| 1. |
- McGinnity, Colm J, et al.
(författare)
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Αlpha 5 subunit-containing GABAA receptors in temporal lobe epilepsy with normal MRI.
- 2021
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Ingår i: Brain communications. - : Oxford University Press (OUP). - 2632-1297. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- GABAA receptors containing the α5 subunit mediate tonic inhibition and are widely expressed in the limbic system. In animals, activation of α5-containing receptors impairs hippocampus-dependent memory. Temporal lobe epilepsy is associated with memory impairments related to neuron loss and other changes. The less selective PET ligand [11C]flumazenil has revealed reductions in GABAA receptors. The hypothesis that α5 subunit receptor alterations are present in temporal lobe epilepsy and could contribute to impaired memory is untested. We compared α5 subunit availability between individuals with temporal lobe epilepsy and normal structural MRI ('MRI-negative') and healthy controls, and interrogated the relationship between α5 subunit availability and episodic memory performance, in a cross-sectional study. Twenty-three healthy male controls (median ± interquartile age 49±13years) and 11 individuals with MRI-negative temporal lobe epilepsy (seven males; 40±8) had a 90-min PET scan after bolus injection of [11C]Ro15-4513, with arterial blood sampling and metabolite correction. All those with epilepsy and six controls completed the Adult Memory and Information Processing Battery on the scanning day. 'Bandpass' exponential spectral analyses were used to calculate volumes of distribution separately for the fast component [VF; dominated by signal from α1 (α2, α3)-containing receptors] and the slow component (VS; dominated by signal from α5-containing receptors). We made voxel-by-voxel comparisons between: the epilepsy and control groups; each individual case versus the controls. We obtained parametric maps of VF and VS measures from a single bolus injection of [11C]Ro15-4513. The epilepsy group had higher VS in anterior medial and lateral aspects of the temporal lobes, the anterior cingulate gyri, the presumed area tempestas (piriform cortex) and the insulae, in addition to increases of ∼24% and ∼26% in the ipsilateral and contralateral hippocampal areas (P<0.004). This was associated with reduced VF:VS ratios within the same areas (P<0.009). Comparisons of VS for each individual with epilepsy versus controls did not consistently lateralize the epileptogenic lobe. Memory scores were significantly lower in the epilepsy group than in controls (mean ± standard deviation -0.4±1.0 versus 0.7±0.3; P=0.02). In individuals with epilepsy, hippocampal VS did not correlate with memory performance on the Adult Memory and Information Processing Battery. They had reduced VF in the hippocampal area, which was significant ipsilaterally (P=0.03), as expected from [11C]flumazenil studies. We found increased tonic inhibitory neurotransmission in our cohort of MRI-negative temporal lobe epilepsy who also had co-morbid memory impairments. Our findings are consistent with a subunit shift from α1/2/3 to α5 in MRI-negative temporal lobe epilepsy.
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| 2. |
- Gryska, Emilia, 1992, et al.
(författare)
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Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.
- 2021
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.Scoping review.Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison.Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity.The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
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| 3. |
- Hortensius, Lisa M., et al.
(författare)
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Serum docosahexaenoic acid levels are associated with brain volumes in extremely preterm born infants
- 2021
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Ingår i: Pediatric Research. - : Springer Science and Business Media LLC. - 0031-3998 .- 1530-0447. ; 90:6, s. 1177-1185
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Tidskriftsartikel (refereegranskat)abstract
- Background: Docosahexaenoic acid (DHA) and arachidonic acid (AA) are important for fetal brain growth and development. Our aim was to evaluate the association between serum DHA and AA levels and brain volumes in extremely preterm infants. Methods: Infants born at <28 weeks gestational age in 2013–2015, a cohort derived from a randomized controlled trial comparing two types of parenteral lipid emulsions, were included (n = 90). Serum DHA and AA levels were measured at postnatal days 1, 7, 14, and 28, and the area under the curve was calculated. Magnetic resonance (MR) imaging was performed at term-equivalent age (n = 66), and volumes of six brain regions were automatically generated. Results: After MR image quality assessment and area under the curve calculation, 48 infants were included (gestational age mean [SD] 25.5 [1.4] weeks). DHA levels were positively associated with total brain (B = 7.966, p = 0.012), cortical gray matter (B = 3.653, p = 0.036), deep gray matter (B = 0.439, p = 0.014), cerebellar (B = 0.932, p = 0.003), and white matter volume (B = 3.373, p = 0.022). AA levels showed no association with brain volumes. Conclusions: Serum DHA levels during the first 28 postnatal days were positively associated with volumes of several brain structures in extremely preterm infants at term-equivalent age. Impact: Higher serum levels of DHA in the first 28 postnatal days are positively associated with brain volumes at term-equivalent age in extremely preterm born infants.Especially the most immature infants suffer from low DHA levels in the first 28 postnatal days, with little increase over time.Future research is needed to explore whether postnatal fatty acid supplementation can improve brain development and may serve as a nutritional preventive and therapeutic treatment option in extremely preterm infants.
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| 4. |
- Srikrishna, Meera, et al.
(författare)
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Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT
- 2021
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 244
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Tidskriftsartikel (refereegranskat)abstract
- Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.
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