Responses to song playback differ in sleeping versus anesthetized songbirds

Vocal learning in songbirds is mediated by a highly localized system of interconnected forebrain regions, including recurrent loops that traverse the cortex, basal ganglia, and thalamus. This brain-behavior system provides a powerful model for elucidating mechanisms of vocal learning, with implications for learning speech in human infants, as well as for advancing our understanding of skill learning in general. A long history of experiments in this area has tested neural responses to playback of different song stimuli in anesthetized birds at different stages of vocal development. These studies have demonstrated selectivity for different song types that provide neural signatures of learning. In contrast to the ease of obtaining responses to song playback in anesthetized birds, song-evoked responses in awake birds are greatly reduced or absent, indicating that behavioral state is an important determinant of neural responsivity. Song-evoked responses can be elicited in sleeping as well as anesthetized zebra finches, and the selectivity of responses to song playback in adult birds tends to be highly similar between anesthetized and sleeping states, encouraging the idea that anesthesia and sleep are highly similar. In contrast to that idea, we report evidence that cortical responses to song playback in juvenile zebra finches (Taeniopygia guttata) differ greatly between sleep and urethane anesthesia. This finding indicates that behavioral states differ in sleep versus anesthesia and raises questions about relationships between developmental changes in sleep activity, selectivity for different song types, and the neural substrate for vocal learning.

A longitudinal neuroimaging dataset on language processing in children ages 5, 7, and 9 years old

This dataset examines language development with a longitudinal design and includes diffusion- and T1-weighted structural magnetic resonance imaging (MRI), task-based functional MRI (fMRI), and a battery of psycho-educational assessments and parental questionnaires. We collected data from 5.5-6.5-year-old children (ses-5) and followed them up when they were 7-8 years old (ses-7) and then again at 8.5-10 years old (ses-9). To increase the sample size at the older time points, another cohort of 7-8-year-old children (ses-7) were recruited and followed up when they were 8.5–10 years old (ses-9). In total, 322 children who completed at least one structural and functional scan were included. Children performed four fMRI tasks consisting of two word-level tasks examining phonological and semantic processing and two sentence-level tasks investigating semantic and syntactic processing. The MRI data is valuable for examining changes over time in interactive specialization due to the use of multiple imaging modalities and tasks in this longitudinal design. In addition, the extensive psycho-educational assessments and questionnaires provide opportunities to explore brain-behavior and brain-environment associations.

Algebra dissociates from arithmetic in the brain semantic network

Background Mathematical expressions mainly include arithmetic (such as 8 − (1 + 3)) and algebra (such as a − (b + c)). Previous studies have shown that both algebraic processing and arithmetic involved the bilateral parietal brain regions. Although previous studies have revealed that algebra was dissociated from arithmetic, the neural bases of the dissociation between algebraic processing and arithmetic is still unclear. The present study uses functional magnetic resonance imaging (fMRI) to identify the specific brain networks for algebraic and arithmetic processing. Methods Using fMRI, this study scanned 30 undergraduates and directly compared the brain activation during algebra and arithmetic. Brain activations, single-trial (item-wise) interindividual correlation and mean-trial interindividual correlation related to algebra processing were compared with those related to arithmetic. The functional connectivity was analyzed by a seed-based region of interest (ROI)-to-ROI analysis. Results Brain activation analyses showed that algebra elicited greater activation in the angular gyrus and arithmetic elicited greater activation in the bilateral supplementary motor area, left insula, and left inferior parietal lobule. Interindividual single-trial brain-behavior correlation revealed significant brain-behavior correlations in the semantic network, including the middle temporal gyri, inferior frontal gyri, dorsomedial prefrontal cortices, and left angular gyrus, for algebra. For arithmetic, the significant brain-behavior correlations were located in the phonological network, including the precentral gyrus and supplementary motor area, and in the visuospatial network, including the bilateral superior parietal lobules. For algebra, significant positive functional connectivity was observed between the visuospatial network and semantic network, whereas for arithmetic, significant positive functional connectivity was observed only between the visuospatial network and phonological network. Conclusion These findings suggest that algebra relies on the semantic network and conversely, arithmetic relies on the phonological and visuospatial networks.

Individual coactivation patterns improve the subject identification and their behavior association

Brain states can be characterized by recurring coactivation patterns (CAPs). Traditional CAP analysis is performed at the group-level, while the human brain is individualized and the functional connectome has shown the uniqueness as fingerprint. Whether stable individual CAPs could be obtained from a single fMRI scan and could individual CAPs improve the identification is unclear. An open dataset, the midnight scan club was used in this study to answer these questions. Four CAP states were identified at three distinct levels (group-, subject- and scan-level) separately, and the CAPs were then reconstructed for each scan. Identification rate and differential identifiability were used to evaluate the identification performance. Our results demonstrated that the individual CAPs were unstable when using a single scan. By maintaining high intra-subject similarity and inter-subject differences, subject-level CAPs achieved the best identification performance. Brain regions that contributed to the identifiability were mainly located in higher-order networks (e.g., frontal-parietal network). Besides, head motion reduced the intra-subject similarity, while its impact on identification rate was non-significant. Finally, a pipeline was developed to depict brain-behavior associations in dataset with few samples but dense sampling, and individualized CAP dynamics showed an above-chance level correlation with IQ.

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.

Dynamic Resting State Motor Network Connectivity of Neurotypical Children, the Groundwork for Network-Guided Therapy in Childhood Movement Disorders

BackgroundNormative childhood motor network resting-state fMRI effective connectivity is undefined, yet necessary for translatable dynamic resting-state network informed treatments in pediatric movement disorders.MethodCross-spectral dynamic causal modelling of resting-state fMRI was investigated in 19 neurotypically developing 5-7-year-old children. Fully connected six-node motor network models were created for each hemisphere including primary motor cortex, striatum, subthalamic nucleus, globus pallidus internus, thalamus, and contralateral cerebellum. Parametric Empirical Bayes with exhaustive Bayesian model reduction and Bayesian modeling averaging were used to create a group model for each hemisphere; Purdue Pegboard Test (PPBT) scores for relevant hand motor behavior were also entered as a covariate at the group level to determine the brain-behavior relationship.ResultsOverall, the resting-state functional MRI effective connectivity of motor cortico-basal ganglia-cerebellar networks was similar across hemispheres, with greater connectivity in the left hemisphere. The motor network effective connectivity relationships between the nodes were consistent and robust across subjects. Additionally, the PPBT score for each hand was positively correlated with the thalamus to contralateral cerebellum connection.DiscussionThe normative effective connectivity from resting-state functional MRI in children largely reflect the direction of inter-nodal signal predicted by other prior modalities and was consistent and robust across subjects, with differences from these prior task-dependent modalities that likely reflect the motor rest-action state during acquisition. Effective connectivity of the motor network was correlated with motor behavior, indicating effective connectivity brain-behavior relationship has physiological meaning in the normally developing. Thus, it may be helpful for future studies in children with movement disorders, wherein comparison to normative effective connectivity will be critical for network-targeted intervention.Impact StatementThis is the first study to use pediatric resting-state functional MRI to create a normative effective connectivity model of the motor network and to also show correlation with behavior, which may have therapeutic implications for children with movement disorders.

Connectivity of the Superior Longitudinal Fasciculus II and Executive Function in Children with ADHD

Attention-deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder marked by inattention, hyperactivity, and impulsiveness. The importance of the Superior Longitudinal Fasciculus II (SLFII), a white matter tract connecting the frontal and parietal regions, to developing executive function has not been established in children who typically have executive function deficits. The present study explored the association between diffusion properties of the SLFII and developing executive function in young children diagnosed with ADHD. A dataset with the performance on the NIH Toolbox Card Sort and the NIH Toolbox Flanker, two executive function tasks, in 59 4-7-year-old children was used, and diffusion-weighted magnetic resonance imaging scans were used to quantify the white matter properties of the bilateral SLFII. The results showed that there was an association between age and performance on the Flanker and Card Sort tasks. As age increased, performance also improved. There was no significant association between white matter properties of the SLFII, birth sex, and the Card Sort and Flanker tasks. As the first study to explore this association in children of this age with ADHD, this result was unexpected suggesting that it may be the case that it is too early in development to detect a strong association. The findings inform contemporary and future investigations into the brain-behavior relations between SLFII and executive function in children with ADHD. With further research, neuroimaging could become a potential diagnostic biomarker for predicting executive function impairments and ADHD symptoms in young children, potentially altering treatment outcomes.