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Unravelling MDD brain circuits and clinical subtypes: A crossmodal ALE meta-analysis of 395 studies

Poster No:

657 

Submission Type:

Abstract Submission 

Authors:

Mónica Sobral1,2, Raquel Guiomar1, Manya Rezaeian3, Maria Vasileiadi4, Sara Cruz5,6, Francisca Pacheco1, Vera Mateus1, Roser Palau-Costafreda7,8, Johanna Pozo-Neira9, Ana Weidenauer10, Helena Moreira1, Martin Tik11, Ana Ganho-Ávila1, Anna-Lisa Schuler12

Institutions:

1Center for Research in Neuropsychology and Cognitive Behavioral Intervention, Coimbra, Portugal, 2Developmental Disorders Program and Mackenzie Center for Research in Childhood and Adolescence, São Paulo, Brazil, 3Counseling Center of Tehran University, Tehran, Iran, 4Medical University of Vienna, Vienna, Vienna, 5The Psychology for Positive Development Research Centre, Lusiada University, Porto, Portugal, 6Department of Psychology, School of Philosophy, Psychology & Language Sciences, University of Edinburgh, Edinburgh, United Kingdom, 7ESIMar (Mar Nursing School), Parc de Salut Mar, Universitat Pompeu Fabra-affiliated, Barcelona, Spain, 8SDHEd (Social Determinants and Health Education Research Group), IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain, 9Institute of Neuroscience, Universidad Católica de Cuenca, Cuenca, Ecuador, 10Medical University of Vienna, Vienna, Austria, 11High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria, 12Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony

First Author:

Mónica Sobral  
Center for Research in Neuropsychology and Cognitive Behavioral Intervention|Developmental Disorders Program and Mackenzie Center for Research in Childhood and Adolescence
Coimbra, Portugal|São Paulo, Brazil

Co-Author(s):

Raquel Guiomar  
Center for Research in Neuropsychology and Cognitive Behavioral Intervention
Coimbra, Portugal
Manya Rezaeian  
Counseling Center of Tehran University
Tehran, Iran
Maria Vasileiadi  
Medical University of Vienna
Vienna, Vienna
Sara Cruz  
The Psychology for Positive Development Research Centre, Lusiada University|Department of Psychology, School of Philosophy, Psychology & Language Sciences, University of Edinburgh
Porto, Portugal|Edinburgh, United Kingdom
Francisca Pacheco  
Center for Research in Neuropsychology and Cognitive Behavioral Intervention
Coimbra, Portugal
Vera Mateus  
Center for Research in Neuropsychology and Cognitive Behavioral Intervention
Coimbra, Portugal
Roser Palau-Costafreda  
ESIMar (Mar Nursing School), Parc de Salut Mar, Universitat Pompeu Fabra-affiliated|SDHEd (Social Determinants and Health Education Research Group), IMIM (Hospital del Mar Medical Research Institute)
Barcelona, Spain|Barcelona, Spain
Johanna Pozo-Neira  
Institute of Neuroscience, Universidad Católica de Cuenca
Cuenca, Ecuador
Ana Weidenauer  
Medical University of Vienna
Vienna, Austria
Helena Moreira  
Center for Research in Neuropsychology and Cognitive Behavioral Intervention
Coimbra, Portugal
Martin Tik  
High Field MR Center, Center for Medical Physics and Biomedical Engineering
Medical University of Vienna, Vienna, Austria
Ana Ganho-Ávila  
Center for Research in Neuropsychology and Cognitive Behavioral Intervention
Coimbra, Portugal
Anna-Lisa Schuler  
Max Planck Institute for Human Cognitive and Brain Sciences
Leipzig, Saxony

Introduction:

The understanding and treatment of major depressive disorder (MDD) have faced challenges due to its heterogeneous clinical presentation and variable treatment outcomes, highlighting that there may exist multiple forms of depression (1). Advancements in neuroimaging have revealed disrupted affective and neurocognitive brain circuitry, offering potential insights into symptom diversity through distinct circuit-based biotypes (e.g., clustering patients based on shared brain dysregulation signatures; 1,2). This emerging approach aims to target specific circuits associated with distinct symptom clusters, such as dysphoric and anxiosomatic, offering a promising avenue for more effective and personalised treatment strategies (3,4). In this meta-analytical study, objectives were two-fold: first, to determine if the same brain pattern observed in MDD populations applies to one known subtype, those experiencing depressive symptoms during the peripartum period (PPD). Second, considering network effects that potentially support specific symptoms.

Methods:

To accomplish this, we conducted a systematic literature search in PubMed, Embase and PsychINFO to identify peer-reviewed original studies in English across brain imaging modalities (functional and structural magnetic resonance imaging [MRI], diffusion tensor Imaging [DTI], positron emission tomography [PET], near‐infrared spectroscopy [NIRS] and magnetic resonance spectroscopy [MRS]). Flow-chart of literature selection is depicted in Figure 1. We then performed a cross-modal coordinate-based meta-analysis using activation likelihood estimation (ALE) to combine peak coordinates from included studies, using GingerALE (cluster-level inference of p<0.05, with 10000 thresholding permutations) for MDD, PPD and MDD female only subtypes, respectively.

Results:

A total of 6624 MDD reports were screened for inclusion. Of these, 369 (11378 participants) were included for the MDD cluster meta-analysis. Regarding subgroup meta-analysis, we included 20 studies with only female MDD participants (318 participants). For the PPD subgroup, 592 reports were screened and 26 studies included (581 participants). Several clusters related to emotional and cognitive processing were found to be disrupted in the MDD full sample, namely right vmPFC, bilateral amygdala, left putamen and right insula (Figure 1). While for MDD female and PPD there was an overlap in the right amygdala, the right putamen and left DLPFC was stronger involved in PPD and the left VMPFC and DLPFC in MDD female (Figure 2). Furthermore, while PPD encompassed similar components as the anxiosomatic depression network, suggested by Cash et al. (4), there was no clear overlap suggesting involvement of other networks. Performing seed-based connectivity analysis from the right amygdala seed in PPD reveals involvement of somatomotor, temporal and prefrontal areas (Figure 2).
Supporting Image: Figure1.png
   ·Figure 1. PRISMA Flow Diagram for Study Selection and results for full sample MDD cross-modal coordinate-based ALE meta-analysis
Supporting Image: Figure2.png
   ·Figure 2. ALE meta-analysis in MDD female and PPD subtypes and seed-based connectivity analysis from the right amygdala seed in PPD
 

Conclusions:

Current classification systems (e.g., 13 subtypes based on symptoms, such as atypical and melancholic) aimed to personalise treatment but fail to improve treatment outcomes (2). The distinct brain patterns differentiating general MDD from PPD sustain the need for tailored treatment approaches that consider MDD subtypes on a fine-grained level. Specifically, better targeting approaches of MDD subtypes might improve non-pharmacological approaches, such as TMS (3,4), considering as well the significance of sex/gender as a biomarker shaping the effects of therapeutic response (5).

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1

Emotion, Motivation and Social Neuroscience:

Emotion and Motivation Other 2

Modeling and Analysis Methods:

Other Methods

Keywords:

Meta- Analysis
Treatment
Other - peripartum depression; major depressive disorder; brain imaging

1|2Indicates the priority used for review

Provide references using author date format

(1) Drysdale, A. T., Grosenick, L., Downar, J., Dunlop, K., Mansouri, F., Meng, Y., Fetcho, R. N., Zebley, B., Oathes, D. J., Etkin, A., Schatzberg, A. F., Sudheimer, K., Keller, J., Mayberg, H. S., Gunning, F. M., Alexopoulos, G. S., Fox, M. D., Pascual-Leone, A., Voss, H. U., Casey, B. J., … Liston, C. (2017), ‘Resting-state connectivity biomarkers define neurophysiological subtypes of depression’, Nature Medicine, vol. 23, no. 1, pp 28–38. https://doi.org/10.1038/nm.4246
(2) Nestor, S. M., & Blumberger, D. M. (2020), ‘Mapping symptom clusters to circuits: Toward personalizing TMS targets to improve treatment outcomes in depression’, The American Journal of Psychiatry, vol. 177, no. 5, pp 373–375. https://doi.org/10.1176/appi.ajp.2020.20030271
(3) Siddiqi, S. H., Taylor, S. F., Cooke, D., Pascual-Leone, A., George, M. S., & Fox, M. D. (2020), ‘Distinct symptom-specific treatment targets for circuit-based neuromodulation’, The American Journal of Psychiatry, vol. 17, no. 5, pp 435–446. https://doi.org/10.1176/appi.ajp.2019.19090915
(4) Cash, R. F. H., Weigand, A., Zalesky, A., Siddiqi, S. H., Downar, J., Fitzgerald, P. B., & Fox, M. D. (2021), ‘Using brain imaging to improve spatial targeting of transcranial magnetic stimulation for depression’, Biological Psychiatry, vol. 90, no. 10, pp. 689–700. https://doi.org/10.1016/j.biopsych.2020.05.033
(5) Hanlon, C. A., & McCalley, D. M. (2022), ‘Sex/gender as a factor that influences transcranial magnetic stimulation treatment outcome: Three potential biological explanations’, Frontiers in Psychiatry’, vol. 13, 869070. https://doi.org/10.3389/fpsyt.2022.869070