Cognition’s dependence on functional network integrity with age is conditional on structural network integrity

Liu, Xulin; Tyler, Lorraine K.; Cam‐CAN,; Davis, Simon W.; Rowe, James B.; Tsvetanov, Kamen A.

doi:10.1016/j.neurobiolaging.2023.06.001

Cited by 4 publications

(4 citation statements)

References 108 publications

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“…While the CamCAN dataset is recognized for its reflection of the general population in terms of demographic variables 58 , 59 , it should be noted that the generalizability of the model's performance across varied populations still demands further examination and validation in future investigations.…”

Section: Discussionmentioning

confidence: 99%

“…The external validation of the proposed algorithms was performed using an independent dataset from the CamCAN (available at http://www.mrc-cbu.cam.ac.uk/datasets/camcan/ ) 73 . Recognized for its approximate reflection of the broader UK demographic profile, this dataset is deemed less biased and more generalizable 58 , 59 . Due to these attributes, the CamCAN set has been the preferred choice for external validation in numerous previous studies regarding brain age prediction models 58 , 59 , 82 – 84 .…”

Section: Methodsmentioning

confidence: 99%

“…Recognized for its approximate reflection of the broader UK demographic profile, this dataset is deemed less biased and more generalizable 58 , 59 . Due to these attributes, the CamCAN set has been the preferred choice for external validation in numerous previous studies regarding brain age prediction models 58 , 59 , 82 – 84 . Specifically, the dataset, consisting of 645 individuals, demonstrated a balanced distribution of age (mean age = 54.7 years, SD = 18.6 years, range = 18.5–88.9 years, Supplementary Fig.…”

Section: Methodsmentioning

confidence: 99%

“…uk/ datas ets/ camcan/) 73 . Recognized for its approximate reflection of the broader UK demographic profile, this dataset is deemed less biased and more generalizable 58,59 . Due to these attributes, the CamCAN set has been the preferred choice for external validation in numerous previous studies regarding brain age prediction models 58,59,[82][83][84] .…”

Section: External Validationmentioning

confidence: 99%

See 3 more Smart Citations

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms

Joo,

Namgung,

Jeong

et al. 2023

Sci Rep

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The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer’s disease groups by identifying variances in brain age gaps between them, highlighting the algorithm’s potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: External Validationmentioning

confidence: 99%

See 2 more Smart Citations

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms

Joo,

Namgung,

Jeong

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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From animal models to human individuality: Integrative approaches to the study of brain plasticity

Hille,

Kühn,

Kempermann

et al. 2024

Neuron

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Explaining tip-of-the-tongue experiences in older adults – contributions of brain function, structure, and perfusion in relation to older adults’ cardiorespiratory fitness

Rahman,

Tsvetanov,

Feron

et al. 2023

Preprint

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Cognitive decline associated with healthy ageing is complex and multifactorial: vascular and lifestyle factors uniquely and jointly contribute to distinct neurocognitive trajectories of ageing. To evaluate existing accounts of neurocognitive ageing that propose mechanisms of compensation, maintenance or reserve, studies should explore how various known brain-based and lifestyle factors intersect to better understand cognitive decline. Here, we bring together brain function, structure, perfusion, and cardiorespiratory fitness to investigate a well-documented, prominent cognitive challenge for older adults: word-finding failures. Commonality analysis on 73 neurologically healthy older adults revealed that functional activation of language networks associated with tip-of-the-tongue states is in part determined by age and, interestingly, cardiorespiratory fitness levels. Age-associated atrophy and perfusion in regions other than those showing functional differences accounted for variance in tip-of-the-tongue states. Our findings can be interpreted in the context of the classic models of neurocognitive ageing, with mechanisms of compensation and reserve interacting with each other.HighlightsThe incidence of word-finding failures is determined by brain health and lifestyle factorsLanguage network activation associated with word-finding failures is determined by age and cardiorespiratory fitness levelsDistinct contribution of brain structure and perfusion also predict this declineBrain health indices in concordance with lifestyle measures provide a holistic explanation of individual differences in age-related cognitive decline

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Cognition’s dependence on functional network integrity with age is conditional on structural network integrity

Cited by 4 publications

References 108 publications

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms

From animal models to human individuality: Integrative approaches to the study of brain plasticity

Explaining tip-of-the-tongue experiences in older adults – contributions of brain function, structure, and perfusion in relation to older adults’ cardiorespiratory fitness

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