7T fMRI changes in subgenual cingulate cortex after repetitive Transcranial Stimulation
Poster No:
35
Submission Type:
Abstract Submission
Authors:
Christiane Licht1,2, Ravichandran Rajkumar3,1,4, Gereon Schnellbaecher1, Jana Hagen1, Shukti Ramkiran3,1,4, N. Jon Shah3,5,6,7, Irene Neuner3,1,4
Institutions:
1Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH, Aachen, Germany, 2INM-4, Forschungszentrum, Juelich, Germany, 3Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany INM-4, Jülich, Germany, 4Center for Computational Life Sciences, RWTH, Aachen, Germany, 5JARA – BRAIN – Translational Medicine, Aachen, Germany, 6Department of Neurology, Faculty of Medicine, RWTH, Aachen, Germany, 7Institute of Neuroscience and Medicine 11 (INM‐11), JARA, Forschungszentrum Juelich, Aachen, Germany
First Author:
Christiane Licht
Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH|INM-4, Forschungszentrum
Aachen, Germany|Juelich, Germany
Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH|INM-4, Forschungszentrum
Aachen, Germany|Juelich, Germany
Co-Author(s):
Ravichandran Rajkumar
Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany INM-4|Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH|Center for Computational Life Sciences, RWTH
Jülich, Germany|Aachen, Germany|Aachen, Germany
Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany INM-4|Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH|Center for Computational Life Sciences, RWTH
Jülich, Germany|Aachen, Germany|Aachen, Germany
Shukti Ramkiran
Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany INM-4|Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH|Center for Computational Life Sciences, RWTH
Jülich, Germany|Aachen, Germany|Aachen, Germany
Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany INM-4|Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH|Center for Computational Life Sciences, RWTH
Jülich, Germany|Aachen, Germany|Aachen, Germany
N. Jon Shah
Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany INM-4|JARA – BRAIN – Translational Medicine|Department of Neurology, Faculty of Medicine, RWTH|Institute of Neuroscience and Medicine 11 (INM‐11), JARA, Forschungszentrum Juelich
Jülich, Germany|Aachen, Germany|Aachen, Germany|Aachen, Germany
Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany INM-4|JARA – BRAIN – Translational Medicine|Department of Neurology, Faculty of Medicine, RWTH|Institute of Neuroscience and Medicine 11 (INM‐11), JARA, Forschungszentrum Juelich
Jülich, Germany|Aachen, Germany|Aachen, Germany|Aachen, Germany
Irene Neuner
Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany INM-4|Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH|Center for Computational Life Sciences, RWTH
Jülich, Germany|Aachen, Germany|Aachen, Germany
Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany INM-4|Dept. of Psychiatry, Psychotherapy and Psychosomatics, RWTH|Center for Computational Life Sciences, RWTH
Jülich, Germany|Aachen, Germany|Aachen, Germany
Introduction:
Repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC) has emerged as a promising non-invasive treatment for severe treatment-resistant depression [1]. Despite its therapeutic potential, remission rates remain highly variable across individuals [2]. One proposed factor contributing to this variability is the interindividual difference in functional connectivity (FC) between the left DLPFC and the subgenual anterior cingulate cortex (sgACC) [3, 4], a region strongly implicated in mood regulation and depressive symptomatology.
Research suggests that rTMS responders exhibit a greater resting-state anti-correlation in functional connectivity between the sgACC and the left DLPFC compared to non-responders, where such changes are typically absent [3,4]. These findings underscore the importance of sgACC connectivity as a potential biomarker for rTMS efficacy. However, most existing studies focus on network-level changes, with limited investigation into direct, localized changes in sgACC connectivity before and after rTMS treatment [5].
Using the global correlation (GCOR) measure derived from resting-state functional MRI (fMRI), we aim to systematically assess sgACC connectivity changes in response to rTMS. Leveraging the enhanced spatial resolution and contrast afforded by 7-Tesla ultra-high-field MRI [6], this study seeks to provide a precise characterization of sgACC connectivity dynamics in a naturalistic setting.
Hypothesis: We hypothesize that rTMS will result in significant changes in sgACC connectivity, measurable by GCOR, particularly in responders. These changes will manifest as increased anti-correlation between the sgACC and left DLPFC, aligning with symptom improvement in treatment responders.
Research suggests that rTMS responders exhibit a greater resting-state anti-correlation in functional connectivity between the sgACC and the left DLPFC compared to non-responders, where such changes are typically absent [3,4]. These findings underscore the importance of sgACC connectivity as a potential biomarker for rTMS efficacy. However, most existing studies focus on network-level changes, with limited investigation into direct, localized changes in sgACC connectivity before and after rTMS treatment [5].
Using the global correlation (GCOR) measure derived from resting-state functional MRI (fMRI), we aim to systematically assess sgACC connectivity changes in response to rTMS. Leveraging the enhanced spatial resolution and contrast afforded by 7-Tesla ultra-high-field MRI [6], this study seeks to provide a precise characterization of sgACC connectivity dynamics in a naturalistic setting.
Hypothesis: We hypothesize that rTMS will result in significant changes in sgACC connectivity, measurable by GCOR, particularly in responders. These changes will manifest as increased anti-correlation between the sgACC and left DLPFC, aligning with symptom improvement in treatment responders.
Methods:
All participants written informed consent, and the study was approved by the RWTH Aachen University Ethics Committee. Patients' symptoms were assessed by the responsible clinicians using the Hamilton Depression Scale (HAMD-21) and Beck's Depression Inventory-II (BDI-II).
MRI data acquisition and processing
MRI data were acquired using a 7T Siemens Terra scanner (Siemens Healthineers). Structural MRI utilized an MP2RAGE sequence (TE = 1.99 ms, TR = 4500 ms) with a 0.75 mm³ isotropic resolution. Resting-state fMRI (rs-fMRI) was conducted with a multiband-accelerated EPI protocol (TR = 2000 ms, TE = 25 ms, 1.3 mm³ isotropic resolution, 100 slices, 305 volumes). Data pre-processing and analysis were performed using CONN (release 22.a), SPM12 (release 12.7771) , and MATLAB (R2022a). The default pipeline for functional and anatomical pre-processing and denoising was applied, followed by bandpass frequency filtering (0.008–0.09 Hz). Subsequently, rs-fMRI global correlation (GCOR) metrics, representing node centrality at each voxel, were calculated.
MRI data acquisition and processing
MRI data were acquired using a 7T Siemens Terra scanner (Siemens Healthineers). Structural MRI utilized an MP2RAGE sequence (TE = 1.99 ms, TR = 4500 ms) with a 0.75 mm³ isotropic resolution. Resting-state fMRI (rs-fMRI) was conducted with a multiband-accelerated EPI protocol (TR = 2000 ms, TE = 25 ms, 1.3 mm³ isotropic resolution, 100 slices, 305 volumes). Data pre-processing and analysis were performed using CONN (release 22.a), SPM12 (release 12.7771) , and MATLAB (R2022a). The default pipeline for functional and anatomical pre-processing and denoising was applied, followed by bandpass frequency filtering (0.008–0.09 Hz). Subsequently, rs-fMRI global correlation (GCOR) metrics, representing node centrality at each voxel, were calculated.
·Patient's data
Results:
The present study demonstrates increased resting-state fMRI GCOR values within the sgACC following rTMS treatment, accompanied by a decrease in BDI-II scores, suggesting a link between enhanced sgACC connectivity and clinical improvement in depressive symptoms. The sgACC, a key region implicated in mood regulation and depressive pathology, showed greater functional integration within the broader brain network post-rTMS. This enhanced connectivity likely reflects a normalization of sgACC function, shifting it from a dysregulated state commonly associated with depression to one of improved centrality with other brain regions.
·Figure 1 & 2: The fMRI GCOR measurements before and after rTMS treatment of Subject 1 and 2, along with permutation test results displaying the mean and standard deviation of voxel values.
Conclusions:
The present study demonstrates increased resting-state fMRI GCOR values within the sgACC following rTMS treatment, accompanied by a decrease in BDI-II scores, suggesting a link between enhanced sgACC connectivity and clinical improvement in depressive symptoms. The sgACC, showed greater functional integration within the broader brain network post-rTMS.
In conclusion, the observed changes underscore the sgACC's importance as a target for understanding and optimizing rTMS interventions in treatment-resistant depression.
In conclusion, the observed changes underscore the sgACC's importance as a target for understanding and optimizing rTMS interventions in treatment-resistant depression.
Brain Stimulation:
Non-invasive Magnetic/TMS 1
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Affective Disorders
MRI
Transcranial Magnetic Stimulation (TMS)
1|2Indicates the priority used for review
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[2] Berlim, M. T., Van den Eynde, F., Tovar-Perdomo, S., & Daskalakis, Z. J. (2014). 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. Psychological medicine, 44(2), 225-239.
[3] Jing Y, Zhao N, Deng XP, Feng ZJ, Huang GF, Meng M, Zang YF, Wang J. Pregenual or subgenual anterior cingulate cortex as potential effective region for brain stimulation of depression. Brain Behav. 2020 Apr;10(4):e01591. doi: 10.1002/brb3.1591.
[4] Benschop, L., Vanhollebeke, G., Li, J., Leahy, R. M., Vanderhasselt, M. A., & Baeken, C. (2022). Reduced subgenual cingulate–dorsolateral prefrontal connectivity as an electrophysiological marker for depression. Scientific reports, 12(1), 16903.
[5] Schiena, G., Franco, G., Boscutti, A., Delvecchio, G., Maggioni, E., & Brambilla, P. (2021). Connectivity changes in major depressive disorder after rTMS: a review of functional and structural connectivity data. Epidemiology and Psychiatric Sciences, 30, e59.
[6] Schnellbächer, G. J., Rajkumar, R., Veselinović, T., Ramkiran, S., Hagen, J., Collee, M., ... & Neuner, I. (2024). Structural alterations as a predictor of depression–a 7-Tesla MRI-based multidimensional approach. Molecular Psychiatry, 1-8.
[7] Whitfield-Gabrieli, S., and Nieto-Castanon, A. (2012). Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks. Brain Connect. 2, 125–141. https://doi.org/10.1089/brain.2012.0073.
[8]
Nieto-Castanon, A., and Whitfield-Gabrieli, S. (2022). CONN functional connectivity toolbox: RRID SCR_009550, release 22 22nd ed. (Hilbert Press) https://doi.org/10.56441/hilbertpress.2246.5840.
[9] Statistical Parametric Mapping (2007). (Elsevier) https://doi.org/10.1016/B978-0-12-372560-8.X5000-1.
[10] Nieto-Castanon, A. (2020). Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN (Hilbert Press) https://doi.org/10.56441/hilbertpress.2207.6598.
[11] Ref https://academic.oup.com/cercor/article/26/8/3