Gaolang Gong

Age- and Gender-Related Differences in the Cortical Anatomical Network

Neuroanatomical differences attributable to aging and gender have been well documented, and these differences may be associated with differences in behaviors and cognitive performance. However, little is known about the dynamic organization of anatomical connectivity within the cerebral cortex, which may underlie population differences in brain function. In this study, we investigated age and sex effects on the anatomical connectivity patterns of 95 normal subjects ranging in age from 19 to 85 years. Using the connectivity probability derived from diffusion magnetic resonance imaging tractography, we characterized the cerebral cortex as a weighted network of connected regions. This approach captures the underlying organization of anatomical connectivity for each subject at a regional level. Advanced graph theoretical analysis revealed that the resulting cortical networks exhibited “small-world” character (i.e., efficient information transfer both at local and global scale). In particular, the precuneus and posterior cingulate gyrus were consistently observed as centrally connected regions, independent of age and sex. Additional analysis revealed a reduction in overall cortical connectivity with age. There were also changes in the underlying network organization that resulted in decreased local efficiency, and also a shift of regional efficiency from the parietal and occipital to frontal and temporal neocortex in older brains. In addition, women showed greater overall cortical connectivity and the underlying organization of their cortical networks was more efficient, both locally and globally. There were also distributed regional differences in efficiency between sexes. Our results provide new insights into the substrates that underlie behavioral and cognitive differences in aging and sex.

Voxel-based detection of white matter abnormalities in mild Alzheimer disease

Objective: To detect white matter abnormalities in patients with mild Alzheimer disease (AD) by diffusion tensor imaging and to determine their topographic relationship with gray matter atrophy. Methods: Thirteen patients with mild AD and 16 normal age-matched volunteers underwent diffusion tensor imaging and three-dimensional spoiled gradient-recalled sequence scanning. Voxel-based morphometry was conducted to detect regions of gray matter atrophy in the AD group relative to the control group. Fractional anisotropy (FA) maps were processed using SPM2 to make voxel-wise comparison of anisotropy in whole brain between the two groups. The relationship between locations of abnormalities in the white and gray matter was examined. Results: Significant reductions in anisotropy were found in the white matter of both medial temporal lobes, bilateral temporal stems, bilateral superior longitudinal fasciculi, bilateral internal capsules, and cerebral peduncles, as well as the white matter of left middle temporal gyrus and right superior parietal lobule, the body and genu of the corpus callosum, and the right lateral capsule in patients with AD. Although the decrease in FA was consistent with cortical volumetric reduction in both temporal lobes, the widespread involvement of bilateral superior longitudinal fasciculi was dominant in these white matter findings. Conclusions: Voxel-wise comparison of whole-brain anisotropy revealed widely distributed disintegration of white matter in mild Alzheimer disease (AD). The white matter shows a different pattern of degeneration from gray matter and may be an independent factor in the progress of AD.

PANDA: a pipeline toolbox for analyzing brain diffusion images

Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named “Pipeline for Analyzing braiN Diffusion imAges” (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.

Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex.

Cortical thickness correlation across individuals has been observed. So far, it remains unclear to what extent such a correlation in thickness is a reflection of underlying fiber connection. Here we explicitly compared the patterns of cortical thickness correlation and diffusion-based fiber connection across the entire cerebral cortex, in 95 normal adults. Interregional thickness correlations were extracted by using computational neuroanatomy algorithms based on structural MRI, and diffusion connections were detected by using diffusion probabilistic tractography. Approximately 35-40% of thickness correlations showed convergent diffusion connections across the cerebral cortex. Intriguingly, the observed convergences between thickness correlation and diffusion connection are mostly focused on the positive thickness correlations, while almost all of the negative correlations (>90%) did not have a matched diffusion connection, suggesting different mechanisms behind the positive and negative thickness correlations, the latter not being mediated by a direct fiber pathway. Furthermore, graph theoretic analysis reveals that the thickness correlation network has a more randomized overall topology, whereas the nodal characteristics of cortical regions in these two networks are statistically correlated. These findings indicate that thickness correlations partly reflect underlying fiber connections but they contains exclusive information, and therefore should not be simply taken as a proxy measure for fiber connections.

Neuronal Networks in Alzheimer's Disease

Alzheimers disease (AD) is a progressive, neurodegenerative disease that can be clinically characterized by impaired memory and many other cognitive functions. Previous studies have demonstrated that the impairment is accompanied by not only regional brain abnormalities but also changes in neuronal connectivity between anatomically distinct brain regions. Specifically, using neurophysiological and neuroimaging techniques as well as advanced graph theory—based computational approaches, several recent studies have suggested that AD patients have disruptive neuronal integrity in large-scale structural and functional brain systems underlying high-level cognition, as demonstrated by a loss of small-world network characteristics. Small world is an attractive model for the description of complex brain networks because it can support both segregated and integrated information processing. The altered small-world organization thus reflects aberrant neuronal connectivity in the AD brain that is most likely to explain cognitive deficits caused by this disease. In this review, we will summarize recent advances in the brain network research on AD, focusing mainly on the large-scale structural and functional descriptions. The literature reviewed here suggests that AD patients are associated with integrative abnormalities in the distributed neuronal networks, which could provide new insights into the disease mechanism in AD and help us to uncover an imaging-based biomarker for the diagnosis and monitoring of the disease.

Asymmetry analysis of cingulum based on scale‐invariant parameterization by diffusion tensor imaging

Current analysis of diffusion tensor imaging (DTI) is based mostly on a region of interest (ROI) in an image dataset, which is specified by users. This method is not always reliable, however, because of the uncertainty of manual specification. We introduce an improved fiber‐based scheme rather than an ROI‐based analysis to study in DTI datasets of 31 normal subjects the asymmetry of the cingulum, which is one of the most prominent white matter fiber tracts of the limbic system. The present method can automatically extract the quantitative anisotropy properties along the cingulum bundles from tractography. Moreover, statistical analysis was carried out after anatomic correspondence specific to the cingulum across subjects was established, rather than the traditional whole‐brain registration. The main merit of our method compared to existing counterparts is that to find such anatomic correspondence in cingulum, a scale‐invariant parameterization method by arc‐angle was proposed. It can give a continuous and exact description on any segment of cingulum. More interestingly, a significant left‐greater‐than‐right asymmetry pattern was obtained in most segments of cingulum bundle (−50–25 degrees), except in the most posterior portion of cingulum (25–50 degrees). Hum Brain Mapp 24:92–98, 2005.

Brain Connectivity: Gender Makes a Difference

It has been well known that gender plays a critical role in the anatomy and function of the human brain, as well as human behaviors. Recent neuroimaging studies have demonstrated gender effects on not only focal brain areas but also the connectivity between areas. Specifically, structural MRI and diffusion MRI data have revealed substantial gender differences in white matter–based anatomical connectivity. Structural MRI data further demonstrated gender differences in the connectivity revealed by morphometric correlation among brain areas. Functional connectivity derived from functional neuroimaging (e.g., functional MRI and PET) data is also modulated by gender. Moreover, male and female human brains display differences in the network topology that represents the organizational patterns of brain connectivity across the entire brain. In this review, the authors summarize recent findings in the multimodal brain connectivity/network research with gender, focusing on large-scale data sets derived from modern neuroimaging techniques. The literature provides convergent evidence for a substantial gender difference in brain connectivity within the human brain that possibly underlies gender-related cognitive differences. Therefore, it should be mandatory to take gender into account when designing experiments or interpreting results of brain connectivity/network in health and disease. Future studies will likely be conducted to explore the interdependence between gender-related brain connectivity/network and the gender-specific nature of brain diseases as well as to investigate gender-related characteristics of multimodal brain connectivity/network in the normal brain.

Age-related alterations in the modular organization of structural cortical network by using cortical thickness from MRI

Normal aging is accompanied by various cognitive functional declines. Recent studies have revealed disruptions in the coordination of large-scale functional brain networks such as the default mode network in advanced aging. However, organizational alterations of the structural brain network at the system level in aging are still poorly understood. Here, using cortical thickness, we investigated the modular organization of the cortical structural networks in 102 young and 97 normal aging adults. Brain networks for both cohorts displayed a modular organization overlapping with functional domains such as executive and auditory/language processing. However, compared with the modular organization of young adults, the aging group demonstrated a significantly reduced modularity that might be indicative of reduced functional segregation in the aging brain. More importantly, the aging brain network exhibited reduced intra-/inter-module connectivity in modules corresponding to the executive function and the default mode network of young adults, which might be associated with the decline of cognitive functions in aging. Finally, we observed age-associated alterations in the regional characterization in terms of their intra/inter-module connectivity. Our results indicate that aging is associated with an altered modular organization in the structural brain networks and provide new evidence for disrupted integrity in the large-scale brain networks that underlie cognition.

Prefrontal white matter abnormalities in young adult with major depressive disorder: A diffusion tensor imaging study

Prefrontal impairments have been hypothesized to be most strongly associated with the cognitive and emotional dysfunction in depression. Recently, white matter microstructural abnormalities in prefrontal lobe have been reported in elderly patients with major depressive disorder (MDD) using diffusion tensor imaging (DTI). However, it is still unclear whether the same changes exist in younger patients. In the present study, we first utilized DTI to detect prefrontal white matter in young adults with MDD. Nineteen first-episode, untreated young adults with MDD and twenty age- and gender-matched healthy controls were recruited. DTI and localizing anatomic data were acquired. Then, the regions of interest (ROIs) were located in prefrontal white matter at 4 mm inferior, and 0, 4, 8, 12, 16 and 20 mm superior to the anterior commissure-posterior commissure (AC-PC) plane, respectively. Compared with healthy controls, patients with MDD showed significantly lower fractional anisotropy (FA) values in prefrontal white matter at bilateral 20 mm, right 16 mm and right 12 mm above the AC-PC. Furthermore, there was no significant correlation between the FA value of any ROI and illness course as well as severity of depression. Together with previous findings, the present results suggest that microstructural abnormalities in prefrontal white matter may occur early in the course of MDD and may be related to the neuropathology of depression throughout adulthood from young to elderly.

Effects of Different Correlation Metrics and Preprocessing Factors on Small-World Brain Functional Networks: A Resting-State Functional MRI Study

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearsons correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01–0.027 Hz) versus slow-4 (0.027–0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the “best” network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearsons correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearsons-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearsons-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027–0.073 Hz band exhibited greater reliability than those in the 0.01–0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies.