SpEx: a German-language dataset of speech and executive function performance

This work presents data from 148 German native speakers (20-55 years of age), who completed several speaking tasks, ranging from formal tests such as word production tests to more ecologically valid spontaneous tasks that were designed to mimic natural speech. This speech data is supplemented by performance measures on several standardised, computer-based executive functioning (EF) tests covering domains of working-memory, cognitive flexibility, inhibition, and attention. The speech and EF data are further complemented by a rich collection of demographic data that documents education level, family status, and physical and psychological well-being. Additionally, the dataset includes information of the participants' hormone levels (cortisol, progesterone, oestradiol, and testosterone) at the time of testing. This dataset is thus a carefully curated, expansive collection of data that spans over different EF domains and includes both formal speaking tests as well as spontaneous speaking tasks, supplemented by valuable phenotypical information. This will thus provide the unique opportunity to perform a variety of analyses in the context of speech, EF, and inter-individual differences, and to our knowledge is the first of its kind in the German language. We refer to this dataset as SpEx since it combines speech and executive functioning data. Researchers interested in conducting exploratory or hypothesis-driven analyses in the field of individual differences in language and executive functioning, are encouraged to request access to this resource. Applicants will then be provided with an encrypted version of the data which can be downloaded.

Convergent abnormality in the subgenual anterior cingulate cortex in insomnia disorder: A revisited neuroimaging meta-analysis of 39 studies

The neurobiological underpinnings of insomnia disorder (ID) are still poorly understood. A previous meta-analysis conducted by our research group in 2018 revealed no consistent regional alterations based on the limited number of eligible studies. Given the number of studies published during the last few years, we revisited the meta-analysis to provide an update to the field. Following the best-practice guidelines for conducting neuroimaging meta-analyses, we searched several databases (PubMed, Web of Science, and BrainMap) and identified 39 eligible structural and functional studies, reporting coordinates reflecting significant group differences between ID patients and healthy controls. A significant convergent regional alteration in the subgenual anterior cingulate cortex (sgACC) was observed using the activation likelihood estimation algorithm. Behavioural decoding using the BrainMap database indicated that this region is involved in fear-related emotional and cognitive processing. The sgACC showed robust task-based co-activation in meta-analytic connectivity modelling and task-free functional connectivity in a resting-state functional connectivity analysis with the main hubs of the salience and default mode networks, including the posterior cingulate cortex and dorsal ACC, amygdala, hippocampus, and medial prefrontal cortex. Collectively, the findings from this large-scale meta-analysis suggest a critical role of the sgACC in the pathophysiology of ID.

Cytoarchitectonic mapping of the human frontal operculum-New correlates for a variety of brain functions

The human frontal operculum (FOp) is a brain region that covers parts of the ventral frontal cortex next to the insula. Functional imaging studies showed activations in this region in tasks related to language, somatosensory, and cognitive functions. While the precise cytoarchitectonic areas that correlate to these processes have not yet been revealed, earlier receptorarchitectonic analysis resulted in a detailed parcellation of the FOp. We complemented this analysis by a cytoarchitectonic study of a sample of ten postmortem brains and mapped the posterior FOp in serial, cell-body stained histological sections using image analysis and multivariate statistics. Three new areas were identified: Op5 represents the most posterior area, followed by Op6 and the most anterior region Op7. Areas Op5-Op7 approach the insula, up to the circular sulcus. Area 44 of Broca's region, the most ventral part of premotor area 6, and parts of the parietal operculum are dorso-laterally adjacent to Op5-Op7. The areas did not show any interhemispheric or sex differences. Three-dimensional probability maps and a maximum probability map were generated in stereotaxic space, and then used, in a first proof-of-concept-study, for functional decoding and analysis of structural and functional connectivity. Functional decoding revealed different profiles of cytoarchitectonically identified Op5-Op7. While left Op6 was active in music cognition, right Op5 was involved in chewing/swallowing and sexual processing. Both areas showed activation during the exercise of isometric force in muscles. An involvement in the coordination of flexion/extension could be shown for the right Op6. Meta-analytic connectivity modeling revealed various functional connections of the FOp areas within motor and somatosensory networks, with the most evident connection with the music/language network for Op6 left. The new cytoarchitectonic maps are part of Julich-Brain, and publicly available to serve as a basis for future analyses of structural-functional relationships in this region.

Neural substrates of motivational dysfunction across neuropsychiatric conditions: Evidence from meta-analysis and lesion network mapping

Motivational dysfunction constitutes one of the fundamental dimensions of psychopathology cutting across traditional diagnostic boundaries. However, it is unclear whether there is a common neural circuit responsible for motivational dysfunction across neuropsychiatric conditions. To address this issue, the current study combined a meta-analysis on psychiatric neuroimaging studies of reward/loss anticipation and consumption (4308 foci, 438 contrasts, 129 publications) with a lesion network mapping approach (105 lesion cases). Our meta-analysis identified transdiagnostic hypoactivation in the ventral striatum (VS) for clinical/at-risk conditions compared to controls during the anticipation of both reward and loss. Moreover, the VS subserves a key node in a distributed brain network which encompasses heterogeneous lesion locations causing motivation-related symptoms. These findings do not only provide the first meta-analytic evidence of shared neural alternations linked to anticipatory motivation-related deficits, but also shed novel light on the role of VS dysfunction in motivational impairments in terms of both network integration and psychological functions. Particularly, the current findings suggest that motivational dysfunction across neuropsychiatric conditions is rooted in disruptions of a common brain network anchored in the VS, which contributes to motivational salience processing rather than encoding positive incentive values.

Convergent regional brain abnormalities in behavioral variant frontotemporal dementia: A neuroimaging meta-analysis of 73 studies

Introduction

Numerous studies have reported brain alterations in behavioral variant frontotemporal dementia (bvFTD). However, they pointed to inconsistent findings.

Methods

We used a meta‐analytic approach to identify the convergent structural and functional brain abnormalities in bvFTD. Following current best‐practice neuroimaging meta‐analysis guidelines, we searched PubMed and Embase databases and performed reference tracking. Then, the coordinates of group comparisons between bvFTD and controls from 73 studies were extracted and tested for convergence using activation likelihood estimation.

Results

We identified convergent abnormalities in the anterior cingulate cortices, anterior insula, amygdala, paracingulate, striatum, and hippocampus. Task‐based and resting‐state functional connectivity pointed to the networks that are connected to the obtained consistent regions. Functional decoding analyses suggested associated dysfunction of emotional processing, interoception, reward processing, higher‐order cognitive functions, and olfactory and gustatory perceptions in bvFTD.

Discussion

Our findings highlighted the key role of the salience network and subcortical regions in the pathophysiology of bvFTD.

Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.

The neural signatures of social hierarchy-related learning and interaction: A coordinate- and connectivity-based meta-analysis

Numerous neuroimaging studies have investigated the neural mechanisms of two mutually independent yet closely related cognitive processes aiding humans to navigate complex societies: social hierarchy-related learning (SH-RL) and social hierarchy-related interaction (SH-RI). To integrate these heterogeneous results into a more fine-grained and reliable characterization of the neural basis of social hierarchy, we combined coordinate-based meta-analyses with connectivity and functional decoding analyses to understand the underlying neuropsychological mechanism of SH-RL and SH-RI. We identified the anterior insula and temporoparietal junction (dominance detection), medial prefrontal cortex (information updating and computation), and intraparietal sulcus region, amygdala, and hippocampus (social hierarchy representation) as consistent activated brain regions for SH-RL, but the striatum, amygdala, and hippocampus associated with reward processing for SH-RI. Our results provide an overview of the neural architecture of the neuropsychological processes underlying how we understand, and interact within, social hierarchy.

The cue-reactivity paradigm: An ensemble of networks driving attention and cognition when viewing drug and natural reward-related stimuli

The cue-reactivity paradigm is a widely adopted neuroimaging probe engendering brain activity linked with attentional, affective, and reward processes following presentation of appetitive stimuli. Given the multiple mental operations invoked, we sought to decompose cue-related brain activity into constituent components employing emergent meta-analytic techniques when considering drug and natural reward-related cues. We conducted coordinate-based meta-analyses delineating common and distinct brain activity convergence across cue-reactivity studies (N = 196 articles) involving drug (n = 133) or natural (n = 63) visual stimuli. Across all studies, convergence was observed in limbic, cingulate, insula, and fronto-parieto-occipital regions. Drug-distinct convergence was observed in posterior cingulate, dorsolateral prefrontal, and temporo-parietal regions, whereas distinct-natural convergence was observed in thalamic, insular, orbitofrontal, and occipital regions. We characterized connectivity profiles of identified regions by leveraging task-independent and task-dependent MRI datasets, grouped these profiles into subnetworks, and linked each with putative mental operations. Outcomes suggest multifaceted brain activity during cue-reactivity can be decomposed into elemental processes and indicate that while drugs of abuse usurp the brain's natural-reward-processing system, some regions appear distinct to drug cue-reactivity.

Neural mechanisms of deliberate dishonesty: Dissociating deliberation from other control processes during dishonest behaviors

Numerous studies have sought proof of whether people are genuinely honest by testing whether cognitive control mechanisms are recruited during honest and dishonest behaviors. The underlying assumption is: Deliberate behaviors require cognitive control to inhibit intuitive responses. However, cognitive control during honest and dishonest behaviors can be required for other reasons than deliberation. Across 58 neuroimaging studies (1,211 subjects), we investigated different forms of honest and dishonest behaviors and demonstrated that many brain regions previously implicated in dishonesty may reflect more general cognitive mechanisms. We argue that the motivational/volitional dimension is central to deliberation and provide evidence that motivated dishonest behaviors recruit the perigenual anterior cingulate cortex. This work questions the view that cognitive control is a hallmark of dishonesty.

Understanding identification-based trust in the light of affiliative bonding: Meta-analytic neuroimaging evidence

Trust is vital for establishing social relationships and is a crucial precursor for affiliative bonds. Investigations explored the neuropsychological bases of trust separately (e.g., measured by the trust game) and affiliative bonding (e.g., measured by parental care, pair-bonding, or friendship). However, direct empirical support for the shared neural mechanisms between trust and affiliative bonding is missing. Here, we conducted a coordinate-based meta-analysis on functional magnetic resonance imaging studies on interpersonal trust and affiliative bonding using the activation likelihood estimation method. Our results demonstrated that decisions to trust strangers in repeated interactions (i.e., identification-based trust) engaged the ventral striatum (vSTR, part of the mesolimbic dopaminergic pathway), likely signaling the reward anticipation. Further, both feedbacks in repeated interactions and affiliative bonding engaged the dorsal striatum (dSTR, part of the nigrostriatal dopaminergic pathway), likely encoding learning dynamics. Our findings suggest that identification-based trust can be understood in the light of affiliative bonding, involving the mesocorticolimbic "reward" pathway (vSTR) and nigrostriatal "habit formation" pathway (dSTR) in building and sustaining social relationships.

Cortical and subcortical gray matter volume in psychopathy: A voxel-wise meta-analysis

Voxel-based morphometry (VBM) studies of gray matter volume (GMV) in psychopathy have produced inconsistent results and few have been replicated. Therefore, to clarify GMV abnormalities associated with psychopathy as operationalized by Hare (2003), we conducted a meta-analysis of VBM studies using both categorical and dimensional analyses. We identified seven studies eligible for the categorical meta-analysis (136 men with psychopathy vs 150 male controls) and 11 studies (N = 519) eligible for dimensional metaregressions. First, we used seed-based d mapping with permutation of subject images for voxel-based meta-analyses. Statistical parametric maps of GMV were available for four (57%) of the studies included in the categorical meta-analysis and for five (45%) of the studies included in the dimensional metaregression analyses, with peak coordinates available for the remaining studies. Second, we used metadata of a large-scale neuroimaging database to provide an objective and quantitative account of psychological processes attributed to the brain regions we identified in our group meta-analysis and metaregressions. Men with psychopathy exhibited reliable GMV abnormalities circumscribed to the left hemisphere in the dorsolateral prefrontal cortex and the medial orbitofrontal cortex. Total psychopathy scores and Factors 1 and 2 scores were all related to decreased GMV within those two prefrontal regions, as well as decreased GMV in a wider set of regions encompassing midline, temporal, parietal, occipital, and subcortical structures. We discuss how decreased GMV in those regions likely account for the impairments in the emotion, cognition, action, and perception domains seen in the disorder. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System

While a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis-Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division between low-level and high-level executive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.

The Aging Brain and Executive Functions Revisited: Implications from Meta-analytic and Functional-Connectivity Evidence

Healthy aging is associated with changes in cognitive performance, including executive functions (EFs) and their associated brain activation patterns. However, it has remained unclear which EF-related brain regions are affected consistently, because the results of pertinent neuroimaging studies and earlier meta-analyses vary considerably. We, therefore, conducted new rigorous meta-analyses of published age differences in EF-related brain activity. Out of a larger set of regions associated with EFs, only left inferior frontal junction and left anterior cuneus/precuneus were found to show consistent age differences. To further characterize these two age-sensitive regions, we performed seed-based resting-state functional connectivity (RS-FC) analyses using fMRI data from a large adult sample with a wide age range. We also assessed associations of the two regions' whole-brain RS-FC patterns with age and EF performance. Although our results largely point toward a domain-general role of left inferior frontal junction in EFs, the pattern of individual study contributions to the meta-analytic results suggests process-specific modulations by age. Our analyses further indicate that the left anterior cuneus/precuneus is recruited differently by older (compared with younger) adults during EF tasks, potentially reflecting inefficiencies in switching the attentional focus. Overall, our findings question earlier meta-analytic results and suggest a larger heterogeneity of age-related differences in brain activity associated with EFs. Hence, they encourage future research that pays greater attention to replicability, investigates age-related differences in deactivation, and focuses on more narrowly defined EF subprocesses, combining multiple behavioral assessments with multimodal imaging.

The functional neural architecture of dysfunctional reward processing in autism

Functional imaging studies have found differential neural activation patterns during reward-paradigms in patients with autism spectrum disorder (ASD) compared to neurotypical controls. However, publications report conflicting results on the directionality and location of these aberrant activations. We here quantitatively summarized relevant fMRI papers in the field using the anatomical likelihood estimation (ALE) algorithm. Patients with ASD consistently showed hypoactivations in the striatum across studies, mainly in the right putamen and accumbens. These regions are functionally involved in the processing of rewards and are enrolled in extensive neural networks involving limbic, cortical, thalamic and mesencephalic regions. The striatal hypo-activations found in our ALE meta-analysis, which pooled over contrasts derived from the included studies on reward-processing in ASD, highlight the role of the striatum as a key neural correlate of impaired reward processing in autism. These changes were present for studies using social and non-social stimuli alike. The involvement of these regions in extensive networks associated with the processing of both positive and negative emotion alike might hint at broader impairments of emotion processing in the disorder.

Common brain networks underlying human social interactions: Evidence from large-scale neuroimaging meta-analysis

Recent overarching frameworks propose that various human social interactions are commonly supported by a set of fundamental neuropsychological processes, including social cognition, motivation, and cognitive control. However, it remains unclear whether brain networks implicated in these functional constructs are consistently engaged in diverse social interactions. Based on ample evidence from human brain imaging studies (342 contrasts, 7234 participants, 3328 foci), we quantitatively synthesized brain areas involved in broad domains of social interactions, including social interactions versus non-social contexts, positive/negative aspects of social interactions, social learning, and social norms. We then conducted brain network analysis on the ensuing brain regions and characterized the psychological function profiles of identified brain networks. Our findings revealed that brain regions consistently involved in diverse social interactions mapped onto default mode network, salience network, subcortical network and central executive network, which were respectively implicated in social cognition, motivation and cognitive control. These findings implicate a heuristic integrative framework to understand human social life from the perspective of component process and network integration.

Comprehensive verbal fluency features predict executive function performance

Semantic verbal fluency (sVF) tasks are commonly used in clinical diagnostic batteries as well as in a research context. When performing sVF tasks to assess executive functions (EFs) the sum of correctly produced words is the main measure. Although previous research indicates potentially better insights into EF performance by the use of finer grained sVF information, this has not yet been objectively evaluated. To investigate the potential of employing a finer grained sVF feature set to predict EF performance, healthy monolingual German speaking participants (n = 230) were tested with a comprehensive EF test battery and sVF tasks, from which features including sum scores, error types, speech breaks and semantic relatedness were extracted. A machine learning method was applied to predict EF scores from sVF features in previously unseen subjects. To investigate the predictive power of the advanced sVF feature set, we compared it to the commonly used sum score analysis. Results revealed that 8 / 14 EF tests were predicted significantly using the comprehensive sVF feature set, which outperformed sum scores particularly in predicting cognitive flexibility and inhibitory processes. These findings highlight the predictive potential of a comprehensive evaluation of sVF tasks which might be used as diagnostic screening of EFs.

Brain Motor Network Changes in Parkinson's Disease: Evidence from Meta-Analytic Modeling

Background

Motor‐related brain activity in Parkinson's disease has been investigated in a multitude of functional neuroimaging studies, which often yielded apparently conflicting results. Our previous meta‐analysis did not resolve inconsistencies regarding cortical activation differences in Parkinson's disease, which might be related to the limited number of studies that could be included. Therefore, we conducted a revised meta‐analysis including a larger number of studies. The objectives of this study were to elucidate brain areas that consistently show abnormal motor‐related activation in Parkinson's disease and to reveal their functional connectivity profiles using meta‐analytic approaches.

Methods

We applied a quantitative meta‐analysis of functional neuroimaging studies testing limb movements in Parkinson's disease comprising data from 39 studies, of which 15 studies (285 of 571 individual patients) were published after the previous meta‐analysis. We also conducted meta‐analytic connectivity modeling to elucidate the connectivity profiles of areas showing abnormal activation.

Results

We found consistent motor‐related underactivation of bilateral posterior putamen and cerebellum in Parkinson's disease. Primary motor cortex and the supplementary motor area also showed deficient activation, whereas cortical regions localized directly anterior to these areas expressed overactivation. Connectivity modeling revealed that areas showing decreased activation shared a common pathway through the posterior putamen, whereas areas showing increased activation were connected to the anterior putamen.

Conclusions

Despite conflicting results in individual neuroimaging studies, this revised meta‐analytic approach identified consistent patterns of abnormal motor‐related activation in Parkinson's disease. The distinct patterns of decreased and increased activity might be determined by their connectivity with different subregions of the putamen. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Neural signatures of prosocial behaviors

Prosocial behaviors are hypothesized to require socio-cognitive and empathic abilities-engaging brain regions attributed to the mentalizing and empathy brain networks. Here, we tested this hypothesis with a coordinate-based meta-analysis of 600 neuroimaging studies on prosociality, mentalizing and empathy (∼12,000 individuals). We showed that brain areas recruited by prosocial behaviors only partially overlap with the mentalizing (dorsal posterior cingulate cortex) and empathy networks (middle cingulate cortex). Additionally, the dorsolateral and ventromedial prefrontal cortices were preferentially activated by prosocial behaviors. Analyses on the functional connectivity profile and functional roles of the neural patterns underlying prosociality revealed that in addition to socio-cognitive and empathic processes, prosocial behaviors further involve evaluation processes and action planning, likely to select the action sequence that best satisfies another person's needs. By characterizing the multidimensional construct of prosociality at the neural level, we provide insights that may support a better understanding of normal and abnormal social cognition (e.g., psychopathy).

Executive functions predict verbal fluency scores in healthy participants

While there is a clear link between impairments of executive functions (EFs), i.e. cognitive control mechanisms that facilitate goal-directed behavior, and speech problems, it is so far unclear exactly which of the complex subdomains of EFs most strongly contribute to speech performance, as measured by verbal fluency (VF) tasks. Furthermore, the impact of intra-individual variability is largely unknown. This study on healthy participants (n = 235) shows that the use of a relevance vector machine approach allows for the prediction of VF performance from EF scores. Based on a comprehensive set of EF scores, results identified cognitive flexibility and inhibition as well as processing speed as strongest predictors for VF performance, but also highlighted a modulatory influence of fluctuating hormone levels. These findings demonstrate that speech production performance is strongly linked to specific EF subdomains, but they also suggest that inter-individual differences should be taken into account.

The emerging neuroscience of social punishment: Meta-analytic evidence

Social punishment (SOP)-third-party punishment (TPP) and second-party punishment (SPP)-sanctions norm-deviant behavior. The hierarchical punishment model (HPM) posits that TPP is an extension of SPP and both recruit common processes engaging large-scale domain-general brain networks. Here, we provided meta-analytic evidence to the HPM by combining the activation likelihood estimation approach with connectivity analyses and hierarchical clustering analyses. Although both forms of SOP engaged the dorsolateral prefrontal cortex and bilateral anterior insula (AI), a functional differentiation also emerged with TPP preferentially engaging social cognitive regions (temporoparietal junction) and SPP affective regions (AI). Further, although both TPP and SPP recruit domain-general networks (salience, default-mode, and central-executive networks), some specificity in network organization was observed. By revealing differences and commonalities of the neural networks consistently activated by different types of SOP, our findings contribute to a better understanding of the neuropsychological mechanisms of social punishment behavior--one of the most peculiar human behaviors.

Variability in the analysis of a single neuroimaging dataset by many teams

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 hypotheses1. 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 dataset2-5. 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.

Electroconvulsive therapy modulates grey matter increase in a hub of an affect processing network

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We here present a structural neuroimaging study reporting on a large multi-site patient sample with unipolar depression that underwent ECT.

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Patients showed grey matter increases in the medial temporal lobe.

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Connectivity modeling revealed that this altered brain region was involved in networks related to affect processing and memory.

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This provides a potential explanation, how these structural changes during ECT are involved in both main and side effects of the treatment.

A growing number of recent studies has suggested that the neuroplastic effects of electroconvulsive therapy (ECT) might be prominent enough to be detected through changes of regional gray matter volumes (GMV) during the course of the treatment. Given that ECT patients are difficult to recruit for imaging studies, most publications, however, report only on small samples. Addressing this challenge, we here report results of a structural imaging study on ECT patients that pooled patients from five German sites.

Whole-brain voxel-based morphometry (VBM) analysis was performed to detect structural differences in 85 patients with unipolar depression before and after ECT, when compared to 86 healthy controls. Both task-independent and task-dependent physiological whole-brain functional connectivity patterns of these regions were modeled using additional data from healthy subjects. All emerging regions were additionally functionally characterized using the BrainMap database.

Our VBM analysis detected a significant increase of GMV in the right hippocampus/amygdala region in patients after ECT compared to healthy controls. In healthy subjects this region was found to be enrolled in a network associated with emotional processing and memory. A region in the left fusiform gyrus was additionally found to have higher GMV in controls when compared with patients at baseline. This region showed minor changes after ECT.

Our data points to a GMV increase in patients post ECT in regions that seem to constitute a hub of an emotion processing network. This appears as a plausible antidepressant mechanism and could explain the efficacy of ECT not only in the treatment of unipolar depression, but also of affective symptoms across heterogeneous disorders.

Functional brain alterations in acute sleep deprivation: An activation likelihood estimation meta-analysis

Sleep deprivation (SD) is a common problem in modern societies, which leads to cognitive dysfunctions including attention lapses, impaired working memory, hindering decision making, impaired emotional processing, and motor vehicle accidents. Numerous neuroimaging studies have investigated the neural correlates of SD, but these studies have reported inconsistent results. Thus, we aimed to identify convergent patterns of abnormal brain functions due to acute SD. Based on the preferred reporting for systematic reviews and meta-analyses statement, we searched the PubMed database and performed reference tracking and finally retrieved 31 eligible functional neuroimaging studies. Then, we applied activation estimation likelihood meta-analysis and found reduced activity mainly in the right intraparietal sulcus and superior parietal lobule. The functional decoding analysis using the BrainMap database indicated that this region is mostly related to visuospatial perception, memory and reasoning. The significant co-activation of this region using the BrainMap database were found in the left superior parietal lobule, intraparietal sulcus, bilateral occipital cortex, left fusiform gyrus and thalamus. This region also connected with the superior parietal lobule, intraparietal sulcus, insula, inferior frontal gyrus, precentral, occipital and cerebellum through resting-state functional connectivity in healthy subjects. Taken together, our findings highlight the role of superior parietal cortex in SD.

Definition and characterization of an extended multiple-demand network

Neuroimaging evidence suggests that executive functions (EF) depend on brain regions that are not closely tied to specific cognitive demands but rather to a wide range of behaviors. A multiple-demand (MD) system has been proposed, consisting of regions showing conjoint activation across multiple demands. Additionally, a number of studies defining networks specific to certain cognitive tasks suggest that the MD system may be composed of a number of sub-networks each subserving specific roles within the system. We here provide a robust definition of an extended MDN (eMDN) based on task-dependent and task-independent functional connectivity analyses seeded from regions previously shown to be convergently recruited across neuroimaging studies probing working memory, attention and inhibition, i.e., the proposed key components of EF. Additionally, we investigated potential sub-networks within the eMDN based on their connectional and functional similarities. We propose an eMDN network consisting of a core whose integrity should be crucial to performance of most operations that are considered higher cognitive or EF. This then recruits additional areas depending on specific demands.

Multi-Modal Imaging of Neural Correlates of Motor Speed Performance in the Trail Making Test

The assessment of motor and executive functions following stroke or traumatic brain injury is a key aspect of impairment evaluation and used to guide further therapy. In clinical routine, such assessments are largely dominated by pen-and-paper tests. While these provide standardized, reliable, and ecologically valid measures of the individual level of functioning, rather little is yet known about their neurobiological underpinnings. Therefore, the aim of this study was to investigate brain regions and their associated networks that are related to upper extremity motor function, as quantified by the motor speed subtest of the trail making test (TMT-MS). Whole-brain voxel-based morphometry and whole-brain tract-based spatial statistics were used to investigate the association between TMT-MS performance with gray-matter volume (GMV) and white-matter integrity, respectively. While results demonstrated no relationship to local white-matter properties, we found a significant correlation between TMT-MS performance and GMV of the lower bank of the inferior frontal sulcus, a region associated with cognitive processing, as indicated by assessing its functional profile by the BrainMap database. Using this finding as a seed region, we further examined and compared networks as reflected by resting state connectivity, meta-analytic connectivity modeling, structural covariance, and probabilistic tractography. While differences between the different approaches were observed, all approaches converged on a network comprising regions that overlap with the multiple-demand network. Our data therefore indicate that performance may primarily depend on executive function, thus suggesting that motor speed in a more naturalistic setting should be more associated with executive rather than primary motor function. Moreover, results showed that while there were differences between the approaches, a convergence indicated that common networks can be revealed across highly divergent methods.