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Extreme deviations from the normative model reveal cortical heterogeneity and associations with negative symptom severity in first-episode psychosis from the OPTiMiSE and GAP studies. Transl Psychiatry 2023; 13:373. [PMID: 38042835 PMCID: PMC10693627 DOI: 10.1038/s41398-023-02661-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 12/04/2023] Open
Abstract
There is currently no quantifiable method to predict long-term clinical outcomes in patients presenting with a first episode of psychosis. A major barrier to developing useful markers for this is biological heterogeneity, where many different pathological mechanisms may underly the same set of symptoms in different individuals. Normative modelling has been used to quantify this heterogeneity in established psychotic disorders by identifying regions of the cortex which are thinner than expected based on a normative healthy population range. These brain atypicalities are measured at the individual level and therefore potentially useful in a clinical setting. However, it is still unclear whether alterations in individual brain structure can be detected at the time of the first psychotic episode, and whether they are associated with subsequent clinical outcomes. We applied normative modelling of cortical thickness to a sample of first-episode psychosis patients, with the aim of quantifying heterogeneity and to use any pattern of cortical atypicality to predict symptoms and response to antipsychotic medication at timepoints from baseline up to 95 weeks (median follow-ups = 4). T1-weighted brain magnetic resonance images from the GAP and OPTiMiSE samples were processed with Freesurfer V6.0.0 yielding 148 cortical thickness features. An existing normative model of cortical thickness (n = 37,126) was adapted to integrate data from each clinical site and account for effects of gender and site. Our test sample consisted of control participants (n = 149, mean age = 26, SD = 6.7) and patient data (n = 295, mean age = 26, SD = 6.7), this sample was used for estimating deviations from the normative model and subsequent statistical analysis. For each individual, the 148 cortical thickness features were mapped to centiles of the normative distribution and converted to z-scores reflecting the distance from the population mean. Individual cortical thickness metrics of +/- 2.6 standard deviations from the mean were considered extreme deviations from the norm. We found that no more than 6.4% of psychosis patients had extreme deviations in a single brain region (regional overlap) demonstrating a high degree of heterogeneity. Mann-Whitney U tests were run on z-scores for each region and significantly lower z-scores were observed in FEP patients in the frontal, temporal, parietal and occipital lobes. Finally, linear mixed-effects modelling showed that negative deviations in cortical thickness in parietal and temporal regions at baseline are related to more severe negative symptoms over the medium-term. This study shows that even at the early stage of symptom onset normative modelling provides a framework to identify individualised cortical markers which can be used for early personalised intervention and stratification.
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Metabolomics dissection of depression heterogeneity and related cardiometabolic risk. Psychol Med 2023; 53:248-257. [PMID: 34078486 PMCID: PMC9874986 DOI: 10.1017/s0033291721001471] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 03/09/2021] [Accepted: 04/06/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND A recent hypothesis postulates the existence of an 'immune-metabolic depression' (IMD) dimension characterized by metabolic dysregulations. Combining data on metabolomics and depressive symptoms, we aimed to identify depressions associated with an increased risk of adverse metabolic alterations. METHOD Clustering data were from 1094 individuals with major depressive disorder in the last 6 months and measures of 149 metabolites from a 1H-NMR platform and 30 depressive symptoms (IDS-SR30). Canonical correlation analyses (CCA) were used to identify main independent metabolite-symptom axes of variance. Then, for the replication, we examined the association of the identified dimensions with metabolites from the same platform and cardiometabolic diseases in an independent population-based cohort (n = 6572). RESULTS CCA identified an overall depression dimension and a dimension resembling IMD, in which symptoms such as sleeping too much, increased appetite, and low energy level had higher relative loading. In the independent sample, the overall depression dimension was associated with lower cardiometabolic risk, such as (i.e. per s.d.) HOMA-1B -0.06 (95% CI -0.09 - -0.04), and visceral adipose tissue -0.10 cm2 (95% CI -0.14 - -0.07). In contrast, the IMD dimension was associated with well-known cardiometabolic diseases such as higher visceral adipose tissue 0.08 cm2 (95% CI 0.04-0.12), HOMA-1B 0.06 (95% CI 0.04-0.09), and lower HDL-cholesterol levels -0.03 mmol/L (95% CI -0.05 - -0.01). CONCLUSIONS Combining metabolomics and clinical symptoms we identified a replicable depression dimension associated with adverse metabolic alterations, in line with the IMD hypothesis. Patients with IMD may be at higher cardiometabolic risk and may benefit from specific treatment targeting underlying metabolic dysregulations.
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Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression. PLoS One 2022; 17:e0278776. [PMID: 36480551 PMCID: PMC9731431 DOI: 10.1371/journal.pone.0278776] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Clinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we approach the problem of estimating a reference normative model across a massive population using a massive multi-center neuroimaging dataset. To this end, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) to complete the life-cycle of normative modeling. The proposed model provides the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed method will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.
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Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models. Neuroimage 2022; 264:119699. [PMID: 36272672 DOI: 10.1016/j.neuroimage.2022.119699] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
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A large-scale ENIGMA multisite replication study of brain age in depression. NEUROIMAGE: REPORTS 2022. [DOI: 10.1016/j.ynirp.2022.100149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models. Neuroimage 2022; 264:119699. [PMID: 36272672 PMCID: PMC7614761 DOI: 10.1101/2021.02.09.430363] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 09/12/2023] Open
Abstract
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
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Abstract
Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus 'healthy' control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1-3 h to complete.
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Neural correlates of anxious distress in depression: A neuroimaging study of reactivity to emotional faces and resting-state functional connectivity. Depress Anxiety 2022; 39:573-585. [PMID: 35536093 PMCID: PMC9543619 DOI: 10.1002/da.23264] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 04/12/2022] [Accepted: 04/16/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Comorbid anxiety disorders and anxious distress are highly prevalent in major depressive disorder (MDD). The presence of the DSM-5 anxious distress specifier (ADS) has been associated with worse treatment outcomes and chronic disease course. However, little is known about the neurobiological correlates of anxious distress in MDD. METHODS We probed the relation between the DSM-5 ADS and task-related reactivity to emotional faces, as well as resting-state functional connectivity patterns of intrinsic salience and basal ganglia networks in unmedicated MDD patients with (MDD/ADS+, N = 24) and without ADS (MDD/ADS-, N = 48) and healthy controls (HC, N = 59). Both categorical and dimensional measures of ADS were investigated. RESULTS MDD/ADS+ patients had higher left amygdala responses to emotional faces compared to MDD/ADS- patients (p = .015)-part of a larger striato-limbic cluster. MDD/ADS+ did not differ from MDD/ADS- or controls in resting-state functional connectivity of the salience or basal ganglia networks. CONCLUSIONS Current findings suggest that amygdala and striato-limbic hyperactivity to emotional faces may be a neurobiological hallmark specific to MDD with anxious distress, relative to MDD without anxious distress. This may provide preliminary indications of the underlying mechanisms of anxious distress in depression, and underline the importance to account for heterogeneity in depression research.
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Classification of suicidal thoughts and behaviour in children: results from penalised logistic regression analyses in the Adolescent Brain Cognitive Development study. Br J Psychiatry 2022; 220:210-218. [PMID: 35135639 DOI: 10.1192/bjp.2022.7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND Despite efforts to predict suicide risk in children, the ability to reliably identify who will engage in suicide thoughts or behaviours has remained unsuccessful. AIMS We apply a novel machine-learning approach and examine whether children with suicide thoughts or behaviours could be differentiated from children without suicide thoughts or behaviours based on a combination of traditional (sociodemographic, physical health, social-environmental, clinical psychiatric) risk factors, but also more novel risk factors (cognitive, neuroimaging and genetic characteristics). METHOD The study included 5885 unrelated children (50% female, 67% White, 9-11 years of age) from the Adolescent Brain Cognitive Development (ABCD) study. We performed penalised logistic regression analysis to distinguish between: (a) children with current or past suicide thoughts or behaviours; (b) children with a mental illness but no suicide thoughts or behaviours (clinical controls); and (c) healthy control children (no suicide thoughts or behaviours and no history of mental illness). The model was subsequently validated with data from seven independent sites involved in the ABCD study (n = 1712). RESULTS Our results showed that we were able to distinguish the suicide thoughts or behaviours group from healthy controls (area under the receiver operating characteristics curve: 0.80 child-report, 0.81 for parent-report) and clinical controls (0.71 child-report and 0.76-0.77 parent-report). However, we could not distinguish children with suicidal ideation from those who attempted suicide (AUROC: 0.55-0.58 child-report; 0.49-0.53 parent-report). The factors that differentiated the suicide thoughts or behaviours group from the clinical control group included family conflict, prodromal psychosis symptoms, impulsivity, depression severity and history of mental health treatment. CONCLUSIONS This work highlights that mostly clinical psychiatric factors were able to distinguish children with suicide thoughts or behaviours from children without suicide thoughts or behaviours. Future research is needed to determine if these variables prospectively predict subsequent suicidal behaviour.
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Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:376-384. [PMID: 33753312 DOI: 10.1016/j.bpsc.2021.03.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/09/2021] [Accepted: 03/09/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Adolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level. METHODS A subsample of a multisite longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into the following: 1) those developing a diagnosis of major depressive disorder or subthreshold major depressive disorder and 2) healthy control subjects. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental, and structural magnetic resonance imaging) at age 14 years were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (n = 407). The features contributing the highest to the prediction were validated in an independent hold-out sample (three independent IMAGEN sites; n = 137). RESULTS The area under the receiver operating characteristic curve for predicting depression onset ranged between 0.70 and 0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events, and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression, with an area under the receiver operating characteristic curve between 0.68 and 0.72 in the independent validation sample. CONCLUSIONS This study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical characteristics, life events, personality traits, and brain structure variables.
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Abstract
Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.
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Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Mol Psychiatry 2021; 26:5124-5139. [PMID: 32424236 PMCID: PMC8589647 DOI: 10.1038/s41380-020-0754-0] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 04/01/2020] [Accepted: 04/23/2020] [Indexed: 01/15/2023]
Abstract
Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.
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Grants
- RF1 AG041915 NIA NIH HHS
- G0802594 Medical Research Council
- R01 MH083968 NIMH NIH HHS
- MR/L010305/1 Medical Research Council
- R01 MH116147 NIMH NIH HHS
- T32 AG058507 NIA NIH HHS
- R01 HD050735 NICHD NIH HHS
- R21 MH113871 NIMH NIH HHS
- T35 AG026757 NIA NIH HHS
- R56 AG058854 NIA NIH HHS
- K23 MH090421 NIMH NIH HHS
- Wellcome Trust
- R61 AT009864 NCCIH NIH HHS
- P41 EB015922 NIBIB NIH HHS
- P20 GM121312 NIGMS NIH HHS
- R37 MH101495 NIMH NIH HHS
- P41 RR008079 NCRR NIH HHS
- T32 MH073526 NIMH NIH HHS
- 104036/Z/14/Z Wellcome Trust
- UL1 TR001872 NCATS NIH HHS
- Department of Health
- U54 EB020403 NIBIB NIH HHS
- R01 MH117601 NIMH NIH HHS
- MR/R024790/2 Medical Research Council
- K01 MH117442 NIMH NIH HHS
- R01 MH085734 NIMH NIH HHS
- R21 AT009173 NCCIH NIH HHS
- RF1 AG051710 NIA NIH HHS
- R01 AG059874 NIA NIH HHS
- CC was supported by NIH grants U54 EB020403, RF1 AG041915, RF1AG051710, P41EB015922, R01MH116147, and R56AG058854
- Russian Science Foundation (RSF)
- The study was supported by a grant from the German Federal Ministry of Education and Research (BMBF; grant FKZ-01ER0816 and FKZ-01ER1506)
- Dr. Busatto was supported by the funding agencies FAPESP and CNPq, Brazil
- Department of Health | National Health and Medical Research Council (NHMRC)
- Deutsche Forschungsgemeinschaft (German Research Foundation)
- This study was funded by National Health and Medical Research Council of Australia (NHMRC) Project Grants 1064643 (Principal Investigator BJH) and 1024570 (Principal Investigator CGD).
- Science Foundation Ireland (SFI)
- This work was supported by NIH grant R37 MH101495
- The Study of Health in Pomerania (SHIP) is part of the Community Medicine Research net (CMR) (http://www.medizin.uni-greifswald.de/icm) of the University Medicine Greifswald, which is supported by the German Federal State of Mecklenburg- West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. This study was further supported by the EU-JPND Funding for BRIDGET (FKZ:01ED1615).
- Gratama Foundation, the Netherlands (2012/35 to NG)
- This work was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via grants to OG (GR1950/5-1 and GR1950/10-1).
- This study was supported by the following National Health and Medical Research Council funding sources: Programme Grant (no. 566529), Centres of Clinical Research Excellence Grant (no. 264611), Australia Fellowship (no. 511921) and Clinical Research Fellowship (no. 402864).
- This study was funded by the National Institute of Mental health grant K23MH090421 (D. Cullen) and Biotechnology Research Center grant P41RR008079 (Center for Magnetic Resonance Research), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, and the Minnesota Medical Foundation. This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute.
- This work was funded by the German Research Foundation (DFG, grant FOR2107 KR 3822/7-2 to AK; FOR2107 KI 588/14-2 to TK and FOR2107 JA 1890/7-2 to AJ)
- The research leading to these results was supported by IMAGEMEND, which received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 602450. This paper reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained therein. This work was also supported by a Wellcome Trust Strategic Award 104036/Z/14/Z
- The QTIM dataset was supported by the Australian National Health and Medical Research Council (Project Grants No. 496682 and 1009064) and US National Institute of Child Health and Human Development(RO1HD050735)
- MJP was funded by Ministerio de Ciencia e Innovación of Spanish Government (ISCIII) through a "Miguel Servet II" (CP16/00020)
- Jair C. Soares supported by the Pat Rutherford Chair in Psychiatry, UTHealth. Jair Soares has received research support from Allergan, Pfizer, Johnson & Johnson, Alquermes and COMPASS. He is a member of the speakers’ bureaus for Sunovion and Sanofi and he is a consultant for Johnson & Johnson.
- The QTIM dataset was supported by the Australian National Health and Medical Research Council (Project Grants No. 496682 and 1009064) and US National Institute of Child Health and Human Development (RO1HD050735)
- SIT was supported in part by NIH grants U54 EB020403, RF1 AG041915, RF1AG051710, P41EB015922, R01MH116147, and R56AG058854
- The CODE cohort was collected from studies funded by Lundbeck and the German Research Foundation (WA 1539/4-1, SCHN 1205/3-1, SCHR443/11-1)
- Canadian Institutes of Health Research (142255)
- Fundet by Research Council of Norway (223273, 248778, 273291), NIH (ENIGMA grants)
- Funded by the South-Eastern Norway Regional Health Authority and a research grant from Mrs. Throne-Holst.
- This work was supported by the Health Research Board, Ireland and the Irish Research Council
- The Cardiff dataset was supported through a 2010 NARSAD Young Investigator Award (ref: 17319) to Dr. Xavier Caseras
- This work was supported by the FRM (Fondation pour la recherche Biomédicale) "Bio-informatique pour la biologie" 2014 grant
- Canadian Institutes of Health Research (103703, 106469), Nova Scotia Health Research Foundation, Dalhousie Clinical Research Scholarship to T. Hajek, Brain & Behavior Research Foundation (formerly NARSAD) 2007 Young Investigator and 2015 Independent Investigator Awards to T. Hajek
- This work was supported by the University Research Council of the University of Cape Town and the National Research Foundation of South Africa.
- Australian NHMRC Program Grant 1037196 and Project Grants 1063960 and 1066177.
- This work was supported by research grants from Grenoble University Hospital
- This work was supported by the Generalitat de Catalunya (2014 SGR 1573) and Instituto de Salud Carlos III (CPII16/00018) and (PI14/01151 and PI14/01148).
- The DIADE dataset was suported by a ZonMW OOG 2007 grant (100-002-034). HG Ruhe was supported by a ZonMW VENI grant (016.126.059)
- JS is supported by the National Institute of General Medical Sciences (P20GM121312) and the National Insitute of Mental Health (R21MH113871)
- Dr. Mauricio was supported by the funding agencies CAPES, Brazil
- This study was supported by R01MH083968, Desert-Pacific Mental Illness Research Education and Clinical Center, and the US National Science Foundation (Science Gateways Community Institutes; XSEDE).
- GT's work was supported by the National Institutes of Health, Grant T35 AG026757/AG/NIA and the University of California San Diego, Stein Institute for Research on Aging
- "EV thanks the support of the Spanish Ministry of Science, Innovation and Universities (PI15/00283) integrated into the Plan Nacional de I+D+I y cofinanciado por el ISCIII-Subdirección General de Evaluación y el Fondo Europeo de Desarrollo Regional (FEDER); CIBERSAM; and the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya to the Bipolar Disorders Group (2017 SGR 1365) and the project SLT006/17/00357, from PERIS 2016-2020 (Departament de Salut). CERCA Programme/Generalitat de Catalunya. "
- Dr. Zanetti was supported by FAPESP, Brazil (grant no. 2013/03905-4).
- NIH grants R01 MH117601, R01 AG059874, U54 EB020403, RF1 AG041915, RF1AG051710, P41EB015922, R01MH116147, and R56AG058854
- PT was supported in part by NIH grants U54 EB020403, RF1 AG041915, RF1AG051710, P41EB015922, R01MH116147, and R56AG058854
- Dr Cole is funded by a UKRI Innovation Fellowship
- This work was supported by NIH grants U54 EB020403 and R01 MH116147. LS is supported by a NHMRC Career Development Fellowship (1140764).
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Rutherford S, Kia SM, Wolfers T, Fraza C, Zabihi M, Dinga R, Berthet P, Worker A, Verdi S, Ruhe HG, Beckmann CF, Marquand AF. The Normative Modeling Framework for Computational Psychiatry.. [PMID: 35650452 PMCID: PMC7613648 DOI: 10.1101/2021.08.08.455583] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus “healthy” control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of down-stream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling. The protocol takes approximately 1-3 hours to complete.
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Fraza CJ, Dinga R, Beckmann CF, Marquand AF. Warped Bayesian Linear Regression for Normative Modelling of Big Data.. [PMID: 34798518 PMCID: PMC7613680 DOI: 10.1101/2021.04.05.438429] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractNormative modelling is becoming more popular in neuroimaging due to its ability to make predictions of deviation from a normal trajectory at the level of individual participants. It allows the user to model the distribution of several neuroimaging modalities, giving an estimation for the mean and centiles of variation. With the increase in the availability of big data in neuroimaging, there is a need to scale normative modelling to big data sets. However, the scaling of normative models has come with several challenges.So far, most normative modelling approaches used Gaussian process regression, and although suitable for smaller datasets (up to a few thousand participants) it does not scale well to the large cohorts currently available and being acquired. Furthermore, most neuroimaging modelling methods that are available assume the predictive distribution to be Gaussian in shape. However, deviations from Gaussianity can be frequently found, which may lead to incorrect inferences, particularly in the outer centiles of the distribution. In normative modelling, we use the centiles to give an estimation of the deviation of a particular participant from the ‘normal’ trend. Therefore, especially in normative modelling, the correct estimation of the outer centiles is of utmost importance, which is also where data are sparsest.Here, we present a novel framework based on Bayesian Linear Regression with likelihood warping that allows us to address these problems, that is, to scale normative modelling elegantly to big data cohorts and to correctly model non-Gaussian predictive distributions. In addition, this method provides also likelihood-based statistics, which are useful for model selection.To evaluate this framework, we use a range of neuroimaging-derived measures from the UK Biobank study, including image-derived phenotypes (IDPs) and whole-brain voxel-wise measures derived from diffusion tensor imaging. We show good computational scaling and improved accuracy of the warped BLR for certain IDPs and voxels if there was a deviation from normality of these parameters in their residuals.The present results indicate the advantage of a warped BLR in terms of; computational scalability and the flexibility to incorporate non-linearity and non-Gaussianity of the data, giving a wider range of neuroimaging datasets that can be correctly modelled.
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Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group. Hum Brain Mapp 2020; 43:341-351. [PMID: 32198905 PMCID: PMC8675412 DOI: 10.1002/hbm.24988] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 03/01/2020] [Accepted: 03/06/2020] [Indexed: 12/11/2022] Open
Abstract
Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD‐related differences in subcortical regions using shape analysis. In this meta‐analysis of subcortical shape from the ENIGMA‐MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta‐analysis. Relative to CTL, patients with adolescent‐onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = −0.164 to −0.180). Relative to first‐episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = −0.173 to −0.184). Our results suggest that previously reported MDD‐associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.
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Predicting individual clinical trajectories of depression with generative embedding. NEUROIMAGE-CLINICAL 2020; 26:102213. [PMID: 32197140 PMCID: PMC7082217 DOI: 10.1016/j.nicl.2020.102213] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/27/2020] [Accepted: 02/13/2020] [Indexed: 12/11/2022]
Abstract
Patients with major depressive disorder (MDD) show variable clinical trajectories. Generative embedding (GE) is used to predict clinical trajectories in MDD patients. GE classifies patients with chronic depression vs. fast remission with 79% accuracy. GE provides mechanistic interpretability and outperforms conventional measures. Proof-of-concept that illustrates the potential of GE for clinical prediction.
Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy – generative embedding (GE) – which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.
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A Closer Look at Depression Biotypes: Correspondence Relating to Grosenick et al. (2019). BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:554-555. [PMID: 31926902 DOI: 10.1016/j.bpsc.2019.09.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 11/08/2019] [Indexed: 11/18/2022]
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Default Mode Network Connectivity and Social Dysfunction in Major Depressive Disorder. Sci Rep 2020; 10:194. [PMID: 31932627 PMCID: PMC6957534 DOI: 10.1038/s41598-019-57033-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 11/20/2019] [Indexed: 01/13/2023] Open
Abstract
Though social functioning is often hampered in Major Depressive Disorder (MDD), we lack a complete and integrated understanding of the underlying neurobiology. Connectional disturbances in the brain's Default Mode Network (DMN) might be an associated factor, as they could relate to suboptimal social processing. DMN connectional integrity, however, has not been explicitly studied in relation to social dysfunctioning in MDD patients. Applying Independent Component Analysis and Dual Regression on resting-state fMRI data, we explored DMN intrinsic functional connectivity in relation to social dysfunctioning (i.e. composite of loneliness, social disability, small social network) among 74 MDD patients (66.2% female, Mean age = 36.9, SD = 11.9). Categorical analyses examined whether DMN connectivity differs between high and low social dysfunctioning MDD groups, dimensional analyses studied linear associations between social dysfunction and DMN connectivity across MDD patients. Threshold-free cluster enhancement (TFCE) with family-wise error (FWE) correction was used for statistical thresholding and multiple comparisons correction (P < 0.05). The analyses cautiously linked greater social dysfunctioning among MDD patients to diminished DMN connectivity, specifically within the rostromedial prefrontal cortex and posterior superior frontal gyrus. These preliminary findings pinpoint DMN connectional alterations as potentially germane to social dysfunction in MDD, and may as such improve our understanding of the underlying neurobiology.
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From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder. Neurosci Biobehav Rev 2019; 104:240-254. [DOI: 10.1016/j.neubiorev.2019.07.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/10/2019] [Accepted: 07/15/2019] [Indexed: 11/17/2022]
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Evaluating the evidence for biotypes of depression: Methodological replication and extension of. NEUROIMAGE-CLINICAL 2019; 22:101796. [PMID: 30935858 PMCID: PMC6543446 DOI: 10.1016/j.nicl.2019.101796] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 03/22/2019] [Accepted: 03/25/2019] [Indexed: 10/29/2022]
Abstract
BACKGROUND Psychiatric disorders are highly heterogeneous, defined based on symptoms with little connection to potential underlying biological mechanisms. A possible approach to dissect biological heterogeneity is to look for biologically meaningful subtypes. A recent study Drysdale et al. (2017) showed promising results along this line by simultaneously using resting state fMRI and clinical data and identified four distinct subtypes of depression with different clinical profiles and abnormal resting state fMRI connectivity. These subtypes were predictive of treatment response to transcranial magnetic stimulation therapy. OBJECTIVE Here, we attempted to replicate the procedure followed in the Drysdale et al. study and their findings in a different clinical population and a more heterogeneous sample of 187 participants with depression and anxiety. We aimed to answer the following questions: 1) Using the same procedure, can we find a statistically significant and reliable relationship between brain connectivity and clinical symptoms? 2) Is the observed relationship similar to the one found in the original study? 3) Can we identify distinct and reliable subtypes? 4) Do they have similar clinical profiles as the subtypes identified in the original study? METHODS We followed the original procedure as closely as possible, including a canonical correlation analysis to find a low dimensional representation of clinically relevant resting state fMRI features, followed by hierarchical clustering to identify subtypes. We extended the original procedure using additional statistical tests, to test the statistical significance of the relationship between resting state fMRI and clinical data, and the existence of distinct subtypes. Furthermore, we examined the stability of the whole procedure using resampling. RESULTS AND CONCLUSION As in the original study, we found extremely high canonical correlations between functional connectivity and clinical symptoms, and an optimal three-cluster solution. However, neither canonical correlations nor clusters were statistically significant. On the basis of our extensive evaluations of the analysis methodology used and within the limits of comparison of our sample relative to the sample used in Drysdale et al., we argue that the evidence for the existence of the distinct resting state connectivity-based subtypes of depression should be interpreted with caution.
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Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach. Transl Psychiatry 2018; 8:241. [PMID: 30397196 PMCID: PMC6218451 DOI: 10.1038/s41398-018-0289-1] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 08/08/2018] [Accepted: 09/10/2018] [Indexed: 12/20/2022] Open
Abstract
Many variables have been linked to different course trajectories of depression. These findings, however, are based on group comparisons with unknown translational value. This study evaluated the prognostic value of a wide range of clinical, psychological, and biological characteristics for predicting the course of depression and aimed to identify the best set of predictors. Eight hundred four unipolar depressed patients (major depressive disorder or dysthymia) patients were assessed on a set involving 81 demographic, clinical, psychological, and biological measures and were clinically followed-up for 2 years. Subjects were grouped according to (i) the presence of a depression diagnosis at 2-year follow-up (yes n = 397, no n = 407), and (ii) three disease course trajectory groups (rapid remission, n = 356, gradual improvement n = 273, and chronic n = 175) identified by a latent class growth analysis. A penalized logistic regression, followed by tight control over type I error, was used to predict depression course and to evaluate the prognostic value of individual variables. Based on the inventory of depressive symptomatology (IDS), we could predict a rapid remission course of depression with an AUROC of 0.69 and 62% accuracy, and the presence of an MDD diagnosis at follow-up with an AUROC of 0.66 and 66% accuracy. Other clinical, psychological, or biological variables did not significantly improve the prediction. Among the large set of variables considered, only the IDS provided predictive value for course prediction on an individual level, although this analysis represents only one possible methodological approach. However, accuracy of course prediction was moderate at best and further improvement is required for these findings to be clinically useful.
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