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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Population modeling with machine learning can enhance measures of mental health Dadi, Kamalaker; Varoquaux, Gaël; Houenou, Josselin; Bzdok, Danilo; Thirion, Bertrand; Engemann, Denis In: ISSN: 2047-217X ; GigaScience ; https://hal.inria.fr/hal-03470466 ; GigaScience, BioMed Central, 2021, ⟨10.1101/2020.08.25.266536⟩ (2021) Abstract: International audience ; Background Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. Instead, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? Results Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures when modeling from brain signals or sociodemographic data, capturing multiple health-related constructs. Conclusions Population modeling with machine learning can derive measures of mental health from brain signals and questionnaire data, which may complement or even substitute for psychometric assessments in clinical populations. Keyword: [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; Brain Imaging; Machine Learning; Mental Health; Proxy Measures; Sociodemographic Factors URL: https://hal.inria.fr/hal-03470466/document https://doi.org/10.1101/2020.08.25.266536 https://hal.inria.fr/hal-03470466/file/Population%20modeling%20with%20machine%20learning%20can%20enhance%20measures%20of%20mental%20health,%20Kamalaker%20D%20et%20al.pdf https://hal.inria.fr/hal-03470466 BASE Hide details 2 Population modeling with machine learning can enhance measures of mental health Dadi, Kamalaker; Varoquaux, Gaël; Houenou, Josselin... In: Gigascience (2021) BASE Show details 3 Distinguishing Social From Private Intentions Through the Passive Observation of Gaze Cues Jording, Mathis; Engemann, Denis; Eckert, Hannah... In: ISSN: 1662-5161 ; Frontiers in Human Neuroscience ; https://hal.archives-ouvertes.fr/hal-02416981 ; Frontiers in Human Neuroscience, Frontiers, 2019, 13, ⟨10.3389/fnhum.2019.00442⟩ (2019) BASE Show details 4 Semantic coding in the occipital cortex of early blind individuals Abboud, Sami; Engemann, Denis; Cohen, Laurent In: https://hal.archives-ouvertes.fr/hal-02018272 ; 2019 (2019) BASE Show details 5 Distinguishing Social From Private Intentions Through the Passive Observation of Gaze Cues Jording, Mathis; Engemann, Denis; Eckert, Hannah. - : Frontiers Media S.A., 2019 BASE Show details 6 Distinguishing Social From Private Intentions Through the Passive Observation of Gaze Cues Jording, Mathis; Engemann, Denis; Eckert, Hannah. - : FRONTIERS MEDIA SA, 2019 BASE Show details

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