Inter- and intra-subject similarity in network functional connectivity across a full narrative movie.

Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging (fMRI), are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. However, in how far NV elicits similarity within and between subjects on a network level, particularly depending on emotions portrayed in movies, is currently unknown. We used the studyforrest dataset to investigate the inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into 8 consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results showed that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. Lowest similarity both within and between subjects was seen in the emotional regulation network and networks associated with long-term memory processing, which might be explained by specific features and content of the movie. We conclude that detailed characterization of movie features is crucial for NV research.

Sex classification from functional brain connectivity: Generalization to multiple datasets.

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or compound samples of two different sizes. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that the generalization performance of parcelwise classifiers (pwCs) trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwCs trained on the compound samples demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that both a large sample size and a heterogeneous data composition of a training sample have a central role in achieving generalizable results.

Sex classification from functional brain connectivity: Generalization to multiple datasets Generalizability of sex classifiers.

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.

Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex.

Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the developmental timing of asymmetry and the extent to which it arises through genetic and later influences in childhood. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in seven datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry is low-moderately heritable (max h2SNP ~19%) and correlates phenotypically and genetically in specific regions, indicating coordinated development of asymmetries partly through genes. In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises early in life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.

A topography-based predictive framework for naturalistic viewing fMRI.

Functional magnetic resonance imaging (fMRI) during naturalistic viewing (NV) provides exciting opportunities for studying brain functions in more ecologically valid settings. Understanding individual differences in brain functions during NV and their behavioural relevance has recently become an important goal. However, methods specifically designed for this purpose remain limited. Here, we propose a topography-based predictive framework (TOPF) to fill this methodological gap. TOPF identifies individual-specific evoked activity topographies in a data-driven manner and examines their behavioural relevance using a machine learning-based predictive framework. We validate TOPF on both NV and task-based fMRI data from multiple conditions. Our results show that TOPF effectively and stably captures individual differences in evoked brain activity and successfully predicts phenotypes across cognition, emotion and personality on unseen subjects from their activity topographies. Moreover, TOPF compares favourably with functional connectivity-based approaches in prediction performance, with the identified predictive brain regions being neurobiologically interpretable. Crucially, we highlight the importance of examining individual evoked brain activity topographies in advancing our understanding of the brain-behaviour relationship. We believe that the TOPF approach provides a simple but powerful tool for understanding brain-behaviour relationships on an individual level with a strong potential for clinical applications.

Naturalistic viewing increases individual identifiability based on connectivity within functional brain networks.

Naturalistic viewing (NV) is currently considered a promising paradigm for studying individual differences in functional brain organization. While whole brain functional connectivity (FC) under NV has been relatively well characterized, so far little work has been done on a network level. Here, we extend current knowledge by characterizing the influence of NV on FC in fourteen meta-analytically derived brain networks considering three different movie stimuli in comparison to resting-state (RS). We show that NV increases identifiability of individuals over RS based on functional connectivity in certain, but not all networks. Furthermore, movie stimuli including a narrative appear more distinct from RS. In addition, we assess individual variability in network FC by comparing within- and between-subject similarity during NV and RS. We show that NV can evoke individually distinct NFC patterns by increasing inter-subject variability while retaining within-subject similarity. Crucially, our results highlight that this effect is not observable across all networks, but rather dependent on the network-stimulus combination. Our results confirm that NV can improve the detection of individual differences over RS and underline the importance of selecting the appropriate combination of movie and cognitive network for the research question at hand.

Interdisciplinary interactive blended learning concept in times of a pandemic - pain medicine "totally digital".

Introduction: Pain medicine is located in different sections of the medical curriculum. In the pandemic situation, an online teaching concept for Q14 which includes several disciplines had to be developed. The goal of the project was to create a fully digitized learning platform for the cross-sectional area Q14 that allows all participating disciplines to address the various learning goals without losing a practical component. Project description: First, the students' expectations regarding education in the field of pain medicine were recorded by means of a survey among medical students. Based on this, a teaching module in a blended learning format was developed, which consisted of two parts. Within a digital learning platform, students were first required to complete consecutive learning units using an interactive learning management system. This was followed by a presence phase (online ZOOM seminar) in which, under the guidance of teaching staff, the therapy suggestions of the individual case studies from the previous learning program were reflected. In the second part, the acquired knowledge was applied to a simulated patient. An evaluation of the online module was carried out through free-text answers and self-assessment of the completion time. The ZOOM seminar was evaluated on the basis of an assessment by the teachers. Results: The survey among students revealed a desire for practical training without "frontal teaching". The resulting project realized this aspect by teaching theory during an online module with case vignettes and interactive learning tasks. The subsequent online presence time during the ZOOM meeting enabled the students to repeat and deepen contents and to ask questions. 170 students completed the entire online program, of which evaluation data were available for 75 students. Self-assessment of completion time averaged at 4-6 hours. In the feedback, 90 aspects were addressed, including mainly comments on content (43%), praise (33%) and comments on technical problems (23%). According to the assessment of the presenters, the students were able to carry out the pain anamnesis survey in a structured manner. The submission of the therapy proposal, however, represents a particular hurdle. Conclusion: With the presented blended learning concept it is possible to address the different learning goals and the interdisciplinarity of Q14 sufficiently. After further processing and improvement of the project, a controlled and more extensive collection of evaluation data is required to further investigate the benefit of the platform for the students regarding achievement of defined learning goals.

Neurite density imaging in amygdala nuclei reveals interindividual differences in neuroticism.

Neuroticism is known to have significant health implications. While previous research revealed that interindividual differences in the amygdala function are associated with interindividual differences in neuroticism, the impact of the amygdala's structure and especially microstructure on variations in neuroticism remains unclear. Here, we present the first study using NODDI to examine the association between the in vivo microstructural architecture of the amygdala and neuroticism at the level of neurites. We, therefore, acquired brain images from 221 healthy participants using advanced multi-shell diffusion-weighted imaging. Because the amygdala comprises several nuclei, we, moreover, used a high-resolution T1 image to automatically segment the amygdala into eight different nuclei. Neuroticism and its facets have been assessed using the NEO-PI-R. Finally, we associated neuroticism and its facets with the volume and microstructure of the amygdala nuclei. Statistical analysis revealed that lower neurite density in the lateral amygdala nucleus (La) was significantly associated with higher scores in depression, one of the six neuroticism facets. The La is the sensory relay of the amygdala, filtering incoming information based on previous experiences. Reduced neurite density and related changes in the dendritic structure of the La could impair its filtering function. This again might cause harmless sensory information to be misevaluated as threatening and lead to the altered amygdala responsivity as reported in previous studies investigating the functional correlates of neuroticism and neuroticism-related disorders like depression.

White matter variability, cognition, and disorders: a systematic review.

Inter-individual differences can inform treatment procedures and-if accounted for-have the potential to significantly improve patient outcomes. However, when studying brain anatomy, these inter-individual variations are commonly unaccounted for, despite reports of differences in gross anatomical features, cross-sectional, and connectional anatomy. Brain connections are essential to facilitate functional organization and, when severed, cause impairments or complete loss of function. Hence, the study of cerebral white matter may be an ideal compromise to capture inter-individual variability in structure and function. We reviewed the wealth of studies that associate cognitive functions and clinical symptoms with individual tracts using diffusion tractography. Our systematic review indicates that tractography has proven to be a sensitive method in neurology, psychiatry, and healthy populations to identify variability and its functional correlates. However, the literature may be biased, as the most commonly studied tracts are not necessarily those with the highest sensitivity to cognitive functions and pathologies. Additionally, the hemisphere of the studied tract is often unreported, thus neglecting functional laterality and asymmetries. Finally, we demonstrate that tracts, as we define them, are not correlated with one, but multiple cognitive domains or pathologies. While our systematic review identified some methodological caveats, it also suggests that tract-function correlations might still be a promising tool in identifying biomarkers for precision medicine. They can characterize variations in brain anatomy, differences in functional organization, and predicts resilience and recovery in patients.

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.

Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations.

We develop an approach to characterize the effects of gating by a multiconformation protein consisting of macrostate conformations that are either accessible or inaccessible to ligand binding. We first construct a Markov state model of the apo-protein from atomistic molecular dynamics simulations from which we identify macrostates and their conformations, compute their relative macrostate populations and interchange kinetics, and structurally characterize them in terms of ligand accessibility. We insert the calculated first-order rate constants for conformational transitions into a multistate gating theory from which we derive a gating factor γ that quantifies the degree of conformational gating. Applied to HIV-1 protease, our approach yields a kinetic network of three accessible (semi-open, open, and wide-open) and two inaccessible (closed and a newly identified, "parted") macrostate conformations. The parted conformation sterically partitions the active site, suggesting a possible role in product release. We find that the binding kinetics of drugs and drug-like inhibitors to HIV-1 protease falls in the slow gating regime. However, because γ = 0.75, conformational gating only modestly slows ligand binding. Brownian dynamics simulations of the diffusional association of eight inhibitors to the protease─having a wide range of experimental association constants (∼104-1010 M-1 s-1)─yields gated rate constants in the range of ∼0.5-5.7 × 108 M-1 s-1. This indicates that, whereas the association rate of some inhibitors could be described by the model, for many inhibitors either subsequent conformational transitions or alternate binding mechanisms may be rate-limiting. For systems known to be modulated by conformational gating, the approach could be scaled computationally efficiently to screen association kinetics for a large number of ligands.

Laterality 2020: Response to the article commentaries.

In our recent opinion paper "Laterality 2020: entering the next decade", we highlighted trends that we thought are likely to shape laterality research in the 2020s. Our opinion paper inspired 11 commentaries by experts from several disciplines which discussed a wide range of topics complementing the 10 trends we identified in the opinion paper. In this reply, we summarize and discuss the 11 commentaries by clustering them into 3 different main topics. The topic that was covered by the largest number of commentaries was the role of comparative and evolutionary approaches in laterality research. Moreover, several comments focused on the ontogenesis of hemispheric asymmetries and the importance of reliability and validity in laterality research. Embracing the technical advances, research trends and controversies laid out in the commentaries will significantly improve our understanding of several of the core questions of laterality research.

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

Imaging evolution of the primate brain: the next frontier?

Evolution, as we currently understand it, strikes a delicate balance between animals' ancestral history and adaptations to their current niche. Similarities between species are generally considered inherited from a common ancestor whereas observed differences are considered as more recent evolution. Hence comparing species can provide insights into the evolutionary history. Comparative neuroimaging has recently emerged as a novel subdiscipline, which uses magnetic resonance imaging (MRI) to identify similarities and differences in brain structure and function across species. Whereas invasive histological and molecular techniques are superior in spatial resolution, they are laborious, post-mortem, and oftentimes limited to specific species. Neuroimaging, by comparison, has the advantages of being applicable across species and allows for fast, whole-brain, repeatable, and multi-modal measurements of the structure and function in living brains and post-mortem tissue. In this review, we summarise the current state of the art in comparative anatomy and function of the brain and gather together the main scientific questions to be explored in the future of the fascinating new field of brain evolution derived from comparative neuroimaging.

Laterality 2020: entering the next decade.

In the 2010s, significant progress has been made in several key areas of laterality research, including neuroimaging, genetics and comparative research. In the present article, we discuss which trends are likely to shape laterality research in the 2020s. These include, but are not limited to: (1) Finding laterality-specific solutions to the replication crisis. (2) Integrating non-W.E.I.R.D. (Western, Educated, Industrialized, Rich, and Democratic) samples into laterality research to a larger extent. (3). Combining meta-analysis and large-scale databank studies to come to unbiased conclusions about true effects. (4) Understanding altered laterality in different psychiatric and neurodevelopmental disorders. (5) Exploring the relevance of laterality research for the treatment of psychiatric and neurodevelopmental disorders. (6) Investigating the molecular correlates of environmental factors that affect laterality. (7) Bridging the gap between laterality research in human subjects and non-human model species. (8) Utilizing "next-generation" neuroimaging in laterality research. (9) Integrating graph-theory and machine learning into laterality research. (10) Enhancing ecological validity in laterality research using mobile EEG and smartphone-based data collection. These trends will likely shape the next decade of laterality research by opening the way for novel questions, enhancing collaborations and boosting the reliability and validity of research findings in our field.

Mapping the principal gradient onto the corpus callosum.

Gradients capture some of the variance of the resting-state functional magnetic resonance imaging (rsfMRI) signal. Amongst these, the principal gradient depicts a functional processing hierarchy that spans from sensory-motor cortices to regions of the default-mode network. While the cortex has been well characterised in terms of gradients little is known about its underlying white matter. For instance, comprehensive mapping of the principal gradient on the largest white matter tract, the corpus callosum, is still missing. Here, we mapped the principal gradient onto the midsection of the corpus callosum using the 7T human connectome project dataset. We further explored how quantitative measures and variability in callosal midsection connectivity relate to the principal gradient values. In so doing, we demonstrated that the extreme values of the principal gradient are located within the callosal genu and the posterior body, have lower connectivity variability but a larger spatial extent along the midsection of the corpus callosum than mid-range values. Our results shed light on the relationship between the brain's functional hierarchy and the corpus callosum. We further speculate about how these results may bridge the gap between functional hierarchy, brain asymmetries, and evolution.

Anchoring the human olfactory system within a functional gradient.

Margulies et al. (2016) demonstrated the existence of at least five independent functional connectivity gradients in the human brain. However, it is unclear how these functional gradients might link to anatomy. The dual origin theory proposes that differences in cortical cytoarchitecture originate from two trends of progressive differentiation between the different layers of the cortex, referred to as the hippocampocentric and olfactocentric systems. When conceptualising the functional connectivity gradients within the evolutionary framework of the Dual Origin theory, the first gradient likely represents the hippocampocentric system anatomically. Here we expand on this concept and demonstrate that the fifth gradient likely links to the olfactocentric system. We describe the anatomy of the latter as well as the evidence to support this hypothesis. Together, the first and fifth gradients might help to model the Dual Origin theory of the human brain and inform brain models and pathologies.

Using Mobile EEG to Investigate Alpha and Beta Asymmetries During Hand and Foot Use.

The Edinburgh Handedness Inventory (EHI) and the Waterloo Footedness Questionnaire (WFQ) are two of the most widely used questionnaires to assess lateralized everyday behavior in human participants. However, it is unclear to what extent the specific behavior assessed in these questionnaires elicit lateralized neural activity when performed in real-life situations. To illuminate this unresolved issue, we assessed EEG alpha and beta asymmetries during real-life performance of the behaviors assessed in the EHI and WFQ using a mobile EEG system. This methodology provides high ecological validity for studying neural correlates of motor behavior under more naturalistic conditions. Our results indicate that behavioral performance of items of both the EHI and WFQ differentiate between left- and right-handers and left- and right-footers on the neural level, especially in the alpha frequency band. These results were unaffected by movement parameters. Furthermore, we could demonstrate that neural activity elicited specifically during left-sided task performance provides predictive power for the EHI or WFQ score of the participants. Overall, our results show that these prominent questionnaires not only distinguish between different motor preferences on the behavioral level, but also on the neurophysiological level. Furthermore, we could show that mobile EEG systems are a powerful tool to investigate motor asymmetries in ecologically valid situations outside of the laboratory setting. Future research should focus on other lateralized behavioral phenotypes in real-life settings to provide more insights into lateralized motor functions.

White matter correlates of hemi-face dominance in happy and sad expression.

The neural underpinnings of human emotional expression are thought to be unevenly distributed among the two brain hemispheres. However, little is known on the anatomy supporting this claim, particularly in the cerebral white matter. Here, we explored the relationship between hemi-face dominance in emotional expression and cerebral white matter asymmetries in 33 healthy participants. Measures of emotional expression were derived from pictures of the participant's faces in a 'happy smiling' and a 'sad frowning' conditions. Chimeric faces were constructed by mirroring right and left hemi-faces, as done in previous studies, resulting in a left mirrored and right mirrored chimeric face per picture. To gain measures of hemi-face dominance per participant, a jury of 20 additional participants rated which chimeric face shows the higher intensity of emotional expressivity, by marking a 155 mm line between the two versions. Measures of the asymmetry of the uncinate, the cingulum and the three branches of superior longitudinal fasciculi were derived from diffusion-weighted imaging tractography dissections. Group effect analyses indicated that the degree of asymmetry in emotional expression was not as prominent as reported in the literature and showed a large inter-individual variability. The degree of asymmetry in emotional expression was, however, significantly associated with the asymmetries in connective properties of the fronto-temporal and fronto-parietal tracts, specifically the uncinate fasciculus and the first branch of the superior longitudinal fasciculus. Therefore, this result raises novel hypotheses on the relationship of specific white matter tracts and emotional expression, especially their role in mood disorders.