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1.
Alzheimers Res Ther ; 15(1): 193, 2023 11 07.
Artigo em Inglês | MEDLINE | ID: mdl-37936236

RESUMO

BACKGROUND: The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established. METHODS: The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods. RESULTS: Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity. CONCLUSIONS: Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.


Assuntos
Doença de Alzheimer , Substância Branca , Humanos , Ratos , Animais , Substância Branca/patologia , Doença de Alzheimer/induzido quimicamente , Doença de Alzheimer/diagnóstico por imagem , Doença de Alzheimer/patologia , Inteligência Artificial , Encéfalo/patologia , Imageamento por Ressonância Magnética/métodos , Aprendizado de Máquina , Glucose
3.
Nat Neurosci ; 26(4): 673-681, 2023 04.
Artigo em Inglês | MEDLINE | ID: mdl-36973511

RESUMO

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.


Assuntos
Mapeamento Encefálico , Encéfalo , Ratos , Animais , Mapeamento Encefálico/métodos , Consenso , Neuroimagem , Imageamento por Ressonância Magnética/métodos
4.
Magn Reson Med ; 89(3): 1193-1206, 2023 03.
Artigo em Inglês | MEDLINE | ID: mdl-36372982

RESUMO

PURPOSE: Biophysical modeling of the diffusion MRI (dMRI) signal provides estimates of specific microstructural tissue properties. Although non-linear least squares (NLLS) is the most widespread fitting method, it suffers from local minima and high computational cost. Deep learning approaches are steadily replacing NLLS, but come with the limitation that the model needs to be retrained for each acquisition protocol and noise level. In this study, a novel fitting approach was proposed based on the encoder-decoder recurrent neural network (RNN) to accelerate model estimation with good generalization to various datasets. METHODS: The white matter tract integrity (WMTI)-Watson model as an implementation of the Standard Model of diffusion in white matter derives its parameters indirectly from the diffusion and kurtosis tensors (DKI). The RNN-based solver, which estimates the WMTI-Watson model from DKI, is therefore more readily translatable to various data, irrespective of acquisition protocols as long as the DKI was pre-computed from the signal. An embedding approach was also used to render the model insensitive to potential differences in distributions between training data and experimental data. The analytical solution, NLLS, RNN-, and a multilayer perceptron (MLP)-based methods were evaluated on synthetic and in vivo datasets of rat and human brain. RESULTS: The proposed RNN solver showed highly reduced computation time over the analytical solution and NLLS, with similar accuracy but improved robustness, and superior generalizability over MLP. CONCLUSION: The RNN estimator can be easily applied to various datasets without retraining, which shows great potential for a widespread use.


Assuntos
Substância Branca , Humanos , Ratos , Animais , Substância Branca/diagnóstico por imagem , Imagem de Tensor de Difusão/métodos , Encéfalo/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética/métodos , Redes Neurais de Computação
5.
Front Neurosci ; 15: 602170, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33841071

RESUMO

Resting state functional MRI (rs-fMRI) is a widespread and powerful tool for investigating functional connectivity (FC) and brain disorders. However, FC analysis can be seriously affected by random and structured noise from non-neural sources, such as physiology. Thus, it is essential to first reduce thermal noise and then correctly identify and remove non-neural artifacts from rs-fMRI signals through optimized data processing methods. However, existing tools that correct for these effects have been developed for human brain and are not readily transposable to rat data. Therefore, the aim of the present study was to establish a data processing pipeline that can robustly remove random and structured noise from rat rs-fMRI data. It includes a novel denoising approach based on the Marchenko-Pastur Principal Component Analysis (MP-PCA) method, FMRIB's ICA-based Xnoiseifier (FIX) for automatic artifact classification and cleaning, and global signal regression (GSR). Our results show that: (I) MP-PCA denoising substantially improves the temporal signal-to-noise ratio, (II) the pre-trained FIX classifier achieves a high accuracy in artifact classification, and (III) both independent component analysis (ICA) cleaning and GSR are essential steps in correcting for possible artifacts and minimizing the within-group variability in control animals while maintaining typical connectivity patterns. Reduced within-group variability also facilitates the exploration of potential between-group FC changes, as illustrated here in a rat model of sporadic Alzheimer's disease.

6.
Neuroimage ; 225: 117498, 2021 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-33164858

RESUMO

Brain glucose hypometabolism has been singled out as an important contributor and possibly main trigger to Alzheimer's disease (AD). Intracerebroventricular injections of streptozotocin (icv-STZ) cause brain glucose hypometabolism without systemic diabetes. Here, a first-time longitudinal study of brain glucose metabolism, functional connectivity and white matter microstructure was performed in icv-STZ rats using PET and MRI. Histological markers of pathology were tested at an advanced stage of disease. STZ rats exhibited altered functional connectivity and intra-axonal damage and demyelination in brain regions typical of AD, in a temporal pattern of acute injury, transient recovery/compensation and chronic degeneration. In the context of sustained glucose hypometabolism, these nonmonotonic trends - also reported in behavioral studies of this animal model as well as in human AD - suggest a compensatory mechanism, possibly recruiting ketone bodies, that allows a partial and temporary repair of brain structure and function. The early acute phase could thus become a valuable therapeutic window to strengthen the recovery phase and prevent or delay chronic degeneration, to be considered both in preclinical and clinical studies of AD. In conclusion, this work reveals the consequences of brain insulin resistance on structure and function, highlights signature nonmonotonic trajectories in their evolution and proposes potent MRI-derived biomarkers translatable to human AD and diabetic populations.


Assuntos
Doença de Alzheimer/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Substância Branca/diagnóstico por imagem , Doença de Alzheimer/metabolismo , Doença de Alzheimer/patologia , Doença de Alzheimer/fisiopatologia , Animais , Encéfalo/metabolismo , Encéfalo/patologia , Encéfalo/fisiopatologia , Doenças Desmielinizantes/diagnóstico por imagem , Doenças Desmielinizantes/patologia , Diabetes Mellitus Experimental/diagnóstico por imagem , Diabetes Mellitus Experimental/metabolismo , Diabetes Mellitus Experimental/fisiopatologia , Imagem de Difusão por Ressonância Magnética , Modelos Animais de Doenças , Fluordesoxiglucose F18 , Neuroimagem Funcional , Glucose/metabolismo , Injeções Intraventriculares , Estudos Longitudinais , Imageamento por Ressonância Magnética , Masculino , Vias Neurais/diagnóstico por imagem , Vias Neurais/metabolismo , Vias Neurais/patologia , Vias Neurais/fisiopatologia , Emaranhados Neurofibrilares/patologia , Placa Amiloide/patologia , Tomografia por Emissão de Pósitrons , Compostos Radiofarmacêuticos , Ratos , Estreptozocina/toxicidade , Substância Branca/metabolismo , Substância Branca/patologia , Substância Branca/fisiopatologia
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