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EEG resting-state alpha asymmetry is one of the most widely investigated forms of functional hemispheric asymmetries in both basic and clinical neuroscience. However, studies yield inconsistent results. One crucial prerequisite to obtain reproducible results is the reliability of the index of interest. There is a body of research suggesting a moderate-to-good reliability of EEG resting-state alpha asymmetry, but unfortunately sample sizes in these studies are typically small. This study presents the first large-scale short-term reliability study of frontal and parietal EEG resting-state alpha asymmetry. We used the Dortmund Vital Study data set containing 370 participants. In each participant, EEG resting state was recorded eight times, twice with their eyes opened, twice with their eyes-closed, each on two different EEG systems. We found good reliability of EEG alpha power and alpha asymmetry on both systems for electrode pairs. We also found that alpha power asymmetry reliability is higher in the eyes-closed condition than in the eyes-open condition. The frontomedial electrode pair showed weaker reliability than the frontolateral and parietal electrode pairs. Interestingly, we found no population-level alpha asymmetry in frontal electrodes, one of the most investigated electrode sites in alpha asymmetry research. In conclusion, our results suggest that while EEG alpha asymmetry is an overall reliable measure, frontal alpha asymmetry should be assessed using multiple electrode pairs.

Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower rates of brain activity during reasoning. However, the microstructural architecture underlying both observations remains unclear. By combining advanced multi-shell diffusion tensor imaging with a culture-fair matrix-reasoning test, we found that higher intelligence in healthy individuals is related to lower values of dendritic density and arborization. These results suggest that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning. Previous studies suggest that individual differences in intelligence correlate with circuit complexity and dendritic arborization in the brain. Here the authors use NODDI, a diffusion MRI technique, to confirm that neurite density and arborization are inversely related to measures of intelligence.

Extinction learning is an important learning process that enables adaptive and flexible behavior. Human neuroimaging studies show that the neural basis of extinction learning consists of a neural network that includes the hippocampus, amygdala, and subcomponents of the prefrontal cortex, but also extends beyond them. The limitations of applying fMRI to actively participating animals have so far restricted the identification of the entire extinction network in non-human animals. Here, we present the first fMRI study of extinction in awake and actively participating pigeons, using a Go/NoGo operant paradigm with a water reward. Our study revealed an extensive and largely left hemispheric telencephalic network of sensory, limbic, executive, and motor areas that slowly ceased to be active during the process of extinction learning. We propose that the beginning of extinction ignites a neuronal updating of the associated consequences of own actions within a large telencephalic neural network until a new association is established which competes with the previously acquired operant response.

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. 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 analyses 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.

Background Myotonic dystrophy is a multisystem disorder characterized by widespread organic involvement including central nervous system symptoms. Although myotonic dystrophy disease types 1 (DM1) and 2 (DM2) cover a similar spectrum of symptoms, more pronounced clinical and brain alterations have been described in DM1. Here, we investigated brain volumetric and white matter alterations in both disease types and compared to healthy controls (HC). Methods MRI scans were obtained from 29 DM1, 27 DM2, and 56 HC. We assessed macro- and microstructural brain changes by surface-based analysis of cortical thickness of anatomical images and tract-based spatial statistics of fractional anisotropy (FA) obtained by diffusion-weighted imaging, respectively. Global MRI measures were related to clinical and neuropsychological scores to evaluate their clinical relevance. Results Cortical thickness was reduced in both patient groups compared to HC, showing similar patterns of regional distribution in DM1 and DM2 (occipital, temporal, frontal) but more pronounced cortical thinning for DM1. Similarly, FA values showed a widespread decrease in DM1 and DM2 compared to HC. Interestingly, FA was significantly lower in DM1 compared to DM2 within most parts of the brain. Conclusion Comparisons between DM1 and DM2 indicate a more pronounced cortical thinning of grey matter and a widespread reduction in microstructural integrity of white matter in DM1. Future studies are required to unravel the underlying and separating mechanisms for the disease courses of the two types and their neuropsychological symptoms.

Intelligence is highly heritable. Genome‐wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto‐frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence. We investigated the mediating effect of surface area, cortical thickness as well as fiber and functional network efficiency on the effect of polygenic scores on intelligence. Fiber network efficiency as well as the surface of brain areas partly located in parieto‐frontal regions were found to mediate the relationship between PGS and cognitive performance.

A growing body of research highlights the fear extinction model as a key framework for understanding the pathology of anxiety disorders and developing therapeutic interventions. Functional imaging studies link successful extinction recall and reduced amygdala reactivity with ventromedial prefrontal cortex (vmPFC) activity, while dysfunctional vmPFC activity is associated with anxiety pathology. Enhancing vmPFC activity through transcranial direct current stimulation (tDCS) has thus emerged as a promising approach to augment cognitive behavioral therapy (CBT) and therapeutic exposure therapies for anxiety disorders. In this study, anodal tDCS was applied over the vmPFC during the extinction learning phase of a 3-day fear acquisition/extinction paradigm (anodal tDCS: 21, sham tDCS: 23 participants). Skin conductance response (SCR) and Blood Oxygenation Level Dependent (BOLD) brain activity were recorded simultaneously. Healthy participants who received real tDCS showed significantly improved extinction recall compared to the sham tDCS group, as indicated by the SCR. Fear acquisition activated core regions of the fear network, including the anterior cingulate cortex, amygdala, orbitofrontal cortex, and insular cortex. During extinction learning, real tDCS activated both fear and safety networks (particularly the posterior cingulate and middle temporal cortex) during early extinction, while the sham tDCS group showed activity only in the fear network. Furthermore, a decoupling of the vmPFC with fear network regions with real tDCS during extinction was observed, indicating strengthened inhibitory control. These findings suggest that anodal tDCS over the vmPFC promotes early activation of the safety network, enhancing extinction learning and consolidation. This suggests its translational potential as an adjunctive intervention for anxiety disorders.

Intelligence is a highly polygenic trait and genome-wide association studies (GWAS) have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured. We used deep-phenotyping to investigate the molecular determinants of individual differences in cognitive ability. We, therefore, studied the association between PGS of intelligence (IQ-PGS), cognitive performance (CP-PGS), and educational attainment (EA-PGS) with a wide range of intelligence facets in a sample of 557 healthy adults. IQ-PGS, CP-PGS, and EA-PGS had the highest incremental R 2 s for general (2.71%; 4.27%; 2.06%), verbal (3.30%; 4.64%; 1.61%), and numerical intelligence (3.06%; 3.24%; 1.26%) and the weakest for non-verbal intelligence (0.89%; 1.47%; 0.70%) and memory (0.80%; 1.06%; 0.67%). These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

Previous research investigating relations between general intelligence and graph-theoretical properties of the brain’s intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

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.