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Machine Learning using Neuroimaging for Psychiatric Disorders

Machine Learning using Neuroimaging for Psychiatric Disorders

Edouard Duchesnay

January 31, 2024
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  1. DE LA RECHERCHE À L’INDUSTRIE
    Commissariat à l’énergie atomique et aux énergies alternatives
    www.cea.fr
    Machine Learning using Neuroimaging for
    Psychiatric Disorders
    Edouard Duchesnay, Ph.D., HDR
    Research director at CEA,
    NeuroSpin – Université Paris-Saclay, France

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  2. 2
    1. INTRODUCTION
    Artificial intelligence + Neuroimaging for mental disorders
    2. APPLICATIONS
    Prognosis of clinical evolution or response to treatment
    Outline

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  3. 3
    INTRODUCTION
    1. ARTIFICIAL INTELLIGENCE
    2. NEUROIMAGING
    3. PSYCHIATRY

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  4. 4
    Artificial intelligence (AI) and Machine Learning (ML)
    Discover general patterns associated to some semantic from set of observations
    Input space: similarities are not related to
    any semantic (cat vs dog)
    Representation space capture similarities related to
    some semantic (cat vs dog)
    dogs
    cats
    Now imagine that:
    – Cats = brain MRI of responder to treatment
    – Dogs = brain MRI of non-responder to treatment
    Artificial intelligence?
    Artificial intelligence → Statistical learning or Machine learning

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  5. 5
    Psychiatric disorders (simplified vision)
    [Adam Nature 2013, Craddock, Br. J. Psychiatry 2010]
    [Solmi Molecular Psychiatry 2021]
    Bipolar
    Disorder
    Depression
    Schizophrenia
    Autism spectrum
    disorders
    Mood disorders
    20.5
    20.5
    Neurodevelopmental disorders
    5.5
    Syndromes
    Symptoms Social cognition
    emotional response
    Delusion
    Disordered thoughts
    Mood swings
    Age of onset
    Causes Genetic Environment (stress)
    Genetic

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  6. 7
    Neuroimaging: Brain maturation and mental disorders
    Synaptogenesis
    Myelination
    Synaptic pruning
    Fetal Childhood (0-12 years) Adolescence (12-20 years) Adulthood (20+)
    Brain
    Maturation
    Mental
    disorders
    Genetic
    vulnerability
    Abuse Stress Toxics
    Environment

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  7. 8
    Brain biomarkers for clinicians
    Psychiatry lacks objective quantitative measures (such as blood dosage) to guide clinicians in
    choosing a therapeutic strategy
    I feel like
    everyone is staring
    at me
    Psychosis ?
    Bipolar ?
    Chronic disorder
    trajectory (risk of,
    with 80% of chance)
    Resilience trajectory
    (risk of, with 20% of
    chance)

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  8. 9
    Machine learning for precision psychiatry (personalized medicine)
    Datasets
    Heterogeneous
    patients
    Deep
    phenotyping
    Personalized
    therapies
    Stratify patients into subgroups:
    ● at-risk vs resilient
    ● responder vs non-responder
    Biomarqueurs
    AI models
    Identify biomarkers of
    homogeneous subgroups

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  9. 10
    Bipolar
    Disorder
    Depression
    Schizophrenia
    Autism spectrum
    disorders
    60-65%
    Brain imaging encode subtitles markers of psychiatric disorders
    AI
    Prediction accuracy
    Disorder
    Present: Machine learning can prediction of clinical status from
    neuroimaging
    65-70% [Duchesnay 2012 Neuroimage]
    [Laidi 2022 Bio. Psy.]
    75-80% [De Pierrefeu 2018 Act. Psy. Scand.]
    [De Pierrefeu 2018 HBM]
    [Iftimovici 2022 NeuBioRev]
    65-75% [Dufumier 2021 MICCAI]

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  10. 11
    Actual situation: cross-sectional (case/control) datasets
    Wide phenotyping: Large (1000) longitudinal datasets with individual trajectories
    vs
    years 14 16 18 20
    Chronic disorder trajectory
    Resilience trajectory
    Deep phenotyping: Small (N<100) with PET, advanced MRI,
    Future: More wide (N>1000) and deep phenotyped longitudinal datasets

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  11. 12
    Data availability is the primary lock to develop personalized medicine
    models
    Collecting large longitudinal datasets is incredibly difficult
    ● Cost: >20k€ / participants (7 M€ for 170 subjects, ie. >200M€ for
    10 000 participants)
    ● Human resources: recruitment require nurses, physicians, etc.
    Health care systems cannot implement large scale study
    Machine learning requires data
    Learning

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  12. 13
    Infrastructure to collect more data
    Prognostic biomarkers of response to
    lithium in Bipolar disorder.
    2018, N~300,
    F Bellivier
    Prognostic biomarkers of transition
    to psychosis patients with first
    episode psychosis
    2020, N~500,
    MO Krebs
    French Minds
    (PEPR, 35M€)
    Expected in 2022,
    N~3000, M Leboyer
    Prognostic biomarkers of clinical
    evolution in transdiagnostic cohorts
    of patients with psychiatric disorder
    NeuroSpin leads data
    analysis (AI) and
    management
    CATI (J. Mangin) for
    harmonized MRI
    collection
    IHU-ICE
    G Dehaene, T Bourgeron, R
    Delorme

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  13. 14
    APPLICATIONS:
    1. Prognostic of clinical evolution
    Asynchronous neural maturation predicts transition to psychosis
    2. Prediction of response to treatment
    Prognosis of response to Lithium in Bipolar Disorder

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  14. 15
    Clinical objective 1/2: early prediction of detrimental outcome
    Example: Prediction of transition chronic psychosis
    Fetal Childhood (0-12 years) Adolescence (12-20 years) Adulthood (20+)
    Chronic
    psychosis
    Toxics
    Care Treatments
    2) Recommend Specific therapeutic strategy
    1) AI models to predict future evolution:
    Identify future subjects at risk
    Prognostic biomarkers of transition to psychosis patients with first episode psychosis (2020, N~500, PI MO Krebs)
    AI
    Resilient
    First episode
    psychosis

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  15. 16
    Asynchronous neural maturation predicts transition to psychosis
    Anatomical
    MRI of 82
    participants
    with Ultra
    High Risk of
    Psychosis
    (UHR) (~ 20
    years)
    Transition to psychosis
    - 27 convertors
    - 55 Non-convertors
    One year
    AI
    Prediction performances
    ROC-AUC=80±4% (p < 0.001)
    BACC=69±5% (p < 0.001)

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  16. 17
    Asynchronous neural maturation predicts transition to psychosis
    1/2. Identify predictive signature
    ventromedial PreFrontal Cortex
    right OrbitoFrontal Cortex
    left Precentral Gyrus
    Anatomical
    MRI of 82
    participants
    with Ultra
    High Risk of
    Psychosis
    (UHR) (~ 20
    years)
    Transition to psychosis
    - 27 convertors
    - 55 Non-convertors
    One year
    AI
    Prediction performances
    ROC-AUC=80±4% (p < 0.001)
    BACC=69±5% (p < 0.001)
    Predictive signature of psychosis
    Model Elastic-Net-Total-Variation (Enet-TV)
    Regions of importance:
    1. ventromedial prefrontal cortex (vmPFC): GM ↑ in convertors
    2. left precentral gyrus (lPG): GM ↓ in convertors
    3. right orbitofrontal cortex (rOFC): GM ↓ in convertors
    4. left posterior cingulate gyrus (lpCG),
    5. right putamen (rP).

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  17. 18
    Train brain age predictor using > 2000 anatomical
    MRI of controls (train: 1605, test: 419).
    AI
    Compute brain age gap of of the
    regions most predictive of
    psychosis, i.e., evaluate brain
    maturation vs the general
    population
    ventromedial PreFrontal Cortex
    right OrbitoFrontal Cortex
    left Precentral Gyrus
    Asynchronous neural maturation predicts transition to psychosis
    2/2. Interpretation: regional maturation analysis

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  18. 19
    Accelerated brain maturation in converters compared to
    non-converters, and controls
    left Precentral Gyrus right OrbitoFrontal Cortex ventromedial PreFrontal Cortex
    Delayed brain maturation in converters
    compared to non-converters
    Asynchronous neural maturation predicts transition to psychosis
    2/2. Interpretation: regional maturation analysis

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  19. 20
    Clinical objective 2/2: Prediction of response to treatment
    Example: Lithium response of patients with Bipolar Disorder
    Prognostic biomarkers of response to lithium in Bipolar disorder.
    (2018, N~300, PI F Bellivier)
    1) Scan patient with
    Bipolar Disorder
    2) Lithium treatment
    4) AI models to predict response
    AI
    3) Response
    Yes
    No
    5) Recommend Lithium for potential responders

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  20. 21
    7Litium brain concentration in patients with Bipolar Disorder
    Initiate Lithium treatment in 25 patient with BD
    Measure lithium distribution in the brain
    → Li concentration in the Hippocampus
    Future step is Li Concentration map predictive of
    response to Lithium?
    Fawzi Boumezber, Frank Bellivier
    [Stout, Boumezber et al. Biol Psychiatry 2020]

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  21. 22
    ….
    Use for clinicians
    Chronic disorder
    trajectory (risk of,
    with 80% of chance)
    Resilience trajectory
    (risk of, with 20% of
    chance)
    Psychiatry would become closer to other medical specialties
    Biological-based measurements can help to define a therapeutic strategy
    Can the introduction of biology-based measures improve the relationship with the patient?
    Brain signature

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  22. 23
    Thank you!
    B Dufumier
    PhD
    R Louiset
    PhD
    A Iftimovici
    PH Psy
    S Petiton
    PhD
    B Dollé
    Engineer
    P Gori
    researcher
    A Grigis
    researcher
    F Boumezber
    researcher
    J Houenou
    PUPH Psy
    P Favre
    researcher
    JF Mangin
    Lab head
    MO Krebs
    PUPH Psy
    M Leboyer
    PUPH Psy
    F Bellivier
    PUPH Psy
    R Jardri
    PUPH Psy
    C Laidi
    PH Psy
    M Chupin
    Engineer
    P Auriau
    PhD
    L Dorval
    Engineer
    A Cachia
    PU Psy
    A De Pierrefeu, F Hadj Selem, T Lofstead, J Victor, V Frouin, C Philippe, P Ciuciu, D Papadopoulos
    Close collaborators in psychiatry

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