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 functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis.

Background: Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems. Methods: Through a comprehensive search in PubMed and Scopus databases, we reviewed 5,258 abstracts and identified 37 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of consistent antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation (TMS) therapy. Last, we studied the association of regional and network-level convergence maps with the selected neurotransmitter receptors/transporters maps. Results: We found regional convergence of the reported treatment-associated increases of functional measures in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. No regional convergence was found across foci of alterations in functional imaging associated with antidepressants. Moreover, we found network-level convergence of functional alterations in a circuit that was prominent in the frontoparietal and salience networks. This circuit was co-aligned with a circuit targeted by anti-subgenual TMS therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters. Conclusion: Our findings highlight the importance of the left dorsolateral prefrontal cortex, as well as frontoparietal network and the salience network in the therapeutic effects of anti-depressants, possibly associated with their role in improving executive functions and emotional processing.

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.

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.

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.

Tract-specific statistics based on diffusion-weighted probabilistic tractography.

Diffusion-weighted neuroimaging approaches provide rich evidence for estimating the structural integrity of white matter in vivo, but typically do not assess white matter integrity for connections between two specific regions of the brain. Here, we present a method for deriving tract-specific diffusion statistics, based upon predefined regions of interest. Our approach derives a population distribution using probabilistic tractography, based on the Nathan Kline Institute (NKI) Enhanced Rockland sample. We determine the most likely geometry of a path between two regions and express this as a spatial distribution. We then estimate the average orientation of streamlines traversing this path, at discrete distances along its trajectory, and the fraction of diffusion directed along this orientation for each participant. The resulting participant-wise metrics (tract-specific anisotropy; TSA) can then be used for statistical analysis on any comparable population. Based on this method, we report both negative and positive associations between age and TSA for two networks derived from published meta-analytic studies (the "default mode" and "what-where" networks), along with more moderate sex differences and age-by-sex interactions. The proposed method can be applied to any arbitrary set of brain regions, to estimate both the spatial trajectory and DWI-based anisotropy specific to those regions.

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.

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.

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.

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).

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.

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.

Better the devil you know than the devil you don't: Neural processing of risk and ambiguity.

Risk and ambiguity are inherent in virtually all human decision-making. Risk refers to a situation in which we know the precise probability of potential outcomes of each option, whereas ambiguity refers to a situation in which outcome probabilities are not known. A large body of research has shown that individuals prefer known risks to ambiguity, a phenomenon known as ambiguity aversion. One heated debate concerns whether risky and ambiguous decisions rely on the same or distinct neural circuits. In the current meta-analyses, we integrated the results of neuroimaging research on decision-making under risk (n = 69) and ambiguity (n = 31). Our results showed that both processing of risk and ambiguity showed convergence in anterior insula, indicating a key role of anterior insula in encoding uncertainty. Risk additionally engaged dorsomedial prefrontal cortex (dmPFC) and ventral striatum, whereas ambiguity specifically recruited the dorsolateral prefrontal cortex (dlPFC), inferior parietal lobe (IPL) and right anterior insula. Our findings demonstrate overlapping and distinct neural substrates underlying different types of uncertainty, guiding future neuroimaging research on risk-taking and ambiguity aversion.

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.