A new species of Orthobula Simon, 1897 (Araneae: Trachelidae) from South America.

Orthobula Simon, 1897 is a group of very small litter-dwelling spiders with a tropical and subtropical distribution. The genus comprises 18 species, without any records in the Neotropical realm yet. Here we describe O. sudamericana sp. nov., distributed in Argentina and Paraguay. The new species appears to be most closely related to O. chayuensis Yang, Song and Zhu, 2003. The male also resembles O. charitonovi (Mikhailov, 1986). Orthobula sudamericana sp. nov. females differ from these species by the straight and parallel insemination ducts, and males by the centrally located, U-shaped sperm duct. Further, we summarize details on its natural history and habitat characteristics.

Intrinsic functional architecture of the human speech processing network.

Speech engages distributed temporo-fronto-parietal brain regions, however a comprehensive understanding of its intrinsic functional network architecture is lacking. Here we investigate the human speech processing network using the largest sample to date, high temporal resolution resting-state fMRI data, network stability analysis, and theoretically informed models. Network consensus analysis revealed three stable functional modules encompassing: (1) superior temporal plane (STP) and Area Spt, (2) superior temporal sulcus (STS) + ventral frontoparietal cortex, and (3) dorsal frontoparietal cortex. The STS + ventral frontoparietal cortex module showed the highest participation coefficient, and a hub-like organization linking STP with frontoparietal cortical nodes. Node-wise analysis revealed key connectivity features underlying this modular architecture, including a leftward asymmetric connectivity profile, and differential connectivity of STS and STP, with frontoparietal cortex. Our findings, replicated across cohorts, reveal a tripartite functional network architecture supporting speech processing and provide a novel template for future studies.

Cortical Observation by Synchronous Multifocal Optical Sampling Reveals Widespread Population Encoding of Actions.

To advance the measurement of distributed neuronal population representations of targeted motor actions on single trials, we developed an optical method (COSMOS) for tracking neural activity in a largely uncharacterized spatiotemporal regime. COSMOS allowed simultaneous recording of neural dynamics at ∼30 Hz from over a thousand near-cellular resolution neuronal sources spread across the entire dorsal neocortex of awake, behaving mice during a three-option lick-to-target task. We identified spatially distributed neuronal population representations spanning the dorsal cortex that precisely encoded ongoing motor actions on single trials. Neuronal correlations measured at video rate using unaveraged, whole-session data had localized spatial structure, whereas trial-averaged data exhibited widespread correlations. Separable modes of neural activity encoded history-guided motor plans, with similar population dynamics in individual areas throughout cortex. These initial experiments illustrate how COSMOS enables investigation of large-scale cortical dynamics and that information about motor actions is widely shared between areas, potentially underlying distributed computations.

The visual word form area (VWFA) is part of both language and attention circuitry.

While predominant models of visual word form area (VWFA) function argue for its specific role in decoding written language, other accounts propose a more general role of VWFA in complex visual processing. However, a comprehensive examination of structural and functional VWFA circuits and their relationship to behavior has been missing. Here, using high-resolution multimodal imaging data from a large Human Connectome Project cohort (N = 313), we demonstrate robust patterns of VWFA connectivity with both canonical language and attentional networks. Brain-behavior relationships revealed a striking pattern of double dissociation: structural connectivity of VWFA with lateral temporal language network predicted language, but not visuo-spatial attention abilities, while VWFA connectivity with dorsal fronto-parietal attention network predicted visuo-spatial attention, but not language abilities. Our findings support a multiplex model of VWFA function characterized by distinct circuits for integrating language and attention, and point to connectivity-constrained cognition as a key principle of human brain organization.

A new species and two new records of the ground spider genus Apopyllus (Araneae, Gnaphosidae) from Paraguay.

The family Gnaphosidae consist of 158 genera and 2530 species worldwide. In South America there are 35 genera (World Spider Catalog 2019) considering Apopyllus Platnick Shadab, 1984, a small genus of ground hunting spiders (Cardoso et al. 2011) that includes ten American species, of which eight have been recorded from southern South America. Their known distribution ranges from southern Mexico through Colombia, Bolivia, Perú, Chile, Brazil and Argentina (World Spider Catalog 2019). In Paraguay, the genus was first mentioned in an invertebrate checklist (see Kochalka et al. 1996). First studies on the genus and its taxonomic placement were made by Platnick Shadab (1984), and more recently a revision of the genus was undertaken by Azevedo et al. (2016), including four new Brazilian species. Even though the external appearance of Apopyllus is similar to other gnaphosids, specifically taking into consideration the Echemus group, spiders with plain colored abdomens, sometimes presenting chevrons in the opisthosoma, and with developed scutum in males (Murphy 2007), females and males can be differentiated by the more elaborated and intricated genitalia structures (see Azevededo et al. 2016). Apopyllus is most similar to Apodrassodes Vellard, 1924 both having a similar elongate embolus (Fig. 3e) and a membranous tegular extension (Fig. 3d) (Platnick Shadab 1984), and to the genera Nopyllus Ott, 2014, but differ from the later by the presence of a scutum (Fig. 3a) in males and by the presence of a median apophysis (Fig. 3e) on the bulb (Ott, 2014).

Impaired voice processing in reward and salience circuits predicts social communication in children with autism.

Engaging with vocal sounds is critical for children's social-emotional learning, and children with autism spectrum disorder (ASD) often 'tune out' voices in their environment. Little is known regarding the neurobiological basis of voice processing and its link to social impairments in ASD. Here, we perform the first comprehensive brain network analysis of voice processing in children with ASD. We examined neural responses elicited by unfamiliar voices and mother's voice, a biologically salient voice for social learning, and identified a striking relationship between social communication abilities in children with ASD and activation in key structures of reward and salience processing regions. Functional connectivity between voice-selective and reward regions during voice processing predicted social communication in children with ASD and distinguished them from typically developing children. Results support the Social Motivation Theory of ASD by showing reward system deficits associated with the processing of a critical social stimulus, mother's voice, in children with ASD. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that minor issues remain unresolved (see decision letter).

Deficits in mesolimbic reward pathway underlie social interaction impairments in children with autism.

Lack of interest in social interaction is a hallmark of autism spectrum disorder. Animal studies have implicated the mesolimbic reward pathway in driving and reinforcing social behaviour, but little is known about the integrity of this pathway and its behavioural consequences in children with autism spectrum disorder. Here we test the hypothesis that the structural and functional integrity of the mesolimbic reward pathway is aberrant in children with autism spectrum disorder, and these aberrancies contribute to the social interaction impairments. We examine structural and functional connectivity of the mesolimbic reward pathway in two independent cohorts totalling 82 children aged 7-13 years with autism spectrum disorder and age-, gender-, and intelligence quotient-matched typically developing children (primary cohort: children with autism spectrum disorder n = 24, typically developing children n = 24; replication cohort: children with autism spectrum disorder n = 17, typically developing children n = 17), using high angular resolution diffusion-weighted imaging and functional MRI data. We reliably identify white matter tracts linking-the nucleus accumbens and the ventral tegmental area-key subcortical nodes of the mesolimbic reward pathway, and provide reproducible evidence for structural aberrations in these tracts in children with autism spectrum disorder. Further, we show that structural aberrations are accompanied by aberrant functional interactions between nucleus accumbens and ventral tegmental area in response to social stimuli. Crucially, we demonstrate that both structural and functional circuit aberrations in the mesolimbic reward pathway are related to parent-report measures of social interaction impairments in affected children. Our findings, replicated across two independent cohorts, reveal that deficits in the mesolimbic reward pathway contribute to impaired social skills in childhood autism, and provide fundamental insights into neurobiological mechanisms underlying reduced social interest in humans.

Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition.

Human cognition is influenced not only by external task demands but also latent mental processes and brain states that change over time. Here, we use novel Bayesian switching dynamical systems algorithm to identify hidden brain states and determine that these states are only weakly aligned with external task conditions. We compute state transition probabilities and demonstrate how dynamic transitions between hidden states allow flexible reconfiguration of functional brain circuits. Crucially, we identify latent transient brain states and dynamic functional circuits that are optimal for cognition and show that failure to engage these states in a timely manner is associated with poorer task performance and weaker decision-making dynamics. We replicate findings in a large sample (N = 122) and reveal a robust link between cognition and flexible latent brain state dynamics. Our study demonstrates the power of switching dynamical systems models for investigating hidden dynamic brain states and functional interactions underlying human cognition.

Mechanisms of interactive specialization and emergence of functional brain circuits supporting cognitive development in children.

Cognitive development is thought to depend on the refinement and specialization of functional circuits over time, yet little is known about how this process unfolds over the course of childhood. Here we investigated growth trajectories of functional brain circuits and tested an interactive specialization model of neurocognitive development which posits that the refinement of task-related functional networks is driven by a shared history of co-activation between cortical regions. We tested this model in a longitudinal cohort of 30 children with behavioral and task-related functional brain imaging data at multiple time points spanning childhood and adolescence, focusing on the maturation of parietal circuits associated with numerical problem solving and learning. Hierarchical linear modeling revealed selective strengthening as well as weakening of functional brain circuits. Connectivity between parietal and prefrontal cortex decreased over time, while connectivity within posterior brain regions, including intra-hemispheric and inter-hemispheric parietal connectivity, as well as parietal connectivity with ventral temporal occipital cortex regions implicated in quantity manipulation and numerical symbol recognition, increased over time. Our study provides insights into the longitudinal maturation of functional circuits in the human brain and the mechanisms by which interactive specialization shapes children's cognitive development and learning.

Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling.

Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of interactions between salience (SN), default mode (DMN), and central executive (CEN) networks-three brain systems that play a critical role in human cognition. In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN, CEN, and DMN. Furthermore, the three "static" networks occurred in a segregated state only intermittently. Findings were replicated in two adult cohorts from the Human Connectome Project. VB-HMM further revealed immature dynamic interactions between SN, CEN, and DMN in children, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our computational techniques provide new insights into human brain network dynamics and its maturation with development.

Neural circuits underlying mother's voice perception predict social communication abilities in children.

The human voice is a critical social cue, and listeners are extremely sensitive to the voices in their environment. One of the most salient voices in a child's life is mother's voice: Infants discriminate their mother's voice from the first days of life, and this stimulus is associated with guiding emotional and social function during development. Little is known regarding the functional circuits that are selectively engaged in children by biologically salient voices such as mother's voice or whether this brain activity is related to children's social communication abilities. We used functional MRI to measure brain activity in 24 healthy children (mean age, 10.2 y) while they attended to brief (<1 s) nonsense words produced by their biological mother and two female control voices and explored relationships between speech-evoked neural activity and social function. Compared to female control voices, mother's voice elicited greater activity in primary auditory regions in the midbrain and cortex; voice-selective superior temporal sulcus (STS); the amygdala, which is crucial for processing of affect; nucleus accumbens and orbitofrontal cortex of the reward circuit; anterior insula and cingulate of the salience network; and a subregion of fusiform gyrus associated with face perception. The strength of brain connectivity between voice-selective STS and reward, affective, salience, memory, and face-processing regions during mother's voice perception predicted social communication skills. Our findings provide a novel neurobiological template for investigation of typical social development as well as clinical disorders, such as autism, in which perception of biologically and socially salient voices may be impaired.

Multivariate dynamical systems-based estimation of causal brain interactions in fMRI: Group-level validation using benchmark data, neurophysiological models and human connectome project data.

BACKGROUND: Causal estimation methods are increasingly being used to investigate functional brain networks in fMRI, but there are continuing concerns about the validity of these methods. NEW METHOD: Multivariate dynamical systems (MDS) is a state-space method for estimating dynamic causal interactions in fMRI data. Here we validate MDS using benchmark simulations as well as simulations from a more realistic stochastic neurophysiological model. Finally, we applied MDS to investigate dynamic casual interactions in a fronto-cingulate-parietal control network using human connectome project (HCP) data acquired during performance of a working memory task. Crucially, since the ground truth in experimental data is unknown, we conducted novel stability analysis to determine robust causal interactions within this network. RESULTS: MDS accurately recovered dynamic causal interactions with an area under receiver operating characteristic (AUC) above 0.7 for benchmark datasets and AUC above 0.9 for datasets generated using the neurophysiological model. In experimental fMRI data, bootstrap procedures revealed a stable pattern of causal influences from the anterior insula to other nodes of the fronto-cingulate-parietal network. COMPARISON WITH EXISTING METHODS: MDS is effective in estimating dynamic causal interactions in both the benchmark and neurophysiological model based datasets in terms of AUC, sensitivity and false positive rates. CONCLUSIONS: Our findings demonstrate that MDS can accurately estimate causal interactions in fMRI data. Neurophysiological models and stability analysis provide a general framework for validating computational methods designed to estimate causal interactions in fMRI. The right anterior insula functions as a causal hub during working memory.

Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions.

State-space multivariate dynamical systems (MDS) (Ryali et al. 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods are poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort, optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in fMRI. More generally, our study demonstrates that the combined use of optogenetics and fMRI provides a powerful new tool for evaluating computational methods designed to estimate causal interactions between distributed brain regions.

Parietal hyper-connectivity, aberrant brain organization, and circuit-based biomarkers in children with mathematical disabilities.

Mathematical disabilities (MD) have a negative life-long impact on professional success, employment, and health outcomes. Yet little is known about the intrinsic functional brain organization that contributes to poor math skills in affected children. It is now increasingly recognized that math cognition requires coordinated interaction within a large-scale fronto-parietal network anchored in the intraparietal sulcus (IPS). Here we characterize intrinsic functional connectivity within this IPS-network in children with MD, relative to a group of typically developing (TD) children who were matched on age, gender, IQ, working memory, and reading abilities. Compared to TD children, children with MD showed hyper-connectivity of the IPS with a bilateral fronto-parietal network. Importantly, aberrant IPS connectivity patterns accurately discriminated children with MD and TD children, highlighting the possibility for using IPS connectivity as a brain-based biomarker of MD. To further investigate regional abnormalities contributing to network-level deficits in children with MD, we performed whole-brain analyses of intrinsic low-frequency fluctuations. Notably, children with MD showed higher low-frequency fluctuations in multiple fronto-parietal areas that overlapped with brain regions that exhibited hyper-connectivity with the IPS. Taken together, our findings suggest that MD in children is characterized by robust network-level aberrations, and is not an isolated dysfunction of the IPS. We hypothesize that intrinsic hyper-connectivity and enhanced low-frequency fluctuations may limit flexible resource allocation, and contribute to aberrant recruitment of task-related brain regions during numerical problem solving in children with MD.

Insula response and connectivity during social and non-social attention in children with autism.

Autism spectrum disorder (ASD) is characterized by reduced attention to salient social stimuli. Here, we use two visual oddball tasks to investigate brain systems engaged during attention to social (face) and non-social (scene) stimuli. We focused on the dorsal and ventral subdivisions of the anterior insula (dAI and vAI, respectively), anatomically distinct regions contributing to a 'salience network' that is known to regulate attention to behaviorally meaningful stimuli. Children with ASD performed comparably to their typically developing (TD) peers, but they engaged the right dAI and vAI differently in response to deviant faces compared with deviant scenes. Multivariate activation patterns in the dAI reliably discriminated between children with ASD and TD children with 85% classification accuracy, and children with ASD activated the vAI more than their TD peers. Children with ASD and their TD peers also differed in dAI connectivity patterns to deviant faces, with stronger within-salience network interactions in the ASD group and stronger cross-network interactions in the TD group. Our findings point to atypical patterns of right anterior insula activation and connectivity in ASD and suggest that multiple functions subserved by the insula, including attention and affective processing of salient social stimuli, are aberrant in children with the disorder.

Causal Interactions Within a Frontal-Cingulate-Parietal Network During Cognitive Control: Convergent Evidence from a Multisite-Multitask Investigation.

Cognitive control plays an important role in goal-directed behavior, but dynamic brain mechanisms underlying it are poorly understood. Here, using multisite fMRI data from over 100 participants, we investigate causal interactions in three cognitive control tasks within a core Frontal-Cingulate-Parietal network. We found significant causal influences from anterior insula (AI) to dorsal anterior cingulate cortex (dACC) in all three tasks. The AI exhibited greater net causal outflow than any other node in the network. Importantly, a similar pattern of causal interactions was uncovered by two different computational methods for causal analysis. Furthermore, the strength of causal interaction from AI to dACC was greater on high, compared with low, cognitive control trials and was significantly correlated with individual differences in cognitive control abilities. These results emphasize the importance of the AI in cognitive control and highlight its role as a causal hub in the Frontal-Cingulate-Parietal network. Our results further suggest that causal signaling between the AI and dACC plays a fundamental role in implementing cognitive control and are consistent with a two-stage cognitive control model in which the AI first detects events requiring greater access to cognitive control resources and then signals the dACC to execute load-specific cognitive control processes.

Brain Structural Integrity and Intrinsic Functional Connectivity Forecast 6 Year Longitudinal Growth in Children's Numerical Abilities.

Early numerical proficiency lays the foundation for acquiring quantitative skills essential in today's technological society. Identification of cognitive and brain markers associated with long-term growth of children's basic numerical computation abilities is therefore of utmost importance. Previous attempts to relate brain structure and function to numerical competency have focused on behavioral measures from a single time point. Thus, little is known about the brain predictors of individual differences in growth trajectories of numerical abilities. Using a longitudinal design, with multimodal imaging and machine-learning algorithms, we investigated whether brain structure and intrinsic connectivity in early childhood are predictive of 6 year outcomes in numerical abilities spanning childhood and adolescence. Gray matter volume at age 8 in distributed brain regions, including the ventrotemporal occipital cortex (VTOC), the posterior parietal cortex, and the prefrontal cortex, predicted longitudinal gains in numerical, but not reading, abilities. Remarkably, intrinsic connectivity analysis revealed that the strength of functional coupling among these regions also predicted gains in numerical abilities, providing novel evidence for a network of brain regions that works in concert to promote numerical skill acquisition. VTOC connectivity with posterior parietal, anterior temporal, and dorsolateral prefrontal cortices emerged as the most extensive network predicting individual gains in numerical abilities. Crucially, behavioral measures of mathematics, IQ, working memory, and reading did not predict children's gains in numerical abilities. Our study identifies, for the first time, functional circuits in the human brain that scaffold the development of numerical skills, and highlights potential biomarkers for identifying children at risk for learning difficulties. SIGNIFICANCE STATEMENT: Children show substantial individual differences in math abilities and ease of math learning. Early numerical abilities provide the foundation for future academic and professional success in an increasingly technological society. Understanding the early identification of poor math skills has therefore taken on great significance. This work provides important new insights into brain structure and connectivity measures that can predict longitudinal growth of children's math skills over a 6 year period, and may eventually aid in the early identification of children who might benefit from targeted interventions.

Sex differences in cortical volume and gyrification in autism.

BACKGROUND: Male predominance is a prominent feature of autism spectrum disorders (ASD), with a reported male to female ratio of 4:1. Because of the overwhelming focus on males, little is known about the neuroanatomical basis of sex differences in ASD. Investigations of sex differences with adequate sample sizes are critical for improving our understanding of the biological mechanisms underlying ASD in females. METHODS: We leveraged the open-access autism brain imaging data exchange (ABIDE) dataset to obtain structural brain imaging data from 53 females with ASD, who were matched with equivalent samples of males with ASD, and their typically developing (TD) male and female peers. Brain images were processed with FreeSurfer to assess three key features of local cortical morphometry: volume, thickness, and gyrification. A whole-brain approach was used to identify significant effects of sex, diagnosis, and sex-by-diagnosis interaction, using a stringent threshold of p < 0.01 to control for false positives. Stability and power analyses were conducted to guide future research on sex differences in ASD. RESULTS: We detected a main effect of sex in the bilateral superior temporal cortex, driven by greater cortical volume in females compared to males in both the ASD and TD groups. Sex-by-diagnosis interaction was detected in the gyrification of the ventromedial/orbitofrontal prefrontal cortex (vmPFC/OFC). Post-hoc analyses revealed that sex-by-diagnosis interaction was driven by reduced vmPFC/OFC gyrification in males with ASD, compared to females with ASD as well as TD males and females. Finally, stability analyses demonstrated a dramatic drop in the likelihood of observing significant clusters as the sample size decreased, suggesting that previous studies have been largely underpowered. For instance, with a sample of 30 females with ASD (total n = 120), a significant sex-by-diagnosis interaction was only detected in 50 % of the simulated subsamples. CONCLUSIONS: Our results demonstrate that some features of typical sex differences are preserved in the brain of individuals with ASD, while others are not. Sex differences in ASD are associated with cortical regions involved in language and social function, two domains of deficits in the disorder. Stability analyses provide novel quantitative insights into why smaller samples may have previously failed to detect sex differences.

Role of the anterior insular cortex in integrative causal signaling during multisensory auditory-visual attention.

Coordinated attention to information from multiple senses is fundamental to our ability to respond to salient environmental events, yet little is known about brain network mechanisms that guide integration of information from multiple senses. Here we investigate dynamic causal mechanisms underlying multisensory auditory-visual attention, focusing on a network of right-hemisphere frontal-cingulate-parietal regions implicated in a wide range of tasks involving attention and cognitive control. Participants performed three 'oddball' attention tasks involving auditory, visual and multisensory auditory-visual stimuli during fMRI scanning. We found that the right anterior insula (rAI) demonstrated the most significant causal influences on all other frontal-cingulate-parietal regions, serving as a major causal control hub during multisensory attention. Crucially, we then tested two competing models of the role of the rAI in multisensory attention: an 'integrated' signaling model in which the rAI generates a common multisensory control signal associated with simultaneous attention to auditory and visual oddball stimuli versus a 'segregated' signaling model in which the rAI generates two segregated and independent signals in each sensory modality. We found strong support for the integrated, rather than the segregated, signaling model. Furthermore, the strength of the integrated control signal from the rAI was most pronounced on the dorsal anterior cingulate and posterior parietal cortices, two key nodes of saliency and central executive networks respectively. These results were preserved with the addition of a superior temporal sulcus region involved in multisensory processing. Our study provides new insights into the dynamic causal mechanisms by which the AI facilitates multisensory attention.