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Purpose: This study investigated to what extent residual hearing and rehabilitation options (e.g., hearing aids [HAs]) affect the auditory cortex in children with hearing loss. Method: Twenty-one children with bilateral congenital sensorineural hearing loss who were candidates for cochlear implantation were recruited. Voxel-based morphometry analysis was conducted to assess the gray matter (GM) volume in the auditory cortex. Children's residual hearing was measured by pure-tone audiometry at different frequencies. Multiple linear regression models were conducted to examine the effects of residual hearing and the use of HAs on GM volume in the auditory cortex with the control of age and gender. Results: Children with more residual hearing at high frequencies had larger GM volume ratio (corrected by total intracranial volume) in the left Heschl's gyrus ( r = −.545, p = .013). An interaction effect between residual hearing and the use of HAs suggested that the effect of residual hearing on GM ratio was moderated by the use of HAs (β = −.791, p = .020). Compared with children with less residual hearing, children who had more residual hearing benefited more from longer use of HAs in terms of a larger GM ratio. Conclusions: Our preliminary findings highlight the impact of residual hearing on the neuroanatomy of the auditory cortex in children with hearing loss. Moreover, our results call for more auditory input via HAs for children with more residual hearing to preserve the auditory cortex before cochlear implantation. For children with less residual hearing who might receive limited benefit from HAs, an early cochlear implant would be necessary.

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating meaningful and conceptual information. Neuroimaging studies of SP typically collapse data from many subjects, but both its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural organizations contribute to the individual differences in SP behaviors. Here we aim to identify the neural signatures underlying SP variabilities by analyzing individual functional connectivity (FC) patterns based on a large-sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two-stage predictive modeling approach to build an internally cross-validated model and to test the model’s generalizability with unseen data from different HCP sub-populations and task states as well as other out-of-sample datasets that are independent of the HCP. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five semantic tasks. This cross-validated predictive model can be used to predict unseen HCP data. The model generalizability was enhanced with FCs in language tasks than resting state and other task states and was better for females than males. The model constructed from the HCP dataset can be generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out-of-sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education and improve intervention practice for patients with SP and language deficits.

A fundamental objective in Auditory Sciences is understanding how people learn to generalize auditory category knowledge in new situations. How we generalize to novel scenarios speaks to the nature of emergent category representations and generalization mechanisms in handling perceptual variabilities and novelty. The dual learning system (DLS) models propose that auditory category learning involves an explicit, hypothesis-testing learning system, which is optimal for learning rule-based (RB) categories, and an implicit, procedural-based learning system, which is optimal for learning categories requiring pre-decisional information integration (II) across acoustic dimensions. Although DLS describes distinct mechanisms of two types of category learning, it is yet clear the nature of acquired representations and how we transfer them to new contexts. Here, we conducted three experiments to examine differences between II and RB category representations by examining what acoustic and perceptual variabilities affect learners’ generalization success. Learners can generalize to different sets of untrained sounds after only eight training blocks for both II and RB categories. The category structures and novel contexts differentially modulated the generalization success. The II learners significantly decreased generalization performances when categorizing new items derived from an untrained perceptual area and in a context with more distributed samples. In contrast, RB learners’ generalizations are resistant to changes in perceptual regions but are sensitive to changes in sound dispersity. Representational similarity modeling revealed that the generalization in the more dispersed sampling context was accomplished differently by II and RB learners. II learners increased representations of perceptual similarity and decision distance to compensate for the decreased transfer of category representations, whereas the RB learners used a more computational cost strategy by default, computing the decision-bound distance to guide generalization decisions. These results suggest that distinct representations emerged after learning the two types of category structures and using different strategies and flexible mechanisms in resolving generalization challenges when facing novel perceptual variability in new contexts. These findings provide new evidence for dissociated representations of auditory categories and reveal novel generalization mechanisms in resolving different variabilities to maintain perceptual constancy.

Purpose This study aimed to construct an objective and cost-effective prognostic tool to forecast the future language and communication abilities of individual infants. Method Speech-evoked electroencephalography (EEG) data were collected from 118 infants during the first year of life during the exposure to speech stimuli that differed principally in fundamental frequency. Language and communication outcomes, namely four subtests of the MacArthur–Bates Communicative Development Inventories (MCDI)–Chinese version, were collected between 3 and 16 months after initial EEG testing. In the two-way classification, children were classified into those with future MCDI scores below the 25th percentile for their age group and those above the same percentile, while the three-way classification classified them into < 25th, 25th–75th, and > 75th percentile groups. Machine learning (support vector machine classification) with cross validation was used for model construction. Statistical significance was assessed. Results Across the four MCDI measures of early gestures, later gestures, vocabulary comprehension, and vocabulary production, the areas under the receiver-operating characteristic curve of the predictive models were respectively .92 ± .031, .91 ± .028, .90 ± .035, and .89 ± .039 for the two-way classification, and .88 ± .041, .89 ± .033, .85 ± .047, and .85 ± .050 for the three-way classification ( p < .01 for all models). Conclusions Future language and communication variability can be predicted by an objective EEG method that indicates the function of the auditory neural pathway foundational to spoken language development, with precision sufficient for individual predictions. Longer-term research is needed to assess predictability of categorical diagnostic status. Supplemental Material https://doi.org/10.23641/asha.15138546

Learning non-native phonetic categories in adulthood is an exceptionally challenging task, characterized by large interindividual differences in learning speed and outcomes. The neurobiological mechanisms underlying the interindividual differences in the learning efficacy are not fully understood. Here we examine the extent to which training-induced neural representations of non-native Mandarin tone categories in English listeners (n = 53) are increasingly similar to those of the native listeners (n = 33) who acquired these categories early in infancy. We assess the extent to which the neural similarities in representational structure between non-native learners and native listeners are robust neuromarkers of interindividual differences in learning success. Using intersubject neural representational similarity (IS-NRS) analysis and predictive modeling on two functional magnetic resonance imaging datasets, we examined the neural representational mechanisms underlying speech category learning success. Learners’ neural representations that were significantly similar to the native listeners emerged in brain regions mediating speech perception following training; the extent of the emerging neural similarities with native listeners significantly predicted the learning speed and outcome in learners. The predictive power of IS-NRS outperformed models with other neural representational measures. Furthermore, neural representations underlying successful learning were multidimensional but cost-efficient in nature. The degree of the emergent native-similar neural representations was closely related to the robustness of neural sensitivity to feedback in the frontostriatal network. These findings provide important insights into the experience-dependent representational neuroplasticity underlying successful speech learning in adulthood and could be leveraged in designing individualized feedback-based training paradigms that maximize learning efficacy.

Current models of auditory category learning argue for a rigid specialization of hierarchically organized regions that are fine-tuned to extracting and mapping acoustic dimensions to categories. We test a competing hypothesis: the neural dynamics of emerging auditory representations are driven by category structures and learning strategies. We designed a category learning experiment where two groups of learners learned novel auditory categories with identical dimensions but differing category structures: rule-based (RB) and information-integration (II) based categories. Despite similar learning accuracies, strategies and cortico-striatal systems processing feedback differed across structures. Emergent neural representations of category information within an auditory frontotemporal pathway exclusively for the II learning task. In contrast, the RB task yielded neural representations within distributed regions involved in cognitive control that emerged at different time-points of learning. Our results demonstrate that learners’ neural systems are flexible and show distinct spatiotemporal patterns that are not dimension-specific but reflect underlying category structures.SignificanceWhether it is an alarm signifying danger or the characteristics of background noise, humans are capable of rapid auditory learning. Extant models posit that novel auditory representations emerge in the superior temporal gyrus, a region specialized for extracting behaviorally relevant auditory dimensions and transformed onto decisions via the dorsal auditory stream. Using a computational cognitive neuroscience approach, we offer an alternative viewpoint: emergent auditory representations are highly flexible, showing distinct spatial and temporal trajectories that reflect different category structures.

We establish a mechanistic account of how the mature human brain functionally reorganizes to acquire and represent new speech sounds. Native speakers of English learned to categorize Mandarin lexical tone categories produced by multiple talkers using trial-by-trial feedback. We hypothesized that the corticostriatal system is a key intermediary in mediating temporal lobe plasticity and the acquisition of new speech categories in adulthood. We conducted a functional magnetic resonance imaging experiment in which participants underwent a sound-to-category mapping task. Diffusion tensor imaging data were collected, and probabilistic fiber tracking analysis was employed to assay the auditory corticostriatal pathways. Multivariate pattern analysis showed that talker-invariant novel tone category representations emerged in the left superior temporal gyrus (LSTG) within a few hundred training trials. Univariate analysis showed that the putamen, a subregion of the striatum, was sensitive to positive feedback in correctly categorized trials. With learning, functional coupling between the putamen and LSTG increased during error processing. Furthermore, fiber tractography demonstrated robust structural connectivity between the feedback-sensitive striatal regions and the LSTG regions that represent the newly learned tone categories. Our convergent findings highlight a critical role for the auditory corticostriatal circuitry in mediating the acquisition of new speech categories.

A significant neural challenge in speech perception includes extracting discrete phonetic categories from continuous and multidimensional signals despite varying task demands and surface-acoustic variability. While neural representations of speech categories have been previously identified in frontal and posterior temporal-parietal regions, the task dependency and dimensional specificity of these neural representations are still unclear. Here, we asked native Mandarin participants to listen to speech syllables carrying 4 distinct lexical tone categories across passive listening, repetition, and categorization tasks while they underwent functional magnetic resonance imaging (fMRI). We used searchlight classification and representational similarity analysis (RSA) to identify the dimensional structure underlying neural representation across tasks and surface-acoustic properties. Searchlight classification analyses revealed significant “cross-task” lexical tone decoding within the bilateral superior temporal gyrus (STG) and left inferior parietal lobule (LIPL). RSA revealed that the LIPL and LSTG, in contrast to the RSTG, relate to 2 critical dimensions (pitch height, pitch direction) underlying tone perception. Outside this core representational network, we found greater activation in the inferior frontal and parietal regions for stimuli that are more perceptually similar during tone categorization. Our findings reveal the specific characteristics of fronto-tempo-parietal regions that support speech representation and categorization processing.

Human language learning differs significantly across individuals in the process and ultimate attainment. Although decades of research exploring the neural substrates of language learning have identified distinct and overlapping neural networks subserving learning of different components, the neural mechanisms that drive the large inter-individual differences are still far from being understood. Here we examine to what extent the neural dynamics of multiple brain networks in men and women across sessions of training contribute to explaining individual differences in learning multiple linguistic components (i.e., vocabulary, morphology, and phrase and sentence structures) of an artificial language in a seven-day training and imaging paradigm with functional Magnetic Resonance Imaging. With machine-learning and predictive modeling, neural activation patterns across training sessions were highly predictive of individual learning success profiles derived from the four components. We identified four neural learning networks (i.e., the Perisylvian, frontoparietal, salience, and default-mode networks) and examined their dynamic contributions to the learning success prediction. Moreover, the robustness of the predictions systematically changes across networks depending on specific training phases and the learning components. We further demonstrated that a subset of network nodes in the inferior frontal, insular, and frontoparietal regions increasingly represent newly acquired language knowledge, while the multivariate connectivity between these representation regions was enhanced during learning for more successful learners. These findings allow us to understand why learners differ and are the first to attribute not only the degree of success but also patterns of language learning across components, to neural fingerprints summarized from multiple neural network dynamics. SIGNIFICANCE STATEMENT: Individual differences in learning a language are widely observed not only within the same component of language but also across components. This study demonstrates that the dynamics of multiple brain networks across four imaging sessions of a seven-day artificial language training contribute to individual differences in learning-outcome profiles derived from four language components. With machine-learning predictive modeling, we identified four neural learning networks, including the Perisylvian, frontoparietal, salience, and default-mode networks that contribute to predicting individual learning-outcome profiles and revealed language-component-general and component-specific prediction patterns across training sessions. These findings provide significant insights in understanding training-dependent neural dynamics underlying individual differences in learning success across language components.

Successful categorization requires listeners to represent the incoming sensory information, resolve the “blooming, buzzing confusion” inherent to noisy sensory signals, and leverage the accumulated evidence towards making a decision. Despite decades of intense debate, the neural systems underlying speech categorization remain unresolved. Here we assessed the neural representation and categorization of lexical tones by native Mandarin speakers (N = 31) across a range of acoustic and contextual variabilities (talkers, perceptual saliences, and stimulus-contexts) using functional magnetic imaging (fMRI) and an evidence accumulation model of decision-making. Univariate activation and multivariate pattern analyses reveal that the acoustic-variability-tolerant representations of tone category are observed within the middle portion of the left superior temporal gyrus (STG). Activation patterns in the frontal and parietal regions also contained category-relevant information that was differentially sensitive to various forms of variability. The robustness of neural representations of tone category in a distributed fronto-temporoparietal network is associated with trial-by-trial decision-making parameters. These findings support a hybrid model involving a representational core within the STG that operates dynamically within an extensive frontoparietal network to support the representation and categorization of linguistic pitch patterns.

Although cochlear implantation enables some children to attain age-appropriate speech and language development, communicative delays persist in others, and outcomes are quite variable and difficult to predict, even for children implanted early in life. To understand the neurobiological basis of this variability, we used presurgical neural morphological data obtained from MRI of individual pediatric cochlear implant (CI) candidates implanted younger than 3.5 years to predict variability of their speech-perception improvement after surgery. We first compared neuroanatomical density and spatial pattern similarity of CI candidates to that of age-matched children with normal hearing, which allowed us to detail neuroanatomical networks that were either affected or unaffected by auditory deprivation. This information enables us to build machine-learning models to predict the individual children's speech development following CI. We found that regions of the brain that were unaffected by auditory deprivation, in particular the auditory association and cognitive brain regions, produced the highest accuracy, specificity, and sensitivity in patient classification and the most precise prediction results. These findings suggest that brain areas unaffected by auditory deprivation are critical to developing closer to typical speech outcomes. Moreover, the findings suggest that determination of the type of neural reorganization caused by auditory deprivation before implantation is valuable for predicting post-CI language outcomes for young children.

Semantic priming is a crucial phenomenon to study the organization of semantic memory. A novel type of priming effect, integrative priming, has been identified behaviorally, whereby a prime word facilitates recognition of a target word when the 2 concepts can be combined to form a unitary representation. We used both functional and anatomical imaging approaches to investigate the neural substrates supporting such integrative priming, and compare them with those in semantic priming. Similar behavioral priming effects for both semantic (Bread–Cake) and integrative conditions (Cherry–Cake) were observed when compared with an unrelated condition. However, a clearly dissociated brain response was observed between these 2 types of priming. The semantic-priming effect was localized to the posterior superior temporal and middle temporal gyrus. In contrast, the integrative-priming effect localized to the left anterior inferior frontal gyrus and left anterior temporal cortices. Furthermore, fiber tractography showed that the integrative-priming regions were connected via uncinate fasciculus fiber bundle forming an integrative circuit, whereas the semantic-priming regions connected to the posterior frontal cortex via separated pathways. The results point to dissociable neural pathways underlying the 2 distinct types of priming, illuminating the neural circuitry organization of semantic representation and integration.

A highly effective algorithmic method is proposed for optimally synthesizing an azeotropic-distillation system from an extensive set of candidate operating units, i.e., functional units. The method has been derived by resorting to the graph-theoretic approach to process-network synthesis based on process graphs (P-graphs); it also resorts to the methodology established in our previous contribution for dividing the residue curve map (RCM) of a material system, i.e., mixture, to be separated into partitioned materials. This allows the entire space of the RCM to be taken into account in composing networks of candidate operating units, thereby preventing the localization of search. Moreover, the RCM is transformed into the flow-rate map, where any material is quantitatively defined by the molar flow rates of its components instead of the concentrations as in the RCM. This renders it possible to eliminate the nonlinearity in the governing equations of the candidate operating units. The efficacy of the method i...

The present work proposes a P-graph based approach capable of systematically generating all combinatorially feasible alternative sequences for both continuous and batch azeotropic distillation systems when the feeds, products, and operating units and their concomitant intermediate materials are known. To facilitate synthesis, a highly effective method has been established to identify processing or operating units necessary for constructing azeotropic-distillation systems by means of first principles and logical sequencing of such units. The materials represented in the space of the residue curve map are partitioned into lumped materials bounded by thermodynamic boundaries and pinches, e.g. distillation boundaries and liquid–liquid phase equilibrium envelope. Subsequently, the operating units are identified on the basis of these lumped materials. The proposed approach automatically discards the operating units and intermediates whose inclusion will lead to infeasible sequences. The efficacy of the approach is illustrated with a realistic example.

A highly effective method has been established to identify processing or operating units necessary for the design or synthesis of azeotropic-distillation systems by resorting to the first principles and logical sequencing of such units. The materials represented in the space of the residue curve map are partitioned into lumped materials bounded by the thermodynamic boundaries and pinches. Subsequently, the operating units are identified on the basis of these lumped materials. The efficacy of the method has been amply illustrated by generating some feasible flowsheets with a set of relatively simple heuristics for the process of producing pure ethanol from its aqueous solution via azeotropic distillation, which is a typical complex process involving thermodynamic pinches, such as azeotropes, phase transition, and/or phase separation. The method is applicable to other complex processes, e.g., crystallization, extraction, reactive distillation, and their combinations. It is highly likely that the method can be an integral part of any conventional heuristic or algorithmic flowsheeting procedure because selecting plausible or candidate operating units is essential for synthesizing a flowsheet by any procedure.

The degree of difficulty of separation (DDS), which is of critical importance in estimating the relative costs of alternative separation sequences or networks, has been hitherto unavailable for fluid flow-facilitated particle separation processes that are rate controlling. The expressions for DDSs are derived from the mechanisms and models of such processes as gravity sedimentation, centrifugation, and hydrocyclone separation. The resultant expressions differ substantially from those for equilibrium-staged separations, e.g. distillation.

For a separation system producing multicomponent products, stream splitting besides stream bypassing has long been recognized as a technique that always reduces total mass load (TML) and should always be preferred over sequential processing. Nevertheless, our preliminary analysis has indicated that stream splitting can increase TML under certain circumstances which, in turn, may increase the cost of the system; furthermore, even when stream splitting reduces TML, it may increase the mass load of one or more difficult separations and increase the cost of the system. An exhaustive parametric study of a class of relatively simple problems is performed in this work to reveal the impact of stream splitting on the sequencing of separation systems. Some significant observations have been made, and some guidelines have been established for evolutionarily improving separation system sequencing involving stream splitting.

Whether left inferior frontal gyrus (LIFG) activation during sentence comprehension reflects semantic integration or domain-general cognitive control remains unclear. To address this issue, 26 participants were presented with sentences word by word during fMRI scanning and were asked to perform two semantic tasks, one explicit (semantic congruency judgment) and one implicit (font size judgment). In the two language tasks, semantic integration load was parametrically manipulated with high cloze, low cloze and semantically violated sentences. Participants also performed a classical Stroop task during scanning. Conjunction analysis of the explicit and implicit tasks revealed two regions in left inferior frontal gyrus associated with semantic integration load: one anterior region (aIFG) and one posterior region (pIFG). However, only the pIFG region was also activated during the Stroop task. These results indicate that different regions in the LIFG play different roles in semantic integration, with aIFG more important for domain-specific processing and pIFG more important for domain-general cognitive control.

Using fMRI, the present study compares the brain activation underlying false belief thinking induced by pictorial, nonverbal material to that instigated by strong non-factive verbs in a sample of adult Chinese speakers. These verbs obligatorily negate their complements which describe the mind content of the sentence agent, and thus may activate part of the false belief network. Some previous studies have shown a behavioral correlation between verb non-factivity/false complementation and conventional false belief but corresponding neural evidence is lacking. Our results showed that the non-factive grammar and false belief commonly implicated the right temporo-parietal junction (TPJ), which had been shown by past studies to play a role in general mentalizing. Regions that were unique to nonverbal false belief were the left TPJ and right middle frontal gyrus (MFG), whereas the unique regions for the non-factive grammar were the left inferior frontal gyrus (IFG) and right superior temporal gyrus (STG). Hence, conventional nonverbal false belief and verb non-factivity have both shared and unique neural representations.

Publisher Summary A systematic and rigorous method for synthesizing azeotropic-distillation systems, which is of utmost practical importance, is yet to be fully established. The available methods are based mainly on heuristics and graphical procedures. In synthesizing a simple separation network, the structure of the optimal solution may be counterintuitive. For synthesizing a complex network structure necessary for an azeotropic-distillation system, therefore, the probability is very high that the solution generated would be far from the optimal one unless the method is systematic and rigorous. The proposed method is capable of algorithmically synthesizing optimal, near optimal, and other feasible structures for an azeotropic-distillation system from a set of candidate operating units. The residue curve map of the system is transformed to a unique multidimensional representation to facilitate the systematic partitioning of the feasible regions into lumped materials bounded by the thermodynamic boundaries and pinches. The process graph (P-graph) representation of these operating units serves as a basis for the synthesis procedure, including combinatorial algorithms. The method is equally applicable to various other complex processes with phase transition and/or phase separation with any number of components. Crystallization, extraction, reactive distillation, and their combinations are examples of such processes. A case study is given in which ethanol is separated from its aqueous solution with toluene as the entrainer.