Doctor of Philosophy, Stanford University, STATS-PMN (2017)
Doctor of Philosophy, Stanford University, EE-PHD (2017)
Master of Science, Stanford University, EE-MS (2013)
Bachelor of Engineering, Tsinghua University, Biomedical Engineering (2011)
M.S., Stanford University, Electrical Engineering (2013)
B.S., Tsinghua University, Biomedical Engineering (2011)
Dissociated patterns of anti-correlations with dorsal and ventral default-mode networks at rest.
Human brain mapping
Previous studies of resting state functional connectivity have demonstrated that the default-mode network (DMN) is negatively correlated with a set of brain regions commonly activated during goal-directed tasks. However, the location and extent of anti-correlations are inconsistent across different studies, which has been posited to result largely from differences in whether or not global signal regression (GSR) was applied as a pre-processing step. Notably, coordinates of seed regions-of-interest defined within the posterior cingulate cortex (PCC)/precuneus, an area often employed to study functional connectivity of the DMN, have been inconsistent across studies. Taken together with recent observations that the DMN contains functionally heterogeneous subdivisions, it is presently unclear whether these seeds map to different DMN subnetworks, whose patterns of anti-correlation may differ. If so, then seed location may be a non-negligible factor that, in addition to differences in preprocessing steps, contributes to the inconsistencies reported among published studies regarding DMN correlations/anti-correlations. In this study, they examined anti-correlations of different subnetworks within the DMN during rest using both seed-based and point process analyses, and discovered that: (1) the ventral branch of the DMN (vDMN) yielded significantly weaker anti-correlations than that associated with the dorsal branch of the DMN (dDMN); (2) vDMN anti-correlations introduced by GSR were distinct from dDMN anti-correlations; (3) PCC/precuneus seeds employed by earlier studies mapped to different DMN subnetworks, which may explain some of the inconsistency (in addition to preprocessing steps) in the reported DMN anti-correlations. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
View details for DOI 10.1002/hbm.23532
View details for PubMedID 28150892
Influence of the cortical midline structures on moral emotion and motivation in moral decision-making.
Behavioural brain research
2016; 302: 237-251
The present study aims to examine the relationship between the cortical midline structures (CMS), which have been regarded to be associated with selfhood, and moral decision making processes at the neural level. Traditional moral psychological studies have suggested the role of moral self as the moderator of moral cognition, so activity of moral self would present at the neural level. The present study examined the interaction between the CMS and other moral-related regions by conducting psycho-physiological interaction analysis of functional images acquired while 16 subjects were solving moral dilemmas. Furthermore, we performed Granger causality analysis to demonstrate the direction of influences between activities in the regions in moral decision-making. We first demonstrate there are significant positive interactions between two central CMS seed regions-i.e., the medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC)-and brain regions associated with moral functioning including the cerebellum, brainstem, midbrain, dorsolateral prefrontal cortex, orbitofrontal cortex and anterior insula (AI); on the other hand, the posterior insula (PI) showed significant negative interaction with the seed regions. Second, several significant Granger causality was found from CMS to insula regions particularly under the moral-personal condition. Furthermore, significant dominant influence from the AI to PI was reported. Moral psychological implications of these findings are discussed. The present study demonstrated the significant interaction and influence between the CMS and morality-related regions while subject were solving moral dilemmas. Given that, activity in the CMS is significantly involved in human moral functioning.
View details for DOI 10.1016/j.bbr.2016.01.001
View details for PubMedID 26772629
- NIRS-Based Hyperscanning Reveals Inter-brain Neural Synchronization during Cooperative Jenga Game with Face-to-Face Communication FRONTIERS IN HUMAN NEUROSCIENCE 2016; 10
Nuisance Regression of High-frequency FMRI Data: De-noising Can Be Noisy.
Recently, emerging studies have demonstrated the existence of brain resting state (RS) spontaneous activity at frequencies higher than the conventional 0.1 Hz. A few groups utilizing accelerated acquisitions have reported persisting signals beyond 1 Hz, which seems too high to be accommodated by the sluggish hemodynamic process underpinning blood-oxygen-level dependent contrasts (the upper limit of the canonical model is ~ 0.3 Hz). It is thus questionable whether the observed high-frequency (HF) functional connectivity (FC) originates from alternative mechanisms (e.g., inflow effects, proton density changes in or near activated neural tissue), or rather is artificially introduced by improper preprocessing operations. Here, we examined the influence of a common preprocessing step - whole-band linear nuisance regression (WB-LNR) - on resting state functional connectivity (RSFC), and demonstrated via both simulation and analysis of real dataset that WB-LNR can introduce spurious network structures into the HF bands of fMRI signals. Findings of present study call into question whether published observations on HF-RSFC are partly attributable to improper data preprocessing instead of actual neural activities.
View details for DOI 10.1089/brain.2016.0441
View details for PubMedID 27875902
Functional Magnetic Resonance Imaging Methods
2015; 25 (3): 289-313
Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.
View details for DOI 10.1007/s11065-015-9294-9
View details for Web of Science ID 000360912800006
View details for PubMedID 26248581
- Erratum to: Functional Magnetic Resonance Imaging Methods. Neuropsychology review 2015; 25 (3): 314-?
Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics
2015; 111: 476-488
Recently, fMRI researchers have begun to realize that the brain's intrinsic network patterns may undergo substantial changes during a single resting state (RS) scan. However, despite the growing interest in brain dynamics, metrics that can quantify the variability of network patterns are still quite limited. Here, we first introduce various quantification metrics based on the extension of co-activation pattern (CAP) analysis, a recently proposed point-process analysis that tracks state alternations at each individual time frame and relies on very few assumptions; then apply these proposed metrics to quantify changes of brain dynamics during a sustained 2-back working memory (WM) task compared to rest. We focus on the functional connectivity of two prominent RS networks, the default-mode network (DMN) and executive control network (ECN). We first demonstrate less variability of global Pearson correlations with respect to the two chosen networks using a sliding-window approach during WM task compared to rest; then we show that the macroscopic decrease in variations in correlations during a WM task is also well characterized by the combined effect of a reduced number of dominant CAPs, increased spatial consistency across CAPs, and increased fractional contributions of a few dominant CAPs. These CAP metrics may provide alternative and more straightforward quantitative means of characterizing brain network dynamics than time-windowed correlation analyses.
View details for DOI 10.1016/j.neuroimage.2015.01.057
View details for Web of Science ID 000352224100042
View details for PubMedID 25662866
BOLD fractional contribution to resting-state functional connectivity above 0.1 Hz
2015; 107: 207-218
Blood oxygen level dependent (BOLD) spontaneous signals from resting-state (RS) brains have typically been characterized by low-pass filtered timeseries at frequencies ≤0.1Hz, and studies of these low-frequency fluctuations have contributed exceptional understanding of the baseline functions of our brain. Very recently, emerging evidence has demonstrated that spontaneous activities may persist in higher frequency bands (even up to 0.8Hz), while presenting less variable network patterns across the scan duration. However, as an indirect measure of neuronal activity, BOLD signal results from an inherently slow hemodynamic process, which in fact might be too slow to accommodate the observed high-frequency functional connectivity (FC). To examine whether the observed high-frequency spontaneous FC originates from BOLD contrast, we collected RS data as a function of echo time (TE). Here we focus on two specific resting state networks - the default-mode network (DMN) and executive control network (ECN), and the major findings are fourfold: (1) we observed BOLD-like linear TE-dependence in the spontaneous activity at frequency bands up to 0.5Hz (the maximum frequency that can be resolved with TR=1s), supporting neural relevance of the RSFC at a higher frequency range; (2) conventional models of hemodynamic response functions must be modified to support resting state BOLD contrast, especially at higher frequencies; (3) there are increased fractions of non-BOLD-like contributions to the RSFC above the conventional 0.1Hz (non-BOLD/BOLD contrast at 0.4-0.5Hz is ~4 times that at <0.1Hz); and (4) the spatial patterns of RSFC are frequency-dependent. Possible mechanisms underlying the present findings and technical concerns regarding RSFC above 0.1Hz are discussed.
View details for DOI 10.1016/j.neuroimage.2014.12.012
View details for Web of Science ID 000348043100023
View details for PubMedID 25497686
Differences in functional activity between boys with pure oppositional defiant disorder and controls during a response inhibition task: a preliminary study
BRAIN IMAGING AND BEHAVIOR
2014; 8 (4): 588-597
Functional Magnetic Resonance Imaging (fMRI) of inhibitory control has only been investigated in patients with attention deficit hyperactivity disorder (ADHD) and conduct disorder (CD). The objective of this study was to investigate the differences of functional areas associated with inhibitory control between boys with pure oppositional defiant disorder (ODD) and controls during a response inhibition task using functional magnetic resonance imaging (fMRI). Eleven boys with pure ODD and ten control boys, aged 10 to 12, performed a GoStop response inhibition task in this study. The task has a series of "go" trials to establish a pre-potent response tendency and a number of "stop" trials to test subjects' ability to withhold their responses. During the GoStop task, greater activation in the dorsolateral parts of the bilateral inferior frontal gyrus, left middle frontal gyrus (lMFG) and right superior frontal gyrus (rSFG) activation was seen in the ODD boys. Additionally, reduced activation in regions of the right inferior frontal gyrus (rIFG) was seen in the ODD boys in comparison with the control group. The results may suggest that the higher activation in areas adjacent to the rIFG could be the cause of reduced activation in the rIFG; although this is speculative and requires additional supporting evidence. The findings further suggest that ODD is a less pronounced functional disorder compared to ADHD and CD.
View details for DOI 10.1007/s11682-013-9275-7
View details for Web of Science ID 000346920400012
View details for PubMedID 24390655