| 1. |
- Dartora, Caroline, et al.
(författare)
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A deep learning model for brain age prediction using minimally preprocessed T1w images as input
- 2023
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Ingår i: Frontiers in Aging Neuroscience. - : Frontiers Media SA. - 1663-4365. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Wahlström, Niklas, et al.
(författare)
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Composition and structure of cell wall ulvans recovered from Ulva spp. along the Swedish west coast
- 2020
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Ingår i: Carbohydrate Polymers. - : Elsevier BV. - 0144-8617 .- 1879-1344. ; 233
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Tidskriftsartikel (refereegranskat)abstract
- The cell wall polysaccharide ulvan was isolated from two species of the seaweed Ulva collected along the Swedish west coast. Acidic extraction was benchmarked against hot water extraction with enzymatic purification and against commercial ulvan. Extracted ulvan contained 11–18 % g/g of ash, some protein (up to 1.3 % g N/g) but minimal colored impurities. The ulvans had high molecular weights (660,000–760,000 g/mol) and were composed of 77–79 % g/g carbohydrates, mainly rhamnose, xylose, glucose, glucuronic acid, and iduronic acid. The extraction protocol and the ulvan source strongly impact the molecular weight and the chemical composition. Acidic extraction caused almost complete desulfation of the isolated ulvan while the other method preserved a significant degree of SO3 substituents. Elemental analysis of ash remaining after thermal degradation showed presence of common mineral elements such as Na, Ca, Mg, Al, and K, but none of the heavy metals Pb, Hg, or As.
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| 3. |
- Hibar, Derrek P., et al.
(författare)
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Novel genetic loci associated with hippocampal volume
- 2017
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (r(g) = -0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.
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| 4. |
- Pereira, Joana B., et al.
(författare)
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Amyloid and tau accumulate across distinct spatial networks and are differentially associated with brain connectivity
- 2019
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Ingår i: eLife. - 2050-084X. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- The abnormal accumulation of amyloid-β and tau targets specific spatial networks in Alzheimer’s disease. However, the relationship between these networks across different disease stages and their association with brain connectivity has not been explored. In this study, we applied a joint independent component analysis to18F-Flutemetamol (amyloid-β) and18F-Flortaucipir (tau) PET images to identify amyloid-β and tau networks across different stages of Alzheimer’s disease. We then assessed whether these patterns were associated with resting-state functional networks and white matter tracts. Our analyses revealed nine patterns that were linked across tau and amyloid-β data. The amyloid-β and tau patterns showed a fair to moderate overlap with distinct functional networks but only tau was associated with white matter integrity loss and multiple cognitive functions. These findings show that amyloid-β and tau have different spatial affinities, which can be used to understand how they accumulate in the brain and potentially damage the brain’s connections.
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