Delayed Auditory Feedback Elicits Specific Patterns of Serial Order Errors in a Paced Syllable Sequence Production Task.

PURPOSE: Delayed auditory feedback (DAF) interferes with speech output. DAF causes distorted and disfluent productions and errors in the serial order of produced sounds. Although DAF has been studied extensively, the specific patterns of elicited speech errors are somewhat obscured by relatively small speech samples, differences across studies, and uncontrolled variables. The goal of this study was to characterize the types of serial order errors that increase under DAF in a systematic syllable sequence production task, which used a closed set of sounds and controlled for speech rate. METHOD: Sixteen adult speakers repeatedly produced CVCVCV (C = consonant, V = vowel) sequences, paced to a "visual metronome," while hearing self-generated feedback with delays of 0-250 ms. Listeners transcribed recordings, and speech errors were classified based on the literature surrounding naturally occurring slips of the tongue. A series of mixed-effects models were used to assess the effects of delay for different error types, for error arrival time, and for speaking rate. RESULTS: DAF had a significant effect on the overall error rate for delays of 100 ms or greater. Statistical models revealed significant effects (relative to zero delay) for vowel and syllable repetitions, vowel exchanges, vowel omissions, onset disfluencies, and distortions. Serial order errors were especially dominated by vowel and syllable repetitions. Errors occurred earlier on average within a trial for longer feedback delays. Although longer delays caused slower speech, this effect was mediated by the run number (time in the experiment) and small compared with those in previous studies. CONCLUSIONS: DAF drives a specific pattern of serial order errors. The dominant pattern of vowel and syllable repetition errors suggests possible mechanisms whereby DAF drives changes to the activity in speech planning representations, yielding errors. These mechanisms are outlined with reference to the GODIVA (Gradient Order Directions Into Velocities of Articulators) model of speech planning and production. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.19601785.

Assessing Ecologically Valid Methods of Auditory Feedback Measurement in Individuals With Typical Speech.

PURPOSE: Auditory feedback is thought to contribute to the online control of speech production. Yet, the standard method of estimating auditory feedback control (i.e., reflexive responses to auditory-motor perturbations), although sound, requires specialized instrumentation, meticulous calibration, unnatural tasks, and specific acoustic environments. The purpose of this study was to explore more ecologically valid features of speech production to determine their relationships with auditory feedback mechanisms. METHOD: Two previously proposed measures of within-utterance variability (centering and baseline variability) were compared with reflexive response magnitudes in 30 adults with typical speech. These three measures were estimated for both the laryngeal and articulatory subsystems of speech. RESULTS: Regardless of the speech subsystem, neither centering nor baseline variability was shown to be related to reflexive response magnitudes. Likewise, no relationships were found between centering and baseline variability. CONCLUSIONS: Despite previous suggestions that centering and baseline variability may be related to auditory feedback mechanisms, this study did not support these assertions. However, the detection of such relationships may have required a larger degree of variability in responses, relative to that found in those with typical speech. Future research on these relationships is warranted in populations with more heterogeneous responses, such as children or clinical populations. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.17330546.

Kinematic Analysis of Speech Sound Sequencing Errors Induced by Delayed Auditory Feedback.

Purpose: Delayed auditory feedback (DAF) causes speakers to become disfluent and make phonological errors. Methods for assessing the kinematics of speech errors are lacking, with most DAF studies relying on auditory perceptual analyses, which may be problematic, as errors judged to be categorical may actually represent blends of sounds or articulatory errors. Method: Eight typical speakers produced nonsense syllable sequences under normal and DAF (200 ms). Lip and tongue kinematics were captured with electromagnetic articulography. Time-locked acoustic recordings were transcribed, and the kinematics of utterances with and without perceived errors were analyzed with existing and novel quantitative methods. Results: New multivariate measures showed that for 5 participants, kinematic variability for productions perceived to be error free was significantly increased under delay; these results were validated by using the spatiotemporal index measure. Analysis of error trials revealed both typical productions of a nontarget syllable and productions with articulatory kinematics that incorporated aspects of both the target and the perceived utterance. Conclusions: This study is among the first to characterize articulatory changes under DAF and provides evidence for different classes of speech errors, which may not be perceptually salient. New methods were developed that may aid visualization and analysis of large kinematic data sets. Supplemental Material: https://doi.org/10.23641/asha.5103067.

Mapping the cortical representation of speech sounds in a syllable repetition task.

Speech repetition relies on a series of distributed cortical representations and functional pathways. A speaker must map auditory representations of incoming sounds onto learned speech items, maintain an accurate representation of those items in short-term memory, interface that representation with the motor output system, and fluently articulate the target sequence. A "dorsal stream" consisting of posterior temporal, inferior parietal and premotor regions is thought to mediate auditory-motor representations and transformations, but the nature and activation of these representations for different portions of speech repetition tasks remains unclear. Here we mapped the correlates of phonetic and/or phonological information related to the specific phonemes and syllables that were heard, remembered, and produced using a series of cortical searchlight multi-voxel pattern analyses trained on estimates of BOLD responses from individual trials. Based on responses linked to input events (auditory syllable presentation), predictive vowel-level information was found in the left inferior frontal sulcus, while syllable prediction revealed significant clusters in the left ventral premotor cortex and central sulcus and the left mid superior temporal sulcus. Responses linked to output events (the GO signal cueing overt production) revealed strong clusters of vowel-related information bilaterally in the mid to posterior superior temporal sulcus. For the prediction of onset and coda consonants, input-linked responses yielded distributed clusters in the superior temporal cortices, which were further informative for classifiers trained on output-linked responses. Output-linked responses in the Rolandic cortex made strong predictions for the syllables and consonants produced, but their predictive power was reduced for vowels. The results of this study provide a systematic survey of how cortical response patterns covary with the identity of speech sounds, which will help to constrain and guide theoretical models of speech perception, speech production, and phonological working memory.

Changes in functional connectivity related to direct training and generalization effects of a word finding treatment in chronic aphasia.

The neural mechanisms that underlie generalization of treatment-induced improvements in word finding in persons with aphasia (PWA) are currently poorly understood. This study aimed to shed light on changes in functional network connectivity underlying generalization in aphasia. To this end, we used fMRI and graph theoretic analyses to examine changes in functional connectivity after a theoretically-based word-finding treatment in which abstract words were used as training items with the goal of promoting generalization to concrete words. Ten right-handed native English-speaking PWA (7 male, 3 female) ranging in age from 47 to 75 (mean=59) participated in this study. Direct training effects coincided with increased functional connectivity for regions involved in abstract word processing. Generalization effects coincided with increased functional connectivity for regions involved in concrete word processing. Importantly, similarities between training and generalization effects were noted as were differences between participants who generalized and those who did not.

An integrative analysis of regional gene expression profiles in the human brain.

Studies of the brain's transcriptome have become prominent in recent years, resulting in an accumulation of datasets with somewhat distinct attributes. These datasets, which are often analyzed only in isolation, also are often collected with divergent goals, which are reflected in their sampling properties. While many researchers have been interested in sampling gene expression in one or a few brain areas in a large number of subjects, recent efforts from the Allen Institute for Brain Sciences and others have focused instead on dense neuroanatomical sampling, necessarily limiting the number of individual donor brains studied. The purpose of the present work is to develop methods that draw on the complementary strengths of these two types of datasets for study of the human brain, and to characterize the anatomical specificity of gene expression profiles and gene co-expression networks derived from human brains using different specific technologies. The approach is applied using two publicly accessible datasets: (1) the high anatomical resolution Allen Human Brain Atlas (AHBA, Hawrylycz et al., 2012) and (2) a relatively large sample size, but comparatively coarse neuroanatomical dataset described previously by Gibbs et al. (2010). We found a relatively high degree of correspondence in differentially expressed genes and regional gene expression profiles across the two datasets. Gene co-expression networks defined in individual brain regions were less congruent, but also showed modest anatomical specificity. Using gene modules derived from the Gibbs dataset and from curated gene lists, we demonstrated varying degrees of anatomical specificity based on two classes of methods, one focused on network modularity and the other focused on enrichment of expression levels. Two approaches to assessing the statistical significance of a gene set's modularity in a given brain region were studied, which provide complementary information about the anatomical specificity of a gene network of interest. Overall, the present work demonstrates the feasibility of cross-dataset analysis of human brain microarray studies, and offers a new approach to annotating gene lists in a neuroanatomical context.

Cell-type-based model explaining coexpression patterns of genes in the brain.

Spatial patterns of gene expression in the vertebrate brain are not independent, as pairs of genes can exhibit complex patterns of coexpression. Two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been studied quantitatively, particularly for the Allen Atlas of the adult mouse brain, but their biological meaning remains obscure. We propose a simple model of the coexpression patterns in terms of spatial distributions of underlying cell types and establish its plausibility using independently measured cell-type-specific transcriptomes. The model allows us to predict the spatial distribution of cell types in the mouse brain.

An informatics approach to integrating genetic and neurological data in speech and language neuroscience.

A number of heritable disorders impair the normal development of speech and language processes and occur in large numbers within the general population. While candidate genes and loci have been identified, the gap between genotype and phenotype is vast, limiting current understanding of the biology of normal and disordered processes. This gap exists not only in our scientific knowledge, but also in our research communities, where genetics researchers and speech, language, and cognitive scientists tend to operate independently. Here we describe a web-based, domain-specific, curated database that represents information about genotype-phenotype relations specific to speech and language disorders, as well as neuroimaging results demonstrating focal brain differences in relevant patients versus controls. Bringing these two distinct data types into a common database ( http://neurospeech.org/sldb ) is a first step toward bringing molecular level information into cognitive and computational theories of speech and language function. One bridge between these data types is provided by densely sampled profiles of gene expression in the brain, such as those provided by the Allen Brain Atlases. Here we present results from exploratory analyses of human brain gene expression profiles for genes implicated in speech and language disorders, which are annotated in our database. We then discuss how such datasets can be useful in the development of computational models that bridge levels of analysis, necessary to provide a mechanistic understanding of heritable language disorders. We further describe our general approach to information integration, discuss important caveats and considerations, and offer a specific but speculative example based on genes implicated in stuttering and basal ganglia function in speech motor control.

Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults.

BACKGROUND: Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets to assess if DNA variation is associated with post-mortem brain gene expression changes based on smoking behavior, a biobehavioral construct that is part of a complex system of genetic and environmental influences. METHODS: We conducted an expression quantitative trait locus (eQTL) study on two independent human brain gene expression datasets assessing G × E for selected psychiatric genes and smoking status. We employed linear regression to model the significance of the Gene × Smoking interaction term, followed by meta-analysis across datasets. RESULTS: Overall, we observed that the effect of DNA variation on gene expression is moderated by smoking status. Expression of 16 genes was significantly associated with single nucleotide polymorphisms that demonstrated G × E effects. The strongest finding (p = 1.9 × 10⁻¹¹) was neurexin 3-alpha (NRXN3), a synaptic cell-cell adhesion molecule involved in maintenance of neural connections (such as the maintenance of smoking behavior). Other significant G × E associations include four glutamate genes. CONCLUSIONS: This is one of the first studies to demonstrate G × E effects within the human brain. In particular, this study implicated NRXN3 in the maintenance of smoking. The effect of smoking on NRXN3 expression and downstream behavior is different based upon SNP genotype, indicating that DNA profiles based on SNPs could be useful in understanding the effects of smoking behaviors. These results suggest that better measurement of psychiatric conditions, and the environment in post-mortem brain studies may yield an important avenue for understanding the biological mechanisms of G × E interactions in psychiatry.

Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects.

Brain imaging methods have long held promise as diagnostic aids for neuropsychiatric conditions with complex behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder. This promise has largely been unrealized, at least partly due to the heterogeneity of clinical populations and the small sample size of many studies. A large, multi-center dataset provided by the ADHD-200 Consortium affords new opportunities to test methods for individual diagnosis based on MRI-observable structural brain attributes and functional interactions observable from resting-state fMRI. In this study, we systematically calculated a large set of standard and new quantitative markers from individual subject datasets. These features (>12,000 per subject) consisted of local anatomical attributes such as cortical thickness and structure volumes, and both local and global resting-state network measures. Three methods were used to compute graphs representing interdependencies between activations in different brain areas, and a full set of network features was derived from each. Of these, features derived from the inverse of the time series covariance matrix, under an L1-norm regularization penalty, proved most powerful. Anatomical and network feature sets were used individually, and combined with non-imaging phenotypic features from each subject. Machine learning algorithms were used to rank attributes, and performance was assessed under cross-validation and on a separate test set of 168 subjects for a variety of feature set combinations. While non-imaging features gave highest performance in cross-validation, the addition of imaging features in sufficient numbers led to improved generalization to new data. Stratification by gender also proved to be a fruitful strategy to improve classifier performance. We describe the overall approach used, compare the predictive power of different classes of features, and describe the most impactful features in relation to the current literature.

fMRI investigation of unexpected somatosensory feedback perturbation during speech.

Somatosensory feedback plays a critical role in the coordination of articulator movements for speech production. In response to unexpected resistance to lip or jaw movements during speech, fluent speakers can use the difference between the somatosensory expectations of a speech sound and the actual somatosensory feedback to adjust the trajectories of functionally relevant but unimpeded articulators. In an effort to investigate the neural substrates underlying the somatosensory feedback control of speech, we used an event-related sparse sampling functional magnetic resonance imaging paradigm and a novel pneumatic device that unpredictably blocked subjects' jaw movements. In comparison to speech, perturbed speech, in which jaw perturbation prompted the generation of compensatory speech motor commands, demonstrated increased effects in bilateral ventral motor cortex, right-lateralized anterior supramarginal gyrus, inferior frontal gyrus pars triangularis and ventral premotor cortex, and bilateral inferior posterior cerebellum (lobule VIII). Structural equation modeling revealed a significant increased influence from left anterior supramarginal gyrus to right anterior supramarginal gyrus and from left anterior supramarginal gyrus to right ventral premotor cortex as well as a significant increased reciprocal influence between right ventral premotor cortex and right ventral motor cortex and right anterior supramarginal gyrus and right inferior frontal gyrus pars triangularis for perturbed speech relative to speech. These results suggest that bilateral anterior supramarginal gyrus, right inferior frontal gyrus pars triangularis, right ventral premotor and motor cortices are functionally coupled and influence speech motor output when somatosensory feedback is unexpectedly perturbed during speech production.

Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy.

Spatial gene expression profiles provide a novel means of exploring the structural organization of the brain. Computational analysis of these patterns is made possible by genome-scale mapping of the C57BL/6J mouse brain in the Allen Brain Atlas. Here we describe methodology used to explore the spatial structure of gene expression patterns across a set of 3041 genes chosen on the basis of consistency across experimental observations (N=2). The analysis was performed on smoothed, co-registered 3D expression volumes for each gene obtained by aggregating cellular resolution image data. Following dimensionality and noise reduction, voxels were clustered according to similarity of expression across the gene set. We illustrate the resulting parcellations of the mouse brain for different numbers of clusters (K) and quantitatively compare these parcellations with a classically-defined anatomical reference atlas at different levels of granularity, revealing a high degree of correspondence. These observations suggest that spatial localization of gene expression offers substantial promise in connecting knowledge at the molecular level with higher-level information about brain organization.

Neural representations and mechanisms for the performance of simple speech sequences.

Speakers plan the phonological content of their utterances before their release as speech motor acts. Using a finite alphabet of learned phonemes and a relatively small number of syllable structures, speakers are able to rapidly plan and produce arbitrary syllable sequences that fall within the rules of their language. The class of computational models of sequence planning and performance termed competitive queuing models have followed K. S. Lashley [The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior (pp. 112-136). New York: Wiley, 1951] in assuming that inherently parallel neural representations underlie serial action, and this idea is increasingly supported by experimental evidence. In this article, we developed a neural model that extends the existing DIVA model of speech production in two complementary ways. The new model includes paired structure and content subsystems [cf. MacNeilage, P. F. The frame/content theory of evolution of speech production. Behavioral and Brain Sciences, 21, 499-511, 1998 ] that provide parallel representations of a forthcoming speech plan as well as mechanisms for interfacing these phonological planning representations with learned sensorimotor programs to enable stepping through multisyllabic speech plans. On the basis of previous reports, the model's components are hypothesized to be localized to specific cortical and subcortical structures, including the left inferior frontal sulcus, the medial premotor cortex, the basal ganglia, and the thalamus. The new model, called gradient order DIVA, thus fills a void in current speech research by providing formal mechanistic hypotheses about both phonological and phonetic processes that are grounded by neuroanatomy and physiology. This framework also generates predictions that can be tested in future neuroimaging and clinical case studies.

The brain atlas concordance problem: quantitative comparison of anatomical parcellations.

Many neuroscientific reports reference discrete macro-anatomical regions of the brain which were delineated according to a brain atlas or parcellation protocol. Currently, however, no widely accepted standards exist for partitioning the cortex and subcortical structures, or for assigning labels to the resulting regions, and many procedures are being actively used. Previous attempts to reconcile neuroanatomical nomenclatures have been largely qualitative, focusing on the development of thesauri or simple semantic mappings between terms. Here we take a fundamentally different approach, discounting the names of regions and instead comparing their definitions as spatial entities in an effort to provide more precise quantitative mappings between anatomical entities as defined by different atlases. We develop an analytical framework for studying this brain atlas concordance problem, and apply these methods in a comparison of eight diverse labeling methods used by the neuroimaging community. These analyses result in conditional probabilities that enable mapping between regions across atlases, which also form the input to graph-based methods for extracting higher-order relationships between sets of regions and to procedures for assessing the global similarity between different parcellations of the same brain. At a global scale, the overall results demonstrate a considerable lack of concordance between available parcellation schemes, falling within chance levels for some atlas pairs. At a finer level, this study reveals spatial relationships between sets of defined regions that are not obviously apparent; these are of high potential interest to researchers faced with the challenge of comparing results that were based on these different anatomical models, particularly when coordinate-based data are not available. The complexity of the spatial overlap patterns revealed points to problems for attempts to reconcile anatomical parcellations and nomenclatures using strictly qualitative and/or categorical methods. Detailed results from this study are made available via an interactive web site at http://obart.info.

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale.

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is critical, however, for both basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brainwide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open-access data repository; compatibility with existing resources; and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.

An anatomic gene expression atlas of the adult mouse brain.

Studying gene expression provides a powerful means of understanding structure-function relationships in the nervous system. The availability of genome-scale in situ hybridization datasets enables new possibilities for understanding brain organization based on gene expression patterns. The Anatomic Gene Expression Atlas (AGEA) is a new relational atlas revealing the genetic architecture of the adult C57Bl/6J mouse brain based on spatial correlations across expression data for thousands of genes in the Allen Brain Atlas (ABA). The AGEA includes three discovery tools for examining neuroanatomical relationships and boundaries: (1) three-dimensional expression-based correlation maps, (2) a hierarchical transcriptome-based parcellation of the brain and (3) a facility to retrieve from the ABA specific genes showing enriched expression in local correlated domains. The utility of this atlas is illustrated by analysis of genetic organization in the thalamus, striatum and cerebral cortex. The AGEA is a publicly accessible online computational tool integrated with the ABA (http://mouse.brain-map.org/agea).

An analysis of the abstracts presented at the annual meetings of the Society for Neuroscience from 2001 to 2006.

Annual meeting abstracts published by scientific societies often contain rich arrays of information that can be computationally mined and distilled to elucidate the state and dynamics of the subject field. We extracted and processed abstract data from the Society for Neuroscience (SFN) annual meeting abstracts during the period 2001-2006 in order to gain an objective view of contemporary neuroscience. An important first step in the process was the application of data cleaning and disambiguation methods to construct a unified database, since the data were too noisy to be of full utility in the raw form initially available. Using natural language processing, text mining, and other data analysis techniques, we then examined the demographics and structure of the scientific collaboration network, the dynamics of the field over time, major research trends, and the structure of the sources of research funding. Some interesting findings include a high geographical concentration of neuroscience research in the north eastern United States, a surprisingly large transient population (66% of the authors appear in only one out of the six studied years), the central role played by the study of neurodegenerative disorders in the neuroscience community, and an apparent growth of behavioral/systems neuroscience with a corresponding shrinkage of cellular/molecular neuroscience over the six year period. The results from this work will prove useful for scientists, policy makers, and funding agencies seeking to gain a complete and unbiased picture of the community structure and body of knowledge encapsulated by a specific scientific domain.

An fMRI investigation of syllable sequence production.

Fluent speech comprises sequences that are composed from a finite alphabet of learned words, syllables, and phonemes. The sequencing of discrete motor behaviors has received much attention in the motor control literature, but relatively little has been focused directly on speech production. In this paper, we investigate the cortical and subcortical regions involved in organizing and enacting sequences of simple speech sounds. Sparse event-triggered functional magnetic resonance imaging (fMRI) was used to measure responses to preparation and overt production of non-lexical three-syllable utterances, parameterized by two factors: syllable complexity and sequence complexity. The comparison of overt production trials to preparation only trials revealed a network related to the initiation of a speech plan, control of the articulators, and to hearing one's own voice. This network included the primary motor and somatosensory cortices, auditory cortical areas, supplementary motor area (SMA), the precentral gyrus of the insula, and portions of the thalamus, basal ganglia, and cerebellum. Additional stimulus complexity led to increased engagement of the basic speech network and recruitment of additional areas known to be involved in sequencing non-speech motor acts. In particular, the left hemisphere inferior frontal sulcus and posterior parietal cortex, and bilateral regions at the junction of the anterior insula and frontal operculum, the SMA and pre-SMA, the basal ganglia, anterior thalamus, and the cerebellum showed increased activity for more complex stimuli. We hypothesize mechanistic roles for the extended speech production network in the organization and execution of sequences of speech sounds.