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Sökning: WFRF:(Wahlund Lars Olof) > Kungliga Tekniska Högskolan > Marseglia Anna > A deep learning mod...

  • Dartora, CarolineKarolinska Institutet,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden (författare)

A deep learning model for brain age prediction using minimally preprocessed T1w images as input

  • Artikel/kapitelEngelska2023

Förlag, utgivningsår, omfång ...

  • Frontiers Media SA,2023
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:kth-342841
  • https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-342841URI
  • https://doi.org/10.3389/fnagi.2023.1303036DOI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:238259636URI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:154744871URI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • QC 20240208
  • Introduction: In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods: We used a multicohort dataset of cognitively healthy individuals (age range = 32.0–95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2–4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset’s images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results: The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1–4, respectively. The model’s performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63–2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion: We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Marseglia, AnnaKarolinska Institutet,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden (författare)
  • Mårtensson, GustavDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden (författare)
  • Rukh, GullDepartment of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden (författare)
  • Dang, JunhuaDepartment of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden (författare)
  • Muehlboeck, J. SebastianDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden (författare)
  • Wahlund, Lars OlofDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden (författare)
  • Moreno, Rodrigo,1973-KTH,Medicinsk avbildning(Swepub:kth)u1osc58y (författare)
  • Barroso, JoséFacultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas, España (författare)
  • Ferreira, DanielDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas, España (författare)
  • Schiöth, Helgi B.Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden (författare)
  • Westman, EricKarolinska Institutet,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom (författare)
  • Karolinska InstitutetDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:Frontiers in Aging Neuroscience: Frontiers Media SA151663-43651663-4365

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