Publications

Abstract

Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) has emerged as the most popular neuroimaging technique used to depict the biological microstructural properties of human brain white matter. However, like other MRI techniques, traditional DW-MRI data remains subject to head motion artifacts during scanning. For example, previous studies have indicated that, with traditional DW-MRI data, head motion artifacts significantly affect the evaluation of diffusion metrics. Actually, DW-MRI data scanned with higher sampling rate are important for accurately evaluating diffusion metrics because it allows for full-brain coverage through the acquisition of multiple slices simultaneously and more gradient directions. Here, we employed a publicly available multiband DW-MRI dataset to investigate the association between motion and diffusion metrics with the standard pipeline, tract-based spatial statistics (TBSS). The diffusion metrics used in this study included not only the commonly used metrics (i.e., FA and MD) in DW-MRI studies, but also newly proposed inter-voxel metric, local diffusion homogeneity (LDH). We found that the motion effects in FA and MD seems to be mitigated to some extent, but the effect on MD still exists. Furthermore, the effect in LDH is much more pronounced. These results indicate that researchers shall be cautious when conducting data analysis and interpretation. Finally, the motion-diffusion association is discussed.

Abstract

Magnetic resonance imaging (MRI) provides valuable data for understanding the human mind and brain disorders, but in-scanner head motion introduces systematic and spurious biases. For example, differences in MRI measures (e.g., network strength, white matter integrity) between patient and control groups may be due to the differences in their head motion. To determine whether head motion is an important variable in itself, or just simply a confounding variable, we explored individual differences in psychological traits that may predispose some people to move more than others during an MRI scan. In the first two studies, we demonstrated in both children (N  =  245) and adults (N  =  581) that head motion, estimated from resting-state functional MRI and diffusion tensor imaging, was reliably correlated with impulsivity scores. Further, the difference in head motion between children with attention deficit hyperactivity disorder (ADHD) and typically developing children was largely due to differences in impulsivity. Finally, in the third study, we confirmed the observation that the regression approach, which aims to deal with motion issues by regressing out motion in the group analysis, would underestimate the effect of interest. Taken together, the present findings provide empirical evidence that links in-scanner head motion to psychological traits.

Abstract

Background Although local features of brain morphology have been widely investigated in neuroscience, the inter-regional relations in brain morphology have rarely been investigated, especially not for individual participants. New method In this paper, we proposed a novel framework for investigating this relation based on an individual's magnetic resonance imaging (MRI) data. The key idea was to estimate the probability density function (PDF) of local morphological features within a brain region to provide a global description of this region. Then, the inter-regional relations were quantified by calculating the similarity of the PDFs for pairs of regions based on the Kullback–Leibler (KL) divergence. Results For illustration, we applied this approach to a pre-post intervention study to investigate the longitudinal changes in morphological relations after long-term sleep deprivation. The results suggest the potential application of this new method for studies on individual differences in brain structure. Comparison with existing methods The current method can be employed to estimate individual morphological relations between regions, which have been largely ignored by previous studies. Conclusions Our morphological relation metric, as a novel quantitative biomarker, can be used to investigate normal individual variability and even within-individual alterations/abnormalities in brain structure.

Abstract

Sleep loss can alter extrinsic, task-related functional MRI signals involved in attention, memory, and executive function. However, the effects of sleep loss on brain structure have not been well characterized. Recent studies with patients with sleep disorders and animal models have demonstrated reduction of regional brain structure in the hippocampus and thalamus. In this study, using T1-weighted MRI, we examined the change of regional gray matter volume in healthy adults after long-term total sleep deprivation (∼72 h). Regional volume changes were explored using voxel-based morphometry with a paired two-sample t-test. The results revealed significant loss of gray matter volume in the thalamus but not in the hippocampus. No overall decrease in whole brain gray matter volume was noted after sleep deprivation. As expected, sleep deprivation significantly reduced visual vigilance as assessed by the continuous performance test, and this decrease was correlated significantly with reduced regional gray matter volume in thalamic regions. This study provides the first evidence for sleep loss-related changes in gray matter in the healthy adult brain.