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A deep learning mod...
A deep learning model for brain age prediction using minimally preprocessed T1w images as input
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- Dartora, Caroline (författare)
- Karolinska Institutet,Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Ctr Alzheimer Res, Stockholm, Sweden.,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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- Marseglia, Anna (författare)
- Karolinska Institutet,Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Ctr Alzheimer Res, Stockholm, Sweden.,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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- Mårtensson, Gustav (författare)
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden,Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Ctr Alzheimer Res, Stockholm, Sweden.
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- Rukh, Gull (författare)
- Uppsala universitet,Funktionell farmakologi och neurovetenskap,Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
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- Dang, Junhua (författare)
- Uppsala universitet,Funktionell farmakologi och neurovetenskap,Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
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- Muehlboeck, J. Sebastian (författare)
- Karolinska Institutet,Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Ctr Alzheimer Res, Stockholm, Sweden.,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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- Wahlund, Lars Olof (författare)
- Karolinska Institutet,Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Ctr Alzheimer Res, Stockholm, Sweden.,Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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- Moreno, Rodrigo, 1973- (författare)
- KTH,Medicinsk avbildning,KTH Royal Inst Technol, Dept Biomed Engn & Hlth Syst, Stockholm, Sweden.
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- Barroso, José (författare)
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas, España,Univ Fernando Pessoa Canarias, Fac Ciencias Salud, Las Palmas Gran Canaria, Spain.
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- Ferreira, Daniel (författare)
- Karolinska Institutet,Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Ctr Alzheimer Res, Stockholm, Sweden.;Univ Fernando Pessoa Canarias, Fac Ciencias Salud, Las Palmas Gran Canaria, Spain.,Division 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
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- Schiöth, Helgi (författare)
- Uppsala universitet,Funktionell farmakologi och neurovetenskap,Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
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- Westman, Eric (författare)
- Karolinska Institutet,Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Ctr Alzheimer Res, Stockholm, Sweden.;Kings Coll London, Inst Psychiat Psychol & Neurosci, Ctr Neuroimaging Sci, Dept Neuroimaging, London, England.,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
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Karolinska Institutet Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Ctr Alzheimer Res, Stockholm, Sweden (creator_code:org_t)
- Frontiers Media SA, 2023
- 2023
- Engelska.
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Ingår i: Frontiers in Aging Neuroscience. - : Frontiers Media SA. - 1663-4365. ; 15
- Relaterad länk:
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https://doi.org/10.3...
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https://uu.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.3...
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http://kipublication...
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http://kipublication...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- ageing prediction
- brain age
- CNN
- neurodegeneration
- normal ageing
- UK Biobank
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- ref (ämneskategori)
- art (ämneskategori)
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Dartora, Carolin ...
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Marseglia, Anna
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Mårtensson, Gust ...
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Rukh, Gull
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Dang, Junhua
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Muehlboeck, J. S ...
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visa fler...
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Wahlund, Lars Ol ...
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Moreno, Rodrigo, ...
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Barroso, José
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Ferreira, Daniel
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Schiöth, Helgi
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Westman, Eric
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TEKNIK OCH TEKNO ...
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och Medicinsk bildbe ...
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MEDICIN OCH HÄLS ...
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och Radiologi och bi ...
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NATURVETENSKAP
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och Datorseende och ...
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Kungliga Tekniska Högskolan
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