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METHODS article

Front. Psychiatry
Sec. Mood Disorders
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1436006
This article is part of the Research Topic Treatment Resistant Depression (TRD): epidemiology, clinic, burden and treatment View all 16 articles

Overcoming treatment-resistant depression with machine-learningbased tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project)

Provisionally accepted
Mauro Pettorruso Mauro Pettorruso 1Giorgio Di Lorenzo Giorgio Di Lorenzo 2Beatrice Benatti Beatrice Benatti 3Giacomo d'Andrea Giacomo d'Andrea 1*Clara Cavallotto Clara Cavallotto 1Rosalba Carullo Rosalba Carullo 1Gianluca Mancusi Gianluca Mancusi 1Ornella D. Marco Ornella D. Marco 1Giovanna Mammarella Giovanna Mammarella 1Antonio D'attilio Antonio D'attilio 1Elisabetta Barlocci Elisabetta Barlocci 1Ilenia Rosa Ilenia Rosa 1Alessio Cocco Alessio Cocco 4Lorenzo P. Padula Lorenzo P. Padula 1Giovanna Bubbico Giovanna Bubbico 1Mauro G. Perrucci Mauro G. Perrucci 1Roberto Guidotti Roberto Guidotti 1Antea D'andrea Antea D'andrea 1Laura Marzetti Laura Marzetti 1Francesca Zoratto Francesca Zoratto 5Bernardo Dell'Osso Bernardo Dell'Osso 3Giovanni Martinotti Giovanni Martinotti 1
  • 1 Department of Neuroscience, Imaging and Clinical Sciences, University of Studies G. d'Annunzio Chieti and Pescara, Chieti, Italy
  • 2 University of Rome Tor Vergata, Roma, Lazio, Italy
  • 3 University of Milan, Milan, Lombardy, Italy
  • 4 Asl Lanciano Vasto Chieti, Chieti, Italy
  • 5 National Institute of Health (ISS), Rome, Lazio, Italy

The final, formatted version of the article will be published soon.

    Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD's clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool's recommendations.Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.

    Keywords: Transcranial magnetic stimulation (rTMS), Esketamine nasal spray, machine-learning (ML) algorithms, treatment resistant depression (TRD), Endophenotyes

    Received: 21 May 2024; Accepted: 01 Jul 2024.

    Copyright: © 2024 Pettorruso, Di Lorenzo, Benatti, d'Andrea, Cavallotto, Carullo, Mancusi, Marco, Mammarella, D'attilio, Barlocci, Rosa, Cocco, Padula, Bubbico, Perrucci, Guidotti, D'andrea, Marzetti, Zoratto, Dell'Osso and Martinotti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Giacomo d'Andrea, Department of Neuroscience, Imaging and Clinical Sciences, University of Studies G. d'Annunzio Chieti and Pescara, Chieti, Italy

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