Multimodal MRI of grey matter, white matter, and functional connectivity in cognitively healthy mutation carriers at risk for frontotemporal dementia and Alzheimer's disease

Background: Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are associated with divergent differences in grey matter volume, white matter diffusion, and functional connectivity. However, it is unknown at what disease stage these differences emerge. Here, we investigate whether divergent differences in grey matter volume, white matter diffusion, and functional connectivity are already apparent between cognitively healthy carriers of pathogenic FTD mutations, and cognitively healthy carriers at increased AD risk. Methods: We acquired multimodal magnetic resonance imaging (MRI) brain scans in cognitively healthy subjects with (n=39) and without (n=36) microtubule-associated protein Tau (MAPT) or progranulin (GRN) mutations, and with (n=37) and without (n=38) apolipoprotein E ϵ4 (APOE4) allele. We evaluated grey matter volume using voxel-based morphometry, white matter diffusion using tract-based spatial statistics (TBSS), and region-to-network functional connectivity using dual regression in the default mode network and salience network. We tested for differences between the respective carriers and controls, as well as for divergence of those differences. For the divergence contrast, we additionally performed region-of-interest TBSS analyses in known areas o

Multimodal MRI of grey matter, white matter, and functional connectivity in cognitively healthy mutation carriers at risk for frontotemporal dementia and Alzheimer's disease

BackgroundFrontotemporal dementia (FTD) and Alzheimer's disease (AD) are associated with divergent differences in grey matter volume, white matter diffusion, and functional connectivity. However, it is unknown at what disease stage these differences emerge. Here, we investigate whether divergent differences in grey matter volume, white matter diffusion, and functional connectivity are already apparent between cognitively healthy carriers of pathogenic FTD mutations, and cognitively healthy carriers at increased AD risk.MethodsWe acquired multimodal magnetic resonance imaging (MRI) brain scans in cognitively healthy subjects with (n=39) and without (n=36) microtubule-associated protein Tau (MAPT) or progranulin (GRN) mutations, and with (n=37) and without (n=38) apolipoprotein E epsilon 4 (APOE4) allele. We evaluated grey matter volume using voxel-based morphometry, white matter diffusion using tract-based spatial statistics (TBSS), and region-to-network functional connectivity using dual regression in the default mode network and salience network. We tested for differences between the respective carriers and controls, as well as for divergence of those differences. For the divergence contrast, we additionally performed region-of-interest TBSS analyses in known areas of white matter diffusion differences between FTD and AD (i.e., uncinate fasciculus, forceps minor, and anterior thalamic radiation).ResultsMAPT/GRN carriers did not differ from controls in any modality. APOE4 carriers had lower fractional anisotropy than controls in the callosal splenium and right inferior fronto-occipital fasciculus, but did not show grey matter volume or functional connectivity differences. We found no divergent differences between both carrier-control contrasts in any modality, even in region-of-interest analyses.ConclusionsConcluding, we could not find differences suggestive of divergent pathways of underlying FTD and AD pathology in asymptomatic risk mutation carriers. Future studies should focus on asymptomatic mutation carriers that are closer to symptom onset to capture the first specific signs that may differentiate between FTD and AD.Multivariate analysis of psychological dat

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification

Multivariate analysis of psychological dat

Prediction of tissue outcome after experimental stroke using MRI-based algorithms

Acute ischemic stroke is a major cause of death and disability in modern western society. Possible benefit of the only clinically approved therapeutic intervention, i.e. thrombolysis, is complicated by complex pathophysiologic interplay of factors resulting from the ischemic insult. As a result, strict therapeutic guidelines limit the applicability of this therapy. Yet these guidelines may not always apply on an individual patient level, thereby unnecessarily excluding patients who could have benefitted from therapeutic intervention. Therefore early and accurate assessment of tissue injury and the prediction of its progression are crucial for individualized strategic therapeutic planning. In this thesis ‘Prediction of tissue outcome after experimental stroke using MRI-based algorithms’ the potentials of novel MRI-based models for prediction of brain tissue outcome after ischemic stroke have been evaluated. Serial MRI data, acquired in different experimental animal models of stroke, were employed to evaluate the potential of various multiparametric statistical models for monitoring of tissue injury progression and prediction of tissue outcome. The studies in this thesis demonstrate that the use of novel voxel-based prediction methods provide significantly improved insights in brain tissue injury progression, with the specific ability to predict variable degrees of cerebral damage. Our data revealed that: (1) multiparametric clustering techniques allow for improved differentiation of tissue injury progression as compared to previously employed volumetric methods; (2) benefit from reperfusion was more specifically predicted by an angiography-diffusion mismatch than a perfusion-diffusion mismatch; (3) specific tissue outcome prediction algorithms enabled infarction risk-based differentiation of tissue amenable for reperfusion from irreversibly injured tissue; and (4) ischemic areas with subsequent hemorrhage were more accurately predicted by multiparametric prediction models than single MRI-parameter thresholding-based methods. The findings in this thesis show that voxel-wise integration of a multitude of (MR imaging-based) biomarkers, representing complex and heterogeneous disease mechanisms within a single easily interpretable index, give the ability to differentiate, predict, and track heterogeneous tissue progression without the need of defining restricted viability thresholds. This offers exciting prospects for multiparametric algorithms in (pre-)clinical stroke treatment trials and points toward appealing opportunities for improved personalized health-care in the near future

Age- and disease-related cerebral white matter changes in patients with Parkinson's disease

Neuro Imaging Researc

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers user multimodal MRI (vol 20, pg 188, 2018)

Neuro Imaging Researc

A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers

BACKGROUND Multimodal MRI-based classification may aid early frontotemporal dementia (FTD) diagnosis. Recently, presymptomatic FTD mutation carriers, who have a high risk of developing FTD, were separated beyond chance level from controls using MRI-based classification. However, it is currently unknown how these scores from classification models progress as mutation carriers approach symptom onset. In this longitudinal study, we investigated multimodal MRI-based classification scores between presymptomatic FTD mutation carriers and controls. Furthermore, we contrasted carriers that converted during follow-up ('converters') and non-converting carriers ('non-converters'). METHODS We acquired anatomical MRI, diffusion tensor imaging and resting-state functional MRI in 55 presymptomatic FTD mutation carriers and 48 healthy controls at baseline, and at 2, 4, and 6 years of follow-up as available. At each time point, FTD classification scores were calculated using a behavioural variant FTD classification model. Classification scores were tested in a mixed-effects model for mean differences and differences over time. RESULTS Presymptomatic mutation carriers did not have higher classification score increase over time than controls (p=0.15), although carriers had higher FTD classification scores than controls on average (p=0.032). However, converters (n=6) showed a stronger classification score increase over time than non-converters (p<0.001). CONCLUSIONS Our findings imply that presymptomatic FTD mutation carriers may remain similar to controls in terms of MRI-based classification scores until they are close to symptom onset. This proof-of-concept study shows the promise of longitudinal MRI data acquisition in combination with machine learning to contribute to early FTD diagnosis.Neuro Imaging Researc

Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept

Frontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10-20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to predict clinical conversion in mutation carriers is lacking. In this retrospective proof-of-concept case-control study, we investigate whether MRI-based and cognition-based classifiers can predict which mutation carriers from genetic frontotemporal dementia families will develop symptoms ('convert') within 4 years. From genetic frontotemporal dementia families, we included 42 presymptomatic frontotemporal dementia mutation carriers. We acquired anatomical, diffusion-weighted imaging, and resting-state functional MRI, as well as neuropsychological data. After 4 years, seven mutation carriers had converted to frontotemporal dementia ('converters'), while 35 had not ('non-converters'). We trained regularized logistic regression models on baseline MRI and cognitive data to predict conversion to frontotemporal dementia within 4 years, and quantified prediction performance using area under the receiver operating characteristic curves. The prediction model based on fractional anisotropy, with highest contribution of the forceps minor, predicted conversion to frontotemporal dementia beyond chance level (0.81 area under the curve, family-wise error corrected P = 0.025 versus chance level). Other MRI-based and cognitive features did not outperform chance level. Even in a small sample, fractional anisotropy predicted conversion in presymptomatic frontotemporal dementia mutation carriers beyond chance level. After validation in larger data sets, conversion prediction in genetic frontotemporal dementia may facilitate early recruitment into clinical trials.Neuro Imaging Researc

Multiple Approaches to Diffusion Magnetic Resonance Imaging in Hereditary Cerebral Amyloid Angiopathy Mutation Carriers

Multivariate analysis of psychological dat

Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison

Neuro Imaging Researc