| 1. |
- Altmann, A, et al.
(författare)
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Analysis of brain atrophy and local gene expression in genetic frontotemporal dementia
- 2020
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Ingår i: Brain communications. - : Oxford University Press (OUP). - 2632-1297. ; 2:2
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Tidskriftsartikel (refereegranskat)abstract
- Frontotemporal dementia is a heterogeneous neurodegenerative disorder characterized by neuronal loss in the frontal and temporal lobes. Despite progress in understanding which genes are associated with the aetiology of frontotemporal dementia, the biological basis of how mutations in these genes lead to cell loss in specific cortical regions remains unclear. In this work, we combined gene expression data for 16 772 genes from the Allen Institute for Brain Science atlas with brain maps of grey matter atrophy in symptomatic C9orf72, GRN and MAPT mutation carriers obtained from the Genetic Frontotemporal dementia Initiative study. No significant association was seen between C9orf72, GRN and MAPT expression and the atrophy patterns in the respective genetic groups. After adjusting for spatial autocorrelation, between 1000 and 5000 genes showed a negative or positive association with the atrophy pattern within each individual genetic group, with the most significantly associated genes being TREM2, SSBP3 and GPR158 (negative association in C9Orf72, GRN and MAPT respectively) and RELN, MXRA8 and LPA (positive association in C9Orf72, GRN and MAPT respectively). An overrepresentation analysis identified a negative association with genes involved in mitochondrial function, and a positive association with genes involved in vascular and glial cell function in each of the genetic groups. A set of 423 and 700 genes showed significant positive and negative association, respectively, with atrophy patterns in all three maps. The gene set with increased expression in spared cortical regions was enriched for neuronal and microglial genes, while the gene set with increased expression in atrophied regions was enriched for astrocyte and endothelial cell genes. Our analysis suggests that these cell types may play a more active role in the onset of neurodegeneration in frontotemporal dementia than previously assumed, and in the case of the positively associated cell marker genes, potentially through emergence of neurotoxic astrocytes and alteration in the blood–brain barrier, respectively.
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| 2. |
- Andersen, Kasper Winther, et al.
(författare)
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Disentangling white-matter damage from physiological fibre orientation dispersion in multiple sclerosis
- 2020
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Ingår i: Brain Communications. - : Oxford University Press (OUP). - 2632-1297. ; 2:2, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- Multiple sclerosis leads to diffuse damage of the central nervous system, affecting also the normal-appearing white matter. Demyelination and axonal degeneration reduce regional fractional anisotropy in normal-appearing white matter, which can be routinely mapped with diffusion tensor imaging. However, the standard fractional anisotropy metric is also sensitive to physiological variations in orientation dispersion of white matter fibres. This complicates the detection of disease-related damage in large parts of cerebral white matter where microstructure physiologically displays a high degree of fibre dispersion. To resolve this ambiguity, we employed a novel tensor-valued encoding method for diffusion MRI, which yields a microscopic fractional anisotropy metric that is unaffected by regional variations in orientation dispersion. In 26 patients with relapsing-remitting multiple sclerosis, 14 patients with primary-progressive multiple sclerosis and 27 age-matched healthy controls, we compared standard fractional anisotropy mapping with the novel microscopic fractional anisotropy mapping method, focusing on normal-appearing white matter. Mean microscopic fractional anisotropy and standard fractional anisotropy of normal-appearing white matter were significantly reduced in both patient groups relative to healthy controls, but microscopic fractional anisotropy yielded a better reflection of disease-related white-matter alterations. The reduction in mean microscopic fractional anisotropy showed a significant positive linear relationship with physical disability, as reflected by the expanded disability status scale. Mean reduction of microscopic fractional anisotropy in normal-appearing white matter also scaled positively with individual cognitive dysfunction, as measured with the symbol digit modality test. Mean microscopic fractional anisotropy reduction in normal-appearing white matter also showed a positive relationship with total white-matter lesion load as well as lesion load in specific tract systems. None of these relationships between normal-appearing white-matter microstructure and clinical, cognitive or structural measures emerged when using mean fractional anisotropy. Together, the results provide converging evidence that microscopic fractional anisotropy mapping substantially advances the assessment of cerebral white matter in multiple sclerosis by disentangling microstructure damage from variations in physiological fibre orientation dispersion at the stage of data acquisition. Since tensor-valued encoding can be implemented in routine diffusion MRI, microscopic fractional anisotropy mapping bears considerable potential for the future assessment of disease progression in normal-appearing white matter in both relapsing-remitting and progressive forms of multiple sclerosis as well as other white-matter-related brain diseases.
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| 3. |
- Arber, C., et al.
(författare)
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Amyloid precursor protein processing in human neurons with an allelic series of the PSEN1 intron 4 deletion mutation and total presenilin-1 knockout
- 2019
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Ingår i: Brain Communications. - : Oxford University Press (OUP). - 2632-1297. ; 1:1
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Tidskriftsartikel (refereegranskat)abstract
- Mutations in presenilin-1 (PSEN1), encoding the catalytic subunit of the amyloid precursor protein-processing enzyme gamma-secretase, cause familial Alzheimer's disease. However, the mechanism of disease is yet to be fully understood and it remains contentious whether mutations exert their effects predominantly through gain or loss of function. To address this question, we generated an isogenic allelic series for the PSEN1 mutation intron 4 deletion; represented by control, heterozygous and homozygous mutant induced pluripotent stem cells in addition to a presenilin-1 knockout line. Induced pluripotent stem cell-derived cortical neurons reveal reduced, yet detectable amyloid-beta levels in the presenilin-1 knockout line, and a mutant gene dosage-dependent defect in amyloid precursor protein processing in PSEN1 intron 4 deletion lines, consistent with reduced processivity of gamma-secretase. The different effects of presenilin-1 knockout and the PSEN1 intron 4 deletion mutation on amyloid precursor protein-C99 fragment accumulation, nicastrin maturation and amyloid-beta peptide generation support distinct consequences of familial Alzheimer's disease-associated mutations and knockout of presenilin-1 on the function of gamma-secretase.
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| 4. |
- Awad, Amar, 1988-, et al.
(författare)
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Deep brain stimulation does not modulate resting-state functional connectivity in essential tremor
- 2024
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Ingår i: Brain Communications. - : Oxford University Press. - 2632-1297. ; 6:2
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Tidskriftsartikel (refereegranskat)abstract
- While the effectiveness of deep brain stimulation in alleviating essential tremor is well-established, the underlying mechanisms of the treatment are unclear. Essential tremor, as characterized by tremor during action, is proposed to be driven by a dysfunction in the cerebello-thalamo-cerebral circuit that is evident not only during motor actions but also during rest. Stimulation effects on resting-state functional connectivity were investigated by functional MRI in 16 essential tremor patients with fully implanted deep brain stimulation in the caudal zona incerta during On-and-Off therapeutic stimulation, in a counterbalanced design. Functional connectivity was calculated between different constellations of sensorimotor as well as non-sensorimotor regions (as derived from seed-based and data-driven approaches), and compared between On and Off stimulation. We found that deep brain stimulation did not modulate resting-state functional connectivity. The lack of modulation by deep brain stimulation during resting-state, in combination with previously demonstrated effects on the cerebello-thalamo-cerebral circuit during motor tasks, suggests an action-dependent modulation of the stimulation in essential tremor.
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| 5. |
- Banote, Rakesh Kumar, et al.
(författare)
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CSF biomarkers in patients with epilepsy in Alzheimer's disease: a nation-wide study.
- 2022
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Ingår i: Brain communications. - : Oxford University Press (OUP). - 2632-1297. ; 4:4
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Tidskriftsartikel (refereegranskat)abstract
- Alzheimer's disease is the most common neurodegenerative dementia. A subset of Alzheimer's disease patients develop epilepsy. The risk is higher in young-onset Alzheimer's disease, but pathophysiological mechanisms remain elusive. The purpose of this study was to assess biomarkers reflecting neurodegeneration in Alzheimer's disease patients with and without epilepsy. By cross-referencing the largest national laboratory database with Swedish National Patient Register, we could identify CSF biomarker results from 17901 Alzheimer's disease patients, and compare levels of neurofilament light, glial fibrillary acidic protein, total tau, phosphorylated tau and amyloid beta 42 in patients with (n = 851) and without epilepsy. The concentrations of total tau and phosphorylated tau were higher in Alzheimer's disease patients with epilepsy than Alzheimer's disease patients without epilepsy and amyloid beta 42 levels were significantly lower in Alzheimer's disease patients with epilepsy. No differences in the levels of neurofilament light and glial fibrillary acidic protein were observed. Our study suggests that epilepsy is more common in Alzheimer's disease patients with more pronounced Alzheimer's pathology, as determined by the CSF biomarkers. Further studies are needed to investigate the biomarker potential of these CSF markers as predictors of epilepsy course or as indicators of epileptogenesis in Alzheimer's disease.
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| 6. |
- Bevan, RJ, et al.
(författare)
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OPA1 deficiency accelerates hippocampal synaptic remodelling and age-related deficits in learning and memory
- 2020
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Ingår i: Brain communications. - : Oxford University Press (OUP). - 2632-1297. ; 2:2, s. fcaa101-
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Tidskriftsartikel (refereegranskat)abstract
- A healthy mitochondrial network is essential for the maintenance of neuronal synaptic integrity. Mitochondrial and metabolic dysfunction contributes to the pathogenesis of many neurodegenerative diseases including dementia. OPA1 is the master regulator of mitochondrial fusion and fission and is likely to play an important role during neurodegenerative events. To explore this, we quantified hippocampal dendritic and synaptic integrity and the learning and memory performance of aged Opa1 haploinsufficient mice carrying the Opa1Q285X mutation (B6; C3-Opa1Q285STOP; Opa1+/−). We demonstrate that heterozygous loss of Opa1 results in premature age-related loss of spines in hippocampal pyramidal CA1 neurons and a reduction in synaptic density in the hippocampus. This loss is associated with subtle memory deficits in both spatial novelty and object recognition. We hypothesize that metabolic failure to maintain normal neuronal activity at the level of a single spine leads to premature age-related memory deficits. These results highlight the importance of mitochondrial homeostasis for maintenance of neuronal function during ageing.
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| 7. |
- Bocchetta, M, et al.
(författare)
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Structural MRI predicts clinical progression in presymptomatic genetic frontotemporal dementia: findings from the GENetic Frontotemporal dementia Initiative cohort
- 2023
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Ingår i: Brain communications. - : Oxford University Press (OUP). - 2632-1297. ; 5:2, s. fcad061-
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Tidskriftsartikel (refereegranskat)abstract
- Biomarkers that can predict disease progression in individuals with genetic frontotemporal dementia are urgently needed. We aimed to identify whether baseline MRI-based grey and white matter abnormalities are associated with different clinical progression profiles in presymptomatic mutation carriers in the GENetic Frontotemporal dementia Initiative. Three hundred eighty-seven mutation carriers were included (160 GRN, 160 C9orf72, 67 MAPT), together with 240 non-carrier cognitively normal controls. Cortical and subcortical grey matter volumes were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans, while white matter characteristics were estimated using diffusion tensor imaging. Mutation carriers were divided into two disease stages based on their global CDR®+NACC-FTLD score: presymptomatic (0 or 0.5) and fully symptomatic (1 or greater). The w-scores in each grey matter volumes and white matter diffusion measures were computed to quantify the degree of abnormality compared to controls for each presymptomatic carrier, adjusting for their age, sex, total intracranial volume, and scanner type. Presymptomatic carriers were classified as ‘normal’ or ‘abnormal’ based on whether their grey matter volume and white matter diffusion measure w-scores were above or below the cut point corresponding to the 10th percentile of the controls. We then compared the change in disease severity between baseline and one year later in both the ‘normal’ and ‘abnormal’ groups within each genetic subtype, as measured by the CDR®+NACC-FTLD sum-of-boxes score and revised Cambridge Behavioural Inventory total score. Overall, presymptomatic carriers with normal regional w-scores at baseline did not progress clinically as much as those with abnormal regional w-scores. Having abnormal grey or white matter measures at baseline was associated with a statistically significant increase in the CDR®+NACC-FTLD of up to 4 points in C9orf72 expansion carriers, and 5 points in the GRN group as well as a statistically significant increase in the revised Cambridge Behavioural Inventory of up to 11 points in MAPT, 10 points in GRN, and 8 points in C9orf72 mutation carriers. Baseline regional brain abnormalities on MRI in presymptomatic mutation carriers are associated with different profiles of clinical progression over time. These results may be helpful to inform stratification of participants in future trials.
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| 8. |
- Boito, Deneb, 1993-, et al.
(författare)
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MRI with generalized diffusion encoding reveals damaged white matter in patients previously hospitalized for COVID-19 and with persisting symptoms at follow-up
- 2023
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Ingår i: Brain Communications. - : Oxford University Press. - 2632-1297. ; 5:6
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Tidskriftsartikel (refereegranskat)abstract
- There is mounting evidence of the long-term effects of COVID-19 on the central nervous system, with patients experiencing diverse symptoms, often suggesting brain involvement. Conventional brain MRI of these patients shows unspecific patterns, with no clear connection of the symptomatology to brain tissue abnormalities, whereas diffusion tensor studies and volumetric analyses detect measurable changes in the brain after COVID-19. Diffusion MRI exploits the random motion of water molecules to achieve unique sensitivity to structures at the microscopic level, and new sequences employing generalized diffusion encoding provide structural information which are sensitive to intravoxel features. In this observational study, a total of 32 persons were investigated: 16 patients previously hospitalized for COVID-19 with persisting symptoms of post-COVID condition (mean age 60 years: range 41–79, all male) at 7-month follow-up and 16 matched controls, not previously hospitalized for COVID-19, with no post-COVID symptoms (mean age 58 years, range 46–69, 11 males). Standard MRI and generalized diffusion encoding MRI were employed to examine the brain white matter of the subjects. To detect possible group differences, several tissue microstructure descriptors obtainable with the employed diffusion sequence, the fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, microscopic anisotropy, orientational coherence (Cc) and variance in compartment’s size (CMD) were analysed using the tract-based spatial statistics framework. The tract-based spatial statistics analysis showed widespread statistically significant differences (P < 0.05, corrected for multiple comparisons using the familywise error rate) in all the considered metrics in the white matter of the patients compared to the controls. Fractional anisotropy, microscopic anisotropy and Cc were lower in the patient group, while axial diffusivity, radial diffusivity, mean diffusivity and CMD were higher. Significant changes in fractional anisotropy, microscopic anisotropy and CMD affected approximately half of the analysed white matter voxels located across all brain lobes, while changes in Cc were mainly found in the occipital parts of the brain. Given the predominant alteration in microscopic anisotropy compared to Cc, the observed changes in diffusion anisotropy are mostly due to loss of local anisotropy, possibly connected to axonal damage, rather than white matter fibre coherence disruption. The increase in radial diffusivity is indicative of demyelination, while the changes in mean diffusivity and CMD are compatible with vasogenic oedema. In summary, these widespread alterations of white matter microstructure are indicative of vasogenic oedema, demyelination and axonal damage. These changes might be a contributing factor to the diversity of central nervous system symptoms that many patients experience after COVID-19.
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| 9. |
- Bonkhoff, A. K., et al.
(författare)
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Sex-specific lesion pattern of functional outcomes after stroke
- 2022
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Ingår i: Brain Communications. - : Oxford University Press (OUP). - 2632-1297. ; 4:2
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Tidskriftsartikel (refereegranskat)abstract
- Relying on neuroimaging and clinical data of 822 acute stroke patients, Bonkhoff et al. report substantially more detrimental effects of lesions in left-hemispheric posterior circulation regions on functional outcomes in women compared to men. These findings may motivate a sex-specific clinical stroke management to improve outcomes in the longer term. Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevant sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale >2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men - women) = -0.295 (90% highest posterior density interval = -0.556 to -0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.
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| 10. |
- Bourached, Anthony, et al.
(författare)
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Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity
- 2023
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Ingår i: BRAIN COMMUNICATIONS. - 2632-1297. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by similar to 20% when increasing the sample size 9x [maximum for 100 patients: 0.279 +/- 0.005 (R2, 95% confidence interval), 900 patients: 0.337 +/- 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes. Bourached et al. contrast linear and deep learning-based algorithms in their prediction performances of stroke severity depending on the training set sample sizes. They find that linear regression outperforms deep learning-based algorithms for smaller training samples comprising lesion location information of 100 patients, while deep learning excels in the case of larger samples (N = 900).
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