LISA improves statistical analysis for fMRI

Cardiovascular and vasomotor pulsations in the brain and periphery during awake and NREM sleep in a multimodal fMRI study

Introduction

The cerebrospinal fluid dynamics in the human brain are driven by physiological pulsations, including cardiovascular pulses and very low-frequency (< 0.1 Hz) vasomotor waves. Ultrafast functional magnetic resonance imaging (fMRI) facilitates the simultaneous measurement of these signals from venous and arterial compartments independently with both classical venous blood oxygenation level dependent (BOLD) and faster arterial spin-phase contrast.

Methods

In this study, we compared the interaction of these two pulsations in awake and sleep using fMRI and peripheral fingertip photoplethysmography in both arterial and venous signals in 10 healthy subjects (5 female).

Results

Sleep increased the power of brain cardiovascular pulsations, decreased peripheral pulsation, and desynchronized them. However, vasomotor waves increase power and synchronicity in both brain and peripheral signals during sleep. Peculiarly, lag between brain and peripheral vasomotor signals reversed in sleep within the default mode network. Finally, sleep synchronized cerebral arterial vasomotor waves with venous BOLD waves within distinct parasagittal brain tissue.

Discussion

These changes in power and pulsation synchrony may reflect systemic sleep-related changes in vascular control between the periphery and brain vasculature, while the increased synchrony of arterial and venous compartments may reflect increased convection of regional neurofluids in parasagittal areas in sleep.

Topological cluster statistic (TCS): Toward structural connectivity-guided fMRI cluster enhancement

Functional magnetic resonance imaging (fMRI) studies most commonly use cluster-based inference to detect local changes in brain activity. Insufficient statistical power and disproportionate false-positive rates reportedly hinder optimal inference. We propose a structural connectivity-guided clustering framework, called topological cluster statistic (TCS), that enhances sensitivity by leveraging white matter anatomical connectivity information. TCS harnesses multimodal information from diffusion tractography and functional imaging to improve task fMRI activation inference. Compared to conventional approaches, TCS consistently improves power over a wide range of effects. This improvement results in a 10%-50% increase in local sensitivity with the greatest gains for medium-sized effects. TCS additionally enables inspection of underlying anatomical networks and thus uncovers knowledge regarding the anatomical underpinnings of brain activation. This novel approach is made available in the PALM software to facilitate usability. Given the increasing recognition that activation reflects widespread, coordinated processes, TCS provides a way to integrate the known structure underlying widespread activations into neuroimaging analyses moving forward.

Additive effects of mild head trauma, blast exposure, and aging within white matter tracts: A novel Diffusion Tensor Imaging analysis approach

Existing diffusion tensor imaging (DTI) studies of neurological injury following high-level blast exposure (hlBE) in military personnel have produced widely variable results. This is potentially due to prior studies often not considering the quantity and/or recency of hlBE, as well as co-morbidity with non-blast head trauma (nbHT). Herein, we compare commonly used DTI metrics: fractional anisotropy and mean, axial, and radial diffusivity, in Veterans with and without history of hlBE and/or nbHT. We use both the traditional method of dividing participants into 2 equally weighted groups and an alternative method wherein each participant is weighted by quantity and recency of hlBE and/or nbHT. While no differences were detected using the traditional method, the alternative method revealed diffuse and extensive changes in all DTI metrics. These effects were quantified within 43 anatomically defined white matter tracts, which identified the forceps minor, middle corpus callosum, acoustic and optic radiations, fornix, uncinate, inferior fronto-occipital and inferior longitudinal fasciculi, and cingulum, as the pathways most affected by hlBE and nbHT. Moreover, additive effects of aging were present in many of the same tracts suggesting that these neuroanatomical effects may compound with age.

Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain

Many functional magnetic resonance imaging (fMRI) studies and presurgical mapping applications rely on mass-univariate inference with subsequent multiple comparison correction. Statistical results are frequently visualized as thresholded statistical maps. This approach has inherent limitations including the risk of drawing overly-selective conclusions based only on selective results passing such thresholds. This article gives an overview of both established and newly emerging scientific approaches to supplement such conventional analyses by incorporating information about subthreshold effects with the aim to improve interpretation of findings or leverage a wider array of information. Topics covered include neuroimaging data visualization, p-value histogram analysis and the related Higher Criticism approach for detecting rare and weak effects. Further examples from multivariate analyses and dedicated Bayesian approaches are provided.

Heart failure decouples the precuneus in interaction with social cognition and executive functions

Aging increases the risk to develop Alzheimer's disease. Cardiovascular diseases might accelerate this process. Our study aimed at investigating the impact of heart failure on brain connectivity using functional magnetic resonance imaging at resting state. Here we show brain connectivity alterations related to heart failure and cognitive performance. Heart failure decreases brain connectivity in the precuneus. Precuneus dysconnectivity was associated with biomarkers of heart failure-left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide-and cognitive performance, predominantly executive function. Meta-analytical data-mining approaches-conducted in the BrainMap and Neurosynth databases-revealed that social and executive cognitive functions are mainly associated with those neural networks. Remarkably, the precuneus, as identified in our study in a mid-life cohort, represents one central functional hub affected by Alzheimer's disease. A long-term follow-up investigation in our cohort after approximately nine years revealed more severe cognitive impairment in the group with heart failure than controls, where social cognition was the cognitive domain mainly affected, and not memory such as in Alzheimer's disease. In sum, our results indicate consistently an association between heart failure and decoupling of the precuneus from other brain regions being associated with social and executive functions. Further longitudinal studies are warranted elucidating etiopathological mechanisms.

Brain-correlates of processing local dependencies within a statistical learning paradigm

Statistical learning refers to the implicit mechanism of extracting regularities in our environment. Numerous studies have investigated the neural basis of statistical learning. However, how the brain responds to violations of auditory regularities based on prior (implicit) learning requires further investigation. Here, we used functional magnetic resonance imaging (fMRI) to investigate the neural correlates of processing events that are irregular based on learned local dependencies. A stream of consecutive sound triplets was presented. Unbeknown to the subjects, triplets were either (a) standard, namely triplets ending with a high probability sound or, (b) statistical deviants, namely triplets ending with a low probability sound. Participants (n = 33) underwent a learning phase outside the scanner followed by an fMRI session. Processing of statistical deviants activated a set of regions encompassing the superior temporal gyrus bilaterally, the right deep frontal operculum including lateral orbitofrontal cortex, and the right premotor cortex. Our results demonstrate that the violation of local dependencies within a statistical learning paradigm does not only engage sensory processes, but is instead reminiscent of the activation pattern during the processing of local syntactic structures in music and language, reflecting the online adaptations required for predictive coding in the context of statistical learning.

CLEAN: Leveraging spatial autocorrelation in neuroimaging data in clusterwise inference

While clusterwise inference is a popular approach in neuroimaging that improves sensitivity, current methods do not account for explicit spatial autocorrelations because most use univariate test statistics to construct cluster-extent statistics. Failure to account for such dependencies could result in decreased reproducibility. To address methodological and computational challenges, we propose a new powerful and fast statistical method called CLEAN (Clusterwise inference Leveraging spatial Autocorrelations in Neuroimaging). CLEAN computes multivariate test statistics by modelling brain-wise spatial autocorrelations, constructs cluster-extent test statistics, and applies a refitting-free resampling approach to control false positives. We validate CLEAN using simulations and applications to the Human Connectome Project. This novel method provides a new direction in neuroimaging that paces with advances in high-resolution MRI data which contains a substantial amount of spatial autocorrelation.

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites¹ that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

Physiological instability is linked to mortality in primary central nervous system lymphoma: A case-control fMRI study

Primary central nervous system lymphoma (PCNSL) is an aggressive brain disease where lymphocytes invade along perivascular spaces of arteries and veins. The invasion markedly changes (peri)vascular structures but its effect on physiological brain pulsations has not been previously studied. Using physiological magnetic resonance encephalography (MREG_BOLD ) scanning, this study aims to quantify the extent to which (peri)vascular PCNSL involvement alters the stability of physiological brain pulsations mediated by cerebral vasculature. Clinical implications and relevance were explored. In this study, 21 PCNSL patients (median 67y; 38% females) and 30 healthy age-matched controls (median 63y; 73% females) were scanned for MREG_BOLD signal during 2018-2021. Motion effects were removed. Voxel-by-voxel Coefficient of Variation (CV) maps of MREG_BOLD signal was calculated to examine the stability of physiological brain pulsations. Group-level differences in CV were examined using nonparametric covariate-adjusted tests. Subject-level CV alterations were examined against control population Z-score maps wherein clusters of increased CV values were detected. Spatial distributions of clusters and findings from routine clinical neuroimaging were compared [contrast-enhanced, diffusion-weighted, fluid-attenuated inversion recovery (FLAIR) data]. Whole-brain mean CV was linked to short-term mortality with 100% sensitivity and 100% specificity, as all deceased patients revealed higher values (n = 5, median 0.055) than surviving patients (n = 16, median 0.028) (p < .0001). After adjusting for medication, head motion, and age, patients revealed higher CV values (group median 0.035) than healthy controls (group median 0.024) around arterial territories (p ≤ .001). Abnormal clusters (median 1.10 × 10⁵ mm³ ) extended spatially beyond FLAIR lesions (median 0.62 × 10⁵ mm³ ) with differences in volumes (p = .0055).

Kimesurface Representation and Tensor Linear Modeling of Longitudinal Data

Many modern techniques for analyzing time-varying longitudinal data rely on parametric models to interrogate the time-courses of univariate or multivariate processes. Typical analytic objectives include utilizing retrospective observations to model current trends, predict prospective trajectories, derive categorical traits, or characterize various relations. Among the many mathematical, statistical, and computational strategies for analyzing longitudinal data, tensor-based linear modeling offers a unique algebraic approach that encodes different characterizations of the observed measurements in terms of state indices. This paper introduces a new method of representing, modeling, and analyzing repeated-measurement longitudinal data using a generalization of event order from the positive reals to the complex plane. Using complex time (kime), we transform classical time-varying signals as 2D manifolds called kimesurfaces. This kime characterization extends the classical protocols for analyzing time-series data and offers unique opportunities to design novel inference, prediction, classification, and regression techniques based on the corresponding kimesurface manifolds. We define complex time and illustrate alternative time-series to kimesurface transformations. Using the Laplace transform and its inverse, we demonstrate the bijective mapping between time-series and kimesurfaces. A proposed general tensor regression based linear model is validated using functional Magnetic Resonance Imaging (fMRI) data. This kimesurface representation method can be used with a wide range of machine learning algorithms, artificial intelligence tools, analytical approaches, and inferential techniques to interrogate multivariate, complex-domain, and complex-range longitudinal processes.

Lateral Prefrontal Cortex Is a Hub for Music Production from Structural Rules to Movements

Complex sequential behaviors, such as speaking or playing music, entail flexible rule-based chaining of single acts. However, it remains unclear how the brain translates abstract structural rules into movements. We combined music production with multimodal neuroimaging to dissociate high-level structural and low-level motor planning. Pianists played novel musical chord sequences on a muted MR-compatible piano by imitating a model hand on screen. Chord sequences were manipulated in terms of musical harmony and context length to assess structural planning, and in terms of fingers used for playing to assess motor planning. A model of probabilistic sequence processing confirmed temporally extended dependencies between chords, as opposed to local dependencies between movements. Violations of structural plans activated the left inferior frontal and middle temporal gyrus, and the fractional anisotropy of the ventral pathway connecting these two regions positively predicted behavioral measures of structural planning. A bilateral frontoparietal network was instead activated by violations of motor plans. Both structural and motor networks converged in lateral prefrontal cortex, with anterior regions contributing to musical structure building, and posterior areas to movement planning. These results establish a promising approach to study sequence production at different levels of action representation.

Cortical Surface-Informed Volumetric Spatial Smoothing of fMRI Data via Graph Signal Processing

Conventionally, as a preprocessing step, functional MRI (fMRI) data are spatially smoothed before further analysis, be it for activation mapping on task-based fMRI or functional connectivity analysis on resting-state fMRI data. When images are smoothed volumetrically, however, isotropic Gaussian kernels are generally used, which do not adapt to the underlying brain structure. Alternatively, cortical surface smoothing procedures provide the benefit of adapting the smoothing process to the underlying morphology, but require projecting volumetric data on to the surface. In this paper, leveraging principles from graph signal processing, we propose a volumetric spatial smoothing method that takes advantage of the gray-white and pial cortical surfaces, and as such, adapts the filtering process to the underlying morphological details at each point in the cortex.

Reduced Hippocampal-Striatal Interactions during Formation of Durable Episodic Memories in Aging

Encoding of durable episodic memories requires cross-talk between the hippocampus and multiple brain regions. Changes in these hippocampal interactions could contribute to age-related declines in the ability to form memories that can be retrieved after extended time intervals. Here we tested whether hippocampal–neocortical– and subcortical functional connectivity (FC) observed during encoding of durable episodic memories differed between younger and older adults. About 48 younger (20–38 years; 25 females) and 43 older (60–80 years; 25 females) adults were scanned with fMRI while performing an associative memory encoding task. Source memory was tested ~20 min and ~6 days postencoding. Associations recalled after 20 min but later forgotten were classified as transient, whereas memories retained after long delays were classified as durable. Results demonstrated that older adults showed a reduced ability to form durable memories and reduced hippocampal–caudate FC during encoding of durable memories. There was also a positive relationship between hippocampal–caudate FC and higher memory performance among the older adults. No reliable age group differences in durable memory–encoding activity or hippocampal–neocortical connectivity were observed. These results support the classic theory of striatal alterations as one cause of cognitive decline in aging and highlight that age-related changes in episodic memory extend beyond hippocampal–neocortical connections.

LayNii: A software suite for layer-fMRI

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 opensource 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.

Diffusion-informed spatial smoothing of fMRI data in white matter using spectral graph filters

Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detectability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject's unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project's 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.

Spectral analysis of physiological brain pulsations affecting the BOLD signal

Physiological pulsations have been shown to affect the global blood oxygen level dependent (BOLD) signal in human brain. While these pulsations have previously been regarded as noise, recent studies show their potential as biomarkers of brain pathology. We used the extended 5 Hz spectral range of magnetic resonance encephalography (MREG) data to investigate spatial and frequency distributions of physiological BOLD signal sources. Amplitude spectra of the global image signals revealed cardiorespiratory envelope modulation (CREM) peaks, in addition to the previously known very low frequency (VLF) and cardiorespiratory pulsations. We then proceeded to extend the amplitude of low frequency fluctuations (ALFF) method to each of these pulsations. The respiratory pulsations were spatially dominating over most brain structures. The VLF pulsations overcame the respiratory pulsations in frontal and parietal gray matter, whereas cardiac and CREM pulsations had this effect in central cerebrospinal fluid (CSF) spaces and major blood vessels. A quasi‐periodic pattern (QPP) analysis showed that the CREM pulsations propagated as waves, with a spatiotemporal pattern differing from that of respiratory pulsations, indicating them to be distinct intracranial physiological phenomenon. In conclusion, the respiration has a dominant effect on the global BOLD signal and directly modulates cardiovascular brain pulsations.

Neocortical substrates of feelings evoked with music in the ACC, insula, and somatosensory cortex

Neurobiological models of emotion focus traditionally on limbic/paralimbic regions as neural substrates of emotion generation, and insular cortex (in conjunction with isocortical anterior cingulate cortex, ACC) as the neural substrate of feelings. An emerging view, however, highlights the importance of isocortical regions beyond insula and ACC for the subjective feeling of emotions. We used music to evoke feelings of joy and fear, and multivariate pattern analysis (MVPA) to decode representations of feeling states in functional magnetic resonance (fMRI) data of n = 24 participants. Most of the brain regions providing information about feeling representations were neocortical regions. These included, in addition to granular insula and cingulate cortex, primary and secondary somatosensory cortex, premotor cortex, frontal operculum, and auditory cortex. The multivoxel activity patterns corresponding to feeling representations emerged within a few seconds, gained in strength with increasing stimulus duration, and replicated results of a hypothesis-generating decoding analysis from an independent experiment. Our results indicate that several neocortical regions (including insula, cingulate, somatosensory and premotor cortices) are important for the generation and modulation of feeling states. We propose that secondary somatosensory cortex, which covers the parietal operculum and encroaches on the posterior insula, is of particular importance for the encoding of emotion percepts, i.e., preverbal representations of subjective feeling.

Altered resting-state functional connectivity of the default mode and central executive networks following cognitive processing therapy for PTSD

Psychotherapy research is increasingly targeting both psychological and neurobiological mechanisms of therapeutic change. This trend is evident in and applicable to post-traumatic stress disorder (PTSD) treatment research given the high nonresponse rate of individuals with PTSD who undergo cognitive-behavioral therapy (CBT). Functional connectivity analyses investigating disrupted brain networks across mental disorders have been employed to understand both mental disorder symptoms and therapeutic mechanisms. However, few studies have examined pre-post CBT brain changes in PTSD using functional connectivity analyses. The current study investigated a) whether brain networks commonly implicated in psychopathology (e.g., default mode network [DMN], central executive network [CEN], and salience network [SN]) changed following Cognitive Processing Therapy (CPT) for PTSD and b) whether change in these networks was associated with PTSD and/or transdiagnostic symptom change. Independent components analysis was implemented to investigate resting-state functional connectivity in DMN, CEN, and SN in 42 women with PTSD and 18 trauma-exposed controls (TEC). Results indicated decreased CEN-cerebellum connectivity in PTSD participants versus TEC prior to CPT and decreased DMN connectivity in PTSD participants after CPT. Additionally, DMN and SN connectivity was related to change in positive and negative affectivity, while exploratory analyses at a cluster threshold of pFDR < .10 indicated DMN and SN connectivity was also related to change in PTSD symptoms and rumination. These findings provide evidence for normalization of CEN connectivity with treatment and implicate the DMN and SN in clinical symptom change following CPT.

Improving the sensitivity of cluster-based statistics for functional magnetic resonance imaging data

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster‐based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false‐alarm rate, (b) that the sensitivity profiles of cluster‐based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.

Neural modelling of the semantic predictability gain under challenging listening conditions

When speech intelligibility is reduced, listeners exploit constraints posed by semantic context to facilitate comprehension. The left angular gyrus (AG) has been argued to drive this semantic predictability gain. Taking a network perspective, we ask how the connectivity within language-specific and domain-general networks flexibly adapts to the predictability and intelligibility of speech. During continuous functional magnetic resonance imaging (fMRI), participants repeated sentences, which varied in semantic predictability of the final word and in acoustic intelligibility. At the neural level, highly predictable sentences led to stronger activation of left-hemispheric semantic regions including subregions of the AG (PGa, PGp) and posterior middle temporal gyrus when speech became more intelligible. The behavioural predictability gain of single participants mapped onto the same regions but was complemented by increased activity in frontal and medial regions. Effective connectivity from PGa to PGp increased for more intelligible sentences. In contrast, inhibitory influence from pre-supplementary motor area to left insula was strongest when predictability and intelligibility of sentences were either lowest or highest. This interactive effect was negatively correlated with the behavioural predictability gain. Together, these results suggest that successful comprehension in noisy listening conditions relies on an interplay of semantic regions and concurrent inhibition of cognitive control regions when semantic cues are available.

Confidence maps: statistical inference of cryo-EM maps

Confidence maps provide complementary information for interpreting cryo-EM densities as they indicate statistical significance with respect to background noise. They can be thresholded by specifying the expected false-discovery rate (FDR), and the displayed volume shows the parts of the map that have the corresponding level of significance. Here, the basic statistical concepts of confidence maps are reviewed and practical guidance is provided for their interpretation and usage inside the CCP-EM suite. Limitations of the approach are discussed and extensions towards other error criteria such as the family-wise error rate are presented. The observed map features can be rendered at a common isosurface threshold, which is particularly beneficial for the interpretation of weak and noisy densities. In the current article, a practical guide is provided to the recommended usage of confidence maps.

Cluster failure or power failure? Evaluating sensitivity in cluster-level inference

Pioneering work in human neuroscience has relied on the ability to map brain function using task-based fMRI, but the empirical validity of these inferential methods is still being characterized. A recent landmark study by Eklund and colleagues showed that popular multiple comparison corrections based on cluster extent suffer from unexpectedly low specificity (i.e., high false positive rate). Yet that study's focus on specificity, while important, is incomplete. The validity of a method depends also on its sensitivity (i.e., true positive rate or power), yet the sensitivity of cluster correction remains poorly understood. Here, we assessed the sensitivity of gold-standard nonparametric cluster correction by resampling real data from five tasks in the Human Connectome Project and comparing results with those from the full "ground truth" datasets (n ​= ​480-493). Critically, we found that sensitivity after correction is lower than may be practical for many fMRI applications. In particular, sensitivity to medium-sized effects (|Cohen's d| ​= ​0.5) was less than 20% across tasks on average, about three times smaller than without any correction. Furthermore, cluster extent correction exhibited a spatial bias in sensitivity that was independent of effect size. In comparison, correction based on the Threshold-Free Cluster Enhancement (TFCE) statistic approximately doubled sensitivity across tasks but increased spatial bias. These results suggest that we have, until now, only measured the tip of the iceberg in the activation-mapping literature due to our goal of limiting the familywise error rate through cluster extent-based inference. There is a need to revise our practices to improve sensitivity; we therefore conclude with a list of modern strategies to boost sensitivity while maintaining respectable specificity in future investigations.

Multiple comparison correction methods for whole-body magnetic resonance imaging

Purpose: Voxel-level hypothesis testing on images suffers from test multiplicity. Numerous correction methods exist, mainly applied and evaluated on neuroimaging and synthetic datasets. However, newly developed approaches like Imiomics, using different data and less common analysis types, also require multiplicity correction for more reliable inference. To handle the multiple comparisons in Imiomics, we aim to evaluate correction methods on whole-body MRI and correlation analyses, and to develop techniques specifically suited for the given analyses.

Approach: We evaluate the most common familywise error rate (FWER) limiting procedures on whole-body correlation analyses via standard (synthetic no-activation) nominal error rate estimation as well as smaller prior-knowledge based stringency analysis. Their performance is compared to our anatomy-based method extensions.

Results: Results show that nonparametric methods behave better for the given analyses. The proposed prior-knowledge based evaluation shows that the devised extensions including anatomical priors can achieve the same power while keeping the FWER closer to the desired rate.

Conclusions: Permutation-based approaches perform adequately and can be used within Imiomics. They can be improved by including information on image structure. We expect such method extensions to become even more relevant with new applications and larger datasets.