation: Independent Validation and Evaluation of Personalization.
      ,
      • Padmanabhan J.L.
      • Cooke D.
      • Joutsa J.
      • Siddiqi S.H.
      • Ferguson M.
      • Darby R.R.
      • et al.
      A Human Depression Circuit Derived From Focal Brain Lesions.
      ). However, these normative connectomes are - by definition - unable to represent individual differences in connectivity.
      A direct comparison between the use of individual MRI data and normative connectome data was performed by Cash et al. (
      • Cash R.F.H.
      • Zalesky A.
      • Thomson R.H.
      • Tian Y.
      • Cocchi L.
      • Fitzgerald P.B.
      Subgenual Functional Connectivity Predicts Antidepressant Treatment Response to Transcranial Magnetic Stimulation: Independent Validation and Evaluation of Personalization.
      ) and Siddiqi et al. (

      Siddiqi SH, Taylor SF, Cooke D, Pascual-Leone A, George MS, Fox MD (2020): Distinct Symptom-Specific Treatment Targets for Circuit-Based Neuromodulation. American Journal of Psychiatry appi.ajp.2019.1.

      ). Both studies found similar results using these types of connectomes in predicting clinical responses based on the connectivity between the stimulation site and the sgACC on the group level. However, a slight trend towards better prediction using the individual data was reported (
      • Cash R.F.H.
      • Zalesky A.
      • Thomson R.H.
      • Tian Y.
      • Cocchi L.
      • Fitzgerald P.B.
      Subgenual Functional Connectivity Predicts Antidepressant Treatment Response to Transcranial Magnetic Stimulation: Independent Validation and Evaluation of Personalization.
      ).
      Recently, the importance of using individual data to derive TMS targets was emphasized in a follow-up study by Cash et al. (

      Cash RFH, Cocchi L, Lv J, Wu Y, Fitzgerald PB, Zalesky A (2021): Personalized connectivity-guided DLPFC-TMS for depression: Advancing computational feasibility, precision and reproducibility. Human Brain Mapping. https://doi.org/10.1002/hbm.25330

      ). Their observations showed that the interindividual variability of TMS target sites was a factor up to 6.85 times bigger than the intraindividual variability using individual MRI data. This result confirms that individual targets do not converge to group-average positions, arguing in favor of using individual rs-fMRI data. Additionally, this study showed high test-retest reliability of TMS targets in contrast to previous work by Ning et al. (
      • Ning L.
      • Makris N.
      • Camprodon J.A.
      • Rathi Y.
      Limits and reproducibility of resting-state functional MRI definition of DLPFC targets for neuromodulation.
      ).
      A few clinical trials prospectively implemented the use of individualized connectomes to determine the cortical target based on the functional connectivity with the sgACC (
      • Cole E.
      • Gulser M.
      • Stimpson K.
      • Bentzley B.
      • Hawkins J.
      • Xiao X.
      • et al.
      Stanford accelerated intelligent neuromodulation therapy for treatment-resistant depression (SAINT-TRD).
      ,
      • Siddiqi S.H.
      • Trapp N.T.
      • Hacker C.D.
      • Laumann T.O.
      • Kandala S.
      • Hong X.
      • 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.
      ,
      • Barbour T.
      • Lee E.
      • Ellard K.
      • Camprodon J.
      Individualized TMS target selection for MDD: clinical outcomes, mechanisms of action and predictors of response.
      ,
      • Williams N.R.
      • Sudheimer K.D.
      • Bentzley B.S.
      • Pannu J.
      • Stimpson K.H.
      • Duvio D.
      • et al.
      High-dose spaced theta-burst TMS as a rapid-acting antidepressant in highly refractory depression.
      ). These studies showed promising clinical efficacy, emphasizing the need for future larger trials to investigate the effect of personalized coil positioning in more detail.

       Structural connectivity

      The effects of TMS also propagate throughout the brain via structural connections (
      • Momi D.
      • Ozdemir R.A.
      • Tadayon E.
      • Boucher P.
      • Shafi M.M.
      • Pascual-Leone A.
      • Santarnecchi E.
      Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation.
      ,
      • Amico E.
      • Bodart O.
      • Rosanova M.
      • Gosseries O.
      • Heine L.
      • van Mierlo P.
      • et al.
      Tracking Dynamic Interactions Between Structural and Functional Connectivity: A TMS/EEG-dMRI Study.
      ,
      • de Geeter N.
      • Crevecoeur G.
      • Leemans A.
      • Dupré L.
      Effective electric fields along realistic DTI-based neural trajectories for modelling the stimulation mechanisms of TMS.
      ,
      • Nummenmaa A.
      • NcNab J.A.
      • Savadjiev P.
      • Okada Y.
      • Hamalainen M.S.
      • Wang R.
      • et al.
      Targeting of White Matter Tracts With Transcranial Magnetic Stimulation.
      ). A recent TMS-EEG study combined with resting-state functional and diffusion weighted MRI showed that the propagation of the TMS effects prefer to follow structural rather than functional pathways (
      • Momi D.
      • Ozdemir R.A.
      • Tadayon E.
      • Boucher P.
      • di Domenico A.
      • Fasolo M.
      • et al.
      Perturbation of resting-state network nodes preferentially propagates to structurally rather than functionally connected regions.
      ). TMS coil positioning based on structural connections has not yet been extensively studied (Figure 1d). It is not straightforward to reproduce findings from rs-fMRI with DW-MRI data since there might be no direct structural connections with the sgACC, as earlier shown in tracer studies in primates (
      • Pandya D.
      • van Hoesen G.
      • Mesulam M.
      • Nourse Rogers Memorial E.
      • Rogers Memorial E.
      Experimental Brain Research Efferent Connections of the Cingulate Gyrus in the Rhesus Monkey.
      ). A first attempt indicated that indirect connections between the stimulation site in the left DLPFC and the caudal and posterior parts of the cingulate cortex were correlated to the clinical response to an accelerated rTMS protocol (

      Klooster DCW, Vos IN, Caeyenberghs K, Leemans A, David S, Besseling RMH, et al. (2020): Indirect frontocingulate structural connectivity predicts clinical response to accelerated rTMS in major depressive disorder. Journal of psychiatry & neuroscience : JPN 45. https://doi.org/10/1503/jpn.190088

      ).

       Symptom specificity

      Defining optimal indirect, and related direct, stimulation targets is further complicated by the possibility that these might differ between subjects with the same pathology. Weigand et al. investigated the potential of resting-state functional connectivity between the DLPFC and the sgACC to predict the clinical response to rTMS in subgroups of patients with cognitive, affective, and somatic symptoms (
      • Weigand A.
      • Horn A.
      • Caballero R.
      • Cooke D.
      • Stern A.P.
      • Taylor S.F.
      • et al.
      Prospective validation that subgenual connectivity predicts antidepressant efficacy of transcranial magnetic stimulation sites.
      ). sgACC connectivity was a significant predictor of improvement in subjects with cognitive and affective symptoms. Siddiqi et al. employed the lesion network mapping technique to show that optimal cortical stimulation targets may be symptom specific (

      Siddiqi SH, Taylor SF, Cooke D, Pascual-Leone A, George MS, Fox MD (2020): Distinct Symptom-Specific Treatment Targets for Circuit-Based Neuromodulation. American Journal of Psychiatry appi.ajp.2019.1.

      ). Here, brain networks related to specific depression symptoms were determined by clustering symptom-response maps. The proposed optimal target for dysphoric symptoms was close to the left DLPFC location with the highest anticorrelation with the subgenual cingulate cortex. Whereas the target for anxiosomatic symptoms was located more medial and posterior in Brodmann area 8. Earlier, also Drysdale et al. showed a relation between symptom profiles, resting-state connectivity, and clinical responses to rTMS (
      • Drysdale A.T.
      • Grosenick L.
      • Downar J.
      • Dunlop K.
      • Mansouri F.
      • Meng Y.
      • et al.
      Resting-state connectivity biomarkers define neurophysiological subtypes of depression.
      ). Four depression subtypes were defined and particularly the subtype mostly related to anhedonia showed the most clinical response to rTMS applied to the dorsomedial prefrontal cortex. However, this depression subtyping could not be replicated by Dinga et al. (

      Dinga R, Schmaal L, Penninx BWJH, van Tol MJ, Veltman DJ, van Velzen L, et al. (2019): Evaluating the evidence for biotypes of depression: Methodological replication and extension of Drysdale et al. (2017). NeuroImage: Clinical 22: 101796.

      ).

       Value of computational models for coil positioning

      Most often, simple projections are used between the coil position at the scalp and the assumed direct cortical stimulation region. However, subject-specific brain geometries uniquely shape the electric fields induced by TMS (
      • Opitz A.
      • Legon W.
      • Rowlands A.
      • Bickel W.K.
      • Paulus W.
      • Tyler W.J.
      Physiological observations validate finite element models for estimating subject-specific electric field distributions induced by transcranial magnetic stimulation of the human motor cortex.
      ,
      • Cocchi L.
      • Zalesky A.
      Personalized Transcranial Magnetic Stimulation in Psychiatry.
      ,
      • Weise K.
      • Numssen O.
      • Thielscher A.
      • Hartwigsen G.
      • Knösche T.R.
      A novel approach to localize cortical TMS effects.
      ). Therefore, the peak of the TMS-induced electric field is not always located directly underneath the stimulation coil and hence the projection method is not optimal to convert between the coil position and the cortical target.
      Computational modeling (Figure 1e,f) of the TMS-induced electric fields can provide insights into the brain areas that are affected by the stimulation. Individual head models for such computational models can be derived from anatomical MRI data (Figure 1, a5). These head models are segmented in different tissue types with specific conductivity values. Additionally, DW-MRI data (Figure 1d) can be used for more accurate orientation-specific conductivity mapping (
      • Opitz A.
      • Windhoff M.
      • Heidemann R.M.
      • Turner R.
      • Thielscher A.
      How the brain tissue shapes the electric field induced by transcranial magnetic stimulation.
      ,
      • Thielscher A.
      • Opitz A.
      • Windhoff M.
      Impact of the gyral geometry on the electric field induced by transcranial magnetic stimulation.
      ).
      Pipelines that are based on a predetermined cortical target and subsequently derive the optimal coil position and orientation by maximizing the magnitude or a directional component of the TMS-induced electric field in this predefined target region were recently proposed (

      Dannhauer M, Huang Z, Beynel L, Wood E, Bukhari-Parlakturk N, Peterchev A v (2021): Title: “TAP: Targeting and analysis pipeline for optimization and verification of TMS coil placement” Article type: Techniques and Methods. https://doi.org/10.1101/2021.05.09.443339

      ,
      • Balderston N.L.
      • Roberts C.
      • Beydler E.M.
      • Deng Z de
      • Radman T.
      • Luber B.
      • et al.
      A generalized workflow for conducting electric field–optimized, fMRI-guided, transcranial magnetic stimulation.
      ,

      Balderston N, Balderston NL, Beer JC, Seok D, Makhoul W, Deng Z-D, et al. (n.d.): Title: Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment Running title: Novel modelling approach for individualized TMS targeting. https://doi.org/10.1101/2020.12.06.408856

      ) . Whereas Balderston et al. (
      • Balderston N.L.
      • Roberts C.
      • Beydler E.M.
      • Deng Z de
      • Radman T.
      • Luber B.
      • et al.
      A generalized workflow for conducting electric field–optimized, fMRI-guided, transcranial magnetic stimulation.
      ) defines the coil position based on the projection from the cortical target to the scalp and iterates the coil orientation at that position, Dannhauer et al. (

      Dannhauer M, Huang Z, Beynel L, Wood E, Bukhari-Parlakturk N, Peterchev A v (2021): Title: “TAP: Targeting and analysis pipeline for optimization and verification of TMS coil placement” Article type: Techniques and Methods. https://doi.org/10.1101/2021.05.09.443339

      ) iterates over both coil location and orientation, using an auxiliary dipole method that allows for fast computation of TMS-induced electric fields (

      Gomez LJ, Dannhauer M, Peterchev A v. (2021): Fast computational optimization of TMS coil placement for individualized electric field targeting. NeuroImage 228. https://doi.org/10.1016/j.neuroimage.2020.117696

      ). Both pipelines define the optimal coil orientation as the one that induces the maximum electric field strength in the predefined target. Additionally, the pipeline by Dannhauer et al. (

      Dannhauer M, Huang Z, Beynel L, Wood E, Bukhari-Parlakturk N, Peterchev A v (2021): Title: “TAP: Targeting and analysis pipeline for optimization and verification of TMS coil placement” Article type: Techniques and Methods. https://doi.org/10.1101/2021.05.09.443339

      ) has the option to maximize a directional component of the electric fields.

       Coil positioning based on concurrent TMS-fMRI

      The use of concurrent TMS-fMRI techniques could also aid in determining the coil position. The causal effects of TMS in the modulation of brain networks can provide a direct proof of target engagement, i.e., activation of the indirect target (
      • Bergmann T.O.
      • Varatheeswaran R.
      • Hanlon C.A.
      • Madsen K.H.
      • Thielscher A.
      • Siebner H.R.
      Concurrent TMS-fMRI for causal network perturbation and proof of target engagement.
      ,

      Oathes DJ, Balderston NL, Kording KP, DeLuisi JA, Perez GM, Medaglia JD, et al. (2021, July 1): Combining transcranial magnetic stimulation with functional magnetic resonance imaging for probing and modulating neural circuits relevant to affective disorders. Wiley Interdisciplinary Reviews: Cognitive Science, vol. 12. John Wiley and Sons Inc. https://doi.org/10.1002/wcs.1553

      ). Vink et al. (
      • Vink J.J.T.
      • Mandija S.
      • Petrov P.I.
      • van den Berg C.A.T.
      • Sommer I.E.C.
      • Neggers S.F.W.
      A novel concurrent TMS-fMRI method to reveal propagation patterns of prefrontal magnetic brain stimulation.
      ) showed activation in the sgACC in four out of nine healthy subjects who received single TMS pulses to the DLPFC, determined by the 5-cm rule, while fMRI scanning. Recently, Oathes et al. (
      • Oathes D.J.
      • Zimmerman J.P.
      • Duprat R.
      • Japp S.S.
      • Scully M.
      • Rosenberg B.M.
      • et al.
      Resting fMRI-guided TMS results in subcortical and brain network modulation indexed by interleaved TMS/fMRI.
      ) used resting-state guided TMS-fMRI to proof downstream target engagement of the sgACC. Note that the use of concurrent TMS-fMRI techniques limits the optimal coil positioning pipeline to a two-step process.

       Stimulation intensity

      To date, the stimulation intensity is the only stimulation parameter that is derived from subject-specific characteristics. The stimulation intensity is most often expressed as a percentage of one's resting motor threshold (rMT), defined as the minimal stimulation intensity that induces a reliable motor evoked potential (MEP) of minimal amplitude in the targeted muscle (
      • Rossini P.M.M.
      • Burke D.
      • Chen R.
      • Cohen L.G.G.
      • Daskalakis Z.
      • di Iorio R.
      • et al.
      Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: Basic principles and procedures for routine clinical and research application. An updated report from an I.F.C.N. Committee.
      ). According to this gold standard, individual adjustment for stimulation intensity is purely based on the electrical responsivity of the primary motor cortex. Previous work showed variation in response to TMS in the prefrontal and motor cortices [80].
      Variation between coil-cortex distance in the motor cortex and other stimulation regions could cause a deviation in effective stimulation intensity. Stokes et al. proposed a method to derive a 'corrected' rMT for distinct stimulation sites based on differences in the coil-cortex distance (
      • Stokes M.G.
      • Barker A.T.
      • Dervinis M.
      • Verbruggen F.
      • Maizey L.
      • Adams R.C.
      • Chambers C.D.
      Biophysical determinants of transcranial magnetic stimulation: Effects of excitability and depth of targeted area.
      ). These distances can be extracted from anatomical MRI data (Figure 1, a3). However, adjusting the stimulation intensity to account for individual coil-to-cortex distance did not enhance the efficacy rates in a previous trial by Blumberger et al.(
      • Blumberger D.M.
      • Maller J.J.
      • Thomson L.
      • Mulsant B.H.
      • Rajji T.K.
      • Maher M.
      • et al.
      Unilateral and bilateral MRI-targeted repetitive transcranial magnetic stimulation for treatmentresistant depression: A randomized controlled study.
      ).
      To address this issue more accurately, simultaneous TMS-fMRI or TMS-EEG might help to further optimize subject-specific stimulation intensities and to validate the correction method proposed by Stokes et al.(
      • Stokes M.G.
      • Barker A.T.
      • Dervinis M.
      • Verbruggen F.
      • Maizey L.
      • Adams R.C.
      • Chambers C.D.
      Biophysical determinants of transcranial magnetic stimulation: Effects of excitability and depth of targeted area.
      ). Dose (i.e., intensity) – response relationships can be derived by systematically varying the stimulation intensity (
      • Bergmann T.O.
      • Varatheeswaran R.
      • Hanlon C.A.
      • Madsen K.H.
      • Thielscher A.
      • Siebner H.R.
      Concurrent TMS-fMRI for causal network perturbation and proof of target engagement.
      ). Responses can be quantified by means of blood-oxygen-level-dependent activity or TMS evoked potentials (TEPs).
      Furthermore, computational models may help to determine the optimal stimulation intensity. Caulfield et al. recently published A Personalized E-field X motor Threshold (APEX MT) for dosing based on electric field simulations (

      Caulfield KA, Li X, George MS (2021, July 1): Four electric field modeling methods of Dosing Prefrontal Transcranial Magnetic Stimulation (TMS): Introducing APEX MT dosimetry. Brain Stimulation, vol. 14. Elsevier Inc., pp 1032–1034.

      ). The proposed approach combines the ability of rMT to determine cortical electric field strengths (derived from electric field simulations at the motor cortex) necessary to induce neuronal activity with the knowledge that electric field strengths scale linearly with the stimulation intensity. Hence, the required intensity necessary to induce neuronal activation in the DLPFC can be derived by a linear scaling of the (random) intensity used to perform electric field simulation at the DLPFC by the ratio of the simulated and intended electric field strengths in the DLPFC cortex.

       Stimulation timing and frequency

      The effects of rTMS do not only differ between subjects but also within subjects, across and even within sessions (
      • Ziemann U.
      • Siebner H.
      Inter-subject and Inter- session Variability of Plasticity Induction by Non-invasive Brain Stimulation: Boon or Bane?.
      ). Even though animal studies already suggested that this variability might reflect dynamics in brain state, this phenomenon was a long time mostly ignored in human research (
      • Huerta P.T.
      • Usman J.E.
      Heightened synaptic plasticity of hippocampal CA1 neurons during a cholinergically induced rhythmic state.
      ,
      • Huerta P.T.
      • Lisman J.E.
      Bidirectional synaptic plasticity induced by a single burst during cholinergic theta oscillation in CA1 in vitro.
      ). In 2008, Silvanto and Pascual-Leone described the potential importance of the baseline cortical activation state when applying TMS (
      • Silvanto J.
      • Pascual-Leone A.
      State-dependency of transcranial magnetic stimulation.
      ). Ongoing brain oscillations can reveal information about the brain's excitability state (Figure 3) (
      • Buzsaki G.
      • Draguhn A.
      Neuronal Oscillations in Cortical Networks.
      ). This information is currently not incorporated in brain stimulation protocols (at least not in clinical settings), which might add to the heterogeneous outcomes (
      • Mansouri F.
      • Fettes P.
      • Schulze L.
      • Giacobbe P.
      • Zariffa J.
      • Downar J.
      A Real-Time Phase-Locking System for Non-invasive Brain Stimulation.
      ). The neuron's excitability state during the application of stimuli is an essential factor that determines the capabilities of the induction of synaptic plasticity within neuronal networks. Based on this fact, it can be hypothesized that the efficacy of (r)TMS can be enhanced when the stimulation is tuned to instantaneous phase or power values that reflect high excitability states (
      • Thut G.
      • Bergmann T.O.
      • Fröhlich F.
      • Soekadar S.R.
      • Brittain J.S.
      • Valero-Cabré A.
      • et al.
      Guiding transcranial brain stimulation by EEG/MEG to interact with ongoing brain activity and associated functions: A position paper.
      ). Real-time analysis of EEG data combined with a forward prediction model allows the optimal timing of the TMS pulses at a preferred brain state. This method is further referred to as brain-oscillation-synchronized (r)TMS (
      • Bergmann T.O.
      • Karabanov A.
      • Hartwigsen G.
      • Thielscher A.
      • Siebner H.R.
      Combining non-invasive transcranial brain stimulation with neuroimaging and electrophysiology: Current approaches and future perspectives.
      ,

      Shirinpour S, Alekseichuk I, Mantell K, Opitz A (2019): Experimental Evaluation of Methods for Real-Time EEG Phase-Specific Transcranial Magnetic Stimulation. BioRxiv.

      ).
      Figure thumbnail gr3
      Figure 3Contribution of EEG to the personalization of the timing and frequency of the stimulation. Effects of stimulation are thought to be more pronounced if the pulses are administered during the brain’s hyper-excitability state. Previous work used five EEG electrodes to derive brain waves and used negative peaks (‘troughs’) as the optimal timing of stimulation. Individualized stimulation frequencies, for example for theta burst stimulation, could also be extracted from EEG.
      The impact of brain-oscillation-synchronized TMS applied to the motor cortex in healthy subjects was represented in different MEP sizes (
      • Zrenner C.
      • Desideri D.
      • Belardinelli P.
      • Ziemann U.
      Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex.
      ,
      • Schaworonkow N.
      • Triesch J.
      • Ziemann U.
      • Zrenner C.
      EEG-triggered TMS reveals stronger brain state-dependent modulation of motor evoked potentials at weaker stimulation intensities.
      ). The negative peak of the μ-rhythm (part of the alpha rhythm extracted from the motor region), extracted from EEG data at the motor cortex, was associated with the high excitability state whereas the positive peak represented low excitability. Hence, motor cortex stimulation during negative peaks of the μ-rhythm resulted in significantly higher MEPs (
      • Zrenner C.
      • Desideri D.
      • Belardinelli P.
      • Ziemann U.
      Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex.
      ). As a first step towards future brain-oscillation-synchronized rTMS treatment for depression, single session alpha-synchronized rTMS applied to the left DLPFC was investigated and showed to be feasible and safe (
      • Zrenner B.
      • Zrenner C.
      • Gordon P.C.
      • Belardinelli P.
      • McDermott E.J.
      • Soekadar S.R.
      • et al.
      Brain oscillation-synchronized stimulation of the left dorsolateral prefrontal cortex in depression using real-time EEG-triggered TMS.
      ). Clinical trials with repeated alpha-synchronized stimulation sessions to investigate the therapeutic potential and efficacy of brain oscillation-synchronized rTMS in MDD compared to current rTMS therapies are warranted.
      Combining brain-oscillation-synchronized rTMS with real-time functional brain imaging can be considered an ideal method to combine optimal targeting (based on concurrent TMS-fMRI) and administering the TMS pulses during predefined brain states. The feasibility of a concurrent TMS-EEG-fMRI system has been shown (
      • Peters J.C.
      • Reithler J.
      • Schuhmann T.
      • de Graaf T.
      • Uludag ˘ K.
      • Goebel R.
      • Sack A.T.
      On the feasibility of concurrent human TMS-EEG-fMRI measurements.
      ) and this setup was used to show the influence of pre-TMS alpha power on the distributed effects in the motor network (
      • Peters J.C.
      • Reithler J.
      • Graaf TA de
      • Schuhmann T.
      • Goebel R.
      • Sack A.T.
      Concurrent human TMS-EEG-fMRI enables monitoring of oscillatory brain state-dependent gating of cortico-subcortical network activity.
      ). However, combining the three techniques is challenging.
      EEG also reveals information about the individual firing patterns. Since every individual has different rhythmic firing patterns, it can be hypothesized that also the stimulation frequency to obtain clinical effects from rTMS is subject-specific and can be personalized for rTMS treatment. Concretely this means that standard 10 Hz rTMS treatment protocols could become more effective if the frequency is tuned to an individual frequency. Previous work showed that smaller deviations between the individual alpha frequency and the stimulation frequency (10 Hz) were related to better responses to 10 Hz rTMS in depression patients (
      • Corlier J.
      • Carpenter L.L.
      • Wilson A.C.
      • Tirrell E.
      • Gobin A.P.
      • Kavanaugh B.
      • Leuchter A.F.
      The relationship between individual alpha peak frequency and clinical outcome with repetitive Transcranial Magnetic Stimulation (rTMS) treatment of Major Depressive Disorder (MDD).
      ,
      • Roelofs C.L.
      • Krepel N.
      • Corlier J.
      • Carpenter L.L.
      • Fitzgerald P.B.
      • Daskalakis Z.J.
      • et al.
      Individual alpha frequency proximity associated with repetitive transcranial magnetic stimulation outcome: An independent replication study from the ICON-DB consortium.
      ). There are various ways to define and calculate the optimal individual stimulation frequency (
      • Leuchter A.F.
      • Wilson A.C.
      • Vince-Cruz N.
      • Corlier J.
      Novel method for identification of individualized resonant frequencies for treatment of Major Depressive Disorder (MDD) using repetitive Transcranial Magnetic Stimulation (rTMS): A proof-of-concept study.
      ,
      • Janssens S.E.W.
      • Sack A.T.
      • ten Oever S.
      • de Graaf T.A.
      Calibrating rhythmic stimulation parameters to individual electroencephalography markers: The consistency of individual alpha frequency in practical lab settings.
      ). Besides a role of frequency in standard rTMS protocols, Chung et al. highlighted the importance of the stimulation frequency in intermittent TBS (iTBS) protocols by comparing 30Hz bursts repeated at 6Hz, 50Hz bursts at 5Hz, or individualized frequency in healthy volunteers (
      • Chung S.W.
      • Sullivan C.M.
      • Rogasch N.C.
      • Hoy K.E.
      • Bailey N.W.
      • Cash R.F.H.
      • Fitzgerald P.B.
      The effects of individualised intermittent theta burst stimulation in the prefrontal cortex: A TMS-EEG study.
      ). In contrast to the two standard protocols, individual iTBS significantly increased the amplitude of the TEPs at specific latencies.

       Challenges towards clinical implementation

      All methods for personalization as described in this manuscript require additional time and effort. It is therefore of paramount importance to prove the superiority (and not simply the non-inferiority) of personalization before these methods can be adopted in daily clinical practice. It is not known if all parameters that can be personalized have equal added value. Standard pipelines to obtain these personalized parameters from neuroimaging data are necessary to extract these parameters in clinical settings.

       Imaging limitations

      Although some previous works used normative connectome data to derive stimulation targets, the methods described in this paper might benefit from the use of individual connectome data (

      Cash RFH, Cocchi L, Lv J, Wu Y, Fitzgerald PB, Zalesky A (2021): Personalized connectivity-guided DLPFC-TMS for depression: Advancing computational feasibility, precision and reproducibility. Human Brain Mapping. https://doi.org/10.1002/hbm.25330

      ). A disadvantage is that they require advanced brain imaging technologies which are not universally available. Also, rs-fMRI time-series are contaminated by many non-neuronal sources of noise and it remains challenging to accurately reconstruct individual structural pathways (
      • Murphy K.
      • Fox M.D.
      Towards a Consensus Regarding Global Signal Regression for Resting State Functional Connectivity MRI.
      ,
      • Maier-Hein K.H.
      • Neher P.F.
      • Houde J.C.
      • Côté M.A.
      • Garyfallidis E.
      • Zhong J.
      • et al.
      The challenge of mapping the human connectome based on diffusion tractography.
      ,
      • Yeh C.H.
      • Jones D.K.
      • Liang X.
      • Descoteaux M.
      • Connelly A.
      Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities.
      ). There is a significant variability between scanners and protocols which forms a huge challenge to obtain and use truly quantitative measures (
      • Ning L.
      • Bonet-Carne E.
      • Grussu F.
      • Sepehrband F.
      • Kaden E.
      • Veraart J.
      • et al.
      Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results.
      ,
      • Tax C.M.
      • Grussu F.
      • Kaden E.
      • Ning L.
      • Rudrapatna U.
      • John Evans C.
      • et al.
      Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms.
      ). One potential compromise between individual and normative connectome methods might be the use of demographically specific connectomes that account for stable neurodiversity, for example sex- and age-based connectomes, or pathology-based connectomes. Initiatives to develop demographically specific connectome methods for MDD treatment are currently underway (http://banda.mit.edu/ and http://enigma.ini.usc.edu/ongoing/enigma-mdd-working-group/).
      Even though electric field simulations can provide valuable information about the optimal coil position and orientation at the scalp and about the stimulation intensity, state-of-the-art head modeling methods are only an approximation of the true individual anatomy. Especially potential segmentation errors, limited number of tissue types, and the use of standard conductivity values might lead to various sources of inaccuracies (
      • Opitz A.
      • Falchier A.
      • Linn G.S.
      • Milham M.P.
      • Schroeder C.E.
      Limitations of ex vivo measurements for in vivo neuroscience.
      ,
      • Puonti O.
      • van Leemput K.
      • Saturnino G.B.
      • Siebner H.R.
      • Madsen K.H.
      • Thielscher A.
      Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling.
      ). Moreover, currently there is no consensus yet on the threshold that should be applied to the simulated electric field distributions to select only the gray matter regions that are activated. Standard depression treatment mostly uses a stimulation intensity of 120% rMT at the left DLPFC. Some studies used a threshold of 83% to comply with the proven activation, i.e., induction of MEPs, when stimulating the motor cortex at rMT (
      • Momi D.
      • Ozdemir R.A.
      • Tadayon E.
      • Boucher P.
      • Shafi M.M.
      • Pascual-Leone A.
      • Santarnecchi E.
      Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation.
      ) and the APEX MT method could also provide insight in this threshold (

      Caulfield KA, Li X, George MS (2021, July 1): Four electric field modeling methods of Dosing Prefrontal Transcranial Magnetic Stimulation (TMS): Introducing APEX MT dosimetry. Brain Stimulation, vol. 14. Elsevier Inc., pp 1032–1034.

      ). This would lead to distributions with a width of approximately 2 cm. However, unexpected focality was shown by Romero et al. (
      • Romero M.C.
      • Davare M.
      • Armendariz M.
      • Janssen P.
      Neural effects of transcranial magnetic stimulation at the single-cell level.
      ) who studied the effects of single TMS pulses on the single-cell level; single neurons in an area less than 2 mm diameter in the cortex were affected. Besides, knowledge about effective electric fields and consecutive TMS signal propagation could benefit from coupling these fields with DW-MRI derived fiber pathways (
      • Wagner T.
      • Valero-Cabre A.
      • Pascual-Leone A.
      Noninvasive Human Brain Stimulation.
      ,
      • de Geeter N.
      • Crevecoeur G.
      • Leemans A.
      • Dupré L.
      Effective electric fields along realistic DTI-based neural trajectories for modelling the stimulation mechanisms of TMS.
      ,
      • Deslauriers-Gauthier S.
      • Lina J.M.
      • Butler R.
      • Whittingstall K.
      • Gilbert G.
      • Bernier P.M.
      • et al.
      White matter information flow mapping from diffusion MRI and EEG.
      ). Additionally, it must be stated that also subthreshold effects might play a role in the responses to TMS (

      Harita S, Momi D, Mazza F, Griffiths JD, Corresponding Author * = (n.d.): Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder. https://doi.org/10.1101/2021.07.15.452518

      ).
      Alpha oscillations could be used to derive the optimal timing and personalized stimulation frequency. However, DLPFC alpha oscillations are not easy to extract because of a low signal-to-noise ratio in rest. An algorithm to automatically reject EEG power spectra that do not contain a clear alpha peak was recently published (
      • Janssens S.E.W.
      • Sack A.T.
      • ten Oever S.
      • de Graaf T.A.
      Calibrating rhythmic stimulation parameters to individual electroencephalography markers: The consistency of individual alpha frequency in practical lab settings.
      ). Individually optimized spatial filters might improve the signal-to-noise ratio in the preferred frequency range in the EEG (
      • Schaworonkow N.
      • Caldana Gordon P.
      • Belardinelli P.
      • Ziemann U.
      • Bergmann T.O.
      • Zrenner C.
      μ-Rhythm Extracted With Personalized EEG Filters Correlates With Corticospinal Excitability in Real-Time Phase-Triggered EEG-TMS.
      ). These filters could furthermore benefit from beamforming techniques incorporating information about the individual’s structural brain connections.
      Randomized controlled trials: Investigating superiority of personalized stimulation parameters
      While there is an emerging body of evidence to support the vision for personalized brain stimulation, large, potentially multi-center prospective clinical trials in which highly treatment resistant depression patients are randomized to receive standard rTMS procedures (in practice usually without neuronavigation) versus personalized protocol are required (

      Modak A, Fitzgerald PB (2021): Personalising transcranial magnetic stimulation for depression using neuroimaging: A systematic review. World Journal of Biological Psychiatry. Taylor and Francis Ltd. https://doi.org/10.1080/15622975.2021.1907710

      ). To prevent patients from having different placebo effects due to varying treatment duration (caused by the additional steps necessary for personalization) the pipeline to derive the personalized stimulation parameters should be done for both groups. Only one group actually receives personalized stimulation. We anticipate that the added value of personalization (compared to standard rTMS) could achieve a moderate effect size (Cohen’s d∼.35). In line with Cole et al. (
      • Cole E.
      • Gulser M.
      • Stimpson K.
      • Bentzley B.
      • Hawkins J.
      • Xiao X.
      • et al.
      Stanford accelerated intelligent neuromodulation therapy for treatment-resistant depression (SAINT-TRD).
      ) we believe that more patients will remit (instead of solely respond) after personalized treatment. A RCT should include 204 subjects (102 per group) to show this potential added value of personalized protocols compared to standard protocols (power 0.8, alpha 0.05) (
      • Faul Franz
      • Erdfelder Edgar
      • Lang Albert-Georg
      • Buchner Axel
      G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences.
      ).

      Conclusion

      Antidepressant treatment by rTMS has shown promising results. However, treatment efficacy is hampered due to the diversity in depressive symptoms and neural phenotypes and the use of overall ‘one-fits-all’ stimulation protocols. We propose that rTMS parameters can be personalized by using multimodal neuroimaging techniques for delivering higher-precision interventions. These personalized stimulation protocols may reduce variability in treatment response and increase overall clinical effectiveness. We anticipate that optimal clinical effectiveness may ultimately be achieved using treatment protocols integrating these personalized stimulation parameters. Even though the focus is on the treatment for depressed patients, the methods proposed here may easily be translated to the treatment of other neuropsychiatric pathologies.

       Author’s contributions

      All four authors have worked on the conceptualization of this manuscript. PB and CB are supervisors of DK. DK wrote the first draft of the manuscript. MF, PB, and CB have carefully reviewed this version and finally DK incorporated all comments and wrote the final version. All authors approved the final version of the manuscript.

      Uncited reference

      • Kähkönen S.
      • Komssi S.
      • Wilenius J.
      • Ilmoniemi R.J.
      Prefrontal TMS produces smaller EEG responses than motor-cortex TMS: Implications for rTMS treatment in depression.
      .

      Acknowledgements

      DK was supported by a grant from ‘Fonds Wetenschappelijk Onderzoek' (FWO), Vlaanderen, Belgium (1259121N). PB was supported by funds from FWO Vlaanderen, BOF UGent, Geneeskundige Stichting Koningin Elisabeth (GSKE-FMRE), and Health Holland. CB was supported by grants of the FWO, including the grant for Applied Biomedical Research PrevenD 2.0, by the GSKE-FMRE and by a Strategic Research Program (SRP 57) from the Free University Brussels (VUB).
      All authors accept responsibility for publication of this manuscript.
      Disclosures:All authors report no biomedical financial interests or potential conflicts of interest.

      Supplementary Material

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