Peripheral reaching in Alzheimer's disease and mild cognitive impairment

Mitchell, A.G.; Rossit, Stéphanie; Pal, Suvankar; Hornberger, Michael; Warman, Annie; Kenning, Elise A.; Williamson, Laura; R, Shapland; McIntosh, Robert D.

doi:10.1016/j.cortex.2022.01.003

Cited by 4 publications

(2 citation statements)

References 65 publications

(103 reference statements)

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“…When using FC to classify STZ/CTL rats, the most important discriminating features were connections involving the RSC, ACC, PPC, Subiculum and Hippocampus, which are regions of the default mode network typically affected by AD (Agosta et al, 2012; Brier et al, 2012; Tristão Pereira et al, 2021), as well as the hypothalamus which is responsible for recruiting alternative sources of energy to glucose, such as ketone bodies, in response to impaired brain glucose metabolism by the STZ (Carneiro et al, 2016; Foll et al, 2014; Gano et al, 2014; Le Foll, 2019; Wu et al, 2018). Many FC connections with top feature importance in the Early stage ( Figure 4 ) also involved the visual and motor cortices, areas that are related to non-cognitive manifestations such as vision and motor decline and have been reported to precede the cognitive deficits (Brewer and Barton, 2014; Do et al, 2018; Hiller and Ishii, 2018; Ishii and Iadecola, 2015; Mitchell et al, 2022; Montero-Odasso et al, 2020; Vidoni et al, 2012). Moreover, the classification accuracy was improved in the Late vs Early timepoint, due to neurodegeneration progression.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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

View full text Add to dashboard Cite

show abstract

“…Discriminating FC connections involved regions of the default mode network such as the hippocampus, cingulate, and posterior parietal cortex [ 2 , 15 , 97 ], as well as the hypothalamus which is responsible for recruiting alternative sources of energy to glucose, such as ketone bodies, in response to impaired brain glucose metabolism by the STZ [ 17 , 33 , 36 , 64 , 106 ]. Many FC connections with top feature importance in the Early stage also involved the visual and motor cortices, areas that are related to non-cognitive manifestations such as vision and motor decline and have been reported to precede the cognitive deficits in humans [ 14 , 27 , 44 , 48 , 73 , 74 , 102 ]. For classification based on WM integrity, microstructural features in the fimbria of the hippocampus played the most important role in distinguishing STZ rats, which was consistent with the fact that hippocampus is especially vulnerable to AD [ 77 , 89 ] and to the icv-STZ rat model of AD [ 4 , 92 ].…”

Section: Discussionmentioning

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,

Lanz,

Jelescu

2023

Alz Res Therapy

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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.

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The “Little Circles Test” (LCT): a dusted-off tool for assessing fine visuomotor function

Ilardi,

La Marra,

Amato

et al. 2023

Aging Clin Exp Res

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Peripheral reaching in Alzheimer's disease and mild cognitive impairment

Cited by 4 publications

References 65 publications

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

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

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

The “Little Circles Test” (LCT): a dusted-off tool for assessing fine visuomotor function

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