Chunshui Yu

Brain anatomical network and intelligence

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.

DYNAMIC FUNCTIONAL REORGANIZATION OF THE MOTOR EXECUTION NETWORK AFTER STROKE

Numerous studies argue that cortical reorganization may contribute to the restoration of motor function following stroke. However, the evolution of changes during the post-stroke reorganization has been little studied. This study sought to identify dynamic changes in the functional organization, particularly topological characteristics, of the motor execution network during the stroke recovery process. Ten patients (nine male and one female) with subcortical infarctions were assessed by neurological examination and scanned with resting-state functional magnetic resonance imaging across five consecutive time points in a single year. The motor execution network of each subject was constructed using a functional connectivity matrix between 21 brain regions and subsequently analysed using graph theoretical approaches. Dynamic changes in topological configuration of the network during the process of recovery were evaluated by a mixed model. We found that the motor execution network gradually shifted towards a random mode during the recovery process, which suggests that a less optimized reorganization is involved in regaining function in the affected limbs. Significantly increased regional centralities within the network were observed in the ipsilesional primary motor area and contralesional cerebellum, whereas the ipsilesional cerebellum showed decreased regional centrality. Functional connectivity to these brain regions demonstrated consistent alterations over time. Notably, these measures correlated with different clinical variables, which provided support that the findings may reflect the adaptive reorganization of the motor execution network in stroke patients. In conclusion, the study expands our understanding of the spectrum of changes occurring in the brain after stroke and provides a new avenue for investigating lesion-induced network plasticity.

Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta-analyses of MRI studies

Numerous studies have reported a smaller hippocampal volume in Alzheimers disease (AD) patients than in aging controls. However, in mild cognitive impairment (MCI), the results are inconsistent. Moreover, the left‐right asymmetry of the hippocampus receives less research attention. In this article, meta‐analyses are designed to determine the extent of hippocampal atrophy in MCI and AD, and to evaluate the asymmetry pattern of the hippocampal volume in control, MCI, and AD groups. From 14 studies including 365 MCI patients and 382 controls, significant atrophy is found in both the left [Effect size (ES), 0.92; 95% confidence interval (CI), 0.72–1.11] and right (ES, 0.78; 95% CI, 0.57–0.98) hippocampus, which is lower than that in AD (ES, 1.60, 95% CI, 1.37–1.84, in left; ES, 1.52, 95% CI, 1.31–1.72, in right). Comparing with aging controls, the average volume reduction weighted by sample size is 12.9% and 11.1% in left and right hippocampus in MCI, and 24.2% and 23.1% in left and right hippocampus in AD, respectively. The findings show a bilateral hippocampal volume loss in MCI and the extent of atrophy is less than that in AD. By comparing the left and right hippocampal volume, a consistent left‐less‐than‐right asymmetry pattern is found, but with different extents in control (ES, 0.39), MCI (ES, 0.56), and AD (ES, 0.30) group.

Diffusion Tensor Tractography Reveals Disrupted Topological Efficiency in White Matter Structural Networks in Multiple Sclerosis

Little is currently known about the alterations in the topological organization of the white matter (WM) structural networks in patients with multiple sclerosis (MS). In the present study, we used diffusion tensor imaging and deterministic tractography to map the WM structural networks in 39 MS patients and 39 age- and gender-matched healthy controls. Graph theoretical methods were applied to investigate alterations in the network efficiency in these patients. The MS patients and the controls exhibited efficient small-world properties in their WM structural networks. However, the global and local network efficiencies were significantly decreased in the MS patients compared with the controls, with the most pronounced changes observed in the sensorimotor, visual, default-mode, and language areas. Furthermore, the decreased network efficiencies were significantly correlated with the expanded disability status scale scores, the disease durations, and the total WM lesion loads. Together, the results suggest a disrupted integrity in the large-scale brain systems in MS, thus providing new insights into the understanding of MS connectome. Our data also suggest that a topology-based brain network analysis can provide potential biomarkers for disease diagnosis and for monitoring the progression and treatment effects for patients with MS.

Regional homogeneity, functional connectivity and imaging markers of Alzheimer's disease: A review of resting-state fMRI studies

Resting-state functional magnetic resonance imaging (fMRI), a promising technique for measuring brain activities during rest, has attracted much attention in the past few years. In this paper, we review recent progress on the study of Alzheimers disease (AD) based on resting-state fMRI. First, we briefly introduce some AD-related studies from other groups. Then we describe our AD-related work in detail from three aspects: (1) alterations in regional homogeneity (ReHo) of the fMRI signal in the resting state, (2) altered patterns of functional connectivity from regions of interest and whole brain analyses, and (3) discriminative analyses based on classification features from resting-state fMRI data for differentiating AD patients from healthy elders. Finally, we summarize the main results and some prospects for future work.

Brain spontaneous functional connectivity and intelligence

Many functional imaging studies have been performed to explore the neural basis of intelligence by detecting brain activity changes induced by intelligence-related tasks, such as reasoning or working memory. However, little is known about whether the spontaneous brain activity at rest is relevant to the differences in intelligence. Here, 59 healthy adult subjects (Wechsler Adult Intelligence Scale score, 90-138) were studied with resting state fMRI. We took the bilateral dorsolateral prefrontal cortices (DLPFC) as the seed regions and investigated the correlations across subjects between individual intelligence scores and the strength of the functional connectivity (FC) between the seed regions and other brain regions. We found that the brain regions in which the strength of the FC significantly correlated with intelligence scores were distributed in the frontal, parietal, occipital and limbic lobes. Stepwise linear regression analysis also revealed that the FCs within the frontal lobe and between the frontal and posterior brain regions were both important predictive factors for the differences in intelligence. These findings support a network view of intelligence, as suggested in previous studies. More importantly, our findings suggest that brain activity may be relevant to the differences in intelligence even in the resting state and in the absence of an explicit cognitive demand. This could provide a new perspective for understanding the neural basis of intelligence.

Increased neural resources recruitment in the intrinsic organization in major depression

OBJECTIVE To investigate the functional connectivity (FC) pattern within an intrinsic functional organization, including both task-positive (TPN) and task-negative (TNN) networks, in major depressive disorder (MDD), and to examine relationships between the involved FCs and clinical variables. METHODS Resting-state FC analyses were used to identify the component brain regions of the intrinsic organization and to investigate the FCs of the individual component regions in 18 first-episode, medication-naïve MDD and 20 healthy control subjects. RESULTS We found that the intrinsic organization of the depressed group recruited more extensive regions than the control group. All of the altered FCs associated with the component regions increased in MDD. Specifically, in the TPN the increased FCs were primarily located in the bilateral lateral prefrontal cortices and the inferior parietal lobes, which have been implicated in attention and adaptive control. In the TNN, the increased FCs were primarily located in the posterior cingulate cortex and the medial orbitofrontal cortex, which are involved in episodic memory, self-reflection and emotional regulation. We also found increased anti-correlations between the two networks. Additionally, the strengths of the FCs associated with the lateral prefrontal cortices were found to be correlated with the duration of the depressive episode and the HDRS scores in the depressed patients. LIMITATIONS Clinical correlates of these abnormal FCs should be cautiously interpreted due to the small sample size in this study. CONCLUSIONS Abnormalities in the intrinsic organization may be an underlying basis for the pronounced and prolonged negative bias in processing emotional information observed in MDD.

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture

The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.

Functional segregation of the human cingulate cortex is confirmed by functional connectivity based neuroanatomical parcellation

The four-region model with 7 specified subregions represents a theoretical construct of functionally segregated divisions of the cingulate cortex based on integrated neurobiological assessments. Under this framework, we aimed to investigate the functional specialization of the human cingulate cortex by analyzing the resting-state functional connectivity (FC) of each subregion from a network perspective. In 20 healthy subjects we systematically investigated the FC patterns of the bilateral subgenual (sACC) and pregenual (pACC) anterior cingulate cortices, anterior (aMCC) and posterior (pMCC) midcingulate cortices, dorsal (dPCC) and ventral (vPCC) posterior cingulate cortices and retrosplenial cortices (RSC). We found that each cingulate subregion was specifically integrated in the predescribed functional networks and showed anti-correlated resting-state fluctuations. The sACC and pACC were involved in an affective network and anti-correlated with the sensorimotor and cognitive networks, while the pACC also correlated with the default-mode network and anti-correlated with the visual network. In the midcingulate cortex, however, the aMCC was correlated with the cognitive and sensorimotor networks and anti-correlated with the visual, affective and default-mode networks, whereas the pMCC only correlated with the sensorimotor network and anti-correlated with the cognitive and visual networks. The dPCC and vPCC involved in the default-mode network and anti-correlated with the sensorimotor, cognitive and visual networks, in contrast, the RSC was mainly correlated with the PCC and thalamus. Based on a strong hypothesis driven approach of anatomical partitions of the cingulate cortex, we could confirm their segregation in terms of functional neuroanatomy, as suggested earlier by task studies or exploratory multi-seed investigations.

A longitudinal diffusion tensor imaging study on Wallerian degeneration of corticospinal tract after motor pathway stroke

Wallerian degeneration of the corticospinal tract (CST) after motor pathway ischemic stroke can be characterized by diffusion tensor imaging (DTI). However, the dynamic evolution of the diffusion indices in the degenerated CST has not previously been completely identified. We investigated this dynamic evolution and the relationship between early changes of the diffusion indices in the degenerated CST and long-term clinical outcomes. DTI and neurological examinations were performed repeatedly in 9 patients with first-onset motor pathway subcortical infarction at 5 consecutive time points, i.e. within 1 week, at 2 weeks, 1 month, 3 months and 1 year. Using a region of interest method, we analyzed the ratios of the fractional anisotropy (rFA), mean diffusivity (rMD), primary eigenvalue (rlambda(1)) and transverse eigenvalue (rlambda(23)) between the affected and unaffected sides of the CSTs. We did not find any significant changes in the diffusion indices of the contralesional CSTs across time points. The rFA decreased monotonously during the first 3 months and then stabilized. The rMD increased after 2 weeks and stabilized after the third month. The rlambda(1) decreased during the first 2 weeks and then remained unchanged. The rlambda(23) increased during the first 3 months and then stabilized. We also found that the changes in the rFA between the first 2 time points were correlated with the NIHSS (P=0.00003) and the Motricity Indices (P=0.0004) after 1 year. Our results suggest that for patients with motor pathway stroke the diffusion indices in the degenerated CST stabilize within 3 months and that early changes in the rFA of the CST may predict long-term clinical outcomes.