T1-/T2-weighted ratio reveals no alterations to gray matter myelination in temporal lobe epilepsy.

Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.

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.

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.

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.

Prediction of Naming Outcome With fMRI Language Lateralization in Left Temporal Epilepsy Surgery.

BACKGROUND AND OBJECTIVES: Naming decline after left temporal lobe epilepsy (TLE) surgery is common and difficult to predict. Preoperative language fMRI may predict naming decline, but this application is still lacking evidence. We performed a large multicenter cohort study of the effectiveness of fMRI in predicting naming deficits after left TLE surgery. METHODS: At 10 US epilepsy centers, 81 patients with left TLE were prospectively recruited and given the Boston Naming Test (BNT) before and ≈7 months after anterior temporal lobectomy. An fMRI language laterality index (LI) was measured with an auditory semantic decision-tone decision task contrast. Correlations and a multiple regression model were built with a priori chosen predictors. RESULTS: Naming decline occurred in 56% of patients and correlated with fMRI LI (r = -0.41, p < 0.001), age at epilepsy onset (r = -0.30, p = 0.006), age at surgery (r = -0.23, p = 0.039), and years of education (r = 0.24, p = 0.032). Preoperative BNT score and duration of epilepsy were not correlated with naming decline. The regression model explained 31% of the variance, with fMRI contributing 14%, with a 96% sensitivity and 44% specificity for predicting meaningful naming decline. Cross-validation resulted in an average prediction error of 6 points. DISCUSSION: An fMRI-based regression model predicted naming outcome after left TLE surgery in a large, prospective multicenter sample, with fMRI as the strongest predictor. These results provide evidence supporting the use of preoperative language fMRI to predict language outcome in patients undergoing left TLE surgery. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that fMRI language lateralization can help in predicting naming decline after left TLE surgery.

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.

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.

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.

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.

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.

Brain aging in temporal lobe epilepsy: Chronological, structural, and functional.

The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients' brain ages. It was found that TLE patients on average have both older structural (+6.6 years) and functional (+8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional - chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.

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.

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.

Cognitive slowing and its underlying neurobiology in temporal lobe epilepsy.

Cognitive slowing is a known but comparatively under-investigated neuropsychological complication of the epilepsies in relation to other known cognitive comorbidities such as memory, executive function and language. Here we focus on a novel metric of processing speed, characterize its relative salience compared to other cognitive difficulties in epilepsy, and explore its underlying neurobiological correlates. Research participants included 55 patients with temporal lobe epilepsy (TLE) and 58 healthy controls from the Epilepsy Connectome Project (ECP) who were administered a battery of tests yielding 14 neuropsychological measures, including selected tests from the NIH Toolbox-Cognitive Battery, and underwent 3T MRI and resting state fMRI. TLE patients exhibited a pattern of generalized cognitive impairment with very few lateralized abnormalities. Using the neuropsychological measures, machine learning (Support Vector Machine binary classification model) classified the TLE and control groups with 74% accuracy with processing speed (NIH Toolbox Pattern Comparison Processing Speed Test) the best predictor. In TLE, slower processing speed was associated predominantly with decreased local gyrification in regions including the rostral and caudal middle frontal gyrus, inferior precentral cortex, insula, inferior parietal cortex (angular and supramarginal gyri), lateral occipital cortex, rostral anterior cingulate, and medial orbital frontal regions, as well as three small regions of the temporal lobe. Slower processing speed was also associated with decreased connectivity between the primary visual cortices in both hemispheres and the left supplementary motor area, as well as between the right parieto-occipital sulcus and right middle insular area. Overall, slowed processing speed is an important cognitive comorbidity of TLE associated with altered brain structure and connectivity.

Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning.

The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.

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.

Effective Connectivity Within the Default Mode Network in Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project.

The Epilepsy Connectome Project examines the differences in connectomes between temporal lobe epilepsy (TLE) patients and healthy controls. Using these data, the effective connectivity of the default mode network (DMN) in patients with left TLE compared with healthy controls was investigated using spectral dynamic causal modeling (spDCM) of resting-state functional magnetic resonance imaging data. Group comparisons were made using two parametric empirical Bayes (PEB) models. The first level of each PEB model consisted of each participant's spDCM. Two different second-level models were constructed: the first comparing effective connectivity of the groups directly and the second using the Rey Auditory Verbal Learning Test (RAVLT) delayed free recall index as a covariate at the second level to assess effective connectivity controlling for the poor memory performance of left TLE patients. After an automated search over the nested parameter space and thresholding parameters at 95% posterior probability, both models revealed numerous connections in the DMN, which lead to inhibition of the left hippocampal formation. Left hippocampal formation inhibition may be an inherent result of the left temporal epileptogenic focus as memory differences were controlled for in one model and the same connections remained. An excitatory connection from the posterior cingulate cortex to the medial prefrontal cortex was found to be concomitant with left hippocampal formation inhibition in TLE patients when including RAVLT delayed free recall at the second level.

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.

Lesion localization of speech comprehension deficits in chronic aphasia.

OBJECTIVE: Voxel-based lesion-symptom mapping (VLSM) was used to localize impairments specific to multiword (phrase and sentence) spoken language comprehension. METHODS: Participants were 51 right-handed patients with chronic left hemisphere stroke. They performed an auditory description naming (ADN) task requiring comprehension of a verbal description, an auditory sentence comprehension (ASC) task, and a picture naming (PN) task. Lesions were mapped using high-resolution MRI. VLSM analyses identified the lesion correlates of ADN and ASC impairment, first with no control measures, then adding PN impairment as a covariate to control for cognitive and language processes not specific to spoken language. RESULTS: ADN and ASC deficits were associated with lesions in a distributed frontal-temporal parietal language network. When PN impairment was included as a covariate, both ADN and ASC deficits were specifically correlated with damage localized to the mid-to-posterior portion of the middle temporal gyrus (MTG). CONCLUSIONS: Damage to the mid-to-posterior MTG is associated with an inability to integrate multiword utterances during comprehension of spoken language. Impairment of this integration process likely underlies the speech comprehension deficits characteristic of Wernicke aphasia.

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.