Is it time to put rest to rest?

Finn, Emily S.

doi:10.1016/j.tics.2021.09.005

Cited by 172 publications

(129 citation statements)

References 124 publications

Supporting

Mentioning

120

Contrasting

Order By: Relevance

“…Resting state FC is a passive measure reflecting global intrinsic brain networks 31,32 . Thus, targeted recruitment of specific brain networks during stressful or engaging tasks is likely necessary in order to use such functional brain signals as a predictor of individual differences in CA-IMT 33 . Indeed, this type of task-based functional brain measure could boost the predictive power of FRS.…”

Section: Discussionmentioning

confidence: 99%

Integrating Multiple Imaging Modalities does not Boost Prediction of Carotid Artery Intima-Media Thickness in Midlife Adults

Sentis

Rasero

Gianaros

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Human neuroimaging evidence suggests that cardiovascular disease (CVD) risk may relate to functional and structural features of the brain. The present study tested whether combining functional and structural (multimodal) brain measures, derived from magnetic resonance imaging (MRI), would yield a multivariate brain biomarker that reliably predicts a subclinical marker of CVD risk, carotid-artery intima-media thickness (CA-IMT). Methods: Neuroimaging, cardiovascular, and demographic data were assessed in 324 midlife and otherwise healthy adults who were free of (a) clinical CVD and (b) use of medications for chronic illness (aged 30-51 years, 49% female). We implemented a prediction stacking algorithm that combined multimodal brain imaging measures and Framingham Risk Scores (FRS) to predict CA-IMT. We included imaging measures that could be easily obtained in clinical settings: resting state functional connectivity and structural morphology measures from T1-weighted images. Results: Our models reliably predicted CA-IMT using FRS, as well as for several individual MRI measures; however, none of the individual MRI measures outperformed FRS. Moreover, stacking functional and structural brain measures with FRS did not boost prediction accuracy above that of FRS alone. Conclusions: Combining multimodal functional and structural brain measures through a stacking algorithm does not appear to yield a reliable brain biomarker of subclinical CVD, as reflected by CA-IMT.

show abstract

Section: Discussionmentioning

confidence: 99%

Integrating Multiple Imaging Modalities does not Boost Prediction of Carotid Artery Intima-Media Thickness in Midlife Adults

Sentis

Rasero

Gianaros

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Participant self-generated thoughts may include planning the future, recalling the past, reflecting, and ruminating. Clearly, the so-called “resting-state” is not a complete standstill of the brain (Buckner, 2012), but rather reflects a mixture of mental activity and intrinsic functional organization (Finn, 2021). Likely because we pay little attention to observing these ongoing mental activities during resting-state scans, the interpretability of resting-state fMRI is inferior (Gonzalez-Castillo et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Neural Representations of Self-Generated Thought during Think-aloud fMRI

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

Is the brain at rest during the so-called resting-state? Ongoing experiences in the resting-state vary in unobserved and uncontrolled ways across time, individuals, and populations. However, the role of self-generated thoughts in resting-state fMRI remains largely unexplored. In this study, we collected real-time self-generated thoughts during resting-state fMRI scans via the think-aloud method (i.e., think-aloud fMRI), which required participants to report whatever they were currently thinking. We first investigated brain activation patterns during a think-aloud condition and found that significantly activated brain areas included all brain regions required for speech, along with a large number of deactivated brain regions. We then calculated the relationship between divergence in thought content and brain activation during think-aloud, and found that divergence in thought content was associated with a large number of brain regions, including those that were significantly activated and deactivated during think-aloud. Finally, we explored the neural representation of self-generated thoughts by performing representational similarity analysis (RSA) at three neural scales: a voxel-wise whole-brain searchlight level; a region-level whole-brain analysis using the Schaefer 400-parcels; a system level using the Yeo seven-network systems and the language subsystems. We found that resting-state self-generated thoughts were distributed across a wide range of brain regions, involving all Yeo seven-network. This study highlights the importance of taking into account ongoing experiences during resting-state fMRI, as well as providing preliminary methodological support for think-aloud fMRI.

show abstract

“…While previously demonstrated with resting-state fMRI data (Tavor et al, 2016), it remains an open question whether resting-state is indeed the optimal task-free paradigm for making such predictions. Recently, it has been suggested that the use of naturalistic stimuli may accelerate progress in areas that have been so far dominated by rest, including characterizing brain functional organization and brain–behavior relationships (Finn, 2021). Our results are in line with previous studies showing that movie-watching data outperform resting-state data in detecting unique functional connectivity patterns (Vanderwal et al, 2017) and that the prediction of cognitive and emotional scores is more accurate when based on movie-watching data than on data collected at rest (Finn & Bandettini, 2021).…”

Section: Discussionmentioning

confidence: 99%

Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

Gal

Coldham

Bernstein‐Eliav

et al. 2021

Preprint

View full text Add to dashboard Cite

The search for an ‘ideal’ approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.

show abstract

Is it time to put rest to rest?

Cited by 172 publications

References 124 publications

Integrating Multiple Imaging Modalities does not Boost Prediction of Carotid Artery Intima-Media Thickness in Midlife Adults

Integrating Multiple Imaging Modalities does not Boost Prediction of Carotid Artery Intima-Media Thickness in Midlife Adults

Neural Representations of Self-Generated Thought during Think-aloud fMRI

Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

Contact Info

Product

Resources

About