An inclusive multivariate approach to neural localization of language components.

To determine how language is implemented in the brain, it is important to know which brain areas are primarily engaged in language processing and which are not. Existing protocols for localizing language are typically univariate, treating each small unit of brain volume as independent. One prominent example that focuses on the overall language network in functional magnetic resonance imaging (fMRI) uses a contrast between neural responses to sentences and sets of pseudowords (pronounceable nonwords). This contrast reliably activates peri-sylvian language areas but is less sensitive to extra-sylvian areas that are also known to support aspects of language such as word meanings (semantics). In this study, we assess areas where a multivariate, pattern-based approach shows high reproducibility across multiple measurements and participants, identifying these areas as multivariate regions of interest (mROI). We then perform a representational similarity analysis (RSA) of an fMRI dataset where participants made familiarity judgments on written words. We also compare those results to univariate regions of interest (uROI) taken from previous sentences > pseudowords contrasts. RSA with word stimuli defined in terms of their semantic distance showed greater correspondence with neural patterns in mROI than uROI. This was confirmed in two independent datasets, one involving single-word recognition, and the other focused on the meaning of noun-noun phrases by contrasting meaningful phrases > pseudowords. In all cases, areas of spatial overlap between mROI and uROI showed the greatest neural association. This suggests that ROIs defined in terms of multivariate reproducibility can help localize components of language such as semantics. The multivariate approach can also be extended to focus on other aspects of language such as phonology, and can be used along with the univariate approach for inclusively mapping language cortex.

Correspondence between cognitive and neural representations for phonology, orthography, and semantics in supramarginal compared to angular gyrus.

The angular and supramarginal gyri (AG and SMG) together constitute the inferior parietal lobule (IPL) and have been associated with cognitive functions that support reading. How those functions are distributed across the AG and SMG is a matter of debate, the resolution of which is hampered by inconsistencies across stereotactic atlases provided by the major brain image analysis software packages. Schematic results from automated meta-analyses suggest primarily semantic (word meaning) processing in the left AG, with more spatial overlap among phonological (auditory word form), orthographic (visual word form), and semantic processing in the left SMG. To systematically test for correspondence between patterns of neural activation and phonological, orthographic, and semantic representations, we re-analyze a functional magnetic resonance imaging data set of participants reading aloud 465 words. Using representational similarity analysis, we test the hypothesis that within cytoarchitecture-defined subregions of the IPL, phonological representations are primarily associated with the SMG, while semantic representations are primarily associated with the AG. To the extent that orthographic representations can be de-correlated from phonological representations, they will be associated with cortex peripheral to the IPL, such as the intraparietal sulcus. Results largely confirmed these hypotheses, with some nuanced exceptions, which we discuss in terms of neurally inspired computational cognitive models of reading that learn mappings among distributed representations for orthography, phonology, and semantics. De-correlating constituent representations making up complex cognitive processes, such as reading, by careful selection of stimuli, representational formats, and analysis techniques, are promising approaches for bringing additional clarity to brain structure-function relationships.

Exposure to violence is associated with decreased neural connectivity in emotion regulation and cognitive control, but not working memory, networks after accounting for socioeconomic status: a preliminary study.

Previous research has demonstrated behavioral and neural differences associated with experiencing adversity. However, adversity is unlikely to be a monolithic construct, and we expect that examining effects of more specific components such as exposure to violence in the home community will yield more concretely interpretable results. Here we account for effects of low socioeconomic status (SES) to examine the specific effects of exposure to violence on functional connectivity between brain areas known to be related to emotion regulation and working memory. Decreased resting state functional connectivity for individuals exposed to high compared to low levels of violence during childhood was predicted for two sets of areas: (1) bilateral amygdala with anterior medial regions involved in cognitive control of emotion, and (2) the right dorsolateral prefrontal cortex (dlPFC) with frontal and parietal regions implicated in working memory. Consistent with our predictions, increasing exposure to violence was related to decreased resting state functional connectivity between the right amygdala and anterior cingulate cortex, even after accounting for SES. Also after accounting for SES, exposure to violence was related to reductions in connectivity between the right dlPFC and frontal regions, but not with parietal regions typically associated with working memory. Overall, this pattern suggests increased exposure to violence in childhood is associated with reduced connectivity among key areas of the circuitry involved in emotion regulation and cognitive control, but not working memory. These results offer insight into the neural underpinnings of behavioral outcomes associated with exposure to violence, laying the foundation for ultimately designing interventions to address the effects of such exposure.

Neural differences in social and figurative language processing on the autism spectrum.

Individuals on the autism spectrum often have trouble with social and figurative language. As social language is often figurative, it can be challenging to disentangle the cognitive and neural sources of these difficulties. Neural systems for social cognition and language comprehension overlap in areas involved in retrieving linguistic meaning (semantics), such as the anterior temporal lobe (ATL), ventro-medial prefrontal cortex (vmPFC), posterior cingulate cortex (PCC), and posterior middle temporal gyrus (pMTG). Using adjective-noun phrases, we manipulated social/nonsocial and figurative/literal dimensions, which we expected to activate distinct but overlapping regions. We hypothesized that activation differences in the group with autism (AUT) would be greater for more social and figurative stimuli. During fMRI, participants in the AUT group (N = 19) and those in the non-autistic comparison (NAC) group (N = 22) made familiarity judgments to 192 phrases in a balanced 2 × 2 (social/nonsocial x figurative/literal) design. Social phrases activated the PCC in all participants, but only the NAC group activated the vmPFC. Figurative phrases were rated as more literal by the AUT group, with the figurative-literal phrase contrast showing no activation in the AUT group, but activating the PCC and right pMTG in the NAC group. The one significant group-level neural difference was for the social-figurative condition predicted to be most different between groups: greater activation for the AUT group in the right ATL. Differences in the right ATL and pMTG in the AUT group suggest altered engagement of right homologues of the canonical semantic network being recruited for processing combined social and figurative language.

Neural Components of Reading Revealed by Distributed and Symbolic Computational Models.

Determining how the cognitive components of reading - orthographic, phonological, and semantic representations - are instantiated in the brain has been a longstanding goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit non-symbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling-sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded with neural activity. However, ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would also greatly facilitate the study of brain-behavior relationships by eliminating the laborious step of having a human expert manually segment the lesion on each brain scan. We propose a multi-modal multi-path convolutional neural network system for automating stroke lesion segmentation. Our system has nine end-to-end UNets that take as input 2-dimensional (2D) slices and examines all three planes with three different normalizations. Outputs from these nine total paths are concatenated into a 3D volume that is then passed to a 3D convolutional neural network to output a final lesion mask. We trained and tested our method on datasets from three sources: Medical College of Wisconsin (MCW), Kessler Foundation (KF), and the publicly available Anatomical Tracings of Lesions After Stroke (ATLAS) dataset. To promote wide applicability, lesions were included from both subacute (1 to 5 weeks) and chronic ( >  3 months) phases post stroke, and were of both hemorrhagic and ischemic etiology. Cross-study validation results (with independent training and validation datasets) were obtained to compare with previous methods based on naive Bayes, random forests, and three recently published convolutional neural networks. Model performance was quantified in terms of the Dice coefficient, a measure of spatial overlap between the model-identified lesion and the human expert-identified lesion, where 0 is no overlap and 1 is complete overlap. Training on the KF and MCW images and testing on the ATLAS images yielded a mean Dice coefficient of 0.54. This was reliably better than the next best previous model, UNet, at 0.47. Reversing the train and test datasets yields a mean Dice of 0.47 on KF and MCW images, whereas the next best UNet reaches 0.45. With all three datasets combined, the current system compared to previous methods also attained a reliably higher cross-validation accuracy. It also achieved high Dice values for many smaller lesions that existing methods have difficulty identifying. Overall, our system is a clear improvement over previous methods for automating stroke lesion segmentation, bringing us an important step closer to the inter-rater accuracy level of human experts.

Neural Effects of Gender and Age Interact in Reading.

There has been an enduring fascination with the possibility of gender differences in the brain basis of language, yet the evidence has been largely equivocal. Evidence does exist, however, for women being at greater risk than men for developing psychomotor slowing and even Alzheimer disease with advancing age, although this may in part at least be due to women living longer. We examined whether gender, age, or their interaction influenced language-related or more general processes in reading. Reading consists of elements related to language, such as the processing of word sound patterns (phonology) and meanings (semantics), along with the lead-in processes of visual perception and orthographic (visual word form) processing that are specific to reading. To test for any influence of gender and age on either semantic processing or orthography-phonology mapping, we tested for an interaction of these factors on differences between meaningful words and meaningless but pronounceable non-words. We also tested for effects of gender and age on how the number of letters in a word modulates neural activity for reading. This lead-in process presumably relates most to orthography. Behaviorally, reading accuracy declined with age for both men and women, but the decline was steeper for men. Neurally, interactions between gender and age were found exclusively in medial orbitofrontal cortex (mOFC). These factors influenced the word-non-word contrast, but not the parametric effect of number of letters. Men showed increasing activation with age for non-words compared to words. Women showed only slightly decreasing activation with age for novel letter strings. Overall, we found interactive effects of gender and age in the mOFC on the left primarily for novel letter strings, but no such interaction for a contrast that emphasized visual form processing. Thus the interaction of gender with age in the mOFC may relate most to orthography-phonology conversion for unfamiliar letter strings. More generally, this suggests that efforts to investigate effects of gender on language-related tasks may benefit from taking into account age and the type of cognitive process being highlighted.

Cerebral perfusion of the left reading network predicts recovery of reading in subacute to chronic stroke.

Better understanding of cerebral blood flow (CBF) perfusion in stroke recovery can help inform decisions about optimal timing and targets of restorative treatments. In this study, we examined the relationship between cerebral perfusion and recovery from stroke-induced reading deficits. Left stroke patients were tested with a noninvasive CBF measure (arterial spin labeling) <5 weeks post-stroke, and a subset had follow up testing >3 months post-stroke. We measured blood flow perfusion within the left and right sides of the brain, in areas surrounding the lesion, and areas belonging to the reading network. Two hypotheses were tested. The first was that recovery of reading function depends on increased perfusion around the stroke lesion. This hypothesis was not supported by our findings. The second hypothesis was that increased perfusion of intact areas within the reading circuit is tightly coupled with recovery. Our findings are consistent with this hypothesis. Specifically, higher perfusion in the left reading network measured during the subacute stroke period predicted better reading ability and phonology competence in the chronic period. In contrast, higher perfusion of the right homologous regions was associated with decreased reading accuracy and phonology competence in the subacute and chronic periods. These findings suggest that recovery of reading and language competence may rely on improved blood flow in the reading network of the language-dominant hemisphere.

Duality of Function: Activation for Meaningless Nonwords and Semantic Codes in the Same Brain Areas.

Studies of the neural substrates of semantic (word meaning) processing have typically focused on semantic manipulations, with less consideration for potential differences in difficulty across conditions. While the idea that particular brain regions can support multiple functions is widely accepted, studies of specific cognitive domains rarely test for co-location with other functions. Here we start with standard univariate analyses comparing words to meaningless nonwords, replicating our recent finding that this contrast can activate task-positive regions for words, and default-mode regions in the putative semantic network for nonwords, pointing to difficulty effects. Critically, this was followed up with a multivariate analysis to test whether the same areas activated for meaningless nonwords contained semantic information sufficient to distinguish high- from low-imageability words. Indeed, this classification was performed reliably better than chance at 75% accuracy. This is compatible with two non-exclusive interpretations. Numerous areas in the default-mode network are task-negative in the sense of activating for less demanding conditions, and the same areas contain information supporting semantic cognition. Therefore, while areas of the default mode network have been hypothesized to support semantic cognition, we offer evidence that these areas can respond to both domain-general difficulty effects, and to specific aspects of semantics.

Elaborative feedback: Engaging reward and task-relevant brain regions promotes learning in pseudoword reading aloud.

Although much is known about the cognitive and neural basis of establishing letter-sound mappings in learning word forms, relatively little is known about what makes for the most effective feedback during this process. We sought to determine the neural basis by which elaborative feedback (EF), which contains both reward-related and content-specific information, may be more helpful than feedback containing only one kind of information (simple positive feedback, PF) or the other (content feedback, CF) in learning orthography-phonology (spelling-sound) mappings for novel letter strings. Compared to CF, EF activated the ventromedial prefrontal cortex, implicated in reward processing. Compared to PF, EF activated the posterior middle temporal, superior temporal, and supramarginal gyri-regions implicated in orthography-phonology conversion. In the same comparison, EF also activated the left fusiform gyrus/visual word form area-implicated in orthographic processing. Also EF, but not CF or PF, modulated activity in the caudate nucleus. In a postscan questionnaire, EF and PF were rated as more pleasant than CF, suggesting that modulation of the caudate for EF may be due to the coupling of reward and skill content. These findings suggest the enhanced effectiveness of EF may be due to concurrent activation of reward-related and task-relevant brain regions.

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.

Reversing the Standard Neural Signature of the Word-Nonword Distinction.

The distinction between letter strings that form words and those that look and sound plausible but are not meaningful is a basic one. Decades of functional neuroimaging experiments have used this distinction to isolate the neural basis of lexical (word level) semantics, associated with areas such as the middle temporal, angular, and posterior cingulate gyri that overlap the default mode network. In two fMRI experiments, a different set of findings emerged when word stimuli were used that were less familiar (measured by word frequency) than those typically used. Instead of activating default mode network areas often associated with semantic processing, words activated task-positive areas such as the inferior pFC and SMA, along with multifunctional ventral occipitotemporal cortices related to reading, whereas nonwords activated default mode areas previously associated with semantics. Effective connectivity analyses of fMRI data on less familiar words showed activation driven by task-positive and multifunctional reading-related areas, whereas highly familiar words showed bottom-up activation flow from occipitotemporal cortex. These findings suggest that functional neuroimaging correlates of semantic processing are less stable than previously assumed, with factors such as word frequency influencing the balance between task-positive, reading-related, and default mode networks. More generally, this suggests that results of contrasts typically interpreted in terms of semantic content may be more influenced by factors related to task difficulty than is widely appreciated.

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.

Neurally dissociable cognitive components of reading deficits in subacute stroke.

According to cognitive models of reading, words are processed by interacting orthographic (spelling), phonological (sound), and semantic (meaning) information. Despite extensive study of the neural basis of reading in healthy participants, little group data exist on patients with reading deficits from focal brain damage pointing to critical neural systems for reading. Here, we report on one such study. We have performed neuropsychological testing and magnetic resonance imaging on 11 patients with left-hemisphere stroke (<=5 weeks post-stroke). Patients completed tasks assessing cognitive components of reading such as semantics (matching picture or word choices to a target based on meaning), phonology (matching word choices to a target based on rhyming), and orthography (a two-alternative forced choice of the most plausible non-word). They also read aloud pseudowords and words with high or low levels of usage frequency, imageability, and spelling-sound consistency. As predicted by the cognitive model, when averaged across patients, the influence of semantics was most salient for low-frequency, low-consistency words, when phonological decoding is especially difficult. Qualitative subtraction analyses revealed lesion sites specific to phonological processing. These areas were consistent with those shown previously to activate for phonology in healthy participants, including supramarginal, posterior superior temporal, middle temporal, inferior frontal gyri, and underlying white matter. Notable divergence between this analysis and previous functional imaging is the association of lesions in the mid-fusiform gyrus and anterior temporal lobe with phonological reading deficits. This study represents progress toward identifying brain lesion-deficit relationships in the cognitive components of reading. Such correspondences are expected to help not only better understand the neural mechanisms of reading, but may also help tailor reading therapy to individual neurocognitive deficit profiles.

Anatomy is strategy: skilled reading differences associated with structural connectivity differences in the reading network.

Are there multiple ways to be a skilled reader? To address this longstanding, unresolved question, we hypothesized that individual variability in using semantic information in reading aloud would be associated with neuroanatomical variation in pathways linking semantics and phonology. Left-hemisphere regions of interest for diffusion tensor imaging analysis were defined based on fMRI results, including two regions linked with semantic processing - angular gyrus (AG) and inferior temporal sulcus (ITS) - and two linked with phonological processing - posterior superior temporal gyrus (pSTG) and posterior middle temporal gyrus (pMTG). Effects of imageability (a semantic measure) on response times varied widely among individuals and covaried with the volume of pathways through the ITS and pMTG, and through AG and pSTG, partially overlapping the inferior longitudinal fasciculus and the posterior branch of the arcuate fasciculus. These results suggest strategy differences among skilled readers associated with structural variation in the neural reading network.

Neural networks underlying contributions from semantics in reading aloud.

Reading is an essential part of contemporary society, yet much is still unknown about the physiological underpinnings of its information processing components. Two influential cognitive models of reading, the connectionist and dual-route cascaded models, offer very different accounts, yet evidence for one or the other remains equivocal. These models differ in several ways, including the role of semantics (word meaning) in mapping spelling to sound. We used a new effective connectivity algorithm, IMaGES, to provide a network-level perspective on these network-level models. Left hemisphere regions of interest were defined based on main effects in functional magnetic resonance imaging and included two regions linked with semantic processing-angular gyrus (AG) and inferior temporal sulcus (ITS)-and two regions linked with phonological processing-posterior superior temporal gyrus (pSTG) and posterior middle temporal gyrus (pMTG). Participants read aloud words of high or low spelling-sound consistency, word frequency, and imageability. Only the connectionist model predicted increased contributions from semantic areas with those computing phonology for low-consistency words. Effective connectivity analyses revealed that areas supporting semantic processing (e.g., the ITS) interacted with phonological areas (e.g., the pSTG), with the pattern changing as a function of word properties. Connectivity from semantic to phonological areas emerged for high- compared to low-imageability words, and a similar pattern emerged for low-consistency words, though only under certain conditions. Analyses of individual differences also showed that variation in the strength of modulation of ITS by AG was associated with reading aloud performance. Overall, these results suggest that connections with semantic processing areas are not only associated with reading aloud, but that these connections are also associated with optimal reading performance.

Noun-noun combination: meaningfulness ratings and lexical statistics for 2,160 word pairs.

The combining of individual concepts to form an emergent concept is a fundamental aspect of language, yet much less is known about it than about processing isolated words or sentences. To facilitate research on conceptual combination, we provide meaningfulness ratings for a large set of (2,160) noun-noun pairs. Half of these pairs (1,080) are reversed versions of the other half (e.g., SKI JACKET and JACKET SKI), to facilitate the comparison of successful and unsuccessful conceptual combination independently of constituent lexical items. The computer code used for obtaining these ratings through a Web interface is provided. To further enhance the usefulness of this resource, ancillary measures obtained from other sources are also provided for each pair. These measures include associate production norms, contextual relatedness in terms of latent semantic analysis distance, total number of letters, phrase-level usage frequency, and word-level usage frequency summed across the words in each pair. Results of correlation and regression analyses are also provided for a quantitative description of the stimulus set. A subset of these stimuli was used to identify neural correlates of successful conceptual combination Graves, Binder, Desai, Conant, & Seidenberg, (NeuroImage 53:638-646, 2010). The stimuli can be used in other research and also provide benchmark data for evaluating the effectiveness of computational algorithms for predicting meaningfulness of noun-noun pairs.

Neural correlates of implicit and explicit combinatorial semantic processing.

Language consists of sequences of words, but comprehending phrases involves more than concatenating meanings: A boat house is a shelter for boats, whereas a summer house is a house used during summer, and a ghost house is typically uninhabited. Little is known about the brain bases of combinatorial semantic processes. We performed two fMRI experiments using familiar, highly meaningful phrases (lake house) and unfamiliar phrases with minimal meaning created by reversing the word order of the familiar items (house lake). The first experiment used a 1-back matching task to assess implicit semantic processing, and the second used a classification task to engage explicit semantic processing. These conditions required processing of the same words, but with more effective combinatorial processing in the meaningful condition. The contrast of meaningful versus reversed phrases revealed activation primarily during the classification task, to a greater extent in the right hemisphere, including right angular gyrus, dorsomedial prefrontal cortex, and bilateral posterior cingulate/precuneus, areas previously implicated in semantic processing. Positive correlations of fMRI signal with lexical (word-level) frequency occurred exclusively with the 1-back task and to a greater spatial extent on the left, including left posterior middle temporal gyrus and bilateral parahippocampus. These results reveal strong effects of task demands on engagement of lexical versus combinatorial processing and suggest a hemispheric dissociation between these levels of semantic representation.

Neural systems for reading aloud: a multiparametric approach.

Reading aloud involves computing the sound of a word from its visual form. This may be accomplished 1) by direct associations between spellings and phonology and 2) by computation from orthography to meaning to phonology. These components have been studied in behavioral experiments examining lexical properties such as word frequency; length in letters or phonemes; spelling-sound consistency; semantic factors such as imageability, measures of orthographic, or phonological complexity; and others. Effects of these lexical properties on specific neural systems, however, are poorly understood, partially because high intercorrelations among lexical factors make it difficult to determine if they have independent effects. We addressed this problem by decorrelating several important lexical properties through careful stimulus selection. Functional magnetic resonance imaging data revealed distributed neural systems for mapping orthography directly to phonology, involving left supramarginal, posterior middle temporal, and fusiform gyri. Distinct from these were areas reflecting semantic processing, including left middle temporal gyrus/inferior-temporal sulcus, bilateral angular gyrus, and precuneus/posterior cingulate. Left inferior frontal regions generally showed increased activation with greater task load, suggesting a more general role in attention, working memory, and executive processes. These data offer the first clear evidence, in a single study, for the separate neural correlates of orthography-phonology mapping and semantic access during reading aloud.