Multimodal predictions of treatment outcome in major depression: A comparison of data-driven predictors with importance ratings by clinicians

Rost, Nicolas; Dwyer, Dominic; Gaffron, Swetlana; Rechberger, Simon; Maier, Dieter; Binder, Elisabeth B.; Brückl, Tanja

doi:10.1016/j.jad.2023.02.007

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…The most common approach is to apply unsupervised clustering algorithms on clinical data. While these studies suggest that specific symptoms-based clusters could be possibly linked to different effectiveness of antidepressant treatments (23)(24)(25), the analysis of symptoms only is likely to be poorly informative given the very large heterogeneity of major depression clinical profile (26). An alternative approach is to identify biologicallydriven subtypes by grouping patients based on shared neuro-biological features (27).…”

Section: Introductionmentioning

confidence: 99%

Multimodal structural neuroimaging data unveil data-driven subtypes of treatment-resistant depression

Colombo,

Calesella,

Bravi

et al. 2023

Preprint

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BackgroundAn estimated 30% of Major Depressive Disorder (MDD) patients exhibit resistance to conventional antidepressant treatments. Identifying reliable biomarkers of treatment-resistant depression (TRD) represents a major goal of precision psychiatry, which is hampered by the clinical and biological heterogeneity underlying MDD.MethodsTo parse heterogeneity and uncover biologically-driven subtypes of MDD, we applied an unsupervised data-driven framework to stratify 102 MDD patients on their neuroimaging signature, including extracted measures of cortical thickness, grey matter volumes, and white matter fractional anisotropy. Our novel analytical pipeline integrated different machine learning algorithms to harmonize neuroimaging data, perform data dimensionality reduction, and provide a stability-based relative clustering validation. The obtained clusters were then characterized for TRD, history of childhood trauma and different profiles of depressive symptoms.ResultsOur results indicated two different clusters of patients, differentiable with 67% of accuracy: 1) one cluster (n=59) was associated with a higher proportion of TRD compared to the other, and higher scores of energy-related depressive symptoms, history of childhood abuse and emotional neglect; this cluster showed a widespread reduction in cortical thickness and volumes, along with fractional anisotropy in the right superior fronto-occipital fasciculus, stria terminalis, and corpus callosum; 2) the second cluster (n=43) was associated with cognitive and affective depressive symptoms and thicker cortices and wider volumes compared to the other.DiscussionOur stratification of MDD patients based on structural neuroimaging identified clinically-relevant subgroups of TRD with specific symptomatic and childhood trauma profiles, which are informative for tailoring personalized and more effective interventions of treatment resistance.

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Section: Introductionmentioning

confidence: 99%

Multimodal structural neuroimaging data unveil data-driven subtypes of treatment-resistant depression

Colombo,

Calesella,

Bravi

et al. 2023

Preprint

View full text Add to dashboard Cite

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Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance

Colombo,

Calesella,

Bravi

et al. 2024

European Neuropsychopharmacology

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Beyond Step Count: Are We Ready to Use Digital Phenotyping to Make Actionable Individual Predictions in Psychiatry?

Ortiz,

Mulsant

2024

J Med Internet Res

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Some models for mental disorders or behaviors (eg, suicide) have been successfully developed, allowing predictions at the population level. However, current demographic and clinical variables are neither sensitive nor specific enough for making individual actionable clinical predictions. A major hope of the “Decade of the Brain” was that biological measures (biomarkers) would solve these issues and lead to precision psychiatry. However, as models are based on sociodemographic and clinical data, even when these biomarkers differ significantly between groups of patients and control participants, they are still neither sensitive nor specific enough to be applied to individual patients. Technological advances over the past decade offer a promising approach based on new measures that may be essential for understanding mental disorders and predicting their trajectories. Several new tools allow us to continuously monitor objective behavioral measures (eg, hours of sleep) and densely sample subjective measures (eg, mood). The promise of this approach, referred to as digital phenotyping, was recognized almost a decade ago, with its potential impact on psychiatry being compared to the impact of the microscope on biological sciences. However, despite the intuitive belief that collecting densely sampled data (big data) improves clinical outcomes, recent clinical trials have not shown that incorporating digital phenotyping improves clinical outcomes. This viewpoint provides a stepwise development and implementation approach, similar to the one that has been successful in the prediction and prevention of cardiovascular disease, to achieve clinically actionable predictions in psychiatry.

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Multimodal predictions of treatment outcome in major depression: A comparison of data-driven predictors with importance ratings by clinicians

Cited by 4 publications

References 47 publications

Multimodal structural neuroimaging data unveil data-driven subtypes of treatment-resistant depression

Multimodal structural neuroimaging data unveil data-driven subtypes of treatment-resistant depression

Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance

Beyond Step Count: Are We Ready to Use Digital Phenotyping to Make Actionable Individual Predictions in Psychiatry?

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