More Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities

Winter, Nils R.; Leenings, Ramona; Ernsting, Jan; Sarink, Kelvin; Fisch, L.; Emden, Daniel; Blanke, Julian; Goltermann, Janik; Barkhau, Carlotta; Meinert, Susanne; Dohm, Katharina; Repple, Jonathan; Mauritz, Marco; Gruber, Marius; Leehr, Elisabeth J.; Grotegerd, Dominik; Redlich, Ronny; Jansen, Andreas; Nenadić, Igor; Nöthen, Markus M.; Forstner, Andreas J.; Rietschel, Marcella; Groß, Joachim; Bauer, Jochen; Heindel, Walter; Andlauer, Till F. M.; Eickhoff, Simon B.; Kircher, Tilo; Dannlowski, Udo; Hahn, Tim

doi:10.48550/arxiv.2112.10730

Cited by 2 publications

(5 citation statements)

References 22 publications

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“…Additional clinical and/or neurobiological factors that contribute to or weaken this association need to be identified. Interestingly, a recent study compared different environmental (CM, social support) and neurobiological factors such as neuroimaging findings and polygenic risk scores (PRS) in depressed patients compared to healthy individuals [57]. Whereas classification accuracy for CM and social support was 71%, it was lower for neuroimaging modalities (ranging between 54 and 55%) and for PRS (58%) underlining the importance of these constructs.…”

Section: Discussionmentioning

confidence: 99%

Association of loneliness and social network size in adulthood with childhood maltreatment: Analyses of a population-based and a clinical sample

et al. 2022

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Background Perceived loneliness and objective social network size are related but distinct factors, which negatively affect mental health and are prevalent in patients who have experienced childhood maltreatment (CM), for example, patients with persistent depressive disorder (PDD) and borderline personality disorder (BPD). This cross-diagnostic study investigated whether loneliness, social network size, or both are associated with self-reported CM. Methods Loneliness and social network size were assessed in a population-based sample at two time points (Study 1, N = 509), and a clinical group of patients with PDD or BPD (Study 2, N = 190) using the UCLA Loneliness Scale and the Social Network Index. Further measures were the Childhood Trauma Questionnaire, and standard depression rating scales. Linear regression analyses were applied to compare associations of loneliness or social network size with CM. Multiple mediation analyses were used to test the relative importance of loneliness and social network size in the relationship between CM and depressive symptoms. Results In both studies, loneliness showed a stronger association than social network size with CM. This was particularly marked for emotional neglect and emotional abuse. Loneliness but not social network size mediated the relationship between CM and depressive symptoms. Conclusions Loneliness is particularly associated with self-reported CM, and in this respect distinct from the social network size. Our results underline the importance of differentiating both psychosocial constructs and suggest focusing on perceived loneliness and its etiological underpinnings by mechanism-based psychosocial interventions.

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Section: Discussionmentioning

confidence: 99%

Association of loneliness and social network size in adulthood with childhood maltreatment: Analyses of a population-based and a clinical sample

et al. 2022

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“…Multicohort, within-participant study designs with multivariate and data-driven analyses have been suggested as improvements for statistical power. 26,27 Furthermore, effect size measures of HC-MDD group differences are often small despite harmonization of study acquisition and methodology. 27 The previously highlighted consistent biomarkers should therefore be taken with caution, even though they been reported in numerous studies.…”

Section: Limitations and Pitfallsmentioning

confidence: 99%

“…26,27 Furthermore, effect size measures of HC-MDD group differences are often small despite harmonization of study acquisition and methodology. 27 The previously highlighted consistent biomarkers should therefore be taken with caution, even though they been reported in numerous studies. Importantly, however, fMRI measures show less distributional overlap between groups and increased out-of-sample replication rates on average compared to structural MRI and diffusion tensor imaging.…”

Section: Limitations and Pitfallsmentioning

confidence: 99%

“…Importantly, however, fMRI measures show less distributional overlap between groups and increased out-of-sample replication rates on average compared to structural MRI and diffusion tensor imaging. 26,27 Moreover, improvements in functional image quality and more advanced analyses might enhance F I G U R E 1 Identified relative consistent functional major depressive disorder biomarkers for task-based and resting-state fMRI for (A) objective diagnosis (MDD > HC) and (B) treatment response. ACC, anterior cingulate cortex; AD, antidepressant treatment; Amyg, amygdala; CCN, cognitive control network; CEN, central executive network; dlPFC, dorsolateral PFC; DMN, default mode network; HC, healthy controls; Ins, insula; MDD, major depressive disorder; mPFC, medial PFC; PCC, posterior cingulate cortex; PFC, prefrontal cortex; rACC, rostral ACC reproducibility and the chances of identifying robust MDD biomarkers.…”

Section: Limitations and Pitfallsmentioning

confidence: 99%

“…ACC, anterior cingulate cortex; AD, antidepressant treatment; Amyg, amygdala; CCN, cognitive control network; CEN, central executive network; dlPFC, dorsolateral PFC; DMN, default mode network; HC, healthy controls; Ins, insula; MDD, major depressive disorder; mPFC, medial PFC; PCC, posterior cingulate cortex; PFC, prefrontal cortex; rACC, rostral ACC reproducibility and the chances of identifying robust MDD biomarkers. 26,27 In this review, we first provide further elucidation of the received criticism by discussing the most notorious limitations and pitfalls of fMRI image acquisition, processing, and analysis methods in MDD studies. The topics that will be discussed explain why these approaches may restrain researchers from identifying robust functional biomarkers of depression.…”

Section: Limitations and Pitfallsmentioning

confidence: 99%

See 2 more Smart Citations

Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects

Pilmeyer

Huijbers

Lamerichs

et al. 2022

Journal of Neuroimaging

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Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging-based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network-based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.

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More Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities

Cited by 2 publications

References 22 publications

Association of loneliness and social network size in adulthood with childhood maltreatment: Analyses of a population-based and a clinical sample

Association of loneliness and social network size in adulthood with childhood maltreatment: Analyses of a population-based and a clinical sample

Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects

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