Publications

Abstract

Schizophrenia is a common and complex mental disorder with neuroimaging alterations. Recent neuroanatomical pattern recognition studies attempted to distinguish individuals with schizophrenia by structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI). 1, 2 Applications of cutting-edge machine learning approaches in structural neuroimaging studies have revealed potential pathways to classification of schizophrenia based on regional gray matter volume (GMV) or density or cortical thickness. 3–5 Additionally, cortical folding may have high discriminatory value in correctly identifying symptom severity in schizophrenia. 6 Regional GMV and cortical thickness have also been combined in attempts to differentiate individuals with schizophrenia from healthy controls (HCs). 7 Applications of machine learning algorithms to diffusion imaging data analysis to predict individuals with first-episode schizophrenia (FES) have achieved encouraging accuracy. 8–10 White matter (WM) abnormalities in schizophrenia as estimated by DTI appear to be present in the early stage of the disorder, most likely reflecting the developmental stage of the sample of interest.

Abstract

Identifying data-driven biotypes of major depressive disorder (MDD) has promise for the clarification of diagnostic heterogeneity. However, few studies have focused on white-matter abnormalities for MDD subtyping. This study included 116 patients with MDD and 118 demographically-matched healthy controls assessed by diffusion tensor imaging and neurocognitive evaluation. Hierarchical clustering was applied to the major fiber tracts, in conjunction with tract-based spatial statistics, to reveal white-matter alterations associated with MDD. Clinical and neurocognitive differences were compared between identified subgroups and healthy controls. With fractional anisotropy extracted from 20 fiber tracts, cluster analysis revealed 3 subgroups based on the patterns of abnormalities. Patients in each subgroup versus healthy controls showed a stepwise pattern of white-matter alterations as follows: subgroup 1 (25.9% of patient sample), widespread white-matter disruption; subgroup 2 (43.1% of patient sample), intermediate and more localized abnormalities in aspects of the corpus callosum and left cingulate; and subgroup 3 (31.0% of patient sample), possible mild alterations, but no statistically significant tract disruption after controlling for family-wise error. The neurocognitive impairment in each subgroup accompanied the white-matter alterations: subgroup 1, deficits in sustained attention and delayed memory; subgroup 2, dysfunction in delayed memory; and subgroup 3, no significant deficits. Three subtypes of white-matter abnormality exist in individuals with major depression, those having widespread abnormalities suffering more neurocognitive impairments, which may provide evidence for parsing the heterogeneity of the disorder and help optimize type-specific treatment approaches.

Abstract

The coordinate system has been proposed as a fundamental and cross-culturally used spatial representation, through which people code location and direction information in the environment. Here we provided direct evidence demonstrating that daily experience with the cardinal coordinate system (i.e., east, west, north, and south) contributed to the representation of cognitive maps. Behaviorally, we found that individuals who relied more on the cardinal coordinate system for daily navigation made smaller errors in an indoor pointing task, suggesting that the cardinal coordinate system is an important element of cognitive maps. Neurally, the extent to which individuals relied on the cardinal coordinate system was positively correlated with the gray matter volume of the entorhinal cortex, suggesting that the entorhinal cortex may serve as the neuroanatomical basis of coordinate-based navigation (the entorhinal coordinate area, ECA). Further analyses on the resting-state functional connectivity revealed that the intrinsic interaction between the ECA and two hippocampal sub-regions, the subiculum and cornu ammonis, might be linked with the representation precision of cognitive maps. In sum, our study reveals an association between daily experience with the cardinal coordinate system and cognitive maps, and suggests that the ECA works in collaboration with hippocampal sub-regions to represent cognitive maps.

Abstract

Spatial navigation is a crucial ability for living. Previous animal studies have shown that the S100B gene is causally related to spatial navigation performance in mice. However, the genetic factors influencing human navigation and its neural substrates remain unclear. Here, we provided the first evidence that the S100B gene modulates neural processing of navigationally relevant scenes in humans. First, with a novel protocol, we demonstrated that the spatial pattern of S100B gene expression in postmortem brains was associated with brain activation pattern for spatial navigation in general, and for scene processing in particular. Further, in a large fMRI cohort of healthy adults of Han Chinese (N = 202), we found that S100B gene polymorphisms modulated scene selectivity in the retrosplenial cortex (RSC) and parahippocampal place area. Finally, the serum levels of S100B protein mediated the association between S100B gene polymorphism and scene selectivity in the RSC. Our study takes the first step toward understanding the neurogenetic mechanism of human spatial navigation and suggests a novel approach to discover candidate genes modulating cognitive functions.

Abstract

Spatial navigation is a crucial ability for living. Previous work has revealed multiple distributed brain regions associated with human navigation. However, little is known about how these regions work together as a network (referred to as navigation network) to support flexible navigation. In a novel protocol, we combined neuroimaging meta-analysis, and functional connectivity and behavioral data from the same subjects. Briefly, we first constructed the navigation network for each participant, by combining a large-scale neuroimaging meta-analysis (with the Neurosynth) and resting-state functional magnetic resonance imaging. Then, we investigated multiple topological properties of the navigation networks, including small-worldness, modularity, and highly connected hubs. Finally, we explored the behavioral relevance of these intrinsic properties in a large sample of healthy young adults (N = 190). We found that navigation networks showed small-world and modular organization at global level. More importantly, we found that increased small-worldness and modularity of the navigation network were associated with better navigation ability. Finally, we found that the right retrosplenial complex (RSC) acted as one of the hubs in the navigation network, and that higher betweenness of this region correlated with better navigation ability, suggesting a critical role of the RSC in modulating the navigation network in human brain. Our study takes one of the first steps toward understanding the underlying organization of the navigation network. Moreover, these findings suggest the potential applications of the novel approach to investigating functionally meaningful networks in human brain and their relations to the behavioral impairments in the aging and psychiatric patients.

Abstract

Spatial navigation is a crucial ability for living. Previous studies have shown that males are better at navigation than females, but little is known about the neural basis underlying the sex differences. In this study, we investigated whether cortical scene processing in three well-established scene-selective regions was sexually different, by examining sex differences in scene selectivity and its behavioral relevance to navigation. To do this, we used functional magnetic resonance imaging (fMRI) to scan the parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA) in a large cohort of healthy young adults viewing navigationally relevant scenes (N = 202), and correlated their neural selectivity to scenes with their self-reported navigational ability. Behaviorally, we replicated the previous finding that males were better at navigation than females. Neurally, we found that the scene selectivity in the bilateral PPA, not in the RSC or OPA, was significantly higher in males than females. Such differences could not be explained by confounding factors including brain size and fMRI data quality. Importantly, males, not females, with stronger scene selectivity in the left PPA possessed better navigational ability. This brain-behavior association could not be accounted for by non-navigational abilities (i.e., intelligence and mental rotation ability). Overall, our study provides novel empirical evidence demonstrating sex differences in the brain activity, inviting further studies on sex differences in the neural network for spatial navigation.

Abstract

Scene-selective regions (SSRs), including the parahippocampal place area (PPA), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS), are among the most widely characterized functional regions in the human brain. However, previous studies have mostly focused on the commonality within each SSR, providing little information on different aspects of their variability. In a large group of healthy adults (N = 202), we used functional magnetic resonance imaging to investigate different aspects of topographical and functional variability within SSRs, including interindividual, interhemispheric, and sex differences. First, the PPA, RSC, and TOS were delineated manually for each individual. We then demonstrated that SSRs showed substantial interindividual variability in both spatial topography and functional selectivity. We further identified consistent interhemispheric differences in the spatial topography of all three SSRs, but distinct interhemispheric differences in scene selectivity. Moreover, we found that all three SSRs showed stronger scene selectivity in men than in women. In summary, our work thoroughly characterized the interindividual, interhemispheric, and sex variability of the SSRs and invites future work on the origin and functional significance of these variabilities. Additionally, we constructed the first probabilistic atlases for the SSRs, which provide the detailed anatomical reference for further investigations of the scene network.

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.