Faculty Opinions recommendation of Ranking brain areas encoding the perceived level of pain from fMRI data.

Background and Objective: Placebo and nocebo responses are widely observed. Herein, we investigated the nocebo hyperalgesia and placebo analgesia responses in brain network in acute lower back pain (ALBP) model using multivariate Granger causality analysis (GCA). This approach analyses functional magnetic resonance imaging (fMRI) data for lagged-temporal correlation between different brain areas.Method: After completing the ALBP model, 20 healthy subjects were given two interventions, once during a placebo intervention and once during a nocebo intervention, pseudo-randomly ordered. fMRI scans were performed synchronously during each intervention, and visual analog scale (VAS) scores were collected at the end of each intervention. The fMRI data were then analyzed using multivariate GCA.Results: Our results found statistically significant differences in VAS scores from baseline (pain status) for both placebo and nocebo interventions, as well as between placebo and nocebo interventions. In placebo network, we found a negative lagged-temporal correlation between multiple brain areas, including the dorsolateral prefrontal cortex (DLPFC), secondary somatosensory cortex area, anterior cingulate cortex (ACC), and insular cortex (IC); and a positive lagged-temporal correlation between multiple brain areas, including IC, thalamus, ACC, as well as the supplementary motor area (SMA). In the nocebo network, we also found a positive lagged-temporal correlation between multiple brain areas, including the primary somatosensory cortex area, caudate, DLPFC and SMA.Conclusion: The results of this study suggest that both pain-related network and reward system are involved in placebo and nocebo responses. The placebo response mainly works by activating the reward system and inhibiting pain-related network, while the nocebo response is the opposite. Placebo network also involves the activation of opioid-mediated analgesia system (OMAS) and emotion pathway, while nocebo network involves the deactivation of emotional control. At the same time, through the construction of the GC network, we verified our hypothesis that nocebo and placebo networks share part of the same brain regions, but the two networks also have their own unique structural features.

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Effect of initial fMRI data modeling on the connectivity reported between brain areas

NeuroImage ◽

10.1016/j.neuroimage.2006.07.019 ◽

2006 ◽

Vol 33 (2) ◽

pp. 515-521 ◽

Cited By ~ 10

Author(s):

Anne Caclin ◽

Pierre Fonlupt

Keyword(s):

Data Modeling ◽

Fmri Data ◽

Brain Areas

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Aberrant resting-state connectivity in non-psychotic individuals with auditory hallucinations

Psychological Medicine ◽

10.1017/s0033291712002541 ◽

2012 ◽

Vol 43 (8) ◽

pp. 1685-1696 ◽

Cited By ~ 37

Author(s):

K. M. J. Diederen ◽

S. F. W. Neggers ◽

A. D. de Weijer ◽

R. van Lutterveld ◽

K. Daalman ◽

...

Keyword(s):

Resting State ◽

Negative Symptoms ◽

Inferior Frontal Gyrus ◽

Fmri Data ◽

Control Group ◽

Temporal Region ◽

Brain Areas ◽

Auditory Verbal Hallucinations ◽

The Right ◽

Resting State Connectivity

BackgroundAlthough auditory verbal hallucinations (AVH) are a core symptom of schizophrenia, they also occur in non-psychotic individuals, in the absence of other psychotic, affective, cognitive and negative symptoms. AVH have been hypothesized to result from deviant integration of inferior frontal, parahippocampal and superior temporal brain areas. However, a direct link between dysfunctional connectivity and AVH has not yet been established. To determine whether hallucinations are indeed related to aberrant connectivity, AVH should be studied in isolation, for example in non-psychotic individuals with AVH.MethodResting-state connectivity was investigated in 25 non-psychotic subjects with AVH and 25 matched control subjects using seed regression analysis with the (1) left and (2) right inferior frontal, (3) left and (4) right superior temporal and (5) left parahippocampal areas as the seed regions. To correct for cardiorespiratory (CR) pulsatility rhythms in the functional magnetic resonance imaging (fMRI) data, heartbeat and respiration were monitored during scanning and the fMRI data were corrected for these rhythms using the image-based method for retrospective correction of physiological motion effects RETROICOR.ResultsIn comparison with the control group, non-psychotic individuals with AVH showed increased connectivity between the left and the right superior temporal regions and also between the left parahippocampal region and the left inferior frontal gyrus. Moreover, this group did not show a negative correlation between the left superior temporal region and the right inferior frontal region, as was observed in the healthy control group.ConclusionsAberrant connectivity of frontal, parahippocampal and superior temporal brain areas can be specifically related to the predisposition to hallucinate in the auditory domain.

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MOU-EC: model-based whole-brain effective connectivity to extract biomarkers for brain dynamics from fMRI data and study distributed cognition

10.1101/531830 ◽

2019 ◽

Cited By ~ 2

Author(s):

M Gilson ◽

G Zamora-López ◽

V Pallarés ◽

MH Adhikari ◽

M Senden ◽

...

Keyword(s):

Distributed Cognition ◽

Dynamic Models ◽

Effective Connectivity ◽

Statistical Testing ◽

Fmri Data ◽

Brain Areas ◽

Whole Brain ◽

Model Based ◽

The Brain ◽

Connectivity Measure

AbstractNeuroimaging techniques are increasingly used to study brain cognition in humans. Beyond their individual activation, the functional associations between brain areas have become a standard proxy to describe how information is distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. In particular, much effort has been devoted to the assessment of directional interactions between brain areas from their observed activity. This paper summarizes our recent approach to analyze fMRI data based on our whole-brain effective connectivity referred to as MOU-EC, while discussing the pros and cons of its underlying assumptions with respect to other established approaches. Once tuned, the model provides a connectivity measure that reflects the dynamical state of BOLD activity obtained using fMRI, which can be used to explore the brain cognition. We focus on two important applications. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools presents some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. To illustrate our framework, we use a dataset where subjects were recorded in two conditions, watching a movie and a black screen (referred to as rest). Our framework provides a comprehensive set of tools that open exciting perspectives for the study of distributed cognition, as well as neuropathologies.

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Carryover Effects Alter fMRI Statistical Analysis in an Acupuncture Study

The American Journal of Chinese Medicine ◽

10.1142/s0192415x08005588 ◽

2008 ◽

Vol 36 (01) ◽

pp. 55-70 ◽

Cited By ~ 20

Author(s):

Tsung-Jung Ho ◽

Jeng-Ren Duann ◽

Chun-Ming Chen ◽

Jeon-Hor Chen ◽

Wu-Chung Shen ◽

...

Keyword(s):

Statistical Analysis ◽

Time Course ◽

Skin Surface ◽

Fmri Data ◽

Coefficient Of Determination ◽

Carryover Effects ◽

Brain Areas ◽

Single Block ◽

Fmri Experiment ◽

Time Courses

Carryover effects can contaminate ON/OFF BOLD contrasts designated in an fMRI experiment. Yet, the ON/OFF contrasts are essential to facilitate statistical analysis based on the significance of contrast levels. Here, we conducted an fMRI experiment with acupuncture stimulation applied on ST42 acupoint as well as with tactile stimulation on its skin surface. Experiment consisted of three two-block acupuncture and one two-block tactile fMRI runs. Each block started with 26-sec OFF period followed by either 26-sec needle manipulation in the acupuncture runs or by scratching skin surface with sand paper in the tactile. To test if carryover effects could alter the BOLD contrasts, we analyzed different portions of fMRI data using GLM method. Our results showed analyses on different portions of acupuncture fMRI data gave significantly different results. Statistical parametric maps of group random effects resulted from the analysis on the very first fMRI trial formed the broadest coverage of the active brain areas. BOLD model time course also best explained the adjusted raw time course at peak active voxel ( coefficient of determination = 0.88). Analyses on other portions of fMRI data only selected subset of the active brain areas delineated by the analysis on the very first data trial and the BOLD model only mildly accounted for the adjusted raw time courses. In tactile runs, results were more consistent across analyses. Therefore, in fMRI experiments with strong carryover effects, a single-block experimental design with multiple repetitions, separated by long enough periods of time, should be more suitable to extract task BOLD effects.

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The Cortex-based Alignment Approach to TMS Coil Positioning

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00635 ◽

2014 ◽

Vol 26 (10) ◽

pp. 2321-2329 ◽

Cited By ~ 9

Author(s):

Felix Duecker ◽

Martin A. Frost ◽

Tom A. de Graaf ◽

Britta Graewe ◽

Christianne Jacobs ◽

...

Keyword(s):

Functional Data ◽

Functional Group ◽

Brain Activity ◽

Fmri Data ◽

Brain Anatomy ◽

Brain Areas ◽

Functional Regions ◽

Anatomical Data ◽

Group Data ◽

The Individual

TMS allows noninvasive manipulation of brain activity in healthy participants and patients. The effectiveness of TMS experiments critically depends on precise TMS coil positioning, which is best for most brain areas when a frameless stereotactic system is used to target activation foci based on individual fMRI data. From a purely scientific perspective, individual fMRI-guided TMS is thus the method of choice to ensure optimal TMS efficiency. Yet, from a more practical perspective, such individual functional data are not always available, and therefore alternative TMS coil positioning approaches are often applied, for example, based on functional group data reported in Talairach coordinates. We here propose a novel method for TMS coil positioning that is based on functional group data, yet only requires individual anatomical data. We used cortex-based alignment (CBA) to transform individual anatomical data to an atlas brain that includes probabilistic group maps of two functional regions (FEF and hMT+/V5). Then, these functional group maps were back-transformed to the individual brain anatomy, preserving functional–anatomical correspondence. As a proof of principle, the resulting CBA-based functional targets in individual brain space were compared with individual FEF and hMT+/V5 hotspots as conventionally localized with individual fMRI data and with targets based on Talairach coordinates as commonly done in TMS research in case only individual anatomical data are available. The CBA-based approach significantly improved localization of functional brain areas compared with traditional Talairach-based targeting. Given the widespread availability of CBA schemes and preexisting functional group data, the proposed procedure is easy to implement and at no additional measurement costs. However, the accuracy of individual fMRI-guided TMS remains unparalleled, and the CBA-based approach should only be the method of choice when individual functional data cannot be obtained or experimental factors argue against it.

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Ranking brain areas encoding the perceived level of pain from fMRI data

NeuroImage ◽

10.1016/j.neuroimage.2014.01.001 ◽

2014 ◽

Vol 90 ◽

pp. 153-162 ◽

Cited By ~ 27

Author(s):

Stefania Favilla ◽

Alexa Huber ◽

Giuseppe Pagnoni ◽

Fausta Lui ◽

Patrizia Facchin ◽

...

Keyword(s):

Fmri Data ◽

Brain Areas

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Model-based whole-brain effective connectivity to study distributed cognition in health and disease

Network Neuroscience ◽

10.1162/netn_a_00117 ◽

2020 ◽

Vol 4 (2) ◽

pp. 338-373 ◽

Cited By ~ 5

Author(s):

Matthieu Gilson ◽

Gorka Zamora-López ◽

Vicente Pallarés ◽

Mohit H. Adhikari ◽

Mario Senden ◽

...

Keyword(s):

Distributed Cognition ◽

Dynamic Models ◽

Effective Connectivity ◽

Statistical Testing ◽

Human Cognition ◽

Fmri Data ◽

Brain Areas ◽

Whole Brain ◽

Model Based ◽

The Many

Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.

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The Effects of Global Signal Regression on Estimates of Resting-state BOLD fMRI and EEG Vigilance Correlations

10.1101/433912 ◽

2018 ◽

Author(s):

Maryam Falahpour ◽

Alican Nalci ◽

Thomas T. Liu

Keyword(s):

Resting State ◽

Brain Activity ◽

Fmri Data ◽

Cingulate Gyrus ◽

Resting State Functional Connectivity ◽

Brain Areas ◽

Bold Fmri ◽

Time Points ◽

Global Signal ◽

Positive Correlations

AbstractGlobal signal regression (GSR) is a commonly used albeit controversial preprocessing approach in the analysis of resting-state BOLD fMRI data. While the effects of GSR on resting-state functional connectivity measures have received much attention, there has been relatively little attention devoted to its effects on studies looking at the relation between resting-state BOLD measures and independent measures of brain activity. In this study we used simultaneously acquired EEG-fMRI data in humans to examine the effects of GSR on the correlation between resting-state BOLD fluctuations and EEG vigilance measures. We show that GSR leads to a positive shift in the correlation between the BOLD and vigilance measures. This shift leads to a reduction in the spatial extent of negative correlations in widespread brain areas, including the visual cortex, but leads to the appearance of positive correlations in other areas, such as the cingulate gyrus. The results obtained using GSR are consistent with those of a temporal censoring process in which the correlation is computed using a temporal subset of the data. Since the data from these retained time points are unaffected by the censoring process, this finding suggests that the positive correlations in cingulate gyrus are not simply an artifact of GSR.

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