Neuroimaging Biomarker in Alzheimer's Disease

82 Pages·2017·4.32 MB·English

by  Farshad Falahati Asrami

Thesis for doctoral degree (Ph.D.)
2017
Neuroimaging Biomarkers in
Alzheimer’s Disease
Farshad Falahati Asrami
From Division of Clinical Geriatrics
Department of Neurobiology, Care Sciences and Society
Karolinska Institutet, Stockholm, Sweden
NEUROIMAGING BIOMARKERS IN
ALZHEIMER’S DISEASE
Farshad Falahati Asrami

Stockholm 2017
All previously published papers were reproduced with permission from the publisher.
Published by Karolinska Institutet.
Printed by Eprint AB 2017
© Farshad Falahati Asrami, 2017
ISBN 978-91-7676-663-7
Neuroimaging Biomarkers in Alzheimer’s Disease
THESIS FOR DOCTORAL DEGREE (Ph.D.)

By
Farshad Falahati Asrami
Principal Supervisor: Opponent:
Associate Professor Eric Westman Associate Professor Pawel Herman
Karolinska Institutet KTH Royal Institute of Technology
Department of Neurobiology, Care Sciences Department of Computational Biology
and Society School of Computer Science and Communication
Division of Clinical Geriatrics
Examination Board:
Co-supervisors: Professor Börje Bjelke
Professor Maria Eriksdotter University of Oslo
Karolinska Institutet Division of Medicine and Laboratory Sciences
Department of Neurobiology, Care Sciences
and Society Professor Magnus Borga
Division of Clinical Geriatrics Linköping University
Department of Biomedical Engineering
Dr. Andrew Simmons
King’s College London Associate Professor Helena Karlström
Department of Neuroimaging Karolinska Institutet
Department of Neurobiology, Care Sciences
Professor Lars-Olof Wahlund and Society
Karolinska Institutet Division of Neurogeriatrics
Department of Neurobiology, Care Sciences
and Society
Division of Clinical Geriatrics
To My Beloved Family
“The more I learn, the more I realize how much I don't know.”
- Albert Einstein
ABSTRACT
Alzheimer’s disease (AD) is characterized by an accumulation of abnormal plaques and tangles
in the brain, a process that is estimated to begin years before the appearance of clinical
symptoms. Individuals with subjective memory decline (SMD) or mild cognitive impairment
(MCI) run a higher risk of developing AD than cognitively normal (CN) people.
The main aim of this thesis was to investigate the potential use of structural neuroimaging
biomarkers in AD. A disease severity index (SI) based on multivariate data analysis of MRI-
derived structural measures was generated. The SI was evaluated to discriminate AD patients
from CN individuals as well as to monitor disease progression and predicting conversion to
AD in SMD/MCI.
In study I, the use of structural imaging and cerebrospinal fluid measures and factors that may
affect the use of these methods in dementia work-up were investigated. The results showed that
94% of the patients had a brain scan performed. The results highlighted the role of MRI as an
extended dementia investigation tool in younger patients with less severe cognitive impairment
and a clinical presentation of less clear dementia symptoms.
In study II, the performance of the SI in discriminating AD patients from CN subjects and in
predicting conversion from MCI to AD was investigated. The role of age correction was also
investigated and how it affected classification/prediction. Age correction did not only
effectively eliminate the effect of age, it also highlighted age associations in other factors such
as APOE genotype, global cognitive impairment and gender.
In study III, the SI was longitudinally evaluated for monitoring disease progression in subjects
with MCI. The results showed the potential of the SI to identify MCI subjects at risk of
converting to AD and that disease progression could be monitored in an accurate way. Further,
using the SI it could be observed that APOE genotype and amyloid pathology may
independently modulate disease-related brain structural changes.
In study IV, the SI was validated in a group of healthy individuals with SMD from a different
cohort. Using the SI, a subgroup of SMD subjects who manifested structural brain patterns
similar to AD was identified. These subjects had lower cognitive performance, higher amyloid
burden and worse clinical progression compared to SMD individuals with structural brain
patterns similar to CN.
The SI as a neuroimaging biomarker was studied in the whole disease continuum from CN and
SMD to MCI and AD. The SI showed strong potential to be used as a sensitive tool for
predicting and monitoring disease progression in clinical trials or clinical practice.
Nevertheless, in future the SI should be validated in clinical cohorts and the relationship
between the SI and factors such as genotype and other AD biomarkers should be further

investigated.
LIST OF SCIENTIFIC PAPERS
I. Falahati F, Fereshtehnejad SM, Religa D, Wahlund L-O, Westman E,
Eriksdotter M. The Use of MRI, CT and Lumbar Puncture in Dementia
Diagnostics: Data from the SveDem Registry. Dementia and geriatric
cognitive disorders (2015) 39(1-2):81-91.
doi:10.1159/000366194

II. Falahati F, Ferreira D, Soininen H, Mecocci P, Vellas B, Tsolaki M,
Kloszewska I, Lovestone S, Eriksdotter M, Wahlund L-O, Simmons A,
Westman E. The Effect of Age Correction on Multivariate Classification in
Alzheimer's Disease, with a Focus on the Characteristics of Incorrectly and
Correctly Classified Subjects. Brain topography (2016) 29(2):296-307.
doi:10.1007/s10548-015-0455-1

III. Falahati F, Ferreira D, Muehlboeck J-S, Eriksdotter M, Simmons A, Wahlund
L-O, Westman E. Monitoring Disease Progression in Mild Cognitive
Impairment: Associations between Atrophy Patterns, Cognition, APOE and
Amyloid. In-manuscript.

IV. Ferreira D, Falahati F, Linden C, Buckley R, Ellis K, Savage G, Villemagne
V, Rowe C, Ames D, Simmons A, Westman E. A ‘Disease Severity Index’ to
identify individuals with Subjective Memory Decline that will progress to mild
cognitive impairment or dementia. Scientific Reports (2017), 7, 44368.
doi:10.1038/srep44368
LIST OF SCIENTIFIC PAPERS NOT INCLUDED IN THE
THESIS
I. Falahati F, Westman E, Simmons A. Multivariate Data Analysis and Machine
Learning in Alzheimer's Disease with a Focus on Structural Magnetic
Resonance Imaging. Journal of Alzheimer's disease: JAD (2014) 41(3):685-
708.
doi:10.3233/JAD-131928

II. Eriksson H, Fereshtehnejad S-M, Falahati F, Farahmand B, Religa D,
Eriksdotter M. Differences in Routine Clinical Practice between Early and Late
Onset Alzheimer's Disease: Data from the Swedish Dementia Registry
(SveDem). Journal of Alzheimer's disease: JAD (2014) 41(2):411-19.
doi:10.3233/JAD-132273

III. Faxen-Irving G, Fereshtehnejad S-M, Falahati F, Cedergren L, Goranzon H,
Wallman K, Garcia-Ptacek S, Eriksdotter M, Religa D. Body Mass Index in
Different Dementia Disorders: Results from the Swedish Dementia Quality
Registry (SveDem). Dement Geriatr Cogn Dis Extra (2014) 4(1):65-75.
doi:10.1159/000360415

IV. Eriksdotter M, Vedin I, Falahati F, Freund-Levi Y, Hjorth E, Faxen-Irving G,
Wahlund LO, Schultzberg M, Basun H, Cederholm T, Palmblad J. Plasma
Fatty Acid Profiles in Relation to Cognition and Gender in Alzheimer's Disease
Patients During Oral Omega-3 Fatty Acid Supplementation: The OmegAD
Study. Journal of Alzheimer's disease: JAD (2015) 48(3):805-812.
doi:10.3233/JAD-150102

V. Maioli S, Lodeiro M, Merino-Serrais P, Falahati F, Khan W, Puerta E, Codita
A, Rimondini R, Ramirez MJ, Simmons A, Gil-Bea F, Westman E, Cedazo-
Minguez A. Alterations in brain leptin signalling in spite of unchanged CSF
leptin levels in Alzheimer's disease. Aging Cell (2015) 14(1):122-129.
doi:10.1111/acel.12281

VI. Bloniecki V, Aarsland D, Blennow K, Cummings J, Falahati F, Winblad B,
Freund-Levi Y. Effects of Risperidone and Galantamine Treatment on
Alzheimer’s Disease Biomarker Levels in Cerebrospinal Fluid. Journal of
Alzheimer's disease: JAD (2017) 57(2):387-393.
doi:10.3233/JAD-160758
TABLE OF CONTENTS
1 Introduction ..................................................................................................................... 1
2 From Normal Aging to Alzheimer’s Disease ................................................................. 3
2.1 Normal Brain Aging .............................................................................................. 3
2.2 Subjective Cognitive Decline................................................................................ 3
2.3 Mild Cognitive Impairment .................................................................................. 4
2.4 Alzheimer’s Disease .............................................................................................. 5
2.4.1 Pathological changes in Alzheimer’s Disease .......................................... 5
2.4.2 Clinical Features of Alzheimer’s Disease ................................................ 6
2.4.3 Risk and Protective Factors of Alzheimer’s Disease ............................... 7
2.4.4 Diagnosis of Alzheimer’s Disease ............................................................ 8
3 Neuroimaging and Biomarkers ....................................................................................... 9
3.1 Biomarkers of Alzheimer’s Disease ..................................................................... 9
3.2 Structural Imaging ................................................................................................. 9
3.2.1 Computed Tomography .......................................................................... 10
3.2.2 Magnetic Resonance Imaging................................................................. 11
3.3 Functional Imaging.............................................................................................. 11
3.3.1 Positron Emission Tomography ............................................................. 11
3.3.2 Functional Magnetic Resonance Imaging .............................................. 12
3.4 Cerebrospinal Fluid Biomarkers ......................................................................... 12
3.5 Neuropsychological Tests ................................................................................... 13
3.5.1 Mini–Mental State Examination ............................................................. 13
3.5.2 Clinical Dementia Rating ........................................................................ 13
3.6 Hypothetical Model of Alzheimer’s Disease Biomarkers ................................. 13
4 Multivariate Data Analysis and Alzheimer’s Disease ................................................. 15
4.1 Data Analysis in Alzheimer’s Disease ............................................................... 15
4.2 Feature Extraction and Selection ........................................................................ 15
4.2.1 Automated Image Processing Tools ....................................................... 17
4.3 Data Analysis Methods ....................................................................................... 17
4.3.1 Model Evaluation .................................................................................... 19
4.3.2 Confounding Factors ............................................................................... 19
5 Aims of the Thesis ......................................................................................................... 21
5.1 General Aims ....................................................................................................... 21
5.2 Specific Aims ...................................................................................................... 21
6 Materials and Methods .................................................................................................. 23
6.1 Study Settings ...................................................................................................... 23
6.1.1 Cohorts .................................................................................................... 23
6.1.2 Inclusion and Diagnostic Criteria ........................................................... 24
6.1.3 Participants .............................................................................................. 24
6.2 Imaging ................................................................................................................ 26