scholarly journals Understanding psychophysiological interaction and its relations to beta series correlation

2018 ◽  
Author(s):  
Xin Di ◽  
Zhiguo Zhang ◽  
Bharat B Biswal
Keyword(s):  
Functional Connectivity ◽  
Effective Connectivity ◽  
General Linear Model ◽  
Fmri Data ◽  
Stop Signal Task ◽  
Physiological Variable ◽  
Context Sensitive ◽  
Psychophysiological Interaction ◽  
Direct Contrast ◽  
Multiple Conditions

AbstractPsychophysiological interaction (PPI) was proposed 20 years ago for study of task modulated connectivity on functional MRI (fMRI) data. A few modifications have since been made, but there remain misunderstandings on the method, as well as on its relations to a similar method named beta series correlation (BSC). Here, we explain what PPI measures and its relations to BSC. We first clarify that the interpretation of a regressor in a general linear model depends on not only itself but also on how other effects are modeled. In terms of PPI, it always reflects differences in connectivity between conditions, when the physiological variable is included as a covariate. Secondly, when there are multiple conditions, we explain how PPI models calculated from direct contrast between conditions could generate identical results as contrasting separate PPIs of each condition (a.k.a. “generalized” PPI). Thirdly, we explicit the deconvolution process that is used for PPI calculation, and how is it related to the trial-by-trial modeling for BSC, and illustrate the relations between PPI and those based upon BSC. In particular, when context sensitive changes in effective connectivity are present, they manifest as changes in correlations of observed trial-by-trial activations or functional connectivity. Therefore, BSC and PPI can detect similar connectivity differences. Lastly, we report empirical analyses using PPI and BSC on fMRI data of an event-related stop signal task to illustrate our points.

scholarly journals Task activations produce spurious but systematic inflation of task functional connectivity estimates

2018 ◽  
Author(s):  
Michael W. Cole ◽  
Takuya Ito ◽  
Douglas Schultz ◽  
Ravi Mill ◽  
Richard Chen ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Computational Model ◽  
Brain Activity ◽  
Pearson Correlation ◽  
Order Effect ◽  
Evoked Response ◽  
Fmri Data ◽  
List Type ◽  
Functional Relationships ◽  
Psychophysiological Interaction

AbstractMost neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) - statistical association between brain activity time series during task performance moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of taskevoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference “likely active or interacting during the task”, rather than the intended inference “likely interacting during the task”. Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the model’s predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.HighlightsComputational model shows task inflation of functional connectivity estimatesHemodynamic responses cause task activations to further inflate estimatesStandard approaches to remove task activations leave many false positivesMethods that flexibly fit hemodynamic response shape effectively correct inflationCorrection of functional connectivity inflation verified with empirical fMRI data

scholarly journals Determining functional connectivity using fMRI data with diffusion-based anatomical weighting

NeuroImage ◽  
2012 ◽  
Vol 62 (3) ◽  
pp. 1769-1779 ◽  
Author(s):  
F. DuBois Bowman ◽  
Lijun Zhang ◽  
Gordana Derado ◽  
Shuo Chen
Keyword(s):  
Functional Connectivity ◽  
Fmri Data

scholarly journals Visual Information Shapes the Dynamics of Corticobasal Ganglia Pathways during Response Selection and Inhibition

2015 ◽  
Vol 27 (7) ◽  
pp. 1344-1359 ◽  
Author(s):  
Sara Jahfari ◽  
Lourens Waldorp ◽  
K. Richard Ridderinkhof ◽  
H. Steven Scholte
Keyword(s):  
Visual Cortex ◽  
Visual Information ◽  
Response Selection ◽  
Selection Process ◽  
Effective Connectivity ◽  
Action Selection ◽  
Visual Input ◽  
Fmri Data ◽  
Response Strategies ◽  
Fast Flow

Action selection often requires the transformation of visual information into motor plans. Preventing premature responses may entail the suppression of visual input and/or of prepared muscle activity. This study examined how the quality of visual information affects frontobasal ganglia (BG) routes associated with response selection and inhibition. Human fMRI data were collected from a stop task with visually degraded or intact face stimuli. During go trials, degraded spatial frequency information reduced the speed of information accumulation and response cautiousness. Effective connectivity analysis of the fMRI data showed action selection to emerge through the classic direct and indirect BG pathways, with inputs deriving form both prefrontal and visual regions. When stimuli were degraded, visual and prefrontal regions processing the stimulus information increased connectivity strengths toward BG, whereas regions evaluating visual scene content or response strategies reduced connectivity toward BG. Response inhibition during stop trials recruited the indirect and hyperdirect BG pathways, with input from visual and prefrontal regions. Importantly, when stimuli were nondegraded and processed fast, the optimal stop model contained additional connections from prefrontal to visual cortex. Individual differences analysis revealed that stronger prefrontal-to-visual connectivity covaried with faster inhibition times. Therefore, prefrontal-to-visual cortex connections appear to suppress the fast flow of visual input for the go task, such that the inhibition process can finish before the selection process. These results indicate response selection and inhibition within the BG to emerge through the interplay of top–down adjustments from prefrontal and bottom–up input from sensory cortex.

scholarly journals The Effects of Functionally Guided, Connectivity-Based rTMS on Amygdala Activation

2021 ◽  
Vol 11 (4) ◽  
pp. 494
Author(s):  
Lysianne Beynel ◽  
Ethan Campbell ◽  
Maria Naclerio ◽  
Jeffrey T. Galla ◽  
Angikar Ghosal ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Psychiatric Disorders ◽  
Interaction Analysis ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Preliminary Evidence ◽  
Therapeutic Effects ◽  
Subcortical Structures ◽  
Video Clips ◽  
Amygdala Activation ◽  
Psychophysiological Interaction

While repetitive transcranial magnetic stimulation (rTMS) is widely used to treat psychiatric disorders, innovations are needed to improve its efficacy. An important limitation is that while psychiatric disorders are associated with fronto-limbic dysregulation, rTMS does not have sufficient depth penetration to modulate affected subcortical structures. Recent advances in task-related functional connectivity provide a means to better link superficial and deeper cortical sources with the possibility of increasing fronto-limbic modulation to induce stronger therapeutic effects. The objective of this pilot study was to test whether task-related, connectivity-based rTMS could modulate amygdala activation through its connectivity with the medial prefrontal cortex (mPFC). fMRI was collected to identify a node in the mPFC showing the strongest connectivity with the amygdala, as defined by psychophysiological interaction analysis. To promote Hebbian-like plasticity, and potentially stronger modulation, 5 Hz rTMS was applied while participants viewed frightening video-clips that engaged the fronto-limbic network. Significant increases in both the mPFC and amygdala were found for active rTMS compared to sham, offering promising preliminary evidence that functional connectivity-based targeting may provide a useful approach to treat network dysregulation. Further research is needed to better understand connectivity influences on rTMS effects to leverage this information to improve therapeutic applications.

Functional connectivity analysis of fMRI data using parameterized regions-of-interest

NeuroImage ◽  
2011 ◽  
Vol 54 (1) ◽  
pp. 410-416 ◽  
Author(s):  
Wouter D. Weeda ◽  
Lourens J. Waldorp ◽  
Raoul P.P.P. Grasman ◽  
Simon van Gaal ◽  
Hilde M. Huizenga
Keyword(s):  
Functional Connectivity ◽  
Regions Of Interest ◽  
Fmri Data ◽  
Connectivity Analysis ◽  
Functional Connectivity Analysis

scholarly journals Aberrant Brain Functional Connectivity Strength and Effective Connectivity in Patients with Type 2 Diabetes Mellitus

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xi Guo ◽  
Su Wang ◽  
Yu-Chen Chen ◽  
Heng-Le Wei ◽  
Gang-Ping Zhou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Disease Duration ◽  
Effective Connectivity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Functional Connectivity Strength ◽  
The Right ◽  
Brain Functional Connectivity ◽  
Connectivity Strength

Alterations of brain functional connectivity in patients with type 2 diabetes mellitus (T2DM) have been reported by resting-state functional magnetic resonance imaging studies, but the underlying precise neuropathological mechanism remains unclear. This study is aimed at investigating the implicit alterations of functional connections in T2DM by integrating functional connectivity strength (FCS) and Granger causality analysis (GCA) and further exploring their associations with clinical characteristics. Sixty T2DM patients and thirty-three sex-, age-, and education-matched healthy controls (HC) were recruited. Global FCS analysis of resting-state functional magnetic resonance imaging was performed to explore seed regions with significant differences between the two groups; then, GCA was applied to detect directional effective connectivity (EC) between the seeds and other brain regions. Correlations of EC with clinical variables were further explored in T2DM patients. Compared with HC, T2DM patients showed lower FCS in the bilateral fusiform gyrus, right superior frontal gyrus (SFG), and right postcentral gyrus, but higher FCS in the right supplementary motor area (SMA). Moreover, altered directional EC was found between the left fusiform gyrus and bilateral lingual gyrus and right medial frontal gyrus (MFG), as well as between the right SFG and bilateral frontal regions. In addition, triglyceride, insulin, and plasma glucose levels were correlated with the abnormal EC of the left fusiform, while disease duration and cognitive function were associated with the abnormal EC of the right SFG in T2DM patients. These results suggest that T2DM patients show aberrant brain function connectivity strength and effective connectivity which is associated with the diabetes-related metabolic characteristics, disease duration, and cognitive function, providing further insights into the complex neural basis of diabetes.

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