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.

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.

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.

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.

The Relationship Between Axon Density, Myelination, and Fractional Anisotropy in the Human Corpus Callosum.

The corpus callosum serves the functional integration and interaction between the two hemispheres. Many studies investigate callosal microstructure via diffusion tensor imaging (DTI) fractional anisotropy (FA) in geometrically parcellated segments. However, FA is influenced by several different microstructural properties such as myelination and axon density, hindering a neurobiological interpretation. This study explores the relationship between FA and more specific measures of microstructure within the corpus callosum in a sample of 271 healthy participants. DTI tractography was used to assess 11 callosal segments and gain estimates of FA. We quantified axon density and myelination via neurite orientation dispersion and density imaging (NODDI) to assess intra-neurite volume fraction and a multiecho gradient spin-echo sequence estimating myelin water fraction. The results indicate three common factors in the distribution of FA, myelin content and axon density, indicating potentially shared rules of topographical distribution. Moreover, the relationship between measures varied across the corpus callosum, suggesting that FA should not be interpreted uniformly. More specific magnetic resonance imaging-based quantification techniques, such as NODDI and multiecho myelin water imaging, may thus play a key role in future studies of clinical trials and individual differences.

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.

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.

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.

Asymmetries in social touch-motor and emotional biases on lateral preferences in embracing, cradling and kissing.

In human social interaction, affective touch plays an integral role to communicate intentions and emotions. Three of the most important forms of social touch are embracing, cradling and kissing. These behaviours have been demonstrated to be lateralized, but the underlying mechanisms are still not well understood. Both motor and emotive biases have been suggested to affect laterality of social touch. We aimed to systematically investigate how motor preferences and emotive biases influence the lateralization of embracing, cradling and kissing within the same sample. Participants performed all three forms of social touch in neutral, positive and negative emotional conditions. Like a previous study, we found a rightward bias for embracing that was modulated by both motor preferences and the emotional content of the situation. Kissing and cradling were not influenced by motor preferences. In general, a negative emotional connotation of the situation led to a reduction of lateral biases in social touch, independent of the individual direction.

Hemispheric asymmetries in cortical gray matter microstructure identified by neurite orientation dispersion and density imaging.

Histological studies have reported microstructural hemispheric asymmetries in several cortical areas of the human brain, but reliable in vivo assessment methods have been lacking so far. Here, we used neurite orientation dispersion and density imaging (NODDI) to examine microstructural asymmetries in in vivo and determine if findings are in accordance with what has been reported in histological studies. We examined intra-neurite volume fraction (INVF), neurite orientation dispersion (ODI), and isotropic volume fraction (ISO) asymmetries in two independent samples of healthy adults (n = 269 and n = 251). Over both samples, we found greater left-hemispheric INVF in early auditory, inferior parietal and temporal-parietal-occipital areas. In contrast, we found greater right-hemispheric INVF in the fusiform and inferior temporal gyrus, reflecting what has been reported in histological studies. ODI was asymmetric towards the left hemisphere in frontal areas and towards the right hemisphere in early auditory areas. ISO showed less pronounced asymmetries. There were hardly any effects of sex or handedness on microstructural asymmetry as determined by NODDI. Taken together, these findings suggest substantial microstructural asymmetries in gray matter, making NODDI a promising marker for future genetic and behavioral studies on laterality.

Fundamental or forgotten? Is Pierre Paul Broca still relevant in modern neuroscience?

The ability to speak is a unique human capacity, but where is it located in our brains? This question is closely connected to the pioneering work of Pierre Paul Broca in the 1860s. Based on post-mortem observations of aphasic patients' brains, Broca located language production in the 3rd convolution of the left frontal lobe and thus reinitiated the localizationist view of brain functions. However, contemporary neuroscience has partially rejected this view in favor of a network-based perspective. This leads to the question, whether Broca's findings are still relevant today. In this mini-review, we discuss current and historical implications of Broca's work by focusing on his original contribution and contrasting it with contemporary knowledge. Borrowing from Broca's famous quote, our review shows that humans indeed "speak with the left hemisphere"- but Broca's area is not the sole "seat of articulatory language".

Beyond frontal alpha: investigating hemispheric asymmetries over the EEG frequency spectrum as a function of sex and handedness.

Frontal alpha EEG asymmetry, an indirect marker of asymmetries in relative frontal brain activity, are widely used in research on lateralization of emotional processing. While most authors focus on frontal electrode pairs (e.g., F3/F4 or F7/F8), several recent studies have indicated that EEG asymmetries can also be observed outside the frontal lobe and in frequency bands other than alpha. Because the focus of most EEG asymmetry research is on the correlations between asymmetry and other traits, much less is known about the distribution of patterns of asymmetry at the population level. To systematically assess these asymmetries in a representative sample, we determined EEG asymmetries across the head in the alpha, beta, delta and theta frequency bands in 235 healthy adults. We found significant asymmetries in all four frequency bands and across several brain areas, indicating that EEG asymmetries are not limited to frontal alpha. Asymmetries were not modulated by sex. They were modulated by direction of hand preference, with stronger right-handedness predicting greater right (relative to left) alpha power, or greater left (relative to right) activity. Taken together, the present results show that EEG asymmetries other than frontal alpha represent markers of asymmetric brain function that should be explored further.

The Structural and Functional Signature of Action Control.

Individuals differ in their ability to initiate self- and emotional-control mechanisms. These differences have been explicitly described in Kuhl's action-control theory. Although interindividual differences in action control make a major contribution to our everyday life, their neural foundation remains unknown. Here, we measured action control in a sample of 264 healthy adults and related interindividual differences in action control to variations in brain structure and resting-state connectivity. Our results demonstrate a significant negative correlation between decision-related action orientation (AOD) and amygdala volume. Further, we showed that the functional resting-state connectivity between the amygdala and the dorsal anterior cingulate cortex was significantly associated with AOD. Specifically, stronger functional connectivity was associated with higher AOD scores. These findings are the first to show that interindividual differences in action control, namely AOD, are based on the anatomical architecture and functional network of the amygdala.

Callosal microstructure affects the timing of electrophysiological left-right differences.

The neural architecture of the corpus callosum shows pronounced inter-individual differences. These differences are thought to affect timing of interhemispheric interactions and, in turn, functional hemispheric asymmetries. The present study aimed at elucidating the neuronal mechanisms underlying this relationship. To this end, we used a combined DTI and EEG study design. In 103 right-handed and healthy adult participants, we determined the microstructural integrity of the posterior third of the corpus callosum and examined in how far this microstructural integrity was related to between-hemisphere timing differences in neurophysiological correlates of attentional processes in the dichotic listening task. The results show that microstructural integrity of the posterior callosal third correlated with attentional timing differences in a verbal dichotic listening condition but not in a noise control condition. Hence, this association between callosal microstructure and between-hemisphere timing differences is specific for stimuli, which trigger hemispheric bottom-up processing in an asymmetric fashion. Specifically, higher microstructural integrity was associated with decreased left-right differences in the latency of the N1 event-related potential component and hence more symmetric processing of dichotic stimuli between the two hemispheres. Our data suggest that microstructure of the posterior callosal third affects functional hemispheric asymmetries by modulating the timing of interhemispheric interactions.

Trends in land use and land cover change in the protected and communal areas of the Zambezi Region, Namibia.

Land management decisions have extensively modified land use and land cover in the Zambezi Region. These decisions are influenced by land tenure classifications, legislation, and livelihoods. Land use and land cover change is an important indicator for quantifying the effectiveness of different land management strategies. However, there has been no evidence on whether protected or communal land tenure is more affected by land use and land cover changes in southern Africa and particularly Namibia. Our study attempted to fill this gap by analyzing the relationship between land use and land cover change and land tenure regimes stratified according to protected and communal area in the Zambezi Region. Multi-temporal Landsat TM and ETM+ imagery were used to determine the temporal dynamics of land use and land cover change from 1984 to 2010. The landscape showed distinctive modifications over the study period; broad trends include the increase in forest land after 1991. However, changes were not uniform across the study areas. Two landscape development stages were deduced: (1) 1984-1991 represented high deforestation and gradual increase in shrub land; (2) 1991-2000 and 2000-2010 represented lower deforestation and slower agropastoral expansion. The results further show clear patterns of the dynamics, magnitude, and direction of land use and land cover change by tenure regime. The study concluded that land tenure has a direct impact on land use and land cover, since it may restrict some activities carried out on the land in the Zambezi Region.

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 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.