Capturing brain‐cognition relationship: Integrating task‐based fMRI across tasks markedly boosts prediction and test‐retest reliability

Tetereva, Alina; Li, Jean; Deng, Jeremiah D.; Stringaris, Argyris; Pornpattananangkul, Narun

doi:10.1016/j.neuroimage.2022.119588

Cited by 17 publications

(29 citation statements)

References 64 publications

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“…For the machine learning algorithm, we used Elastic Net (Zou & Hastie, 2005) as implemented in sklearn (Pedregosa et al, 2011). Not only does Elastic Net provide a relatively good predictive performance for fMRI (Dubois et al, 2018; Pat, Wang, Anney, et al, 2022; Pat, Wang, Bartonicek, et al, 2022; Tetereva et al, 2022), but it also offers easy-to-interpret feature importance (Molnar, 2019). In our grid search, we tuned two Elastic Net hyperparameters: a using 70 numbers in log space, ranging from .1 and 100, and ℓ 1 -ratio using 25 numbers in linear space, ranging from 0 and 1.…”

Section: Methodsmentioning

confidence: 99%

“…Third and finally, can we further improve our ability to capture the decline in cognitionfluid by using, not only Brain Age and chronological age, but also another biomarker, Brain Cognition? Analogous to Brain Age, Brain Cognition is defined as a predicted value from machine-learning models that predict cognitionfluid based on a person's brain data (Dubois et al, 2018;Pat, Wang, Anney, et al, 2022;Rasero et al, 2021;Sripada et al, 2020;Tetereva et al, 2022; for review, see Vieira et al, 2022). Age-related cognitive decline is not only related to the changes in age, but also to the changes in cognition.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tetereva¹,

Pornpattananangkul²

2023

Preprint

Self Cite

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“…Increasingly, tasks are used as an alternative to resting‐state acquisitions to increase participant engagement and reduce head motion (D. J. Greene et al, 2018) and elicit states of interest (Finn, 2021). Many studies collect fMRI data across multiple rest and task acquisitions and concatenate the data (J. Chen et al, 2022; Gao et al, 2019) to increase data quantity toward improved reliability (Elliott et al, 2019, 2020; Herting et al, 2018; Tetereva et al, 2022). This work underscores the importance of considering how tasks can impact fMRI‐FC measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies collect fMRI data across multiple rest and task acquisitions and concatenate the data (J. Chen et al, 2022;Gao et al, 2019) to increase data quantity toward improved reliability (Elliott et al, 2019(Elliott et al, , 2020Herting et al, 2018;Tetereva et al, 2022). This work underscores the importance of considering how tasks can impact fMRI-FC measurements.…”

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confidence: 98%

How do tasks impact the reliability of fMRI functional connectivity?

Rai,

Graff,

Tansey

et al. 2024

Human Brain Mapping

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While there is growing interest in the use of functional magnetic resonance imaging‐functional connectivity (fMRI‐FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal‐to‐noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region‐wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task‐engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task‐engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.

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“…One way is correlational, whereas the second and most effective way is using a machine learning algorithm to model the relationship between brain activity and ANS. In contrast to correlation, which uses intrinsic properties of features via statistics, machine learning algorithms aim to find optimal performance and can generally achieve higher prediction and reliable results in comparison with correlation analysis (Tetereva et al, 2022; Xue et al, 2012). More so, while a correlational analysis assumes a linear dependency between variables (Pat et al, 2022), machine learning allows different types of relationships (both linear and non‐linear), which gives an open way of approaching this prediction problem.…”

Section: Introductionmentioning

confidence: 99%

Prediction of moment‐by‐moment heart rate and skin conductance changes in the context of varying emotional arousal

et al. 2023

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Autonomic nervous system (ANS) responses such as heart rate (HR) and galvanic skin responses (GSR) have been linked with cerebral activity in the context of emotion. Although much work has focused on the summative effect of emotions on ANS responses, their interaction in a continuously changing context is less clear. Here, we used a multimodal data set of human affective states, which includes electroencephalogram (EEG) and peripheral physiological signals of participants' moment‐by‐moment reactions to emotional provoking video clips and modeled HR and GSR changes using machine learning techniques, specifically, long short‐term memory (LSTM), decision tree (DT), and linear regression (LR). We found that LSTM achieved a significantly lower error rate compared with DT and LR due to its inherent ability to handle sequential data. Importantly, the prediction error was significantly reduced for DT and LR when used together with particle swarm optimization to select relevant/important features for these algorithms. Unlike summative analysis, and contrary to expectations, we found a significantly lower error rate when the prediction was made across different participants than within a participant. Moreover, the predictive selected features suggest that the patterns predictive of HR and GSR were substantially different across electrode sites and frequency bands. Overall, these results indicate that specific patterns of cerebral activity track autonomic body responses. Although individual cerebral differences are important, they might not be the only factors influencing the moment‐by‐moment changes in ANS responses.

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Capturing brain‐cognition relationship: Integrating task‐based fMRI across tasks markedly boosts prediction and test‐retest reliability

Cited by 17 publications

References 64 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

How do tasks impact the reliability of fMRI functional connectivity?

Prediction of moment‐by‐moment heart rate and skin conductance changes in the context of varying emotional arousal

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