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Brain mechanisms of error processing have often been investigated using response interference tasks and focusing on the posterior medial frontal cortex, which is also implicated in resolving response conflict in general. Thereby, the role other brain regions may play has remained undervalued. Here, activation likelihood estimation meta-analyses were used to synthesize the neuroimaging literature on brain activity related to committing errors versus responding successfully in interference tasks and to test for commonalities and differences. The salience network and the temporoparietal junction were commonly recruited irrespective of whether responses were correct or incorrect, pointing towards a general involvement in coping with situations that call for increased cognitive control. The dorsal posterior cingulate cortex, posterior thalamus, and left superior frontal gyrus showed error-specific convergence, which underscores their consistent involvement when performance goals are not met. In contrast, successful responding revealed stronger convergence in the dorsal attention network and lateral prefrontal regions. Underrecruiting these regions in error trials may reflect failures in activating the task-appropriate stimulus-response contingencies necessary for successful response execution.

Crosstalk between conflicting response codes contributes to interference in dual-tasking, an effect exacerbated in advanced age. Here, we investigated (1) brain activity correlates of such response-code conflicts, (2) activity modulations by individual dual-task performance and related cognitive abilities, (3) task-modulated connectivity within the task network, and (4)age-related differences in all these aspects. Young and older adults underwent fMRI while responding to the pitch of tones through spatially mapped speeded button presses with one or two hands concurrently. Using opposing stimulus–response mappings between hands, we induced conflict between simultaneously activated response codes. These response-code conflicts elicited activation in key regions of the multiple-demand network. Older adults showed non-compensatory hyperactivity in left superior frontal gyrus, and higher left intraparietal sulcus activity associated with lower attentional performance. While motor and parietal areas of the conflict-related network were modulated by attentional and task-switching abilities, efficient conflict resolution in dual-tasking was linked to suppressing visual cortex activity. Finally, connectivity between premotor or parietal seed regions and the conflict-sensitive network was neither conflict-specific nor age-sensitive. Overall, resolving dual-task response-code conflict recruited substantial parts of the multiple-demand network, whose activity and coupling, however, were only little affected by individual differences in task performance or age.

Current major efforts in human neuroimaging research aim to understand individual differences and identify biomarkers for clinical applications. One particularly promising approach is the prediction of individual-level behavioural phenotypes (e.g. treatment response, cognition) from brain imaging data. An essential prerequisite to identify replicable brain-behaviour prediction models is sufficient measurement reliability. By attenuating the relationship between two variables, low reliability increases the sample size necessary to identify an effect, making large datasets a necessity rather than an advantage. While previous work has evaluated the reliability of brain-based measures, the impact of the reliability of behavioural phenotypes has been largely neglected, as target selection for prediction is often guided by scientific interest or data availability. Here we demonstrate the impact of low phenotypic reliability on individual-level prediction performance. Using simulated and empirical data from the Human Connectome Projects, we found that even moderate reliability levels of commonly used behavioural phenotypes can markedly limit the ability to link brain and behaviour when underlying relations are weak. Next, using 5000 subjects from the UK Biobank, we show that highly reliable data in smaller samples outperform large amounts of moderately reliable data. These findings suggest that research programmes focused on identifying generalizable brain–behaviour associations must seriously consider the reliability of outcome measures. Ultimately, a stronger focus on reliability will help reduce the financial and societal costs incurred in acquiring large-scale datasets with unreliable “markers” of behaviour.

Healthy aging is associated with altered executive functioning (EF). Earlier studies found age-related differences in EF performance to be partially accounted for by changes in resting-state functional connectivity (RSFC) within brain networks associated with EF. However, it remains unclear which role RSFC in EF-associated networks plays as a marker for individual differences in EF performance. Here, we investigated to what degree individual abilities across 3 different EF tasks can be predicted from RSFC within EF-related, perceptuo-motor, whole-brain, and random networks separately in young and old adults. Specifically, we were interested if (i) young and old adults differ in predictability depending on network or EF demand level (high vs. low), (ii) an EF-related network outperforms EF-unspecific networks when predicting EF abilities, and (iii) this pattern changes with demand level. Both our uni- and multivariate analysis frameworks analyzing interactions between age × demand level × networks revealed overall low prediction accuracies and a general lack of specificity regarding neurobiological networks for predicting EF abilities. This questions the idea of finding markers for individual EF performance in RSFC patterns and calls for future research replicating the current approach in different task states, brain modalities, different, larger samples, and with more comprehensive behavioral measures.

The human brain's cerebral cortex exhibits a topographic division into higher-order transmodal core and lower-order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale-free dynamics during resting-state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f-like pink noise manifestation of scale-free dynamics in the core-periphery topography during rest and task states applying infra-slow inter-trial intervals up to 1 min falling inside the BOLD's infra-slow frequency band. The results demonstrate (1) higher resting-state power-law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task-related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter-trial interval = 52-60 s; 0.016-0.019 Hz). A computational model and a replication dataset that used similar infra-slow inter-trial intervals provide further support for our main findings. Altogether, the results show that scale-free dynamics differentiate core and periphery regions in the resting-state and mediate task-related effects.

No evidence-based prosthetic treatment concept for 3-unit fixed-dental-prostheses (FDPs) on ceramic implants is currently available. Therefore, the aim of this in vitro study was to investigate the failure load and fatigue behavior of monolithic and bi-layer zirconia FDPs supported by one-piece ceramic implants. Eighty 3-unit FDPs supported by 160 zirconia-implants (ceramic.implant; vitaclinical) were divided into 4 groups (n = 20 each): Group Z-HT: 3Y-TZP monolithic-zirconia (Vita-YZ-HT); Group Z-ST: 4Y-TZP monolithic-zirconia (Vita-YZ-ST); Group FL: 3Y-TZP zirconia (Vita-YZ-HT) with facial-veneer (Vita-VM9); Group RL (Rapid-layer): PICN “table-top” (Vita-Enamic), 3Y-TZP-framework (Vita-YZ-HT). Half of the test samples (n = 10/group) were fatigued in a mouth-motion chewing-simulator (F = 98 N, 1.2 million-cycles) with simultaneous thermocycling (5–55 °C). All specimens (fatigued and non-fatigued) were afterwards exposed to single-load-to-failure-testing (Z010, Zwick). Statistical analysis was performed using ANOVA, Tukey’s post-hoc tests and two-sample t-tests (p < 0.05, Bonferroni-corrected where appropriate). All specimens withstood fatigue application. While the effect of fatigue was not significant in any group (p = 0.714), the choice of material had a significant effect (p < 0.001). Material FL recorded the highest failure loads, followed by Z-ST, Z-HT and RL, both with and without fatigue application. Taken together, all tested FDP material combinations survived chewing forces that exceeded physiological levels. Bi-Layer FL and monolithic Z-ST showed the highest resilience and might serve as reliable prosthetic reconstruction concepts for 3-unit FDPs on ceramic implants.

The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure–function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large thalamic-centered systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure–function relationships at the brain network level, and to build viable starting points for future studies.

In neu errichteten Einfamilienhäusern hat sich die Wärmepumpe inzwischen etabliert. Rund 45 Prozent der Neubaueigentümer entschieden sich 2019 für diese Heiztechnologie. Die Wärmeerzeuger funktionieren jedoch auch in bestehenden Gebäuden zuverlässig. Das zeigen Ergebnisse einer breit angelegten Feldstudie des Fraunhofer-Instituts für Solare Energiesysteme ISE. Hauptfokus des Projektes war die Ermittlung der Effizienz von Wärmepumpen unter Berücksichtigung der Einsatzbedingungen, insbesondere mit Blick auf die Gebäudeeigenschaften und Wärmeübergabesysteme.

Human brain mapping 43(13), 3987-3997 (2022). doi:10.1002/hbm.25898, Published by Wiley-Liss, New York, NY

Neurofeedback allows for learning voluntary control over one’s own brain activity, aiming to enhance cognition and clinical symptoms. A recent study improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network (SAN) minus the default mode network (DMN). However, long-term learning effects of functional magnetic resonance imaging (fMRI) neurofeedback training remain under-explored. Here, we evaluate the effects of network-based fMRI neurofeedback training for sustained attention by assessing behavioral and brain measures before, one day after, and two months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs. Neurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training-induced increase in FC between the DMN and occipital gyrus was maintained during follow-up transfer runs, but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up. The discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior long-term, a better understanding of the brain-behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool.Key pointsParticipants were still able to self-regulate the differential activity between large-scale networks two months after the end of neurofeedback training and this during transfer runs without feedback.Lasting brain changes were also observed in the functional connectivity of trained regions in runs during which participants engaged in active self-regulation as well as during resting-state runs without concomitant self-regulation.The increased sustained attention we observed right after the end of neurofeedback training did not persist two months later.

Skilled visual object and pattern recognition form the basis of many everyday behaviours. The game of chess has often been used as a model case for studying how long-term experience aides in perceiving objects and their spatio-functional interrelations. Earlier research revealed two brain regions, posterior middle temporal gyrus (pMTG) and collateral sulcus (CoS), to be linked to chess experts' superior object and pattern recognition, respectively. Here we elucidated the brain networks these two expertise-related regions are embedded in, employing resting-state functional connectivity analysis and meta-analytic connectivity modelling with the BrainMap database. pMTG was preferentially connected with dorsal visual stream areas and a parieto-prefrontal network for action planning, while CoS was preferentially connected with posterior medial cortex and hippocampus, linked to scene perception, perspective-taking and navigation. Functional profiling using BrainMap meta-data revealed that pMTG was linked to semantic processing as well as inhibition and attention, while CoS was linked to face and shape perception as well as passive viewing. Our findings suggest that pMTG subserves skilled object recognition by mediating the link between object identity and object affordances, while CoS subserves skilled pattern recognition by linking the position of individual objects with typical spatio-functional layouts of their environment stored in memory.

Background With this study we aimed to analyze if the separate consideration of body mass index (BMI) could provide any superior predictive values compared with the established risk scores in isolated minimally invasive mitral valve surgery (MIMVS). This might facilitate future therapeutic decision-making, e.g., regarding the question surgery versus transcatheter mitral valve repair (TMVr). Methods We assessed the relevance of BMI in non-underweight patients who underwent isolated MIMVS. The risk predictive potential of BMI for mortality and several postoperative adverse events was assessed in 429 consecutive patients. This predictive potential was compared with that of European System for Cardiac Outcome Risk Evaluation II (EuroSCORE II) and the Society of Thoracic Surgeons score (STS score) using a comparative receiver operating characteristic curve analysis. Results BMI was a significant numeric predictor of wound healing disorders (p = 0.001) and proved to be significantly superior in case of this postoperative adverse event compared with the EuroSCORE II (p = 0.040) and STS score (p = 0.015). Except for this, the predictive potential of BMI was significantly inferior compared with that of the EuroSCORE II and STS score for several end points, including 30-day (p = 0.029 and p = 0.006) and 1-year (p = 0.012 and p = 0.001) mortality. Conclusion Therefore, we suggest that, in the course of decision-making regarding the right treatment modality for non-underweight patients with isolated mitral valve regurgitation, the sole factor of BMI should not be given a predominant weight.

Meta-analytic connectivity modelling (MACM) capitalizes on the rich corpus of published neuroimaging studies that have accumulated over the years. Querying large neuroimaging databases such as BrainMap, MACM employs meta-analytic algorithms like activation likelihood estimation to identify brain regions that are consistently activated together with a given region of interest, across task-based studies investigating different functions. This allows for delineating the functional network of this region and, therefore, allows investigating the region’s functional connectivity (FC). The MACM approach can also be extended to objectively characterize cognitive/behavioral function(s) of a given brain region, based on the meta-data of the experiments stored in the database used. MACM is a useful tool for delineating functional networks in the brain as it yields robust and highly generalizable patterns of coordinated brain activity in response to a broad range of tasks. It is thus complementary to other, task-free approaches to FC, providing a firm basis for normative FC models of particular brain regions.

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 (IFJ) and left anterior cuneus/precuneus (aC/PrC) 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 functional profiling and RS-FC analyses point towards a domain-general role of left IFJ in EFs, the pattern of individual study contributions to the meta-analytic results suggests process-specific modulations by age. Our analyses further indicate that left aC/PrC is recruited differently by older (compared to 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 multi-modal imaging.Highlights- Healthy aging is linked to deterioration in executive functions (EFs)- ALE meta-analyses examined consistent age differences in brain activity linked to EFs- In a larger set of EF regions, only left IFJ and (pre)cuneus were sensitive to age- Advanced age was linked to weaker functional coupling within EF-related networks- Our findings question earlier meta-analytic findings

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

Self-regulation refers to controlling our emotions and actions in the pursuit of higher-order goals. Although research suggests commonalities in the cognitive control of emotion and action, evidence for a shared neural substrate is scant and largely circumstantial. Here we report on two large-scale meta-analyses of human neuroimaging studies on emotion or action control, yielding two fronto-parieto-insular networks. The networks’ overlap, however, was restricted to four brain regions: posteromedial prefrontal cortex, bilateral anterior insula, and right temporo-parietal junction. Conversely, meta-analytic contrasts revealed major between-network differences, which were independently corroborated by clustering domain-specific regions based on their intrinsic functional connectivity, as well as by functionally characterizing network sub-clusters using the BrainMap database for quantitative forward and reverse inference. Collectively, our analyses identified a core system for implementing self-control across emotion and action, beyond which, however, either regulation facet appears to rely on broadly similar yet distinct subnetworks. These insights into the neurocircuitry subserving affective and executive facets of self-control suggest both processing commonalities and differences between the two aspects of human self-regulation.

Contains fulltext : 187792.pdf (Publisher’s version ) (Closed access) Despite the common conception of the dorsal premotor cortex (PMd) as a single brain region, its diverse connectivity profiles and behavioral heterogeneity argue for a differentiated organization of the PMd. A previous study revealed that the right PMd is characterized by a rostro-caudal and a ventro-dorsal distinction dividing it into five subregions: rostral, central, caudal, ventral and dorsal. The present study assessed whether a similar organization is present in the left hemisphere, by capitalizing on a multimodal data-driven approach combining connectivity-based parcellation (CBP) based on meta-analytic modeling, resting-state functional connectivity, and probabilistic diffusion tractography. The resulting PMd modules were then characterized based on multimodal functional connectivity and a quantitative analysis of associated behavioral functions. Analyzing the clusters consistent across all modalities revealed an organization of the left PMd that mirrored its right counterpart to a large degree. Again, caudal, central and rostral modules reflected a cognitive-motor gradient and a premotor eye-field was found in the ventral part of the left PMd. In addition, a distinct module linked to abstract cognitive functions was observed in the rostro-ventral left PMd across all CBP modalities, implying greater differentiation of higher cognitive functions for the left than the right PMd. 12 p.

We provide a psychometric analysis of commonly used performance indices of the d2 sustained-attention test, and give methodical guidelines and recommendations, based on this research. We examined experimental effects of repeated testing on performance speed and accuracy (omission and commission errors), and further evaluated aspects of test reliability by means of cumulative reliability function (CRF) analysis. These aspects were also examined for a number of alternative (yet commonly used) scoring techniques and valuation methods. Results indicate that performance is sensitive to change, both differentially within (time-on-task) and between (test-retest) sessions. These effects did not severely affect test reliability, since perfect score reliability was observed for measures of speed (and was even preserved with half the test length) while variability and error scores were more problematic with respect to reliability. Notably, limitations particularly hold for commission but less so for omission errors. Our recommendations to researchers and practitioners are that (a) only the speed score (and error-corrected speed score) is eligible for highly reliable assessment, that (b) error scores might be used as a secondary measure (e.g., to check for aberrant behavior), that (c) variability scores might not be used at all. Given the exceptional reliability of performance speed, and (d) test length may be reduced up to 50%, if necessary for time-economic reasons, to serve purposes of population screening and field assessment. (PsycINFO Database Record

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.

Predicting individual mental traits and behavioral dispositions from brain imaging data through machine-learning approaches is becoming a rapidly evolving field in neuroscience. Beyond scientific and clinical applications, such approaches also hold the potential to gain substantial influence in fields such as human resource management, education, or criminal law. Although several challenges render real-life applications of such tools difficult, future conflicts of individual, economic, and public interests are preprogrammed, given the prospect of improved personalized predictions across many domains. In this Perspective paper, we thus argue for the need to engage in a discussion on the ethical, legal, and societal implications of the emergent possibilities for brain-based predictions and outline some of the aspects for this discourse., Advances in machine learning on neuroimaging data have opened up the possibility of objectively predicting individual traits like intelligence, personality, or clinical risks from brain scans. This Perspective article discusses the methodological and ethical challenges arising from these advances.

Research in cognitive neuroscience employing resting-state functional magnetic resonance imaging (rs-fMRI) has experienced an exponential boom since Biswal et al.’s pioneering work in the mid-nineties of the last century, when it was demonstrated that rs-fMRI is capable to reveal enhanced functional connectivity within functional subsystems of the human brain (Biswal et al., 1997, Biswal et al., 1995). Rs-fMRI derives its name from the fact that hemodynamic activity is recorded while participants lie in the MR scanner without any experimental task. Instead, participants are usually instructed to let their minds wander freely and not fall asleep. Under these conditions, the body obviously is in a “resting position”; the brain, however, is never at rest but rather enters a so-called default mode involving increased activity in a set of brain areas that are usually deactivated during externally structured tasks ...

OBJECTIVES Mitral valve repair is the preferred method used to address mitral valve regurgitation, whereas transcatheter mitral valve repair is recommended for high-risk patients. We evaluated the risk-predictive value of the age-adjusted Charlson comorbidity index (aa-CCI) in the setting of minimally invasive mitral valve surgery. METHODS The perioperative course and 1-year follow-up of 537 patients who underwent isolated or combined minimally invasive mitral valve surgery were evaluated for 1-year mortality as the primary end point and other adverse events. The predictive values of the EuroSCORE II and STS score were compared to that of the aa-CCI by a comparative analysis of receiver operating characteristic curves. Restricted cubic splines were applied to find optimal aa-CCI cut-off values for the increased likelihood of experiencing the predefined adverse end points. Consequently, the perioperative course and postoperative outcome of the aa-CCI ≥8 patients and the remainder of the sample were analysed. RESULTS The predictive value of the aa-CCI does not significantly differ from those of the EuroSCORE II or STS score. Patients with an aa-CCI ≥8 were identified as a subgroup with a significant increase of mortality and other adverse events. CONCLUSIONS The aa-CCI displays a suitable predictive ability for patients undergoing minimally invasive mitral valve surgery. In particular, multimorbid or frail patients may benefit from the extension of the objectively assessed parameters, in addition to the STS score or EuroSCORE II. Patients with an aa-CCI ≥8 have a very high surgical risk and should receive very careful attention.