Disease State Index and Neuroimaging in Frontotemporal Dementia, Alzheimer's Disease and Mild ...

157 Pages·2014·2 MB·English

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isbn 978-952-61-1479-8
MIGUEL ÁNGEL MUÑOZ-RUIZ

Disease State Index and neuroimaging in
frontotemporal dementia, Alzheimer’s
disease and mild cognitive impairment

Neuroimaging and Disease State Index in dementia diseases

To be presented by permission of the Faculty of Health Sciences, University of Eastern Finland for
public examination in Canthia L3, Kuopio, on Wednesday, June 11th 2014, at 12 noon

Publications of the University of Eastern Finland
Dissertations in Health Sciences
Number 235

Department of Neurology, Institute of Clinical Medicine, School of Medicine, Faculty of Health
Sciences, University of Eastern Finland
Neurocenter / Neurology
Kuopio University Hospital
Kuopio

2014
Kopijyvä Oy
Kuopio, 2014

Series Editors:
Professor Veli-Matti Kosma, M.D., Ph.D.
Institute of Clinical Medicine, Pathology
Faculty of Health Sciences

Professor Hannele Turunen, Ph.D.
Department of Nursing Science
Faculty of Health Sciences

Professor Olli Gröhn, Ph.D.
A.I. Virtanen Institute for Molecular Sciences
Faculty of Health Sciences

Professor Kai Kaarniranta, M.D., Ph.D.
Institute of Clinical Medicine, Ophthalmology
Faculty of Health Sciences

Lecturer Veli-Pekka Ranta, Ph.D. (pharmacy)
School of Pharmacy
Faculty of Health Sciences

Distributor:
University of Eastern Finland
Kuopio Campus Library
P.O.Box 1627
FI-70211 Kuopio, Finland
http://www.uef.fi/kirjasto

ISBN (print): 978-952-61-1479-8
ISBN (pdf): 978-952-61-1480-4
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
III

Author’s address: Department of Neurology, Institute of Clinical Medicine, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND

Supervisors: Professor Hilkka Soininen, M.D., Ph.D.
Department of Neurology, Institute of Clinical Medicine, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND

Docent Päivi Hartikainen, M.D., Ph.D.
Department of Neurology
Kuopio University Hospital
KUOPIO
FINLAND

Docent Yawu Liu, M.D., Ph.D.
Department of Neurology, Institute of Clinical Medicine, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND

Reviewers: Professor Matti Viitanen, M.D., Ph.D.
Department of Geriatrics
University of Turku
TURKU
FINLAND

Associate Professor Vesa Kiviniemi, M.D., Ph.D.
Department of Radiology
University of Oulu
OULU
FINLAND

Opponent: Professor Alberto Lleó Bisa, M.D., Ph.D.
Hospital de la Santa Creu I Sant Pau
BARCELONA
SPAIN
IV
V

Muñoz-Ruiz, Miguel Ángel
Disease State Index and neuroimaging in frontotemporal dementia, Alzheimer’s disease and mild cognitive
impairment; Neuroimaging and Disease State Index in dementia diseases
University of Eastern Finland, Faculty of Health Sciences
Publications of the University of Eastern Finland. Dissertations in Health Sciences 235. 2014. 135 p.

ISBN (print): 978-952-61-1479-8
ISBN (pdf): 978-952-61-1480-4
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706

ABSTRACT:

The differential diagnosis of dementia diseases represents a challenge particularly in early
phases of the diseases. Many studies have focused on predictive factors for conversion from
mild cognitive impairment (MCI) to dementia, most often to Alzheimer’s disease (AD).
Several methods have been proposed for differentiating between AD and frontotemporal
dementia (FTD), another relative common degenerative dementia. The differential
diagnosis is not easy due to overlapping clinical and biomarker findings.
This thesis introduces the use of a new combination of different methods in the differential
diagnosis of AD, MCI and FTD, and describes a tool, Disease State Index (DSI) and its
visual counterpart, Disease State Fingerprint which collates data from different modalities
and facilitates clinicians to profile a patient as having either AD or FTD.
The first publication compared the benefits of hippocampal volumetry (HV), tensor-based
morphometry (TBM) and voxel-based morphometry (VBM), in order to identify the most
accurate method for differentiating FTD from controls, AD, stable MCI and progressive
MCI. Controls can clearly be differentiated from FTD by using HV (Accuracy=0.83), TBM
(0.82) and VBM (0.83). VBM achieved the highest accuracy of the methods used in its ability
to differentiate between FTD and AD (0.72).
The second report described a comparison of FTD cases with AD, MCI and controls,
including into the DSI in addition to the imaging methods assessed in study I, also values
from CSF, APOE and MMSE. The highest accuracy was reached when comparing FTD with
controls (0.84), followed by FTD compared with MCI (0.79) and AD (0.69). MRI is the most
relevant feature in FTD in comparison to the situation for MCI and AD, however in the
controls vs. FTD comparison, the most relevant feature was the MMSE.
The third publication compared FTD cases with AD and controls, including in DSI data
from clinical symptoms, Hachinski ischemic score, Webster total score, Hamilton
depression scale, MMSE, and tests for assessing functions such as language, memory,
visuo-construction and executive-function, MRI, SPECT, APOE genotype and CSF
biomarker results. The highest accuracy was achieved in differentiating controls from
patients with AD (0.99) and from FTD (0.97). In addition, AD could be differentiated from
FTD with a high degree of accuracy (0.86). Clinical symptoms and neuropsychological tests
were the most relevant categories in differentiating between AD and FTD. With respect to
the imaging methods, MRI was particularly useful in differentiating a healthy state from
AD, while SPECT was more relevant in separating FTD from controls and AD.
The fourth publication investigated the generalizability of DSI in 875 MCI cases from four
cohorts (ADNI, DESCRIPA, AddNeuroMed and Kuopio L-MCI). This report examined the
VI

accuracy to predict progression from MCI to AD and included MRI imaging analysis,
HV, TBM, VBM and as well as CSF biomarkers, neuropsychological tests, MMSE and
APOE. MRI features alone achieved good classification accuracies (0.67-0.81) in the four
cohorts studied, which can be slightly improved by adding values from MMSE, APOE, CSF
and neuropsychological test data. The results revealed that the prediction accuracy of the
combined cohort (0.70) was close to the average of the individual cohort accuracies (0.68-
0.82). It is feasible to use different cohorts as training sets for the DSI, as long as they are
sufficiently similar.
Results from this thesis point to the conclusion that HV, TBM and VBM provide accurate
results when comparing the healthy state with disease and for predicting the conversion to
AD and may also help in differentiating between AD and FTD. DSI incorporating data from
several tests and biomarkers can be supportive in the differentiation of different patients
group i.e. controls, MCI, AD, FTD.

National Library of Medicine Classification: WL 141.5, WL 358.5, WT 155, WN 185
Medical Subject Headings: Alzheimer Disease; Biological Markers; Diagnosis, Computer-Assisted; Dementia;
Diagnosis, Differential; Frontotemporal Dementia; Hippocampus; Magnetic Resonance Imaging; Mild
Cognitive Impairment; Neuroimaging; Neuropsychological Tests
VII

Muñoz-Ruiz, Miguel Ángel
Disease State Index and neuroimaging in frontotemporal dementia, Alzheimer’s disease and mild cognitive
impairment; Neuroimaging and Disease State Index in dementia diseases
Itä-Suomen yliopisto, terveystieteiden tiedekunta
Publications of the University of Eastern Finland. Dissertations in Health Sciences 235. 2014. 135 s.

ISBN (print): 978-952-61-1479-8
ISBN (pdf): 978-952-61-1480-4
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706

TIIVISTELMÄ:

Muistisairauden erotusdiagnoosi on haastavaa erityisesti sairauden alkuvaiheessa. Monet
tutkimukset ovat keskittyneet tutkimaan niitä tekijöitä, jotka ennustavat lievän
kognitiivisen heikentymisen (mild cognitive impairment, MCI)) etenemistä dementiaan,
tavallisimmin Alzheimerin tautiin (AT). Useita menetelmiä on ehdottu erottelemaan AT ja
otsalohko dementia (frontotemporaali dementia (FTD), joka on melko yleinen muistisairaus
nuoremmissa ikäryhmissä. Erotusdiagnoosi ei ole helppoa, koska on kliinisissä oireissa ja
biologisissa merkkiaineissa on osittain samankaltaisuutta näissä sairauksissa.
Tässä väitöskirjassa tutkittiin uutta menetelmien yhdistelmää AT, MCI ja FTD välisessä
erotusdiagnostiikassa. Työssä käytetään työkalua, Disease State Index (DSI, sairausindeksi)
ja sen visuaalinen vastinetta, Disease State Fingerprint (taudin sormenjälki), mikä yhdistää
tietoja ja helpottaa lääkäriä profiilimaan potilaan.
Ensimmäisessä osatyössä vertailtiin hippokampuksen tilavuusmittauksen (volumetrian,
HV), tensor-based morphometrian (TBM) ja voxel-based morphometrian (VBM) tarkkuutta
erottaa FTD, kontrolleista sekä AT ja MCI potilaista. Kontrollit voitiin erottaa hyvin FTD
potilaista käyttämällä HV (tarkkuus=0.83), TBM (0.82) ja VBM menetelmiä (0.83). VBM oli
tarkin erottamaan FTD ja AT potilaat (0.72).
Toisessa osatyössä verrattiin FTD, AT, MCI ja kontrolli ryhmiä siten, että DSI sisälsi
MRI:n lisäksi myös likvorin (CSF) biologiset merkkiaineet, APOE ja MMSE testin tulokset.
Paras tarkkuus saatiin FTD ja kontrolli ryhmien vertailussa (0.84), FTD MCI vertailussa
(0.79) ja alhaisin FTD / AT vertailussa (0.69). MRI oli tärkein FTD /MCI ja FTD/AT
erottelussa.
Kolmannessa osatyössä FTD / AT / kontrollit vertailussa DSI sisälsi myös oireiden
arviointiasteikkoja, laajempia neuropsykologisia testejä, MRI, SPECT, APOE ja CSF
tuloksia. Paras tarkkuus saavutettiin kontrolli / AT (0.99) ja kontrolli / FTD (0.97)
vertailuissa. Myös AT potilaat voitiin erottaa FTD potilaista (0.86). Kliiniset oireet ja
neuropsykologiset testit olivat tärkeimmät AT ja FTD erottelussa.
Kuvantamistutkimuksista MRI oli erityisen hyödyllinen erottamaan terveet AT potilaista,
mutta SPECT oli merkityksellinen erottamaan FTD kontrolleista ja AT potilaista.
Neljännessä osatyössä tutkittiin DSI:n yleistettävyyttä 875 MCI potilaalla neljässä
kohortissa (ADNI, DESCRIPA, AddNeuroMed and Kuopio L-MCI). Tämä työ tutki
tarkkuutta ennustaa MCI:n etenemistä dementiaan (AT). Analyysiin otettiin mukaan MRI
(HV, TBM, VBM) sekä CSF tulokset, neuropsykologisia testejä, MMSE ja APOE. MRI yksin
saavutti hyvän tarkkuuden (0.67-0.81) neljässä kohortissa. Tuloksen paranivat hieman
lisäämällä MMSE:n arvot, APOE, CSF ja neuropsykologia testituloksia. Ennustearvon
tarkkuus yhdistetyssä kohortissa (0.70) oli lähellä keskimääräisen yksittäisten kohorttien
tarkkuutta (0.68-0.82). Tutkimus osoitti DSI:n yleistettävyyden myös eri kohortteja
käytettäessä, jos kohortit ovat riittävän samanlaisia ja sisältävät samoja muuttujia.
VIII

Tulokset osoittivat, että käytetyillä MRI menetelmillä päästään hyvään tarkkuuteen
tutkittujen muistisairauksien erotusdiagnostiikassa. Testien, kuvantamisen ja biologisten
merkkiaineiden tuloksia yhdistävä DSI voi tukea diagnostiikkaa muistisairauksissa.

Luokitus: WL 141.5, WL 358.5, WT 155, WN 185
Yleinen Suomalainen asiasanasto: Alzheimerin tauti; merkkiaineet; Diagnoosi-tietokoneavusteisuus;
Dementia; Erotusdiagnostiikka; Otsalohkodementia; Hippokampus; Magneettitutkimus; Neurologia-
kuvantaminen; Neuropsykologia-testit
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To my family