White-matter structure in the right hemisphere predicts Mandarin Chinese learning success

AbstractSecond language learning becomes increasingly difficult with age, but some adults learn more successfully than others. We examined whether inter-subject variability in the microstructure of white matter pathways, as measured by diffusion tensor imaging (DTI), would predict native English speakers' outcomes in learning Mandarin Chinese. Twenty-one adults were scanned before participating in an intensive 4-week Mandarin course. At the end of the Mandarin course, participants completed a final exam that assessed their skills in both spoken and written Mandarin. Individual participants' white-matter tracts were reconstructed from their native DTI data and related to final-exam performance. Superior language learning was correlated with DTI measures in the right hemisphere, but not in the left hemisphere. In particular, greater initial fractional anisotropy (FA) in both the right superior longitudinal fasciculus (parietal bundle) and the right inferior longitudinal fasciculus was associated with more successful Mandarin learning. The relation between white-matter structure in the right hemisphere of native English speakers and successful initial language learning may reflect the tonal and visuo-spatial properties, respectively, of spoken and written Mandarin Chinese

Projective Ribbon Permutation Statistics: a Remnant of non-Abelian Braiding in Higher Dimensions

In a recent paper, Teo and Kane proposed a 3D model in which the defects support Majorana fermion zero modes. They argued that exchanging and twisting these defects would implement a set R of unitary transformations on the zero mode Hilbert space which is a 'ghostly' recollection of the action of the braid group on Ising anyons in 2D. In this paper, we find the group T_{2n} which governs the statistics of these defects by analyzing the topology of the space K_{2n} of configurations of 2n defects in a slowly spatially-varying gapped free fermion Hamiltonian: T_{2n}\equiv {\pi_1}(K_{2n})$. We find that the group T_{2n}= Z \times T^r_{2n}, where the 'ribbon permutation group' T^r_{2n} is a mild enhancement of the permutation group S_{2n}: T^r_{2n} \equiv \Z_2 \times E((Z_2)^{2n}\rtimes S_{2n}). Here, E((Z_2)^{2n}\rtimes S_{2n}) is the 'even part' of (Z_2)^{2n} \rtimes S_{2n}, namely those elements for which the total parity of the element in (Z_2)^{2n} added to the parity of the permutation is even. Surprisingly, R is only a projective representation of T_{2n}, a possibility proposed by Wilczek. Thus, Teo and Kane's defects realize `Projective Ribbon Permutation Statistics', which we show to be consistent with locality. We extend this phenomenon to other dimensions, co-dimensions, and symmetry classes. Since it is an essential input for our calculation, we review the topological classification of gapped free fermion systems and its relation to Bott periodicity.Comment: Missing figures added. Fixed some typos. Added a paragraph to the conclusio

Shared Neuroanatomical Substrates of Impaired Phonological Working Memory Across Reading Disability and Autism

Background Individuals with reading disability and individuals with autism spectrum disorder (ASD) are characterized, respectively, by their difficulties in reading and social communication, but both groups often have impaired phonological working memory (PWM). It is not known whether the impaired PWM reflects distinct or shared neuroanatomical abnormalities in these two diagnostic groups. Methods White-matter structural connectivity via diffusion weighted imaging was examined in 64 children, age 5 to 17 years, with reading disability, ASD, or typical development, who were matched on age, gender, intelligence, and diffusion data quality. Results Children with reading disability and children with ASD exhibited reduced PWM compared with children with typical development. The two diagnostic groups showed altered white matter microstructure in the temporoparietal portion of the left arcuate fasciculus and in the occipitotemporal portion of the right inferior longitudinal fasciculus (ILF), as indexed by reduced fractional anisotropy and increased radial diffusivity. Moreover, the structural integrity of the right ILF was positively correlated with PWM ability in the two diagnostic groups but not in the typically developing group. Conclusions These findings suggest that impaired PWM is transdiagnostically associated with shared neuroanatomical abnormalities in ASD and reading disability. Microstructural characteristics in left arcuate fasciculus and right ILF may play important roles in the development of PWM. The right ILF may support a compensatory mechanism for children with impaired PWM

O10.5. ABNORMAL MODULAR ORGANIZATION OF THE FUNCTIONAL CONNECTOME PREDICTS CONVERSION TO PSYCHOSIS IN CLINICAL HIGH-RISK YOUTH

Abstract Background: The first episode of schizophrenia is typically preceded by a prodromal phase characterized by sub-threshold symptoms and declining functioning. Elucidating the neurobiological substrate of prodromal symptoms that progress into overt psychotic illness is crucial to the development of early detection and intervention strategies for schizophrenia. In this study, we performed a functional connectome analysis in a large group of adolescents and young adults at Clinical High Risk (CHR) for schizophrenia. We aim to assess whether, and if so how, baseline connectome organization distinguishes CHR youth that go on to develop psychosis. Methods: This study comprises a total of 251 subjects, including 158 psychotropically-naïve CHR subjects (CHRs) and 93 healthy controls (HCs), who were matched to CHRs on age, gender, and level of education. Prodromal symptoms and cognition were assessed using the SIPS structured interview and MATRICS cognitive battery. Anatomical T1 MRI and resting-state fMRI scans were collected at baseline and processed using Freesurfer v6.0 and CONN v17.d software. For each subject, a functional connectome map was reconstructed consisting of 162 nodes representing 148 cortical regions from the Destrieux atlas and 14 subcortical structures. Functional connectomes were analyzed in terms of modular topology using the Louvain community detection method. Modular network partitions of individual CHRs were compared to a group-averaged HC network using the rand similarity coefficient (SR), providing a measure of the level of (ab)normality of the CHRs’ modular partitions. Analysis of covariance (correcting for age- and gender) was used to compare SR levels between CHRs who developed psychosis during follow-up (CHR+; N = 23) as compared to CHRs who did not develop psychosis (CHR-; N = 135). Kaplan-Meier analysis was used to estimate psychosis-free survival functions for CHRs with below- versus above-average SR, which were compared using log-rank tests. Cox regression analysis was used to assess how baseline connectome organization and clinical measures (i.e., demographics, symptoms, IQ) predicted time to conversion. Results: Modular community detection in HCs yielded five major modules including a posterior ‘visual’, central ‘sensorimotor’, medial frontoparietal ‘default-mode’, lateral frontoparietal ‘central-executive’, and inferior ‘limbic’ module. Modular connectome organization of CHR+ was significantly less similar to HCs than CHR- (F(1,154) = 7.14, p = 0.008). A region-specific analysis to identify which regions contributed most to aberrant modular connectome organization in CHR+ showed that superior temporal (including STG), medial temporal (including amygdala), and ventromedial prefrontal regions were most abnormal in terms of their modular assignment. Psychosis-free survival functions of CHRs with low versus high SR were significantly different (z = 2.5, p = 0.013), with a Hazard ratio of 3.3 indicating an over 3-fold relative event rate (i.e., conversion to psychosis) in CHRs with abnormal baseline connectome organization. Cox regression analysis indicated that baseline connectome organization (z = -2.3, p = 0.019), IQ (z = -2.7, p = 0.007), and gender (z = 2.0, p = 0.048) predicted time to conversion. Discussion This study indicates that abnormalities in functional connectome organization precede the first psychotic episode. Conversion to psychosis was found to be over three times more likely in CHRs with abnormal modular organization of the functional connectome at baseline. Our results suggest that functional connectome reorganization may underlie the gradual manifestation of prodromal symptoms. These findings may contribute to early diagnosis and intervention in schizophrenia

Altered engagement of the speech motor network is associated with reduced phonological working memory in autism

Nonword repetition, a common clinical measure of phonological working memory, involves component processes of speech perception, working memory, and speech production. Autistic children often show behavioral challenges in nonword repetition, as do many individuals with communication disorders. It is unknown which subprocesses of phonological working memory are vulnerable in autistic individuals, and whether the same brain processes underlie the transdiagnostic difficulty with nonword repetition. We used functional magnetic resonance imaging (fMRI) to investigate the brain bases for nonword repetition challenges in autism. We compared activation during nonword repetition in functional brain networks subserving speech perception, working memory, and speech production between neurotypical and autistic children. Autistic children performed worse than neurotypical children on nonword repetition and had reduced activation in response to increasing phonological working memory load in the supplementary motor area. Multivoxel pattern analysis within the speech production network classified shorter vs longer nonword-repetition trials less accurately for autistic than neurotypical children. These speech production motor-specific differences were not observed in a group of children with reading disability who had similarly reduced nonword repetition behavior. These findings suggest that atypical function in speech production brain regions may contribute to nonword repetition difficulties in autism.R01 DC011339 - NIDCD NIH HHS; R21 DC017576 - NIDCD NIH HHS; R03 DC014045 - NIDCD NIH HHS; T32 DC000038 - NIDCD NIH HHSPublished versio

Neurocognitive plasticity in verb bias learning in children and adults

Verb-specific preference for syntactic structure (verb bias) is considered as a critical parsing constraint that guides online sentence comprehension. Both adults and preschoolers show great sensitivity to verb bias in their temporary parsing commitment as sentences unfold in time. How do people learn verb bias in the first place? In natural language, frequency-sensitive verb argument structure is closely intertwined with the event information delivered by the verb and its argument, which raises complexity in teasing apart the information from linguistic co-occurrence frequency and the information from the event semantics. In this dissertation I began by examining the independent roles of each information source during the process of updating familiar verb bias. The rest of the study focused on the verb bias learning without event cues from verb semantics. Two parallel approaches were applied to explore the details of the learning mechanisms. One set of studies used eye tracking to monitor the time course of online usage of newly learned verb bias during sentence ambiguity resolution across different age ranges. The other set of studies examined the neural stages of verb bias learning as well as the individual differences of verb bias retrieval during online sentence reading with event-related brain potential (ERP) techniques. I demonstrated with very brief training paradigm in both listening and reading modality that children and adults were capable of quickly adapting to new information about verb-specific structural preference from the dynamic language input. The results provided evidence for a central role of linguistic distributional information in verb bias learning. Newly learned verb bias plays a similar role as the existing verb bias knowledge in affecting language users’ parsing commitment and online ambiguity resolution. In addition ERP results revealed separate neural stages that transits from semantic prediction to syntactic rule-based processing as learners continuously collected distributional information of verb-specific structural preference. Individuals who were highly sensitive to familiar verb bias also showed greater use of newly learned verb bias during conflict detection, further indicating the same mechanism underlying natural verb bias acquisition

DTI predicts Mandarin Learning

The nifti files, bvals, bvecs of each deidentified participants are included. A separate csv file describes basic demographic information and these participants' learning scores reported in the paper: Qi Z., Han M., Garel K., Chen E. S., & Gabrieli J. D. E. (2014). White-matter structure in the right hemisphere predicts Mandarin Chinese learning success. Journal of Neurolinguistics, 33: 14-28

Prediction of Green Properties of Flux Pellets Based on Improved Generalized Regression Neural Network

In order to improve the quality of magnesia flux pellets and meet the production needs of the iron and steel industry, a pellet formation experiment was carried out. The effects of alkalinity R, SiO2 mass fraction, MgO mass fraction on the green pellets’ burst temperature, compressive strength, and falling strength were studied. The results showed that with the increase in alkalinity, the bursting temperature of green pellets decreases, but has no obvious effect on the compressive strength or drop strength; with the increase in SiO2 content, the bursting temperature of green pellets decreases gradually, and the green pellets’ strength also decreases slightly; with the increase in MgO content, the compressive strength of green pellets shows an upward trend, while the falling strength gradually decreases, and the burst temperature of green pellets shows a trend of rising first and then decreasing. The change trend is coupled with the software test data amplification method algorithm, based on the search algorithm of longicorn (MBAS), to expand a small amount of experimental data. Through data analysis and algorithm comparison, an improved generalized regression neural network (CFA-GRNN), based on culture firefly, was proposed to establish an optimization model for green pellet performance prediction. CFA uses the weights in the input layer and hidden layer of GRNN, the weights in the hidden layer and output layer, the threshold of the hidden layer and the threshold of the output layer as codes for optimization. The evolutionary goal is to obtain the most appropriate and optimal neural network structure. The results show that the MBAS algorithm, combined with the experimental research, can expand the effective data to 1000 pieces. Secondly, the green pellets’ burst temperature, compressive strength and falling strength predicted by the improved generalized regression neural network are in good agreement with the real values, and the average relative errors were 1.88%, 3.18% and 3.62%, respectively. The error analysis shows that the improved model algorithm has higher accuracy, meets the classification of pellets, and can be used to guide the production of pellets