eRapport

Improving early identification of dementia risk by means of multimodal neuroimaging

Prosjekt
Prosjektnummer
912152
Ansvarlig person
Ketil Oppedal
Institusjon
Helse Stavanger HF
Prosjektkategori
Postdoktorstipend
Helsekategori
Neurological
Forskningsaktivitet
4. Detection and Diagnosis
Rapporter
2021 - sluttrapport
Prosjektet har bestått av flere delprosjekter. Det har retrospektivt blitt samlet inn MRI av hjerne fra mennesker med demens, som er samlet inn i en stor database i samarbeid med flere europeiske samarbeidende sentre. Det har blitt publisert en rekke publikasjoner som viser ulikheter mellom ulike demensformer og sub-grupper innenfor demensformer og hvordan dette relaterer seg til hjerneforandringer. Det har blitt publisert flere artikler hvor man viser sammenhenger mellom hjerneforandringer og bla. pyskologiske, psykiatriske og geriatriske markører/mål. Det har blitt gjennomført en klinisk utprøvingsstudie i demens med en avansert hjernebildeprotokoll, som nå er ferdig og hvor det kommer mange publikasjoner i nær framtid. Det har blitt utviklet KI (kunstig intelligens) verktøy for klassifisering av sykdom tilpasset utfordringer i medisinske applikasjoner knyttet til små datasett og forklarbarhet, som også har munnet ut i publikasjoner. Videre har flere PhD-kandidater startet prosjekter innenfor tema knyttet til nevrodegenerative sykdommer og hjernebilder under veiledning av meg, både som hoved- og biveileder. Jeg har i dag avsluttet min postdok og gått over i full og fast stilling som førsteamanuensis ved UiS hvor jeg viderefører arbeidet jeg utførte som postdok. Det vil først og fremst komme indirekte konsekvenser for helsetjenesten gjennom at resultater fra forskningen bidrar til økt forståelse av hjerneforandringer i demenssykdom og dets relasjoner til andre symptomer man erfarer som del av sykdomsbildet. Videre åpner forskningen på KI for at slike verktøy i framtiden kan bidra i diagnostisering og forbedret forståelse av sykdom. I tiden som kommer vil vi også kunne forstå bedre hvordan man kan bruke hjernebilder i gjennomføringen av kliniske utprøvingsstudier i nevrodegenerative sykdommer.
2020
In 2020 I've contributed to eight scientific papers. I am a main supervisor for four PhD-candidates, co-supervisor for two PhD-candidates and main supervisor for five MSc-students within my research field. I have contributed to several funding applications.Alzheimer’s disease (AD) and dementia with Lewy bodies are the two most common dementias. A definite diagnosis can only be made post-mortem and clinical diagnosis is prone to inter- and intra subject variation. Mild cognitive impairment (MCI) is an early pre-dementia phase. However, MCI is a heterogeneous group, many remaining stable or even reversing to normal. Thus, early and accurate identification of patients with MCI who progress to AD and DLB is a key clinical and scientific challenge. AD and DLB have many similar symptoms and co-morbidity is common. Since they need different care and medication, differential diagnosis is important. In this project we are using advanced statistical approaches and machine learning with paramters from both structural and functional brain images in combination with biomarkers, neuropsychiatric symptoms and results from neuropsychologic testing for high precision early detection and differential diagnosis of AD and DLB. The main objective is to develop a high precision computer assisted diagnosis (CAD) system for dementia diagnosis and prediction of conversion from MCI to dementia as well as differential diagnosis of different dementia types using multimodal neuroimage information from MRI and PET data togther with machine learning methods. A secondary aim is to build a scientific group working in the field of brain imaging and machine learning for the definition and diagnosis of neurological and psychiatric disorders. Clinical implementation of these methods will also be of high priority. During 2020 I have contributed to eight scientific journal papers plus additional conference papers and posters. As responsible for the neuroimaging part of the ACID study (Anthocyanins in dementia), which has now finished data collection, I have started the work developing an analysis plan for the different brain images studying brain function, brain blood perfusion as well as brain metabolism using MRI and PET imaging. I am also responsible for the development of the neuroimaging protocol in the PRO-LBD study, studying the prodromal phases of Lewy body disease starting these days and the same for the contribution from Stavanger to the prospective European LBD consortium study. I am currently contributing as a main supervisor for four PhD candidates and co-supervisor for two PhD candidates, all of them working in the field of medical imaing and machine learning. Additionally, I am co-supervising one radiographer in her work towards an MSc. As a part of my position as an Adjunct Associate Professor at University of Stavanger I have supervised five MSc. students in 2020 in the field of computer science and medical imaging. Additionally, I was also course responsible for a 10 ECT MSc course in machine learning spring 2020. I have been responsible for the acquisition of an 8 GPU AI computer as part of Stavanger Medical Imaging Laboratory (SMIL) - centre for medical imaging and machine learning at SUS. I have contributed to several funding applications, some of them with positive outcome.
2019
During 2019 I have published an article, have contributed as co-author to several manuscripts that is currently under review. I am a main supervisor for three PhD-candidates and several MSc-students in my research field. I am heading the startup of Stavanger Medical Imaging Laboratory (SMIL). I have contributed to several funding applications.Alzheimer’s disease (AD) and dementia with Lewy bodies are the two most common dementias. A definite diagnosis can only be made post-mortem and clinical diagnosis is prone to inter- and intra subject variation. Mild cognitive impairment (MCI) is an early pre-dementia phase. However, MCI is a heterogeneous group, many remaining stable or even reversing to normal. Thus, early and accurate identification of patients with MCI who progress to AD and DLB is a key clinical and scientific challenge. AD and DLB have many similar symptoms and co-morbidity is common. Since they need different care and medication, differential diagnosis is important. In this project we are using advanced statistical approaches and machine learning with paramters from both structural and functional brain images in combination with biomarkers, neuropsychiatric symptoms and results from neuropsychologic testing for high precision early detection and differential diagnosis of AD and DLB. The main objective is to develop a high precision computer assisted diagnosis (CAD) system for dementia diagnosis and prediction of conversion from MCI to dementia as well as differential diagnosis of different dementia types using multimodal structural, functional, and textural neuroimage information from MRI and PET data and machine learning methods. A secondary aim is to build a scientific group working in the field of brain imaging and image analysis using multimodal and multiparameter methods as well as machine learning for the definition and diagnosis of neurological and psychiatric disorders. Clinical implementation of these methods will also be of high priority. During 2019 I have published an article based on the neuroimaging work done in the prospective European DLB (E-DLB) study and am continuing to contribute to the neuroimaing work done as part of the E-DLB consortium through several co-authorships to submitted manuscripts that is currently under review. As responsible for the neuroimaging part of the ACID study (Anthocyanins in dementia), I have continued to contribute to the inclusion of a multimodal neuroimaging protocol for the study of brain function, brain blood perfusion as well as brain metabolism using MRI and PET imaging currently reaching more than 100 included subjects with brain images. I am also responsible for the development of the neuroimaging protocol in the upcoming PRO-LBD study, studying the prodromal phases of Lewy body disease. I am currently contributing as a main supervisor for three PhD candidates, all of them working in the field of medical imaing and machine learning. Additionally, I am co-supervising two radiographers in their work towards an MSc and a research radiologist in specialisation. As a part of my position as an Adjunct Associate Professor at University of Stavanger I have supervised two MSc. students in 2019 in the field of computer science developing a CAD system based on deep learning for the diagnosis and early detection of dementia. I am also supervising five MSc. students in 2020. Additionally, I am lecturing a 10 ECT course in machine learning this semester. I am heading the startup of Stavanger Medical Imaging Laboratory (SMIL) which is a centre focusing on medical imaging and machine learning at SUS. I have contributed to several funding applications, some of them with positive outcome.
2018
During 2018 I have published an article where we show that subjects with dementia with Lewy bodies hava a signature pattern of atrophy that is different from subjects with Alzheimer's disease. I am a main supervisor for a PhD-candidate and several researchers and MSc-students in my research field. I have contributed to several funding applications.47.4 million people suffers from dementia worldwide and 7.7 million new cases are expected each year, with enormous impact for patients, families and society. Alzheimer’s disease (AD) accounts for 60-70% of people with dementia. A definite diagnosis can only be made post-mortem and clinical diagnosis is prone to inter- and intra subject variation. Mild cognitive impairment (MCI) is an early pre-dementia phase of AD. However, MCI is a heterogeneous group, many remaining stable or even reversing to normal. Thus, early and accurate identification of patients with MCI who progress to AD is a key clinical and scientific challenge. Dementia with Lewy bodies (DLB) is the second most recognised dementia. AD and DLB have many similar symptoms and co-morbidity is common. Since they need different care and medication, differential diagnosis is important. In this project we are using advanced statistical approaches and machine learning with paremters from both structural and functional brain images for high precision early detection of AD and differential diagnosis. The main objective is to develop a high precision computer assisted diagnosis (CAD) system for dementia diagnosis and prediction of conversion from MCI to dementia as well as differential diagnosis of different dementia types using multimodal structural, functional, and textural neuroimage information from MRI and PET data and machine learning methods. A secondary aim is to build a scientific group working in the field of brain imaging and image analysis using multimodal and multiparameter methods as well as machine learning for the definition and diagnosis of neurological and psychiatric disorders. Clinical implementation of these methods will also be of high priority. During 2018 I have published an article based on the retrospectively collected MR images of 1360 subjects with different dementia diagnoses as well as healthy controls as part of the pan-european collaboration with 15 other centers called E-DLB. We show that subjects with DLB have a signature pattern of atrophy (cell death and brain shrinkage) where frontal- and posterior regions are affected and the hippocampal regions are spared, which is different from a typical AD atrophy pattern. As a part of the ACID study (Anthocyanins in dementia) - a randomised controlled trial studying the effect of anthocyanins on neurodegeneration, cognition, inflammation and oxidative stress financed by the National Association for Public Health - the candidate has continued to contribute to the inclusion of a multimodal neuroimaging protocol for the study of brain function, brain blood perfusion as well as brain metabolism using MRI and PET imaging currently reaching 46 included subjects with brain images. I am contributing as a main supervisor for a PhD candidate in neuroimaging focusing on dementia research. Additionally I am supervising a research radiographer, a research radiologist in specialisation, and a research medical physicist. As a part of my position as an Associate Professor II at University of Stavanger I am supervising two M.Sc. students in computer science developing a CAD system based on deep learning for the diagnosis and early detection of dementia. I have contributed to several funding applications, some of them with positive outcome.
2017
Images from 1360 dementia subjects has been collected in a European collaboration with 15 centers. The first manuscript is in its final phase. A protocol for the study of brain function, -perfusion, and -metabolism combining MRI and PET in the ACID study has been developed. I work as main supervisor for a PhD candidate and two M.Sc. students.47.4 million people suffers from dementia worldwide and 7.7 million new cases are expected each year, with enormous impact for patients, families and society. Alzheimer’s disease (AD) accounts for 60-70% of people with dementia. A definite diagnosis can only be made post-mortem and clinical diagnosis is prone to inter- and intra subject variation. Mild cognitive impairment (MCI) is an early pre-dementia phase of AD. However, MCI is a heterogeneous group, many remaining stable or even reversing to normal. Thus, early and accurate identification of patients with MCI who progress to AD is a key clinical and scientific challenge. A number of different imaging modalities have been shown to aid in the prediction of progression from MCI to dementia. However, combining different imaging techniques, i.e. multimodal neuroimaging, may better detect the earliest changes in brain structure and function before cognitive impairment appears compared to single imaging domains. Dementia with Lewy bodies (DLB) is the second most recognised dementia. AD and DLB have many similar symptoms and co-morbidity is common. Since they need different care and medication, differential diagnosis is important. In this project we will use advanced statistical approaches and machine learning to combine structural features and texture analysis (TA) in MRI, DTI, and FLAIR images in combination with functional features from PET images for high precision early detection of AD and differential diagnosis. The main objective is to develop a high precision computer assisted diagnosis (CAD) system for dementia diagnosis and prediction of conversion from MCI to dementia as well as differential diagnosis of different dementia types using multimodal structural, functional, and textural neuroimage information from MRI and PET data and machine learning methods. A secondary aim is to build a scientific group working in the field of brain imaging and image analysis using multimodal and multiparameter methods as well as machine learning for the definition and diagnosis of neurological and psychiatric disorders. Clinical implementation of these methods will also be of high priority. During 2017 the candidate has collected MR images retrospectivly from 1360 subjects with different dementia diagnoses as well as healthy controls as part of a pan-european collaboration with 15 other centers. This will be one of the largest MR imgaing databases with subjects diagnosed with DLB worldwide. The first manuscript from this work is in its final phase and will be submitted in a couple of days. As part of the ACID study (Anthocyanins in dementia) - a randomised controlled trial studying the effect of anthocyanins on neurodegeneration, cognition, inflammation and oxidative stress financed by the National Association for Public Health - the candidate has contributed to including a multimodal neuroimaging protocol for the study of brain function, brain blood perfusion as well as brain metabolism using MRI and PET imaging. The first patient is included. The candidate has also contributed as a main supervisor for a PhD candidate in radioogy as well as two M.Sc. students in computer science, UiS, dveloping a CAD system based on deep learning - which are machine learning techniques inspired by the development in artifical intelligence - for the diagnosis and early detection using MRI of AD and DLB. The candidate has also contributed to several funding applications, some of them with positive outcome.

The population of study consists of subjects with dementia, subjects with cognitive decline not interfering with the ability to perform daily activities, and normal controls. A user in these terms will be the study population, people in close relations to the subjects in the study population, and health care professionals. This study focuses on advanced analysis of brain images of the study population with the aim to identify subjects with high risk for developing dementia from already acquired data. Direct contributions from users to the research work may seem difficult. On the other hand, updated information regarding research results will be of interest for all the abovementioned users. The research environment where this study work will be performed arranges weekly lunch seminars where health care professionals and other users are invited to take part, both as contributors and as listeners. Additionally, the yearly event called ”SESAM-konferansen” will arrange an ”open day” where users are welcome to participate. The candidate will be responsible for an update on the research work accustomed to the user group. The candidate will also be responsible for acquiring feedback from users during these meetings.

Vitenskapelige artikler
Jaramillo-Jimenez A, Giil LM, Tovar-Rios DA, Borda MG, Ferreira D, Brønnick K, Oppedal K, Aarsland D

Association Between Amygdala Volume and Trajectories of Neuropsychiatric Symptoms in Alzheimer's Disease and Dementia With Lewy Bodies.

Front Neurol 2021;12():679984. Epub 2021 jul 7

PMID: 34305791

Borda MG, Bani Hassan E, Weon JH, Wakabayashi H, Tovar-Rios DA, Oppedal K, Aarsland D, Duque G

Muscle Volume and Intramuscular Fat of the Tongue Evaluated With MRI Predict Malnutrition in People Living With Dementia: A 5-Year Follow-up Study.

J Gerontol A Biol Sci Med Sci 2022 Feb 03;77(2):228.

PMID: 34338751

, Jalbrzikowski M, Hayes RA, Wood SJ, Nordholm D, Zhou JH, Fusar-Poli P, Uhlhaas PJ, Takahashi T, Sugranyes G, Kwak YB, Mathalon DH, Katagiri N, Hooker CI, Smigielski L, Colibazzi T, Via E, Tang J, Koike S, Rasser PE, Michel C, Lebedeva I, Hegelstad WTV, de la Fuente-Sandoval C, Waltz JA, Mizrahi R, Corcoran CM, Resch F, Tamnes CK, Haas SS, Lemmers-Jansen ILJ, Agartz I, Allen P, Amminger GP, Andreassen OA, Atkinson K, Bachman P, Baeza I, Baldwin H, Bartholomeusz CF, Borgwardt S, Catalano S, Chee MWL, Chen X, Cho KIK, Cooper RE, Cropley VL, Dolz M, Ebdrup BH, Fortea A, Glenthøj LB, Glenthøj BY, De Haan L, Hamilton HK, Harris MA, Haut KM, He Y, Heekeren K, Heinz A, Hubl D, Hwang WJ, Kaess M, Kasai K, Kim M, Kindler J, Klaunig MJ, Koppel A, Kristensen TD, Kwon JS, Lawrie SM, Lee J, León-Ortiz P, Lin A, Loewy RL, Ma X, McGorry P, McGuire P, Mizuno M, Møller P, Moncada-Habib T, Muñoz-Samons D, Nelson B, Nemoto T, Nordentoft M, Omelchenko MA, Oppedal K, Ouyang L, Pantelis C, Pariente JC, Raghava JM, Reyes-Madrigal F, Roach BJ, Røssberg JI, Rössler W, Salisbury DF, Sasabayashi D, Schall U, Schiffman J, Schlagenhauf F, Schmidt A, Sørensen ME, Suzuki M, Theodoridou A, Tomyshev AS, Tor J, Værnes TG, Velakoulis D, Venegoni GD, Vinogradov S, Wenneberg C, Westlye LT, Yamasue H, Yuan L, Yung AR, van Amelsvoort TAMJ, Turner JA, van Erp TGM, Thompson PM, Hernaus D

Association of Structural Magnetic Resonance Imaging Measures With Psychosis Onset in Individuals at Clinical High Risk for Developing Psychosis: An ENIGMA Working Group Mega-analysis.

JAMA Psychiatry 2021 07 01;78(7):753-766.

PMID: 33950164

Ferreira D, Nedelska Z, Graff-Radford J, Przybelski SA, Lesnick TG, Schwarz CG, Botha H, Senjem ML, Fields JA, Knopman DS, Savica R, Ferman TJ, Graff-Radford NR, Lowe VJ, Jack CR, Petersen RC, Lemstra AW, van de Beek M, Barkhof F, Blanc F, Loureiro de Sousa P, Philippi N, Cretin B, Demuynck C, Hort J, Oppedal K, Boeve BF, Aarsland D, Westman E, Kantarci K

Cerebrovascular disease, neurodegeneration, and clinical phenotype in dementia with Lewy bodies.

Neurobiol Aging 2021 09;105():252-261. Epub 2021 mai 14

PMID: 34130107

Mårtensson G, Ferreira D, Granberg T, Cavallin L, Oppedal K, Padovani A, Rektorova I, Bonanni L, Pardini M, Kramberger MG, Taylor JP, Hort J, Snædal J, Kulisevsky J, Blanc F, Antonini A, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Soininen H, Lovestone S, Simmons A, Aarsland D, Westman E

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Med Image Anal 2020 12;66():101714. Epub 2020 mai 1

PMID: 33007638

Fernandez-Quilez A, Germán Borda M, Leonardo Carreño G, Castellanos-Perilla N, Soennesyn H, Oppedal K, Reidar Kjosavik S

Prostate cancer screening and socioeconomic disparities in Mexican older adults.

Salud Publica Mex 2020 Mar-Apr;62(2):121-122.

PMID: 32237553

Borda MG, Lopera F, Buritica O, Cerquera-Cleves C, Gonzalez MC, Garcia-Cifuentes E, Jaramillo-Jimenez A, Aguillon D, Bocanegra Y, Munoz-Ospina BE, Cano-Gutierrez CA, Patiño-Hernandez D, Tobón C, Santamaría-García H, Santacruz JM, Chavarro-Carvajal DA, Pinilla G, Morros-González E, Pantoja C, Quintana-Peña V, Valderrama J, Oppedal K, Aarsland D, Orozco J

Colombian consortium for the study of Lewy body dementia COL-DLB.

J Neurol Sci 2020 05 15;412():116807. Epub 2020 mar 27

PMID: 32247904

Abdelnour C, Ferreira D, Oppedal K, Cavallin L, Bousiges O, Wahlund LO, Hort J, Nedelska Z, Padovani A, Pilotto A, Bonanni L, Kramberger MG, Boada M, Westman E, Pagonabarraga J, Kulisevsky J, Blanc F, Aarsland D

The combined effect of amyloid-β and tau biomarkers on brain atrophy in dementia with Lewy bodies.

Neuroimage Clin 2020;27():102333. Epub 2020 jul 2

PMID: 32674011

Khalifa K, Bergland AK, Soennesyn H, Oppedal K, Oesterhus R, Dalen I, Larsen AI, Fladby T, Brooker H, Wesnes KA, Ballard C, Aarsland D

Effects of Purified Anthocyanins in People at Risk for Dementia: Study Protocol for a Phase II Randomized Controlled Trial.

Front Neurol 2020;11():916. Epub 2020 sep 2

PMID: 32982933

Borda MG, Jaramillo-Jimenez A, Tovar-Rios DA, Ferreira D, Garcia-Cifuentes E, Vik-Mo AO, Aarsland V, Aarsland D, Oppedal K

Hippocampal subfields and decline in activities of daily living in Alzheimer's disease and dementia with Lewy bodies.

Neurodegener Dis Manag 2020 12;10(6):357-367. Epub 2020 sep 23

PMID: 32967534

Ferreira D, Przybelski SA, Lesnick TG, Lemstra AW, Londos E, Blanc F, Nedelska Z, Schwarz CG, Graff-Radford J, Senjem ML, Fields JA, Knopman DS, Savica R, Ferman TJ, Graff-Radford NR, Lowe VJ, Jack CR, Petersen RC, Mollenhauer B, Garcia-Ptacek S, Abdelnour C, Hort J, Bonanni L, Oppedal K, Kramberger MG, Boeve BF, Aarsland D, Westman E, Kantarci K

β-Amyloid and tau biomarkers and clinical phenotype in dementia with Lewy bodies.

Neurology 2020 12 15;95(24):e3257-e3268. Epub 2020 sep 28

PMID: 32989106

Borda MG, Aarsland D, Tovar-Rios DA, Giil LM, Ballard C, Gonzalez MC, Brønnick K, Alves G, Oppedal K, Soennesyn H, Vik-Mo AO

Neuropsychiatric Symptoms and Functional Decline in Alzheimer's Disease and Lewy Body Dementia.

J Am Geriatr Soc 2020 Oct;68(10):2257-2263. Epub 2020 aug 1

PMID: 32738062

Oppedal K, Borda MG, Ferreira D, Westman E, Aarsland D,

European DLB consortium: diagnostic and prognostic biomarkers in dementia with Lewy bodies, a multicenter international initiative.

Neurodegener Dis Manag 2019 Oct;9(5):247-250.

PMID: 31580225

Oppedal K, Ferreira D, Cavallin L, Lemstra AW, Ten Kate M, Padovani A, Rektorova I, Bonanni L, Wahlund LO, Engedal K, Nobili F, Kramberger M, Taylor JP, Hort J, Snædal J, Blanc F, Walker Z, Antonini A, Westman E, Aarsland D,

A signature pattern of cortical atrophy in dementia with Lewy bodies: A study on 333 patients from the European DLB consortium.

Alzheimers Dement 2018 Nov 12. Epub 2018 nov 12

PMID: 30439333

Deltagere
  • Morten Goodwin Prosjektdeltaker
  • Daniel Ferreira Prosjektdeltaker
  • Eric Westman Prosjektdeltaker
  • Trygve Christian Eftestøl Prosjektdeltaker
  • Dag Årsland Leder av forskningsgruppe
  • Solveig Kristina Hammonds Ph.d.-kandidat
  • Tormod Fladby Prosjektdeltaker
  • Kolbjørn Selvåg Brønnick Prosjektdeltaker
  • Ketil Oppedal Prosjektleder

eRapport er utarbeidet av Sølvi Lerfald og Reidar Thorstensen, Regionalt kompetansesenter for klinisk forskning, Helse Vest RHF, og videreutvikles av de fire RHF-ene i fellesskap, med støtte fra Helse Vest IKT

Alle henvendelser rettes til Faglig rapportering, Helse Vest

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