An Updated Survey on Statistical Thresholding and Sample Size of fMRI Studies

Yeung, Andy Wai Kan

doi:10.3389/fnhum.2018.00016

Cited by 53 publications

(57 citation statements)

References 24 publications

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“…Another relevant consideration pertains to the use of a more relaxed clusterforming threshold of p < 0.005, which may have resulted in slightly increased risk for false positives. 57 However, even though this threshold is commonly adopted by exploratory studies, 58 our findings were interpreted in line with previous reports. Another relevant question involved the characteristics of the risky decision-making task used in this study.…”

Section: Limitationssupporting

confidence: 91%

Altered response to risky decisions and reward in patients with obsessive–compulsive disorder

Moreira

Macoveanu

Marques

et al. 2020

jpn

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Background: Patients with obsessive-compulsive disorder (OCD) employ ritualistic behaviours to reduce or even neutralize the anxiety provoked by their obsessions. The presence of excessive rumination and indecision has motivated the view of OCD as a disorder of decision-making. Most studies have focused on the "cold," cognitive aspects of decision-making. This study expands current understanding of OCD by characterizing the abnormalities associated with affective, or "hot" decision-making. Methods: We performed a functional MRI study in a sample of 34 patients with OCD and 33 sex-and age-matched healthy controls, during which participants made 2-choice gambles taking varying levels of risk. Results: During risky decisions, patients showed significantly reduced task-related activation in the posterior cingulum, lingual gyrus and anterior cingulate cortex. We identified significant group × risk interactions in the calcarine cortex, precuneus, amygdala and anterior cingulate cortex. During the outcome phase, patients with OCD showed stronger activation of the orbitofrontal cortex, anterior cingulate cortex and putamen in response to unexpected losses. Limitations: The group of patients not receiving medication was very small (n = 5), which precluded us from assessing the effect of medication on risk-taking behaviour in these patients. Conclusion: Obsessive-compulsive disorder is associated with abnormal brain activity patterns during risky decision-making in a set of brain regions that have been consistently implicated in the processing of reward prediction errors. Alterations in affective "hot" processes implicated in decision-making may contribute to increased indecisiveness and intolerance to uncertainty in patients with OCD.

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Section: Limitationssupporting

confidence: 91%

Altered response to risky decisions and reward in patients with obsessive–compulsive disorder

Moreira

Macoveanu

Marques

et al. 2020

jpn

View full text Add to dashboard Cite

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“…While small, these sample sizes are fairly representative of a typical functional neuroimaging study over the past two decades—between 1995 and 2015, the median sample size of an fMRI study increased steadily from 8 to 22 (Poldrack et al, ). This increase has continued, and a review of 2017 publications found a median sample size of 33 (Yeung, ). Hence while our datasets are important for judging previous work, a future comparison exercise with larger datasets would be a valuable addition to the literature.…”

Section: Discussionmentioning

confidence: 99%

Exploring the impact of analysis software on task fMRI results

Bowring

Maumet

Nichols

2019

Human Brain Mapping

136

104

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A wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this “methodological plurality” comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. In this work, our goal is to understand how choice of software package impacts on analysis results. We use publicly shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL, and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyse, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. Qualitatively we find similarities between packages, backed up by Neurosynth association analyses that correlate similar words and phrases to all three software package's unthresholded results for each of the studies we reanalyse. However, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 to 0.684 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our efforts to make this research reproducible.

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“…Though there is no survey on the overall dental fMRI literature, a meta‐analysis on fMRI studies of dental anxiety found that the original studies had 7‐40 participants . Besides, surveys on the general neuroimaging literature of human subjects reported a median sample size of around 30 and that 4.4%‐40.9% of studies reported results with uncorrected statistics, that is without correcting for multiple testing . These shortcomings increased the chance of spurious, false positives, and limited the estimated power of studies to be in the range of 8%‐31% .…”

Section: Pros and Cons Of Mri‐based Neuroimagingmentioning

confidence: 99%

What do we learn from brain imaging?—A primer for the dentists who want to know more about the association between the brain and human stomatognathic functions

Lin

Yeung

2020

J of Oral Rehabilitation

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The number of neuroimaging studies on the brain and oral sensorimotor functions has increased recently. Behind the dazzling "brain maps," what does the neuroimaging evidence truly tell us? What can dentists learn from it to improve clinical practice?We summarise the pros and cons of applying magnetic resonance imaging (MRI)based neuroimaging to study oral behaviours of the dental patients. This is a narrative review of previous neuroimaging research of oral functions, focusing on MRI-related studies of human subjects. MRI has gained popularity in dental research due to its non-invasive nature, its approachability and its versatility in quantifying a variety of brain signatures. We argue that MRI-based neuroimaging is suitable for investigating the association between the between-individual variations in brain structure (eg grey matter volume)/brain functions (eg brain activation) and oral behaviours of the patients. Two specific topics of the daily dental practice, mastication and dental fear and anxiety, are discussed to exemplify the potential of neuroimaging methods. The methodological and interpretive limitations of MRI techniques are highlighted, and most importantly, we emphasise that the neuroimaging findings should be carefully interpreted given these limitations. MRI-based neuroimaging techniques can provide a better evaluation of the association between the brain and stomatognathic functions, which could be pivotal to the evidence-based clinical management of dental patients. K E Y W O R D Sbrain, magnetic resonance imaging, neuroimaging, oral medicine, stomatognathic system

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An Updated Survey on Statistical Thresholding and Sample Size of fMRI Studies

Cited by 53 publications

References 24 publications

Altered response to risky decisions and reward in patients with obsessive–compulsive disorder

Altered response to risky decisions and reward in patients with obsessive–compulsive disorder

Exploring the impact of analysis software on task fMRI results

What do we learn from brain imaging?—A primer for the dentists who want to know more about the association between the brain and human stomatognathic functions

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