Background TDP-43 proteinopathies represent a spectrum of neurological disorders,anchored clinically on either end by amyotrophic lateral sclerosis(ALS)and frontotemporal degeneration(FTD).The ALS-FTD spectrum exhibit...
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Background TDP-43 proteinopathies represent a spectrum of neurological disorders,anchored clinically on either end by amyotrophic lateral sclerosis(ALS)and frontotemporal degeneration(FTD).The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes,highlighting its *** study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD *** We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD(bvFTD;with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants),103 ALS,and 47 ALS-FTD patients with likely TDP-43.A Subtype and Stage Inference(SuStaIn)model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns,and we related subtypes and stages to clinical,genetic,and neuropathological features of *** SuStaIn identified three novel subtypes:two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions,and a normal-appearing group without obvious brain *** limbic-predominant subtype tended to present with more impaired cognition,higher frequencies of pathogenic variants in TBK1 and TARDBP genes,and a higher proportion of TDP-43 types B,E and *** contrast,the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type *** normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients,higher cognitive capacity,higher proportion of lower motor neuron onset,milder motor symptoms,and lower frequencies of genetic pathogenic *** overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration,higher King’s stage,and cog
Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnos...
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Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.
Deformable image registration (DIR) is crucial in medical image analysis, enabling the exploration of biological dynamics such as organ motions and longitudinal changes in imaging. Leveraging Neural Ordinary Different...
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Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not ...
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Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using mac...
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ISBN:
(数字)9798350313338
ISBN:
(纸本)9798350313345
Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.
Deep learning models perform best when tested on target (test) data domains whose distribution is similar to the set of source (train) domains. However, model generalization can be hindered when there is significant d...
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Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing ...
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ISBN:
(数字)9781728165530
ISBN:
(纸本)9781728165547
Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data. Generative Adversarial Networks (GAN) have proven to perform well in image recovery tasks. In this work, we followed the GAN framework and developed a generator coupled with discriminator to tackle the task of 3D SISR on T1 brain MRI images. We developed a novel 3D memory-efficient residual-dense block generator (MRDG) that achieves state-of-the-art performance in terms of SSIM (Structural Similarity), PSNR (Peak Signal to Noise Ratio) and NRMSE (Normalized Root Mean Squared Error) metrics. We also designed a pyramid pooling discriminator (PPD) to recover details on different size scales simultaneously. Finally, we introduced model blending, a simple and computational efficient method to balance between image and texture quality in the final output, to the task of SISR on 3D images.
Background The relationship between tau neurofibrillary tangles (T) and neurodegeneration (N) may offer clues to potential presence of comorbidities, as well as relative resilience and vulnerability to Alzheimer’s di...
Background The relationship between tau neurofibrillary tangles (T) and neurodegeneration (N) may offer clues to potential presence of comorbidities, as well as relative resilience and vulnerability to Alzheimer’s disease (AD) pathology. We have previously developed measures of tau PET (T) and cortical thickness (N) mismatch based on linear model residuals. However, the underlying relationships between T and N are likely complex and non-linear. Moreover, predicting cortical thickness based on local tau level may miss non-local contributions of tauopathy. Here we investigate T-N mismatch using a deep-learning 3D image translation neural network to estimate synthetic maps of cortical thickness based on tau PET images. Deviation between the synthetic and actual cortical thickness serves as a metric of T-N mismatch. Method We derived 3D cortical thickness from T1-MRI and SUVR from 18F-flortaucipir tracer uptake to represent N and T maps respectively. We predicted cortical thickness maps from tau SUVR by training a 3D U-Net (Ronneberger et al. 2015) to learn the relationship between paired cortical tau SUVR and thickness maps from 70 symptomatic patients from ADNI. Approximately 70% of the training set was A+. We used regional standardized mean absolute error between predicted and actual thickness across 104 bilateral gray matter regions of interest as T-N mismatch for clustering in a separate independent sample of 194 A+ symptomatic patients. Result The voxel-wise mean absolute error of thickness translation on 194 tested patients was 0.54 mm. We obtained six T-N data-driven clusters (Figure 2). The group with lowest reconstruction error was defined as canonical – meaning that the degree of neurodegeneration was accurately predicted by tau PET. There were three groups with lower thickness than predicted, which were denoted as temporal-limbic (TL) vulnerable, posterior vulnerable and diffuse vulnerable based on their spatial patterns. Additionally, two groups with greate
Background The extent to which pathological processes in aging and Alzheimer’s disease (AD) relate to functional disruption of the medial temporal lobe (MTL)-dependent brain networks is poorly understood. To address ...
Background The extent to which pathological processes in aging and Alzheimer’s disease (AD) relate to functional disruption of the medial temporal lobe (MTL)-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity (FC) alterations between anterior and posterior regions of the MTL and in MTL-associated functional communities – the Anterior-Temporal (AT) and Posterior-Medial (PM) networks – in normal agers, individuals with preclinical AD, and patients with Mild Cognitive Impairment or mild dementia due to AD. Method In this cross-sectional study, we analyzed data from 179 individuals from the Aging Brain Cohort study of the penn ADRC. Detailed information about participants is provided in Table 1. For intra-MTL FC comparisons, the MTL subregions were segmented using the automated segmentation of hippocampal subfields-T1 (ASHS-T1) pipeline (Fig. 1a). When modeling the MTL’s interactions with the rest of the cortex, we employed four MTL ROIs (left/right × anterior/posterior) derived from an ex vivo atlas of tau accumulation in the MTL. Our functional datasets were preprocessed using a customized fMRIprep pipeline. Sparse network estimations and modularity-based consensus clustering were used to reconstruct the AT and PM network systems (Fig. 1b). Age effect analyses and group comparisons along the AD continuum were performed using the General Linear Model within the network-based statistical framework. Result The preclinical stage of AD was characterized by increased FC between the perirhinal cortex and other regions of the MTL, as well as between the anterior MTL and its direct neighbors in the AT network (Fig. 1c-d). This effect was not present in symptomatic AD. Instead, symptomatic patients displayed reduced hippocampal and intra-PM connectivity. For normal aging, our results led to three main conclusions (for visuals, see Fig. 2). First, intra-network connectivity of both the AT and PM networks decreases wi
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