Publications

Abstract

Hemispheric asymmetry is a cardinal feature of human brain organization. Altered brain asymmetry has also been linked to some cognitive and neuropsychiatric disorders. Here the ENIGMA consortium presents the largest ever analysis of cerebral cortical asymmetry and its variability across individuals. Cortical thickness and surface area were assessed in MRI scans of 17,141 healthy individuals from 99 datasets worldwide. Results revealed widespread asymmetries at both hemispheric and regional levels, with a generally thicker cortex but smaller surface area in the left hemisphere relative to the right. Regionally, asymmetries of cortical thickness and/or surface area were found in the inferior frontal gyrus, transverse temporal gyrus, parahippocampal gyrus, and entorhinal cortex. These regions are involved in lateralized functions, including language and visuospatial processing. In addition to population-level asymmetries, variability in brain asymmetry was related to sex, age, and intracranial volume. Interestingly, we did not find significant associations between asymmetries and handedness. Finally, with two independent pedigree datasets (N = 1,443 and 1,113, respectively), we found several asymmetries showing significant, replicable heritability. The structural asymmetries identified, and their variabilities and heritability provide a reference resource for future studies on the genetic basis of brain asymmetry and altered laterality in cognitive, neurological, and psychiatric disorders.

Abstract

Statistical inference plays a critical role in modern scientific research, however, the dominant method for statistical inference in science, null hypothesis significance testing (NHST), is often misunderstood and misused, which leads to unreproducible findings. To address this issue, researchers propose to adopt the Bayes factor as an alternative to NHST. The Bayes factor is a principled Bayesian tool for model selection and hypothesis testing, and can be interpreted as the strength for both the null hypothesis H0 and the alternative hypothesis H1 based on the current data. Compared to NHST, the Bayes factor has the following advantages: it quantifies the evidence that the data provide for both the H0 and the H1, it is not “violently biased” against H0, it allows one to monitor the evidence as the data accumulate, and it does not depend on sampling plans. Importantly, the recently developed open software JASP makes the calculation of Bayes factor accessible for most researchers in psychology, as we demonstrated for the t-test. Given these advantages, adopting the Bayes factor will improve psychological researchers’ statistical inferences. Nevertheless, to make the analysis more reproducible, researchers should keep their data analysis transparent and open.

Abstract

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive features may be represented by neurocognitive graphs based on cognitive features, modeled as Gaussian Markov random fields. However, it is unclear whether it is possible to differentiate between phenotypic patterns associated with the differential diagnosis of schizophrenia and depression using this neurocognitive graph approach. In this study, we enrolled 215 first-episode patients with schizophrenia (FES), 125 with MDD, and 237 demographically-matched healthy controls (HCs). The cognitive performance of all participants was evaluated using a battery of neurocognitive tests. The graphical LASSO model was trained with a one-vs-one scenario to learn the conditional independent structure of neurocognitive features of each group. Participants in the holdout dataset were classified into different groups with the highest likelihood. A partial correlation matrix was transformed from the graphical model to further explore the neurocognitive graph for each group. The classification approach identified the diagnostic class for individuals with an average accuracy of 73.41% for FES vs HC, 67.07% for MDD vs HC, and 59.48% for FES vs MDD. Both of the neurocognitive graphs for FES and MDD had more connections and higher node centrality than those for HC. The neurocognitive graph for FES was less sparse and had more connections than that for MDD. Thus, neurocognitive graphs based on cognitive features are promising for describing endophenotypes that may discriminate schizophrenia from depression.

Abstract

Introduction: Navigation is a fundamental and multidimensional cognitive function that individuals rely on to move around the environment. In this study, we investigated the neural basis of human spatial navigation ability. Methods: A large cohort of participants (N > 200) was examined on their navigation ability behaviorally and structural and functional magnetic resonance imaging (MRI) were then used to explore the corresponding neural basis of spatial navigation. Results: The gray matter volume (GMV) of the bilateral parahippocampus (PHG), retrosplenial complex (RSC), entorhinal cortex (EC), hippocampus (HPC), and thalamus (THAL) was correlated with the participants’ self-reported navigational ability in general, and their sense of direction in particular. Further fMRI studies showed that the PHG, RSC, and EC selectively responded to visually presented scenes, whereas the HPC and THAL showed no selectivity, suggesting a functional division of labor among these regions in spatial navigation. The resting-state functional connectivity analysis further revealed a hierarchical neural network for navigation constituted by these regions, which can be further categorized into three relatively independent components (i.e., scene recognition component, cognitive map component, and the component of heading direction for locomotion, respectively). Conclusions: Our study combined multi-modality imaging data to illustrate that multiple brain regions may work collaboratively to extract, integrate, store, and orientate spatial information to guide navigation behaviors.

Abstract

The functional region of interest (fROI) approach has increasingly become a favored methodology in functional magnetic resonance imaging (fMRI) because it can circumvent inter-subject anatomical and functional variability, and thus increase the sensitivity and functional resolution of fMRI analyses. The standard fROI method requires human experts to meticulously examine and identify subject-specific fROIs within activation clusters. This process is time-consuming and heavily dependent on experts’ knowledge. Several algorithmic approaches have been proposed for identifying subject-specific fROIs; however, these approaches cannot easily incorporate prior knowledge of inter-subject variability. In the present study, we improved the multi-atlas labeling approach for defining subject-specific fROIs. In particular, we used a classifier-based atlas-encoding scheme and an atlas selection procedure to account for the large spatial variability across subjects. Using a functional atlas database for face recognition, we showed that with these two features, our approach efficiently circumvented inter-subject anatomical and functional variability and thus improved labeling accuracy. Moreover, in comparison with a single-atlas approach, our multi-atlas labeling approach showed better performance in identifying subject-specific fROIs.

Abstract

A major principle of human brain organization is “integrating” some regions into networks while “segregating” other sets of regions into separate networks. However, little is known about the cognitive function of the integration and segregation of brain networks. Here, we examined the well-studied brain network for face processing, and asked whether the integration and segregation of the face network (FN) are related to face recognition performance. To do so, we used a voxel-based global brain connectivity method based on resting-state fMRI to characterize the within-network connectivity (WNC) and the between-network connectivity (BNC) of the FN. We found that 95.4% of voxels in the FN had a significantly stronger WNC than BNC, suggesting that the FN is a relatively encapsulated network. Importantly, individuals with a stronger WNC (i.e., integration) in the right fusiform face area were better at recognizing faces, whereas individuals with a weaker BNC (i.e., segregation) in the right occipital face area performed better in the face recognition tasks. In short, our study not only demonstrates the behavioral relevance of integration and segregation of the FN but also provides evidence supporting functional division of labor between the occipital face area and fusiform face area in the hierarchically organized FN.

Abstract

Faces contain a variety of information such as one’s identity and expression. One prevailing model suggests a functional division of labor in processing faces that different aspects of facial information are processed in anatomically separated and functionally encapsulated brain regions. Here, we demonstrate that facial identity and expression can be processed in the same region, yet with different neural coding strategies. To this end, we employed functional magnetic resonance imaging to examine two types of coding schemes, namely univariate activity and multivariate pattern, in the posterior superior temporal cortex (pSTS) - a face-selective region that is traditionally viewed as being specialized for processing facial expression. With the individual difference approach, we found that participants with higher overall face selectivity in the right pSTS were better at differentiating facial expressions measured outside of the scanner. In contrast, individuals whose spatial pattern for faces in the right pSTS was less similar to that for objects were more accurate in identifying previously presented faces. The double dissociation of behavioral relevance between overall neural activity and spatial neural pattern suggests that the functional-division-of-labor model on face processing is over-simplified, and that coding strategies shall be incorporated in a revised model.

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.