Marco LORENZI — Inria, Université Côte d'Azur — Statistical Learning for Understanding and Predictin

Statistical Learning for Understanding and Predicting the Course of Neurological Disorders Marco Lorenzi Université Côte d’Azur, Inria Sophia Antipolis Epione Research Group

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William Utermolhen (1933-2007) Self-portraits

1967

1996

1997

1998

1999

2000

1995: diagnosis of Alzheimer’s disease -2

Alzheimer’s disease and Neurodegenerative disorders Language problems

Memory loss

Functionality loss

Apraxia

Memory impairment

Mood alterations

Human and social costs

Most costly disease in Europe and USA Long-term care Mean life expectancy of 7 years

Impact on caregivers ~20,000 $ per year in 1998

[Mölsä et al., Acta Neurol Scand 1986]

[Moore et al., J Gerontol B Psychol Sci Soc Sci 1998]

Health care 160 billion $ per year worldwide [Wimo et al., Dement Geriatr Cogn Disord 1998] -3

Dynamics of neurodegeneration Jack et al, Lancet Neurol 2010; Frisoni et al, Nature Rev Neurol 2010

Genetics

Vascularity

15-30 years time span

?

Microbiome

Sociodemographic

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Introduction

Data Science meets biomedical research Neurology

Statistical learning

+ Statistical learning - formalizes hypothesis into models - verifies models by means of data A discipline to answer challenging questions in medicine -5

Introduction

Data Science meets biomedical research

- Better understanding of the pathology

How to integrate heterogenous biomedical measures?

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Introduction

Data Science meets biomedical research

- Monitoring and studying diseases world-wide How to analyse private healthcare data?

single hospital: 100s – 1’000s patients Big Data 100s – 1’000s hospitals 1’000’000s patients

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Introduction

Data Science meets biomedical research

- Early diagnosis for better cure

How to predict a pathological evolution for a given individual?

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Introduction

Challenges

- How to integrate heterogenous biomedical measures?

- How to analyse private healthcare data collected worldwide?

- How to predict pathological evolution for a given individual?

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Introduction

Challenges

- How to integrate heterogenous biomedical measures?

- How to analyse private healthcare data collected worldwide?

- How to predict pathological evolution for a given individual?

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Imaging-genetics

Identifying genetic modulators of the brain phenotype

Brain imaging 100’000 to 1’000’000 voxels

Genetics Genetic variants (Single Nucleotide Polymorphism - SNP - )

> 1’000’000 known SNPs - 11

…

…

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…

…

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…

…

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…

…

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…

…

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…

…

Iterate for > 1’000’000 variants

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…

…

Iterate for > 1’000’000 variants Iterate for > 1’000’000 image locations

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…

…

Iterate for > 1’000’000 variants Iterate for > 1’000’000 image locations • Hard interpretability • False positive discoveries • No interaction across brain and genetic areas

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Multivariate modeling and dimensionality reduction ~106 SNPs

~105 brain features

X

N individuals

Y

N individuals

‌

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Multivariate modeling and dimensionality reduction ~106 SNPs

~105 brain features

X

N individuals

Y

N individuals

‌ f(X)

g(Y)

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Multivariate modeling and dimensionality reduction ~106 SNPs

~105 brain features

X

Y

N individuals

N individuals

‌ f(X)

g(Y) maximal association

f(X)

f(X)

g(Y)

Correlation: CCA, Covariance: PLS g(Y) - 22

Multivariate modeling and dimensionality reduction ~106 SNPs

~105 brain features

X

Y

N individuals

N individuals

‌ f(X)

g(Y) maximal association

f(X)

f(X)

g(Y)

Correlation: CCA, Covariance: PLS g(Y) - 23

Multivariate Imaging-genetics modeling

Partial least squares association

639 individuals 401 healthy 238 Alzheimer’s Lorenzi et al; PNAS, 115 (12), 2018

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Multivariate Imaging-genetics modeling PLS statistical result From 1’000’000 to 148 variants Meta analysis on gene annotation databases

p relevant locus

proximal areas (+/- 5kbp) Significance (p-value) training testing

TRIB3 • neuronal cell death, • modulation of PSEN1 stability, • interaction with APP.

TM2D1 IL10RA TRIB3 ZBTB7A LYSMD4 CRYL1 FAM135B ...

Lorenzi et al; PNAS, 115 (12), 2018

0.005 0.107 0.003 0.036 0.000 0.621 0.000 …

0.053 0.620 0.003 0.913 0.206 0.118 0.559 …

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Further direction: How to integrate heterogenous biomedical measures from several modalities?

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Analysis of multichannel data

atrophy

function

amyloid

Antelmi, Ayache, Robert and Lorenzi; MLCN @ MICCAI 2018

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Introduction

Challenges

How to integrate heterogenous biomedical measures?

Answer from Statistical Learning: - In principle, data can tell how to combine 1’000’000s of measurements to extract meaningful information - In practice, we need synergy with other experts: biologist and clinicians - More data required to enhanced the discovery potential

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Introduction

Challenges

- How to integrate heterogenous biomedical measures - How to analyse private healthcare data collected worldwide?

- How to predict pathological evolution for a given individual?

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Big Data in medicine Single hospital: 100s – 1’000s patients Data from many hospitals needed

Access to multiple centers data falls into General Data Protection Regulation (GDPR): Privacy, confidentiality, security, ...

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Big Data in medicine Possible Solution: Federated-analysis (or meta-analysis)

Is the association significant?

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Big Data in medicine Possible Solution: Federated-analysis (or meta-analysis)

Is the association significant?

Hospital

1

2

3

4

…

Answer

yes

yes

no

yes

…

Meta-answer: yes

• •

•

No data sharing Ok for standard statistical testing (p-values, effect size)

No complex modeling possible - 32

Advanced Data Science through federated learning C2

C3

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C1 CN

Silva, Gutman, Romero, Thompson, Altmann and Lorenzi. arXiv:1810.08553, 2018

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Advanced Data Science through federated learning C2

C3

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C1 CN Parameters are computed locally Silva, Gutman, Romero, Thompson, Altmann and Lorenzi. arXiv:1810.08553, 2018

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Advanced Data Science through federated learning C3

C2

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C1 CN Parameters are shared Silva, Gutman, Romero, Thompson, Altmann and Lorenzi. arXiv:1810.08553, 2018

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Advanced Data Science through federated learning C2

C3

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C1 CN A federated model is learned Silva, Gutman, Romero, Thompson, Altmann and Lorenzi. arXiv:1810.08553, 2018

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Advanced Data Science through federated learning C2

C3

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C1 CN The federated model is shared Silva, Gutman, Romero, Thompson, Altmann and Lorenzi. arXiv:1810.08553, 2018

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Advanced Data Science through federated learning C2

C3

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C1 CN The procedure is iterated Silva, Gutman, Romero, Thompson, Altmann and Lorenzi. arXiv:1810.08553, 2018

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Federated analysis toolkit An established methodology for distributed = Linear modeling

Matrix factorization

Allows a federated framework for several key statistical operations: Data standardization, accounting for covariates, dimensionality reduction, ...

Silva, Gutman, Romero, Thompson, Altmann and Lorenzi. arXiv:1810.08553, 2018

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Federated analysis of subcortical brain regions in dementia ADNI

PPMI

UK Biobank

Miriad

Alzheimer’s

Parkinson’s

Healthy

Alzheimer’s

802

232

208

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Projection on latent components

Brain subcortical components

Silva, Gutman, Romero, Thompson, Altmann and Lorenzi. arXiv:1810.08553, 2018

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Introduction

Challenges

How to analyse private healthcare data collected worldwide?

Answer from Statistical Learning: - Advanced statistical modeling can be compatible with Data privacy and anonymity - Federated learning applies for simple linear models as well as for more complex ones (e.g. neural networks or Gaussian processes) - Future research needed for improving data security, data transfer bottlenecks, modeling flexibility

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Introduction

Challenges

- How to integrate heterogenous biomedical measures - How to analyse private healthcare data collected worldwide?

- How to predict pathological evolution for a given individual?

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Introduction

Modeling the natural history of neurodegeneration

Hypothetical model

The reality

?

Assessing • Patient severity • Drugs effect

Many years time span

Short clinical trial time span - 43

Introduction

Statistical disease progression model Patient C

Biomarker X

Patient B

Patient A Start clinical trial

End clinical trial Lorenzi et al. NeuroImage, 2017 --- Lorenzi & Filippone, ICML, 2018

time - 44

Introduction

Statistical disease progression model Patient C

Biomarker X

Patient B

Modeling contraints - smoothness - monotonicity

Patient A Start clinical trial

End clinical trial Lorenzi et al. NeuroImage, 2017 --- Lorenzi & Filippone, ICML, 2018

time - 45

Introduction

Statistical disease progression model Patient C

Biomarker X

Patient B

Modeling contraints - smoothness - monotonicity

Patient A Start clinical trial

End clinical trial Lorenzi et al. NeuroImage, 2017 --- Lorenzi & Filippone, ICML, 2018

time - 46

Introduction

Statistical disease progression model Patient C

Disease progression

Biomarker X

Patient B

Patient A Start clinical trial

End clinical trial Lorenzi et al. NeuroImage, 2017 --- Lorenzi & Filippone, ICML, 2018

time - 47

Introduction

Statistical disease progression model Patient C

Disease stage Patient B

Disease progression

Biomarker X

Patient B Disease stage Patient C

Patient A Start clinical trial

End clinical trial Lorenzi et al. NeuroImage, 2017 --- Lorenzi & Filippone, ICML, 2018

time - 48

Introduction

Short term data

Statistical disease progression model

Estimated long term progressions

Lorenzi et al. NeuroImage, 2017 --- Lorenzi & Filippone, ICML, 2018

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Application to clinical data

Introduction OASIS AMYPAD cohort 571 individuals Cognitively normal: 494 AD dementia: 57 Impaired: 37 Non AD Dementia: 4

Data-driven model

Automatic disease severity assessment

Hypothetical model

Lorenzi et al. NeuroImage, 2017 --- Lorenzi & Filippone, ICML, 2018

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A digital model of neurodegeneration

voxels’ intensities

Representing the full disease process space (3D coord)

time

space, time

Lorenzi et al. IPMI 2015

- Structure MRI - Pathological protein deposition Amyloid PET Tau PET - Hypometabolism FDG PET

Collaboration with IRCCS FBF and University of Geneva - 51

A digital model of neurodegeneration 572 individuals 131 healthy 320 impaired 121 Alzheimer’s 5 years average follow-up

Collaboration with IRCCS FBF and University of Geneva Lorenzi, Ribaldi & Frisoni. Submitted

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Neurodegeneration model as reference for independent studies Geneva Memory Clinic cohort (91 individuals)

Most likely time point?

test subject

scan X

scan Y

Lorenzi, Ribaldi & Frisoni. Submitted

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Neurodegeneration model as reference for independent studies Geneva Memory Clinic cohort (91 individuals)

Most likely time point?

test subject

scan X

scan Y

Lorenzi, Ribaldi & Frisoni. Submitted

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Improved prediction with respect to state-of-art assessment Prediction based on progression model

Prediction based on standard imaging measures

Lorenzi, Ribaldi & Frisoni. Submitted

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Revealing independent neurodegeneration patterns in Alzheimer’s disease

Lef

hippocampi

right

Abi Nader, Ayache, Robert and Lorenzi , MLCN@MICCAI, 2018

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Introduction

Challenges

How to predict pathological evolution for a given individual?

Answer from Statistical Learning: - A long-term progression model can be estimated from short-term clinical data - Clinically inspired statistical constraints improve model plausibility and reliability - The progression model is a valuable diagnostic tool, potential improvement of the state-of-the art in clinical assessment

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Conclusions

+

+

• Bridging domains through statistical learning • Integrating biomedical hypothesis is fundamental: plausibility, interpretability, no Big Data (yet) • Challenges: data integration, model federation - 58

mNC3

N. Ayache

P. Robert

C. Abi Nader

Meta-ImaGen

S.S. Silva

V. Manera

UCA MDLab

L. Antelmi

M. Milanesio

UCA-Ville de Nice Young Researcher award

S. Garbarino

Brain-Heart

J. Banus

M. Sermesant

A. Altmann M. Filippone

G.B Frisoni

F. Ribaldi

B. Gutman

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Thank you!

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Understanding Alzheimer’s disease Dr. Aloysius “Alois” Alzheimer (1864-1915)

Amyloid plaques and neurofibrillary tangles

Widespread brain atrophy Normal

Normal

“Alzheimer’s”

“Alzheimer’s”

[Kahn et al, PNAS 2007]

source http://www.alz.org

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Random effects modeling Individuals biomarkers’ observations

Lorenzi et al. NeuroImage, 2017; Lorenzi & Filippone, ICML, 2018

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Random effects modeling Individuals biomarkers’ observations

Fixed effect

Lorenzi et al. NeuroImage, 2017; Lorenzi & Filippone, ICML, 2018

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Random effects modeling Individuals biomarkers’ observations

Random effect + observational noise

Lorenzi et al. NeuroImage, 2017; Lorenzi & Filippone, ICML, 2018

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Random effects modeling Individuals biomarkers’ observations

Subject-specific time reparameterization

Lorenzi et al. NeuroImage, 2017; Lorenzi & Filippone, ICML, 2018

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Random effects modeling df/dt>0

Individuals biomarkers’ observations

Subject-specific time reparameterization Formulation through Gaussian process regression with constraint on dynamics

Lorenzi et al. NeuroImage, 2017; Lorenzi & Filippone, ICML, 2018

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Analysis of multichannel data

Antelmi, Ayache, Robert and Lorenzi; MLCN @ MICCAI 2018

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Analysis of multichannel data

Antelmi, Ayache, Robert and Lorenzi; MLCN @ MICCAI 2018

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Analysis of multichannel data

Antelmi, Ayache, Robert and Lorenzi; MLCN @ MICCAI 2018

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Analysis of multichannel data

Antelmi, Ayache, Robert and Lorenzi; MLCN @ MICCAI 2018

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Analysis of multichannel data

Antelmi, Ayache, Robert and Lorenzi; MLCN @ MICCAI 2018

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Analysis of multichannel data

Antelmi, Ayache, Robert and Lorenzi; MLCN @ MICCAI 2018

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Analysis of multichannel data

Antelmi, Ayache, Robert and Lorenzi; MLCN @ MICCAI 2018

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AD cases worldwide 26,6 millions in 2006

3,1 3,1

7,21 7,21

12,63

1,33 2,3

0,23

[Brookmeyer et al., Alzheimers Dement. 2007] 74

AD cases worldwide 106 millions in 2050

16,51

8,85

62,85

Intervention urgently needed Delaying the disease onset of 1 year

6,33 10,85 10,85

0,84

“Looming epidemy�

2018: No cure nor preventive measures

-12 millions patients worldwide

[Brookmeyer et al., Alzheimers Dement. 2007] 75

gpprogressionmodel.inria.fr Step 1

Step 2

Thanks to Inria SED Team: Julia Elizabeth Luna, Thibaud Kloczko, David Rey - 76

Introduction

Application to clinical data

OASIS AMYPAD cohort 571 individuals Cognitively normal: 494 AD dementia: 57 Impaired: 37 Non AD Dementia: 4

Amyloid Biomarkers Structural Biomarkers

Clinical Measures - 77