Decoding the information structure underlying the neural representation of concepts.

The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional "semantic hubs" contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

A Distributed Network for Multimodal Experiential Representation of Concepts.

Neuroimaging, neuropsychological, and psychophysical evidence indicate that concept retrieval selectively engages specific sensory and motor brain systems involved in the acquisition of the retrieved concept. However, it remains unclear which supramodal cortical regions contribute to this process and what kind of information they represent. Here, we used representational similarity analysis of two large fMRI datasets with a searchlight approach to generate a detailed map of human brain regions where the semantic similarity structure across individual lexical concepts can be reliably detected. We hypothesized that heteromodal cortical areas typically associated with the default mode network encode multimodal experiential information about concepts, consistent with their proposed role as cortical integration hubs. In two studies involving different sets of concepts and different participants (both sexes), we found a distributed, bihemispheric network engaged in concept representation, composed of high-level association areas in the anterior, lateral, and ventral temporal lobe; inferior parietal lobule; posterior cingulate gyrus and precuneus; and medial, dorsal, ventrolateral, and orbital prefrontal cortex. In both studies, a multimodal model combining sensory, motor, affective, and other types of experiential information explained significant variance in the neural similarity structure observed in these regions that was not explained by unimodal experiential models or by distributional semantics (i.e., word2vec similarity). These results indicate that during concept retrieval, lexical concepts are represented across a vast expanse of high-level cortical regions, especially in the areas that make up the default mode network, and that these regions encode multimodal experiential information.SIGNIFICANCE STATEMENT Conceptual knowledge includes information acquired through various modalities of experience, such as visual, auditory, tactile, and emotional information. We investigated which brain regions encode mental representations that combine information from multiple modalities when participants think about the meaning of a word. We found that such representations are encoded across a widely distributed network of cortical areas in both hemispheres, including temporal, parietal, limbic, and prefrontal association areas. Several areas not traditionally associated with semantic cognition were also implicated. Our results indicate that the retrieval of conceptual knowledge during word comprehension relies on a much larger portion of the cerebral cortex than previously thought and that multimodal experiential information is represented throughout the entire network.

Coordinated anatomical and functional variability in the human brain during adolescence.

Adolescence represents a time of unparalleled brain development. In particular, developmental changes in morphometric and cytoarchitectural features are accompanied by maturation in the functional connectivity (FC). Here, we examined how three facets of the brain, including myelination, cortical thickness (CT), and resting-state FC, interact in children between the ages of 10 and 15. We investigated the pattern of coordination in these measures by computing correlation matrices for each measure as well as meta-correlations among them both at the regional and network levels. The results revealed consistently higher meta-correlations among myelin, CT, and FC in the sensory-motor cortical areas than in the association cortical areas. We also found that these meta-correlations were stable and little affected by age-related changes in each measure. In addition, regional variations in the meta-correlations were consistent with the previously identified gradient in the FC and therefore reflected the hierarchy of cortical information processing, and this relationship persists in the adult brain. These results demonstrate that heterogeneity in FC among multiple cortical areas are closely coordinated with the development of cortical myelination and thickness during adolescence.

Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning.

Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge. Recent advances have begun to explain brain activation elicited by sentences using vector models of word meaning derived from patterns of word co-occurrence in text corpora. These studies have helped map out semantic representation across a distributed brain network spanning temporal, parietal, and frontal cortex. However, it remains unclear whether activation patterns within regions reflect unified representations of sentence-level meaning, as opposed to superpositions of context-independent component words. This is because models have typically represented sentences as "bags-of-words" that neglect sentence-level structure. To address this issue, we interrogated fMRI activation elicited as 240 sentences were read by 14 participants (9 female, 5 male), using sentences encoded by a recurrent deep artificial neural-network trained on a sentence inference task (InferSent). Recurrent connections and nonlinear filters enable InferSent to transform sequences of word vectors into unified "propositional" sentence representations suitable for evaluating intersentence entailment relations. Using voxelwise encoding modeling, we demonstrate that InferSent predicts elements of fMRI activation that cannot be predicted by bag-of-words models and sentence models using grammatical rules to assemble word vectors. This effect occurs throughout a distributed network, which suggests that propositional sentence-level meaning is represented within and across multiple cortical regions rather than at any single site. In follow-up analyses, we place results in the context of other deep network approaches (ELMo and BERT) and estimate the degree of unpredicted neural signal using an "experiential" semantic model and cross-participant encoding.SIGNIFICANCE STATEMENT A modern-day scientific challenge is to understand how the human brain transforms word sequences into representations of sentence meaning. A recent approach, emerging from advances in functional neuroimaging, big data, and machine learning, is to computationally model meaning, and use models to predict brain activity. Such models have helped map a cortical semantic information-processing network. However, how unified sentence-level information, as opposed to word-level units, is represented throughout this network remains unclear. This is because models have typically represented sentences as unordered "bags-of-words." Using a deep artificial neural network that recurrently and nonlinearly combines word representations into unified propositional sentence representations, we provide evidence that sentence-level information is encoded throughout a cortical network, rather than in a single region.

Late dominance of the right hemisphere during narrative comprehension.

PET and fMRI studies suggest that auditory narrative comprehension is supported by a bilateral multilobar cortical network. The superior temporal resolution of magnetoencephalography (MEG) makes it an attractive tool to investigate the dynamics of how different neuroanatomic substrates engage during narrative comprehension. Using beta-band power changes as a marker of cortical engagement, we studied MEG responses during an auditory story comprehension task in 31 healthy adults. The protocol consisted of two runs, each interleaving 7 blocks of the story comprehension task with 15 blocks of an auditorily presented math task as a control for phonological processing, working memory, and attention processes. Sources at the cortical surface were estimated with a frequency-resolved beamformer. Beta-band power was estimated in the frequency range of 16-24 Hz over 1-sec epochs starting from 400 msec after stimulus onset until the end of a story or math problem presentation. These power estimates were compared to 1-second epochs of data before the stimulus block onset. The task-related cortical engagement was inferred from beta-band power decrements. Group-level source activations were statistically compared using non-parametric permutation testing. A story-math contrast of beta-band power changes showed greater bilateral cortical engagement within the fusiform gyrus, inferior and middle temporal gyri, parahippocampal gyrus, and left inferior frontal gyrus (IFG) during story comprehension. A math-story contrast of beta power decrements showed greater bilateral but left-lateralized engagement of the middle frontal gyrus and superior parietal lobule. The evolution of cortical engagement during five temporal windows across the presentation of stories showed significant involvement during the first interval of the narrative of bilateral opercular and insular regions as well as the ventral and lateral temporal cortex, extending more posteriorly on the left and medially on the right. Over time, there continued to be sustained right anterior ventral temporal engagement, with increasing involvement of the right anterior parahippocampal gyrus, STG, MTG, posterior superior temporal sulcus, inferior parietal lobule, frontal operculum, and insula, while left hemisphere engagement decreased. Our findings are consistent with prior imaging studies of narrative comprehension, but in addition, they demonstrate increasing right-lateralized engagement over the course of narratives, suggesting an important role for these right-hemispheric regions in semantic integration as well as social and pragmatic inference processing.

An Integrated Neural Decoder of Linguistic and Experiential Meaning.

The brain is thought to combine linguistic knowledge of words and nonlinguistic knowledge of their referents to encode sentence meaning. However, functional neuroimaging studies aiming at decoding language meaning from neural activity have mostly relied on distributional models of word semantics, which are based on patterns of word co-occurrence in text corpora. Here, we present initial evidence that modeling nonlinguistic "experiential" knowledge contributes to decoding neural representations of sentence meaning. We model attributes of peoples' sensory, motor, social, emotional, and cognitive experiences with words using behavioral ratings. We demonstrate that fMRI activation elicited in sentence reading is more accurately decoded when this experiential attribute model is integrated with a text-based model than when either model is applied in isolation (participants were 5 males and 9 females). Our decoding approach exploits a representation-similarity-based framework, which benefits from being parameter free, while performing at accuracy levels comparable with those from parameter fitting approaches, such as ridge regression. We find that the text-based model contributes particularly to the decoding of sentences containing linguistically oriented "abstract" words and reveal tentative evidence that the experiential model improves decoding of more concrete sentences. Finally, we introduce a cross-participant decoding method to estimate an upper bound on model-based decoding accuracy. We demonstrate that a substantial fraction of neural signal remains unexplained, and leverage this gap to pinpoint characteristics of weakly decoded sentences and hence identify model weaknesses to guide future model development.SIGNIFICANCE STATEMENT Language gives humans the unique ability to communicate about historical events, theoretical concepts, and fiction. Although words are learned through language and defined by their relations to other words in dictionaries, our understanding of word meaning presumably draws heavily on our nonlinguistic sensory, motor, interoceptive, and emotional experiences with words and their referents. Behavioral experiments lend support to the intuition that word meaning integrates aspects of linguistic and nonlinguistic "experiential" knowledge. However, behavioral measures do not provide a window on how meaning is represented in the brain and tend to necessitate artificial experimental paradigms. We present a model-based approach that reveals early evidence that experiential and linguistically acquired knowledge can be detected in brain activity elicited in reading natural sentences.

Mapping language from MEG beta power modulations during auditory and visual naming.

Evaluation of language dominance is an essential step prior to epilepsy surgery. There is no consensus on an optimal methodology for determining language dominance using magnetoencephalography (MEG). Oscillatory dynamics are increasingly recognized as being of fundamental importance for brain function and dysfunction. Using task-related beta power modulations in MEG, we developed an analysis framework for localizing and lateralizing areas relevant to language processing in patients with focal epilepsy. We examined MEG responses from 29 patients (age 42 â€‹± â€‹13 years, 15M/14F) during auditory description naming (ADN) and visual picture naming (PN). MEG data were preprocessed using a combination of spatiotemporal filtering, signal thresholding, and ICA decomposition. Beta-band 17-25Hz power decrements were examined at both sensor and source levels. Volumetric grids of anatomical source space were constructed in MNI space at 8 â€‹mm isotropic resolution, and beta-band power changes were estimated using the dynamic imaging of coherent sources beamformer technique. A 600 â€‹ms temporal-window that ends 100 â€‹ms before speech onset was selected for analysis, to focus on later stages of word production such as phonologic selection and motor speech preparation. Cluster-based permutation testing was employed for patient- and group-level statistical inferences. Automated anatomic labeling atlas-driven laterality indices (LIs) were computed for 13 left and right language- and motor speech-related cortical regions. Group localization of ADN and PN consistently revealed significant task-related decrements of beta-power within language-related areas in the frontal, temporal and parietal lobes as well as motor-related regions of precentral/premotor and postcentral/somatomotor gyri. A region-of-interest analysis of ADN and PN suggested a strong correlation of r â€‹= â€‹0.74 (p â€‹< â€‹0.05, FDR corrected) between the two tasks within the language-related brain regions, with the highest spatial overlap in the prefrontal areas. Laterality indices (LIs) consistently showed left dominance (LI â€‹> â€‹0.1) for most individuals (93% and 82% during ADN and PN, respectively), with average LIs of 0.40 â€‹± â€‹0.25 and 0.34 â€‹± â€‹0.20 for ADN and PN, respectively. Source analysis of task-related beta power decrements appears to be a reliable method for lateralizing and localizing brain activations associated with language processing in patients with epilepsy.

Temporal lobe regions essential for preserved picture naming after left temporal epilepsy surgery.

OBJECTIVE: To define left temporal lobe regions where surgical resection produces a persistent postoperative decline in naming visual objects. METHODS: Pre- and postoperative brain magnetic resonance imaging data and picture naming (Boston Naming Test) scores were obtained prospectively from 59 people with drug-resistant left temporal lobe epilepsy. All patients had left hemisphere language dominance at baseline and underwent surgical resection or ablation in the left temporal lobe. Postoperative naming assessment occurred approximately 7 months after surgery. Surgical lesions were mapped to a standard template, and the relationship between presence or absence of a lesion and the degree of naming decline was tested at each template voxel while controlling for effects of overall lesion size. RESULTS: Patients declined by an average of 15% in their naming score, with wide variation across individuals. Decline was significantly related to damage in a cluster of voxels in the ventral temporal lobe, located mainly in the fusiform gyrus approximately 4-6 cm posterior to the temporal tip. Extent of damage to this region explained roughly 50% of the variance in outcome. Picture naming decline was not related to hippocampal or temporal pole damage. SIGNIFICANCE: The results provide the first statistical map relating lesion location in left temporal lobe epilepsy surgery to picture naming decline, and they support previous observations of transient naming deficits from electrical stimulation in the basal temporal cortex. The critical lesion is relatively posterior and could be avoided in many patients undergoing left temporal lobe surgery for intractable epilepsy.

Changes in description naming for common and proper nouns after left anterior temporal lobectomy.

Numerous studies have shown that surgical resection of the left anterior temporal lobe (ATL) is associated with a decline in object naming ability (Hermann et al., 1999). In contrast, few studies have examined the effects of left ATL surgery on auditory description naming (ADN) or category-specific naming. Compared with object naming, which loads heavily on visual recognition processes, ADN provides a more specific measure of concept retrieval. The present study examined ADN declines in a large group of patients who were tested before and after left ATL surgery, using a 2 × 2 × 2 factorial manipulation of uniqueness (common vs. proper nouns), taxonomic category (living vs. nonliving things), and time (pre- vs. postsurgery). Significant declines occurred across all categories but were substantially larger for proper living (PL) concepts, i.e., famous individuals. The disproportionate decline in PL noun naming relative to other conditions is consistent with the notion that the left ATL is specialized not only for retrieval of unique entity concepts, but also plays a role in processing social concepts and person-specific features.

Multiple Regions of a Cortical Network Commonly Encode the Meaning of Words in Multiple Grammatical Positions of Read Sentences.

Deciphering how sentence meaning is represented in the brain remains a major challenge to science. Semantically related neural activity has recently been shown to arise concurrently in distributed brain regions as successive words in a sentence are read. However, what semantic content is represented by different regions, what is common across them, and how this relates to words in different grammatical positions of sentences is weakly understood. To address these questions, we apply a semantic model of word meaning to interpret brain activation patterns elicited in sentence reading. The model is based on human ratings of 65 sensory/motor/emotional and cognitive features of experience with words (and their referents). Through a process of mapping functional Magnetic Resonance Imaging activation back into model space we test: which brain regions semantically encode content words in different grammatical positions (e.g., subject/verb/object); and what semantic features are encoded by different regions. In left temporal, inferior parietal, and inferior/superior frontal regions we detect the semantic encoding of words in all grammatical positions tested and reveal multiple common components of semantic representation. This suggests that sentence comprehension involves a common core representation of multiple words' meaning being encoded in a network of regions distributed across the brain.

Neuroanatomical correlates of personality traits in temporal lobe epilepsy: Findings from the Epilepsy Connectome Project.

Behavioral and personality disorders in temporal lobe epilepsy (TLE) have been a topic of interest and controversy for decades, with less attention paid to alterations in normal personality structure and traits. In this investigation, core personality traits (the Big 5) and their neurobiological correlates in TLE were explored using the Neuroticism Extraversion Openness-Five Factor Inventory (NEO-FFI) and structural magnetic resonance imaging (MRI) through the Epilepsy Connectome Project (ECP). NEO-FFI scores from 67 individuals with TLE (34.6 ±â€¯9.5 years; 67% women) were compared to 31 healthy controls (32.8 ±â€¯8.9 years; 41% women) to assess differences in the Big 5 traits (agreeableness, openness, conscientiousness, neuroticism, and extraversion). Individuals with TLE showed significantly higher neuroticism, with no significant differences on the other traits. Neural correlates of neuroticism were then determined in participants with TLE including cortical and subcortical volumes. Distributed reductions in cortical gray matter volumes were associated with increased neuroticism. Subcortically, hippocampal and amygdala volumes were negatively associated with neuroticism. These results offer insight into alterations in the Big 5 personality traits in TLE and their brain-related correlates.

The Neural Basis of Successful Word Reading in Aphasia.

Understanding the neural basis of recovery from stroke is a major research goal. Many functional neuroimaging studies have identified changes in brain activity in people with aphasia, but it is unclear whether these changes truly support successful performance or merely reflect increased task difficulty. We addressed this problem by examining differences in brain activity associated with correct and incorrect responses on an overt reading task. On the basis of previous proposals that semantic retrieval can assist pronunciation of written words, we hypothesized that recruitment of semantic areas would be greater on successful trials. Participants were 21 patients with left-hemisphere stroke with phonologic retrieval deficits. They read words aloud during an event-related fMRI paradigm. BOLD signals obtained during correct and incorrect trials were contrasted to highlight brain activity specific to successful trials. Successful word reading was associated with higher BOLD signal in the left angular gyrus. In contrast, BOLD signal in bilateral posterior inferior frontal cortex, SMA, and anterior cingulate cortex was greater on incorrect trials. These data show for the first time the brain regions where neural activity is correlated specifically with successful performance in people with aphasia. The angular gyrus is a key node in the semantic network, consistent with the hypothesis that additional recruitment of the semantic system contributes to successful word production when phonologic retrieval is impaired. Higher activity in other brain regions during incorrect trials likely reflects secondary engagement of attention, working memory, and error monitoring processes when phonologic retrieval is unsuccessful.

Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.

We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.

Heteromodal Cortical Areas Encode Sensory-Motor Features of Word Meaning.

UNLABELLED: The capacity to process information in conceptual form is a fundamental aspect of human cognition, yet little is known about how this type of information is encoded in the brain. Although the role of sensory and motor cortical areas has been a focus of recent debate, neuroimaging studies of concept representation consistently implicate a network of heteromodal areas that seem to support concept retrieval in general rather than knowledge related to any particular sensory-motor content. We used predictive machine learning on fMRI data to investigate the hypothesis that cortical areas in this "general semantic network" (GSN) encode multimodal information derived from basic sensory-motor processes, possibly functioning as convergence-divergence zones for distributed concept representation. An encoding model based on five conceptual attributes directly related to sensory-motor experience (sound, color, shape, manipulability, and visual motion) was used to predict brain activation patterns associated with individual lexical concepts in a semantic decision task. When the analysis was restricted to voxels in the GSN, the model was able to identify the activation patterns corresponding to individual concrete concepts significantly above chance. In contrast, a model based on five perceptual attributes of the word form performed at chance level. This pattern was reversed when the analysis was restricted to areas involved in the perceptual analysis of written word forms. These results indicate that heteromodal areas involved in semantic processing encode information about the relative importance of different sensory-motor attributes of concepts, possibly by storing particular combinations of sensory and motor features. SIGNIFICANCE STATEMENT: The present study used a predictive encoding model of word semantics to decode conceptual information from neural activity in heteromodal cortical areas. The model is based on five sensory-motor attributes of word meaning (color, shape, sound, visual motion, and manipulability) and encodes the relative importance of each attribute to the meaning of a word. This is the first demonstration that heteromodal areas involved in semantic processing can discriminate between different concepts based on sensory-motor information alone. This finding indicates that the brain represents concepts as multimodal combinations of sensory and motor representations.

Surface errors without semantic impairment in acquired dyslexia: a voxel-based lesion-symptom mapping study.

Patients with surface dyslexia have disproportionate difficulty pronouncing irregularly spelled words (e.g. pint), suggesting impaired use of lexical-semantic information to mediate phonological retrieval. Patients with this deficit also make characteristic 'regularization' errors, in which an irregularly spelled word is mispronounced by incorrect application of regular spelling-sound correspondences (e.g. reading plaid as 'played'), indicating over-reliance on sublexical grapheme-phoneme correspondences. We examined the neuroanatomical correlates of this specific error type in 45 patients with left hemisphere chronic stroke. Voxel-based lesion-symptom mapping showed a strong positive relationship between the rate of regularization errors and damage to the posterior half of the left middle temporal gyrus. Semantic deficits on tests of single-word comprehension were generally mild, and these deficits were not correlated with the rate of regularization errors. Furthermore, the deep occipital-temporal white matter locus associated with these mild semantic deficits was distinct from the lesion site associated with regularization errors. Thus, in contrast to patients with surface dyslexia and semantic impairment from anterior temporal lobe degeneration, surface errors in our patients were not related to a semantic deficit. We propose that these patients have an inability to link intact semantic representations with phonological representations. The data provide novel evidence for a post-semantic mechanism mediating the production of surface errors, and suggest that the posterior middle temporal gyrus may compute an intermediate representation linking semantics with phonology.

Concept Representation Reflects Multimodal Abstraction: A Framework for Embodied Semantics.

Recent research indicates that sensory and motor cortical areas play a significant role in the neural representation of concepts. However, little is known about the overall architecture of this representational system, including the role played by higher level areas that integrate different types of sensory and motor information. The present study addressed this issue by investigating the simultaneous contributions of multiple sensory-motor modalities to semantic word processing. With a multivariate fMRI design, we examined activation associated with 5 sensory-motor attributes--color, shape, visual motion, sound, and manipulation--for 900 words. Regions responsive to each attribute were identified using independent ratings of the attributes' relevance to the meaning of each word. The results indicate that these aspects of conceptual knowledge are encoded in multimodal and higher level unimodal areas involved in processing the corresponding types of information during perception and action, in agreement with embodied theories of semantics. They also reveal a hierarchical system of abstracted sensory-motor representations incorporating a major division between object interaction and object perception processes.

Toward a brain-based componential semantic representation.

Componential theories of lexical semantics assume that concepts can be represented by sets of features or attributes that are in some sense primitive or basic components of meaning. The binary features used in classical category and prototype theories are problematic in that these features are themselves complex concepts, leaving open the question of what constitutes a primitive feature. The present availability of brain imaging tools has enhanced interest in how concepts are represented in brains, and accumulating evidence supports the claim that these representations are at least partly "embodied" in the perception, action, and other modal neural systems through which concepts are experienced. In this study we explore the possibility of devising a componential model of semantic representation based entirely on such functional divisions in the human brain. We propose a basic set of approximately 65 experiential attributes based on neurobiological considerations, comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences. We provide normative data on the salience of each attribute for a large set of English nouns, verbs, and adjectives, and show how these attribute vectors distinguish a priori conceptual categories and capture semantic similarity. Robust quantitative differences between concrete object categories were observed across a large number of attribute dimensions. A within- versus between-category similarity metric showed much greater separation between categories than representations derived from distributional (latent semantic) analysis of text. Cluster analyses were used to explore the similarity structure in the data independent of a priori labels, revealing several novel category distinctions. We discuss how such a representation might deal with various longstanding problems in semantic theory, such as feature selection and weighting, representation of abstract concepts, effects of context on semantic retrieval, and conceptual combination. In contrast to componential models based on verbal features, the proposed representation systematically relates semantic content to large-scale brain networks and biologically plausible accounts of concept acquisition.

Cerebral localization of impaired phonological retrieval during rhyme judgment.

OBJECTIVE: Computation of a prearticulatory phonological representation (phonological access, or phonological retrieval) is an essential process in speech production whose neural localization is not clear. This study combined a specific behavioral measure of phonological access and multivariate voxel-based lesion-symptom mapping (VLSM) in a series of left hemisphere stroke patients to identify brain regions critical for this process. METHODS: Phonological access was assessed in 40 chronic ischemic stroke patients using a silent rhyming task to avoid confounds with motor planning and articulation deficits. Additional covariates were incorporated in the VLSM analysis to control for orthographic and working memory demands of the rhyming task, and for age, education, and total lesion volume. The resulting t statistic maps were thresholded at voxelwise p < 0.001 and cluster-corrected at a familywise error of p < 0.05. RESULTS: Phonological access impairment was correlated with damage to a focal region of cortex and white matter caudal to the posterior sylvian fissure, which included the posterior supramarginal gyrus and adjacent anterior angular gyrus, planum temporale, and posterior superior temporal gyrus. No correlation was observed with Broca's area, insula, or sensorimotor cortex. An additional VLSM showed no correlation between damage in this posterior perisylvian region and spoken word comprehension. INTERPRETATION: This is the first demonstration of a specific lesion correlate for phonological access impairment. Although this posterior perisylvian region overlaps with some versions of the classical Wernicke area, the present results demonstrate its involvement in prearticulatory phonological production rather than speech perception or lexical-semantic processes.

The role of left occipitotemporal cortex in reading: reconciling stimulus, task, and lexicality effects.

Although the left posterior occipitotemporal sulcus (pOTS) has been called a visual word form area, debate persists over the selectivity of this region for reading relative to general nonorthographic visual object processing. We used high-resolution functional magnetic resonance imaging to study left pOTS responses to combinatorial orthographic and object shape information. Participants performed naming and visual discrimination tasks designed to encourage or suppress phonological encoding. During the naming task, all participants showed subregions within left pOTS that were more sensitive to combinatorial orthographic information than to object information. This difference disappeared, however, when phonological processing demands were removed. Responses were stronger to pseudowords than to words, but this effect also disappeared when phonological processing demands were removed. Subregions within the left pOTS are preferentially activated when visual input must be mapped to a phonological representation (i.e., a name) and particularly when component parts of the visual input must be mapped to corresponding phonological elements (consonant or vowel phonemes). Results indicate a specialized role for subregions within the left pOTS in the isomorphic mapping of familiar combinatorial visual patterns to phonological forms. This process distinguishes reading from picture naming and accounts for a wide range of previously reported stimulus and task effects in left pOTS.

Perceptual demand modulates activation of human auditory cortex in response to task-irrelevant sounds.

In the visual modality, perceptual demand on a goal-directed task has been shown to modulate the extent to which irrelevant information can be disregarded at a sensory-perceptual stage of processing. In the auditory modality, the effect of perceptual demand on neural representations of task-irrelevant sounds is unclear. We compared simultaneous ERPs and fMRI responses associated with task-irrelevant sounds across parametrically modulated perceptual task demands in a dichotic-listening paradigm. Participants performed a signal detection task in one ear (Attend ear) while ignoring task-irrelevant syllable sounds in the other ear (Ignore ear). Results revealed modulation of syllable processing by auditory perceptual demand in an ROI in middle left superior temporal gyrus and in negative ERP activity 130-230 msec post stimulus onset. Increasing the perceptual demand in the Attend ear was associated with a reduced neural response in both fMRI and ERP to task-irrelevant sounds. These findings are in support of a selection model whereby ongoing perceptual demands modulate task-irrelevant sound processing in auditory cortex.