Number of studies with resting and state and fmri in their title/abstract

Transcription

1 430 Chapter 11 seed voxel A voxel chosen as a starting point for a connectivity analysis. Figure Studies of spontaneous BOLD fluctuations in the resting state have become an increasingly important part of fmri research. Plotted for each year is the number of PubMed-indexed studies that contained the terms fmri, resting, and state in their titles or abstracts. This search necessarily underestimates the true volume of research (and potentially includes some studies using active-task fmri instead), but it illustrates the impressive increase in how researchers are using restingstate data to draw conclusions about brain function and provides a rough indicator of the change in research volume since If studies published in 2014 (not shown here), are included there are now more than 2000 studies using resting-state fmri, of which more than 90% have been published since the second edition of this textbook (i.e., since 2009). a single time course constructed by averaging the entire brain at each time point) to minimize effects of physiological variability; this practice has also become controversial because it can theoretically introduce (or exacerbate) anticorrelations between functional networks in resting-state data. Because there is no task to guide the creation of a design matrix during analyses, researchers must adopt a different approach to resting-state fmri data. One such approach involves identifying a seed voxel or a small region of interest (ROI), characterizing its BOLD signal time course, and then examining what other regions exhibit similar fluctuations in activation over time. This approach has been used in many studies, but it suffers from a significant limitation: because the measured time course can differ dramatically from voxel to voxel (or region to region), this approach can generate very different patterns of connectivity based on the seed chosen. Alternatively, broad patterns of common activation can be identified using data exploration techniques like PCA or ICA. Recent work has shown that such techniques, including the ICA dual-regression approach described earlier in this chapter (see Figure 11.6), have improved sensitivity and consistency across participants compared to seed voxel approaches. When analyzing resting-state data, researchers have found coherent activation in sets of regions involved in visual, auditory, memory, and attentional processes, among others. In general, regions that are coactivated during active tasks also show resting-state coactivation, which suggests that brain regions with similar functionality tend to express similar patterns of spontaneous BOLD activation (and presumably neural activity as well). There has been an explosion of interest in resting-state studies, which are now commonplace in current fmri practice (Figure 11.13), partially because the data can be collected without the usual challenges of optimizing task design and partially because of the development of automated approaches for data analysis. In addition, large-scale fmri projects those involving hundreds or even thousands of participants often include resting-state data sets as part of their overall program (Box 11.1) Year Number of studies with resting and state and fmri in their title/abstract

2 Statistical Analysis II: Advanced Approaches 431 Box 11.1 Increasing the Scale of fmri Research: The Human Connectome Nearly all the human fmri research highlighted in this book was conducted in relatively small samples of participants. In many cases, important conclusions about brain function have been drawn from data collected on perhaps a few dozen individuals and often fewer. Such sample sizes do not render those studies conclusions suspect; the standard analysis approaches for combining data across participant account for sample size in their statistics. But increasing the scale of fmri research moving to hundreds or even thousands of participants in a study could have at least three important consequences. First, it would improve the effectiveness of research on a variety of individual differences (e.g., traits like impulsiveness or anxiety). As samples get larger, researchers can more effectively isolate the effects of one variable (e.g., impulsiveness) from potential effects of other related variables (e.g., age, socioeconomic status, intelligence). An analogy can be seen in genetics research, which now involves exceedingly stringent statistical testing (in part because of highprofile false-positive results) that in turn requires very large samples, often including thousands of individuals. Second, large-scale studies have inherent advantages when relating fmri data to other biological measures. Measurements of brain structure, blood flow, or hormone levels can be highly informative but also highly variable, especially in small samples. Increasing the sample size allows researchers to better understand the variability within each of their diverse measures, which improves the inferences that can be drawn when combining disparate sources of data. Third, large-scale studies have the potential to generate more representative samples by collecting data from a set of individuals whose characteristics more accurately reflect those of the population at large (e.g., having more educational diversity than an university sample of convenience). A large sample size does not in itself lead to representativeness. Suppose, for example, that an fmri researcher scanned all students at one selective university. By collecting information from those students about important markers of diversity, it may be possible to show that conclusions drawn from a full sample also hold in diverse subgroups of that sample. In 2010, the United States National Institutes of Health announced an ambitious $40 million project to map the structural and functional connections in the human brain in a sample of unprecedented size. The goal was to create a systematic map of the patterns of connectivity in the brain a goal given the evocative title of the human connectome ( The researchers leading this project proposed to scan 1200 young adult participants (focusing on groups of siblings) in sessions comprising standard task-based fmri, resting-state fmri, structural MRI, and diffusion tensor imaging (DTI). All subjects were to be scanned on a single 3.0-T scanner, and many were to be also scanned on a 7.0-T scanner. To provide data about individual differences that might contribute to observed differences in brain structure and function, the same participants completed a battery of behavioral tests outside of the scanner and were genotyped. Most remarkably of all, the data acquired for this project have been made available to the research community at large. In June 2014, for example, the project made available its 500 subjects release (which actually included more than 520 subjects), containing approximately 20 terabytes of data available to researchers via download or physical delivery (via five large-capacity hard drives). While data collection remains ongoing, initial results have been published from subsamples of the data (Figure 1). Collecting data at such scale as in this project and a few others with similar goals holds promise for changing how fmri research is conducted. As discussed in the text, such large data sets allow researchers to combine fmri with other sources of data while maintaining sufficient power to draw clear conclusions. Moreover, the data sets are not locked away within a single laboratory but instead are made freely available to (Continued on next page) Figure 1 Examining resting-state fmri data from the Human Connectome Project. The researchers characterized the variability in the functional connectivity of each voxel, across subjects, to identify those voxels that represent transitions between one functional region to another (shown as bright colors on the overlaid color map). The dark regions of the map indicate sets of voxels whose functional connectivity was generally similar; they provide candidates for functional regions (i.e., sets of contiguous voxels that share functional connectivity with the remainder of the brain). (From Wig et al., 2014.)

3 432 Chapter 11 Box 11.1 (continued) Functional connectivity (arbitrary units) Age Younger Older Functional connectivity (arbitrary units) Age Figure 2 Combing fmri data across many studies to draw conclusions about individual differences in brain function. A consortium of investigators from 24 sites pooled their restingstate fmri data to generate a final sample of 1093 adult participants. Note that the imaging protocols differed from site to site, consistent with the normal variability in the literature. Approximately 90% of the scans were at 3.0 T, with the remainder at 1.5 T. Other parameters like voxel size, slice thickness, and pulse sequence also varied (e.g., some data sets had more than three times the spatial resolution of others). Different analysis techniques were applied, including examination of functional connectivity with particular seeds, analysis of low-frequency fluctuations in the BOLD signal, and application of dual-regression ICA. Shown here are examples of ICA analyses that reveal age- and sex-related differences in functional connectivity. For example, younger adults tended to show increased connectivity between voxels in the posterior cingulate cortex and restingstate networks, whereas older adults tended to have increased connectivity in lateral prefrontal cortex voxels. To provide a sense of the scale of this work, each dot represents a single participant. (After Biswal et al., 2010). researchers in the field. In principle, the key discoveries from the Human Connectome Project might not come from the researchers who were initially funded to conduct the study, but rather from other laboratories that applied insightful analysis methods to these data. This model one consortium collects the data, and then the entire research community benefits might seem odd to many neuroscientists, but it has a long history in the social sciences, where many large-scale surveys provide data for researchers throughout entire fields (e.g., the National Longitudinal Survey of Youth, the Health and Retirement Study). Few researchers would criticize the goals of having comprehensive, well-curated data about brain structure and function and of making such data freely available to the research community. Moreover, the leaders of the Human Connectome Project were neuroscientists with established track records conducting traditional fmri studies, including those targeted at both methodological and clinical questions. So, the key question was not whether such data would be valuable in itself, but whether the resources devoted to collect and curate those data represented an efficient use of a limited funding budget. The $40 million budget for this project which corresponds to several tens of thousands of dollars per subject in the final data set would be approximately equivalent to that of 50 standard 5-year, single-investigator grants. Each of those diverse grants could tackle a separate aspect of brain function at much smaller scale. The tension between small-scale and large-scale fmri cannot be easily resolved. Each has clear advantages. The traditional small-scale, singlelaboratory approach incentivizes small groups of researchers to develop creative paradigms or new analysis approaches that distinguish their work from that of their peers. Such research is necessarily exploratory; that is, individual projects must break new ground to be published in the leading journals and to attract continued research funding. Thus, such research becomes more likely to generate unexpected advances and novel methods than large-scale projects. Large-scale studies gain power from their consistency; by collecting data using the same imaging parameters, the same tasks, and same protocols, researchers minimize variation associated with data collection so as to detect intersubject differences and to identify subtle but common features of brain function. These properties make the data accessible to a larger audience than data generated by smaller-scale projects, at considerably increased cost (e.g., for quality assurance testing, documentation). Funding will need to balance the advantages of both approaches to create a portfolio of research, especially if the stakes get larger (e.g., the

4 Statistical Analysis II: Advanced Approaches 433 Box 11.1 (continued) recently announced BRAIN initiative, which proposes $4.5 billion in funding for systems and cellular neuroscience research). A potential hybrid model involves contributions of data from many individual laboratories to a common pool. Most commonly, this model has been used for resting-state data that can be readily combined across participants collected in different studies, regardless of the specific goals of each study (Figure 2; see also connectivity maps in Figure 14.1). Studies of resting-state connectivity have become a primary tool for understanding the default network (see Box 9.1). Imagine that you are a subject in a resting-state fmri scan. Before the scan begins, the experimenter instructs you to close your eyes and relax, asking you to remain still without thinking about anything in particular. Perfect adherence to these instructions is, of course, impossible. One cannot shut off active thought in the same way that one closes a running faucet; indeed, the very act of remembering the task instructions violates them. You will spontaneously engage in all sorts of cognition, from remembering recent events to daydreaming about the future, and these thoughts lead to systematic and coordinated activation throughout the brain. However, not all aspects of resting-state connectivity can be attributed to ongoing complex cognitive processes. For example, in a 2007 study, Vincent and colleagues scanned anesthetized monkeys and demonstrated robust resting-state connectivity in various brain systems. These and related findings indicate that coherent low-frequency fluctuations in the BOLD signal may reflect some general property of brain function. default network A set of brain regions whose activation tends to decrease during the performance of active, engaging tasks, but to increase during conditions of resting and reflection. psychophysiological interaction (PPI) A statistical approach for identifying the effect of an experimental manipulation on the functional connectivity between two brain regions. Psychophysiological interactions Another approach for combining fmri and behavioral data involves the identification of psychophysiological interactions (PPIs), a method developed by Friston and colleagues in The core concept of PPI is that mental processes, whether evoked by a stimulus condition or by the subject s behavior, modulate the influence of one brain region over another. Like other connectivity approaches, PPI uses the time course of activation in one seed region to predict changes in activation in another region. However, the process for identifying a PPI sets this method apart from other techniques. Two regressors serve as inputs to the PPI: a psychological regressor, typically a time course of values of some experimental variable of interest; and a physiological regressor, typically the BOLD time course in a selected ROI. The psychological regressor can have categorical values (e.g., conditions of a blocked design) or continuous values (e.g., response time on each trial), and is not convolved with the fmri hemodynamic response. Then, a third regressor, the PPI regressor, is constructed by deconvolving the physiological regressor with a hemodynamic response to estimate the underlying neuronal activity, calculating the interaction term between the (deconvolved) physiological regressor and the psychological regressor, and then convolving that interaction term with a hemodynamic response. The PPI regressor is referred to as psychophysiological because the approach is intended to model interactions between cognitive processes and physiological changes in the connectivity between brain regions. Note that the psychological and physiological regressors should still be included in the model to control for the effects of the task itself and for simple co-activation with the region of interest, independent of any

5 434 Chapter 11 connectivity effects; this step is particularly critical for cases in which the task regressor and task-by-activation interaction regressor are partially correlated. The chief advantage of PPI is that it provides a hypothesis-driven approach for examining the effects of an experimental manipulation (or behavior) on functional connectivity. This advantage is illustrated by a study of how pain captures attention as reported by Bingel and colleagues in Previous work from the same researchers had shown that cognitive distraction, achieved by requiring subjects to remember letters presented at the center of a display, attenuated visual cortex activation in response to task-irrelevant background image. To create painful distractions, they shone infrared laser light onto the hands of their fmri subjects. The associated pain evoked activation in the insular, cingulate, and somatosensory cortices. They used PPI to examine whether activation in the anterior cingulate cortex (i.e., a marker for pain-related responses) interacted with the effects of image visibility on visual cortex activation (i.e., decreased activation to passive viewing of lowcontrast, compared to high-contrast, background images). As shown in Figure 11.14A, their research hypothesis was confirmed: the cingulate cortex activity exerted a pain-related modulation on the lateral visual cortex, and this potential pathway for pain-related distraction was distinct from one for cognitive distraction (not shown). Figure Identification of a psychophysiological interaction (PPI). (A) Researchers hypothesized that processing in the visual cortex could be modulated by the distracting effects of painful stimuli, as indexed by activation in brain regions associated with pain. Using a PPI analysis, they found that the magnitude of pain-related activation in the anterior cingulate cortex modulated the influence of image contrast on the lateral occipital cortex, a region important for processing complex visual scenes. (B) A key topic for research in cognitive neuroscience has been interactions between executive control regions like the prefrontal cortex and emotional regions like the amygdala. In this example, PPI models were constructed by combining physiological regressors associated with activation time courses from the amygdala and psychological time courses reflecting conditions of emotional regulation (i.e., when viewing emotion-evoking photographs, either experience one s emotions naturally or try to decrease one s emotional responses). The PPI analysis identified regions in lateral prefrontal cortex that had different patterns of connectivity with the amygdala depending on whether emotions were being experienced or decreased. (A from Bingel et al., 2007; B from Winecoff et al., 2011.) (A) Pain distraction Object processing (B) Cognitive distraction Decrease Experience Regulation R R amygdala seed L amygdala seed z = 2.4 L 3.0

6 Statistical Analysis II: Advanced Approaches 435 Because the PPI regressor combines a physiological time course (e.g., activation from a source region) with a psychological time course (e.g., conditions of interest), the results of PPI analyses do not in themselves reveal information about causality, or the direction of information flow between the regions. Rather, the results can be expressed as modulatory effects of one region on stimulus-driven activation in another region as in Figure 11.14A or as modulatory effects of an experimental condition on connectivity between two regions as in Figure 11.14B. In either case, the directionality of the relationship between the two brain regions is left undetermined. Inferring causality from fmri data One well-recognized limitation of fmri research is its difficulty with assigning causality. In Chapter 9, we raised this issue by discussing the idea that fmri data are epiphenomenal, meaning that they do not reflect information processing itself, and thus cannot be used to produce models of brain function. Although this objection does not undermine the fact that fmri data provide a valuable index of the underlying information processing, more subtle causal problems remain. Suppose that we observe fmri activation in two regions, A and B. How can we determine whether activation in region A caused changes in region B, or activation in Region B caused changes in Region A, or if both were influenced by some other region? Determining how regions interact is necessary for understanding the flow of information throughout the brain, which in turn allows researchers to construct more biologically plausible models of brain function. The limited temporal resolution of fmri data poses many challenges for causal modeling. Nearly all connections between brain regions are bidirectional, such that the flow of information in one direction may be shortly followed by a flow of information in the other direction, and this back-and-forth signaling may occur repeatedly in the repetition time (TR) between consecutive image acquisitions. Collecting data with a relatively short TR greatly improves the robustness of causal analyses. Moreover, fmri does not measure neuronal activity directly, but instead measures a hemodynamic marker of that activity. Thus, any small time differences in activation between regions may simply reflect differences in the timing of hemodynamic responses in those regions (see Chapter 7). Likewise, there exists strong evidence that integrative activity constitutes a major contributor to the BOLD signal through the metabolic demands associated with restoring dendritic membrane potentials. Therefore, some BOLD signal changes could, at least in principle, represent feedback from downstream regions rather than signaling output, completely skewing assignments of causality. These and related caveats indicate that sophisticated approaches are needed for inferring causality using fmri data. One method for improving the robustness of connectivity analyses is to restrict those analyses to connections among a particular set of regions. A powerful technique for investigating relationships among regions activated in an fmri study is structural equation modeling (SEM), which identifies the combination of connections between variables that can best account for the observed data. A structural equation model consists of a set of nodes, each representing a single measured or latent variable from the data set, and a set of paths between those variables. By including both direct and indirect paths between variables, the model can evaluate whether one variable mediates the covariation between two other variables. (Note that when the model includes only measured data and no inferred or latent variables, the approach is a subset of SEM called path analysis.) causality The judgment that one event led to another. For fmri data, measures of causality attempt to determine whether changes in the activation of one brain region led to changes in the activation in another region. epiphenomenal A secondary consequence of a causal chain of processes, but playing no causal role in the process of interest. repetition time (TR) The time interval between successive excitation pulses, usually expressed in seconds. structural equation modeling (SEM) A statistical approach for testing a model of the relationships among a set of independent and dependent variables.