A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity

Jiang, Rongtao; Scheinost, Dustin; Zuo, Nianming; Wu, Jing; Qi, Shile; Liang, Qinghao; Zhi, Dongmei; Luo, Na; Chung, Young‐Chul; Li, Sha; Xu, Yong; Sui, Jing; Calhoun, Vince D.

doi:10.1002/advs.202201621

Cited by 31 publications

(18 citation statements)

References 88 publications

(135 reference statements)

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“…Based on the commonality analyses, the multiple regression models that explained higher variation were driven predominantly by the common effects that Brain Cognition had with the other two, in the case of (Corrected) Brain Age, or with chronological age, in the case of (Corrected) Brain Age Gap. This overlapped variation was consistent with a recent study (Jiang et al, 2022). Altogether, while both the effects of Brain Age and Brain Cognition overlapped with chronological age, Brain Cognition offered additional unique effects that helped improve the ability to capture cognition fluid .…”

Section: Discussionsupporting

confidence: 93%

The (Limited?) Utility of Brain Age as a Biomarker for Capturing Fluid Cognition in Older Individuals

Tetereva¹,

Pornpattananangkul²

2023

Preprint

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For decades, neuroscientists have been on a quest to search for a biomarker that can help capture age-related cognitive decline. One well-known candidate is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI data. Here we aim to formally evaluate the utility of Brain Age as a biomarker for capturing cognitive decline. Using 504 aging participants (36-100 years old) from the Human Connectome Project in Aging, we created 26 age-prediction models for Brain Age based on different combinations of MRI modalities. We first tested how much Brain Age from these age-prediction models added to what we had already known from a chronological age in capturing cognitive decline. Based on the commonality analyses, we found a large degree of overlap between Brain Age and chronological age, so much so that, at best, Brain Age could uniquely add only around 1.6% in explaining the variation in cognitive decline. Next, the age-prediction models that performed better at predicting chronological age did not necessarily create better Brain Age for capturing cognitive decline beyond chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining cognitive decline. Lastly, unlike Brain Age, Brain Cognition, or a predicted value based on machine-learning models built to predict cognitive abilities from brain MRI data, provided much higher unique effects. Brain Cognition added over 11% to explain the variation in cognitive decline beyond chronological age, leading to around a 1/3-time improvement of the total variation explained. Accordingly, while demonstrating the limited utility of Brain Age, we provided a solution to improve our ability to use brain MRI data as a biomarker for cognitive decline.

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Section: Discussionsupporting

confidence: 93%

The (Limited?) Utility of Brain Age as a Biomarker for Capturing Fluid Cognition in Older Individuals

Tetereva¹,

Pornpattananangkul²

2023

Preprint

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“…Unlike atlas-based analysis, this framework allows single-scan variability in network representations, which can therefore retain more scan-specific information in the connectivity features (Du et al, 2020). Secondly, we capitalized on advances in machine learning technology to characterize the connectivity signatures of antidepressant outcomes and cognitive changes in a purely data-driven manner (Jiang et al, 2022). Instead of focusing on isolated FNC pairs, the PLS prediction Frontiers in Pharmacology frontiersin.org model concentrates on aggregating thousands of antidepressant outcome-and cognitive change-related FNC changes into a single measure that best captures their neural representations.…”

Section: Discussionmentioning

confidence: 99%

Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes

Abbott

Sui

et al. 2023

Front. Pharmacol.

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Introduction: Electroconvulsive therapy (ECT) remains one of the most effective approaches for treatment-resistant depressive episodes, despite the potential cognitive impairment associated with this treatment. As a potent stimulator of neuroplasticity, ECT might normalize aberrant depression-related brain function via the brain’s reconstruction by forming new neural connections. Multiple lines of evidence have demonstrated that functional connectivity (FC) changes are reliable indicators of antidepressant efficacy and cognitive changes from static and dynamic perspectives. However, no previous studies have directly ascertained whether and how different aspects of FC provide complementary information in terms of neuroimaging-based prediction of clinical outcomes.Methods: In this study, we implemented a fully automated independent component analysis framework to an ECT dataset with subjects (n = 50, age = 65.54 ± 8.92) randomized to three treatment amplitudes (600, 700, or 800 milliamperes [mA]). We extracted the static functional network connectivity (sFNC) and dynamic FNC (dFNC) features and employed a partial least square regression to build predictive models for antidepressant outcomes and cognitive changes.Results: We found that both antidepressant outcomes and memory changes can be robustly predicted by the changes in sFNC (permutation test p < 5.0 × 10−3). More interestingly, by adding dFNC information, the model achieved higher accuracy for predicting changes in the Hamilton Depression Rating Scale 24-item (HDRS24, t = 9.6434, p = 1.5 × 10−21). The predictive maps of clinical outcomes show a weakly negative correlation, indicating that the ECT-induced antidepressant outcomes and cognitive changes might be associated with different functional brain neuroplasticity.Discussion: The overall results reveal that dynamic FC is not redundant but reflects mechanisms of ECT that cannot be captured by its static counterpart, especially for the prediction of antidepressant efficacy. Tracking the predictive signatures of static and dynamic FC will help maximize antidepressant outcomes and cognitive safety with individualized ECT dosing.

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“…In Figure 7C, blocks on the main diagonal of the FC matrices represent connections within-module, while blocks off the main diagonal represent connections between-module. Recently, Jiang et al found that, in an older population, connections between the DMN, SMT, and SUB networks were highly predictive for age [43]. They also found that a DMN-SUB connection was correlated with high cognitive performance.…”

Section: A Significant Functional Networkmentioning

confidence: 99%

Latent Similarity Identifies Important Functional Connections for Phenotype Prediction

Orlichenko

Zhang

et al. 2023

IEEE Trans. Biomed. Eng.

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Objective: Endophenotypes like brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects but high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size but high feature dimension datasets. Methods: LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. Results: LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. Conclusion:We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work should lead to new insights in both algorithm design and neuroscience research. 1

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A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity

Cited by 31 publications

References 88 publications

The (Limited?) Utility of Brain Age as a Biomarker for Capturing Fluid Cognition in Older Individuals

The (Limited?) Utility of Brain Age as a Biomarker for Capturing Fluid Cognition in Older Individuals

Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes

Latent Similarity Identifies Important Functional Connections for Phenotype Prediction

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