Neurite architecture of the planum temporale predicts neurophysiological processing of auditory speech

Speech-language within and between network disruptions in primary progressive aphasia variants

Primary progressive aphasia (PPA) variants present with distinct disruptions in speech-language functions with little known about the interplay between affected and spared regions within the speech-language network and their interaction with other functional networks. The Neurodegenerative Research Group, Mayo Clinic, recruited 123 patients with PPA (55 logopenic (lvPPA), 44 non-fluent (nfvPPA) and 24 semantic (svPPA)) who were matched to 60 healthy controls. We investigated functional connectivity disruptions between regions within the left-speech-language network (Broca, Wernicke, anterior middle temporal gyrus (aMTG), supplementary motor area (SMA), planum temporale (PT) and parietal operculum (PO)), and disruptions to other networks (visual association, dorsal-attention, frontoparietal and default mode networks (DMN)). Within the speech-language network, multivariate linear regression models showed reduced aMTG-Broca connectivity in all variants, with lvPPA and nfvPPA findings remaining significant after Bonferroni correction. Additional loss in Wernicke-Broca connectivity in nfvPPA, Wernicke-PT connectivity in lvPPA and greater aMTG-PT connectivity in svPPA were also noted. Between-network connectivity findings in all variants showed reduced aMTG-DMN and increased aMTG-dorsal-attention connectivity, with additional disruptions between aMTG-visual association in both lvPPA and svPPA, aMTG-frontoparietal in lvPPA, and Wernicke-DMN breakdown in svPPA. These findings suggest that aMTG connectivity breakdown is a shared feature in all PPA variants, with lvPPA showing more extensive connectivity disruptions with other networks.

Clinical implications of brain asymmetries

No two human brains are alike, and with the rise of precision medicine in neurology, we are seeing an increased emphasis on understanding the individual variability in brain structure and function that renders every brain unique. Functional and structural brain asymmetries are a fundamental principle of brain organization, and recent research suggests substantial individual variability in these asymmetries that needs to be considered in clinical practice. In this Review, we provide an overview of brain asymmetries, variations in such asymmetries and their relevance in the clinical context. We review recent findings on brain asymmetries in neuropsychiatric and neurodevelopmental disorders, as well as in specific learning disabilities, with an emphasis on large-scale database studies and meta-analyses. We also highlight the relevance of asymmetries for disease symptom onset in neurodegenerative diseases and their implications for lateralized treatments, including brain stimulation. We conclude that alterations in brain asymmetry are not sufficiently specific to act as diagnostic biomarkers but can serve as meaningful symptom or treatment response biomarkers in certain contexts. On the basis of these insights, we provide several recommendations for neurological clinical practice.

Cortical neurite microstructural correlates of time perception in healthy older adults

The human experience is significantly impacted by timing as it structures how information is processed. Nevertheless, the neurological foundation of time perception remains largely unresolved. Understanding cortical microstructure related to timing is crucial for gaining insight into healthy aging and recognizing structural alterations that are typical of neurodegenerative diseases associated with age. Given the importance, this study aimed to determine the brain regions that are accountable for predicting time perception in older adults using microstructural measures of the brain. In this study, elderly healthy adults performed the Time-Wall Estimation task to measure time perception through average error time. We used support vector regression (SVR) analyses to predict the average error time using cortical neurite microstructures derived from orientation dispersion and density imaging based on multi-shell diffusion magnetic resonance imaging (dMRI). We found significant correlations between observed and predicted average error times for neurite arborization (ODI) and free water (FISO). Neurite arborization and free water properties in specific regions in the medial and lateral prefrontal, superior parietal, and medial and lateral temporal lobes were among the most significant predictors of timing ability in older adults. Further, our results revealed that greater branching along with lower free water in cortical structures result in shorter average error times. Future studies should assess whether these same networks are contributing to time perception in older adults with mild cognitive impairment (MCI) and whether degeneration of these networks contribute to early diagnosis or detection of dementia.

The uniqueness of the human brain: a review

The purpose of this review is to highlight the most important aspects of the anatomical and functional uniqueness of the human brain. For this, a comparison is made between our brains and those of our closest ancestors (chimpanzees and bonobos) and human ancestors. During human evolution, several changes occurred in the brain, such as an absolute increase in brain size and number of cortical neurons, in addition to a greater degree of functional lateralization and anatomical asymmetry. Also, the cortical cytoarchitecture became more diversified and there was an increase in the number of intracortical networks and networks extending from the cerebral cortex to subcortical structures, with more neural networks being invested in multisensory and sensory-motor-affective-cognitive integration. These changes permitted more complex, flexible and versatile cognitive abilities and social behavior, such as shared intentionality and symbolic articulated language, which, in turn, made possible the formation of larger social groups and cumulative cultural evolution that are characteristic of our species.

Response stopping under conflict: The integrative role of representational dynamics associated with the insular cortex

Coping with distracting inputs during goal-directed behavior is a common challenge, especially when stopping ongoing responses. The neural basis for this remains debated. Our study explores this using a conflict-modulation Stop Signal task, integrating group independent component analysis (group-ICA), multivariate pattern analysis (MVPA), and EEG source localization analysis. Consistent with previous findings, we show that stopping performance is better in congruent (nonconflicting) trials than in incongruent (conflicting) trials. Conflict effects in incongruent trials compromise stopping more due to the need for the reconfiguration of stimulus-response (S-R) mappings. These cognitive dynamics are reflected by four independent neural activity patterns (ICA), each coding representational content (MVPA). It is shown that each component was equally important in predicting behavioral outcomes. The data support an emerging idea that perception-action integration in action-stopping involves multiple independent neural activity patterns. One pattern relates to the precuneus (BA 7) and is involved in attention and early S-R processes. Of note, three other independent neural activity patterns were associated with the insular cortex (BA13) in distinct time windows. These patterns reflect a role in early attentional selection but also show the reiterated processing of representational content relevant for stopping in different S-R mapping contexts. Moreover, the insular cortex's role in automatic versus complex response selection in relation to stopping processes is shown. Overall, the insular cortex is depicted as a brain hub, crucial for response selection and cancellation across both straightforward (automatic) and complex (conditional) S-R mappings, providing a neural basis for general cognitive accounts on action control.

Using rare genetic mutations to revisit structural brain asymmetry

Asymmetry between the left and right hemisphere is a key feature of brain organization. Hemispheric functional specialization underlies some of the most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variants, which typically exert small effects on brain-related phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We designed a pattern-learning approach to dissect the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior data fusion highlights the consequences of genetically controlled brain lateralization on uniquely human cognitive capacities.

Bridging the Divide: Brain and Behavior in Developmental Language Disorder

Developmental language disorder (DLD) is a heterogenous neurodevelopmental disorder that affects a child's ability to comprehend and/or produce spoken and/or written language, yet it cannot be attributed to hearing loss or overt neurological damage. It is widely believed that some combination of genetic, biological, and environmental factors influences brain and language development in this population, but it has been difficult to bridge theoretical accounts of DLD with neuroimaging findings, due to heterogeneity in language impairment profiles across individuals and inconsistent neuroimaging findings. Therefore, the purpose of this overview is two-fold: (1) to summarize the neuroimaging literature (while drawing on findings from other language-impaired populations, where appropriate); and (2) to briefly review the theoretical accounts of language impairment patterns in DLD, with the goal of bridging the disparate findings. As will be demonstrated with this overview, the current state of the field suggests that children with DLD have atypical brain volume, laterality, and activation/connectivity patterns in key language regions that likely contribute to language difficulties. However, the precise nature of these differences and the underlying neural mechanisms contributing to them remain an open area of investigation.

Hand Preference in Stuttering: Meta-Analyses

Reduced hemispheric asymmetries, as well as their behavioral manifestation in the form of atypical handedness (i.e., non-right, left-, or mixed-handedness), are linked to neurodevelopmental disorders, such as autism spectrum disorder, and several psychiatric disorders, such as schizophrenia. One neurodevelopmental disorder that is associated with reduced hemispheric asymmetries, but for which findings on behavioral laterality are conflicting, is stuttering. Here, we report a series of meta-analyses of studies that report handedness (assessed as hand preference) levels in individuals who stutter (otherwise healthy) compared to controls. For this purpose, articles were identified via a search in PubMed, Scopus, and PsycInfo (13 June 2023). On the basis of k = 52 identified studies totaling n = 2590 individuals who stutter and n = 17,148 controls, five random effects meta-analyses were conducted: four using the odds ratio [left-handers (forced choice); left-handers (extreme); mixed-handers; non-right-handers vs. total)] and one using the standardized difference in means as the effect size. We did not find evidence of a left (extreme)- or mixed-handedness difference or a difference in mean handedness scores, but evidence did emerge, when it came to left-handedness (forced-choice) and (inconclusively for) non-right-handedness. Risk-of-bias analysis was not deemed necessary in the context of these meta-analyses. Differences in hand skill or strength of handedness could not be assessed as no pertinent studies were located. Severity of stuttering could not be used s a moderator, as too few studies broke down their data according to severity. Our findings do not allow for firm conclusions to be drawn on whether stuttering is associated with reduced hemispheric asymmetries, at least when it comes to their behavioral manifestation.

The neurophysiology of continuous action monitoring

Monitoring actions is essential for goal-directed behavior. However, as opposed to short-lasting, and regularly reinstating monitoring functions, the neural processes underlying continuous action monitoring are poorly understood. We investigate this using a pursuit-tracking paradigm. We show that beta band activity likely maintains the sensorimotor program, while theta and alpha bands probably support attentional sampling and information gating, respectively. Alpha and beta band activity are most relevant during the initial tracking period, when sensorimotor calibrations are most intense. Theta band shifts from parietal to frontal cortices throughout tracking, likely reflecting a shift in the functional relevance from attentional sampling to action monitoring. This study shows that resource allocation mechanisms in prefrontal areas and stimulus-response mapping processes in the parietal cortex are crucial for adapting sensorimotor processes. It fills a knowledge gap in understanding the neural processes underlying action monitoring and suggests new directions for examining sensorimotor integration in more naturalistic experiments.

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.

TrGANet: Transforming 3T to 7T dMRI using Trapezoidal Rule and Graph based Attention Modules

Diffusion MRI (dMRI) is a non-invasive tool for assessing the white matter region of the brain by approximating the fiber streamlines, structural connectivity, and estimation of microstructure. This modality can yield useful information for diagnosing several mental diseases as well as for surgical planning. The higher angular resolution diffusion imaging (HARDI) technique is helpful in obtaining more robust fiber tracts by getting a good approximation of regions where fibers cross. Moreover, HARDI is more sensitive to tissue changes and can accurately represent anatomical details in the human brain at higher magnetic strengths. In other words, magnetic strengths affect the quality of the image, and hence high magnetic strength has good tissue contrast with better spatial resolution. However, a higher magnetic strength scanner (like 7T) is costly and unaffordable to most hospitals. Hence, in this work, we have proposed a novel CNN architecture for the transformation of 3T to 7T dMRI. Additionally, we have also reconstructed the multi-shell multi-tissue fiber orientation distribution function (MSMT fODF) at 7T from single-shell 3T. The proposed architecture consists of a CNN-based ODE solver utilizing the Trapezoidal rule and graph-based attention layer alongwith L₁ and total variation loss. Finally, the model has been validated on the HCP data set quantitatively and qualitatively.

The role of visual association cortices during response selection processes in interference-modulated response stopping

Response inhibition and the ability to navigate distracting information are both integral parts of cognitive control and are imperative to adaptive behavior in everyday life. Thus far, research has only inconclusively been able to draw inferences regarding the association between response stopping and the effects of interfering information. Using a novel combination of the Simon task and a stop signal task, the current study set out to investigate the behavioral as well as the neurophysiological underpinnings of the relationship between response stopping and interference processing. We tested n = 27 healthy individuals and combined temporal EEG signal decomposition with source localization methods to delineate the precise neurophysiological dynamics and functional neuroanatomical structures associated with conflict effects on response stopping. The results showed that stopping performance was compromised by conflicts. Importantly, these behavioral effects were reflected by specific aspects of information coded in the neurophysiological signal, indicating that conflict effects during response stopping are not mediated via purely perceptual processes. Rather, it is the processing of specific, stop-relevant stimulus features in the sensory regions during response selection, which underlies the emergence of conflict effects in response stopping. The findings connect research regarding response stopping with overarching theoretical frameworks of perception-action integration.

A ventral stream-prefrontal cortex processing cascade enables working memory gating dynamics

The representation of incoming information, goals and the flexible processing of these are required for cognitive control. Efficient mechanisms are needed to decide when it is important that novel information enters working memory (WM) and when these WM 'gates' have to be closed. Compared to neural foundations of maintaining information in WM, considerably less is known about what neural mechanisms underlie the representational dynamics during WM gating. Using different EEG analysis methods, we trace the path of mental representations along the human cortex during WM gate opening and closing. We show temporally nested representational dynamics during WM gate opening and closing depending on multiple independent neural activity profiles. These activity profiles are attributable to a ventral stream-prefrontal cortex processing cascade. The representational dynamics start in the ventral stream during WM gate opening and WM gate closing before prefrontal cortical regions are modulated. A regional specific activity profile is shown within the prefrontal cortex depending on whether WM gates are opened or closed, matching overarching concepts of prefrontal cortex functions. The study closes an essential conceptual gap detailing the neural dynamics underlying how mental representations drive the WM gate to open or close to enable WM functions such as updating and maintenance.

Cognitive science theory-driven pharmacology elucidates the neurobiological basis of perception-motor integration

An efficient integration of sensory and motor processes is crucial to goal-directed behavior. Despite this high relevance, and although cognitive theories provide clear conceptual frameworks, the neurobiological basis of these processes remains insufficiently understood. In a double-blind, randomized placebo-controlled pharmacological study, we examine the relevance of catecholamines for perception-motor integration processes. Using EEG data, we perform an in-depth analysis of the underlying neurophysiological mechanisms, focusing on sensorimotor integration processes during response inhibition. We show that the catecholaminergic system affects sensorimotor integration during response inhibition by modulating the stability of the representational content. Importantly, catecholamine levels do not affect the stability of all aspects of information processing during sensorimotor integration, but rather-as suggested by cognitive theory-of specific codes in the neurophysiological signal. Particularly fronto-parietal cortical regions are associated with the identified mechanisms. The study shows how cognitive science theory-driven pharmacology can shed light on the neurobiological basis of perception-motor integration and how catecholamines affect specific information codes relevant to cognitive control.

Lip movements enhance speech representations and effective connectivity in auditory dorsal stream

Viewing speaker's lip movements facilitates speech perception, especially under adverse listening conditions, but the neural mechanisms of this perceptual benefit at the phonemic and feature levels remain unclear. This fMRI study addressed this question by quantifying regional multivariate representation and network organization underlying audiovisual speech-in-noise perception. Behaviorally, valid lip movements improved recognition of place of articulation to aid phoneme identification. Meanwhile, lip movements enhanced neural representations of phonemes in left auditory dorsal stream regions, including frontal speech motor areas and supramarginal gyrus (SMG). Moreover, neural representations of place of articulation and voicing features were promoted differentially by lip movements in these regions, with voicing enhanced in Broca's area while place of articulation better encoded in left ventral premotor cortex and SMG. Next, dynamic causal modeling (DCM) analysis showed that such local changes were accompanied by strengthened effective connectivity along the dorsal stream. Moreover, the neurite orientation dispersion of the left arcuate fasciculus, the bearing skeleton of auditory dorsal stream, predicted the visual enhancements of neural representations and effective connectivity. Our findings provide novel insight to speech science that lip movements promote both local phonemic and feature encoding and network connectivity in the dorsal pathway and the functional enhancement is mediated by the microstructural architecture of the circuit.

Improved Speech Hearing in Noise with Invasive Electrical Brain Stimulation

Speech perception in noise is a challenging everyday task with which many listeners have difficulty. Here, we report a case in which electrical brain stimulation of implanted intracranial electrodes in the left planum temporale (PT) of a neurosurgical patient significantly and reliably improved subjective quality (up to 50%) and objective intelligibility (up to 97%) of speech in noise perception. Stimulation resulted in a selective enhancement of speech sounds compared with the background noises. The receptive fields of the PT sites whose stimulation improved speech perception were tuned to spectrally broad and rapidly changing sounds. Corticocortical evoked potential analysis revealed that the PT sites were located between the sites in Heschl's gyrus and the superior temporal gyrus. Moreover, the discriminability of speech from nonspeech sounds increased in population neural responses from Heschl's gyrus to the PT to the superior temporal gyrus sites. These findings causally implicate the PT in background noise suppression and may point to a novel potential neuroprosthetic solution to assist in the challenging task of speech perception in noise.SIGNIFICANCE STATEMENT Speech perception in noise remains a challenging task for many individuals. Here, we present a case in which the electrical brain stimulation of intracranially implanted electrodes in the planum temporale of a neurosurgical patient significantly improved both the subjective quality (up to 50%) and objective intelligibility (up to 97%) of speech perception in noise. Stimulation resulted in a selective enhancement of speech sounds compared with the background noises. Our local and network-level functional analyses placed the planum temporale sites in between the sites in the primary auditory areas in Heschl's gyrus and nonprimary auditory areas in the superior temporal gyrus. These findings causally implicate planum temporale in acoustic scene analysis and suggest potential neuroprosthetic applications to assist hearing in noise.

Structural Brain Asymmetries for Language: A Comparative Approach across Primates

Humans are the only species that can speak. Nonhuman primates, however, share some ‘domain-general’ cognitive properties that are essential to language processes. Whether these shared cognitive properties between humans and nonhuman primates are the results of a continuous evolution [homologies] or of a convergent evolution [analogies] remain difficult to demonstrate. However, comparing their respective underlying structure—the brain—to determinate their similarity or their divergence across species is critical to help increase the probability of either of the two hypotheses, respectively. Key areas associated with language processes are the Planum Temporale, Broca’s Area, the Arcuate Fasciculus, Cingulate Sulcus, The Insula, Superior Temporal Sulcus, the Inferior Parietal lobe, and the Central Sulcus. These structures share a fundamental feature: They are functionally and structurally specialised to one hemisphere. Interestingly, several nonhuman primate species, such as chimpanzees and baboons, show human-like structural brain asymmetries for areas homologous to key language regions. The question then arises: for what function did these asymmetries arise in non-linguistic primates, if not for language per se? In an attempt to provide some answers, we review the literature on the lateralisation of the gestural communication system, which may represent the missing behavioural link to brain asymmetries for language area’s homologues in our common ancestor.

Conditional generative adversarial networks applied to EEG data can inform about the inter-relation of antagonistic behaviors on a neural level

Goal-directed actions frequently require a balance between antagonistic processes (e.g., executing and inhibiting a response), often showing an interdependency concerning what constitutes goal-directed behavior. While an inter-dependency of antagonistic actions is well described at a behavioral level, a possible inter-dependency of underlying processes at a neuronal level is still enigmatic. However, if there is an interdependency, it should be possible to predict the neurophysiological processes underlying inhibitory control based on the neural processes underlying speeded automatic responses. Based on that rationale, we applied artificial intelligence and source localization methods to human EEG recordings from N = 255 participants undergoing a response inhibition experiment (Go/Nogo task). We show that the amplitude and timing of scalp potentials and their functional neuroanatomical sources during inhibitory control can be inferred by conditional generative adversarial networks (cGANs) using neurophysiological data recorded during response execution. We provide insights into possible limitations in the use of cGANs to delineate the interdependency of antagonistic actions on a neurophysiological level. Nevertheless, artificial intelligence methods can provide information about interdependencies between opposing cognitive processes on a neurophysiological level with relevance for cognitive theory.

An artificial intelligence algorithm trained on EEG recordings can be used to predict brain dynamics underpinning motor inhibition processes using neurophysiological information from motor execution.

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.

Single-shell to multi-shell dMRI transformation using spatial and volumetric multilevel hierarchical reconstruction framework

Single or Multi-shell high angular resolution diffusion imaging (HARDI) has become an important dMRI acquisition technique for studying brain white matter fibers. Existing single-shell HARDI makes it challenging to estimate the intravoxel structure up to the desired resolution. However, multi-shell acquisition (with multiple b-values) can provide higher resolution for the intravoxel structure, which further helps in getting accurate fiber tracts; But, this comes at the cost of larger acquisition time and larger setup. Hence, we propose a novel deep learning architecture for the reconstruction of diffusion MRI volumes for different b-values (degree of diffusion weighting) using acquisitions at a fixed b-value (termed as single-shell) acquisition. This reconstruction has been performed in the spherical harmonics space to better manage varying gradient directions. In this work, we have demonstrated such a reconstruction for b = 3000 s/mm² and b = 2000 s/mm² from b = 1000 s/mm². The proposed Multilevel Hierarchical Spherical Harmonics Coefficients Reconstruction (MHSH) framework takes advantage of contextual information within each slice as well as across the slices by involving Slice Level ReconNet (SLRNet) network and a Volumetric ROI Level ReconNet (VPLRNet) network, respectively. Three-loss functions have been used to optimize network learning, i.e., L₁, Adversarial, and Total Variation Loss. Finally, the network is trained and validated on the publicly available HCP data-set with standard qualitative and quantitative performance measures and achieves promising results.

Common principles in the lateralisation of auditory cortex structure and function for vocal communication in primates and rodents

This review summarises recent findings on the lateralisation of communicative sound processing in the auditory cortex (AC) of humans, non-human primates, and rodents. Functional imaging in humans has demonstrated a left hemispheric preference for some acoustic features of speech, but it is unclear to which degree this is caused by bottom-up acoustic feature selectivity or top-down modulation from language areas. Although non-human primates show a less pronounced functional lateralisation in AC, the properties of AC fields and behavioral asymmetries are qualitatively similar. Rodent studies demonstrate microstructural circuits that might underlie bottom-up acoustic feature selectivity in both hemispheres. Functionally, the left AC in the mouse appears to be specifically tuned to communication calls, whereas the right AC may have a more 'generalist' role. Rodents also show anatomical AC lateralisation, such as differences in size and connectivity. Several of these functional and anatomical characteristics are also lateralized in human AC. Thus, complex vocal communication processing shares common features among rodents and primates. We argue that a synthesis of results from humans, non-human primates, and rodents is necessary to identify the neural circuitry of vocal communication processing. However, data from different species and methods are often difficult to compare. Recent advances may enable better integration of methods across species. Efforts to standardise data formats and analysis tools would benefit comparative research and enable synergies between psychological and biological research in the area of vocal communication processing.

Asymmetry in the Central Nervous System: A Clinical Neuroscience Perspective

Recent large-scale neuroimaging studies suggest that most parts of the human brain show structural differences between the left and the right hemisphere. Such structural hemispheric asymmetries have been reported for both cortical and subcortical structures. Interestingly, many neurodevelopmental and psychiatric disorders have been associated with altered functional hemispheric asymmetries. However, findings concerning the relation between structural hemispheric asymmetries and disorders have largely been inconsistent, both within specific disorders as well as between disorders. In the present review, we compare structural asymmetries from a clinical neuroscience perspective across different disorders. We focus especially on recent large-scale neuroimaging studies, to concentrate on replicable effects. With the notable exception of major depressive disorder, all reviewed disorders were associated with distinct patterns of alterations in structural hemispheric asymmetries. While autism spectrum disorder was associated with altered structural hemispheric asymmetries in a broader range of brain areas, most other disorders were linked to more specific alterations in brain areas related to cognitive functions that have been associated with the symptomology of these disorders. The implications of these findings are highlighted in the context of transdiagnostic approaches to psychopathology.

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.

Perception-action integration in young age-A cross-sectional EEG study

Humans differ in their capacity for integrating perceived events and related actions. The "Theory of event coding" (TEC) conceptualizes how stimuli and actions are cognitively bound into a common functional representation (or "code"), known as the "event file". To date, however, the neural processes underlying the development of event file coding mechanisms across age are largely unclear. We investigated age-related neural changes of event file coding from late childhood to early adulthood, using EEG signal decompositions methods. We included a group of healthy participants (n = 91) between 10 and 30 years, performing an event file paradigm. Results of this study revealed age-related effects on event file coding processes both at the behavioural and the neurophysiological level. Performance accuracy data showed that event file unbinding und rebinding processes become more efficient from late childhood to early adulthood. These behavioural effects are reflected by age-related effects in two neurophysiological subprocesses associated with the superior parietal cortex (BA7) as revealed in the analyses using EEG signal decomposition. The first process entails mapping and association processes between stimulus and response; whereas, the second comprises inhibitory control subprocesses subserving the selection of the relevant motor programme amongst competing response options.

Neural dynamics of stimulus-response representations during inhibitory control

The investigation of action control processes is one major field in cognitive neuroscience and several theoretical frameworks have been proposed. One established framework is the "Theory of Event Coding" (TEC). However, only rarely, this framework has been used in the context of response inhibition and how stimulus-response association or binding processes modulate response inhibition performance. Particularly the neural dynamics of stimulus-response representations during inhibitory control are elusive. To address this, we examined n = 40 healthy controls and combined temporal EEG signal decomposition with source localization and temporal generalization multivariate pattern analysis (MVPA). We show that overlaps in features of stimuli used to trigger either response execution or inhibition compromised task performance. According to TEC, this indicates that binding processes in event file representations impact response inhibition through partial repetition costs. In the EEG data, reconfiguration of event files modulated processes in time windows well-known to reflect distinct response inhibition mechanisms. Crucially, event file coding processes were only evident in a specific fraction of neurophysiological activity associated with the inferior parietal cortex (BA40). Within that specific fraction of neurophysiological activity, the decoding of the dynamics of event file representations using temporal generalization MVPA suggested that event file representations are stable across several hundred milliseconds, and that event file coding during inhibitory control is reflected by a sustained activation pattern of neural dynamics.NEW & NOTEWORTHY The "mental representation" of how stimulus input translate into the appropriate response is central for goal-directed behavior. However, little is known about the dynamics of such representations on the neurophysiological level when it comes to the inhibition of motor processes. This dynamic is shown in the current study.

Neurophysiological and functional neuroanatomical coding of statistical and deterministic rule information during sequence learning

Humans are capable of acquiring multiple types of information presented in the same information stream. It has been suggested that at least two parallel learning processes are important during learning of sequential patterns-statistical learning and rule-based learning. Yet, the neurophysiological underpinnings of these parallel learning processes are not fully understood. To differentiate between the simultaneous mechanisms at the single trial level, we apply a temporal EEG signal decomposition approach together with sLORETA source localization method to delineate whether distinct statistical and rule-based learning codes can be distinguished in EEG data and can be related to distinct functional neuroanatomical structures. We demonstrate that concomitant but distinct aspects of information coded in the N2 time window play a role in these mechanisms: mismatch detection and response control underlie statistical learning and rule-based learning, respectively, albeit with different levels of time-sensitivity. Moreover, the effects of the two learning mechanisms in the different temporally decomposed clusters of neural activity also differed from each other in neural sources. Importantly, the right inferior frontal cortex (BA44) was specifically implicated in visuomotor statistical learning, confirming its role in the acquisition of transitional probabilities. In contrast, visuomotor rule-based learning was associated with the prefrontal gyrus (BA6). The results show how simultaneous learning mechanisms operate at the neurophysiological level and are orchestrated by distinct prefrontal cortical areas. The current findings deepen our understanding on the mechanisms of how humans are capable of learning multiple types of information from the same stimulus stream in a parallel fashion.

Planum temporale grey matter volume asymmetries in newborn monkeys (Papio anubis)

The Planum temporale (PT) is one of the key hubs of the language network in the human brain. The gross asymmetry of this perisylvian region toward the left brain was considered as the most emblematic marker of hemispheric specialization of language processes in the brain. Interestingly, this neuroanatomical signature was documented also in newborn infants and preterms, suggesting the early brain's readiness for language acquisition. Nevertheless, this latter interpretation was questioned by a recent report in non-human primates of a potential similar signature in newborn baboons Papio anubis based on PT surface measures. Whether this "tip of the iceberg" PT asymmetry is actually reflecting asymmetry of its underlying grey matter volume remains unclear but critical to investigate potential continuities of cortical specialization with human infants. Here we report a population-level leftward asymmetry of the PT grey matter volume in in vivo 34 newborn baboons P. anubis, which showed intra-individual positive correlation with PT surface's asymmetry measures but also a more pronounced degree of leftward asymmetry at the population level. This finding demonstrates that PT leftward structural asymmetry in this Old World monkey species is a robust phenomenon in early primate development, which clearly speaks for a continuity with early human brain specialization. Results also strengthen the hypothesis that early PT asymmetry might be not a human-specific marker for the pre-wired language-ready brain in infants.

Bilateral age-related atrophy in the planum temporale is associated with vowel discrimination difficulty in healthy older adults

In this study we investigated the association between age-related brain atrophy and behavioural as well as electrophysiological markers of vowel perception in a sample of healthy younger and older adults with normal pure-tone hearing. Twenty-three older adults and 27 younger controls discriminated a set of vowels with altered second formants embedded in consonant-vowel syllables. Additionally, mismatch negativity (MMN) responses were recorded in a separate oddball paradigm with the same set of stimuli. A structural magnet resonance scan was obtained for each participant to determine cortical architecture of the left and right planum temporale (PT). The PT was chosen for its function as a major processor of auditory cues and speech. Results suggested that older adults performed worse in vowel discrimination despite normal-for-age pure-tone hearing. In the older group, we found evidence that those with greater age-related cortical atrophy (i.e., lower cortical surface area and cortical volume) in the left and right PT also showed weaker vowel discrimination. In comparison, we found a lateralized correlation in the younger group suggesting that those with greater cortical thickness in only the left PT performed weaker in the vowel discrimination task. We did not find any associations between macroanatomical traits of the PT and MMN responses. We conclude that deficient vowel processing is not only caused by pure-tone hearing loss but is also influenced by atrophy-related changes in the ageing auditory-related cortices. Furthermore, our results suggest that auditory processing might become more bilateral across the lifespan.

Acoustics versus linguistics? Context is Part and Parcel to lateralized processing of the parts and parcels of speech

The purpose of this review is to provide an accessible exploration of key considerations of lateralization in speech and non-speech perception using clear and defined language. From these considerations, the primary arguments for each side of the linguistics versus acoustics debate are outlined and explored in context of emerging integrative theories. This theoretical approach entails a perspective that linguistic and acoustic features differentially contribute to leftward bias, depending on the given context. Such contextual factors include stimulus parameters and variables of stimulus presentation (e.g., noise/silence and monaural/binaural) and variances in individuals (sex, handedness, age, and behavioural ability). Discussion of these factors and their interaction is also aimed towards providing an outline of variables that require consideration when developing and reviewing methodology of acoustic and linguistic processing laterality studies. Thus, there are three primary aims in the present paper: (1) to provide the reader with key theoretical perspectives from the acoustics/linguistics debate and a synthesis of the two viewpoints, (2) to highlight key caveats for generalizing findings regarding predominant models of speech laterality, and (3) to provide a practical guide for methodological control using predominant behavioural measures (i.e., gap detection and dichotic listening tasks) and/or neurophysiological measures (i.e., mismatch negativity) of speech laterality.

Neurophysiology of embedded response plans: age effects in action execution but not in feature integration from preadolescence to adulthood

Performing a goal-directed movement consists of a chain of complex preparatory mechanisms. Such planning especially requires integration (or binding) of various action features, a process that has been conceptualized in the "theory of event coding." Theoretical considerations and empirical research suggest that these processes are subject to developmental effects from adolescence to adulthood. The aim of the present study was to investigate age-related modulations in action feature binding processes and to shed light on underlying neurophysiological development from preadolescence to early adulthood. We examined a group of healthy participants (n = 61) between 10 and 30 yr of age, who performed a task that requires a series of bimanual response selections in an embedded paradigm. For an in-depth analysis of the underlying neural correlates, we applied EEG signal decomposition together with source localization analyses. Behavioral results across the whole group did not show binding effects in reaction times but in intraindividual response variability. From age 10 to 30 yr, there was a decrease in reaction times and reaction time variability but no age-related effect in action file binding. The latter were corroborated by Bayesian data analyses. On the brain level, the developmental effects on response selection were associated with activation modulations in the superior parietal cortex (BA7). The results show that mechanisms of action execution and speed, but not those of action feature binding, are subject to age-related changes between the age of 10 and 30 yr.NEW & NOTEWORTHY Different aspects of an action need to be integrated to allow smooth unfolding of behavior. We examine developmental effects in these processes and show that mechanisms of action execution and speed, but not those of action feature binding, are subject to age-related changes between the age of 10 and 30 yr.

MORC1 methylation and BDI are associated with microstructural features of the hippocampus and medial prefrontal cortex

BACKGROUND

Alterations in the hippocampus and prefrontal cortex (PFC) have frequently been reported in depressed patients. These parameters might prove to be a consistent finding in depression. In addition, peripheral DNA methylation of the MORC1 gene promoter showed stable associations with depression across independent samples. However, the question arises whether MORC1, supposedly acting as transcription factor, might also be involved in neurobiological alterations accompanying depression. This study further analyses the role of MORC1 in depression by investigating a potential correlation between peripheral MORC1 DNA methylation and neuronal structural properties previously associated with depression in humans.

METHODS

Beck Depression Inventory (BDI) was assessed in 52 healthy participants. DNA was extracted from buccal cells and MORC1 methylation correlated with micro- and macrostructural properties derived from magnetic resonance imaging (MRI) and neurite orientation dispersion and density imaging (NODDI) in the hippocampus and medial prefrontal cortex (mPFC).

RESULTS

MORC1 methylation was associated with volume reduction and neurite orientation dispersion and density markers in the hippocampus and mPFC. BDI was positively associated with neurite orientation dispersion and density markers in the hippocampus.

LIMITATIONS

The study was conducted in a small sample of healthy participants with subclinical depressive symptoms. Peripheral tissue was analyzed.

CONCLUSION

We found significant negative associations between peripheral MORC1 methylation and macro- and microstructural markers in the hippocampus and mPFC. Thus, MORC1 might be involved in neurobiological properties. Studies investigating neuronal methylation patterns of MORC1 are needed to support this hypothesis.

Relations between hemispheric asymmetries of grey matter and auditory processing of spoken syllables in 281 healthy adults

Most people have a right-ear advantage for the perception of spoken syllables, consistent with left hemisphere dominance for speech processing. However, there is considerable variation, with some people showing left-ear advantage. The extent to which this variation is reflected in brain structure remains unclear. We tested for relations between hemispheric asymmetries of auditory processing and of grey matter in 281 adults, using dichotic listening and voxel-based morphometry. This was the largest study of this issue to date. Per-voxel asymmetry indexes were derived for each participant following registration of brain magnetic resonance images to a template that was symmetrized. The asymmetry index derived from dichotic listening was related to grey matter asymmetry in clusters of voxels corresponding to the amygdala and cerebellum lobule VI. There was also a smaller, non-significant cluster in the posterior superior temporal gyrus, a region of auditory cortex. These findings contribute to the mapping of asymmetrical structure–function links in the human brain and suggest that subcortical structures should be investigated in relation to hemispheric dominance for speech processing, in addition to auditory cortex.

Super-Resolved q-Space deep learning with uncertainty quantification

Diffusion magnetic resonance imaging (dMRI) provides a noninvasive method for measuring brain tissue microstructure. q-Space deep learning(q-DL) methods have been developed to accurately estimate tissue microstructure from dMRI scans acquired with a reduced number of diffusion gradients. In these methods, deep networks are trained to learn the mapping directly from diffusion signals to tissue microstructure. However, the quality of tissue microstructure estimation can be limited not only by the reduced number of diffusion gradients but also by the low spatial resolution of typical dMRI acquisitions. Therefore, in this work we extend q-DL to super-resolved tissue microstructure estimation and propose super-resolvedq-DL (SR-q-DL), where deep networks are designed to map low-resolution diffusion signals undersampled in the q-space to high-resolution tissue microstructure. Specifically, we use a patch-based strategy, where a deep network takes low-resolution patches of diffusion signals as input and outputs high-resolution tissue microstructure patches. The high-resolution patches are then combined to obtain the final high-resolution tissue microstructure map. Motivated by existing q-DL methods, we integrate the sparsity of diffusion signals in the network design, which comprises two functional components. The first component computes sparse representation of diffusion signals for the low-resolution input patch, and the second component maps the low-resolution sparse representation to high-resolution tissue microstructure. The weights in the two components are learned jointly and the trained network performs end-to-end tissue microstructure estimation. In addition to SR-q-DL, we further propose probabilistic SR-q-DL, which can quantify the uncertainty of the network output as well as achieve improved estimation accuracy. In probabilistic SR-q-DL, a deep ensemble strategy is used. Specifically, the deep network for SR-q-DL is revised to produce not only tissue microstructure estimates but also the uncertainty of the estimates. Then, multiple deep networks are trained and their results are fused for the final prediction of high-resolution tissue microstructure and uncertainty quantification. The proposed method was evaluated on two independent datasets of brain dMRI scans. Results indicate that our approach outperforms competing methods in terms of estimation accuracy. In addition, uncertainty measures provided by our method correlate with estimation errors, which indicates potential application of the proposed uncertainty quantification method in brain studies.

Reproducibility of leftward planum temporale asymmetries in two genetically isolated populations of chimpanzees (Pan troglodytes)

Once considered a hallmark of human uniqueness, brain asymmetry has emerged as a feature shared with several other species, including chimpanzees, one of our closest living relatives. Most notable has been the discovery of asymmetries in homologues of cortical language areas in apes, particularly in the planum temporale (PT), considered a central node of the human language network. Several lines of evidence indicate a role for genetic mechanisms in the emergence of PT asymmetry; however, the genetic determinants of cerebral asymmetries have remained elusive. Studies in humans suggest that there is heritability of brain asymmetries of the PT, but this has not been explored to any extent in chimpanzees. Furthermore, the potential influence of non-genetic factors has raised questions about the reproducibility of earlier observations of PT asymmetry reported in chimpanzees. As such, the present study was aimed at examining both the heritability of phenotypic asymmetries in PT morphology, as well as their reproducibility. Using magnetic resonance imaging, we evaluated morphological asymmetries of PT surface area (mm²) and mean depth (mm) in captive chimpanzees (n = 291) derived from two genetically isolated populations. Our results confirm that chimpanzees exhibit a significant population-level leftward asymmetry for PT surface area, as well as significant heritability in the surface area and mean depth of the PT. These results conclusively demonstrate the existence of a leftward bias in PT asymmetry in chimpanzees and suggest that genetic mechanisms play a key role in the emergence of anatomical asymmetry in this region.

Brain Lateralization: A Comparative Perspective

Comparative studies on brain asymmetry date back to the 19th century but then largely disappeared due to the assumption that lateralization is uniquely human. Since the reemergence of this field in the 1970s, we learned that left-right differences of brain and behavior exist throughout the animal kingdom and pay off in terms of sensory, cognitive, and motor efficiency. Ontogenetically, lateralization starts in many species with asymmetrical expression patterns of genes within the Nodal cascade that set up the scene for later complex interactions of genetic, environmental, and epigenetic factors. These take effect during different time points of ontogeny and create asymmetries of neural networks in diverse species. As a result, depending on task demands, left- or right-hemispheric loops of feedforward or feedback projections are then activated and can temporarily dominate a neural process. In addition, asymmetries of commissural transfer can shape lateralized processes in each hemisphere. It is still unclear if interhemispheric interactions depend on an inhibition/excitation dichotomy or instead adjust the contralateral temporal neural structure to delay the other hemisphere or synchronize with it during joint action. As outlined in our review, novel animal models and approaches could be established in the last decades, and they already produced a substantial increase of knowledge. Since there is practically no realm of human perception, cognition, emotion, or action that is not affected by our lateralized neural organization, insights from these comparative studies are crucial to understand the functions and pathologies of our asymmetric brain.

Constructing an Axonal-Specific Myelin Developmental Graph and its Application to Childhood Absence Epilepsy

BACKGROUND AND PURPOSE

The process of myelination starts in utero around 20 weeks of gestation and continues through adulthood. We first set out to characterize the maturation of the tract-specific myelin content in healthy subjects from childhood (7-12 years) into adulthood (18-32 years). Second, we apply the resulting development graph to children with childhood absence epilepsy (CAE), a pediatric epilepsy that was previously characterized by changes in myelin content.

METHODS

In a prospective cross-sectional study, 15 healthy children (7-12 years), 14 healthy adult participants (18-32 years) and 17 children with a clinical diagnosis of CAE (6-12 years) were included. For each participant, diffusion weighted images were acquired to reconstruct bundles of white matter tracts and multi-echo multi-slice GRASE images were acquired for myelin-water estimation. Subsequently, a tract-specific myelin development graph was constructed using the percentual difference in myelin-water content from childhood (12 year) to adulthood (25 year).

RESULTS

The graph revealed myelination patterns, where tracts in the central regions myelinate prior to peripheral tracts and intra-hemispheric tracts as well as tracts in the left hemisphere myelinate prior to inter-hemispheric tracts and tracts in the right hemisphere, respectively. No significant differences were found in myelin-water content between children with CAE and healthy children for neither the early developing tracts, nor the tracts that develop in a later stage. However, the difference between the myelin-water of late and early developing tracts is significantly smaller in the children with CAE.

CONCLUSION

These results indicate that CAE is associated with widespread neurodevelopmental myelin differences.

In vivo assessment of the neural substrate linked with vocal imitation accuracy

Human speech and bird song are acoustically complex communication signals that are learned by imitation during a sensitive period early in life. Although the brain areas indispensable for speech and song learning are known, the neural circuits important for enhanced or reduced vocal performance remain unclear. By combining in vivo structural Magnetic Resonance Imaging with song analyses in juvenile male zebra finches during song learning and beyond, we reveal that song imitation accuracy correlates with the structural architecture of four distinct brain areas, none of which pertain to the song control system. Furthermore, the structural properties of a secondary auditory area in the left hemisphere, are capable to predict future song copying accuracy, already at the earliest stages of learning, before initiating vocal practicing. These findings appoint novel brain regions important for song learning outcome and inform that ultimate performance in part depends on factors experienced before vocal practicing.

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.

An improved deep network for tissue microstructure estimation with uncertainty quantification

Deep learning based methods have improved the estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients. These methods learn the mapping from diffusion signals in a voxel or patch to tissue microstructure measures. In particular, it is beneficial to exploit the sparsity of diffusion signals jointly in the spatial and angular domains, and the deep network can be designed by unfolding iterative processes that adaptively incorporate historical information for sparse reconstruction. However, the number of network parameters is huge in such a network design, which could increase the difficulty of network training and limit the estimation performance. In addition, existing deep learning based approaches to tissue microstructure estimation do not provide the important information about the uncertainty of estimates. In this work, we continue the exploration of tissue microstructure estimation using a deep network and seek to address these limitations. First, we explore the sparse representation of diffusion signals with a separable spatial-angular dictionary and design an improved deep network for tissue microstructure estimation. The procedure for updating the sparse code associated with the separable dictionary is derived and unfolded to construct the deep network. Second, with the formulation of sparse representation of diffusion signals, we propose to quantify the uncertainty of network outputs with a residual bootstrap strategy. Specifically, because of the sparsity constraint in the signal representation, we perform a Lasso bootstrap strategy for uncertainty quantification. Experiments were performed on brain dMRI scans with a reduced number of diffusion gradients, where the proposed method was applied to two representative biophysical models for describing tissue microstructure and compared with state-of-the-art methods of tissue microstructure estimation. The results show that our approach compares favorably with the competing methods in terms of estimation accuracy. In addition, the uncertainty measures provided by our method correlate with estimation errors and produce reasonable confidence intervals; these results suggest potential application of the proposed uncertainty quantification method in brain studies.

Schizotypy and altered hemispheric asymmetries: The role of cilia genes

Schizophrenia patients have a higher probability of altered structural and functional differences between the left and right hemisphere. Schizotypy as its nonclinical manifestation has been related to a higher incidence of non-right-handedness and atypical right-hemispheric language dominance. It has been suggested that genes involved in cilia function might link brain asymmetry and neurodevelopmental disorders. We assessed DNA methylation in the promoter regions of seven candidate genes involved in cilia function and psychiatric disorders from buccal cells and investigated their association with schizotypy and language lateralization in 60 healthy adults. Moreover, we determined microstructural properties of the planum temporale in a subsample of 52 subjects using neurite orientation dispersion and density imaging (NODDI). We found a significant association between schizotypy and DNA methylation in the AHI1 promoter region. Moreover, AHI1 DNA methylation significantly predicted language lateralization and asymmetry in estimated planum temporale neurite density. Finally, stronger leftward asymmetry in estimated neurite density was associated with a more pronounced right ear advantage (left hemisphere dominance) in the forced-right condition of the dichotic listening task, measuring attentional modulation of language lateralization. Our results are in line with a shared molecular basis of schizotypy and functional hemispheric asymmetries that is based on cilia function.

Genetic effects on planum temporale asymmetry and their limited relevance to neurodevelopmental disorders, intelligence or educational attainment

Previous studies have suggested that altered asymmetry of the planum temporale (PT) is associated with neurodevelopmental disorders, including dyslexia, schizophrenia, and autism. Shared genetic factors have been suggested to link PT asymmetry to these disorders. In a dataset of unrelated subjects from the general population (UK Biobank, N = 18,057), we found that PT volume asymmetry had a significant heritability of roughly 14%. In genome-wide association analysis, two loci were significantly associated with PT asymmetry, including a coding polymorphism within the gene ITIH5 that is predicted to affect the protein's function and to be deleterious (rs41298373, p = 2.01 × 10-15), and a locus that affects the expression of the genes BOK and DTYMK (rs7420166, p = 7.54 × 10-10). DTYMK showed left-right asymmetry of mRNA expression in post mortem PT tissue. Cortex-wide mapping of these SNP effects revealed influences on asymmetry that went somewhat beyond the PT. Using publicly available genome-wide association statistics from large-scale studies, we saw no significant genetic correlations of PT asymmetry with autism spectrum disorder, attention deficit hyperactivity disorder, schizophrenia, educational attainment or intelligence. Of the top two individual loci associated with PT asymmetry, rs41298373 showed a tentative association with intelligence (unadjusted p = .025), while the locus at BOK/DTYMK showed tentative association with educational attainment (unadjusted Ps < .05). These findings provide novel insights into the genetic contributions to human brain asymmetry, but do not support a substantial polygenic association of PT asymmetry with cognitive variation and mental disorders, as far as can be discerned with current sample sizes.

Neurite orientation dispersion and density imaging reveals white matter and hippocampal microstructure changes produced by Interleukin-6 in the TgCRND8 mouse model of amyloidosis

Extracellular β-amyloid (Aβ) plaque deposits and inflammatory immune activation are thought to alter various aspects of tissue microstructure, such as extracellular free water, fractional anisotropy and diffusivity, as well as the density and geometric arrangement of axonal processes. Quantifying these microstructural changes in Alzheimer's disease and related neurodegenerative dementias could serve to monitor or predict disease course. In the present study we used high-field diffusion magnetic resonance imaging (dMRI) to investigate the effects of Aβ and inflammatory interleukin-6 (IL6), alone or in combination, on in vivo tissue microstructure in the TgCRND8 mouse model of Alzheimer's-type Aβ deposition. TgCRND8 and non-transgenic (nTg) mice expressing brain-targeted IL6 or enhanced glial fibrillary protein (EGFP controls) were scanned at 8 months of age using a 2-shell, 54-gradient direction dMRI sequence at 11.1 T. Images were processed using the diffusion tensor imaging (DTI) model or the neurite orientation dispersion and density imaging (NODDI) model. DTI and NODDI processing in TgCRND8 mice revealed a microstructure pattern in white matter (WM) and hippocampus consistent with radial and longitudinal diffusivity deficits along with an increase in density and geometric complexity of axonal and dendritic processes. This included reduced FA, mean, axial and radial diffusivity, and increased orientation dispersion (ODI) and intracellular volume fraction (ICVF) measured in WM and hippocampus. IL6 produced a 'protective-like' effect on WM FA in TgCRND8 mice, observed as an increased FA that counteracted a reduction in FA observed with endogenous Aβ production and accumulation. In addition, we found that ICVF and ODI had an inverse relationship with the functional connectome clustering coefficient. The relationship between NODDI and graph theory metrics suggests that currently unknown microstructure alterations in WM and hippocampus are associated with diminished functional network organization in the brain.

Structural Asymmetry in the Frontal and Temporal Lobes Is Associated with PCSK6 VNTR Polymorphism

The nodal cascade influences the development of bodily asymmetries in humans and other vertebrates. The gene PCSK6 has shown a regulatory function during left-right axis formation and is therefore thought to influence bodily left-right asymmetries. However, it is not clear if variation in this gene is also associated with structural asymmetries in the brain. We genotyped an intronic 33bp PCSK6 variable number tandem repeat (VNTR) polymorphism that has been associated with handedness in a cohort of healthy adults. We acquired T1-weighted structural MRI images of 320 participants and defined cortical surface and thickness for each HCP region. The results demonstrate a significant association between PCSK6 VNTR genotypes and gray matter asymmetry in the superior temporal sulcus, which is involved in voice perception. Heterozygous individuals who carry a short (≤ 6 repeats) and a long (≥ 9 repeats) PCSK6 VNTR allele show stronger rightward asymmetry. Further associations were evident in the dorsolateral prefrontal cortex. Here, individuals homozygous for short alleles show a more pronounced asymmetry. This shows that PCSK6, a gene that has been implicated in the ontogenesis of bodily asymmetries by regulating the nodal cascade, is also relevant for structural asymmetries in the human brain.

A deep network for tissue microstructure estimation using modified LSTM units

Diffusion magnetic resonance imaging (dMRI) offers a unique tool for noninvasively assessing tissue microstructure. However, accurate estimation of tissue microstructure described by complicated signal models can be challenging when a reduced number of diffusion gradients are used. Deep learning based microstructure estimation has recently been developed and achieved promising results. In particular, optimization-based learning, where deep network structures are constructed by unfolding the iterative processes performed for solving optimization problems, has demonstrated great potential in accurate microstructure estimation with a reduced number of diffusion gradients. In this work, using the optimization-based learning strategy, we propose a deep network structure that is motivated by the use of historical information in iterative optimization for tissue microstructure estimation, and such incorporation of historical information has not been previously explored in the design of deep networks for microstructure estimation. We assume that (1) diffusion signals can be sparsely represented by a dictionary and its coefficients jointly in the spatial and angular domain, and (2) tissue microstructure can be computed from the sparse representation. Following these assumptions, our network comprises two cascaded stages. The first stage takes image patches as input and computes the spatial-angular sparse representation of the input with learned weights. Specifically, the network structure in the first stage is constructed by unfolding an iterative process for solving sparse reconstruction problems, where historical information is incorporated. The components in this network can be shown to correspond to modified long short-term memory (LSTM) units. In the second stage, fully connected layers are added to compute the mapping from the sparse representation to tissue microstructure. The weights in the two stages are learned jointly by minimizing the mean squared error of microstructure estimation. Experiments were performed on dMRI scans with a reduced number of diffusion gradients. For demonstration, we evaluated the estimation of tissue microstructure described by three signal models: the neurite orientation dispersion and density imaging (NODDI) model, the spherical mean technique (SMT) model, and the ensemble average propagator (EAP) model. The results indicate that the proposed approach outperforms competing methods.