Xiaoyun Liang

Neural activation in the “reward circuit” shows a nonlinear response to facial attractiveness

Positive behavioral responses to attractive faces have led neuroscientists to investigate underlying neural mechanisms in a “reward circuit” that includes brain regions innervated by dopamine pathways. Using male faces ranging from attractive to extremely unattractive, disfigured ones, this study is the first to demonstrate heightened responses to both rewarding and aversive faces in numerous areas of this putative reward circuit. Parametric analyses employing orthogonal linear and nonlinear regressors revealed positive nonlinear effects in anterior cingulate cortex, lateral orbital frontal cortex (LOFC), striatum (nucleus accumbens, caudate, putamen), and ventral tegmental area, in addition to replicating previously documented linear effects in medial orbital frontal cortex (MOFC) and LOFC and nonlinear effects in amygdala and MOFC. The widespread nonlinear responses are consistent with single cell recordings in animals showing responses to both rewarding and aversive stimuli, and with some human fMRI investigations of non-face stimuli. They indicate that the reward circuit does not process face valence with any simple dissociation of function across structures. Perceiver gender modulated some responses to our male faces: Women showed stronger linear effects, and men showed stronger nonlinear effects, which may have functional implications. Our discovery of nonlinear responses to attractiveness throughout the reward circuit echoes the history of amygdala research: Early work indicated a linear response to threatening stimuli, including faces; later work also revealed a nonlinear response with heightened activation to affectively salient stimuli regardless of valence. The challenge remains to determine how such dual coding influences feelings, such as pleasure and pain, and guides goal-related behavioral responses, such as approach and avoidance.

Graph analysis of resting-state ASL perfusion MRI data: Nonlinear correlations among CBF and network metrics

Human connectome mapping is important to understand both normal brain function and disease-related dysfunction. Although blood-oxygen-level-dependent (BOLD) fMRI has been the most commonly used method for human connectome mapping, arterial spin labeling (ASL) is an fMRI technique to measure cerebral blood flow (CBF) directly and noninvasively, and thus provides a more direct quantitative correlate of neural activity. In this study, investigations on properties of CBF networks using ASL perfusion data have been conducted on 10 healthy subjects. As with BOLD fMRI studies, the extracted networks exhibited small-world network properties. In addition, highly connected brain regions are shown to overlap mostly with hub regions detected from BOLD fMRI studies. Taken together, this demonstrates the capability of ASL fMRI for mapping the brain connectome. Furthermore, a sigmoid model was then employed to fit the extracted network metrics vs. CBF measurements. Interestingly, the relationships between 4 specific network metrics and region-wise CBF demonstrate that consistently nonlinear patterns exist across all subjects. In contrast to the positive nonlinear pattern of other network metrics (degree, vulnerability, and eigenvector centrality), the characteristic path length shows a negative nonlinear pattern, reflecting the mechanism underlying the small-world properties. To our knowledge, this is the first study to unravel the intrinsic relationships between specific network metrics and CBF estimates. This should have diagnostic and therapeutic implications for those studies focusing on patients who suffer from abnormal functional connectivity.

A k -space sharing 3D GRASE pseudocontinuous ASL method for whole-brain resting-state functional connectivity

Magnetic resonance imaging (MRI) investigations of resting‐state functional connectivity (RSFC) typically use blood oxygen level‐dependent (BOLD)‐weighted imaging because of its ability to provide whole‐brain coverage and high temporal resolution. Single‐shot 3D gradient‐ and spin‐echo (GRASE) arterial spin labeling (ASL) offers a number of potential advantages for RSFC measurements, such as a more direct quantitative correlate of neural activity and lower variability across subjects; however, current sequences are usually not suitable for whole‐brain acquisitions because of T2 decay during the long echo train. In this study, we proposed a k‐space sharing 3D GRASE ASL sequence to achieve whole‐brain coverage, applied it to measure RSFC on a group of healthy subjects, and compared it with BOLD data. Similar RSFC networks were estimated using both techniques, providing corroboration of the capability of our method for RSFC analysis. Furthermore, ASL data enable calculation of mean cerebral blood flow (CBF) values within the RSFC networks, thus assigning them biologically meaningful values. The inherently quantitative nature of CBF measurements should provide a more stable and interpretable biomarker in comparison to BOLD and may, therefore, be particularly useful for applications such as longitudinal studies of RSFC.

Improved partial volume correction for single inversion time arterial spin labeling data.

Arterial spin labeling has relatively low spatial resolution, which affects cerebral blood flow measurements by partial volume effect occurring at tissue interfaces, e.g., between gray matter, white matter, and cerebrospinal fluid. This can be an important source of cerebral blood flow quantification error. To correct for partial volume effect in arterial spin labeling, a linear regression method was recently proposed. Because this method assumes that tissue magnetization and cerebral blood flow are constant over an n2 × 1 regression kernel, an inherent spatial blurring is introduced. In this study, a modified least trimmed squares algorithm is proposed for partial volume effect correction. It is demonstrated using simulations that the modified least trimmed square method can correct for partial volume effect and produce less blurring than the linear regression method. This is achieved without either acquiring additional datasets or increasing the computation burden. These capabilities were further demonstrated in vivo. The modified least trimmed square method should, therefore, play an important role in arterial spin labeling studies. Magn Reson Med, 2013.

Effective connectivity between amygdala and orbitofrontal cortex differentiates the perception of facial expressions

Abstract Emotion research is guided both by the view that emotions are points in a dimensional space, such as valence or approach–withdrawal, and by the view that emotions are discrete categories. We determined whether effective connectivity of amygdala with medial orbitofrontal cortex (MOFC) and lateral orbitofrontal cortex (LOFC) differentiates the perception of emotion faces in a manner consistent with the dimensional and/or categorical view. Greater effective connectivity from left MOFC to amygdala differentiated positive and neutral expressions from negatively valenced angry, disgust, and fear expressions. Greater effective connectivity from right LOFC to amygdala differentiated emotion expressions conducive to perceiver approach (happy, neutral, and fear) from angry expressions that elicit perceiver withdrawal. Finally, consistent with the categorical view, there were unique patterns of connectivity in response to fear, anger, and disgust, although not in response to happy expressions, which did not differ from neutral ones.

Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics.

Diffusion MRI streamlines tractography has become a major technique for inferring structural networks through reconstruction of brain connectome. However, quantification of structural connectivity based on the number of streamlines interconnecting brain grey matter regions is known to be problematic in a number of aspects, such as the ill-posed nature of streamlines terminations and the non-quantitative nature of streamline counts. This study investigates the effects of state-of-the-art connectome construction methods on the subsequent analyses of structural brain networks using graph theoretical approaches. Our results demonstrate that the characteristics of structural connectivity, including connectome variability, global network metrics, small-world attributes and network hubs, alter significantly following the improvement in biological accuracy of streamlines tractograms provided by anatomically-constrained tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT). Importantly, the commonly-used correction for connection density based on scaling the contribution of each streamline to the connectome by its inverse length is shown to provide incomplete correction, highlighting the necessity for the use of advanced tractogram reconstruction techniques in structural connectomics research.

Voxel-Wise Functional Connectomics Using Arterial Spin Labeling Functional Magnetic Resonance Imaging: The Role of Denoising

The objective of this study was to investigate voxel-wise functional connectomics using arterial spin labeling (ASL) functional magnetic resonance imaging (fMRI). Since ASL signal has an intrinsically low signal-to-noise ratio (SNR), the role of denoising is evaluated; in particular, a novel denoising method, dual-tree complex wavelet transform (DT-CWT) combined with the nonlocal means (NLM) algorithm is implemented and evaluated. Simulations were conducted to evaluate the performance of the proposed method in denoising images and in detecting functional networks from noisy data (including the accuracy and sensitivity of detection). In addition, denoising was applied to in vivo ASL datasets, followed by network analysis using graph theoretical approaches. Efficiencies cost was used to evaluate the performance of denoising in detecting functional networks from in vivo ASL fMRI data. Simulations showed that denoising is effective in detecting voxel-wise functional networks from low SNR data and/or from data with small total number of time points. The capability of denoised voxel-wise functional connectivity analysis was also demonstrated with in vivo data. We concluded that denoising is important for voxel-wise functional connectivity using ASL fMRI and that the proposed DT-CWT-NLM method should be a useful ASL preprocessing step.

A novel joint sparse partial correlation method for estimating group functional networks

Advances in graph theory have provided a powerful tool to characterize brain networks. In particular, functional networks at group‐level have great appeal to gain further insight into complex brain function, and to assess changes across disease conditions. These group networks, however, often have two main limitations. First, they are popularly estimated by directly averaging individual networks that are compromised by confounding variations. Secondly, functional networks have been estimated mainly through Pearson cross‐correlation, without taking into account the influence of other regions. In this study, we propose a sparse group partial correlation method for robust estimation of functional networks based on a joint graphical models approach. To circumvent the issue of choosing the optimal regularization parameters, a stability selection method is employed to extract networks. The proposed method is, therefore, denoted as JGMSS. By applying JGMSS across simulated datasets, the resulting networks show consistently higher accuracy and sensitivity than those estimated using an alternative approach (the elastic‐net regularization with stability selection, ENSS). The robustness of the JGMSS is evidenced by the independence of the estimated networks to choices of the initial set of regularization parameters. The performance of JGMSS in estimating group networks is further demonstrated with in vivo fMRI data (ASL and BOLD), which show that JGMSS can more robustly estimate brain hub regions at group‐level and can better control intersubject variability than it is achieved using ENSS. Hum Brain Mapp 37:1162–1177, 2016.

A variable flip angle-based method for reducing blurring in 3D GRASE ASL

Arterial Spin Labeling (ASL) is an MRI technique to measure cerebral blood flow directly and noninvasively, and thus provides a more direct quantitative correlate of neural activity than blood-oxygen-level-dependent fMRI. A 3D gradient and spin-echo (GRASE) sequence is capable of enhancing signal-to-noise ratio, and has been shown to be a very useful readout module for ASL sequences. Nonetheless, the introduction of significant blurring in its single-shot version, due to T2 decay along the partition dimension, compromises the achievable spatial resolution, limiting the potential of this technique for whole-brain coverage. To address this issue, a method for reducing blurring based on a variable flip angle (VFA) scheme is proposed in this study for 3D GRASE ASL perfusion. Numerical simulations show that the proposed method is capable of reducing the blurring significantly compared to the standard constant flip angle approach; this result was further confirmed using in vivo data. The proposed VFA method should therefore be of significance to 3D GRASE ASL fMRI studies, since it is able to reduce blurring without sacrificing temporal resolution.

Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods

Arterial spin labeling (ASL) is an emerging MRI technique for non-invasive measurement of cerebral blood flow (CBF). Compared to invasive perfusion imaging modalities, ASL suffers from low sensitivity due to poor signal-to-noise ratio (SNR), susceptibility to motion artifacts and low spatial resolution, all of which limit its reliability. In this work, the effects of various state of the art image processing techniques for addressing these ASL limitations are investigated. A processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, partial volume effect correction, and CBF quantification was developed and assessed. To further improve the SNR for pseudo-continuous ASL (PCASL) by accounting for errors in tagging efficiency, the data from multiphase (MP) acquisitions were analyzed using a novel weighted-averaging scheme. The performances of each step in terms of SNR and reproducibility were evaluated using test-retest ASL data acquired from 12 young healthy subjects. The proposed processing pipeline was shown to improve the within-subject coefficient of variation and regional reproducibility by 17% and 16%, respectively, compared to CBF maps computed following motion correction but without the other processing steps. The CBF measurements of MP-PCASL compared to PCASL had on average 23% and 10% higher SNR and reproducibility, respectively.