| 1. |
- Abdelnour, Carla, et al.
(författare)
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Parsing heterogeneity within dementia with Lewy bodies using clustering of biological, clinical, and demographic data
- 2022
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Ingår i: Alzheimer's Research & Therapy. - : Springer Science and Business Media LLC. - 1758-9193. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Dementia with Lewy bodies (DLB) includes various core clinical features that result in different phenotypes. In addition, Alzheimer's disease (AD) and cerebrovascular pathologies are common in DLB. All this increases the heterogeneity within DLB and hampers clinical diagnosis. We addressed this heterogeneity by investigating subgroups of patients with similar biological, clinical, and demographic features.Methods: We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions.Results: We identified 3 dimensions using FAMD that explained 38% of the variance. Subsequent hierarchical clustering identified 4 clusters. Cluster 1 was characterized by amyloid-beta and cerebrovascular pathologies, medial temporal atrophy, and cognitive fluctuations. Cluster 2 had posterior atrophy and showed the lowest frequency of visual hallucinations and cognitive fluctuations and the worst cognitive performance. Cluster 3 had the highest frequency of tau pathology, showed posterior atrophy, and had a low frequency of parkinsonism. Cluster 4 had virtually normal AD biomarkers, the least regional brain atrophy and cerebrovascular pathology, and the highest MMSE scores.Conclusions: This study demonstrates that there are subgroups of DLB patients with different biological, clinical, and demographic characteristics. These findings may have implications in the diagnosis and prognosis of DLB, as well as in the treatment response in clinical trials.
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| 2. |
- Albrecht, Franziska, et al.
(författare)
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Effects of a Highly Challenging Balance Training Program on Motor Function and Brain Structure in Parkinson's Disease
- 2021
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Ingår i: Journal of Parkinson's Disease. - : IOS Press. - 1877-7171 .- 1877-718X. ; 11:4, s. 2057-2071
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Tidskriftsartikel (refereegranskat)abstract
- Background: Parkinson's disease (PD) is characterized by motor deficits and brain alterations having a detrimental impact on balance, gait, and cognition. Intensive physical exercise can induce changes in the neural system, potentially counteracting neurodegeneration in PD and improving clinical symptoms. Objective: This randomized controlled trial investigated effects of a highly challenging, cognitively demanding, balance and gait training (HiBalance) program in participants with PD on brain structure. Methods: 95 participants were assigned to either the HiBalance or an active control speech training program. The group-based interventions were performed in 1-hour sessions, twice per week over a 10-week period. Participants underwent balance, gait, cognitive function, and structural magnetic resonance imaging assessments before and after the interventions. Voxel-based morphometry was analyzed in 34 HiBalance and 31 active controls. Additionally, structural covariance networks were assessed. Results: There was no significant time by group interaction between the HiBalance and control training in balance, gait, or brain volume. Within-HiBalance-group analyses showed higher left putamen volumes post-training. In repeated measures correlation a positive linear, non-significant relationship between gait speed and putamen volume was revealed. In the HiBalance group we found community structure changes and stronger thalamic-cerebellar connectivity in structural covariance networks. Neither brain volume changes nor topology changes were found for the active controls after the training. Conclusion: Thus, subtle structural brain changes occur after balance and gait training. Future studies need to determine whether training modifications or other assessment methods lead to stronger effects.
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| 3. |
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| 4. |
- Borda, Miguel German, et al.
(författare)
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Temporal Muscle Thickness: A Practical Approximation for Assessing Muscle Mass in Older Adults
- 2024
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Ingår i: JOURNAL OF THE AMERICAN MEDICAL DIRECTORS ASSOCIATION. - 1525-8610 .- 1538-9375. ; 25:4
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Ongoing research has evidenced the importance of muscle measurement in predicting adverse outcomes. Measurement of other muscles is promising in current research. This study aimed to determine the correlation between temporal muscle thickness (TMT) and appendicular lean soft tissue (ALSTI) in older adults. Design: Cross-sectional study. Settings and Participants: Single cohort gathered in Gothenburg, Sweden, consisting of individuals born in 1944 (n = 1203). Methods: We studied 657 magnetic resonance images to measure TMT. Comparisons of TMT with dual -energy X-ray absorptiometry ALSTI (kg/m 2 ) as a reference standard were performed. Finally, TMT associations with cognition evaluated using the Mini -Mental State Examination (MMSE), gait speed, and handgrip strength were explored with linear regressions. Results: The correlation between TMT and ALSTI was weak yet signi ficant (r = 0.277, P < .001). TMT exhibited signi ficant associations with MMSE (estimate = 0.168, P = .002), gait speed (estimate =1.795, P < .001), and ALSTI (estimate = 0.508, P < .001). These associations varied when analyzed by sex. In women, TMT was signi ficantly associated with gait speed (estimate = 1.857, P = .005) and MMSE (estimate = 0.223, P = .003). In men, TMT scores were signi ficantly correlated with ALSTI scores (estimate = 0.571, P < .001). Conclusion and Implications: Repurposing head images can be an accessible alternative to detect muscle mass and ultimately detect sarcopenia. These studies have the potential to trigger interventions or further evaluation to improve the muscle and overall health of individuals. However, additional research is warranted before translating these findings into clinical practice. (c) 2024 AMDA - The Society for Post -Acute and Long -Term Care Medicine.
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| 5. |
- Borda, Miguel German, et al.
(författare)
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Using magnetic resonance imaging to measure head muscles: An innovative method to opportunistically determine muscle mass and detect sarcopenia
- 2024
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Ingår i: Journal of Cachexia, Sarcopenia and Muscle. - 2190-5991 .- 2190-6009. ; 15:1, s. 189-197
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Tidskriftsartikel (refereegranskat)abstract
- Background: Sarcopenia is associated with multiple adverse outcomes. Traditional methods to determine low muscle mass for the diagnosis of sarcopenia are mainly based on dual-energy X-ray absorptiometry (DXA), whole-body magnetic resonance imaging (MRI) and bioelectrical impedance analysis. These tests are not always available and are rather time consuming and expensive. However, many brain and head diseases require a head MRI. In this study, we aim to provide a more accessible way to detect sarcopenia by comparing the traditional method of DXA lean mass estimation versus the tongue and masseter muscle mass assessed in a standard brain MRI. Methods: The H70 study is a longitudinal study of older people living in Gothenburg, Sweden. In this cross-sectional analysis, from 1203 participants aged 70years at baseline, we included 495 with clinical data and MRI images available. We used the appendicular lean soft tissue index (ALSTI) in DXA images as our reference measure of lean mass. Images from the masseter and tongue were analysed and segmented using 3D Slicer. For the statistical analysis, the Spearman correlation coefficient was used, and concordance was estimated with the Kappa coefficient. Results: The final sample consisted of 495 participants, of which 52.3% were females. We found a significant correlation coefficient between both tongue (0.26) and masseter (0.33) with ALSTI (P<0.001). The sarcopenia prevalence confirmed using the alternative muscle measure in MRI was calculated using the ALSTI (tongue=2.0%, masseter=2.2%, ALSTI=2.4%). Concordance between sarcopenia with masseter and tongue versus sarcopenia with ALSTI as reference has a Kappa of 0.989 (P<0.001) for masseter and a Kappa of 1 for the tongue muscle (P<0.001). Comorbidities evaluated with the Cumulative Illness Rating Scale were significantly associated with all the muscle measurements: ALSTI (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.07–1.26, P<0.001), masseter (OR 1.16, 95% CI 1.07–1.26, P<0.001) and tongue (OR 1.13, 95% CI 1.04–1.22, P=0.002); the higher the comorbidities, the higher the probability of having abnormal muscle mass. Conclusions: ALSTI was significantly correlated with tongue and masseter muscle mass. When performing the sarcopenia diagnostic algorithm, the prevalence of sarcopenia calculated with head muscles did not differ from sarcopenia calculated using DXA, and almost all participants were correctly classified using both methods.
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| 6. |
- Brusini, Irene
(författare)
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Methods for the analysis and characterization of brain morphology from MRI images
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. These measures can both allow a better understanding of how the brain changes due to multiple factors (e.g., environmental and pathological) and contribute to the identification of new imaging biomarkers of neurological and psychiatric diseases. The overall goal of the present thesis is to advance the knowledge on how brain MRI image processing can be effectively used to analyze and characterize brain structure.The first two works presented in this thesis are animal studies that primarily aim to use MRI data for analyzing differences between groups of interest. In Paper I, MRI scans from wild and domestic rabbits were processed to identify structural brain differences between these two groups. Domestication was found to significantly reshape brain structure in terms of both regional gray matter volume and white matter integrity. In Paper II, rat brain MRI scans were used to train a brain age prediction model. This model was then tested on both controls and a group of rats that underwent long-term environmental enrichment and dietary restriction. This healthy lifestyle intervention was shown to significantly affect the predicted brain age trajectories by slowing the rats' aging process compared to controls. Furthermore, brain age predicted on young adult rats was found to have a significant effect on survival.Papers III to V are human studies that propose deep learning-based methods for segmenting brain structures that can be severely affected by neurodegeneration. In particular, Papers III and IV focus on U-Net-based 2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS) patients. In both studies, good segmentation accuracy was obtained and a significant correlation was found between CC area and the patient's level of cognitive and physical disability. Additionally, in Paper IV, shape analysis of the segmented CC revealed a significant association between disability and both CC thickness and bending angle. Conversely, in Paper V, a novel method for automatic segmentation of the hippocampus is proposed, which consists of embedding a statistical shape prior as context information into a U-Net-based framework. The inclusion of shape information was shown to significantly improve segmentation accuracy when testing the method on a new unseen cohort (i.e., different from the one used for training). Furthermore, good performance was observed across three different diagnostic groups (healthy controls, subjects with mild cognitive impairment and Alzheimer's patients) that were characterized by different levels of hippocampal atrophy.In summary, the studies presented in this thesis support the great value of MRI image analysis for the advancement of neuroscientific knowledge, and their contribution is mostly two-fold. First, by applying well-established processing methods on datasets that had not yet been explored in the literature, it was possible to characterize specific brain changes and disentangle relevant problems of a clinical or biological nature. Second, a technical contribution is provided by modifying and extending already-existing brain image processing methods to achieve good performance on new datasets.
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| 7. |
- Brusini, Irene, et al.
(författare)
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MRI-derived brain age as a biomarker of ageing in rats : validation using a healthy lifestyle intervention
- 2022
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Ingår i: Neurobiology of Aging. - : Elsevier BV. - 0197-4580 .- 1558-1497. ; 109, s. 204-215
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Tidskriftsartikel (refereegranskat)abstract
- The difference between brain age predicted from MRI and chronological age (the so-called BrainAGE) has been proposed as an ageing biomarker. We analyse its cross-species potential by testing it on rats undergoing an ageing modulation intervention. Our rat brain age prediction model combined Gaussian process regression with a classifier and achieved a mean absolute error (MAE) of 4.87 weeks using cross-validation on a longitudinal dataset of 31 normal ageing rats. It was then tested on two groups of 24 rats (MAE = 9.89 weeks, correlation coefficient = 0.86): controls vs. a group under long-term environmental enrichment and dietary restriction (EEDR). Using a linear mixed-effects model, BrainAGE was found to increase more slowly with chronological age in EEDR rats ( p = 0 . 015 for the interaction term). Cox re-gression showed that older BrainAGE at 5 months was associated with higher mortality risk ( p = 0 . 03 ). Our findings suggest that lifestyle-related prevention approaches may help to slow down brain ageing in rodents and the potential of BrainAGE as a predictor of age-related health outcomes.
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| 8. |
- Brusini, Irene, et al.
(författare)
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Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus
- 2020
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Ingår i: Frontiers in Neuroscience. - : Frontiers Media S.A.. - 1662-4548 .- 1662-453X. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer's disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.
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| 9. |
- Budhiraja, Meenal, et al.
(författare)
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Cortical structure abnormalities in females with conduct disorder prior to age 15
- 2019
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Ingår i: Psychiatry Research. - : Elsevier. - 0925-4927 .- 1872-7506. ; 289, s. 37-44
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Tidskriftsartikel (refereegranskat)abstract
- Among females, conduct disorder (CD) before age 15 is associated with multiple adverse outcomes in adulthood. The few existing structural neuroimaging studies of females with CD report abnormalities of gray matter volumes. The present study compared cortical thickness and surface area of young women with childhood/adolescent CD and healthy women to determine whether cortical abnormalities were present in adulthood and whether they were related to prior CD. Structural brain images from 31 women with CD and 25 healthy women were analyzed using FreeSurfer. Group differences between cortical thickness and surface area were assessed using cluster-wise corrections with Monte Carlo simulations. Women with prior CD, relative to healthy women, showed: (1) reduced cortical thickness in left fusiform gyrus extending up to entorhinal cortex and lingual gyrus; (2) reduced surface area in right superior parietal cortex; (3) increased surface area in left superior temporal gyrus, and right precentral gyrus. These differences remained significant after adjusting for past comorbid disorders, current symptoms of anxiety and depression, current substance use as well as maltreatment. The study suggests that among females, CD prior to age 15 is associated with cortical structure abnormalities in brain regions involved in emotion processing and social interaction.
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| 10. |
- Canal-Garcia, Anna, et al.
(författare)
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Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease
- 2024
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Ingår i: CEREBRAL CORTEX. - 1047-3211 .- 1460-2199. ; 34:2
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Tidskriftsartikel (refereegranskat)abstract
- Increasing evidence suggests that patients with Alzheimer's disease present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional magnetic resonance imaging across different stages of Alzheimer's disease using a novel multilayer brain network approach. Participants from preclinical and clinical Alzheimer's disease stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE epsilon 4 carriership. Dynamic multilayer functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early- and late-stage Alzheimer's disease diagnosis.
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