Lab for Prediction and Prevention in Mental Health
In order to understand mental health, it is crucial to consider the complex interplay of an individual with its environment. This includes biological and psychological factors as well as social interactions or the use of digital media. In our lab, we conduct research on this exciting field with the aim to:
- identify adverse influences for mental health as well as protective factors
- provide modern tools to allow individualised diagnostics and preventive strategies
- develop personalised interventions to support mental health
Our team consists of a mix of methodologically-oriented clinicians and clinically-oriented methodologists, which allows us to live a close interaction between research and clinical application. Together we aim at the clinical translation of novel tools inspired by data-science oriented approaches such as:
- machine-learning and big data analytics to identify informative patterns in behavioural or biological data
- network models to characterise complex relationships between symptoms, risk and protective factors
- dynamic systems modelling to provide a mechanistic understanding of brain in health and disease
- meta-analysis to address essential clinical questions based on a large body of evidence
We hope that by extending the focus to the development of behavioural, cognitive and neuro-imaging biomarkers, we can improve our understanding of mental health and generate models for individualised prediction in psychiatric patients or individuals at risk. Ultimately, we aim to personalise behavioural, pharmacological and psychotherapeutic interventions in the complex human-digital setting of psychiatry today using our expertise in machine learning and computational methods.
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Buciuman M-O, Oeztuerk OF, Popovic D, Enrico P, Ruef A, Bieler N, et al. (2023): Structural and functional brain patterns predict formal thought disorder’s severity and its persistence in recent-onset psychosis: Results from the PRONIA Study. Biol Psychiatry Cogn Neurosci Neuroimaging. https://doi.org/10.1016/j.bpsc.2023.06.001
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Betz LT, Penzel N, Rosen M, Bhui K, Upthegrove R, Kambeitz J (2023): Disentangling heterogeneity of psychosis expression in the general population: sex-specific moderation effects of environmental risk factors on symptom networks. Psychol Med 53: 1860–1869.
Penzel N, Sanfelici R, Antonucci LA, Betz LT, Dwyer D, Ruef A, et al. (2022): Pattern of predictive features of continued cannabis use in patients with recent-onset psychosis and clinical high-risk for psychosis. NPJ Schizophr 8. https://doi.org/10.1038/s41537-022-00218-y
Betz LT, Penzel N, Kambeitz J (2022): A network approach to relationships between cannabis use characteristics and psychopathology in the general population. Sci Rep 12. https://doi.org/10.1038/s41598-022-11092-0
Kambeitz-Ilankovic L, Rzayeva U, Völkel L, Wenzel J, Weiske J, Jessen F, et al. (2022): A systematic review of digital and face-to-face cognitive behavioral therapy for depression. NPJ Digit Med 5: 144.
Haas SS, Doucet GE, Antoniades M, Modabbernia A, Corcoran CM, Kahn RS, et al. (2022): Evidence of discontinuity between psychosis-risk and non-clinical samples in the neuroanatomical correlates of social function. Schizophr Res Cogn 29: 100252.
Müller H, Betz LT, Kambeitz J, Falkai P, Gaebel W, Heinz A, et al. (2022): Bridging the phenomenological gap between predictive basic-symptoms and attenuated positive symptoms: a cross-sectional network analysis. Schizophr 8. https://doi.org/10.1038/s41537-022-00274-4
Schwarzer JM, Meyhoefer I, Antonucci LA, Kambeitz-Ilankovic L, Surmann M, Bienek O, et al. (2022): The impact of visual dysfunctions in recent-onset psychosis and clinical high-risk state for psychosis. Neuropsychopharmacology. https://doi.org/10.1038/s41386-022-01385-3
Koutsouleris N, Pantelis C, Velakoulis D, McGuire P, Dwyer DB, Urquijo-Castro M-F, et al. (2022): Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning: Dementia Praecox Revisited. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2022.2075
Rosen M, Betz LT, Montag C, Kannen C, Kambeitz J (2022): Transdiagnostic Psychopathology in a Help-Seeking Population of an Early Recognition Center for Mental Disorders: Protocol for an Experience Sampling Study. JMIR Res Protoc 11: e35206.
Betz LT, Rosen M, Salokangas RKR, Kambeitz J (2022): Disentangling the impact of childhood abuse and neglect on depressive affect in adulthood: A machine learning approach in a general population sample. J Affect Disord 315: 17–26.
Dwyer DB, Buciuman M-O, Ruef A, Kambeitz J, Sen Dong M, Stinson C, et al. (2022): Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2022.1163
Lalousis PA, Schmaal L, Wood SJ, Reniers RLEP, Barnes NM, Chisholm K, et al. (2022): Neurobiologically based stratification of recent-onset depression and psychosis: Identification of two distinct transdiagnostic phenotypes. Biol Psychiatry. https://doi.org/10.1016/j.biopsych.2022.03.021
Jimeno N, Gomez-Pilar J, Poza J, Hornero R, Vogeley K, Meisenzahl E, et al. (2022): (Attenuated) hallucinations join basic symptoms in a transdiagnostic network cluster analysis. Schizophr Res 243: 43–54.
Antonucci LA, Penzel N, Sanfelici R, Pigoni A, Kambeitz-Ilankovic L, Dwyer D, et al. (2022): Using combined environmental-clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression. Br J Psychiatry 220: 1–17.
Squarcina L, Kambeitz-Ilankovic L, Bonivento C, Prunas C, Oldani L, Wenzel J, et al. (2022): Relationships between global functioning and neuropsychological predictors in subjects at high risk of psychosis or with a recent onset of depression. World J Biol Psychiatry 1–9.
Hanke N, Penzel N, Betz LT, Rohde M, Kambeitz-Ilankovic L, Kambeitz J (2022): Personality traits differentiate patients with bipolar disorder and healthy controls - a meta analytic approach. J Affect Disord. https://doi.org/10.1016/j.jad.2022.01.067
Dizinger JMB, Doll CM, Rosen M, Gruen M, Daum L, Schultze-Lutter F, et al. (2022): Does childhood trauma predict schizotypal traits? A path modelling approach in a cohort of help-seeking subjects. Eur Arch Psychiatry Clin Neurosci. https://doi.org/10.1007/s00406-021-01373-6
