Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 8 de 8
Filtrar
1.
Ann Neurol ; 94(6): 1080-1085, 2023 12.
Artigo em Inglês | MEDLINE | ID: mdl-37753809

RESUMO

The minor allele of the genetic variant rs10191329 in the DYSF-ZNF638 locus is associated with unfavorable long-term clinical outcomes in multiple sclerosis patients. We investigated if rs10191329 is associated with brain atrophy measured by magnetic resonance imaging in a discovery cohort of 748 and a replication cohort of 360 people with relapsing multiple sclerosis. We observed an association with 28% more brain atrophy per rs10191329*A allele. Our results encourage stratification for rs10191329 in clinical trials. Unraveling the underlying mechanisms may enhance our understanding of pathophysiology and identify treatment targets. ANN NEUROL 2023;94:1080-1085.


Assuntos
Doenças do Sistema Nervoso Central , Esclerose Múltipla , Doenças Neurodegenerativas , Humanos , Esclerose Múltipla/diagnóstico por imagem , Esclerose Múltipla/genética , Esclerose Múltipla/patologia , Encéfalo/patologia , Imageamento por Ressonância Magnética/métodos , Doenças Neurodegenerativas/patologia , Atrofia/patologia
2.
Brain ; 145(10): 3522-3535, 2022 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-35653498

RESUMO

Cortical lesions constitute a key manifestation of multiple sclerosis and contribute to clinical disability and cognitive impairment. Yet it is unknown whether local cortical lesions and cortical lesion subtypes contribute to domain-specific impairments attributable to the function of the lesioned cortex. In this cross-sectional study, we assessed how cortical lesions in the primary sensorimotor hand area relate to corticomotor physiology and sensorimotor function of the contralateral hand. Fifty relapse-free patients with relapsing-remitting or secondary-progressive multiple sclerosis and 28 healthy age- and sex-matched participants underwent whole-brain 7 T MRI to map cortical lesions. Brain scans were also used to estimate normalized brain volume, pericentral cortical thickness, white matter lesion fraction of the corticospinal tract, infratentorial lesion volume and the cross-sectional area of the upper cervical spinal cord. We tested sensorimotor hand function and calculated a motor and sensory composite score for each hand. In 37 patients and 20 healthy controls, we measured maximal motor-evoked potential amplitude, resting motor threshold and corticomotor conduction time with transcranial magnetic stimulation and the N20 latency from somatosensory-evoked potentials. Patients showed at least one cortical lesion in the primary sensorimotor hand area in 47 of 100 hemispheres. The presence of a lesion was associated with worse contralateral sensory (P = 0.014) and motor (P = 0.009) composite scores. Transcranial magnetic stimulation of a lesion-positive primary sensorimotor hand area revealed a decreased maximal motor-evoked potential amplitude (P < 0.001) and delayed corticomotor conduction (P = 0.002) relative to a lesion-negative primary sensorimotor hand area. Stepwise mixed linear regressions showed that the presence of a primary sensorimotor hand area lesion, higher white-matter lesion fraction of the corticospinal tract, reduced spinal cord cross-sectional area and higher infratentorial lesion volume were associated with reduced contralateral motor hand function. Cortical lesions in the primary sensorimotor hand area, spinal cord cross-sectional area and normalized brain volume were also associated with smaller maximal motor-evoked potential amplitude and longer corticomotor conduction times. The effect of cortical lesions on sensory function was no longer significant when controlling for MRI-based covariates. Lastly, we found that intracortical and subpial lesions had the largest effect on reduced motor hand function, intracortical lesions on reduced motor-evoked potential amplitude and leucocortical lesions on delayed corticomotor conduction. Together, this comprehensive multilevel assessment of sensorimotor brain damage shows that the presence of a cortical lesion in the primary sensorimotor hand area is associated with impaired corticomotor function of the hand, after accounting for damage at the subcortical level. The results also provide preliminary evidence that cortical lesion types may affect the various facets of corticomotor function differentially.


Assuntos
Esclerose Múltipla , Córtex Sensório-Motor , Humanos , Esclerose Múltipla/patologia , Estudos Transversais , Imageamento por Ressonância Magnética/métodos , Potencial Evocado Motor , Tratos Piramidais/patologia , Córtex Sensório-Motor/diagnóstico por imagem
3.
Neuroimage ; 225: 117471, 2021 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-33099007

RESUMO

Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures. We further demonstrate that the contrast-adaptive method can also be safely applied to MRI scans of healthy controls, and replicate previously documented atrophy patterns in deep gray matter structures in MS. The algorithm is publicly available as part of the open-source neuroimaging package FreeSurfer.


Assuntos
Encéfalo/patologia , Interpretação de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Esclerose Múltipla/patologia , Algoritmos , Atrofia/patologia , Encéfalo/diagnóstico por imagem , Substância Cinzenta/patologia , Humanos , Esclerose Múltipla/diagnóstico por imagem , Neuroimagem , Substância Branca/patologia
4.
Neuroimage Clin ; 38: 103354, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36907041

RESUMO

In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.


Assuntos
Substância Branca , Humanos , Substância Branca/diagnóstico por imagem , Substância Branca/patologia , Reprodutibilidade dos Testes , Estudos Transversais , Encéfalo/patologia , Imageamento por Ressonância Magnética/métodos , Processamento de Imagem Assistida por Computador
5.
J Comp Neurol ; 531(18): 2062-2079, 2023 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-37700618

RESUMO

Investigating interindividual variability is a major field of interest in neuroscience. The entorhinal cortex (EC) is essential for memory and affected early in the progression of Alzheimer's disease (AD). We combined histology ground-truth data with ultrahigh-resolution 7T ex vivo MRI to analyze EC interindividual variability in 3D. Further, we characterized (1) entorhinal shape as a whole, (2) entorhinal subfield range and midpoints, and (3) subfield architectural location and tau burden derived from 3D probability maps. Our results indicated that EC shape varied but was not related to demographic or disease factors at this preclinical stage. The medial intermediate subfield showed the highest degree of location variability in the probability maps. However, individual subfields did not display the same level of variability across dimensions and outcome measure, each providing a different perspective. For example, the olfactory subfield showed low variability in midpoint location in the superior-inferior dimension but high variability in anterior-posterior, and the subfield entorhinal intermediate showed a large variability in volumetric measures but a low variability in location derived from the 3D probability maps. These findings suggest that interindividual variability within the entorhinal subfields requires a 3D approach incorporating multiple outcome measures. This study provides 3D probability maps of the individual entorhinal subfields and respective tau pathology in the preclinical stage (Braak I and II) of AD. These probability maps illustrate the subfield average and may serve as a checkpoint for future modeling.


Assuntos
Doença de Alzheimer , Hipocampo , Humanos , Hipocampo/patologia , Imageamento por Ressonância Magnética/métodos , Córtex Entorrinal , Doença de Alzheimer/diagnóstico por imagem , Doença de Alzheimer/patologia
6.
Sci Rep ; 12(1): 19744, 2022 11 17.
Artigo em Inglês | MEDLINE | ID: mdl-36396681

RESUMO

Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct anatomical-functional interpretation: They measure the deformation caused by the tumor on the surrounding brain structures, comparing the shape of various structures in the patient's brain to their expected shape in healthy individuals. To obtain the required segmentations, we use an automatic method that is contrast-adaptive and robust to missing modalities, making the features generalizable across scanners and imaging protocols. Since the features we propose do not depend on characteristics of the tumor region itself, they are also applicable to post-operative images, which have been much less studied in the context of survival prediction. Using experiments involving both pre- and post-operative data, we show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features.


Assuntos
Neoplasias Encefálicas , Glioblastoma , Humanos , Glioblastoma/patologia , Imageamento por Ressonância Magnética/métodos , Encéfalo/patologia , Neoplasias Encefálicas/patologia , Prognóstico
7.
Front Neuroimaging ; 1: 977491, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-37555157

RESUMO

Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. This work evaluates the influence of multiple sequences, namely T1-weighted (T1), T2-weighted (T2), contrast enhanced T1-weighted (T1-CE), and T2 Fluid Attenuated Inversion Recovery (FLAIR), and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1-CE achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1-CE obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1-CE. Both approaches utilizing the T1-CE sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.

8.
Proc IEEE Int Symp Biomed Imaging ; 2021: 1971-1974, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-34367472

RESUMO

We present the first deep learning method to segment Multiple Sclerosis lesions and brain structures from MRI scans of any (possibly multimodal) contrast and resolution. Our method only requires segmentations to be trained (no images), as it leverages the generative model of Bayesian segmentation to generate synthetic scans with simulated lesions, which are then used to train a CNN. Our method can be retrained to segment at any resolution by adjusting the amount of synthesised partial volume. By construction, the synthetic scans are perfectly aligned with their labels, which enables training with noisy labels obtained with automatic methods. The training data are generated on the fly, and aggressive augmentation (including artefacts) is applied for improved generalisation. We demonstrate our method on two public datasets, comparing it with a state-of-the-art Bayesian approach implemented in FreeSurfer, and dataset specific CNNs trained on real data. The code is available at https://github.com/BBillot/SynthSeg.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA