Associations between white matter hyperintensities, lacunes, entorhinal cortex thickness, declarative memory and leisure activity in cognitively healthy older adults: A 7-year study.

INTRODUCTION: Cerebral small vessel disease (cSVD) is a growing epidemic that affects brain health and cognition. Therefore, a more profound understanding of the interplay between cSVD, brain atrophy, and cognition in healthy aging is of great importance. In this study, we examined the association between white matter hyperintensities (WMH) volume, number of lacunes, entorhinal cortex (EC) thickness, and declarative memory in cognitively healthy older adults over a seven-year period, controlling for possible confounding factors. Because there is no cure for cSVD to date, the neuroprotective potential of an active lifestyle has been suggested. Supporting evidence, however, is scarce. Therefore, a second objective of this study is to examine the relationship between leisure activities, cSVD, EC thickness, and declarative memory. METHODS: We used a longitudinal dataset, which consisted of five measurement time points of structural MRI and psychometric cognitive ability and survey data, collected from a sample of healthy older adults (baseline N = 231, age range: 64-87 years, age M = 70.8 years), to investigate associations between cSVD MRI markers, EC thickness and verbal and figural memory performance. Further, we computed physical, social, and cognitive leisure activity scores from survey-based assessments and examined their associations with brain structure and declarative memory. To provide more accurate estimates of the trajectories and cross-domain correlations, we applied latent growth curve models controlling for potential confounders. RESULTS: Less age-related thinning of the right (ß = 0.92, p<.05) and left EC (ß = 0.82, p<.05) was related to less declarative memory decline; and a thicker EC at baseline predicted less declarative memory loss (ß = 0.54, p<.05). Higher baseline levels of physical (ß = 0.24, p<.05), and social leisure activity (ß = 0.27, p<.01) predicted less thinning of right EC. No relation was found between WMH or lacunes and declarative memory or between leisure activity and declarative memory. Higher education was initially related to more physical activity (ß = 0.16, p<.05) and better declarative memory (ß = 0.23, p<.001), which, however, declined steeper in participants with higher education (ß = -.35, p<.05). Obese participants were less physically (ß = -.18, p<.01) and socially active (ß = -.13, p<.05) and had thinner left EC (ß = -.14, p<.05) at baseline. Antihypertensive medication use (ß = -.26, p<.05), and light-to-moderate alcohol consumption (ß = -.40, p<.001) were associated with a smaller increase in the number of lacunes whereas a larger increase in the number of lacunes was observed in current smokers (ß = 0.30, p<.05). CONCLUSIONS: Our results suggest complex relationships between cSVD MRI markers (total WMH, number of lacunes, right and left EC thickness), declarative memory, and confounding factors such as antihypertensive medication, obesity, and leisure activitiy. Thus, leisure activities and having good cognitive reserve counteracting this neurodegeneration. Several confounding factors seem to contribute to the extent or progression/decline of cSVD, which needs further investigation in the future. Since there is still no cure for cSVD, modifiable confounding factors should be studied more intensively in the future to maintain or promote brain health and thus cognitive abilities in older adults.

A neurostructural biomarker of dissociative amnesia: a hippocampal study in dissociative identity disorder.

BACKGROUND: Little is known about the neural correlates of dissociative amnesia, a transdiagnostic symptom mostly present in the dissociative disorders and core characteristic of dissociative identity disorder (DID). Given the vital role of the hippocampus in memory, a prime candidate for investigation is whether total and/or subfield hippocampal volume can serve as biological markers of dissociative amnesia. METHODS: A total of 75 women, 32 with DID and 43 matched healthy controls (HC), underwent structural magnetic resonance imaging (MRI). Using Freesurfer (version 6.0), volumes were extracted for bilateral global hippocampus, cornu ammonis (CA) 1-4, the granule cell molecular layer of the dentate gyrus (GC-ML-DG), fimbria, hippocampal-amygdaloid transition area (HATA), parasubiculum, presubiculum and subiculum. Analyses of covariance showed volumetric differences between DID and HC. Partial correlations exhibited relationships between the three factors of the dissociative experience scale scores (dissociative amnesia, absorption, depersonalisation/derealisation) and traumatisation measures with hippocampal global and subfield volumes. RESULTS: Hippocampal volumes were found to be smaller in DID as compared with HC in bilateral global hippocampus and bilateral CA1, right CA4, right GC-ML-DG, and left presubiculum. Dissociative amnesia was the only dissociative symptom that correlated uniquely and significantly with reduced bilateral hippocampal CA1 subfield volumes. Regarding traumatisation, only emotional neglect correlated negatively with bilateral global hippocampus, bilateral CA1, CA4 and GC-ML-DG, and right CA3. CONCLUSION: We propose decreased CA1 volume as a biomarker for dissociative amnesia. We also propose that traumatisation, specifically emotional neglect, is interlinked with dissociative amnesia in having a detrimental effect on hippocampal volume.

Laterality indices consensus initiative (LICI): A Delphi expert survey report on recommendations to record, assess, and report asymmetry in human behavioural and brain research.

Laterality indices (LIs) quantify the left-right asymmetry of brain and behavioural variables and provide a measure that is statistically convenient and seemingly easy to interpret. Substantial variability in how structural and functional asymmetries are recorded, calculated, and reported, however, suggest little agreement on the conditions required for its valid assessment. The present study aimed for consensus on general aspects in this context of laterality research, and more specifically within a particular method or technique (i.e., dichotic listening, visual half-field technique, performance asymmetries, preference bias reports, electrophysiological recording, functional MRI, structural MRI, and functional transcranial Doppler sonography). Experts in laterality research were invited to participate in an online Delphi survey to evaluate consensus and stimulate discussion. In Round 0, 106 experts generated 453 statements on what they considered good practice in their field of expertise. Statements were organised into a 295-statement survey that the experts then were asked, in Round 1, to independently assess for importance and support, which further reduced the survey to 241 statements that were presented again to the experts in Round 2. Based on the Round 2 input, we present a set of critically reviewed key recommendations to record, assess, and report laterality research for various methods.

Musical Expertise Shapes Functional and Structural Brain Networks Independent of Absolute Pitch Ability.

Professional musicians are a popular model for investigating experience-dependent plasticity in human large-scale brain networks. A minority of musicians possess absolute pitch, the ability to name a tone without reference. The study of absolute pitch musicians provides insights into how a very specific talent is reflected in brain networks. Previous studies of the effects of musicianship and absolute pitch on large-scale brain networks have yielded highly heterogeneous findings regarding the localization and direction of the effects. This heterogeneity was likely influenced by small samples and vastly different methodological approaches. Here, we conducted a comprehensive multimodal assessment of effects of musicianship and absolute pitch on intrinsic functional and structural connectivity using a variety of commonly used and state-of-the-art multivariate methods in the largest sample to date (n = 153 female and male human participants; 52 absolute pitch musicians, 51 non-absolute pitch musicians, and 50 non-musicians). Our results show robust effects of musicianship in interhemispheric and intrahemispheric connectivity in both structural and functional networks. Crucially, most of the effects were replicable in both musicians with and without absolute pitch compared with non-musicians. However, we did not find evidence for an effect of absolute pitch on intrinsic functional or structural connectivity in our data: The two musician groups showed strikingly similar networks across all analyses. Our results suggest that long-term musical training is associated with robust changes in large-scale brain networks. The effects of absolute pitch on neural networks might be subtle, requiring very large samples or task-based experiments to be detected.SIGNIFICANCE STATEMENT A question that has fascinated neuroscientists, psychologists, and musicologists for a long time is how musicianship and absolute pitch, the rare talent to name a tone without reference, are reflected in large-scale networks of the human brain. Much is still unknown as previous studies have reported widely inconsistent results based on small samples. Here, we investigate the largest sample of musicians and non-musicians to date (n = 153) using a multitude of established and novel analysis methods. Results provide evidence for robust effects of musicianship on functional and structural networks that were replicable in two separate groups of musicians and independent of absolute pitch ability.

Longitudinal functional connectivity patterns of the default mode network in healthy older adults.

Cross-sectional studies have consistently identified age-associated alterations in default mode network (DMN) functional connectivity (FC). Yet, research on longitudinal trajectories of FC changes of the DMN in healthy aging is less conclusive. For the present study, we used a resting state functional MRI dataset drawn from the Longitudinal Healthy Aging Brain Database Project (LHAB) collected in 5 occasions over a course of 7 years (baseline N = 232, age range: 64-87 y, mean age = 70.85 y). FC strength changes within the DMN and its regions were investigated using a network-based statistical method suitable for the analysis of longitudinal data. The average DMN FC strength remained stable, however, various DMN components showed differential age- and time-related effects. Our results revealed a complex pattern of longitudinal change seen as decreases and increases of FC strength encompassing the majority of DMN regions, while age-related effects were negative and present in select brain areas. These findings testify to the growing importance of longitudinal studies using more sophisticated fine-grained tools needed to highlight the complexity of the functional reorganization of DMN with healthy aging.

Identification of individual subjects based on neuroanatomical measures obtained 7 years earlier.

We analysed a dataset comprising 118 subjects who were scanned three times (at baseline, 1-year follow-up, and 7-year follow-up) using structural magnetic resonance imaging (MRI) over the course of 7 years. We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7 years previously. We used FreeSurfer to compute 15 standard brain measures (total intracranial volume [ICV], total cortical thickness [CT], total cortical surface area [CA], cortical grey matter [CoGM], cerebral white matter [CeWM], cerebellar cortex [CBGM], cerebellar white matter [CBWM], subcortical volumes [thalamus, putamen, pallidum, caudatus, hippocampus, amygdala and accumbens] and brain stem volume). We used linear discriminant analysis (LDA), random forest machine learning (RF) and a newly developed rule-based identification approach (RBIA) for the identification process. Using RBIA, different sets of neuroanatomical features (ranging from 2 to 14) obtained at baseline were combined by if-then rules and compared to the same set of neuroanatomical features derived from the 7-year follow-up measurement. We achieved excellent identification results with LDA, while the identification results for RF were very good but not perfect. The RBIA produced the best results, achieving perfect participant identification for some four-feature sets. The identification results improved substantially when using larger feature sets, with 14 neuroanatomical features providing perfect identification. Thus, this study shows again that the human brain is highly individual in terms of neuroanatomical features. These results are discussed in the context of the current literature on brain plasticity and the scientific attempts to develop brain-fingerprinting techniques.

Tracking lexical access and code switching in multilingual participants with different degrees of simultaneous interpretation expertise.

With the worldwide increase in people speaking more than one language, a better understanding of the behavioural and neural mechanisms governing lexical selection, lexical access in multiple languages and code switching has attracted widespread interest from several disciplines. Previous studies documented higher costs when processing a non-native (L2) than a native (L1) language or when switching from L2 to L1. However, studies on auditory language reception are still scarce and did not take into account the degree of switching experience. Accordingly, in the present study, we combined behavioural and electrophysiological measurements to assess lexical access in L1 and L2 as well as code switching in professional simultaneous interpreters, trainee interpreters, foreign language teachers and Anglistics students, while the participants performed a bilingual auditory lexical decision task. The purpose of this study was to expand the knowledge on code switching in auditory language processing and examine whether the degree of simultaneous interpretation experience might reduce switching costs. As a main result, we revealed that L2 compared to L1 trials, as well as switch compared to non-switch trials, generally resulted in lower accuracies, longer reaction times and increased N400 amplitudes in all groups of participants. Otherwise, we did not reveal any influence of switching direction and interpretation expertise on N400 parameters. Taken together, these results suggest that a late age of L2 acquisition leads to switching costs, irrespective of proficiency level. Furthermore, we provided first evidence that simultaneous interpretation training does not diminish switching costs, at least when focusing on lexical access.

Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector, and BIANCA.

White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (rs  = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (rs  = 0.41), and many outlier WMH volumes were detected when exploring within-person trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: rs  = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.

Phonetic Skills and Verbal Memory Capacity Predict Phonetic-based Word Learning: An Event-related Potential Study.

The learning of new words is a challenge that accompanies human beings throughout the entire life span. Although the main electrophysiological markers of word learning have already been described, little is known about the performance-dependent neural machinery underlying this exceptional human faculty. Furthermore, it is currently unknown how word learning abilities are related to verbal memory capacity, auditory attention functions, phonetic discrimination skills, and musicality. Accordingly, we used EEG and examined 40 individuals, who were assigned to two groups (low [LPs] and high performers [HPs]) based on a median split of word learning performance, while they completed a phonetic-based word learning task. Furthermore, we collected behavioral data during an attentive listening and a phonetic discrimination task with the same stimuli to address relationships between auditory attention and phonetic discrimination skills, word learning performance, and musicality. The phonetic-based word learning task, which also included a nonlearning control condition, was sensitive enough to segregate learning-specific and unspecific N200/N400 manifestations along the anterior-posterior topographical axis. Notably, HPs exhibited enhanced verbal memory capacity and we also revealed a performance-dependent spatial N400 pattern, with maximal amplitudes at posterior electrodes in HPs and central maxima in LPs. Furthermore, phonetic-based word learning performance correlated with verbal memory capacity and phonetic discrimination skills, whereas the latter was related to musicality. This experimental approach clearly highlights the multifaceted dimensions of phonetic-based word learning and is helpful to disentangle learning-specific and unspecific ERPs.

Age-related decline in the brain: a longitudinal study on inter-individual variability of cortical thickness, area, volume, and cognition.

Magnetic Resonance Imaging (MRI) studies have shown that cortical volume declines with age. Although volume is a multiplicative measure consisting of thickness and area, few studies have focused on both its components. Information on decline variability and associations between person-specific changes of different brain metrics, brain regions, and cognition is sparse. In addition, the estimates have often been biased by the measurement error, because three repeated measures are minimally required to separate the measurement error from person-specific changes. With a sample size of N = 231, five repeated measures, and an observational time span of seven years, this study explores the associations between changes of different brain metrics, brain regions, and cognitive abilities in aging. Person-specific changes were obtained by latent growth curve models using Bayesian estimation. Our data indicate that both thickness and area are important contributors to volumetric changes. In most brain regions, area clearly declined on average over the years, while thickness showed only little decline. However, there was also substantial variation around the average slope in thickness and area. The correlation pattern of changes in thickness between brain regions was strong and largely homogenous. The pattern for changes in area was similar but weaker, indicating that factors affecting area may be more region-specific. Changes in thickness and volume were substantially correlated with changes in cognition. In some brain regions, changes in area were also related to changes in cognition. Overall, studying the associations between the trajectories of brain regions in different brain metrics provides insights into the regional heterogeneity of structural changes. SIGNIFICANCE STATEMENT: Many studies have described volumetric brain changes in aging. Few studies have focused on both its individual components: area and thickness. Longitudinal studies with three or more time points are highly needed, because they provide more precise average change estimates and, more importantly, allow us to quantify the associations between changes in the different brain metrics, brain regions, and other variables (e.g. cognitive abilities). Studying these associations is important because they can provide information regarding possible underlying factors of these changes. Our study, with a large sample size, five repeated measures, and an observational time span of seven years, provides new insights about the associations between person-specific changes in thickness, area, volume, and cognitive abilities.

Are language skills related to structural features in Broca's and Wernicke's area?

This study used structural magnetic resonance imaging to examine whether specific anatomical features of Broca's and Wernicke's areas are related to language functions in typically developing older subjects with no specific language expertize. Data from 231 subjects from the Zurich LHAB-study are used for this study. For these subjects, we obtained several psychometric measures from which we calculated performance measures reflecting specific psychological functions (language comprehension, verbal fluency, perceptual speed, visual memory, recognition of regularities, and logical thinking). From the MRI measurements, we calculated the cortical thickness and cortical surface of Broca's and Wernicke's areas. Applying multiple regression analyses, we identified a moderately strong relationship between language comprehension and the brain metrics from Broca's and Wernicke's areas and showed that approximately 10% of the variance in language comprehension performance is explained by the linear combination of all perisylvian brain metrics. The other psychological functions (verbal fluency, perceptual speed, visual memory, recognition of regularities, and logical thinking) are not related to these brain metrics. Subsequent detailed analyses revealed that the cortical thickness of Wernicke's area, in particular, contributed most to this structure-function relationship. The better performance in the language comprehension tests was related to a thicker cortex in Wernicke's area. Thus, this study demonstrates a structure-function relationship between the anatomical features of the perisylvian language areas and language comprehension, suggesting that particular anatomical features are associated with better language performance.

Solve problems and answer questions instead of following trends!

With this comment, I comment on the key ideas of the opinion paper by Ocklenburg et al. The authors describe trends in lateralization research for the next decade. With my commentary, I take the liberty of pointing out that it is first more important to focus on the relevant questions to be answered in the context of lateralization research before calling out research trends. Furthermore, the focus of lateralization research in humans should be more on the human brain and human behaviour because the human brain is highly specialized despite many similarities with other species' brains.

Diminished large-scale functional brain networks in absolute pitch during the perception of naturalistic music and audiobooks.

Previous studies have reported the effects of absolute pitch (AP) and musical proficiency on the functioning of specific brain regions or distinct subnetworks, but they provided an incomplete account of the effects of AP and musical proficiency on whole-brain networks. In this study, we used EEG to estimate source-space whole-brain functional connectivity in a large sample comprising AP musicians (n â€‹= â€‹46), relative pitch (RP) musicians (n â€‹= â€‹45), and Non-musicians (n â€‹= â€‹34) during resting state, naturalistic music listening, and audiobook listening. First, we assessed the global network density of the participants' functional networks in these conditions. As revealed by cluster-based permutation testing, AP musicians showed a decreased mean degree compared to Non-musicians whereas RP musicians showed an intermediate mean degree not statistically different from Non-musicians or AP-musicians. This effect was present during naturalistic music and audiobook listening, but, crucially, not during resting state. Second, we identified the subnetworks that drove group differences in global network density using the network-based statistic approach. We found that AP musicians showed decreased functional connectivity between major hubs of the default mode network during both music and audiobook listening compared to Non-musicians. Third, we assessed group differences in global network topology while controlling for network density. We did not find evidence for group differences in the clustering coefficient and characteristic path length. Taken together, we found first evidence of diminished whole-brain functional networks in AP musicians during the perception of naturalistic auditory stimuli. These differences might reflect a complex interplay between AP ability, musical proficiency, music processing, and auditory processing per se.

The importance of the fibre tracts connecting the planum temporale in absolute pitch possessors.

In the present study we investigated 90 musicians of both sexes who possess different degrees of absolute pitch (AP) using diffusion tensor imaging in association with a correlational approach and evaluated whether there are differences in perisylvian connections depending on the proficiency level of AP. We expected higher fractional anisotropy with increasing AP ability in the white matter underlying perisylvian brain areas. Results revealed a significant positive correlation between the proficiency level of AP and fractional anisotropy values in the left-sided white matter underlying the planum temporale. We interpret this result as an indicator of auditory processing differences between musicians possessing different degrees of AP, reflecting early auditory encoding and categorization processes. The present study provides further evidence for the substantial importance of the left-sided planum temporale for the ability of AP.

Functional dedifferentiation of associative resting state networks in older adults - A longitudinal study.

Healthy aging is associated with weaker functional connectivity within resting state brain networks and stronger functional interaction between these networks. This phenomenon has been characterized as reduced functional segregation and has been investigated mainly in cross-sectional studies. Here, we used a longitudinal dataset which consisted of four occasions of resting state fMRI and psychometric cognitive ability data, collected from a sample of healthy older adults (baseline N = 232, age range: 64-87 y, age M = 70.8 y), to investigate the functional segregation of several well-defined resting state networks encompassing the whole brain. We characterized the ratio of within-network and between-network correlations via the well-established segregation index. Our findings showed a decrease over a 4-year interval in the functional segregation of the default mode, frontoparietal control and salience ventral attention networks. In contrast, we showed an increase in the segregation of the limbic network over the same interval. More importantly, the rate of change in functional segregation of the frontoparietal control network was associated with the rate of change in processing speed. These findings support the hypothesis of functional dedifferentiation in healthy aging as well as its role in cognitive function in elderly.

Longitudinal functional brain network reconfiguration in healthy aging.

Healthy aging is associated with changes in cognitive performance and functional brain organization. In fact, cross-sectional studies imply lower modularity and significant heterogeneity in modular architecture across older subjects. Here, we used a longitudinal dataset consisting of four occasions of resting-state-fMRI and cognitive testing (spanning 4 years) in 150 healthy older adults. We applied a graph-theoretic analysis to investigate the time-evolving modular structure of the whole-brain network, by maximizing the multilayer modularity across four time points. Global flexibility, which reflects the tendency of brain nodes to switch between modules across time, was significantly higher in healthy elderly than in a temporal null model. Further, global flexibility, as well as network-specific flexibility of the default mode, frontoparietal control, and somatomotor networks, were significantly associated with age at baseline. These results indicate that older age is related to higher variability in modular organization. The temporal metrics were not associated with simultaneous changes in processing speed or learning performance in the context of memory encoding. Finally, this approach provides global indices for longitudinal change across a given time span and it may contribute to uncovering patterns of modular variability in healthy and clinical aging populations.

Altered limbic and autonomic processing supports brain-heart axis in Takotsubo syndrome.

AIMS: Takotsubo syndrome (TTS) is characterized by acute left ventricular dysfunction often triggered by emotional or physical stress. Severe activation of the sympathetic nervous system with catecholamine release caused by a dysfunctional limbic system has been proposed as a potential mechanism. We hypothesize that brain regions responsible for autonomic integration and/or limbic processing might be involved in the development of TTS. Here, we investigated alterations in resting state functional connectivity in TTS patients compared with healthy controls. METHODS AND RESULTS: Using brain functional magnetic resonance imaging (fMRI), resting state functional connectivity has been assessed in 15 subjects with TTS and 39 healthy controls. Network-based statistical analyses were conducted to identify subnetworks with altered resting state functional connectivity. Sympathetic and parasympathetic networks have been constructed in addition to the default mode network and whole-brain network. We found parasympathetic- and sympathetic-associated subnetworks both showing reduced resting state functional connectivity in TTS patients compared with controls. Important brain regions constituting parasympathetic- and sympathetic-associated subnetworks included the amygdala, hippocampus, and insula as well as cingulate, parietal, temporal, and cerebellar regions. Additionally, the default mode network as well as limbic regions in the whole-brain analysis demonstrated reduced resting state functional connectivity in TTS, including the hippocampus, parahippocampal, and medial prefrontal regions. CONCLUSION: For the first time, we demonstrate hypoconnectivity of central brain regions associated with autonomic functions and regulation of the limbic system in patients with TTS. These findings suggest that autonomic-limbic integration might play an important role in the pathophysiology and contribute to the understanding of TTS.

Univariate and multivariate analyses of functional networks in absolute pitch.

Absolute pitch (AP) refers to the rare ability to identify the pitch of any given tone without an external reference tone. Previous studies have shown that during auditory processing, AP musicians activate the auditory cortex (AC), the prefrontal cortex (PFC), and parietal areas of the brain. Therefore, it has been hypothesized that AP is sustained by a widespread functional network. In the present functional magnetic resonance imaging (fMRI) study, we tested this hypothesis by employing a mass-univariate analysis of resting-state functional connectivity within the AC, the PFC, and parietal areas in a large sample of musicians with and without AP (N = 100). AP musicians showed increased functional connectivity in the left middle frontal gyrus (MFG), left intraparietal sulcus (IPS), and right superior parietal lobule (SPL). These results provide the first evidence for an AP-specific network characterized by increased functional connections in higher-order cognitive areas. Interestingly, AP was not associated with increases in functional connectivity of the AC, but AP was successfully decoded from functional connectivity patterns in the left AC using multi-voxel pattern analysis (MVPA, also known as multivariate pattern analysis), with group classification accuracy being highest for the left Heschl's gyrus (HG). MVPA can capture fine-grained patterns in the brain connectivity profile of AP musicians, whilst a mass-univariate analysis is sensitive to macroscopic trends in the data. The successful differentiation of AP musicians by MVPA but not by a mass-univariate analysis of connectivity in the AC thus indicates that AP musicians differ in the fine-grained rather than the macroscopic AC function. Based on our findings, and in light of current literature, we propose pitch-label associations, tonal working memory, pitch categorization, and multimodal integration as potential mechanisms underlying the AP ability. This set of psychological functions is controlled by a distributed functional network and a particular AC connectivity pattern only present in AP musicians.

Decrypting the electrophysiological individuality of the human brain: Identification of individuals based on resting-state EEG activity.

Biometric identification (BI) of individuals is a fast-growing field of research that is producing increasingly sophisticated applications in several spheres of everyday life. Previous magnetic resonance imaging (MRI) studies have demonstrated that based on the high inter-individual variability of brain structure and function, it is possible to identify individuals with high accuracy. Otherwise, there is the common belief that electroencephalographic (EEG) data recorded at the surface of the scalp are too noisy for identification purposes with a comparably high hit rate. In the present work, we compared BI quality (F1-scores, accuracy, sensitivity, and specificity) between different types of functional (instantaneous, lagged, and total coherence, phase synchronization, correlation, and mutual information) and effective (Granger causality, phase synchronization, and coherence) connectivity measures. Results revealed that across functional connectivity metrics, identification accuracy was in the range of 0.98-1, whereas sensitivity and F1-scores were between 0.00 and 1 and specificity was between 0.99 and 1. BI was higher for the connectivity metrics that are contaminated by volume conduction (instantaneous connectivity) compared to those that are unaffected by this variable (lagged connectivity). Support vector machine and neural network algorithms yielded the highest BI, followed by random forest and weighted k-nearest neighborhood, whereas linear discriminant analysis was less accurate. These results provide cross-validated counterevidence to the belief that EEG data are too noisy for identification purposes and demonstrate that functional and effective connectivity metrics are particularly suited for BI applications with comparable accuracy to MRI. Our results have important implications for fast, low-cost, and mobile BI applications.

Neural patterns reveal single-trial information on absolute pitch and relative pitch perception.

Pitch is a fundamental attribute of sounds and yet is not perceived equally by all humans. Absolute pitch (AP) musicians perceive, recognize, and name pitches in absolute terms, whereas relative pitch (RP) musicians, representing the large majority of musicians, perceive pitches in relation to other pitches. In this study, we used electroencephalography (EEG) to investigate the neural representations underlying tone listening and tone labeling in a large sample of musicians (n = 105). Participants performed a pitch processing task with a listening and a labeling condition during EEG acquisition. Using a brain-decoding framework, we tested a prediction derived from both theoretical and empirical accounts of AP, namely that the representational similarity of listening and labeling is higher in AP musicians than in RP musicians. Consistent with the prediction, time-resolved single-trial EEG decoding revealed a higher representational similarity in AP musicians during late stages of pitch perception. Time-frequency-resolved EEG decoding further showed that the higher representational similarity was present in oscillations in the theta and beta frequency bands. Supplemental univariate analyses were less sensitive in detecting subtle group differences in the frequency domain. Taken together, the results suggest differences between AP and RP musicians in late pitch processing stages associated with cognition, rather than in early processing stages associated with perception.