The hopelessness theory of depression posits that individuals with negative cognitive styles are ... more The hopelessness theory of depression posits that individuals with negative cognitive styles are at risk of developing depression following negative life events. The purpose of this work was to examine whether individuals with cognitive vulnerability to depression (CVD) exhibit similar spontaneous brain activity patterns as compared to patients with major depressive disorder (MDD). Subjects with CVD (N=32), drug-naïve first-episode patients with major depressive disorder (N=32), and sex-, age- and education-matched healthy controls (HCs; N=35) were subjected to resting state functional magnetic resonance imaging (RS-fMRI) and amplitudes of low-frequency fluctuation (ALFF) was compared between the groups. Pearson correlation analysis was performed between regional ALFFs and psychometric scores, namely the Cognitive Style Questionnaire (CSQ) and the Center for Epidemiologic Studies Depression (CES-D) scale scores. Significant group differences in ALFF values were observed bilaterally ...
Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI... more Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI) is critical to understand brain functions that support cognitive and affective processes. Existing methods such as psychophysiological interaction (PPI) and dynamic causal modeling (DCM) usually implicitly assume that the connectivity patterns are stable over a block-designed task with identical stimuli. However, this assumption lacks empirical verification on high-temporal resolution fMRI data with reliable data-driven analysis methods. The present study performed a detailed examination of dynamic changes of functional connectivity (FC) in a simple block-designed visual checkerboard experiment with a sub-second sampling rate (TR = 0.645 s) by estimating time-varying correlation coefficient (TVCC) between BOLD responses of different brain regions. We observed reliable task-related FC changes (i.e., FCs were transiently decreased after task onset and went back to the baseline afterward)...
Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI... more Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI) is critical to understand brain functions that support cognitive and affective processes. Existing methods such as psychophysiological interaction (PPI) and dynamic causal modeling (DCM) usually implicitly assume that the connectivity patterns are stable over a block-designed task with identical stimuli. However, this assumption lacks empirical verification on high-temporal resolution fMRI data with reliable data-driven analysis methods. The present study performed a detailed examination of dynamic changes of functional connectivity (FC) in a simple block-designed visual checkerboard experiment with a sub-second sampling rate (TR = 0.645 s) by estimating time-varying correlation coefficient (TVCC) between BOLD responses of different brain regions. We observed reliable task-related FC changes (i.e., FCs were transiently decreased after task onset and went back to the baseline afterward) among several visual regions of the bilateral middle occipital gyrus (MOG) and the bilateral fusiform gyrus (FuG). Importantly, only the FCs between higher visual regions (MOG) and lower visual regions (FuG) exhibited such dynamic patterns. The results suggested that simply assuming a sustained FC during a task block may be insufficient to capture distinct task-related FC changes. The investigation of FC dynamics in tasks could improve our understanding of condition shifts and the coordination between different activated brain regions.
Functional connectivity between two brain regions measured using functional MRI (fMRI) have been ... more Functional connectivity between two brain regions measured using functional MRI (fMRI) have been shown to be modulated by other regions even in resting-state, i.e. without performing specific tasks. We aimed to characterize large scale modulatory interactions by performing ROI-based (region of interest) physiophysiological interaction (PPI) analysis on resting-state fMRI data. Modulatory interactions were calculated for every possible combination of three ROIs among 160 ROIs sampling the whole brain. Firstly, among all the significant modulatory interactions, there were considerably more negative than positive effects, i.e. in more cases an increase of activity in one region was associated with decreased functional connectivity between two other regions. Next, modulatory interactions were categorized as whether the three ROIs were from one single network module, two modules, or three different modules (defined by a modularity analysis on their functional connectivity). Positive modulatory interactions were more represented than expected in cases that the three ROIs were from a single module, suggesting increased within module processing efficiency through positive modulatory interactions. In contrast, negative modulatory interactions were more represented than expected in cases that the three ROIs were from two modules, suggesting a tendency of between modules segregation through negative modulatory interactions. Regions that were more likely to have modulatory interactions were then identified. The numbers of significant modulatory interactions for different regions were correlated with the regions' connectivity strengths and connection degrees. These results demonstrate whole brain characteristics of modulatory interactions, and may provide guidance for future studies of connectivity dynamics in both resting-state and task-state.
With increasing popularity of high resolution neuroimaging techniques, such as functional magneti... more With increasing popularity of high resolution neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and position emission computerized tomography (PET), a large number of neuroimaging studies have been accumulated in the last two decades.These new data brought both opportunities and challenges for cognitive neuroscientists,enabling them to generate and examine new hypotheses. Given the main goal of neuroimaging is to explore the relationship between cognitive processes and corresponding locations in brain, coordinate-based meta-analysis become the dominant method for neuroimaging data. One such method, activation likelihood estimation (ALE), is the most widely used, because of its methodological superiority and usability. The current review first introduced basic principles of ALE method. Next, the two most common approaches of conducting meta-analysis of neuroimaging data were discussed: finding consistency across studies and finding modulators of brain acti...
The two major brain networks, i.e., the default mode network (DMN) and the task positive network,... more The two major brain networks, i.e., the default mode network (DMN) and the task positive network, typically reveal negative and variable connectivity in resting-state. In the present study, we examined whether the connectivity between the DMN and different components of the task positive network were modulated by other brain regions by using physiophysiological interaction (PPI) on resting-state functional magnetic resonance imaging data. Spatial independent component analysis was first conducted to identify components that represented networks of interest, including the anterior and posterior DMNs, salience, dorsal attention, left and right executive networks. PPI analysis was conducted between pairs of these networks to identify networks or regions that showed modulatory interactions with the two networks. Both network-wise and voxel-wise analyses revealed reciprocal positive modulatory interactions between the DMN, salience, and executive networks. Together with the anatomical pr...
Studies on functional brain lateralization using functional magnetic resonance imaging (fMRI) hav... more Studies on functional brain lateralization using functional magnetic resonance imaging (fMRI) have generally focused on lateralization of local brain regions. To explore the lateralization on the whole-brain level, lateralization of functional connectivity using resting-state fMRI (N=87, right handed) was analyzed and left- and right-lateralized networks were mapped. Four hundred two equally spaced regions of interest (ROI) covering the entire gray matter were divided into 358 task-positive and 44 task-negative ROIs. Lateralization of functional connectivity was analyzed separately for the task-positive and task-negative regions to prevent spuriously high lateralization indices caused by negative correlations between task-positive and task-negative regions. Lateralized functional connections were obtained using k-means clustering analysis. Within the task-positive network, the right-lateralized functional connections were between the occipital and inferior/middle frontal regions amo...
To investigate the effects of a functional polymorphism of the monoamine oxidase A (MAOA) gene on... more To investigate the effects of a functional polymorphism of the monoamine oxidase A (MAOA) gene on spontaneous brain activity in healthy male adolescents. Thirty-one healthy male adolescents with the low-activity MAOA genotype (MAOA-L) and 25 healthy male adolescents with the high-activity MAOA genotype (MAOA-H) completed the 11-item Barratt Impulsiveness Scale (BIS-11) questionnaire and were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) scans. The amplitude of low-frequency fluctuation (ALFF) of the blood oxygen level-dependent (BOLD) signal was calculated using REST software. ALFF data were related to BIS scores and compared between genotype groups. Compared with the MAOA-H group, the MAOA-L group showed significantly lower ALFFs in the pons. There was a significant correlation between the BIS scores and the ALFF values in the pons for MAOA-L group, but not for the MAOA-H group. Further regression analysis showed a significant genotype by ALFF values in...
The neural representation of self is a fundamental question for brain research. Employing activat... more The neural representation of self is a fundamental question for brain research. Employing activation likelihood estimation (ALE) meta-analyses, we assessed the commonalities and distinctions between different components of the self by focusing on the 'physical' self and the 'psychological' self – assessed respectively through face processing and self-referential tasks. We first conducted ALE meta-analyses by computing the convergence of findings on brain activation in self-face recognition and self-referential studies respectively. Contrast and conjunction analyses of these two meta-analytic results were then applied to extract the distinctions and commonalities in self-face and self-reference tasks. Facial self processing was particularly associated with lateral brain regions with a right hemispheric dominance, while processing psychological self predominantly activated cortical midline structures, more specifically the anterior cingulate cortex/superior frontal cortex. In contrast, the conjunction analyses showed that the two aspects of self-processing recruit the dorsal anterior cingulate cortex and the left inferior frontal gyrus extending to the insula. A framework including both distinct and common neural representation of selfs is discussed.
The hopelessness theory of depression posits that individuals with negative cognitive styles are ... more The hopelessness theory of depression posits that individuals with negative cognitive styles are at risk of developing depression following negative life events. The purpose of this work was to examine whether individuals with cognitive vulnerability to depression (CVD) exhibit similar spontaneous brain activity patterns as compared to patients with major depressive disorder (MDD). Subjects with CVD (N=32), drug-naïve first-episode patients with major depressive disorder (N=32), and sex-, age- and education-matched healthy controls (HCs; N=35) were subjected to resting state functional magnetic resonance imaging (RS-fMRI) and amplitudes of low-frequency fluctuation (ALFF) was compared between the groups. Pearson correlation analysis was performed between regional ALFFs and psychometric scores, namely the Cognitive Style Questionnaire (CSQ) and the Center for Epidemiologic Studies Depression (CES-D) scale scores. Significant group differences in ALFF values were observed bilaterally ...
Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI... more Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI) is critical to understand brain functions that support cognitive and affective processes. Existing methods such as psychophysiological interaction (PPI) and dynamic causal modeling (DCM) usually implicitly assume that the connectivity patterns are stable over a block-designed task with identical stimuli. However, this assumption lacks empirical verification on high-temporal resolution fMRI data with reliable data-driven analysis methods. The present study performed a detailed examination of dynamic changes of functional connectivity (FC) in a simple block-designed visual checkerboard experiment with a sub-second sampling rate (TR = 0.645 s) by estimating time-varying correlation coefficient (TVCC) between BOLD responses of different brain regions. We observed reliable task-related FC changes (i.e., FCs were transiently decreased after task onset and went back to the baseline afterward)...
Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI... more Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI) is critical to understand brain functions that support cognitive and affective processes. Existing methods such as psychophysiological interaction (PPI) and dynamic causal modeling (DCM) usually implicitly assume that the connectivity patterns are stable over a block-designed task with identical stimuli. However, this assumption lacks empirical verification on high-temporal resolution fMRI data with reliable data-driven analysis methods. The present study performed a detailed examination of dynamic changes of functional connectivity (FC) in a simple block-designed visual checkerboard experiment with a sub-second sampling rate (TR = 0.645 s) by estimating time-varying correlation coefficient (TVCC) between BOLD responses of different brain regions. We observed reliable task-related FC changes (i.e., FCs were transiently decreased after task onset and went back to the baseline afterward) among several visual regions of the bilateral middle occipital gyrus (MOG) and the bilateral fusiform gyrus (FuG). Importantly, only the FCs between higher visual regions (MOG) and lower visual regions (FuG) exhibited such dynamic patterns. The results suggested that simply assuming a sustained FC during a task block may be insufficient to capture distinct task-related FC changes. The investigation of FC dynamics in tasks could improve our understanding of condition shifts and the coordination between different activated brain regions.
Functional connectivity between two brain regions measured using functional MRI (fMRI) have been ... more Functional connectivity between two brain regions measured using functional MRI (fMRI) have been shown to be modulated by other regions even in resting-state, i.e. without performing specific tasks. We aimed to characterize large scale modulatory interactions by performing ROI-based (region of interest) physiophysiological interaction (PPI) analysis on resting-state fMRI data. Modulatory interactions were calculated for every possible combination of three ROIs among 160 ROIs sampling the whole brain. Firstly, among all the significant modulatory interactions, there were considerably more negative than positive effects, i.e. in more cases an increase of activity in one region was associated with decreased functional connectivity between two other regions. Next, modulatory interactions were categorized as whether the three ROIs were from one single network module, two modules, or three different modules (defined by a modularity analysis on their functional connectivity). Positive modulatory interactions were more represented than expected in cases that the three ROIs were from a single module, suggesting increased within module processing efficiency through positive modulatory interactions. In contrast, negative modulatory interactions were more represented than expected in cases that the three ROIs were from two modules, suggesting a tendency of between modules segregation through negative modulatory interactions. Regions that were more likely to have modulatory interactions were then identified. The numbers of significant modulatory interactions for different regions were correlated with the regions' connectivity strengths and connection degrees. These results demonstrate whole brain characteristics of modulatory interactions, and may provide guidance for future studies of connectivity dynamics in both resting-state and task-state.
With increasing popularity of high resolution neuroimaging techniques, such as functional magneti... more With increasing popularity of high resolution neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and position emission computerized tomography (PET), a large number of neuroimaging studies have been accumulated in the last two decades.These new data brought both opportunities and challenges for cognitive neuroscientists,enabling them to generate and examine new hypotheses. Given the main goal of neuroimaging is to explore the relationship between cognitive processes and corresponding locations in brain, coordinate-based meta-analysis become the dominant method for neuroimaging data. One such method, activation likelihood estimation (ALE), is the most widely used, because of its methodological superiority and usability. The current review first introduced basic principles of ALE method. Next, the two most common approaches of conducting meta-analysis of neuroimaging data were discussed: finding consistency across studies and finding modulators of brain acti...
The two major brain networks, i.e., the default mode network (DMN) and the task positive network,... more The two major brain networks, i.e., the default mode network (DMN) and the task positive network, typically reveal negative and variable connectivity in resting-state. In the present study, we examined whether the connectivity between the DMN and different components of the task positive network were modulated by other brain regions by using physiophysiological interaction (PPI) on resting-state functional magnetic resonance imaging data. Spatial independent component analysis was first conducted to identify components that represented networks of interest, including the anterior and posterior DMNs, salience, dorsal attention, left and right executive networks. PPI analysis was conducted between pairs of these networks to identify networks or regions that showed modulatory interactions with the two networks. Both network-wise and voxel-wise analyses revealed reciprocal positive modulatory interactions between the DMN, salience, and executive networks. Together with the anatomical pr...
Studies on functional brain lateralization using functional magnetic resonance imaging (fMRI) hav... more Studies on functional brain lateralization using functional magnetic resonance imaging (fMRI) have generally focused on lateralization of local brain regions. To explore the lateralization on the whole-brain level, lateralization of functional connectivity using resting-state fMRI (N=87, right handed) was analyzed and left- and right-lateralized networks were mapped. Four hundred two equally spaced regions of interest (ROI) covering the entire gray matter were divided into 358 task-positive and 44 task-negative ROIs. Lateralization of functional connectivity was analyzed separately for the task-positive and task-negative regions to prevent spuriously high lateralization indices caused by negative correlations between task-positive and task-negative regions. Lateralized functional connections were obtained using k-means clustering analysis. Within the task-positive network, the right-lateralized functional connections were between the occipital and inferior/middle frontal regions amo...
To investigate the effects of a functional polymorphism of the monoamine oxidase A (MAOA) gene on... more To investigate the effects of a functional polymorphism of the monoamine oxidase A (MAOA) gene on spontaneous brain activity in healthy male adolescents. Thirty-one healthy male adolescents with the low-activity MAOA genotype (MAOA-L) and 25 healthy male adolescents with the high-activity MAOA genotype (MAOA-H) completed the 11-item Barratt Impulsiveness Scale (BIS-11) questionnaire and were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) scans. The amplitude of low-frequency fluctuation (ALFF) of the blood oxygen level-dependent (BOLD) signal was calculated using REST software. ALFF data were related to BIS scores and compared between genotype groups. Compared with the MAOA-H group, the MAOA-L group showed significantly lower ALFFs in the pons. There was a significant correlation between the BIS scores and the ALFF values in the pons for MAOA-L group, but not for the MAOA-H group. Further regression analysis showed a significant genotype by ALFF values in...
The neural representation of self is a fundamental question for brain research. Employing activat... more The neural representation of self is a fundamental question for brain research. Employing activation likelihood estimation (ALE) meta-analyses, we assessed the commonalities and distinctions between different components of the self by focusing on the 'physical' self and the 'psychological' self – assessed respectively through face processing and self-referential tasks. We first conducted ALE meta-analyses by computing the convergence of findings on brain activation in self-face recognition and self-referential studies respectively. Contrast and conjunction analyses of these two meta-analytic results were then applied to extract the distinctions and commonalities in self-face and self-reference tasks. Facial self processing was particularly associated with lateral brain regions with a right hemispheric dominance, while processing psychological self predominantly activated cortical midline structures, more specifically the anterior cingulate cortex/superior frontal cortex. In contrast, the conjunction analyses showed that the two aspects of self-processing recruit the dorsal anterior cingulate cortex and the left inferior frontal gyrus extending to the insula. A framework including both distinct and common neural representation of selfs is discussed.
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