Francisco del Pozo

Centro de Tecnología Biomédica CTBUniversidad Politécnica de Madrid UPM

Parque Científico y Tecnológico de Montegancedo

www.ctb.up.es

EA-DIAGNOSTIC

Early detection of Alzheimer

Disease

20

14

The Cognitive Continuum

MCI

Mild cognitive

Impairment

Normal

Alzheimer's disease

SMCSUBJECTIVE

MEMORY

COMPLAINTS

Memory complaints

with normal scores

on

neuropsychological

test: no medical

evidence of disease

Experimental

target

4,2 - 6 %

The problem – the market

Alzheimer Disease (AD)

affects 4.2 - 6% of the

population over 65

Spain: 600.000 people

with dementia (400.000

AD)

AD vs Healthy ageing

Are there early functional synchronization differences?

The Objectives

1. RESEARCH ON BIOMARKERS FOR THE

EARLY DETECTION OF ALZHEIMER DISEASE

2. THE CLINICAL TRANSLACION. CLINICAL

TRIALS

3. INDUSTRIAL TRANSFER TO MAKE THAT

POSSIBLE

Toward the early detection of

Alzheimer Disease (AD)

Multicenter-

Clinical trial

Amiloid

Genetics

TAU

RMI

Neuro-

psicology

DTI/MEGAD Biomarker

Clinical individual

AD Biomarkers

Biomarkers

exploitation

MCI/SMC

Analysis

(resting/memory

)

MEG-based brain

conectivity analysis

Graph models

Multidimentional

Classification

Synchronization

MEG analysis

Describing brain connectivity

• Cross-correlation

• Wavelet coherence

• Sync. likelihood

• Generalized Sync.

• Phase Sync.

- EEG

- MEG

- fMRI

- Histological Analysis

- DTI (MRI)

Anatomical Networks Functional Networks

From Bullmore & Sporns, Nature Rev. 10, 186

(2009)

MEG continous recordings

Channel #1

Channel #340TIME

MAGNETIC

FIELD

Visualización conectividad

306 sensors: Planar

radiometers and

magnetometers

Powerful connectivity tools are required

Statistics between groups/conditions

Functional and Effective Connectivity

Signals: Time domain

Signals: Frecuency domain

a)C

PU

/GP

U

Pa

ralle

liza

tio

n

CU

DA

/Op

en

MP

HERMES Integrated toolbox to characterize

functional and effective brain connectivity

1. CLASSICAL MEASURES◦ Cross-Correlation

◦ Correlation

◦ Coherence

◦ [C PARAMETERS]

2. PHASE SYNCRONIZATION

MEASURES◦ Phase Locking Value (PLV)

◦ Phase-Lag Index (PLI)

◦ Weighted Phase-Lag Index (WPLI)

◦ RHO

◦ [PS PARAMETERS]

3. GENERALIZED SYNCRONIZATION

MEASURES◦ S Index

◦ H Index

◦ N Index

◦ M Index

◦ L Index

◦ Synchronization Likelihood

◦ [GS PARAMETERS]

4. GRANGER CAUSALITY◦ Clasical Linear Granger Causality

◦ Direct Transfer Function (DTF)

◦ Partial Directed Coherence (PDC)

◦ [GC PARAMETERS]

There are conectivity differences

Synchronization profiles

Frontoparietal Synchronization

Memory tasks

MCI vs Controls

Synchronization profiles

Lateral Synchronization

Memory tasks

MCI vs Controls

There are conectivity differences

(López, Bruña et al, 2014)

MCI AD Conversion (memory tasks)

Subjetive Memory Complaints (SMC)

Alzheimer'

s disease

SMC Memory

complaints with

normal scores on

neuropsychological

test: no medical

evidence of disease

SMCSUBJECTIVE

MEMORY

COMPLAINT

S

MCI

Normal

Default Mode Network

How to describe synchronization changes.

Modeling Brain Newtworks

Control

MCI

Degree, clustering, outreach

and knn distributions:

MCI networks have nodes with

higher connectivity.

The clustering increases with the

degree (in both Control and MCI).

For the same degree, outreach is

higher at the MCI group.

Networks are assortative.

How to describe synchronization changes.

Modeling Brain Newtworks

Differences between the MCI and

Control groups:

Randomness

Ne

two

rk s

tre

ng

th

Control

M.C.I.

High

Synchronization

Low clustering

Higher outreach

Low modularity

Higher Randomness

Alzheimer

Low

Synchronization

Low clustering

Higher Randomness

Synchronization results vs. other AD-

related potential markers

Multicenter-

Clinical trial

Amiloid

Genetics

TAU

RMI

Neuro-psicology

DTI/MEG

Biomarkers

Clinical individual

AD Biomarkers

Biomarkers

exploitation

MCI/SMC

Resting/memory

analysis

MEG-based brain

conectivity analysis

Graph models

Multidimentional

Classification

Synchronization

MEG analysis

Looking for biomarkers from image

multimodalities

Neuroimaging

Neuroimaging Biomarkers in Aging and Dementia. Application of volumetric

techniques (Voxel-Based Morphometry, VBM) at different stages of

neurodegenerative diseases.

Default Mode Network

SC FC

Pr: Precuneus

IP: Inferior parietal

PC: Posterior cingulate

AC: Anterior cingulate

Green lines

significant

decrease

in connectivity of

MCI vs Controls

Correlation between structural and

functional connectivity

Relationship between

neuropsychology and network scores

MEG-Functional connectivity vs p-TAU in the CSF

TAU concentration

has been associated

with neuronal damage

and cognitive

impairment

CSF

Frontal Inferior OrbLeft

Frontal Superior OrbRight

(R2=-0.52; p<0.05)

Frontal SuperiorLeft

Frontal Middle OrbLeft

(R2=-0.38; p<0.05)

Functional connectivity and

genetic profiles

lLIOC <PLV>

<PLV

>

ApoE Genotype Effect p = 0.049 (F = 4.03)

lLIOC Disrupted Links

lLIOC rTP

mPFClPFC

Delta Band APOE effect

carriers versus non

carriers of apoe 3/4

Multicenter clinical trial

Multicenter-

Clinical trial

Amiloid

Genetics

TAU

RMI

Neuro-psicology

DTI/MEG

Biomarkers

Clinical individual

AD Biomarkers

Biomarkers

exploitation

MCI/SMC

Resting/memory

analysis

MEG-based brain

conectivity analysis

Graph models

Multidimentional

Classification

Synchronization

MEG analysis

Clinical trial: MAGIC-AD

It has been examined differences in functional connectivity between

MCI and healthy controls with MEG at the group level.

- In order for the MEG-based biomarkers to be useful, it must be able

to detect abnormal function at the level of the individual patient.

-There were two goals to the present study:

- To develop a model, using data mining techniques, that reliably

distinguishes between MCI patients and healthy controls.

- Test this model using an unseen dataset of MCI and control

subjects acquired by the MAGIC-AD consortium.

MAGIC-AD: MEG International Consortium

for the study of Alzheimer’s Disease

Elekta-Neuromag supported the annual meeting of the

consortium

Obu – Japan

Pittsburg – USA

Salt Lake – USA

Houston – USA

Cambridge – UK

Helsinki – Finland

Brussels - Belgium

Madrid - Spain

DMN

MEG Preprocessing

102

Magnetometer

Sensor Data

Synchronization

Matrix

102

Magnetometer

Sensor Data

Synchronization

Matrix

102

Magnetometer

Sensor Data

Synchronization

Matrix

Severa

lepochs

(2 s

ec)

per

subje

ct

Aggregated

Synchronization

Matrix

One matrix per subject

Several statistics for each pair of

channels

Mean

Standard Deviation

Median

Median Absolute Deviation (MAD)

Variance coefficient

Mutual Information / Broadband

Resting state

data

.03-330 Hz

Analysis of MAGIC-AD DATA

Center for Biomedical

Technology

(Madrid, Spain)

Recordings of MEG

data

University of Pittsburgh

(USA)

University of Houston (USA)

University of Cambridge

(UK)

University of Helsinki (FN)

National Center for

Gerontology and Geriatrics

(Obu-Japan)

Blind

Data Mining

analysis

University of Utah (SLC,

USA)

Meeting to assess

results

Classification Phase• Random Forest [RF] (Breiman, 2001)

• Naïve Bayes Network [NB] (Buntine, 1991)

• C4.5 induction tree [C4.5] (Quinlan, 1993)

• K-Nearest Neighbour [KNN] (Cover and Hart,

1967)

• Logistic Regression [Logistic](Ng and

Jordan, 2002)

• Support vector machine [SVM] (Platt, 1999),

Information

gain filter

Features: 5150x5

statistics for a pairs

of channels

Subjects: 83 MCI

and 54 Control

1 subject

for

validation

146

subjects for

trainingMulti

Boruta

Classifier

Validation: Internal

validation (leave-one-out

validation scheme)

Classification

algorithms: State-of-the-

art data mining

techniques.

Validation

Bootstrapx147 repetitions

Results of the data mining blind

classification. MAGIC-AD DATA

External validation

Real class

Predicted class MCI Control

MCI 12 4 75,00% PPV

Control 1 11 91,67% NPV

92,31% 73,33% 82,14% Accuracy

Sensitivity Specificity

(Jack et al, 2010; Sperling et al, 2011)

MEG-based

Functional

ConnectivityYears