Essentials of Functional Neuroimaging

Wager, Tor D.; Hernández, Luis; Lindquist, Martin A.

doi:10.1002/9780470478509.neubb001010

Cited by 5 publications

(8 citation statements)

References 178 publications

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“…Correction methods such as those based on Gaussian Random Field Theory are often just as conservative, but nonparametric correction improves power substantially (Nichols and Hayasaka, 2003). With nonparametric correction, approximately only 30 participants are needed for 80% power (Wager et al, in press), though this sample size is still larger than all but the largest studies in our samples 2 . Thus, performing proper correction is impractical without relatively large sample sizes, but failing to make appropriate corrections leads to increased false positive rates.…”

Section: Introductionmentioning

confidence: 92%

“…A basic power calculation (see Fig. 12 in Wager et al, in press) shows that with a standard effect size of d =1 (Cohen's d , an effect divided by its standard deviation), approximately 45 participants are required to achieve 80% power using Bonferroni correction in a typical whole brain, voxel-wise analysis. Correction methods such as those based on Gaussian Random Field Theory are often just as conservative, but nonparametric correction improves power substantially (Nichols and Hayasaka, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the consistency and specificity of neuroimaging data using meta-analysis

Wager¹,

Lindquist²,

Nichols³

et al. 2009

NeuroImage

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233

224

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Making sense of a neuroimaging literature that is growing in scope and complexity will require increasingly sophisticated tools for synthesizing findings across studies. Meta-analysis of neuroimaging studies fills a unique niche in this process: It can be used to evaluate the consistency of findings across different laboratories and task variants, and it can be used to evaluate the specificity of findings in brain regions or networks to particular task types. This review discusses examples, implementation, and considerations when choosing meta-analytic techniques. It focuses on the multilevel kernel density analysis (MKDA) framework, which has been used in recent studies to evaluate consistency and specificity of regional activation, identify distributed functional networks from patterns of co-activation, and test hypotheses about functional cortical-subcortical pathways in healthy individuals and patients with mental disorders. Several tests of consistency and specificity are described.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Evaluating the consistency and specificity of neuroimaging data using meta-analysis

Wager¹,

Lindquist²,

Nichols³

et al. 2009

NeuroImage

Self Cite

233

224

View full text Add to dashboard Cite

show abstract

“…We have previously evaluated that with an effect size of d = 1 (Cohen’s d , an effect divided by its standard deviation), approximately 45 participants are required to achieve 80% power using Bonferroni correction on the whole brain. 1,2 This sample size is larger than that used in most imaging studies. Furthermore, based on a sample of 195 long-term memory studies published between 1993 and 2003, we found that a modal threshold of P < 0.001 un corrected for multiple comparisons and estimated 663 false positives in that sample of studies (which is about 17% of the total number of reported peaks 3 ).…”

Section: Why Use Meta-analysis?mentioning

confidence: 99%

“…In order to make claims about the relationship between brain activation and a psychological process (e.g., 'reverse inference'), we must ask whether activity in amygdala is specific to the experience of emotion, which requires comparison of activation across a wide variety of tasks. This, in turn, requires that we assess the consistency of activation in two ways: (1) in the amygdala during tasks that involve the experience of emotion and (2) in many types of tasks that do not involve the experience of emotion. Indeed, only if the amygdala is consistently activated by emotional experience and not by tasks that do not involve emotional experience, will we be able to use amygdala activity to predict that such a psychological process has occurred.…”

Section: Evaluating Specificitymentioning

confidence: 99%

“…Analyses of consistency are particularly important because the small sample sizes typical in neuroimaging studies and the many tests performed across the brain create an unusually high rate of false‐positive results. We have previously evaluated that with an effect size of d = 1 (Cohen's d , an effect divided by its standard deviation), approximately 45 participants are required to achieve 80% power using Bonferroni correction on the whole brain 1, 2. This sample size is larger than that used in most imaging studies.…”

Section: Why Use Meta‐analysis?mentioning

confidence: 99%

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Meta‐analysis of neuroimaging data

Kober

Wager

2010

WIRES Cognitive Science

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As the number of neuroimaging studies that investigate psychological phenomena grows, it becomes increasingly difficult to integrate the knowledge that has accrued across studies. Metaanalyses are designed to serve this purpose, as they allow the synthesis of findings not only across studies but also across laboratories and task variants. Meta-analyses are uniquely suited to answer questions about whether brain regions or networks are consistently associated with particular psychological domains, including broad categories such as working memory or more specific categories such as conditioned fear. Meta-analysis can also address questions of specificity, which pertains to whether activation of regions or networks is unique to a particular psychological domain, or is a feature of multiple types of tasks. This review discusses several techniques that have been used to test consistency and specificity in published neuroimaging data, including the kernel density analysis (KDA), activation likelihood estimate (ALE), and the recently developed multilevel kernel density analysis (MKDA). We discuss these techniques in light of current and future directions in the field.Studies that use neuroimaging methods such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) allow us to investigate the function of the human brain, in both healthy and disordered populations. In recent years, the number of such investigations increased dramatically across a range of psychological domains. Indeed, some areas of investigation such as cognitive control and emotion have been the subject of many such studies, representing an ever-increasing body of knowledge that complements findings from animal and human lesion studies, electrophysiology, transcranial magnetic stimulation, and other methods. However, activation of a brain region in a single fMRI or PET study cannot usually provide conclusive evidence until it is replicated across laboratories, task variants, and scanning procedures. Meta-analyses of neuroimaging data can therefore serve a crucial function of integrating over research findings to evaluate the consistency of activation in any particular domain. In this way, meta-analyses can determine which regions are consistently activated across a large group of studies that address a single psychological function. A second crucial function served by meta-analysis is the evaluation of functional specificity of activations in a region to a particular type of psychological process. Metaanalysis can determine whether a particular brain region that is consistently activated by a single psychological domain (e.g., cognitive control) is unique to that domain, or whether it is shared by a broader set of cognitive processes (e.g., working memory). WHY USE META-ANALYSIS? Evaluating consistencyIn order to understand and evaluate hundreds of neuroimaging studies published in a single domain (such as emotion), we need to know which regions are most consistently activated.

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Making sense by making sentient: Effectance motivation increases anthropomorphism.

Waytz

Morewedge

Epley

et al. 2010

Journal of Personality and Social Psychology

400

338

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People commonly anthropomorphize nonhuman agents, imbuing everything from computers to pets to gods with humanlike capacities and mental experiences. Although widely observed, the determinants of anthropomorphism are poorly understood and rarely investigated. We propose that people anthropomorphize, in part, to satisfy effectance motivation-the basic and chronic motivation to attain mastery of one's environment. Five studies demonstrated that increasing effectance motivation by manipulating the perceived unpredictability of a nonhuman agent or by increasing the incentives for mastery increases anthropomorphism. Neuroimaging data demonstrated that the neural correlates of this process are similar to those engaged when mentalizing other humans. A final study demonstrated that anthropomorphizing a stimulus makes it appear more predictable and understandable, suggesting that anthropomorphism satisfies effectance motivation. Anthropomorphizing nonhuman agents seems to satisfy the basic motivation to make sense of an otherwise uncertain environment.

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Essentials of Functional Neuroimaging

Cited by 5 publications

References 178 publications

Evaluating the consistency and specificity of neuroimaging data using meta-analysis

Evaluating the consistency and specificity of neuroimaging data using meta-analysis

Meta‐analysis of neuroimaging data

Making sense by making sentient: Effectance motivation increases anthropomorphism.

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