Identifying predictors of within-person variance in MRI-based brain volume estimates

Karch, Julian D.; Filevich, Elisa; Wenger, Elisabeth; Lisofsky, Nina; Becker, Maxi; Butler, Oisín; Mårtensson, Johan; Lindenberger, Ulman; Brandmaier, Andreas M.; Kühn, Simone

doi:10.1016/j.neuroimage.2019.05.030

Cited by 44 publications

(51 citation statements)

References 55 publications

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“…Time of day effects have been observed not only in studies of brain activity but also in studies of brain structure [105][106][107][108][109][110]. Notably, some studies have reported morning-to-evening reductions in total brain and/or grey matter volume at rates comparable to age-related annual atrophy [107,110]. Hydration levels have been mentioned as a possible mechanism, given that hydration has been shown to modulate measurements of total brain volumes in some studies [111,112], though not all [110].…”

Section: Relationship To Studies Of Diurnal Variation In Brain Structurementioning

confidence: 99%

“…Notably, some studies have reported morning-to-evening reductions in total brain and/or grey matter volume at rates comparable to age-related annual atrophy [107,110]. Hydration levels have been mentioned as a possible mechanism, given that hydration has been shown to modulate measurements of total brain volumes in some studies [111,112], though not all [110]. Dehydration could also potentially impact BOLD signal fluctuation by reducing the amplitude of brain (or cerebrospinal fluid) pulsation or the dynamic range of cardiac and respiratory activity.…”

Section: Relationship To Studies Of Diurnal Variation In Brain Structurementioning

confidence: 99%

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Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

Orban

Kong

et al. 2020

PLoS Biol

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The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain's global signal (GS) amplitude (henceforth referred to as "fluctuation") increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

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Section: Relationship To Studies Of Diurnal Variation In Brain Structurementioning

confidence: 99%

Section: Relationship To Studies Of Diurnal Variation In Brain Structurementioning

confidence: 99%

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

Orban

Kong

et al. 2020

PLoS Biol

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“…All previously calculated confounds (487) are first regressed out of each IDP to ensure that we are focusing on time/date effects not already covered by the known above confounds. After regressing out all the previously mentioned confounds, there may still be some variance explained by the acquisition date or its time of day [Karch, 2019]. This kind of information may be useful for some studies (e.g.…”

Section: Date/time-related Confounds (120 Variables)mentioning

confidence: 99%

Confound modelling in UK Biobank brain imaging

Alfaro-Almagro

McCarthy

Afyouni

et al. 2020

Preprint

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Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including non-linear effects and interactions) that researchers may wish to use, to arrive at more valid conclusions in their studies using such data. We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.

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“…For the true squared multiple correlation ρ 2 , I included the values investigated in the previous comparison performed by Shieh (2008), 0.0, 0.1, 0.2,…, 0.9 as they sample the space of possible values in a relatively dense matter. I additionally included two small values: 0.01 and 0.05 as small squared multiple correlation values are relatively common in psychology (see, for example, Karch et al, 2019).…”

Section: Squared Multiple Correlationmentioning

confidence: 99%

Improving on Adjusted R-Squared

Karch

2020

Collabra: Psychology

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The amount of variance explained is widely reported for quantifying the model fit of a multiple linear regression model. The default adjusted R-squared estimator has the disadvantage of not being unbiased. The theoretically optimal Olkin-Pratt estimator is unbiased. Despite this, it is not being used due to being difficult to compute. In this paper, I present an algorithm for the exact and fast computation of the Olkin-Pratt estimator, which facilitates its use. I compare the Olkin-Pratt, the adjusted R-squared, and 18 alternative estimators using a simulation study. The metrics I use for comparison closely resemble established theoretical optimality properties. Importantly, the exact Olkin-Pratt estimator is shown to be optimal under the standard metric, which considers an estimator optimal if it has the least mean squared error among all unbiased estimators. Under the important alternative metric, which aims for the estimator with the lowest mean squared error, no optimal estimator could be identified. Based on these results, I provide careful recommendations on when to use which estimator, which first and foremost depends on the choice of which metric is deemed most appropriate. If such a choice is infeasible, I recommend using the exact Olkin-Pratt instead of the default adjusted R-squared estimator. To facilitate this, I provide the R package altR2, which implements the Olkin-Pratt estimator as well as all other estimators.

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Identifying predictors of within-person variance in MRI-based brain volume estimates

Cited by 44 publications

References 55 publications

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

Confound modelling in UK Biobank brain imaging

Improving on Adjusted R-Squared

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