I am a postdoc scholar at Department of Psychiatry and Behavioral Sciences. I am interested in studying brain dynamics, measured using fMRI, EEG and fNIRs using manifold learning, machine learning and pattern recognition, and discovering link between brain dynamics to psychiatric diseases.
Member, Maternal & Child Health Research Institute (MCHRI)
Doctor of Philosophy, Texas Tech University (2018)
Master of Science, Texas Tech University (2015)
Bachelor of Engineering, Jinan University (2013)
Manish Saggar, Postdoctoral Faculty Sponsor
Questions and controversies in the study of time-varying functional connectivity in resting fMRI.
Network neuroscience (Cambridge, Mass.)
2020; 4 (1): 30–69
The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
View details for DOI 10.1162/netn_a_00116
View details for PubMedID 32043043
- Creativity slumps and bumps: Examining the neurobehavioral basis of creativity development during middle childhood NEUROIMAGE 2019; 196: 94–101
Creativity slumps and bumps: Examining neurobehavioral basis of creativity development during middle childhood.
Developmental research has found that children's creative thinking ability tends to decline during middle childhood. However, this decline has not been consistently demonstrated, and the underlying neural and behavioral factors that affect fluctuations in children's creative thinking ability remain uncharacterized. Using a longitudinal cohort-sequential experimental design, we investigated the neurobehavioral basis of creative thinking ability during middle childhood in a sample of 48 children (n = 21 starting 3rd grade, n = 27 starting 4th grade) assessed longitudinally at three time-points across one year. For the first time, we used data-driven methods to reveal distinct trajectories in creative thinking ability during middle childhood. We found that although some children show a classic decline in creative ability, others exhibit a significant increase in creativity over time. These trajectories were not associated with differences in intelligence, age, or sex, but rather other developmentally-relevant constructs, including heightened externalizing behavior (i.e., rule-breaking and aggression). Using functional near-infrared spectroscopy (fNIRS) in a smaller cohort (n = 26), we examined longitudinal changes in bilateral frontal neural connectivity and found that increased right lateral frontal segregation or functional specialization tracked developmental improvements in creative thinking ability. Taken together, the findings reveal distinct profiles of change in creative thinking ability during middle childhood and identify behavioral and neural mechanisms potentially underlying changes in children's ability to think creatively.
View details for PubMedID 30959195
- Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information NEUROIMAGE 2019; 188: 502–14
Time-varying whole-brain functional network connectivity coupled to task engagement.
Network neuroscience (Cambridge, Mass.)
2019; 3 (1): 49–66
Brain functional connectivity (FC), as measured by blood oxygenation level-dependent (BOLD) signal, fluctuates at the scale of 10s of seconds. It has recently been found that whole-brain dynamic FC (dFC) patterns contain sufficient information to permit identification of ongoing tasks. Here, we hypothesize that dFC patterns carry fine-grained information that allows for tracking short-term task engagement levels (i.e., 10s of seconds long). To test this hypothesis, 25 subjects were scanned continuously for 25 min while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns by using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement by using k-means clustering. Then, we derived three metrics from whole-brain dFC patterns to track engagement level, that is, dissimilarity between dFC patterns and engagement-specific FC patterns, and the level of brainwide integration level. Finally, those engagement markers were evaluated against windowed task performance by using a linear mixed effects model. Significant relationships were observed between abovementioned metrics and windowed task performance for the working memory task only. These findings partially confirm our hypothesis and underscore the potential of whole-brain dFC to track short-term task engagement levels.
View details for PubMedID 30793073
View details for PubMedCentralID PMC6326730
Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information.
Given the dynamic nature of the human brain, there has been an increasing interest in investigating short-term temporal changes in functional connectivity, also known as dynamic functional connectivity (dFC), i.e., the time-varying inter-regional statistical dependence of blood oxygenation level-dependent (BOLD) signal within the constraints of a single scan. Numerous methodologies have been proposed to characterize dFC during rest and task, but few studies have compared them in terms of their efficacy to capture behavioral and clinically relevant dynamics. This is mostly due to lack of a well-defined ground truth, especially for rest scans. In this study, with a multitask dataset (rest, memory, video, and math) serving as ground truth, we investigated the efficacy of several dFC estimation techniques at capturing cognitively relevant dFC modulation induced by external tasks. We evaluated two framewise methods (dFC estimates for a single time point): dynamic conditional correlation (DCC) and jackknife correlation (JC); and five window-based methods: sliding window correlation (SWC), sliding window correlation with L1-regularization (SWC_L1), a combination of DCC and SWC called moving average DCC (DCC_MA), multiplication of temporal derivatives (MTD), and a variant of jackknife correlation called delete-d jackknife correlation (dJC). The efficacy is defined as each dFC metric's ability to successfully subdivide multitask scans into cognitively homogenous segments (even if those segments are not temporally continuous). We found that all window-based dFC methods performed well for commonly used window lengths (WL ≥ 30sec), with sliding window methods (SWC, SWC_L1) as well as the hybrid DCC_MA approach performing slightly better. For shorter window lengths (WL ≤ 15sec), DCC_MA and dJC produced the best results. Neither framewise method (i.e., DCC and JC) led to dFC estimates with high accuracy.
View details for PubMedID 30576850
- Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study NEUROIMAGE 2018; 180: 495–504
Altered Functional Brain Connectomes between Sporadic and Familial Parkinson's Patients
FRONTIERS IN NEUROANATOMY
2017; 11: 99
Familial Parkinson's disease (PD) is often caused by mutation of a certain gene, while sporadic PD is associated with variants of genes which can influence the susceptibility to PD. The goal of this study was to investigate the difference between the two forms of PD in terms of brain abnormalities using resting-state functional MRI and graph theory. Thirty-one familial PD patients and 36 sporadic PD patients underwent resting-state functional MRI scanning. Frequency-dependent functional connectivity was calculated for each subject using wavelet-based correlations of BOLD signal over 246 brain regions from Brainnetome Atlas. Graph theoretical analysis was then performed to analyze the topology of the functional network, and functional connectome differences were identified with a network-based statistical approach. Our results revealed a frequency-specific (0.016 and 0.031 Hz) connectome difference between familial and sporadic forms of PD, as indicated by an increase in assortativity and decrease in the nodal strength in the left medial amygdala of the familial PD group. In addition, the familial PD patients also showed a distinctive functional network between the left medial amygdala and regions related to retrieval of motion information. The present study indicates that the medial amygdala might be most vulnerable to both sporadic and familial PD. Our findings provide some new insights into disrupted resting-state functional connectomes between sporadic PD and familial PD.
View details for PubMedID 29163072
Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study.
Functional connectivity (FC) has been widely used to study the functional organization of temporally correlated and spatially distributed brain regions. Recent studies of FC dynamics, quantified by windowed correlations, provide new insights to analyze dynamic, context-dependent reconfiguration of brain networks. A set of reoccurring whole-brain connectivity patterns at rest, referred to as FC states, have been identified, hypothetically reflecting underlying cognitive processes or mental states. We posit that the mean FC information for a given subject represents a significant contribution to the group-level FC dynamics. We show that the subject-specific FC profile, termed as FC individuality, can be removed to increase sensitivity to cognitively relevant FC states. To assess the impact of the FC individuality and task-specific FC modulation on the group-level FC dynamics analysis, we generate and analyze group studies of four subjects engaging in four cognitive conditions (rest, simple math, two-back memory, and visual attention task). We also propose a model to quantitatively evaluate the effect of two factors, namely, subject-specific and task-specific modulation on FC dynamics. We show that FC individuality is a predominant factor in group-level FC variability, and the embedded cognitively relevant FC states are clearly visible after removing the individual's connectivity profile. Our results challenge the current understanding of FC states and emphasize the importance of individual heterogeneity in connectivity dynamics analysis.
View details for PubMedID 28549798
Aberrant functional brain connectome in people with antisocial personality disorder
2016; 6: 26209
Antisocial personality disorder (ASPD) is characterised by a disregard for social obligations and callous unconcern for the feelings of others. Studies have demonstrated that ASPD is associated with abnormalities in brain regions and aberrant functional connectivity. In this paper, topological organisation was examined in resting-state fMRI data obtained from 32 ASPD patients and 32 non-ASPD controls. The frequency-dependent functional networks were constructed using wavelet-based correlations over 90 brain regions. The topology of the functional networks of ASPD subjects was analysed via graph theoretical analysis. Furthermore, the abnormal functional connectivity was determined with a network-based statistic (NBS) approach. Our results revealed that, compared with the controls, the ASPD patients exhibited altered topological configuration of the functional connectome in the frequency interval of 0.016-0.031 Hz, as indicated by the increased clustering coefficient and decreased betweenness centrality in the medial superior frontal gyrus, precentral gyrus, Rolandic operculum, superior parietal gyrus, angular gyrus, and middle temporal pole. In addition, the ASPD patients showed increased functional connectivity mainly located in the default-mode network. The present study reveals an aberrant topological organisation of the functional brain network in individuals with ASPD. Our findings provide novel insight into the neuropathological mechanisms of ASPD.
View details for PubMedID 27257047