Psychophysiological and modulatory interactions in neuroimaging.
TL;DR: Interactions among extrastriate, inferotemporal, and posterior parietal regions during visual processing, under different attentional and perceptual conditions, are focused on.
About: This article is published in NeuroImage.The article was published on 1997-10-01 and is currently open access. It has received 2917 citations till now. The article focuses on the topics: Psychophysiological Interaction & Visual processing.
Figures (4)
FIG. 3. The effects used to model psychophysiological interaction between activity in V1 (12, 281, 24 mm) and attentional set. These variables (top) correspond to xk (dashed and dotted line) and gp (solid line) in the main text. The bottom panel shows the element-wise product of these effects and corresponds to the interaction effect (xk 3 gp). 
FIG. 5. Schematic illustrating the two perspectives on psychophysiological interactions. On the left the psychological or experimental factor can be thought of as modulating the contribution of one area to another in terms of neuronal activity. This can be understood as a condition or context-sensitive change in the contributional aspects of effective connectivity between the two areas. On the right the alternative interpretation is that the contribution modulates responsiveness of the target area to the psychological factor. This perspective translates into a modulation of condition-specific responses by afferents from another area. 
FIG. 7. (Top) The activity in the most significant voxel (Z 5 3.97, P , 0.0007 uncorrected at 238, 230, 220 mm) is displayed as a function of (mean corrected) activity in the medial parietal voxel used to specify the psychophysiological interaction (at 6, 274, 36 mm). The dark gray dots correspond to (adjusted for confounds only) activity while viewing faces and the light gray dots correspond to nonface stimuli. The essence of this effect can be seen by (i) noting that this region differentiates between faces and nonfaces when, and only when, medial parietal activity is high, or alternatively (ii) noting that the contribution of parietal activity to inferotemporal activity is greater in the context of face-processing. (Bottom) Suprathreshold regions from the previous figure superimposed on a T1-weighted structural image. The cross-hairs localize the voxel from which the above data were taken. ![FIG. 2. As for Fig. 1 but for a voxel more anteriorly and superiorly placed in relation to V5 (57, 257 6 mm). In this instance the regression slope is negative, suggesting a negative modulation of V1 = V5 influences by PP activity [Z 5 5.32, P 5 0.007 (corrected)].](/figures/fig-2-as-for-fig-1-but-for-a-voxel-more-anteriorly-and-2qe6dfhx.png)
FIG. 2. As for Fig. 1 but for a voxel more anteriorly and superiorly placed in relation to V5 (57, 257 6 mm). In this instance the regression slope is negative, suggesting a negative modulation of V1 = V5 influences by PP activity [Z 5 5.32, P 5 0.007 (corrected)].
![FIG. 2. As for Fig. 1 but for a voxel more anteriorly and superiorly placed in relation to V5 (57, 257 6 mm). In this instance the regression slope is negative, suggesting a negative modulation of V1 = V5 influences by PP activity [Z 5 5.32, P 5 0.007 (corrected)].](/figures/fig-2-as-for-fig-1-but-for-a-voxel-more-anteriorly-and-2qe6dfhx.webp)
Citations
More filters
TL;DR: The meaning of the terms "method" and "method bias" are explored and whether method biases influence all measures equally are examined, and the evidence of the effects that method biases have on individual measures and on the covariation between different constructs is reviewed.
Abstract: Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
8,719 citations
Cites background from "Psychophysiological and modulatory ..."
...Another important fMRI tool for examining interactions between different brain regions is known as psychophysiological interaction (PPI), which is an analysis conducted on task-driven BOLD activations (Friston et al. 1997)....
[...]
TL;DR: As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling, but unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
4,182 citations
TL;DR: The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fc MRI measures.
Abstract: Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...
3,388 citations
Cites methods from "Psychophysiological and modulatory ..."
...…et al., 1995; Fox et al., 2005; Greicius et al., 2003), and psychophysiological interactions used to characterize activation in a particular brain region in terms of the interaction between the influence of another area and an experimental parameter (Friston et al., 1997; Gitelman et al., 2003)....
[...]
...…filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data....
[...]
TL;DR: The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses.
Abstract: Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.
2,822 citations
Cites background from "Psychophysiological and modulatory ..."
...It should be said that there are many qualifications to the use of these simple linear models of effective connectivity (because they belong to the class of structural equation or regression models; Friston et al., 1997)....
[...]
TL;DR: It is proposed that information is gated by inhibiting task-irrelevant regions, thus routing information to task-relevant regions and the empirical support for this framework is discussed.
Abstract: In order to understand the working brain as a network, it is essential to identify the mechanisms by which information is gated between regions. We here propose that information is gated by inhibiting task-irrelevant regions, thus routing information to task-relevant regions. The functional inhibition is reflected in oscillatory activity in the alpha band (8-13 Hz). From a physiological perspective the alpha activity provides pulsed inhibition reducing the processing capabilities of a given area. Active processing in the engaged areas is reflected by neuronal synchronization in the gamma band (30-100 Hz) accompanied by an alpha band decrease. According to this framework the brain should be studied as a network by investigating cross-frequency interactions between gamma and alpha activity. Specifically the framework predicts that optimal task performance will correlate with alpha activity in task-irrelevant areas. In this review we will discuss the empirical support for this framework. Given that alpha activity is by far the strongest signal recorded by EEG and MEG, we propose that a major part of the electrophysiological activity detected from the working brain reflects gating by inhibition.
2,448 citations
Cites background from "Psychophysiological and modulatory ..."
...Network questions are often addressed by asking how different regions become functionally coupled depending on the task at hand (Friston et al., 1997)....
[...]
References
More filters
TL;DR: In this paper, the authors present a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors).
Abstract: + Abstract: Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. (1991): J Cereb Blood Flow Metab 11:690-699; Worsley et al. 119921: J Cereb Blood Flow Metab 12:YOO-918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data. The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis.
9,614 citations
TL;DR: A general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment is presented that minimizes the sum of squares between two images following non linear spatial deformations and transformations of the voxel (intensity) values.
Abstract: This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions.
Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time-series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley-Liss, Inc.
3,715 citations
"Psychophysiological and modulatory ..." refers background or methods in this paper
...Examples of the latter include modulatory interactions between two or more brain areas that could be inferred on the basis of measured neuronal or hemodynamic activity (Friston et al., 1995a; Buechel and Friston, submitted for publication)....
[...]
...An equivalent inference could be obtained by using the t statistic and a contrast of parameter estimates that was 1 for bi, and zero elsewhere in the usual way (see Friston et al., 1995b)....
[...]
...A similar approach was used to assess modulatory connections between V2 and V1 (Friston et al., 1995a)....
[...]
...The data were realigned, spatially normalized, and analyzed using the general linear model as implemented in SPM96 (Wellcome Department of Neurology; Friston et al. 1995c,d, 1996)....
[...]
TL;DR: The empirical analyses suggest that (in extreme situations) over 90% of fMRI signal can be attributed to movement, and that this artifactual component can be successfully removed.
Abstract: This paper concerns the spatial and intensity transformations that are required to adjust for the confounding effects of subject movement during functional MRI (fMRI) activation studies. An approach is presented that models, and removes, movement-related artifacts from fMRI time-series. This approach is predicated on the observation that movement-related effects are extant even after perfect realignment. Movement-related effects can be divided into those that are a function of position of the object in the frame of reference of the scanner and those that are due to movement in previous scans. This second component depends on the history of excitation experienced by spins in a small volume and consequent differences in local saturation. The spin excitation history thus will itself be a function of previous positions, suggesting an autoregression-moving average model for the effects of previous displacements on the current signal. A model is described as well as the adjustments for movement-related components that ensue. The empirical analyses suggest that (in extreme situations) over 90% of fMRI signal can be attributed to movement, and that this artifactual component can be successfully removed.
3,083 citations
"Psychophysiological and modulatory ..." refers methods in this paper
...First, the explanatory variables used to predict activity (i.e., the response variable) in any brain region comprise a standard predictor variable based on the experimental design (e.g., the presence or absence of a particular stimulus attribute) and a response variable from another part of the…...
[...]
TL;DR: The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing, and uses the effective degrees of freedom associated with the error term.
2,647 citations
TL;DR: The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components and highlighted an intentional brain system seen in previous studies of verbal fluency.
Abstract: The distributed brain systems associated with performance of a verbal fluency task were identified in a nondirected correlational analysis of neurophysiological data obtained with positron tomography. This analysis used a recursive principal-component analysis developed specifically for large data sets. This analysis is interpreted in terms of functional connectivity, defined as the temporal correlation of a neurophysiological index measured in different brain areas. The results suggest that the variance in neurophysiological measurements, introduced experimentally, was accounted for by two independent principal components. The first, and considerably larger, highlighted an intentional brain system seen in previous studies of verbal fluency. The second identified a distributed brain system including the anterior cingulate and Wernicke's area that reflected monotonic time effects. We propose that this system has an attentional bias.
1,876 citations
"Psychophysiological and modulatory ..." refers background or methods in this paper
...The most closely related work in this viewpoint is the regression analysis of changes in effective connectivity presented in Friston et al. (1993b)....
[...]
...Functional integration on the other hand is usually assessed by examining the correlations among activity in different brain areas or by trying to explain the activity in one area in relation to others (e.g., Friston et al., 1993a,b; McIntosh et al., 1994a,b; Paus et al., 1996)....
[...]
...The third section considers how interactions between physiological measurements can be understood in terms of effective connectivity, using concepts from the first section....
[...]
...These are strong and unrealistic assumptions but they have proved useful in making the analysis of effective connectivity more robust and tractable, particularly in relation to multiple regression approaches and structural equation modeling (e.g., Friston et al., 1993b; McIntosh et al., 1994a)....
[...]
...K in the model (Friston et al., 1993b)....
[...]