Disease state index and neuroimaging in frontotemporal dementia, Alzheimer's disease and mild cognitive impairment

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Publications of the University of Eastern Finland Dissertations in Health Sciences

isbn 978-952-61-1479-8

Publications of the University of Eastern Finland Dissertations in Health Sciences

is se rt at io n s

| 235 | Miguel Ángel Muñoz-Ruiz | Disease State Index and Neuroimaging in Frontotemporal Dementia, Alzheimer’s...

Miguel Ángel Muñoz-Ruiz Disease State Index and Neuroimaging in Frontotemporal Dementia, Alzheimer’s Disease and Mild Cognitive Impairment

Miguel Ángel Muñoz-Ruiz

Disease State Index and

Neuroimaging in Frontotemporal Dementia, Alzheimer’s Disease and Mild Cognitive Impairment

Alzheimer’s disease (AD) is the most prevalent disease of the dementia diseases while frontotemporal dementia (FTD) is relatively common in people younger than 65 years of age. Early and precise diagnosis of these two diseases is a major challenge. There is a need to identify new methods that could achieve an earlier and more precise diagnosis, and to integrate all these data originating from multiple sources, in order to facilitate the clinical diagnosis. This thesis introduces the use of a new combination of different methods in the differential diagnosis of AD, mild cognitive impairment stages and FTD, and a tool (Disease State Index and Disease State Fingerprint) that collates data from different sources to help clinicians to profile a patient as having either AD or FTD.

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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 11^th 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

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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

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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

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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

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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

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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.

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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

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Acknowledgements

This work has been possible due to the multidisciplinary work of several partners, from a great variety of fields.

I want to thank everyone who has helped, supported, shared moments with me, which are lots of people. Special thanks to:

First of all, my greatest thanks to Professor Hilkka Soininen, main supervisor. Example of hard-working, leadership, always available although the lots of work she had, especially when she became dean of the Faculty of Health Sciences. For being an excellent supervisor and also an excellent person who has encouraged me to persuade in working hard for staying in Finland.

Thanks to Päivi Hartikainen, co-supervisor. Still I remember when I was an exchange student at KYS and she gave me the chance to meet Hilkka and come back to Finland after my graduation as MD. A great supervisor and an excellent person, also with lots of work, who always has found time for attending me and supervising my research.

Thanks to Yawu Liu, co-supervisor, for his great help in the daily work concerning my PhD articles and general research work.

Thanks to Associate Professor Vesa Kiviniemi and Professor Matti Viitanen for reviewing this thesis and to Professor Alberto Lleó Bisa for being the opponent during the defense.

Thanks to Anette Hall, for his enormous help and collaboration, always ready for answering every question kindly. Also for her help to write the methods part of this thesis and her work as co-author in the fourth article.

Thanks to people working in PredictAD project, from which all the work has been developed. Particularly thanks to Jyrki Lötjönen, Juha Koikkalainen and Jussi Mattila from VTT in Tampere. Without their help, all these work could not have been done. It is always a pleasure visiting VTT and learning from them.

Thanks to Ewen Macdonald, for making the language check of this thesis.

Thanks to Professor Hannele Turunen for editing this thesis.

Thanks to the other co-authors and collaborators, whose contribution has been essential for coming up with all the publications.

Thanks to all the funding sources: Doctoral Programme in Molecular Medicine, European Union 7^th Framework Program PredictAD, European Union 7^th Framework Program VPH- DARE@IT (grant agreement No 601055), EVO grant from Kuopio University Hospital, Instrumentarium grant and the Faculty of Health Sciences.

To Esa Koivisto, Tuija Parsons, Mari Tikkanen and Sari Palviainen from UEF and Tuula Toivanen from KYS, for their great help and assistance always when needed.

To Merja Hallikainen, Tuomo Hänninen, Anne Koivisto, Professor Anne Remes and Professor Mikko Hiltunen for their comments when writing this thesis and/or preparing its’

defense.

Thanks to people from VPH-DARE@IT in Sheffield, in particular to Professor Alejandro Frangi, Professor Annalena Venneri, Daerdre McGrath, Luigi Di Marco and Leandro Beltrachini. These few months in the UK have been a new and refreshing experience to me.

To Álex for his tutoring and his kindness. To Annalena for her teaching about

neuropsychological testing and her kindness during my time in Hallamshire Hospital. To

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Leandro, Daerdre and Luigi: working with them in CISTIB was an invaluable experience which I do recommend; I learned that engineers and medical people can do a great work together, no matter how distant our fields are. In particular thanks to Leo for his help writing the imaging section of the methods part and his time in Sheffield. We definitely have to go together to watch a Premier League match in Manchester!

Thanks to Lauri Kivikoski for his collaboration within the PredictAD pilot project at KYS.

To Sanna-Kaisa Herukka, Laura Kela, Maria Falkenberg, Ossi Nerg, Niina Happonen, Anna Railo and Terhi Laitinen for their support and help with Finnish language. Especially to Anna, who helped me during winter time in 2011. The first steps are always the hardest to take, and without her help it could not have been possible. Also to Ossi for being a friend who has made of me a new KalPa fan.

To Taru Heikkinen for her friendship and the phrase of 2014: voe tokkiisa!

Particularly thanks to Arja and Kari Savolainen, who have treated me like one of their children. I have learned Finnish with them, but because of them I have learned to

understand and appreciate Finnish people like them, which is even more important. Most of the best moments I have lived in Finland have been with them. Nothing is more important than a family, and with them I have found a new one in Kuopio.

Also special thanks to Alberto and Sharon Salgado, who have accompanied me during this time in Finland since the very first moment I met them in 2010.

Thanks to Jussi Nokelainen and Valtteri Kokkonen. I met them in 2010 when they were my tutors, and then started an invaluable friendship that remains. Looking forward to watch the next Champions League match on TV with them!

Thanks to Heli Nuutinen and her family for their support and friendship since 2010.

Thanks to Harri Ruhanen for the Spanish conversations while having beers in Malja.

To Carmen Plumed and Javier Ortega. With them I have felt like being in Spain again, despite the rainy and snowing days typical from the Finnish weather.

To Bhima, Lili and Lan for their friendship and the funny moments lived; our coffee time at 16.00 is now a tradition that should continue. Especially to Bhima, for all the good moments lived since we met in the Winter School in 2012.

Thanks to Anu Jormanainen, Vicent Tortosa, Alvaro Herrera and Pepe and his family, for the great moments we have shared either in Tampere, Helsinki, Jyväskylä or Joensuu. I remembered when I was with Alvaro in the EILC in Jyväskylä in 2010, when we wondered who would be coming back to Finland… and we both were here in Finland again during 2013. Life is always unpredictable!

Thanks to Nacho Navero, best friend and permanent link from London, for the never- ending Facebook and Skype conversations, always there for cheering up my day.

To Jordi Guinot for all the conversations about football, medicine and life in general. To Rogelio Monfort, Juan Hervás and Herminio Morillas for their friendship during these years.

Thanks to Miguel Martínez for his support and guidance within the years.

Last but not least, my biggest thanks go to my family: to my brothers Álex and Fran, and my parents Francisco and Trinidad. Always source of support and love, no matter how long is the distance. I would need all the pages in a book for thanking what they have done, and it never would be enough. They always supported me in the good and in the bad moments, encouraging me to study medicine at first and then doing a PhD in dementia and starting my medical specialty training in Finland. But above all, they encourage me to be a

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better person day after day and to be a good professional; this is working hard every day, with dignity and respect. It does not matter if it is big or small the goal you try to achieve, what matters is that you do your best for getting it. This thesis, or better saying, all the work done through these years, is entirely dedicated to my family.

Finally, I would like to quote the words from Pascual Maragall, ex-former president of the Generalitat of Catalunya (Spain), currently suffering from Alzheimer’s disease, as our road route for studying dementia within the following years: “I want to help to defeat this disease;

personally and collectively. Nowhere is it written that is invincible”

Kuopio, May 2014 Miguel Ángel Muñoz-Ruiz

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List of the original publications

This thesis is based on the following original publications, referred by their Roman numbers in the text.

I. Muñoz-Ruiz MÁ, Hartikainen P, Koikkalainen J, Wolz R, Julkunen V, Niskanen E, Herukka S-K, Kivipelto M, Vanninen R, Rueckert D, Liu Y, Lötjönen J and Soininen H. Structural MRI in Frontotemporal Dementia: Comparisons between Hippocampal volumetry, Tensor-based morphometry and Voxel-based morphometry. PLoS ONE 7(12): e52531.

II. Muñoz-Ruiz MÁ, Hartikainen P, Hall A, Mattila J, Koikkalainen J, Herukka S-K, Julkunen V, Vanninen R, Liu Y, Lötjönen J and Soininen H. Disease Fingerprint in frontotemporal degeneration with reference to Alzheimer’s disease and mild cognitive impairment. J Alzheimers Dis. 2013 Jan 1;35(4):727-39. doi: 10.3233/JAD- 122260.

III. Muñoz-Ruiz MÁ, Hall A, Mattila J, Koikkalainen J, Herukka S-K, Husso M, Hänninen T, Vanninen R, Liu Y, Hallikainen M, Lötjönen J, Soininen H and Hartikainen P. Validating the Disease State Fingerprint for diagnosing frontotemporal lobar degeneration. Submitted.

IV. Hall A*, Muñoz-Ruiz M*, Mattila J, Koikkalainen J, Tsolaki M, Mecocci P, Kloszewska I, Vellas B, Lovestone S, Visser PJ, Lötjönen J, Soininen H and and collaborators from DESCRIPA, the Kuopio L-MCI study, AddNeuroMed consortium and Alzheimer Disease Neuroimaging Initiative. Generalizability of the Disease State Index in Predicting Mild Cognitive Impairment Progression to Alzheimer’s Disease in Four Different Cohorts. Submitted. * Authors have equal contribution to this study.

The publications were adapted with the permission of the copyright owners.

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Abbreviations

Aβ Amyloid beta

AD Alzheimer’s disease

ADL Activities of daily living ALS Amyotrophic lateral sclerosis APOE Apolipoprotein E APP Amyloid precursor protein ARWMC Age-related white matter changes

ASL Arterial spin labelling

AV Automatic volumetry

BIBD Basophilic inclusion disease

BMI Body mass index

bvFTD Behavioral variant of FTD BOLD Blood oxygen level dependent

C Controls

C9ORF72 Chromosome 9 open reading frame 72

CBD Corticobasal degeneration CBF Cerebral blood flow

CBS Corticobasal syndrome CDR Clinical dementia rating CHMP2B Charge multivesicular body protein 2B

Cho Choline

COPD Chronic obstructive pulmonary disease

Cr Creatine/phosphocreatine CSF Cerebrospinal fluid

CTH Cortical thickness DLB Dementia with Lewy bodies DMN Default mode network

DSC Dynamic susceptibility contrast

DSF Disease State Fingerprint DSI Disease State Index

DTI Diffusion tensor-imaging EOAD Early onset Alzheimer’s

disease

EOD Early onset dementia

FA Fractional anisotropy

FBI Frontal behavioral inventory FLAIR Fluid-attenuated inversion recovery

fMRI Functional MRI

FTD Frontotemporal dementia FTLD Frontotemporal lobar degeneration

FUS Fused in sarcoma

GDS Global deterioration scale

GM Grey matter

GRN Progranulin GWAS Genome wide association studies

HC Hippocampus

HIS Hachinski ischemic score

HV Hippocampal volumetry

LA Logopenic aphasia

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LOAD Late onset Alzheimer’s disease

LOD Late onset dementia

MAPT Microtubule associated protein tau

MB Microbleed

MBL Manifold-based learning MCI Mild cognitive impairment

MD Mean diffusifity

MI Myoinositol MMSE Mini-Mental State Examination

MND Motoneuron disease

MRI Magnetic resonance imaging

MRS Magnetic resonance

spectroscopy

MTA Medial temporal lobe atrophy MTL Medial temporal lobe

NAA N-acetyl aspartate NFT Neurofibrillary tangles NIFID Neuronal intermediate filament inclusion disease

NPI Neuropsychiatric inventory P-Tau Phosphorylated Tau

PET Positron emission

tomography

PIB Pittsburg Compound B

PMCI Progressive mild cognitive impairment

PNFA Progressive non-fluent aphasia

PPA Primary progressive aphasia PS-1 Presenilin-1

PS-2 Presenilin-2

PSP Progressive supranuclear palsy

PWI Perfusion weighted imaging

RF Risk factor

ROI Region of interest RSN Resting state networks SCI Subjective cognitive impairment

SD Semantic dementia

SMCI Stable mild cognitive impairment

SN Salience network

SPECT Single photon emission computed tomography

TMT Trail making test T-Tau Total Tau

TARDBP TAR DNA binding protein TBM Tensor-based morphometry

UPS Unknown pathological

substrate

VaD Vascular dementia

VBM Voxel-based morphometry VCP Valosin containing protein

WM White matter

WMS Weschler memory scale

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1 Introduction

Among different memory patients there is always a real-life drama, with the patient as the main character and family and friends who have to live and help the patient - these are the secondary characters. The patient deserves a reasonable explanation and care for the memory or behavioral symptoms causing mental and physical disability.

In 2008, the total cost of dementia in Europe was €177 billion, of which 56% were the costs attributable to informal care (Wimo et al. 2011). With respect to Finland, the latest report from 2004 estimated dementia to be the most expensive brain disorder, in particular due to the direct non-medical costs (Sillanpaa, Andlin-Sobocki & Lonnqvist 2008).

It has been estimated than 35.6 million people were living with dementia worldwide in 2010 (Prince et al. 2013). Alzheimer’s disease (AD) is the most prevalent disease of the dementia diseases, followed by frontotemporal dementia (FTD) in people younger than 65 years of age (Ratnavalli et al. 2002).

FTD is a neurodegenerative disease characterized by behavioural and/or language impairments (Neary et al. 1998). There is no cure, only symptomatic treatment and even its efficacy is not impressive. FTD has been reported to be under diagnosed in the elderly (Baborie et al. 2012, Rossor et al. 2010), being confused with psychiatric syndromes.

AD is a neurodegenerative disease for which there is no cure. This disease starts with forgetfulness of minor things and then it spreads to affect several domains which hinder daily routines. There are some drugs available, however there is no current treatment which can delay or stop the onset of the disease.

AD and other dementias are the 11^th most important cause of disability-adjusted life years in western-Europe. The incidence of neurological disorders including dementia has increased from 1.9 to 3% over the two decades (Murray et al. 2012).

Both early and precise diagnosis of these two dementia diseases is needed if one wishes to plan new drug trials. This is the only way to truly benefit these patients and it will require that these patients receive treatment as soon as possible. The prevention and control of risk factors (Qiu, Kivipelto & von Strauss 2009) have been stated as a door which can lead to a better understanding and hopefully to a decrease in the risk of developing AD.

Several advances have been made in the recent years, especially in developing new diagnostic methods. New guidelines have been postulated for both FTD (Rascovsky et al.

2011) and AD (Dubois et al. 2010) have attempted to gather all of the possible findings that can lead to a certain diagnosis.

There is still a need to identify new methods that could achieve an earlier and more precise diagnosis, and to integrate all these data originating from multiple sources, in order to facilitate the clinical diagnosis. This thesis introduces the use of a new combination of different methods in the differential diagnosis of AD, mild cognitive impairment stages and FTD, and a tool that collates data from different sources to help clinicians to profile a patient as having either AD or FTD.

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2 Review of literature

2.1 AGEING AND DEMENTIA

Brain ageing can be classified as normal ageing and pathological aging (Barkhof et al. 2011).

Within the normal group there are two apparent types i.e. successful aging and typical aging (Barkhof et al. 2011).

With successful ageing, there is no any discernible deterioration i.e. minimal morphological and physiological loss relative to younger individuals. These are elderly people with borderline normal appearance of the brain when imaged. Some of these changes may appear gradually and do not represent disease.

Typical (usual) ageing may encompass a variety of changes in the brain, not only overall shrinkage, but also distinct local alterations such as white matter changes or specific findings associated with vascular risk factors.

There are some changes that can be identified in typical ageing and which are wrongly associated only with pathological ageing: brain volume loss, enlarged perivascular (Virchow-Robin) spaces, punctiform or minor white matter (WM) abnormalities associated with vascular risk factors and other cerebrovascular lesions also known as age-related white matter changes (ARWMC), iron accumulation in the basal ganglia, amyloid deposition, ventricular enlargement and cerebral microbleed (MB) (Barkhof et al. 2011).

Finally, certain degenerative changes may make the individual susceptible to the appearance of certain age related diseases ultimately leading to ageing.

There is a theory that aging starts when the brain, the last organ which concludes its development in early adolescence, starts to lose its volume steadily; this occurs through middle adulthood and sharply after 55 years of age (Courchesne et al. 2000). There is a more specific cut-off established usually at the age of 65 years of age, which basically matches the time when people can retire from work. Thus one can define early-onset dementia (EOD) as occurring below the age of 65 years, and late-onset dementia (LOD) after this age.

According to WHO, dementia can be defined as a syndrome in which there is deterioration in memory, thinking, behavior and the ability to perform everyday activities.

Dementia is a cause of disability and dependency among the elderly and it exerts a major physical, mental, social and economic impact on caregivers, families and society.

There are two common assumptions made by many lay people: first, the tendency to consider dementia as a part of normal ageing, when it is not, although it is strongly associated with aging. In fact it is pathological aging. Second, dementia is a syndrome, it is not a disease. There are several diseases that can be considered to cause dementia.

According to the widely accepted Diagnostic and Statistical Manual of Mental Disorders, 4^th ed (DSM-IV) (American Psychiatric Association, 1994), the following criteria can be used for diagnosing dementia: memory deficits, one or more cognitive deficit as aphasia, apraxia, agnosia and executive dysfunction; the symptoms have to cause impairments in social and occupational functioning, registered by the activities of daily life questionnaire

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(ADL); evidence for a systemic or brain disorder; and no clouding of consciousness, i.e. the deficits do not occur exclusively in the course of a period of delirium.

Apraxia refers to difficulty in sequencing voluntary purposeful motor movements (such as dressing), agnosia is a difficulty in processing sensory input (i.e. in recognizing objects by sight), and disturbances in executive functioning to difficulty in planning and organizing activities (Kawas 2003). Aphasia refers to impairment in language function (i.e.

difficulties in finding words, pronunciation or some other kind of speech problem).

Another criteria classification widely accepted is that by the National Institute on Aging- Alzheimer’s Association (NIA-AA) workgroup or McKhann criteria (McKhann et al. 2011), in which have criteria for the threshold of dementia. It contains the requirements from DSM-IV, except that in NIA-AA criteria one does not necessarily need to identify a memory problem.

Nonetheless the latest DSM-V includes a new modification: because of the stigmatization associated with the term dementia, it has been proposed that physicians should use the terms major and mild neurocognitive disorders instead of dementia.

The International Classification of Diseases (ICD-10) used in clinical work also contains the term dementia, and different criteria for diagnosing various dementia diseases.

One can classify dementia diseases into two groups: neurodegenerative or progressive and non-neurodegenerative or non-progressive dementia diseases.

The neurodegenerative group contains the five most common dementia diseases:

Alzheimer’s disease (AD), vascular dementia (VaD), frontotemporal dementia (FTD), dementia with Lewy-bodies (DLB) and Parkinson’s disease with dementia.

The frequency of these main dementia diseases has been revised over the years. AD is by far the most common subtype (50-70%), followed by VaD (10-25%), DLB (15%) and FTD (5- 10%) (Lobo et al. 2000, Fratiglioni et al. 2000, McKeith et al. 1996). Today, there have been proposals for a new distribution, and although AD still would be the most common subtype of dementia, FTD and DLB would have higher rates and a mixed-dementia due to the combination of AD and VaD would also display a higher rate. This could be a consequence of more precise and definitive diagnosis, and due to the fact that now there is considered that there are more dementias than simply those due to AD and it is accepted that mixed cases are more common than previously believed.

Despite this new window for diagnosing each particular dementia disease and not simply considering all types of dementia as being AD, there is another problem; even in high income countries, only 20-50% of dementia cases are recognised and documented in primary care (alz.co.uk).

The increase of age is the major risk factor for developing dementia nevertheless one should not forget that below 65 years of age there is the so-called pre-senile dementia or EOD, of which AD is the most common type followed by VaD and FTD (Vieira et al. 2013), and the oldest-old range (>85 years of age), where AD is the most common subtype, frequently associated with vascular cerebrovascular changes as demonstrated in some pathological studies (Corrada, Berlau & Kawas 2012).

Within the non-neurodegenerative group, there are etiologies that one usually does not link with dementia, and these may be reversible at least to some extent, such as depression or hydrocephalus (Hejl, Hogh & Waldemar 2002).

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2.2 MILD COGNITIVE IMPAIRMENT

The term mild cognitive impairment (MCI) generally describes the transitional zone between normal cognitive function and clinical AD (Winblad et al. 2004). If one considers that there is a continuum of brain ageing, then MCI would represent the intermediate stage on the route from age-associated memory impairment, the term used before MCI (Crook et al. 1986) , to AD (Small et al. 2008).

According to NIA-AA, the main difference between MCI and dementia is based on whether there is interference in the ability to function at work or in usual daily activities (McKhann et al. 2011).

The probability of developing dementia from MCI still is a question open to debate. One meta-analysis has suggested that less than half of the MCI cases actually progress to AD, with an annual rate of conversion of around 10%, and many cases do not convert even after 10 years of follow-up (Mitchell, Shiri-Feshki 2009). Nevertheless some studies have found a higher rate of progression, such as in the study from Bennet et al., with over 30% of MCI cases converting to AD on an average of 4.5 years’ follow-up (Bennett et al. 2002), and the study of Petersen et al., which described an up to 80% rate of conversion after 6 years (Petersen et al. 2001).

MCI starts with a cognitive complaint: not normal for age, not for a demented subject, displaying a cognitive decline and maintaining normal functional activities. Once one concludes that these fulfill the criteria for MCI, we need to consider which cognitive domains are affected: if there is memory impairment, this is referred to as amnestic MCI or multi-domain amnestic MCI if the amnestic episode is accompanied by other features, while if there is no memory impairment, it may be necessary to distinguish between single non-memory MCI and multidomain non-amnestic MCI (Winblad et al. 2004).

Most studies mainly focus on the amnestic and the multi-domain amnestic MCI subtypes, which are more likely to progress to AD (Petersen et al. 2001). However there is also a need to classify MCI according to the domains affected, and include all these subtypes into studies and trials, because most of the studies simply consider the amnestic subtype as MCI, and do not follow any standardized criteria (Christa Maree Stephan et al. 2013).

In clinical practice it is usual to follow the criteria proposed by Petersen et al., (Petersen et al. 1997) (Table 1).

Table 1. Clinical diagnosis of Mild Cognitive Impairment devised by Petersen et al., (Petersen et al. 1997)

MCI

Diagnostic criteria A. Complaint of defective memory B. Normal activities of daily living C. Normal general cognitive function D. Abnormal memory function for age E. Absence of dementia

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When MCI patients are followed over time, they may develop AD, some other type of dementia, some patients remain stable and a very few number of cases may even recover (Winblad et al. 2004).

There are no large studies measuring the rate of progression of MCI to other types of dementia, only studies with small cohorts including MCI and neurodegenerative dementia subtypes (Galluzzi et al. 2013).

The emergence of new biomarkers, and new research criteria for MCI and AD (Dubois et al. 2007, Sperling et al. 2011), could predict the appearance of dementia earlier than when symptoms arise.

In order to clarify the use of the term MCI and AD, Dubois et al., (Dubois et al. 2010) have tried to collect the terms related to MCI in order that they can be more accurately defined.

A summary with the terminology and the information required for each term can be found in Table 2.

Table 2. MCI lexicon. Modified from (Dubois et al. 2010)

MCI AD diagnosis Impairment

in specific memory tests

Biomarkers in vivo

Additional requirements

MCI No Not required Not required Not required

Preclinical AD

- Asymptomatic at risk for AD

No Not present Required Absence of

symptoms of AD - Presymptomatic AD No Not present Not required

Prodromal AD Yes Required Required Absence of

dementia

In addition, three terms should be considered: first, subjective cognitive impairment (SCI) as a phase prior to MCI where there are already differences in cognitive tests and hippocampal volumes between SCI subjects and age-matched non-SCI subjects (Reisberg et al. 2008); second, Albert et al., have categorized different MCI subgroups depending on the positivity of certain biomarkers which were already included in the Dubois criteria.

According to this proposal, there is a high, an intermediate or no likelihood of having MCI due to AD (Albert et al. 2011). Third, vascular cognitive impairment and MCI are considered as different entities, and diseases and vascular risk factors are not usually considered in the criteria for MCI. It would be useful to study the association between MCI and these vascular risk factors, as half of the patients with vascular cognitive impairment do develop AD or mixed AD during follow-up (Stephan et al. 2009). Finally, there is an on- going effort to adapt the term MCI not only to general pre-dementia stage or pre-AD, but to other dementia diseases as proposed (Dubois, Albert 2004), such as VaD (Gorelick et al.

2011) or Parkinson’s disease (Goldman, Litvan 2011).

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2.3 FRONTOTEMPORAL LOBAR DEGENERATION

Frontotemporal lobar degeneration (FTLD) is an umbrella term that includes diseases which affect the frontal and temporal lobes in the brain, and cause behavioral and/or language impairment (Neary et al. 1998). FTLD is composed of a variety of different clinical and pathological syndromes caused by different histopathological atrophic changes in the anterior brain areas and related anatomical connections.

Frontotemporal dementia (FTD) is the most common clinical subtype in FTLD. FTD consists of a variety of frontal type behavioral symptoms, referred to also as behavioural variant of FTD (bvFTD). FTD predominantly affects young patients, it has been underestimated as a cause of dementia in the elderly (Baborie et al. 2012), as it includes a symptomatic memory loss, resembling more AD than FTD (Baborie et al. 2012).

2.3.1 History and nosology

Arnold Pick in 1892 described atrophy in the frontal and temporal lobes of patients who presented personality change and language impairment (Kertesz et al. 2005). Then Warrington (1975) and Mesulam (1982) described progressive language disorders in the Western literature. Warrington depicted a group of patients displaying a selective impairment of semantic memory (Warrington 1975). Mesulam depicted patients who were exhibiting progressive language problems that he called “primary progressive aphasia”

(PPA), this included impairment in both production and comprehension (Mesulam 1982).

In 1994 the Lund-Manchester criteria (The Lund and Manchester Groups, 1994) included in addition to the neuropathologic signs, clinical criteria for FTD. The FTD core diagnostic features consisted of a combination of behavioural disorders, affective symptoms, speech disorder, preservation of spatial orientation and praxis. Clinical motor neuron disease (MND) was noticed among supportive diagnostic features as well as the pathological changes of the MND type. The still current consensus clinical criteria for FTLD were described in 1998 by Neary and colleagues (Neary et al. 1998). The Neary criteria included clinical features for the bvFTD, progressive non-fluent aphasia (PNFA) and semantic dementia (SD). In 2001, McKhann and colleagues (McKhann et al. 2001) published a large detailed report about clinical and neuropathological correlations and specifications of terms. Furthermore some clarification of nosology was given after a consensus conference held in 2003 about Pick’s disease, originally regarded as a clinical disease, but subsequently the use of the term was narrowed to limited cases with specific Pick bodies as a microscopic finding in conjuction with macroscopic anterior brain atrophy. Pick type neuropathology was already described in the Lund & Manchester criteria. Pick’s disease manifests with frontotemporal dementia and typical Pick type neuropathological changes. Similarly, progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) were given more diverse meanings correlating with neuropathological changes in the spectrum of FTLD pathologies. In 2003, Kertesz described the so-called Pick complex or clinical Pick’s disease, which included the same entities as the McKhann criteria, adding the association of MND with FTD; the term Pick does not have to be associated with the appearance of Pick bodies, and this complex should be viewed as representing the commonalities between these diseases rather than the differences (Kertesz 2003).

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2.3.2 Epidemiology, classification and risk factors Epidemiology:

FTD is a frequent cause of dementia in people less than 65 years of age (Ratnavalli et al.

2002), consisting up to 27% of all cases (Vieira et al. 2013). Earlier, the diseases of the FTLD group were thought to represent only 5-10% of all dementia cases at different age groups (Lobo et al. 2000, Fratiglioni et al. 2000). FTLD syndromes commonly occurs when people are in their sixties (Hodges et al. 2004, Rosso et al. 2003a, Harvey, Skelton-Robinson &

Rossor 2003) although it could start already in younger individuals in their thirties (Harvey, Skelton-Robinson & Rossor 2003) and also in the very old (Gislason et al. 2003, Pikkarainen, Hartikainen & Alafuzoff 2008). The mean of age at onset is lower in FTD compared to AD (Ratnavalli et al. 2002). Of all FTLD cases, only 20-25% occur in people over 65 years of age (Rosso et al. 2003a). In the elderly, FTLD syndromes are believed to be a different entity from early onset FTLD and it is considered to be underdiagnosed in the elderly (Baborie et al. 2012). Familial cases are more prevalent than sporadic cases, even in the elderly (Borroni et al. 2013). The average age of onset does not differ greatly between familial and sporadic cases (Piguet et al. 2004). Further, no male or female preponderance has been described (Rosso et al. 2003a, Borroni et al. 2011) albeit there are some studies which reported mild (Harvey, Skelton-Robinson & Rossor 2003) or even a high male predominance (Ratnavalli et al. 2002).

In Japan, one study described the prevalence of FTLD as 12.7 % among all dementia cases (Ikeda, Ishikawa & Tanabe 2004); another study showed a frequency of 14.7% in early-onset dementia while in late-onset dementia FTLD accounted only for 1.6% of all cases (Yokota et al. 2005). The highest prevalence has been reported in two studies performed in the UK among patients between 45 and 65 years of age, with a prevalence rate between 81 and 98.1 per 100,000 inhabitants (Ratnavalli et al. 2002, Harvey, Skelton-Robinson & Rossor 2003).

Harvey et al., stated that the prevalence increased with age. One northern Italian study found a FTLD prevalence of 22 per 100,000 inhabitants between 44 and 65 years of age, 78 per 100,000 inhabitants aged 66-74 and 54 per 100,000 inhabitants over 75 years of age (Borroni et al. 2010). Another study from the UK stated that FTLD accounted for 7.9% of all dementia cases among people over 65 years of age (Stevens et al. 2002). Very few studies have been conducted in the oldest-old however one can presume that there are FTLD cases, as was reported in one Swedish study which found a 3% of cases with the bvFTD in a population over 85 years of age (Gislason et al. 2003). The lowest rates were described in one study from the Netherlands where the prevalence of FTD was of 3.6 per 100,000 in patients aged 50-59, 9.4 per 100,000 at age 60-69 and 3.8 per 100.000 in patients between 70 and 79 years of age (Rosso et al. 2003a). This variability is among studies is due to the different settings and criteria established for selecting the patients.

There is a strong association between age at onset of the diagnosis and length of survival.

The mean survival ranged from 1.3 to 6.5 years depending on the study and whether only one disease was considered from the FTLD spectrum or a particular disease and the age of diagnosis. The lowest survival (1.3 years) was found when FTD was associated with MND (Brodaty, Seeher & Gibson 2012).

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Classification:

FTLD can be classified according to three patterns: clinical syndrome, neuropathological syndrome and molecular genetic syndrome (Barkhof et al. 2011, Sieben et al. 2012). These groups are divided as follows:

Clinical syndromes: According to Neary and colleagues (Neary et al. 1998) (Table 3), FTLD includes 3 clinical variants which have brain atrophy or hypometabolism restricted to the frontal and mostly anterior regional brain areas, and display behavioural and/or language manifestation: bvFTD, and 2 language variants which are PNFA and SD. For PPA there are updated criteria in which PPA includes logopenic aphasia (LA) as well (Gorno-Tempini et al. 2011). Finally, there are other clinical presentations from the FTLD spectrum which present with a very low percentage of cases.

The most common subtype of FTLD is bvFTD, accounting for between 30% to 50% of all cases depending on the study (Ikeda, Ishikawa & Tanabe 2004, Kertesz et al. 2007). BvFTD is characterized by changes in the personality and social conduct. Table 4 presents the recent criteria (Rascovsky et al. 2011), which have been proposed to replace FTD from the previous Neary criteria (Neary et al. 1998).

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10 Table 3. Diagnostic criteria of FTLD according to Neary and colleagues (Neary et al. 1998) FTD PNFASD Core diagnostic features (must be present for making a diagnosis): A.Insidious onset and gradual progression B.Early decline in social interpersonal conduct C.Early impairment in regulation of personal conduct D.Early emotional blunting E.Early loss of insight Supportive diagnostic features: A.Behavioral disorder a.Decline in personal hygiene and grooming b.Mental rigidity and inflexibility c.Distractibility and impersistence d.Hyperorality and dietary changes e.Perseverative and stereotyped behaviour f.Utilization behavior B.Speech and language a.Altered speech output b.Stereotypy of speech c.Echolalia

Core diagnostic features (must be present for making a diagnosis): A.Insidious onset and gradual progression B.Nonfluent spontaneous speech Supportive diagnostic features: A.Speech and language a.Stuttering or oral apraxia b.Impaired repetition c.Alexia, agraphia d.Early preservation of word meaning e.Late mutism B.Behavior a.Early preservation of social skills b.Late behavioural changes similar to FTD C.Physical signs: late contralateral primitive reflexes, akinesia, rigidity, and tremor D.Investigations Core diagnostic features (must be present for making a diagnosis): A.Insidious onset and gradual progression B.Language disorder characterized by e.Progressive, fluent, empty spontaneous speech f.Loss of word meaning g.Semantic paraphasias and/or perceptual disorder C.Preserved perceptual matching and drawing reproduction D.Preserved single-word reproduction E.Preserved ability to read aloud and write to dictation orthographically regular words Supportive diagnostic features: A.Speech and language: press of speech, idiosyncratic word usage, absence of phonemic paraphasias, surface dyslexia and dysgraphia B.Behavior: loss of sympathy and empathy, narrowed preoccupations, parsimony

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11 d.Perseveration e.Mutism C.Physical signs a.Primitive reflexes b.Incontinence c.Akinesia, rigidity and tremor d.Low and labile blood pressure D.Investigations a.Neuropsychology: significant impairment of frontal lobe tests in absence of severe amnesia, aphasia or perceptuospatial disorder b.EEG: normal or conventional EEG despite clinically evident dementia c.Brain imaging (structural and/or functional): predominant frontal and /or anterior temporal abnormality a.Neuropsychology: nonfluent aphasia in the absence of severe amnesia or perceptuospatial disorder b.EEG: normal or minor asymmetric slowing c.Brain imaging (structural and/or functional): asymmetric abnormality predominantly affecting dominant (usually left) hemisphere

C.Physical signs: absent or late primitive reflexes, akinesia, rigidity, and tremor D.Investigations a.Neuropsychology: profound semantic loss, comprehension and naming and/or face and object recognition perceptual processing, spatial skills, and day-to-day memorizing. Preserved phonology and syntax, and elementary b.EEG: normal c.Brain imaging (structural and/or functional): predominant anterior temporal abnormality (symmetric or asymmetric)

Table 3. (continued) Diagnostic criteria of FTLD according to Neary and colleagues(Neary et al. 1998)

Disease state index and neuroimaging in frontotemporal dementia, Alzheimer's disease and mild cognitive impairment

is se rt at io n s

Miguel Ángel Muñoz-Ruiz Disease State Index and Neuroimaging in Frontotemporal Dementia, Alzheimer’s Disease and Mild Cognitive Impairment

Miguel Ángel Muñoz-Ruiz

Disease State Index and

Neuroimaging in Frontotemporal Dementia, Alzheimer’s Disease and Mild Cognitive Impairment

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

Neuroimaging and Disease State Index in dementia diseases

Acknowledgements

List of the original publications

Contents

Abbreviations

1 Introduction

2 Review of literature