Semantic memory refers to knowledge about people, objects, actions, relations, self, and culture acquired through experience. The neural systems that store and retrieve this information have been ...studied for many years, but a consensus regarding their identity has not been reached. Using strict inclusion criteria, we analyzed 120 functional neuroimaging studies focusing on semantic processing. Reliable areas of activation in these studies were identified using the activation likelihood estimate (ALE) technique. These activations formed a distinct, left-lateralized network comprised of 7 regions: posterior inferior parietal lobe, middle temporal gyrus, fusiform and parahippocampal gyri, dorsomedial prefrontal cortex, inferior frontal gyrus, ventromedial prefrontal cortex, and posterior cingulate gyrus. Secondary analyses showed specific subregions of this network associated with knowledge of actions, manipulable artifacts, abstract concepts, and concrete concepts. The cortical regions involved in semantic processing can be grouped into 3 broad categories: posterior multimodal and heteromodal association cortex, heteromodal prefrontal cortex, and medial limbic regions. The expansion of these regions in the human relative to the nonhuman primate brain may explain uniquely human capacities to use language productively, plan, solve problems, and create cultural and technological artifacts, all of which depend on the fluid and efficient retrieval and manipulation of semantic knowledge.
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.
•Time and effort for hand-segmenting lesions bottlenecks brain lesion-deficit studies.•We provide a fully-automated tool for stroke brain lesion segmentation.•This tool significantly outperforms ...existing techniques, including on small lesions.•Freely available tool should accelerate progress in brain lesion-deficit studies.
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.
Abstract
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.
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.
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.
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.
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.
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.
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