A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework
The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied...
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| Published in: | Artificial intelligence in medicine Vol. 161; p. 103063 |
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| Main Authors: | , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.03.2025
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| ISSN: | 0933-3657, 1873-2860, 1873-2860 |
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| Abstract | The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation.
The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs.
Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.
[Display omitted]
•A normative autoencoder model is used to study BD brain deviations.•Our end-to-end pipeline includes harmonization of external test sets with training set.•We investigated both group- and individual-level brain deviations.•BD showed higher deviations, heterogeneity, and extreme values compared to controls.•Individual-level brain deviating maps showed lower overlap in BD than in controls. |
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| AbstractList | The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry. The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry. The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry. [Display omitted] •A normative autoencoder model is used to study BD brain deviations.•Our end-to-end pipeline includes harmonization of external test sets with training set.•We investigated both group- and individual-level brain deviations.•BD showed higher deviations, heterogeneity, and extreme values compared to controls.•Individual-level brain deviating maps showed lower overlap in BD than in controls. |
| ArticleNumber | 103063 |
| Author | Yatham, Lakshmi Maggioni, Eleonora Bianchi, Anna Maria Sampaio, Inês Won Tassi, Emma Benedetti, Francesco Phillips, Mary L. Piras, Fabrizio Bellani, Marcella Nenadić, Igor Brambilla, Paolo |
| Author_xml | – sequence: 1 givenname: Inês Won surname: Sampaio fullname: Sampaio, Inês Won organization: Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy – sequence: 2 givenname: Emma surname: Tassi fullname: Tassi, Emma organization: Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy – sequence: 3 givenname: Marcella surname: Bellani fullname: Bellani, Marcella organization: Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy – sequence: 4 givenname: Francesco surname: Benedetti fullname: Benedetti, Francesco organization: Division of Neuroscience, Unit of Psychiatry and Clinical Psychobiology, IRCCS Ospedale San Raffaele, Milan, Italy – sequence: 5 givenname: Igor surname: Nenadić fullname: Nenadić, Igor organization: Cognitive Neuropsychiatry Lab, Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany – sequence: 6 givenname: Mary L. surname: Phillips fullname: Phillips, Mary L. organization: Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA – sequence: 7 givenname: Fabrizio surname: Piras fullname: Piras, Fabrizio organization: Fondazione IRCCS Santa Lucia, Roma, Italy – sequence: 8 givenname: Lakshmi surname: Yatham fullname: Yatham, Lakshmi organization: Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada – sequence: 9 givenname: Anna Maria surname: Bianchi fullname: Bianchi, Anna Maria organization: Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy – sequence: 10 givenname: Paolo surname: Brambilla fullname: Brambilla, Paolo email: paolo.brambilla1@unimi.it organization: Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy – sequence: 11 givenname: Eleonora surname: Maggioni fullname: Maggioni, Eleonora organization: Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy |
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| Keywords | Normative modelling Multi-site harmonization Brain MRI Psychiatric disorders Anomaly detection |
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| SubjectTerms | Adult Anomaly detection Autoencoder Bipolar Disorder - diagnostic imaging Bipolar Disorder - pathology Brain - diagnostic imaging Brain - pathology Brain MRI Case-Control Studies Connectome Female Humans Magnetic Resonance Imaging Male Multi-site harmonization Normative modelling Psychiatric disorders |
| Title | A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework |
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