Why small low-powered studies are worse than large high-powered studies and how to protect against “trivial” findings in research: Comment on Friston (2012)

Bias in data-driven replicability analysis of univariate brain-wide association studies

Recent studies have used big neuroimaging datasets to answer an important question: how many subjects are required for reproducible brain-wide association studies? These data-driven approaches could be considered a framework for testing the reproducibility of several neuroimaging models and measures. Here we test part of this framework, namely estimates of statistical errors of univariate brain-behaviour associations obtained from resampling large datasets with replacement. We demonstrate that reported estimates of statistical errors are largely a consequence of bias introduced by random effects when sampling with replacement close to the full sample size. We show that future meta-analyses can largely avoid these biases by only resampling up to 10% of the full sample size. We discuss implications that reproducing mass-univariate association studies requires tens-of-thousands of participants, urging researchers to adopt other methodological approaches.

Similarity and characterization of structural and functional neural connections within species under isoflurane anesthesia in the common marmoset

The common marmoset is an essential model for understanding social cognition and neurodegenerative diseases. This study explored the structural and functional brain connectivity in a marmoset under isoflurane anesthesia, aiming to statistically overcome the effects of high inter-individual variability and noise-related confounds such as physiological noise, ensuring robust and reliable data. Similarities and differences in individual subject data, including assessments of functional and structural brain connectivities derived from resting-state functional MRI and diffusion tensor imaging were meticulously captured. The findings highlighted the high consistency of structural neural connections within the species, indicating a stable neural architecture, while functional connectivity under anesthesia displayed considerable variability. Through independent component and dual regression analyses, several distinct brain connectivities were identified, elucidating their characteristics under anesthesia. Insights into the structural and functional features of the marmoset brain from this study affirm its value as a neuroscience research model, promising advancements in the field through fundamental and translational studies.

Industry Sponsorship Bias in Collagenase Clinical Trials for Dupuytren Disease: A Meta-analysis

BACKGROUND

Collagenase clostridium histolyticum (collagenase) was introduced in 2010 creating a nonoperative treatment option for Dupuytren disease with promising results in sponsored clinical trials. A meta-analysis was performed to investigate industry sponsorship bias.

METHODS

A systematic review of collagenase treatment of Dupuytren contracture was conducted. Articles containing mesh terms including "microbial collagenase" and "Dupuytren's contracture" were searched and limited to only clinical trials with similar protocols for inclusion. Meta-analysis of treatment endpoints of correction of contracture to 0-5 degrees after first and last injection was conducted comparing sponsored versus nonsponsored studies.

RESULTS

Sixteen of the 29 identified articles met criteria for inclusion. Nonsponsored studies reported a significantly higher rate of meeting the primary treatment endpoint compared to sponsored studies after single injection for all joints (69.6% vs 56% P < 0.01), metacarpophalangeal joint (96% vs 64% P < 0.01), and proximal interphalangeal joint (67% vs 36% P = 0.011). The correction in contracture rates was similar between groups with studies evaluating more than one injection.

CONCLUSIONS

Nonsponsored studies published higher success rates in meeting the primary endpoint of full correction after single injection than sponsored studies; however, similar results with multiple injections. This study demonstrated that sponsored studies of collagenase produced highly powered studies that may be reliably depended on for evidence-based clinical application.

Oral language interventions can improve language outcomes in children with neurodevelopmental disorders: A systematic review and meta-analysis

Background

Young people who fail to develop language as expected face significant challenges in all aspects of life. Unfortunately, language disorders are common, either as a distinct condition (e.g., Developmental Language Disorder) or as a part of another neurodevelopmental condition (e.g., autism). Finding ways to attenuate language problems through intervention has the potential to yield great benefits not only for the individual but also for society as a whole.

Objectives

This meta-analytic review examined the effect of oral language interventions for children with neurodevelopmental disorders.

Search Methods

The last electronic search was conducted in April 2022.

Selection Criteria

Intervention studies had to target language skills for children from 2 to 18 years of age with Developmental Language Disorder, autism, intellectual disability, Down syndrome, Fragile X syndrome, and Williams syndrome in randomised controlled trials or quasi-experimental designs. Control groups had to include business-as-usual, waiting list, passive or active conditions. However, we excluded studies in which the active control group received a different type, delivery, or dosage of another language intervention. Eligible interventions implemented explicit and structured activities (i.e., explicit instruction of vocabulary, narrative structure or grammatical rules) and/or implicit and broad activities (i.e., shared book reading, general language stimulation). The intervention studies had to assess language skills in receptive and/or expressive modalities.

Data Collection and Analysis

The search provided 8195 records after deduplication. Records were screened by title and abstract, leading to full-text examinations of 448 records. We performed Correlated and Hierarchical Effects models and ran a retrospective power analysis via simulation. Publication bias was assessed via p-curve and precision-effect estimate.

Main Results

We examined 38 studies, with 46 group comparisons and 108 effects comparing pre-/post-tests and eight studies, with 12 group comparisons and 21 effects at follow-up. The results showed a mean effect size of d = 0.27 at the post-test and d = 0.18 at follow-up. However, there was evidence of publication bias and overestimation of the mean effects. Effects from the meta-analysis were significantly related to these elements: (1) receptive vocabulary and omnibus receptive measures showed smaller effect sizes relative to expressive vocabulary, grammar, expressive and receptive discourse, and omnibus expressive tests; and (2) the length of the intervention, where longer sessions conducted over a longer period of time were more beneficial than brief sessions and short-term interventions. Neither moderators concerning participants' characteristics (children's diagnosis, diagnostic status, age, sex, and non-verbal cognitive ability and severity of language impairment), nor those regarding of the treatment components and implementation of the language interventions (intervention content, setting, delivery agent, session structure of the intervention or total number of sessions) reached significance. The same occurred to indicators of study quality. The risk of bias assessment showed that reporting quality for the studies examined in the review was poor.

Authors’ Conclusions

In sum, the current evidence base is promising but inconclusive. Pre-registration and replication of more robust and adequately powered trials, which include a wider range of diagnostic conditions, together with more long-term follow-up comparisons, are needed to drive evidence-based practice and policy.

Efficacy and safety of yoga for the management of chronic low back pain: an overview of systematic reviews

Background

Yoga is a non-pharmacological conservative therapeutic modality that can be employed for the management of chronic low back pain (CLBP). In this overview, we have summarized and evaluated data from current systematic reviews (SRs) on the use of yoga for CLBP.

Methods

We comprehensively searched SRs on the use of yoga for CLBP in nine electronic databases from inception to September 2023. The methodological quality was evaluated using the Assessment of Multiple Systematic Review Scale-2 (AMSTAR-2). The reporting quality of the included SRs was evaluated using the Preferred Reporting Item for Systematic Review and Meta-Analysis-2020 (PRISMA-2020), and the quality of data was graded using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Two independent researchers performed the screening, data extraction, and quality assessment process of SRs.

Results

A total of 13 SRs were included. The results of the AMSTAR-2 indicated that the methodological quality of the included studies was relatively low. The PRISMA-2020 checklist evaluation results indicated that methodological limitations in reporting, especially regarding data processing and presentation, were the main weaknesses. The GRADE assessment indicated that 30 outcomes were rated moderate, 42 were rated low level, and 20 were rated very low level. Downgrading factors were mainly due to the limitations of the included studies.

Conclusion

Yoga appears to be an effective and safe non-pharmacological therapeutic modality for the Management of CLBP. Currently, it may exhibit better efficacy in improving pain and functional disability associated with CLBP. However, the methodological quality and quality of evidence for SRs/MAs in the included studies were generally low, and these results should be interpreted cautiously.

Replicable brain-phenotype associations require large-scale neuroimaging data

Numerous neuroimaging studies have investigated the neural basis of interindividual differences but the replicability of brain-phenotype associations remains largely unknown. We used the UK Biobank neuroimaging dataset (N = 37,447) to examine associations with six variables related to physical and mental health: age, body mass index, intelligence, memory, neuroticism and alcohol consumption, and assessed the improvement of replicability for brain-phenotype associations with increasing sampling sizes. Age may require only 300 individuals to provide highly replicable associations but other phenotypes required 1,500 to 3,900 individuals. The required sample size showed a negative power law relation with the estimated effect size. When only comparing the upper and lower quarters, the minimally required sample sizes for imaging decreased by 15-75%. Our findings demonstrate that large-scale neuroimaging data are required for replicable brain-phenotype associations, that this can be mitigated by preselection of individuals and that small-scale studies may have reported false positive findings.

Evaluation of a decided sample size in machine learning applications

BACKGROUND

An appropriate sample size is essential for obtaining a precise and reliable outcome of a study. In machine learning (ML), studies with inadequate samples suffer from overfitting of data and have a lower probability of producing true effects, while the increment in sample size increases the accuracy of prediction but may not cause a significant change after a certain sample size. Existing statistical approaches using standardized mean difference, effect size, and statistical power for determining sample size are potentially biased due to miscalculations or lack of experimental details. This study aims to design criteria for evaluating sample size in ML studies. We examined the average and grand effect sizes and the performance of five ML methods using simulated datasets and three real datasets to derive the criteria for sample size. We systematically increase the sample size, starting from 16, by randomly sampling and examine the impact of sample size on classifiers' performance and both effect sizes. Tenfold cross-validation was used to quantify the accuracy.

RESULTS

The results demonstrate that the effect sizes and the classification accuracies increase while the variances in effect sizes shrink with the increment of samples when the datasets have a good discriminative power between two classes. By contrast, indeterminate datasets had poor effect sizes and classification accuracies, which did not improve by increasing sample size in both simulated and real datasets. A good dataset exhibited a significant difference in average and grand effect sizes. We derived two criteria based on the above findings to assess a decided sample size by combining the effect size and the ML accuracy. The sample size is considered suitable when it has appropriate effect sizes (≥ 0.5) and ML accuracy (≥ 80%). After an appropriate sample size, the increment in samples will not benefit as it will not significantly change the effect size and accuracy, thereby resulting in a good cost-benefit ratio.

CONCLUSION

We believe that these practical criteria can be used as a reference for both the authors and editors to evaluate whether the selected sample size is adequate for a study.

The effects of intranasal oxytocin on black participants’ responses to outgroup acceptance and rejection

Social acceptance (vs. rejection) is assumed to have widespread positive effects on the recipient; however, ethnic/racial minorities often react negatively to social acceptance by White individuals. One possibility for such reactions might be their lack of trust in the genuineness of White individuals’ positive evaluations. Here, we examined the role that oxytocin—a neuropeptide putatively linked to social processes—plays in modulating reactions to acceptance or rejection during interracial interactions. Black participants (N = 103) received intranasal oxytocin or placebo and interacted with a White, same-sex stranger who provided positive or negative social feedback. After positive feedback, participants given oxytocin (vs. placebo) tended to display approach-oriented cardiovascular responses of challenge (vs. threat), exhibited more cooperative behavior, and perceived the partner to have more favorable attitudes toward them after the interaction. Following negative feedback, oxytocin reduced anger suppression. Oxytocin did not modulate testosterone reactivity directly, but our exploratory analysis showed that the less participants suppressed anger during the interaction with their partner, the greater testosterone reactivity they displayed after the interaction. These results survived the correction for multiple testing with a false discovery rate (FDR) of 20%, but not with a rate of 10 or 5%. Discussion centers on the interplay between oxytocin and social context in shaping interracial interactions.

Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders

Structural magnetic resonance imaging (sMRI) offers immense potential for increasing our understanding of how anatomical brain development relates to clinical symptoms and functioning in neurodevelopmental disorders. Clinical developmental sMRI may help identify neurobiological risk factors or markers that may ultimately assist in diagnosis and treatment. However, researchers and clinicians aiming to conduct sMRI studies of neurodevelopmental disorders face several methodological challenges. This review offers hands-on guidelines for clinical developmental sMRI. First, we present brain morphometry metrics and review evidence on typical developmental trajectories throughout adolescence, together with atypical trajectories in selected neurodevelopmental disorders. Next, we discuss challenges and good scientific practices in study design, image acquisition and analysis, and recent options to implement quality control. Finally, we discuss choices related to statistical analysis and interpretation of results. We call for greater completeness and transparency in the reporting of methods to advance understanding of structural brain alterations in neurodevelopmental disorders.

Research Review: Language and specific learning disorders in children and their co-occurrence with internalizing and externalizing problems: a systematic review and meta-analysis

BACKGROUND

Some studies suggest that children with language and learning disorders (LLDs) show more internalizing and externalizing problems than their peers. However, the available evidence remains inconsistent, especially regarding the conditions under which these psychological problems occur.

METHODS

We performed a meta-analysis of studies comparing children with LLDs and controls on internalizing (53 independent samples, 135 effect sizes) and externalizing problems (37 independent samples, 61 effect sizes) separately.

RESULTS

Children with LLDs showed higher internalizing (Hedges' g = 0.36) and externalizing problems (Hedges' g = 0.42) than controls did. The group standardized difference in internalizing problems was moderated by the primary disorder, with children with language disorders showing more internalizing problems than those with reading disorders. The severity of the primary disorder, IQ, and age did not moderate Hedge's g between children with LLDs and controls in internalizing and externalizing outcomes. The same pattern was found for gender as a moderator of Hedge's g in internalizing problems, while findings for externalizing problems were inconclusive. The results were consistent when methodological variables were assessed, also for informant, sample size, and geographical area. Clinical samples with LLDs reported higher internalizing problems respect to those with difficulties, but findings on externalizing outcomes were limited. Similarly, results on the presence of additional symptoms in learning and language, self-concept, and socioeconomic status were inconclusive, as few studies reported this information. Results were robust when publication bias, publication year, and study quality were assessed.

CONCLUSIONS

There is evidence that children with LLDs report higher internalizing and externalizing problems than controls do. Children with language disorders seemed more vulnerable to report more internalizing problems, and clinical samples reported higher problems than those with difficulties. For clinical practice, assessment and interventions should target socioemotional skills to support the psychological well-being of children with LLDs.

Effect sizes of associations between neuroimaging measures and affective symptoms: A meta-analysis

BACKGROUND

The utility of brain-based biomarkers for psychiatric disorders hinges among other factors on their ability to explain a significant portion of the phenotypic variance. In particular, many small scale studies have been unable to arbitrate whether structural or functional magnetic resonance imaging has potential to be a biological marker for these disorders.

METHODS

This study conducted a meta-analysis to examine the relationship between study power and published effect sizes for the relationship between affective symptoms and structural or functional magnetic resonance imaging measures. The current analyses are based on 821 brain-affective symptom association effect sizes derived from 120 publications, which employed a univariate region-of-interest approach.

RESULTS

For self-assessed affective symptoms published brain imaging measures accounted for on average 8% (confidence interval: 1.6%-23%) of between-subject variation. This average effect size was based mostly on studies with small sample sizes, which have likely led to inflation of these effect size estimates.

CONCLUSIONS

These findings support the conclusion that brain imaging measures currently account for a smaller proportion of the interindividual variance in affective symptoms than has been previously reported. The current findings support the need for both large-sample clinical studies and new statistical and theoretical models to more robustly capture systematic variance of brain-affective symptom relationships.

Detecting Suicide Ideation in the Era of Social Media: The Population Neuroscience Perspective

Social media platforms are increasingly used across many population groups not only to communicate and consume information, but also to express symptoms of psychological distress and suicidal thoughts. The detection of suicidal ideation (SI) can contribute to suicide prevention. Twitter data suggesting SI have been associated with negative emotions (e.g., shame, sadness) and a number of geographical and ecological variables (e.g., geographic location, environmental stress). Other important research contributions on SI come from studies in neuroscience. To date, very few research studies have been conducted that combine different disciplines (epidemiology, health geography, neurosciences, psychology, and social media big data science), to build innovative research directions on this topic. This article aims to offer a new interdisciplinary perspective, that is, a Population Neuroscience perspective on SI in order to highlight new ways in which multiple scientific fields interact to successfully investigate emotions and stress in social media to detect SI in the population. We argue that a Population Neuroscience perspective may help to better understand the mechanisms underpinning SI and to promote more effective strategies to prevent suicide timely and at scale.

Updating and characterizing neuroanatomical markers in high-risk subjects, recently diagnosed and chronic patients with schizophrenia: A revised coordinate-based meta-analysis

Characterizing neuroanatomical markers of different stages of schizophrenia (SZ) to assess pathophysiological models of how the disorder develops is an important target for the clinical practice. We performed a meta-analysis of voxel-based morphometry studies of genetic and clinical high-risk subjects (g-/c-HR), recently diagnosed (RDSZ) and chronic SZ patients (ChSZ). We quantified gray matter (GM) changes associated with these four conditions and compared them with contrast and conjunctional data. We performed the behavioral analysis and networks decomposition of alterations to obtain their functional characterization. Results reveal a cortical-subcortical, left-to-right homotopic progression of GM loss. The right anterior cingulate is the only altered region found altered among c-HR, RDSZ and ChSZ. Contrast analyses show left-lateralized insular, amygdalar and parahippocampal GM reduction in RDSZ, which appears bilateral in ChSZ. Functional decomposition shows involvement of the salience network, with an enlargement of the sensorimotor network in RDSZ and the thalamus-basal nuclei network in ChSZ. These findings support the current neuroprogressive models of SZ and integrate this deterioration with the clinical evolution of the disease.

Influence of sample size and analytic approach on stability and interpretation of brain-behavior correlations in task-related fMRI data

Limited statistical power due to small sample sizes is a problem in fMRI research. Most of the work to date has examined the impact of sample size on task‐related activation, with less attention paid to the influence of sample size on brain‐behavior correlations, especially in actual experimental fMRI data. We addressed this issue using two large data sets (a working memory task, N = 171, and a relational processing task, N = 865) and both univariate and multivariate approaches to voxel‐wise correlations. We created subsamples of different sizes and calculated correlations between task‐related activity at each voxel and task performance. Across both data sets the magnitude of the brain‐behavior correlations decreased and similarity across spatial maps increased with larger sample sizes. The multivariate technique identified more extensive correlated areas and more similarity across spatial maps, suggesting that a multivariate approach would provide a consistent advantage over univariate approaches in the stability of brain‐behavior correlations. In addition, the multivariate analyses showed that a sample size of roughly 80 or more participants would be needed for stable estimates of correlation magnitude in these data sets. Importantly, a number of additional factors would likely influence the choice of sample size for assessing such correlations in any given experiment, including the cognitive task of interest and the amount of data collected per participant. Our results provide novel experimental evidence in two independent data sets that the sample size commonly used in fMRI studies of 20–30 participants is very unlikely to be sufficient for obtaining reproducible brain‐behavior correlations, regardless of analytic approach.

Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals

We evaluated 1038 of the most cited structural and functional (fMRI) magnetic resonance brain imaging papers (1161 studies) published during 1990-2012 and 270 papers (300 studies) published in top neuroimaging journals in 2017 and 2018. 96% of highly cited experimental fMRI studies had a single group of participants and these studies had median sample size of 12, highly cited clinical fMRI studies (with patient participants) had median sample size of 14.5, and clinical structural MRI studies had median sample size of 50. The sample size of highly cited experimental fMRI studies increased at a rate of 0.74 participant/year and this rate of increase was commensurate with the median sample sizes of neuroimaging studies published in top neuroimaging journals in 2017 (23 participants) and 2018 (24 participants). Only 4 of 131 papers in 2017 and 5 of 142 papers in 2018 had pre-study power calculations, most for single t-tests and correlations. Only 14% of highly cited papers reported the number of excluded participants whereas 49% of papers with their own data in 2017 and 2018 reported excluded participants. Publishers and funders should require pre-study power calculations necessitating the specification of effect sizes. The field should agree on universally required reporting standards. Reporting formats should be standardized so that crucial study parameters could be identified unequivocally.

Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies

Clinical multicenter imaging studies are frequent and rely on a wide range of existing tools for sharing data and processing pipelines. This is not the case for preclinical (small animal) studies. Animal population imaging is still in infancy, especially because a complete standardization and control of initial conditions in animal models across labs is still difficult and few studies aim at standardization of acquisition and post-processing techniques. Clearly, there is a need of appropriate tools for the management and sharing of data, post-processing and analysis methods dedicated to small animal imaging. Solutions developed for Human imaging studies cannot be directly applied to this specific domain. In this paper, we present the Small Animal Shanoir (SAS) solution for supporting animal population imaging using tools compatible with open data. The integration of automated workflow tools ensures accessibility and reproducibility of research outputs. By sharing data and imaging processing tools, hosted by SAS, we promote data preparation and tools for reproducibility and reuse, and participation in multicenter or replication "open science" studies contributing to the improvement of quality science in preclinical domain. SAS is a first step for promoting open science for small animal imaging and a contribution to the valorization of data and pipelines of reference.

An appraisal of methodology reporting in lifestyle interventions among people with psychosis: A systematic review

ISSUE ADDRESSED

Lifestyle interventions use nutrition and physical activity behaviour modification techniques to decrease obesity and cardio-metabolic risk in people with psychosis. Evidence on the specific behaviour modification strategies applied to decrease obesity is weakened by inadequate methodology reporting of lifestyle interventions. A systematic review that we conducted earlier highlighted a possible deficiency in reporting; hence we aim to critically appraise lifestyle intervention studies that target weight outcomes for people with psychosis against the methods component of the CONSORT statement for randomised trials of nonpharmacologic treatments.

METHODS COMPONENT

We considered randomised controlled studies which delivered lifestyle interventions to community-dwelling adults with psychotic disorders, and included those with the following outcomes of interest: weight, body mass index, waist circumference and waist-to-hip ratio. The Cochrane Library, MEDLINE/PREMEDLINE, EMBASE, CINAHL, Scopus and PsycINFO were searched for English publications between 1985 and 2018. Methodology and reporting of studies were evaluated using the CONSORT statement for randomised trials of nonpharmacologic treatments.

RESULTS

Thirty-two studies met the inclusion criteria. Critical appraisals revealed that reporting of lifestyle intervention studies was generally incomplete. Fewer than 50% provided the recommended information on trial design, participant characteristics, detail of interventions, outcomes, sample size, randomisation, blinding and statistical methods.

CONCLUSIONS

Application of guidelines, like the CONSORT statement, in future publications of lifestyle interventions for people with psychosis will improve accuracy of reporting. SO WHAT?: Enhanced reporting in lifestyle intervention studies for people with psychosis will promote guideline creation and translation of research, which is likely to positively impact physical health outcomes.

Visual Perceptual Load Does Not Affect the Frequency Mismatch Negativity

The mismatch negativity (MMN) has been of particular interest in auditory perception because of its sensitivity to auditory change. It is typically measured in an oddball task and is computed as the difference of deviant minus standard tones. Previous studies suggest that the oddball MMN can be reduced by crossmodal attention to a concurrent, difficult visual task. However, more recent studies did not replicate this effect. Because previous findings seem to be biased, we preregistered the present study and used Bayesian hypothesis testing to measure the strength of evidence for or against an effect of visual task difficulty. We manipulated visual perceptual load (high and low load). In the task, the visual stimuli were identical for both loads to avoid confounding effects from physical differences of the visual stimuli. We also measured the corrected MMN because the oddball MMN may be confounded by physical differences between deviant and standard tones. The corrected MMN is obtained with a separate control condition in which the same tone as the deviant (critical tone) is equiprobable with other tones. The corrected MMN is computed as deviant minus critical tones. Furthermore, we assessed working memory capacity to examine its moderating role. In our large sample (N = 49), the evidential strength in support of no effect of visual load was moderate for the oddball MMN (9.09 > BF01 > 3.57) and anecdotal to moderate for the corrected MMN (4.55 > BF01 > 2.17). Also, working memory capacity did not correlate with the visual load effect on the oddball MMN and the corrected MMN. The present findings support the robustness of the auditory frequency MMN to manipulations of crossmodal, visual attention and suggest that this relationship is not moderated by working memory capacity.

Discovery reliability

Whenever an experiment yields a statistically significant outcome you should ask yourself: To what extent can I trust this result? This is especially important for pre-clinical drug studies because of the frequent failures of phase III clinical trials of neurological diseases, which has put the reliability of pre-clinical research into question. Two important factors, the pre-study likelihood of treatment benefit, and statistical power, affects the reliability of the result in a quantifiable way. This can be used to assess to what extent the result of a study can be trusted (discovery reliability), and to guide the design of pre-clinical research.

Sparse Sampling of Silence Type I Errors With an Emphasis on Primary Auditory Cortex

Sparse sampling functional MRI (ssfMRI) enables stronger primary auditory cortex blood oxygen level-dependent (BOLD) signal by acquiring volumes interspersed with silence, reducing the physiological artifacts associated with scanner noise. Recent calculations of type I error rates associated with resting-state fMRI suggest that the techniques used to model the hemodynamic response function (HRF) might be resulting in higher false positives than is generally acceptable. In the present study, we analyze ssfMRI to determine type I error rates associated with whole brain and primary auditory cortex voxel-wise activation patterns. Study participants (n = 15, age 27.62 ± 3.21 years, range: 22–33 years; 6 females) underwent ssfMRI. An optimized paradigm was used to determine the HRF to auditory stimuli, which was then substituted for silent stimuli to ascertain false positives. We report that common techniques used for analyzing ssfMRI result in high type I error rates. The whole brain and primary auditory cortex voxel-wise analysis resulted in similar error distributions. The number of type I errors for P < 0.05, P < 0.01, and P < 0.001 for the whole brain was 7.88 ± 9.29, 2.37 ± 3.54, and 0.53 ± 0.96% and for the auditory cortex was 9.02 ± 1.79, 2.95 ± 0.91, and 0.58 ± 0.21%, respectively. When conducting a ssfMRI analysis, conservative α level should be employed (α < 0.001) to bolster the results in the face of false positive results.

Sleep restriction caused impaired emotional regulation without detectable brain activation changes-a functional magnetic resonance imaging study

Sleep restriction has been proposed to cause impaired emotional processing and emotional regulation by inhibiting top-down control from prefrontal cortex to amygdala. Intentional emotional regulation after sleep restriction has, however, never been studied using brain imaging. We aimed here to investigate the effect of partial sleep restriction on emotional regulation through cognitive reappraisal. Forty-seven young (age 20-30) and 33 older (age 65-75) participants (38/23 with complete data and successful sleep intervention) performed a cognitive reappraisal task during fMRI after a night of normal sleep and after restricted sleep (3 h). Emotional downregulation was associated with significantly increased activity in the dorsolateral prefrontal cortex (p _FWE < 0.05) and lateral orbital cortex (p _FWE < 0.05) in young, but not in older subjects. Sleep restriction was associated with a decrease in self-reported regulation success to negative stimuli (p < 0.01) and a trend towards perceiving all stimuli as less negative (p = 0.07) in young participants. No effects of sleep restriction on brain activity nor connectivity were found in either age group. In conclusion, our data do not support the idea of a prefrontal-amygdala disconnect after sleep restriction, and neural mechanisms underlying behavioural effects on emotional regulation after insufficient sleep require further investigation.

The Value of Electroencephalogram in Assessing Children With Speech and Language Impairments

Purpose We sought to estimate the prevalence of isolated epileptiform activity (IEA) in children with speech and language impairments and discuss the utility of an electroencephalogram (EEG) in assessing these children. Method We conducted a systematic review and searched for eligible studies in 8 databases. All languages were included, and meta-analyses were performed. Results We found 55 prevalence estimates (8 with control group). The odds of having IEA were 6 times greater for children with speech and language impairments than for typically developing children. The overall pooled prevalence of IEA was 27.3%. A wide variation between the prevalence estimates was, to a certain degree, explained by type of impairment (8.1% in speech impairments, 25.8% in language impairments, and 51.5% in language regression). Sleep EEGs detected a significantly higher prevalence than awake EEGs. Although the presence of epilepsy gave a significantly higher prevalence than if epilepsy was not present, 33.5% of children with language impairment but without epilepsy were found to have IEA in sleep EEGs. Conclusions This systematic review shows that IEA is 6 times more prevalent in children with speech and language impairment than in typically developing children. However, the prevalence rates vary to a great extent. Uncovering IEA will, in addition to information from other clinical assessments, provide a more comprehensive understanding of the child's impairments. We argue that, although EEG is of questionable value when assessing children with speech impairments, sleep EEG could be valuable when assessing children with language impairments and, in particular, children who experience language regression.

Estimating statistical power, posterior probability and publication bias of psychological research using the observed replication rate

In this paper, we show how Bayes' theorem can be used to better understand the implications of the 36% reproducibility rate of published psychological findings reported by the Open Science Collaboration. We demonstrate a method to assess publication bias and show that the observed reproducibility rate was not consistent with an unbiased literature. We estimate a plausible range for the prior probability of this body of research, suggesting expected statistical power in the original studies of 48-75%, producing (positive) findings that were expected to be true 41-62% of the time. Publication bias was large, assuming a literature with 90% positive findings, indicating that negative evidence was expected to have been observed 55-98 times before one negative result was published. These findings imply that even when studied associations are truly NULL, we expect the literature to be dominated by statistically significant findings.

Do Status-Legitimizing Beliefs Moderate Effects of Racial Progress on Perceptions of Anti-White Bias? A Replication of Wilkins and Kaiser (2014)

In three studies, Wilkins and Kaiser found that both chronic and experimental salience of racial progress in the United States increased the perceptions of anti-White bias only among people high in status-legitimizing beliefs (SLBs). We conducted four preregistered high-powered replications of this research. Studies 1, 2, and 3a were close replications of studies 1–3, respectively. Study 3b was a close replication that included an additional experimental condition. Contrary to the original findings, none of the four expected interaction effects tested were statistically significant in the predicted direction, and only one of the four survived a “small telescopes” analysis. We provide additional tests addressing whether changing social contexts explain our failures to replicate, with mixed conclusions. Whereas it is possible that changing social contexts may have eliminated a once true effect, it is also possible that the original results were false positives.

The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings

This study investigated how sample size affects the reproducibility of findings from univariate voxel-based lesion-deficit analyses (e.g., voxel-based lesion-symptom mapping and voxel-based morphometry). Our effect of interest was the strength of the mapping between brain damage and speech articulation difficulties, as measured in terms of the proportion of variance explained. First, we identified a region of interest by searching on a voxel-by-voxel basis for brain areas where greater lesion load was associated with poorer speech articulation using a large sample of 360 right-handed English-speaking stroke survivors. We then randomly drew thousands of bootstrap samples from this data set that included either 30, 60, 90, 120, 180, or 360 patients. For each resample, we recorded effect size estimates and p values after conducting exactly the same lesion-deficit analysis within the previously identified region of interest and holding all procedures constant. The results show (1) how often small effect sizes in a heterogeneous population fail to be detected; (2) how effect size and its statistical significance varies with sample size; (3) how low-powered studies (due to small sample sizes) can greatly over-estimate as well as under-estimate effect sizes; and (4) how large sample sizes (N ≥ 90) can yield highly significant p values even when effect sizes are so small that they become trivial in practical terms. The implications of these findings for interpreting the results from univariate voxel-based lesion-deficit analyses are discussed.

Small sample sizes reduce the replicability of task-based fMRI studies

Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N = 100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences

Individual differences in cognitive paradigms are increasingly employed to relate cognition to brain structure, chemistry, and function. However, such efforts are often unfruitful, even with the most well established tasks. Here we offer an explanation for failures in the application of robust cognitive paradigms to the study of individual differences. Experimental effects become well established - and thus those tasks become popular - when between-subject variability is low. However, low between-subject variability causes low reliability for individual differences, destroying replicable correlations with other factors and potentially undermining published conclusions drawn from correlational relationships. Though these statistical issues have a long history in psychology, they are widely overlooked in cognitive psychology and neuroscience today. In three studies, we assessed test-retest reliability of seven classic tasks: Eriksen Flanker, Stroop, stop-signal, go/no-go, Posner cueing, Navon, and Spatial-Numerical Association of Response Code (SNARC). Reliabilities ranged from 0 to .82, being surprisingly low for most tasks given their common use. As we predicted, this emerged from low variance between individuals rather than high measurement variance. In other words, the very reason such tasks produce robust and easily replicable experimental effects - low between-participant variability - makes their use as correlational tools problematic. We demonstrate that taking such reliability estimates into account has the potential to qualitatively change theoretical conclusions. The implications of our findings are that well-established approaches in experimental psychology and neuropsychology may not directly translate to the study of individual differences in brain structure, chemistry, and function, and alternative metrics may be required.

A Systematic Review and Meta-Analysis of the Relationship Between Brain Data and the Outcome in Disorders of Consciousness

A systematic search revealed 68 empirical studies of neurophysiological [EEG, event-related brain potential (ERP), fMRI, PET] variables as potential outcome predictors in patients with Disorders of Consciousness (diagnoses Unresponsive Wakefulness Syndrome [UWS] and Minimally Conscious State [MCS]). Data of 47 publications could be presented in a quantitative manner and systematically reviewed. Insufficient power and the lack of an appropriate description of patient selection each characterized about a half of all publications. In more than 80% studies, neurologists who evaluated the patients' outcomes were familiar with the results of neurophysiological tests conducted before, and may, therefore, have been influenced by this knowledge. In most subsamples of datasets, effect size significantly correlated with its standard error, indicating publication bias toward positive results. Neurophysiological data predicted the transition from UWS to MCS substantially better than they predicted the recovery of consciousness (i.e., the transition from UWS or MCS to exit-MCS). A meta-analysis was carried out for predictor groups including at least three independent studies with N > 10 per predictor per improvement criterion (i.e., transition to MCS versus recovery). Oscillatory EEG responses were the only predictor group whose effect attained significance for both improvement criteria. Other perspective variables, whose true prognostic value should be explored in future studies, are sleep spindles in the EEG and the somatosensory cortical response N20. Contrary to what could be expected on the basis of neuroscience theory, the poorest prognostic effects were shown for fMRI responses to stimulation and for the ERP component P300. The meta-analytic results should be regarded as preliminary given the presence of numerous biases in the data.

Reward motivation in humans and its relationship to dopamine D2/3 receptor availability: A pilot study with dual [11C]-raclopride and [11C]-(+)-PHNO imaging

Rodent studies suggest that dopamine signaling at D_2/3 receptors in the ventral striatum is critical for reward motivation. Whether this is also true in humans is unclear. Positron emission tomography studies in healthy humans have generally not observed a relationship between D_2/3 receptor availability in the ventral striatum and motivation. We developed the "mounting-effort for reward task" to assess high motivational demand for (a) gaining money (CS+), (b) losing money or avoiding electric shock (CS-), and (c) non-reward (Neutral). Receipt was contingent on participants making sufficient button responses relative to a "reward-threshold" determined by prior motor performance. This reward-threshold was dynamically increased if surpassed, making the task increasingly more difficult on every trial. The mounting-effort for reward task was preliminarily validated in 29 healthy volunteers (mean age: 25.83±3.58; 15 female). In this sample, %CS+ and %CS- significantly correlated with different dimensions of self-reported apathy. In a sub-sample of eight healthy volunteers (mean age: 25.75±1.91; four female), the mounting-effort for reward task demonstrated good test-retest reliability (%variance: 0.20-2.61%). Seven healthy male volunteers (mean age: 31.14±5.43) completed the mounting-effort for reward task and provided both [¹¹C]-raclopride and [¹¹C]-(+)-PHNO PET scans to assess D_2/3 receptor availability. %CS+ and %CS- were positively correlated with [¹¹C]-raclopride binding in the dorsal striatum. %CS+, %Cs-, and %Neutral were positively correlated with [¹¹C]-(+)-PHNO binding in the globus pallidus. Thus, increased expression of D₂ receptors in the dorsal striatum, and D₃ receptors in the globus pallidus, may be related to motivation for rewards. Larger positron emission tomography studies are required to formally validate the mounting-effort for reward task and replicate our pilot findings.

The Cultivation of Pure Altruism via Gratitude: A Functional MRI Study of Change with Gratitude Practice

Gratitude is an emotion and a trait linked to well-being and better health, and welcoming benefits to oneself is instrumentally valuable. However, theoretical and empirical work highlights that gratitude is more fully understood as an intrinsically valuable moral emotion. To understand the role of neural reward systems in the association between gratitude and altruistic motivations we tested two hypotheses: First, whether self-reported propensity toward gratitude relates to fMRI-derived indicators of "pure altruism," operationalized as the neural valuation of passive, private transfers to a charity versus to oneself. In young adult female participants, self-reported gratitude and altruism were associated with "neural pure altruism" in ventromedial prefrontal cortex (VMPFC) and nucleus accumbens. Second, whether neural pure altruism can be increased through practicing gratitude. In a double-blind study, we randomly assigned participants to either a gratitude-journal or active-neutral control journal group for 3 weeks. Relative to pre-test levels, gratitude journaling increased the neural pure altruism response in the VMPFC. We posit that as a context-dependent value-sensitive cortical region, the VMPFC supports change with gratitude practice, a change that is larger for benefits to others versus oneself.

A hitchhiker's guide to lesion-behaviour mapping

Lesion-behaviour mapping is an influential and popular approach to anatomically localise cognitive brain functions in the human brain. Multiple considerations, ranging from patient selection, assessment of lesion location and patient behaviour, spatial normalisation, statistical testing, to the anatomical interpretation of obtained results, are necessary to optimize a lesion-behaviour mapping study and arrive at meaningful conclusions. Here, we provide a hitchhiker's guide, giving practical guidelines and references for each step of the typical lesion-behaviour mapping study pipeline.

'Whose atlas I use, his song I sing?' - The impact of anatomical atlases on fiber tract contributions to cognitive deficits after stroke

Nowadays, different anatomical atlases exist for the anatomical interpretation of the results from neuroimaging and lesion analysis studies that investigate the contribution of white matter fiber tract integrity to cognitive (dys)function. A major problem with the use of different atlases in different studies, however, is that the anatomical interpretation of neuroimaging and lesion analysis results might vary as a function of the atlas used. This issue might be particularly prominent in studies that investigate the contribution of white matter fiber tract integrity to cognitive (dys)function. We used a single large-sample dataset of right brain damaged stroke patients with and without cognitive deficit (here: spatial neglect) to systematically compare the influence of three different, widely-used white matter fiber tract atlases (1 histology-based atlas and 2 DTI tractography-based atlases) on conclusions concerning the involvement of white matter fiber tracts in the pathogenesis of cognitive dysfunction. We both calculated the overlap between the statistical lesion analysis results and each long association fiber tract (topological analyses) and performed logistic regressions on the extent of fiber tract damage in each individual for each long association white matter fiber tract (hodological analyses). For the topological analyses, our results suggest that studies that use tractography-based atlases are more likely to conclude that white matter integrity is critical for a cognitive (dys)function than studies that use a histology-based atlas. The DTI tractography-based atlases classified approximately 10 times as many voxels of the statistical map as being located in a long association white matter fiber tract than the histology-based atlas. For hodological analyses on the other hand, we observed that the conclusions concerning the overall importance of long association fiber tract integrity to cognitive function do not necessarily depend on the white matter atlas used, but conclusions may vary as a function of atlas used at the level of individual fiber tracts. Moreover, these analyses revealed that hodological studies that express the individual extent of injury to each fiber tract as a binomial variable are more likely to conclude that white matter integrity is critical for a cognitive function than studies that express the individual extent of injury to each fiber tract as a continuous variable.

Searching for behavior relating to grey matter volume in a-priori defined right dorsal premotor regions: Lessons learned

Recently, we showed that the functional heterogeneity of the right dorsal premotor (PMd) cortex could be better understood by dividing it into five subregions that showed different behavioral associations according to task-based activations studies. The present study investigated whether the revealed behavioral profile could be corroborated and complemented by a structural brain behavior correlation approach in two healthy adults cohorts. Grey matter volume within the five volumes of interest (VOI-GM) was computed using voxel-based morphometry. Associations between the inter-individual differences in VOI-GM and performance across a range of neuropsychological tests were assessed in the two cohorts with and without correction for demographical variables. Additional analyses were performed in random smaller subsamples drawn from each of the two cohorts. In both cohorts, correlation coefficients were low; only few were significant and a considerable number of correlations were counterintuitive in their directions (i.e., higher performance related to lower grey matter volume). Furthermore, correlation patterns were inconsistent between the two cohorts. Subsampling revealed that correlation patterns could vary widely across small samples and that negative correlations were as likely as positive correlations. Thus, the structural brain-behavior approach did not corroborate the functional profiles of the PMd subregions inferred from activation studies, suggesting that local recruitment by fMRI studies does not necessarily imply covariance of local structure with behavioral performance in healthy adults. We discuss the limitations of such studies and related recommendations for future studies.

Processing of gaze direction within the N170/M170 time window: A combined EEG/MEG study

Gaze direction is an important social signal for human beings. Beside the role of gaze in attention orienting, direct gaze (that is, gaze directed toward an observer) is a highly relevant biological stimulus that elicits attention capture and increases face encoding. Brain imaging studies have emphasized the role of the superior temporal sulcus (STS) in the coding of gaze direction and in the integration of gaze and head cues of social attention. The dynamics of the processing and integration of these cues remains, however, unclear. In order to address this question, we used deviated and frontal faces with averted and direct gaze in a combined electro- and magneto- encephalography (EEG-MEG) study. We showed distinct effects of gaze direction on the N170 and M170 responses. There was an interaction between gaze direction and head orientation between 134 and 162ms in MEG and a main effect of gaze direction between 171 and 186ms in EEG. These effects involved the posterior and anterior regions of the STS respectively. Both effects also emphasized the sensitivity to direct gaze. These data highlight the central role of the STS in gaze processing.

Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data

BACKGROUND

Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity.

NEW METHOD

The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence.

RESULTS

The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs.

CONTROLS

The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method.

COMPARISON

The proposed architecture leads to reliable estimates of EC than the existing latent models.

CONCLUSIONS

This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process.

Stimulus-Driven Reorienting Impairs Executive Control of Attention: Evidence for a Common Bottleneck in Anterior Insula

A classical model of human attention holds that independent neural networks realize stimulus-driven reorienting and executive control of attention. Questioning full independence, the two functions do, however, engage overlapping networks with activations in cingulo-opercular regions such as anterior insula (AI) and a reverse pattern of activation (stimulus-driven reorienting), and deactivation (executive control) in temporoparietal junction (TPJ). To test for independent versus shared neural mechanisms underlying stimulus-driven and executive control of attention, we used fMRI and a task that isolates individual from concurrent demands in both functions. Results revealed super-additive increases of left AI activity and behavioral response costs under concurrent demands, suggesting a common bottleneck for stimulus-driven reorienting and executive control of attention. These increases were mirrored by non-additive decreases of activity in the default mode network (DMN), including posterior TPJ, regions where activity increased with off-task processes. The deactivations in posterior TPJ were spatially separated from stimulus-driven reorienting related activation in anterior TPJ, a differentiation that replicated in task-free resting state. Furthermore, functional connectivity indicated inhibitory coupling between posterior TPJ and AI during concurrent attention demands. These results demonstrate a role of AI in stimulus-driven and executive control of attention that involves down-regulation of internally directed processes in DMN.

Visual Task Demands and the Auditory Mismatch Negativity: An Empirical Study and a Meta-Analysis

Because the auditory system is particularly useful in monitoring the environment, previous research has examined whether task-irrelevant, auditory distracters are processed even if subjects focus their attention on visual stimuli. This research suggests that attentionally demanding visual tasks decrease the auditory mismatch negativity (MMN) to simultaneously presented auditory distractors. Because a recent behavioral study found that high visual perceptual load decreased detection sensitivity of simultaneous tones, we used a similar task (n = 28) to determine if high visual perceptual load would reduce the auditory MMN. Results suggested that perceptual load did not decrease the MMN. At face value, these nonsignificant findings may suggest that effects of perceptual load on the MMN are smaller than those of other demanding visual tasks. If so, effect sizes should differ systematically between the present and previous studies. We conducted a selective meta-analysis of published studies in which the MMN was derived from the EEG, the visual task demands were continuous and varied between high and low within the same task, and the task-irrelevant tones were presented in a typical oddball paradigm simultaneously with the visual stimuli. Because the meta-analysis suggested that the present (null) findings did not differ systematically from previous findings, the available evidence was combined. Results of this meta-analysis confirmed that demanding visual tasks reduce the MMN to auditory distracters. However, because the meta-analysis was based on small studies and because of the risk for publication biases, future studies should be preregistered with large samples (n > 150) to provide confirmatory evidence for the results of the present meta-analysis. These future studies should also use control conditions that reduce confounding effects of neural adaptation, and use load manipulations that are defined independently from their effects on the MMN.

Cross-validation and hypothesis testing in neuroimaging: An irenic comment on the exchange between Friston and Lindquist et al

The "ten ironic rules for statistical reviewers" presented by Friston (2012) prompted a rebuttal by Lindquist et al. (2013), which was followed by a rejoinder by Friston (2013). A key issue left unresolved in this discussion is the use of cross-validation to test the significance of predictive analyses. This note discusses the role that cross-validation-based and related hypothesis tests have come to play in modern data analyses, in neuroimaging and other fields. It is shown that such tests need not be suboptimal and can fill otherwise-unmet inferential needs.

Is Playing Video Games Related to Cognitive Abilities?

The relations between video-game experience and cognitive abilities were examined in the current study. In two experiments, subjects performed a number of working memory, fluid intelligence, and attention-control measures and filled out a questionnaire about their video-game experience. In Experiment 1, an extreme-groups analysis indicated that experienced video-game players outperformed nonplayers on several cognitive-ability measures. However, in Experiments 1 and 2, when analyses examined the full range of subjects at both the task level and the latent-construct level, nearly all of the relations between video-game experience and cognitive abilities were near zero. These results cast doubt on recent claims that playing video games leads to enhanced cognitive abilities. Statistical and methodological issues with prior studies of video-game experience are discussed along with recommendations for future studies.

Desperately seeking grey matter volume changes in sleep apnea: A methodological review of magnetic resonance brain voxel-based morphometry studies

Cognitive impairment related to obstructive sleep apnea might be explained by subtle changes in brain anatomy. This has been mainly investigated using magnetic resonance brain scans coupled with a voxel-based morphometry analysis. However, this approach is prone to several methodological pitfalls that may explain the large discrepancy in the results reported in the literature. We critically reviewed twelve papers addressing grey matter volume modifications in association with obstructive sleep apnea. Finally, based on strict methodological criteria, only three studies reported robust, but conflicting, results. No clear evidence has emerged and exploring brain alteration due to obstructive sleep apnea should thus be considered as an open field. We provide recommendations for designing additional robust voxel-based morphometry studies, notably the use of larger cohorts, which is the only way to solve the underpowered issue and the underestimated role of confounders in neuroimaging studies.

Statistical inferences under the Null hypothesis: common mistakes and pitfalls in neuroimaging studies

Published studies using functional and structural MRI include many errors in the way data are analyzed and conclusions reported. This was observed when working on a comprehensive review of the neural bases of synesthesia, but these errors are probably endemic to neuroimaging studies. All studies reviewed had based their conclusions using Null Hypothesis Significance Tests (NHST). NHST have yet been criticized since their inception because they are more appropriate for taking decisions related to a Null hypothesis (like in manufacturing) than for making inferences about behavioral and neuronal processes. Here I focus on a few key problems of NHST related to brain imaging techniques, and explain why or when we should not rely on "significance" tests. I also observed that, often, the ill-posed logic of NHST was even not correctly applied, and describe what I identified as common mistakes or at least problematic practices in published papers, in light of what could be considered as the very basics of statistical inference. MRI statistics also involve much more complex issues than standard statistical inference. Analysis pipelines vary a lot between studies, even for those using the same software, and there is no consensus which pipeline is the best. I propose a synthetic view of the logic behind the possible methodological choices, and warn against the usage and interpretation of two statistical methods popular in brain imaging studies, the false discovery rate (FDR) procedure and permutation tests. I suggest that current models for the analysis of brain imaging data suffer from serious limitations and call for a revision taking into account the "new statistics" (confidence intervals) logic.

Attempted and successful compensation in preclinical and early manifest neurodegeneration - a review of task FMRI studies

Several models of neural compensation in healthy aging have been suggested to explain brain activity that aids to sustain cognitive function. Applying recently suggested criteria of "attempted" and "successful" compensation, we reviewed existing literature on compensatory mechanisms in preclinical Huntington's disease (HD) and amnestic mild cognitive impairment (aMCI). Both disorders constitute early stages of neurodegeneration ideal for examining compensatory mechanisms and developing targeted interventions. We strived to clarify whether compensation criteria derived from healthy aging populations can be applied to early neurodegeneration. To concentrate on the close coupling of cognitive performance and brain activity, we exclusively addressed task fMRI studies. First, we found evidence for parallels in compensatory mechanisms between healthy aging and neurodegenerative disease. Several studies fulfilled criteria of attempted compensation, while reports of successful compensation were largely absent, which made it difficult to conclude on. Second, comparing working memory studies in preclinical HD and aMCI, we identified similar compensatory patterns across neurodegenerative disorders in lateral and medial prefrontal cortex. Such patterns included an inverted U-shaped relationship of neurodegeneration and compensatory activity spanning from preclinical to manifest disease. Due to the lack of studies systematically targeting all criteria of compensation, we propose an exemplary study design, including the manipulation of compensating brain areas by brain stimulation. Furthermore, we delineate the benefits of targeted interventions by non-invasive brain stimulation, as well as of unspecific interventions such as physical activity or cognitive training. Unambiguously detecting compensation in early neurodegenerative disease will help tailor interventions aiming at sustained overall functioning and delayed clinical disease onset.

Two distinct dynamic modes subtend the detection of unexpected sounds

The brain response to auditory novelty comprises two main EEG components: an early mismatch negativity and a late P300. Whereas the former has been proposed to reflect a prediction error, the latter is often associated with working memory updating. Interestingly, these two proposals predict fundamentally different dynamics: prediction errors are thought to propagate serially through several distinct brain areas, while working memory supposes that activity is sustained over time within a stable set of brain areas. Here we test this temporal dissociation by showing how the generalization of brain activity patterns across time can characterize the dynamics of the underlying neural processes. This method is applied to magnetoencephalography (MEG) recordings acquired from healthy participants who were presented with two types of auditory novelty. Following our predictions, the results show that the mismatch evoked by a local novelty leads to the sequential recruitment of distinct and short-lived patterns of brain activity. In sharp contrast, the global novelty evoked by an unexpected sequence of five sounds elicits a sustained state of brain activity that lasts for several hundreds of milliseconds. The present results highlight how MEG combined with multivariate pattern analyses can characterize the dynamics of human cortical processes.

Attention shifts the language network reflecting paradigm presentation

OBJECTIVES

Functional magnetic resonance imaging (fMRI) is a reliable and non-invasive method with which to localize language function in pre-surgical planning. In clinical practice, visual stimulus presentation is often difficult or impossible, due to the patient's restricted language or attention abilities. Therefore, our aim was to investigate modality-specific differences in visual and auditory stimulus presentation.

METHODS

Ten healthy subjects participated in an fMRI study comprising two experiments with visual and auditory stimulus presentation. In both experiments, two language paradigms (one for language comprehension and one for language production) used in clinical practice were investigated. In addition to standard data analysis by the means of the general linear model (GLM), independent component analysis (ICA) was performed to achieve more detailed information on language processing networks.

RESULTS

GLM analysis revealed modality-specific brain activation for both language paradigms for the contrast visual > auditory in the area of the intraparietal sulcus and the hippocampus, two areas related to attention and working memory. Using group ICA, a language network was detected for both paradigms independent of stimulus presentation modality. The investigation of language lateralization revealed no significant variations. Visually presented stimuli further activated an attention-shift network, which could not be identified for the auditory presented language.

CONCLUSION

The results of this study indicate that the visually presented language stimuli additionally activate an attention-shift network. These findings will provide important information for pre-surgical planning in order to preserve reading abilities after brain surgery, significantly improving surgical outcomes. Our findings suggest that the presentation modality for language paradigms should be adapted on behalf of individual indication.

Potential reporting bias in fMRI studies of the brain

Background

Functional magnetic resonance imaging (fMRI) studies have reported multiple activation foci associated with a variety of conditions, stimuli or tasks. However, most of these studies used fewer than 40 participants.

Methodology

After extracting data (number of subjects, condition studied, number of foci identified and threshold) from 94 brain fMRI meta-analyses (k = 1,788 unique datasets) published through December of 2011, we analyzed the correlation between individual study sample sizes and number of significant foci reported. We also performed an analysis where we evaluated each meta-analysis to test whether there was a correlation between the sample size of the meta-analysis and the number of foci that it had identified. Correlation coefficients were then combined across all meta-analyses to obtain a summary correlation coefficient with a fixed effects model and we combine correlation coefficients, using a Fisher’s z transformation.

Principal Findings

There was no correlation between sample size and the number of foci reported in single studies (r = 0.0050) but there was a strong correlation between sample size and number of foci in meta-analyses (r = 0.62, p<0.001). Only studies with sample sizes <45 identified larger (>40) numbers of foci and claimed as many discovered foci as studies with sample sizes ≥45, whereas meta-analyses yielded a limited number of foci relative to the yield that would be anticipated from smaller single studies.

Conclusions

These results are consistent with possible reporting biases affecting small fMRI studies and suggest the need to promote standardized large-scale evidence in this field. It may also be that small studies may be analyzed and reported in ways that may generate a larger number of claimed foci or that small fMRI studies with inconclusive, null, or not very promising results may not be published at all.

Sample size and the fallacies of classical inference

I would like to thank Michael Ingre, Martin Lindquist and their co-authors for their thoughtful responses to my ironic Comments and Controversies piece. I was of two minds about whether to accept the invitation to reply - largely because I was convinced by most of their observations. I concluded that I should say this explicitly, taking the opportunity to consolidate points of consensus and highlight outstanding issues.