Synchronous nonmonotonic changes in functional connectivity and white matter integrity in a rat model of sporadic Alzheimer's disease

Tristão‐Pereira, Catarina; Diao, Yujian; Yin, Ting; Silva, Analina R da; Lanz, Bernard; Pierzchała, Katarzyna; Poitry‐Yamate, Carole; Jelescu, Ileana O.

doi:10.1016/j.neuroimage.2020.117498

Cited by 14 publications

(24 citation statements)

References 91 publications

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“…In terms of the reliability of the differences pointed by the PIRACY pipeline, while false positives cannot be excluded due to the relatively small sample size, we stress that group differences were indeed expected at all chosen timepoints and the pattern that PIRACY showcased for these changes agreed with previous literature on this animal model assessed from histology, behavior, and volumetry (Lester-Coll et al, 2006;Kraska et al, 2012;Knezovic et al, 2015). The FC analysis resulting from Cohort 1 and its pertinence with respect to the animal model and to AD have been published separately (Tristão Pereira et al, 2021). Briefly, STZ rats in Cohort 1 exhibited not only altered FC but also intra-axonal damage and demyelination (assessed using diffusion MRI) in brain regions typical of AD, in a temporal pattern of acute injury, transient recovery/compensation, and chronic degeneration.…”

Section: Discussionsupporting

confidence: 81%

PIRACY: An Optimized Pipeline for Functional Connectivity Analysis in the Rat Brain

et al. 2021

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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.

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Section: Discussionsupporting

confidence: 81%

PIRACY: An Optimized Pipeline for Functional Connectivity Analysis in the Rat Brain

et al. 2021

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“…In the end, differences in WMTI‐Watson parameters in white matter between STZ and CTL groups in rat Cohort1 were evaluated at each timepoint, based on the estimation of NLLS and RNN fitting approaches, respectively. Statistical test was performed as follows: (1) average WMTI model parameters in each white matter ROI were compared between STZ and CTL groups using two‐tailed Mann–Whitney U test, at α = 0.05 significance; (2) longitudinal alterations within each group were calculated by one‐way analysis of variance (ANOVA) and Tukey‐Cramer correction, at α = 0.05 significance 31 …”

Section: Methodsmentioning

confidence: 99%

“…Data were reused from a previous longitudinal study of WM microstructure alterations in a rat model of Alzheimer's disease using a intracerebroventricular injection of streptozotocin (STZ) 31 . Details of experimental procedures can be found in the original publication.…”

Section: Methodsmentioning

confidence: 99%

Parameter estimation for WMTI‐Watson model of white matter using encoder–decoder recurrent neural network

Diao

Jelescu

2022

Magnetic Resonance in Med

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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.

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“…In a previous work, we performed a comprehensive longitudinal study (Tristão Pereira et al, 2021) in a icv-STZ rat model to quantitatively characterize alterations in FC and in WM microstructure using resting-state functional MRI (fMRI) and advanced diffusion MRI techniques, respectively, as well as in brain glucose uptake captured by 18 FDG-PET. By comparing the STZ group to the control group, non-invasive MRI-derived measures of functional breakdown and WM degeneration were identified and evaluated in the context of brain glucose hypometabolism.…”

Section: Introductionmentioning

confidence: 99%

Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer’s using machine learning

Diao

Jelescu

2023

Preprint

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Background: The pathological process of Alzheimer's disease (AD) typically takes up decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as aItered 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.

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Synchronous nonmonotonic changes in functional connectivity and white matter integrity in a rat model of sporadic Alzheimer's disease

Cited by 14 publications

References 91 publications

PIRACY: An Optimized Pipeline for Functional Connectivity Analysis in the Rat Brain

PIRACY: An Optimized Pipeline for Functional Connectivity Analysis in the Rat Brain

Parameter estimation for WMTI‐Watson model of white matter using encoder–decoder recurrent neural network

Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer’s using machine learning

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