Age differences in head motion and estimates of cortical morphology

Abstract

12Head motion during MRI scanning influence estimates of cortical morphology. 13 Previous studies have found that older adults demonstrate greater head motion than 14 younger adults, while other studies have found task-related differences in head motion. 15Cortical morphology also differ with age, as measured by cortical thickness, fractal 16 dimensionality, and gyrification. Here I replicated all three of these prior findings 17 within a larger dataset than these results have been demonstrated previously, with a 18 sample size of more than 600 adults across the adult lifespan (Centre for Ageing and 19 Neuroscience, CamCAN). Furthermore, I additionally test for the influence of head 20 motion on cortical fractal dimensionality and gyrification; effects were statistically 21 significant in some cases, but small in magnitude. 22HEAD MOTION, AGING, AND BRAIN MORPHOLOGY 3 Age differences in head motion and estimates of cortical morphology 23 131Of particular interest, I also examined the influence of head motion on the cortical 132 morphology estimates. For all three measures, head motion explained only a small 133 amount of additional variance beyond age, as shown in Table 1. Nonetheless, head 134 motion from the movie scan did explain significant additional variance, as measured 135 by ∆BIC, however, this only accounted for an additional 1% variance. In the model of 136 cortical thickness including head motion from the movie scan (but not the interaction), 137 age related changes corresponded to −0.0398 mm/decade, while head motion 138 contributed −0.0135 mm/(mm/min).

Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity

“…In broad strokes, our findings concur with previous studies which have demonstrated a connection between motion metrics and age and/or BMI (Madan, 2018;Savalia et al, 2017). However, true (lower frequency) head motion may also be correlated with these demographic characteristics, even when HFmotion is explicitly removed (Siegel et al, 2017), suggesting that some participant characteristics may relate to both true and factitious motion.…”

“…Head motion introduces bias in measured FC through both common effects across all pair-wise regional correlations as well as distance-dependent biases, where correlations are increased most for adjacent regions and relatively decreased for regions that are distant (Power et al, 2012;Satterthwaite et al, 2012). These distortions are of particular concern in studies comparing groups or conditions that differ systematically in head motion, for example in comparisons between children and young adults (Greene et al, 2016;Nielsen et al, 2019;Satterthwaite et al, 2013), adults of different ages (Madan, 2018;Savalia et al, 2017), or clinical populations vs. normative controls Fair et al, 2012;Gratton et al, 2019). Fortunately, effective approaches have been developed to reduce biases introduced by head motion (Power et al, 2015).…”

Weight loss reduces head motion: Revisiting a major confound in neuroimaging

“…While our estimate of a weight loss‐induced bias in neuroimaging outcomes (i.e., 5% increase in total gray matter volume after bariatric surgery) relied on between‐subject estimates of the effect of head motion (Alexander‐Bloch et al, ) and therefore has to be interpreted with caution, this finding still stresses the importance to control for head motion differences in future MRI analyses. Different techniques, such as multi‐echo sequences (Power et al, ), fixation through head molds (Power et al, ) and tactile feedback during scanning (Krause et al, ) have been proposed to considerably reduce head motion a priori — which is probably the best way to handle this important confound for practically all imaging outcomes (Baum et al, ; Beyer et al, ; Madan, ; Reuter et al, ).…”

“…In this pre‐registered analysis (https://osf.io/epsxt), we therefore aimed to test whether a radical weight‐loss intervention (bariatric surgery) compared to a control group induces consistent changes of head motion during magnetic resonance imaging (MRI) in obese individuals. In addition, we aimed to estimate the confounding effects of a potential change in head motion after bariatric surgery on structural brain measures, according to previous literature (Madan, ; Reuter et al, ; Savalia et al, ).…”

Altered orbitofrontal sulcogyral patterns in gambling disorder: a multicenter study

“…While a few studies have reported decreased OFC gray matter volume [13][14][15] and decreased cortical thickness 16 in pathological gamblers compared with healthy controls, other studies have failed to report significant group differences [17][18][19][20] . These inconsistencies might reflect the influence of factors such as age, comorbidities and head motion acting as confounds on structural brain measures 21 , as well as the heterogeneity existing among gamblers, as suggested by a recent study which found decreased OFC gray matter volume specifically in gamblers showing low risk-taking 22 . As a matter of fact, structural abnormalities observed in pathological gamblers are less consistent and of more modest magnitude than those reported in substance addiction 19,20,23 .…”