Neuroimaging research

on ageing,

neurodegenerative and

cerebrovascular diseases

Assess brain biomarkers reflected in

micro/macrostructural changes and study

their impact on cognition and health

Maria del C. Valdés

Hernández

Row Fogo Lecturer in Image

Analysis

Centre for Clinical Brain

Sciences

University of Edinburgh

The Dementia Research Institute at the University of Edinburgh

Clinical practice vs. clinical research

Clinical practice Clinical research

Validation in clinical trials, phases 1 to 4

http://uk.gsk.com/en-gb/research/trials-in-people/clinical-trial-phases/

Validation in synthetic images, phantoms, images from clinical and/or

research protocols and against published results

Users are patients/caregivers/practitioners,

therefore tailored for individual use by specific patient populations

Users are researchers,therefore tailored for broad use and

management of big data

User-friendly interface with possibilities of user interaction

Fully automatic methods preferable with minimal user input

Real-time output required Real-time output not required. Batch background processing preferred

Cost-effective Cost-effectiveness not relevant

Clinical practice - SymptomApp

Corbett and Valdés Hernández @ Future Health Product Forge

https://www.youtube.

com/watch?v=qc8a4

pp5lAc

Overview

Structural Imaging

Biomarkers

Identification and Characterisation of Abnormalities

Measurement of Physical

Tissue Properties

Measurement of Tissue

Microstructural Characteristics

3D Shape Morphology

Analysis

Clinical Research

Structural Imaging

Biomarkers

Identification and Characterisation of Abnormalities

Measurement of Physical

Tissue Properties

Measurement of Tissue

Microstructural Characteristics

3D Shape Morphology

Analysis

Overview

How common are they?

Incidental findings Systematic Review BMJ 2009 339:b3016

(16 studies, involving 19,559 people)

Brain Abnormalities

Are all abnormalities incidental findings?

Brain Abnormalities

Sandeman EM, Valdes Hernandez MC, Morris Z, et al. (2013) Incidental Findings on Brain MR Imaging in Older

Community-Dwelling Subjects Are Common but Serious Medical Consequences Are Rare: A Cohort Study. PLOS

ONE 8(8): e71467. https://doi.org/10.1371/journal.pone.0071467

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0071467

Number (%) of subjects with incidental findings (IF), from a birth cohort of 700 (~72years old) individuals (determined against normal ageing templates)

Are all incidental findings clinically relevant?

Brain Abnormalities

Booth TC, Boyd-Ellison JM (2015) The Current Impact of Incidental Findings Found during

Neuroimaging on Neurologists’ Workloads. PLOS ONE 10(2): e0118155.

https://doi.org/10.1371/journal.pone.0118155

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118155

T2 hyper-

inten-

sities

Strokes

and

lacunes

Mineral

deposits

and

haemor-

rhages

Peri-

vascular

spaces

Vascular disease

Brain Vascular Abnormalities

T2 hyper-

inten-

sities

Strokes

and

lacunes

Mineral

deposits

and

haemor-

rhages

Peri-

vascular

spaces

Vascular disease

Brain Vascular Abnormalities

T2 hyperintensities and the white matter

Prefrontal

brain slice

of an 88-yrs

old female

with

Alzheimer’s

disease

FLAIR

MRI

Histochemically

stained

hemisphere

Bodian Silver stained sections

Figure adapted from Gouw AA et al. Brain (2008), 131:3286-3298

T2 hyperintensities and the white matter

T2 hyperintensities and the white matter

Meijboom et al. 2018 (under review)

T2 hyperintensities and the white matter

https://sourceforge.net/projects/bric1936/files/

https://github.com/febrianrachmadi/lots-iam-gpu

T2 hyperintensities and the white matter

https://github.com/febrianrachmadi/lots-iam-gpu

T2 hyperintensities and the white matter

FLAIR MRI 2D Patch-CNN (DeepMedic) Patch-uNet

LOTS-IAM LST-LGA MVQ-100

https://github.com/febrianrachmadi/lots-iam-gpu

T2 hyperintensities and the white matter

FLAIR MRI with WMH Label 2D Patch-CNN (DeepMedic) Patch-uNet

LOTS-IAM LST-LGA MVQ-100

https://link.springer.com/chapter/10.1007/

978-3-030-00931-1_58

T2 hyperintensities and the white matter

Automatic Irregular Texture Detection in Brain MRI Without Human Supervision

Rachmadi, Valdés Hernández and KomuraLNCS, volume 11072 MICCAI 2018 pp 506-513

Extended version in:https://www.biorxiv.org/content/early/2018/07/23/334292

T2 hyper-

inten-

sities

Strokes

and

lacunes

Mineral

deposits

and

haemor-

rhages

Peri-

vascular

spaces

Vascular disease

Brain Vascular Abnormalities

The multispectral approach – cortical stroke lesions

Study on 57 patients with mild to moderate cortical strokes

(Poster at the Annual Meeting of the European Stroke Organisation, 2017,

Christopher and Valdés Hernández)

Volumetric differences between FLAIR

assessments guided by Diffusion-

weighted imaging (DWI) vs. blind to DWI

Volumetric differences between

assessments using FLAIR vs. T1-

weighted

The multispectral approach – cortical stroke lesions

Measurements were not associated with the clinical parameters evaluated:

age, basal ganglia perivascular spaces burden, blood pressure, pulse

frequency, small vessel disease load, lesion or atrophy scores, and all

associations yielded similar B and p values.

The multispectral approach – cortical stroke lesions

T2 hyper-

inten-

sities

Strokes

and

lacunes

Mineral

deposits

and

haemor-

rhages

Peri-

vascular

spaces

Vascular disease

Brain Vascular Abnormalities

Brain mineral deposition

As per

McCarthy &

Kosman, 2014

Valdés Hernández et al. Eur J Neurol 2016

T1W

T2*W

Ca Fe

Glatz A. et al NeuroImage 2014

Brain mineral deposition in routine clinical MRI scans

Valdés Hernández et al. JMRI 2014

Ca+ ions trapped in Alginate gel have an effect on the MRI signal: Formation of

hypointensities in GRE and hyperintensities in T1W; If Ca+ density increases (e.g.

CaCO3) then the water density goes down and hypointensities are visible in T1W.

Outer ring: CaCl2 solutions and Alginate Gel (and one MnCl2solution without Alginate)

Glatz A. et al NeuroImage 2014

Brain mineral deposition – Validation in phantoms

Trapped Cu+ ions cause pronounced hypointensities in GRE, FLAIR and

hyperintensities in T1W (Cu5, mostly contains Cu+ ions dissolved in water).

Inner ring: CuSO4 solutions and Alginate Gel

Glatz A. et al NeuroImage 2014

Brain mineral deposition – Validation in phantoms

T2 hyper-

inten-

sities

Strokes

and

lacunes

Mineral

deposits

and

haemor-

rhages

Peri-

vascular

spaces

Vascular disease

Brain Vascular Abnormalities

http://datashare.is.ed.ac.uk/handle/10283/2216

Gonzalez-Castro and Valdés Hernández Clin Sci 2017

Enlarged perivascular spaces – Automatic scoring

Predictors for Model 1: age, total atrophy, hypertension, Fazekas score, whether the

patient had a previous lacunar infarct or not, index stroke subtype and SVD score.

Predictors for Model 2: the same as Model 1 with the exception of SVD score.

Predictors for Model 3: the same as Model 1 with the exception of Fazekas score

and whether the patient had a previous lacunar infarct or not, as these two

parameters are contemplated within the SVD score.

No SVD score

Enlarged perivascular spaces – Automatic scoring ?

Gonzalez-Castro and Valdés Hernández Clin Sci 2017

Enlarged perivascular spaces – Automatic quantification

Ballerini et al. 2018

Are these biomarkers related?

Valdés Hernández et al. Eur J Neurol 2016

Valdés Hernández et al. 2018 (under review)

Are they associated with cognition?

Microvesseldysfunction

Cognition in late adulthood

Childhood intelligence

Deary et al. 2009

Penke et al. 2012

Perivascular spaces

Brain mineral

deposition

Valdés Hernández et al. 2018 (under review)

Bivariate correlations

G-factor G-speed G-memory

% ID volin ICV

% CSO-PVS vol in ICV

CSO-PVS count

% WMH vol in ICV

Age

G-factor 1

G-speed 0.74** 1

G-memory 0.68** 0.46** 1

% ID vol

in ICV

-0.090* -0.077 -0.087 1

%CSO-PVS

vol in ICV

-0.095* -0.11* -0.10* 0.13** 1

CSO-PVS

count

-0.011 -0.033 -0.060 0.14** 0.83** 1

% WMH vol

in ICV

-0.18** -0.25** -0.14** 0.039 0.48** 0.19** 1

Age -0.14** -0.15** -0.040 -0.027 0.084 0.063 0.20** 1

Are they associated with cognition?

Valdés Hernández et al. Eur J Neurol 2016

IDs in brain stem 0.14 (p=0.006)

Total ID volume0.13 (p=0.047)

-0.14 (p=0.004) in g-0.17 (p<0.001) in g-speed-0.08 (p=0.03) in g-memory

IDs in brain stem -0.02 (p=0.02)

-0.17 (p=0.003) in g-0.13 (p=0.03) in g-speed-0.13 (p=0.02) in g-memory

(Driven by IDs in corpus stratum)

Structural Imaging

Biomarkers

Identification and Characterisation of Abnormalities

Measurement of Physical

Tissue Properties

Measurement of Tissue

Microstructural Characteristics

3D Shape Morphology

Analysis

Overview

Thrippleton, Valdés Hernández and Wardlaw (under review)

Physical tissue properties

1) Second order statistical textural features (e.g. extracted from a Grey Level

Co-occurrence Matrix)

Physical tissue properties – Texture analysis

Valdés Hernández and Gonzalez-Castro et al.

Front Neurol 2017

m

m

m

𝐫𝐞𝐥𝐚𝐭𝐢𝐯𝐞 𝐦𝐞𝐚𝐧 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲 =𝐦𝐞𝐚𝐧 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲

𝐦𝐚𝐱𝐢𝐦𝐮𝐦 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲 𝐢𝐧 𝐭𝐢𝐬𝐬𝐮𝐞 𝐭𝐲𝐩𝐞∙ 𝟏𝟎𝟎%

0

0 1 3

1 1 2 3 3

1 2 2

3

𝐫𝐞𝐥𝐚𝐭𝐢𝐯𝐞 𝐒𝐃 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲 =𝐒𝐃 𝐨𝐟 𝐑𝐎𝐈 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐢𝐞𝐬

𝐦𝐚𝐱𝐢𝐦𝐮𝐦 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲 𝐢𝐧 𝐭𝐢𝐬𝐬𝐮𝐞 𝐭𝐲𝐩𝐞∙ 𝟏𝟎𝟎%

𝐫𝐞𝐥𝐚𝐭𝐢𝐯𝐞 𝐦𝐚𝐱𝐢𝐦𝐮𝐦 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲 =𝐦𝐚𝐱𝐢𝐦𝐮𝐦 𝐑𝐎𝐈 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲

𝐦𝐚𝐱𝐢𝐦𝐮𝐦 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲 𝐢𝐧 𝐭𝐢𝐬𝐬𝐮𝐞 𝐭𝐲𝐩𝐞∙ 𝟏𝟎𝟎%

2) First order statistical textural features

Physical tissue properties – Texture analysis

Viksne and Valdés Hernández et al. 2015 MIUA, Lincoln, UK

http://miua.blogs.lincoln.ac.uk/files/2015/07/MIUA2015_Proceedings_Final.pdf (p 66)

In ROIs

on FLAIR and T1-weighted

In tissues and CSF

on pre-/post-Gd FLAIR

Viksne and Valdés Hernández et al. 2015 MIUA,

Lincoln, UK

http://miua.blogs.lincoln.ac.uk/files/2015/07/MIU

A2015_Proceedings_Final.pdf (p 66)

Valdés Hernández and Gonzalez-Castro et al.

Front Neurol 2017

Physical tissue properties – Texture analysis

Perivascular spaces in

basal ganglia

Total scores of white

matter hyperintensity

burden

Small Vessel Disease

scores

GLCM Contrast (variability) GLCM Homogeneity

Disease progression

Physical tissue properties – Texture analysis

In relation to:

Structural Imaging

Biomarkers

Identification and Characterisation of Abnormalities

Measurement of Physical

Tissue Properties

Measurement of Tissue

Microstructural Characteristics

3D Shape Morphology

Analysis

Overview

Pre-requisites:

1) Smooth surface representing individual shape characteristics

2) Inter-subject point-to-point shape correspondence

3) Robust restoration of individual shape details across large variations

of shape and size

3D Shape Morphology Analysis

Image courtesy of J. Kim, KAIST, South Korea

Progressive deformation – Kim and Park

http://www.nitrc.org/projects/

dtmframework/

3D Shape Morphology Analysis

Kim and Valdés Hernández et al. IEEE TMI 2015

General memory < SD General memory > SD

Left

Hippocampus

Right

Hippocampus

3rd.

ventricle

Example - Regional deformation pattern for general memory

3D Shape Morphology Analysis

Valdés Hernández, Cox et al. NBA 2016

Structural Imaging

Biomarkers

Identification and Characterisation of Abnormalities

Measurement of Physical

Tissue Properties

Measurement of Tissue

Microstructural Characteristics

3D Shape Morphology

Analysis

Overview

(a) Fractional Anisotropy

(b) T1 pulse

(c) Magnetisation

Transfer Ratio

(d) Mean Diffusivity

(e) Radial diffusivity

(f) Axial diffusivity

Tissue Microstructure

Image courtesy of M.E. Bastin, University of Edinburgh, UK

Muñoz Maniega, Valdés Hernández et al. ISMRM 2010

Tissue Microstructure

Some of the severe lesions at baseline correspond to tissue

loss or remain severe (i.e. very hyperintense) after a year.

WMH at baseline only WMH unchanged WMH at follow-up only

Tissue Microstructure –Longitudinal changes

Valdés Hernández et al. AHA 2015

Tissue Microstructure –Longitudinal changes

Valdés Hernández et al. AHA 2015

Row Fogo Charitable Trust

Development of the software/methods on this presentation

received funds from:

Funding