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Recent data suggest that interactions between systems involved in higher order knowledge and associative learning drive responses during value-based learning. However, it is unknown how these systems impact subjective responses, such as pain. We tested how instructions and reversal learning influence pain and pain-evoked brain activation. Healthy volunteers (n=40) were either instructed about contingencies between cues and aversive outcomes or learned through experience in a paradigm where contingencies reversed three times. We measured predictive cue effects on pain and heat-evoked brain responses using functional magnetic resonance imaging. Predictive cues dynamically modulated pain perception as contingencies changed, regardless of whether participants received contingency instructions. Heat-evoked responses in the insula, anterior cingulate, and other regions updated as contingencies changed, and responses in the prefrontal cortex mediated dynamic cue effects on pain, whereas responses in the brainstem’s rostroventral medulla (RVM) were shaped by initial contingencies throughout the task. Quantitative modeling revealed that expected value was shaped purely by instructions in the Instructed Group, whereas expected value updated dynamically in the Uninstructed Group as a function of error-based learning. These differences were accompanied by dissociations in the neural correlates of value-based learning in the rostral anterior cingulate, thalamus, and posterior insula, among other regions. These results show how predictions dynamically impact subjective pain. Moreover, imaging data delineate three types of networks involved in pain generation and value-based learning: those that respond to initial contingencies, those that update dynamically during feedback-driven learning as contingencies change, and those that are sensitive to instruction. Together, these findings provide multiple points of entry for therapies designs to impact pain.

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present “only the significant results” to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, we propose that studies should “highlight” the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and “digestible” findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams’ results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative “highlighting” approach shows this relative agreement, while one using the standard “hiding” approach does not. We describe how this simple but powerful change in practice---focusing on highlighting results, rather than hiding all but the strongest ones---can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses.HighlightsMost studies do not present all results of their analysis, hiding subthreshold ones.Hiding results negatively affects the interpretation and understanding of the study.Neuroimagers should present all results of their study, highlighting key ones.Using the public NARPS data, we show several benefits of the “highlighting” approach.The highlighting approach improves individual studies and meta-analyses.

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While a large body of literature documents the impairing effect of anxiety on cognition, performing a demanding task was shown to be effective in reducing anxiety. Here we explored the mechanisms of this anxiolytic effect by examining how a pharmacological challenge designed to improve attentional processes influences the interplay between the neural networks engaged during anxiety and cognition. Using a double-blind between-subject design, we pharmacologically manipulated working memory (WM) using a single oral dose of 20 mg methylphenidate (MPH, cognitive enhancer) or placebo. Fifty healthy adults (25/drug group) performed two runs of a WM N-back task in a 3 T magnetic resonance imaging scanner. This task comprised a low (1-Back) and high (3-Back) WM load, which were performed in two contexts, safety or threat of shocks (induced-anxiety). Analyses revealed that (1) WM accuracy was overall improved by MPH and (2) MPH (vs. placebo) strengthened the engagement of regions within the fronto-parietal control network (FPCN) and reduced the default mode network (DMN) deactivation. These MPH effects predominated in the most difficult context, i.e., threat condition, first run (novelty of the task), and 3-Back task. The facilitation of neural activation can be interpreted as an expansion of cognitive resources, which could foster both the representation and integration of anxiety-provoking stimuli as well as the top–down regulatory processes to protect against the detrimental effect of anxiety. This mechanism might establish an optimal balance between FPCN (cognitive processing) and DMN (emotion regulation) recruitment.

Recent data suggest that interactions between systems involved in higher order knowledge and associative learning drive responses during appetitive and aversive learning. However, it is unknown how these systems impact subjective responses, such as pain. We tested how instructions and reversal learning influence pain and pain-evoked brain activation. Healthy volunteers (n = 40) were either instructed about contingencies between cues and aversive outcomes or learned through experience in a paradigm where contingencies reversed three times. We measured predictive cue effects on pain and heat-evoked brain responses using functional magnetic resonance imaging. Predictive cues dynamically modulated pain perception as contingencies changed, regardless of whether participants received contingency instructions. Heat-evoked responses in the insula, anterior cingulate, and putamen updated as contingencies changed, whereas the periaqueductal gray and thalamus responded to initial contingencies throughout the task. Quantitative modeling revealed that expected value was shaped purely by instructions in the Instructed Group, whereas expected value updated dynamically in the Uninstructed Group as a function of error-based learning. These differences were accompanied by dissociations in the neural correlates of value-based learning in the rostral anterior cingulate, medial prefrontal cortex, and orbitofrontal cortex. These results show how predictions impact subjective pain. Moreover, imaging data delineate three types of networks involved in pain generation and value-based learning: those that respond to initial contingencies, those that update dynamically during feedback-driven learning as contingencies change, and those that are sensitive to instruction. Together, these findings provide multiple points of entry for therapies designs to impact pain.

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed open-source and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain ‘layerification’ and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data.HighlightsA new software toolbox is introduced for layer-specific functional MRI: LayNii.LayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS.LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints.LayNii performs layerification in the native voxel space of functional data.LayNii performs layer-smoothing, GE-BOLD deveining, QA, and VASO analysis.Abstract FigureGraphical abstract

Object: A real-time functional magnetic resonance imaging (fMRI) feedback during ventral intermediate nucleus (VIM) deep brain stimulation (DBS) under general anesthesia (or “asleep” DBS) does not exist. We hypothesized that it was feasible to acquire a reliable and responsive fMRI during asleep VIM DBS surgery.Methods: We prospectively enrolled 10 consecutive patients who underwent asleep DBS for the treatment of medication-refractory essential tremor. Under general anesthesia, we acquired resting-state functional MRI immediately before and after the cannula insertion. Reliability was determined by a temporal signal-to-noise-ratio >100. Responsiveness was determined based on the fMRI signal change upon insertion of the cannula to the VIM.Results: It was feasible to acquire reliable fMRI during asleep DBS surgery. The fMRI signal was responsive to the brain cannula insertion, revealing a reduction in the tremor network's functional connectivity, which did not reach statistical significance in the group analysis.Conclusions: It is feasible to acquire a reliable and responsive fMRI signal during asleep DBS. The acquisition steps and the preprocessing pipeline developed in these experiments will be useful for future investigations to develop fMRI-based feedback for asleep DBS surgery.

OBJECTIVES: Deconvolution-derived maps of CT perfusion (CTP) data may be confounded by transit delays. We propose temporal similarity perfusion (TSP) analysis to decrease CTP maps' dependence on transit times and investigate its sensitivity to detect perfusion deficits.METHODS: CTP data of acute stroke patients obtained within 9 h of symptom onset was analyzed using a delay-insensitive singular value decomposition method and with TSP. The TSP method applies an iterative process whereby a pixel's highest Pearson's R value is obtained through comparison of a pixel's time-shifted signal density time-series curve and the average whole brain signal density time-series curve. Our evaluation included a qualitative and quantitative rating of deconvolution maps (MTT, CBV, and TTP), of TSP maps, and of follow-up CT.RESULTS: Sixty-five patients (mean 68 (SD 13) years, 34 male) were included. A perfusion deficit was identified in 90%, 86%, 65%, and 84% of MTT, TTP, CBV, and TSP maps. The agreement of MTT, TTP, and TSP with CT follow-up was comparable but noticeably lower for CBV. CBV had the best relationship with final infarct volume (R2 = 0.77, p CONCLUSIONS: TSP maps are easier to interpret for inexperienced readers. Perfusion deficits detected by TSP are smaller which may suggest less dependence on transit delays although more investigation is required.KEY POINTS: • Temporal similarity perfusion mapping assesses CTP data based on similarities in signal time-curves. • TSP maps are comparable in perfusion deficit detection to deconvolution maps. • TSP maps are easier to interpret for inexperienced readers.

OBJECTIVES Little is known about potential differences in the pathophysiology of bipolar disorder (BD) across development. The present study aimed to characterize age-related neural mechanisms of BD. METHODS Youths and adults with and without BD (N = 108, age range = 9.8-55.9 years) completed an emotional face labeling task during fMRI acquisition. We leveraged three different fMRI analytic tools to identify age-related neural mechanisms of BD, investigating (a) change in neural responses over the course of the task, (b) neural activation averaged across the entire task, and (c) amygdala functional connectivity. RESULTS We found converging Age Group × Diagnosis patterns across all three analytic methods. Compared to healthy youths vs adults, youths vs adults with BD show an altered pattern in response to repeated presentation of emotional faces in medial prefrontal, amygdala, and temporoparietal regions, as well as amygdala-temporoparietal connectivity. Specifically, medial prefrontal and lingual activation decreases over the course of repeated emotional face presentations in healthy youths vs adults but increases in youths with BD compared to adults with BD. Moreover, youths vs adults with BD show less medial prefrontal activation and amygdala-temporoparietal junction connectivity averaged over the task, but this difference is not found for healthy youths vs adults. CONCLUSION Although longitudinal confirmation and replication will be necessary, these findings suggest that neural development may be aberrant in BD and that some neural mechanisms mediating BD may differ in adults vs children with the illness.

Irritability is impairing and prevalent across pediatric psychiatric disorders and typical development, yet its neural mechanisms are largely unknown. This study evaluated the relation between adolescent irritability and reward-related brain function as a candidate neural mechanism. Adolescents from intervention-seeking families in the community (N = 52; mean age = 13.80, SD = 1.94) completed a monetary incentive delay task to assess reward anticipation and feedback (reward receipt and omission) during fMRI acquisition. Whole-brain analyses, controlling for age, examined brain activation and striatal and amygdala connectivity in relation to irritability. Irritability was measured using the parent- and youth-reported Affective Reactivity Index. Irritability was associated with altered reward processing-related activation and connectivity in multiple networks during reward anticipation and feedback, including increased striatal activation and altered ventral striatum connectivity with prefrontal areas. Our findings suggest that irritability is associated with altered neural patterns during reward processing and that aberrant prefrontal cortex-mediated top-down control may be related to irritability. These findings inform our understanding of the etiology of youth irritability and the development of mechanism-based interventions.

Task, resting state, and diffusion MRI data are usually acquired from subjects using echo-planar based imaging techniques. These techniques are highly susceptible to B0 homogeneity effects that result in geometric distortions in the reconstructed images. As researchers work to link the information from these scans back to various developmental stages, or to conditions and diseases in specific regions or structures of the brain, it becomes critical to have accurate correspondence between more geometrically distorted echo-planar images and less geometrically distorted anatomical images. A variety of techniques and tools have been developed to improve this correspondence. The basic premise behind most techniques used to mitigate geometric distortion is to acquire enough information to inform software tools how echo-planar images are warped, then have them undo that warping. Here, we investigate the application of two common methods: B0 correction, and reverse-polarity phase-encoding (or reverse blip) correction. We implement each of these in two separate, widely used software packages in the field: AFNI and FSL. We find that using either technique in either software package results in reduced geometric distortions in the EPI images. We discuss the practical implementations of these methods (e.g., increased scan and processing time). In general, however, both methods possess readily available data acquisition schemes, and are highly efficient to include in processing streams. Due to the overall data improvement, we strongly recommend that researchers include one of these methods in their standard protocols.

Botvinik-Nezer, Rotem et al., Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., European Union’s Horizon 2020 research and innovation programme (https://ec.europa.eu/programmes/horizon2020/en) under the Marie Skłodowska-Curie grant agreement no. 665501. A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa. S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2).

The field of cognitive neuroscience is weighing evidence about whether to move from standard field strength to ultra-high field (UHF). The present study contributes to the evidence by comparing a cognitive neuroscience paradigm at 3 Tesla (3T) and 7 Tesla (7T). The goal was to test and demonstrate the practical effects of field strength on a standard GO/NOGO task using accessible preprocessing and analysis tools. Two independent matched healthy samples (N = 31 each) were analyzed at 3T and 7T. Results show gains at 7T in statistical strength, the detection of smaller effects and group-level power. With an increased availability of UHF scanners, these gains may be exploited by cognitive neuroscientists and other neuroimaging researchers to develop more efficient or comprehensive experimental designs and, given the same sample size, achieve greater statistical power at 7T.

Objective Presurgical language assessment can help minimize damage to eloquent cortex during resective epilepsy surgery. Two methods for presurgical language mapping are functional MRI (fMRI) and direct cortical stimulation (DCS) of implanted subdural electrodes. We compared fMRI results to DCS to help optimize noninvasive language localization and assess its validity. Methods We studied 19 patients referred for presurgical evaluation of drug-resistant epilepsy. Patients completed four language tasks during preoperative fMRI. After subdural electrode implantation, we used DCS to localize language areas. For each stimulation site, we determined whether language positive electrode pairs intersected with significant fMRI activity clusters for language tasks. Results Sensitivity and specificity depended on electrode region of interest radii and statistical thresholding. For patients with at least one language positive stimulation site, an auditory description decision task provided the best trade-off between sensitivity and specificity. For patients with no language positive stimulation sites, fMRI was a dependable method of excluding eloquent language processing. Interpretation Language fMRI is an effective tool for determining language lateralization before electrode implantation and is especially useful for excluding unexpected critical language areas. It can help guide subdural electrode implantation and narrow the search for eloquent cortical areas by DCS. Ann Neurol 2017;81:526-537.

It has been argued that naturalistic conditions in FMRI studies provide a useful paradigm for investigating perception and cognition through a synchronization measure, inter-subject correlation (ISC). However, one analytical stumbling block has been the fact that the ISC values associated with each single subject are not independent, and our previous paper (Chen et al., 2016) used simulations and analyses of real data to show that the methodologies adopted in the literature do not have the proper control for false positives. In the same paper, we proposed nonparametric subject-wise bootstrapping and permutation testing techniques for one and two groups, respectively, which account for the correlation structure, and these greatly outperformed the prior methods in controlling the false positive rate (FPR); that is, subject-wise bootstrapping (SWB) worked relatively well for both cases with one and two groups, and subject-wise permutation (SWP) testing was virtually ideal for group comparisons. Here we seek to explicate and adopt a parametric approach through linear mixed-effects (LME) modeling for studying the ISC values, building on the previous correlation framework, with the benefit that the LME platform offers wider adaptability, more powerful interpretations, and quality control checking capability than nonparametric methods. We describe both theoretical and practical issues involved in the modeling and the manner in which LME with crossed random effects (CRE) modeling is applied. A data-doubling step further allows us to conveniently track the subject index, and achieve easy implementations. We pit the LME approach against the best nonparametric methods, and find that the LME framework achieves proper control for false positives. The new LME methodologies are shown to be both efficient and robust, and they will be added as an additional option and settings in an existing open source program, 3dLME, in AFNI (http://afni.nimh.nih.gov).

Psychophysiological interaction (PPI) is a widely used regression-based method to study connectivity changes in different experimental conditions. A PPI effect is generated by point-by-point multiplication of a psychological variable (experimental design) and a physiological variable (time series of a seed region). If the psychological variable is non-centered with a constant component, the constant component will add a physiological variable to the PPI term. The physiological component would in theory be accounted for by the physiological main effect in the model. But due to imperfect deconvolution and convolution with hemodynamic response function, the physiological component in PPI may no longer be exactly the same as the physiological main effect. This issue was illustrated by analyzing two block-designed fMRI datasets, one simple visual checkerboard task and a set of different tasks designed to activate different hemispheres. When PPI was calculated with deconvolution but without centering, significant results were usually observed between regions that are known to have baseline functional connectivity. These results could be suppressed by simply centering the psychological variable when calculating the PPI term or adding a deconvolve-reconvolved version of the physiological covariate. The PPI results with centering and with deconvolve-reconvolved physiological covariate are consistent with an explicit test for differences in coupling between conditions. It was, therefore, suggested that centering of the psychological variable or the addition of a deconvolve-reconvolved covariate is necessary for PPI analysis. Hum Brain Mapp 38:1723-1740, 2017. © 2017 Wiley Periodicals, Inc.

We compared resting state (RS) functional connectivity and task‐based fMRI to lateralize language dominance in 30 epilepsy patients (mean age = 33; SD = 11; 12 female), a measure used for presurgical planning. Language laterality index (LI) was calculated from task fMRI in frontal, temporal, and frontal + temporal regional masks using LI bootstrap method from SPM12. RS language LI was assessed using two novel methods of calculating RS language LI from bilateral Broca's area seed based connectivity maps across regional masks and multiple thresholds (p

Social anxiety disorder involves biased cognition and altered neural responses to social stimuli. This study further assesses the precise ways in which neural activation associated with perceptual ...

Bipolar disorder and disruptive mood dysregulation disorder (DMDD) are clinically and pathophysiologically distinct, yet irritability can be a clinical feature of both illnesses. The authors examine whether the neural mechanisms mediating irritability differ between bipolar disorder and DMDD, using a face emotion labeling paradigm because such labeling is deficient in both patient groups. The authors hypothesized that during face emotion labeling, irritability would be associated with dysfunctional activation in the amygdala and other temporal and prefrontal regions in both disorders, but that the nature of these associations would differ between DMDD and bipolar disorder.During functional MRI acquisition, 71 youths (25 with DMDD, 24 with bipolar disorder, and 22 healthy youths) performed a labeling task with happy, fearful, and angry faces of varying emotional intensity.Participants with DMDD and bipolar disorder showed similar levels of irritability and did not differ from each other or from healthy youths in face emotion labeling accuracy. Irritability correlated with amygdala activity across all intensities for all emotions in the DMDD group; such correlation was present in the bipolar disorder group only for fearful faces. In the ventral visual stream, associations between neural activity and irritability were found more consistently in the DMDD group than in the bipolar disorder group, especially in response to ambiguous angry faces.These results suggest diagnostic specificity in the neural correlates of irritability, a symptom of both DMDD and bipolar disorder. Such evidence of distinct neural correlates suggests the need to evaluate different approaches to treating irritability in the two disorders.

Intra-subject variation in reaction time (ISVRT) is a developmentally-important phenomenon that decreases from childhood through young adulthood in parallel with the development of executive functions and networks. Prior work has shown a significant association between trial-by-trial variations in reaction time (RT) and trial-by-trial variations in brain activity as measured by the blood-oxygenated level-dependent (BOLD) response in functional magnetic resonance imaging (fMRI) studies. It remains unclear, however, whether such “RT-BOLD” relationships vary with age. Here, we determined whether such trial-by-trial relationships vary with age in a cross-sectional design. We observed an association between age and RT-BOLD relationships in 11 clusters located in visual/occipital regions, frontal and parietal association cortex, precentral/postcentral gyrus, and thalamus. Some of these relationships were negative, reflecting increased BOLD associated with decreased RT, manifesting around the time of stimulus presentation and positive several seconds later. Critically for present purposes, all RT-BOLD relationships increased with age. Thus, RT-BOLD relationships may reflect robust, measurable changes in the brain-behavior relationship across development.

Image quality control (QC) is a critical and computationally intensive component of functional magnetic resonance imaging (fMRI). Artifacts caused by physiologic signals or hardware malfunctions are usually identified and removed during data processing offline, well after scanning sessions are complete. A system with the computational efficiency to identify and remove artifacts during image acquisition would permit rapid adjustment of protocols as issues arise during experiments. To improve the speed and accuracy of QC and functional image correction, we developed Fast Anatomy-Based Image Correction (Fast ANATICOR) with newly implemented nuisance models and an improved pipeline. We validated its performance on a dataset consisting of normal scans and scans containing known hardware-driven artifacts. Fast ANATICOR's increased processing speed may make real-time QC and image correction feasible as compared with the existing offline method.

Childhood irritability is a common, impairing problem with changing age-related manifestations that predict long-term adverse outcomes. However, more investigation of overall and age-specific neural correlates is needed. Because youths with irritability exhibit exaggerated responses to frustrating stimuli, the authors used a frustration functional MRI (fMRI) paradigm to examine associations between irritability and neural activation and tested the moderating effect of age.The authors studied a transdiagnostic sample of 195 youths with varying levels of irritability (disruptive mood dysregulation disorder, N=52; anxiety disorder, N=42; attention deficit hyperactivity disorder, N=40; and healthy volunteers, N=61). Irritability was measured by parent and child reports on the Affective Reactivity Index. The fMRI paradigm was a cued-attention task differentiating neural activity in response to frustration (rigged feedback) from activity during attention orienting in the trial following frustration.Whole-brain activation analyses revealed associations with irritability during attention orienting following frustration. Irritability was positively associated with frontal-striatal activation, specifically in the dorsolateral prefrontal cortex, inferior frontal gyrus, and caudate. Age moderated the association between irritability and activation in some frontal and posterior regions (the anterior cingulate cortex, medial frontal gyrus, cuneus, precuneus, and superior parietal lobule [F=19.04-28.51, df=1, 189, partial eta squared=0.09-0.13]). Specifically, higher irritability was more strongly related to increased activation in younger youths compared with older youths.Following frustration, levels of irritability correlated with activity in neural systems mediating attention orienting, top-down regulation of emotions, and motor execution. Although most associations were independent of age, dysfunction in the anterior cingulate cortex and posterior regions was more pronounced in young children with irritability.

One-sided t-tests are widely used in neuroimaging data analysis. While such a test may be applicable when investigating specific regions and prior information about directionality is present, we argue here that it is often mis-applied, with severe consequences for false positive rate (FPR) control. Conceptually, a pair of one-sided t-tests conducted in tandem (e.g., to test separately for both positive and negative effects), effectively amounts to a two-sided t-test. However, replacing the two-sided test with a pair of one-sided tests without multiple comparisons correction essentially doubles the intended FPR of statements made about the same study; that is, the actual family-wise error (FWE) of results at the whole brain level would be 10% instead of the 5% intended by the researcher. Therefore, we strongly recommend that, unless otherwise explicitly justified, two-sided t-tests be applied instead of two simultaneous one-sided t-tests.

A recent study posted on bioRxiv by Bowring, Maumet and Nichols aimed to compare results of FMRI data that had been processed with three commonly used software packages (AFNI, FSL and SPM). Their stated purpose was to use “default” settings of each software’s pipeline for task-based FMRI, and then to quantify overlaps in final clustering results and to measure similarity/dissimilarity in the final outcomes of packages. While in theory the setup sounds simple (implement each package’s defaults and compare results), practical realities make this difficult. For example, different softwares would recommend different spatial resolutions of the final data, but for the sake of comparisons, the same value must be used across all. Moreover, we would say that AFNI does not have an explicit default pipeline available: a wide diversity of datasets and study designs are acquired across the neuroimaging community, often requiring bespoke tailoring of basic processing rather than a “one-size-fits-all” pipeline. However, we do have strong recommendations for certain steps, and we are also aware that the choice of a given step might place requirements on other processing steps. Given the very clear reporting of the AFNI pipeline used in Bowring et al. paper, we take this opportunity to comment on some of these aspects of processing with AFNI here, clarifying a few mistakes therein and also offering recommendations. We provide point-by-point considerations of using AFNI’s processing pipeline design tool at the individual level, afni_proc.py, along with supplementary programs; while specifically discussed in the context of the present usage, many of these choices may serve as useful starting points for broader processing. It is our intention/hope that the user should examine data quality at every step, and we demonstrate how this is facilitated in AFNI, as well.

A fundamental feature of cortical visual processing is the separation of visual processing for the upper and lower visual fields. In early visual cortex (EVC), the upper visual field is processed ventrally, with the lower visual field processed dorsally. This distinction persists into several category-selective regions of occipitotemporal cortex, with ventral and lateral scene-, face-, and object-selective regions biased for the upper and lower visual fields, respectively. Here, using an elliptical population receptive field (pRF) model, we systematically tested the sampling of visual space within ventral and dorsal divisions of human EVC in both male and female participants. We found that (1) pRFs tend to be elliptical and oriented toward the fovea with distinct angular distributions for ventral and dorsal divisions of EVC, potentially reflecting a radial bias; and (2) pRFs in ventral areas were larger (∼1.5×) and more elliptical (∼1.2×) than those in dorsal areas. These differences potentially reflect a tendency for receptive fields in ventral temporal cortex to overlap the fovea with less emphasis on precise localization and isotropic representation of space compared with dorsal areas. Collectively, these findings suggest that ventral and dorsal divisions of EVC sample visual space differently, likely contributing to and/or stemming from the functional differentiation of visual processing observed in higher-level regions of the ventral and dorsal cortical visual pathways.SIGNIFICANCE STATEMENTThe processing of visual information from the upper and lower visual fields is separated in visual cortex. Although ventral and dorsal divisions of early visual cortex (EVC) are commonly assumed to sample visual space equivalently, we demonstrate systematic differences using an elliptical population receptive field (pRF) model. Specifically, we demonstrate that (1) ventral and dorsal divisions of EVC exhibit diverging distributions of pRF angle, which are biased toward the fovea; and (2) ventral pRFs exhibit higher aspect ratios and cover larger areas than dorsal pRFs. These results suggest that ventral and dorsal divisions of EVC sample visual space differently and that such differential sampling likely contributes to different functional roles attributed to the ventral and dorsal pathways, such as object recognition and visually guided attention, respectively.

Introduction: Studies investigating the value of CT perfusion (CTP) imaging in treatment decision or outcome prediction of acute stroke patients have found conflicting results. This may be attributed to variable accuracy and reliability across the different deconvolution algorithms. The aim of this study was to introduce a new, standardized and model-free method, based on similarities in signal time-curves (Pearson’s correlation coefficient) and to increase robustness of CTP analysis and perfusion deficit detection. Methods: Acute stroke patients from the Dutch Acute Stroke Study were included. CTP data acquired at admission was analyzed using a deconvolution method (Philips Brain Perfusion software) and with TSP. Acute CTP and follow-up non-contrast images after 3 days were interpreted by experienced and inexperienced raters for presence of perfusion deficits, intra-rater and inter-rater agreement. Results: 65 patients (68±13 years) were included. Example images are shown in the Figure. A perfusion deficit was detected in 56 patients on the MTT deconvolution maps; TSP detected 54 of these perfusion deficits. The agreement of MTT, TTP and TSP with the presence of ischemia on follow-up was comparable, but noticeably lower for CBV. CBV had the best relationship with final infarct volume (R 2 =0.77, p2 =0.63, p Conclusion: TSP offers a very fast, automated, non-proprietary, analysis of CTP data. TSP maps are highly comparable to deconvolution maps, but easier to interpret for inexperienced readers. The relationship of TSP perfusion deficit volume with final infarct volume is comparable to CBV. This suggests that TSP might be a valuable alternative to deconvolution-derived maps, which are known to be prone to tracer delays.

BACKGROUND: We measured and compared heritability estimates for measures of functional brain connectivity extracted using the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) rsfMRI analysis pipeline in two cohorts: the GOBS (Genetics of Brain Structure) cohort and the HCP (the Human Connectome Project) cohort. These two cohorts were assessed using conventional (GOBS) and advanced (HCP) rsfMRI protocols, offering a test case for harmonization of rsfMRI phenotypes, and to determine measures that show consistent heritability for in-depth genome-wide analysis. METHODS: The GOBS cohort consisted of 334 Mexican-American individuals (124M/210F, average age=47.9±13.2 years) from 29 extended pedigrees (average family size=9 people; range 5–32). The GOBS rsfMRI data was collected using a 7.5-minute acquisition sequence (spatial resolution=1.72×1.72×3 mm(3)). The HCP cohort consisted of 518 twins and family members (240M/278F; average age=28.7± 3.7 years). rsfMRI data was collected using 28.8-minute sequence (spatial resolution=2×2×2 mm(3)). We used the single-modality ENIGMA rsfMRI preprocessing pipeline to estimate heritability values for measures from eight major functional networks, using (1) seed-based connectivity and (2) dual regression approaches. RESULTS: We observed significant heritability (h(2)=0.2–0.4, p

The bed nucleus of the stria terminalis (BNST), a portion of the "extended amygdala," is implicated in the pathophysiology of anxiety and addiction disorders. Its small size and connection to other small regions prevents standard imaging techniques from easily capturing it and its connectivity with confidence. Seed-based resting state functional connectivity is an established method for mapping functional connections across the brain from a region of interest. We, therefore, mapped the BNST resting state network with high spatial resolution using 7 Tesla fMRI, demonstrating the in vivo reproduction of many human BNST connections previously described only in animal research. We identify strong BNST functional connectivity in amygdala, hippocampus and thalamic subregions, caudate, periaqueductal gray, hypothalamus, and cortical areas such as the medial PFC and precuneus. This work, which demonstrates the power of ultra-high field for mapping functional connections in the human, is an important step toward elucidating cortical and subcortical regions and subregions of the BNST network.

It remains unclear the degree to which youths with episodic mania (bipolar disorder; BD) vs. those with chronic, severe irritability (severe mood dysregulation, SMD) should be placed in similar or distinct diagnostic groups. Addressing this clinically meaningful question requires greater understanding of the neural alterations underlying both disorders. We evaluated resting state functional magnetic resonance imaging data of 53 youths (14 BD, 20 healthy volunteers (HV), and 19 SMD, ages 9–18.5 years). Seed regions of interest were the bilateral basolateral, superficial and centromedial amygdala, defined using the Juelich probabilistic atlas. We found a significant between-group difference in functional connectivity between the left basolateral amygdala and the medial aspect of the left frontal pole plus the posterior cingulate/precuneus. This finding was driven by hyperconnectivity among BD vs. HV or SMD youths. As with earlier data, these findings suggest that the pathophysiology of BD and SMD may differ.

Big data initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analysis consortium (ENIGMA), combine data collected by independent studies worldwide to achieve more generalizable estimates of effect sizes and more reliable and reproducible outcomes. Such efforts require harmonized image analyses protocols to extract phenotypes consistently. This harmonization is particularly challenging for resting state fMRI due to the wide variability of acquisition protocols and scanner platforms; this leads to site-to-site variance in quality, resolution and temporal signal-to-noise ratio (tSNR). An effective harmonization should provide optimal measures for data of different qualities. We developed a multi-site rsfMRI analysis pipeline to allow research groups around the world to process rsfMRI scans in a harmonized way, to extract consistent and quantitative measurements of connectivity and to perform coordinated statistical tests. We used the single-modality ENIGMA rsfMRI preprocessing pipeline based on model-free Marchenko-Pastur PCA based denoising to verify and replicate resting state network heritability estimates. We analyzed two independent cohorts, GOBS (Genetics of Brain Structure) and HCP (the Human Connectome Project), which collected data using conventional and connectomics oriented fMRI protocols, respectively. We used seed-based connectivity and dual-regression approaches to show that the rsfMRI signal is consistently heritable across twenty major functional network measures. Heritability values of 20–40% were observed across both cohorts.

Recent reports of inflated false-positive rates (FPRs) in FMRI group analysis tools by Eklund and associates in 2016 have become a large topic within (and outside) neuroimaging. They concluded that existing parametric methods for determining statistically significant clusters had greatly inflated FPRs (“up to 70%,” mainly due to the faulty assumption that the noise spatial autocorrelation function is Gaussian shaped and stationary), calling into question potentially “countless” previous results; in contrast, nonparametric methods, such as their approach, accurately reflected nominal 5% FPRs. They also stated that AFNI showed “particularly high” FPRs compared to other software, largely due to a bug in 3dClustSim. We comment on these points using their own results and figures and by repeating some of their simulations. Briefly, while parametric methods show some FPR inflation in those tests (and assumptions of Gaussian-shaped spatial smoothness also appear to be generally incorrect), their emphasis on reporting the single worst result from thousands of simulation cases greatly exaggerated the scale of the problem. Importantly, FPR statistics depends on “task” paradigm and voxelwise p value threshold; as such, we show how results of their study provide useful suggestions for FMRI study design and analysis, rather than simply a catastrophic downgrading of the field's earlier results. Regarding AFNI (which we maintain), 3dClustSim's bug effect was greatly overstated—their own results show that AFNI results were not “particularly” worse than others. We describe further updates in AFNI for characterizing spatial smoothness more appropriately (greatly reducing FPRs, although some remain >5%); in addition, we outline two newly implemented permutation/randomization-based approaches producing FPRs clustered much more tightly about 5% for voxelwise p ≤ 0.01.

Introduction: Interpretation of the extent of perfusion deficits in stroke MRI is highly dependent on the method used for analyzing the perfusion-weighted signal intensity time-series after gadolinium injection. In this study, we introduce a new model-free standardized method of temporal similarity perfusion (TSP) mapping for perfusion deficit detection and test its ability and reliability in acute ischemia.Materials and methods: Forty patients with an ischemic stroke or transient ischemic attack were included. Two blinded readers compared real-time generated interactive maps and automatically generated TSP maps to traditional TTP/MTT maps for presence of perfusion deficits. Lesion volumes were compared for volumetric inter-rater reliability, spatial concordance between perfusion deficits and healthy tissue and contrast-to-noise ratio (CNR).Results: Perfusion deficits were correctly detected in all patients with acute ischemia. Inter-rater reliability was higher for TSP when compared to TTP/MTT maps and there was a high similarity between the lesion volumes depicted on TSP and TTP/MTT (r(18) = 0.73). The Pearson's correlation between lesions calculated on TSP and traditional maps was high (r(18) = 0.73, pDiscussion: TSP maps provide a reliable and robust model-free method for accurate perfusion deficit detection and improve lesion delineation compared to traditional methods. This simple method is also computationally faster and more easily automated than model-based methods. This method can potentially improve the speed and accuracy in perfusion deficit detection for acute stroke treatment and clinical trial inclusion decision-making.

Recently, Eklund et al. (2016) analyzed clustering methods in standard FMRI packages: AFNI (which we maintain), FSL, and SPM [1]. They claimed: 1) false positive rates (FPRs) in traditional approaches are greatly inflated, questioning the validity of "countless published fMRI studies"; 2) nonparametric methods produce valid, but slightly conservative, FPRs; 3) a common flawed assumption is that the spatial autocorrelation function (ACF) of FMRI noise is Gaussian-shaped; and 4) a 15-year-old bug in AFNI's 3dClustSim significantly contributed to producing "particularly high" FPRs compared to other software. We repeated simulations from [1] (Beijing-Zang data [2], see [3]), and comment on each point briefly., Comment: 3 pages, 1 figure. A Letter accepted in PNAS

Both children and adults with bipolar disorder (BD) exhibit face emotion labeling deficits and neural circuitry dysfunction in response to emotional faces. However, few studies have compared these groups directly to distinguish effects of age and diagnosis. Such studies are important to begin to elucidate the developmental trajectory of BD and facilitate its diagnosis, prevention and treatment. This functional magnetic resonance imaging study compares 41 individuals with BD (19 children; 22 adults) and 44 age-matched healthy individuals (25 children; 19 adults) when making explicit or implicit judgments about angry or happy face morphs across a range of emotion intensity. Linear trend analyses revealed that BD patients, irrespective of age, failed to recruit the amygdala in response to increasing angry face. This finding was no longer significant when the group was restricted to euthymic youth or those without comorbid attention deficit hyperactivity disorder although this may reflect low statistical power. Deficits in subgenual anterior cingulate modulation were observed in both patient groups but were related to implicit processing for child patients and explicit processing for adult patients. Abnormalities in face emotion labeling and the circuitry mediating it may be biomarkers of BD that are present across development.

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html., National Institutes of Health (U.S.) (Grant 1R01EB020740-01A1), National Institutes of Health (U.S.) (Grant 1P41EB019936-01A)

Available online 9 December 2016 http://www.sciencedirect.com/science/article/pii/S1053811916307418?via%3Dihub http://www.sciencedirect.com/science/article/pii/S1053811916307418?via%3Dihub Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies. This work was supported by the Spanish Ministry of Economy and Competitiveness [Grant PSI 2013–42343 Neuroimagen Multimodal], the Severo Ochoa Programme for Centres/Units of Excellence in R & D [SEV-2015-490], and the research and writing of the paper were supported by the NIMH and NINDS Intramural Research Programs (ZICMH002888) of the NIH/HHS.