What can we learn and predict when we model the brain as a graph?

Jonas Richiardijonas.richiardi@chuv.ch - http://richiardi.net

Lausanne University HospitalRadiology Department

Advanced Clinical Imaging Technologies

MAIN2017 18/11/2017

DisclaimerThe information and views set out in this talk are those of the author and do not necessarily reflect the official

opinion of the CHUV or Siemens Healthineers.

AgendaGraphs as representation of the brain What can we learn from graphs?

What can machines learn from graphs? Applications in clinical neuroscience

Graph topological features correspond to clinically relevant subtypes

Applications in basic neuroscience

Functional networks have strong genetic underpinnings

Graphs as representation of the brain

Broad theories of brain organisation

Wernicke’s associationism: higher functions emerge by integration between specialised, segregated brain regions

5

Geschwind’s model of cross-modal association, from [Catani and Ffytche, Brain,2005]

1885 1965

higher function needs parallel, distributed, bidirectional processing

Current

e.g. [Mesulam, Brain, 1998]e.g. [Wernicke, Fortschr Med, 1885]

Brain networks: physical

“default mode network”: coordinated deactivation

61[Raichle et al., PNAS, 2001]

PET 15O blood flow, mass-univariate meta-analysis, N=1321,2

2[Shulman et al., J. Cog. Neurosci., 1997]

WM tractography of arcuate fasciclus

[Catani and Thiebaut de Schotten, Cortex, 2008]

We can see in-vivo, non-invasive evidence of distributed activation…

…supported by an underlying white matter connectome

Brain graphs: mathematical modelsfMRI: from voxels to vertices2

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Entities are not to be multiplied without necessity (Occam)make the model as simple as possible, but no simpler / Everything should be made as simple as possible, but no simpler

all models are wrong, but some are useful (Box, 1976)

2[Richiardi et al., IEEE Sig. Proc. Mag., 2013]

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Staining: from tracts to edges1

1Literature-based macaque connectome from [Felleman & Van Essen, Cer. Cortex, 1991]

g = (V,E,�,⇥)

V: the set of vertices (voxels, ROIs, ICA components, sources...)

E: the set of edges

α: vertex labelling function (returns a scalar or vector for each vertex)

β: edge labelling function (returns a scalar, or vector for each edge)

What can we learn from graphs?

Brain graphs for cognitive neuroscience

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brain organisation graph property

balance between integration and segregation small-worldness

composition of subsystems (e.g. Hubel & Wiesel) modularity

presence of hubs fat-tailed degree distribution

Many essential aspects of brain organisation can be captured by graph topological properties1

1e.g.[Sporns, Ann. N.Y. Acad. Sci., 2011][Rubinov & Sporns, Neuroimage, 2010]

Brain graphs for clinical neuroscience

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Similar brains have similar network communities

Disease progresses along network connections

Disease targets network hubs

More precisely…

11[Fornito et al., Nature Rev. neuroci., 2015]

Graphs as interlingua for neuroimaging

12[Plis et al., Comp. Bio. Med, 2011]

oddball in fMRI and MEG

[Richiardi, Altmann et al., Science, 2015]

mouse axonal connectivity vs transcriptional similarity

What can machines learn from graphs?

Learning and prediction with graphs

Clinical neuroscienceDiagnosis (CAD, Dx, DDx,

subtyping…)

Prognosis (clinical scores, stratification…)

Treatment planning (responders…)

Basic neuroscienceUnderstand (molecular) biology of

networks

What we’d like to do How stats and ML can help

Classification

Hypothesis testing

Regression

Clustering

Factorization/representation learning

Overview of approaches

Stats

mass-univariate, non-parametric, relaxed/two-step

Network science

community structures

Machine Learning

embeddings, kernels, neural nets

matrix statstopological properties

topological properties

[Richiardi et al., IEEE Sig. Proc. Mag., 2013] [Richiardi & Ng, GlobalSIP, 2013]

ML approaches on graphs

Kernels

substructure, global

Neural nets

Spectral, topological filters

Embeddings

direct, algebraic, tangent space

dissim

ilarit

y embe

dding

1D vector input to NN

graph kernel CNNs

Applications in clinical neuroscience

Graph topological features correspond to clinically relevant subtypes

Diagnosis from graphs

Minimally-disabled MS diagnosisCan resting-state functional connectivity serve as a surrogate marker of MS ?

Data: 14 HC, 22 MS (EDSS 1.5-2.5), 450 volumes @ TR 1.1s, 3T scannerGraph: AAL 90, 0.06-0.11 HzPerformance: 82% sens (CI 62-93%)86% spec (CI 60-96%)

[Richiardi et al., NeuroImage, 2012]

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

reduced connectivity index

incre

ased

conn

ectiv

ity in

dex

controls (N=14)patients (N=22)

discriminative projection correlates with WM lesion load

(r=0.61, p < 0.001)

Diagnosis is only the first stepBrain graph classification now yields useful accuracies for several diseases1, although sample sizes are generally small:

Depression: 99%-100% sens / 90%-100% spec

Schizophrenia: 67%-94% sens / 64%-100% spec

also MS, Psychosis, MCI, AD…

But

- this is only as good as the gold standard the model is trained with - a clinical diagnosis

- HC vs disease not very useful in clinic, where DDx is more common

Nevertheless, it is a good sanity check for a new method

Prognosis applications, e.g. recovery of function in stroke2, or conversion in MCI, look very promising

201[Castellanos et al.,NeuroImage, 2013] [Arbabshirani et al, NeuroImage, 2017]

3[Carter et al.,NeuroImage, 2012]

Prognosis from graphs

Survival prognosisPrediction of ALS survival time-frame (short/medium/long)

N=135 (eval: 32!)

Algo: embedding (direct) + MLP, stack imaging and clinical

22[van der Burgh et al., NeuroImage: clinical, 2017]

Preemies development prognosis

Predict pre-term infants cog/motor scores @ 18m from 90-nodes DTI connectivity @ birth:

N=115 (total 168 scans)

Best result ρ=0.31 (<10% var. exp.)

Much better than direct embedding + SVM (ρ=0.18)

231[Kawahara et al., NeuroImage, 2017]

Lesion clinical impact predictionSample: 30 brain injury (19+11) + 120 HC

voxel-level 16 FNs from 120 HC, colourised by nFNs

lesions in high-density regions should impact more domains

[Warren et al., PNAS, 2014]

consistent results with participation coeff on 264-vertices graph, infomap for edge

communities

Treatment planning from graphs

Depression subtyping and responsiveness

[Drysdale et al., Nature Medicine, 2016]

Sample: training (multisite 333D + 378HC), replication (multisite 125D + 352 HC)Algorithm: LogReg / LDA / SVC on CCA loadings

4 subtypes defined from CCA of connectivity (258 vertices)

and HAMD items

group

rTMS response prediction from subtypes (N=124)

individuals

Applications in basic neuroscience

Functional networks have strong genetic underpinnings

Is intrinsic brain activity brain related to genetics?

In humans and lower mammals, spatially consistent, synchronised, intrinsic activity is observed reproducibly across the lifespan

281[Biswal et al., PNAS, 2010]

Are there genetic correlates to this consistency?

BOLD fMRI, ICA(K=20), N=1093, C=24

Functional connectivity genes

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N=15, 18-29 y.o. in vivo

fMRI data(ICA-defined networks)

N=6 (1777 samples), 24-57 y.o. post-mortem~17,000 genes

Gene expression microarray data

xy

xy

xz

yz

dDMNSalience

VisuospatialSensorimotor

A B

[Richiardi, Altmann et al., Science, 2015]

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Is gene expression ‘similar’ in functional networks ?

Gene expression per region Regions grouped as belongingto FNs (1) or rest of brain (0)

4.4

4.6

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ngth

Fra

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grouped by networks

random groupings

p < 0.001

I 1 1 0 0

Sg =

P1P

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1

Holds with distance-corrected data, distance-preserving

permutation

Putative ‘genes of connectivity’

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A few well-known genes like SNAP25 or GABRA5Many potassium channels (KCN*)Significant enrichment for voltage-gated ion channels

Do SNPs modulate FNs in vivo?Except in some cases (e.g. epilepsy resection), brain gene expression is measured post mortem

Gene expression levels can be influenced by single mutations (SNPs) in (or around) the gene

Opposite approach: define strength fraction Sf on fMRI connectivity, see how it is modulated by SNPs of our list

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Sf =

P1P

1+0 �P

1

I 1 1 0 0

Mouse orthologs relate to axonal connectivity

Compare connectivity and transcriptional similarity graphs

Test strength of association between connectivity graph and transcriptional similarity graph via modified Mantel test (use only significant edges). Null model: random selection of 57 genes.

331[Oh et al., 2014]

Structural connectivity Transcriptional similarity Permutation test0

0.05

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p=0.01

More gene-connectivity associationsSome genes individually modulate DMN slow activity1

Common: IL13RA2, NECAB2, NEFH, PVALB, SCN1B, SYT2 (6/38)

342[Krienen et al., PNAS, 2016]

Functional network topography matches transcriptional similarity2

Common:NEFH, SCN4B, SV2C, SYT2 (4/19)

1[W

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et a

l., N

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n, 2

015]

Gene expression correlates with ECoG connectivity in all bands3

Common: ontology results: ion channels; our genes

predict above whole-genome

3[Be

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al., A

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Network imaging genomics for stratification

[Rudie et al.,, Neuron, 2012]

MET genotype: CC lowers PCC↔MPFC conn

CC CG GG ∑TD 9 15 9 33

ASD 7 24 7 38∑ 16 39 16 71

Vertical view of brain disease

[XX

X]

[Dendrou et al., Nat. Rev. Neurol. 2016]

Challenges

ChallengesMotion (mostly in fMRI)

Progress in acquisition (prospective MoCo), correction, analysis, but still major confound

Annotated data still small

Especially ICD-10 subcategories, especially non-structural

How to decide between sensitivity and specificity?

Cost of false positive / false negatives differs widely across diseases

How to integrate data across scanners?

Most theory is for IID case. fMRI connectivity quite sensitive.

[Pow

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t al

, Neu

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age,

2012

]

Edge weight distributions

39[Castrillon et al., 2014]

ABIDE

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ConclusionsApplications of predictive modelling of connectivity are starting to show robustness: connectivity should be considered when looking

for a sensitive marker

Methodological advances, especially in kernels and neural nets, together with large open datasets, are driving prediction

performance upwards rapidly across the board

The biological and molecular bases of functional connectivity are being uncovered, with many contributions by “dry lab” scientists

ThanksAllen Institute

C.K. Lee

IMAGEN consortium

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Modelling and Inference on Brain networks for Diagnosis, MC IOF #299500

MIPLab, EPFL/UNIGE

D. Van De Ville, N. Leonardi

Kelp Lab, UCSCC. Otter

DataAllen Institute

http://brain-map.orgIMAGEN Consortium

http://imagen-europe.com

Funding

UNIGEP. Vuilleumier, M. Gscwhind, C. Quairaux

Collaborators

FINDLab, StanfordM. Greicius

TIG, UCLA. Altmann

CHUVP. Hagmann

BerkeleyJB Poline

Bavaria-California Technology Center

Further readingCalhoun et al. (eds), NeuroImage special issue on Individual Subject Prediction, 2017Fox and Greicius, Clinical applications of resting-state functional connectivity, Front. Sys. Neurosci., 2010 Castellanos et al., Clinical applications of the functional connectome, Neuroimage, 2013 Rao et al., Predictive modelling using neuroimaging data in the presence of confounds, NeuroImage, 2017Shen et al., Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort, NeuroImage, 2010Miller et al., Multimodal population brain imaging in the UK Biobank prospective epidemiological study, Nature Neuroscience, 2016