Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions

Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, c ingulate, and insular regions. Activity in this “multiple demand” (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people (n=45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks. Keywords: comprehension; functional localization; intersubject correlation; language network; multiple-demand network; naturalistic cognitio

Functional Network Dynamics of the Language System

During linguistic processing, a set of brain regions on the lateral surfaces of the left frontal, temporal, and parietal cortices exhibit robust responses. These areas display highly correlated activity while a subject rests or performs a naturalistic language comprehension task, suggesting that they form an integrated functional system. Evidence suggests that this system is spatially and functionally distinct from other systems that support high-level cognition in humans. Yet, how different regions within this system might be recruited dynamically during task performance is not well understood. Here we use network methods, applied to fMRI data collected from 22 human subjects performing a language comprehension task, to reveal the dynamic nature of the language system. We observe the presence of a stable core of brain regions, predominantly located in the left hemisphere, that consistently coactivate with one another. We also observe the presence of a more flexible periphery of brain regions, predominantly located in the right hemisphere, that coactivate with different regions at different times. However, the language functional ROIs in the angular gyrus and the anterior temporal lobe were notable exceptions to this trend. By highlighting the temporal dimension of language processing, these results suggest a trade-off between a region's specialization and its capacity for flexible network reconfiguration

The Functional architecture of language comprehension mechanisms : fundamental principles revealed with fMRI

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2016.Cataloged from PDF version of thesis.Includes bibliographical references.A key requirement from cognitive models of language comprehension is that they specify the distinct computational mechanisms that are engaged in language processing and the division of linguistic labor across them. Here, I address this requirement from a cognitive neuroscience perspective by employing functional MRI to study the neural implementation of comprehension processes. My experimental approach, unprecedented in studies of language, combines available methods to simultaneously achieve (i) increased functional resolution, via localization of functional brain regions at the single-participant level; (ii) ecological validity, through datadriven, model-free paradigms using naturalistic stimuli; and (iii) statistical rigor, by explicit comparison of functional profiles across regions. Using this approach, I first contrast two cortical networks engaged in comprehension: one, the "high-level language network", is selectively recruited by linguistic processing but not by other cognitive functions; another, the "multiple-demand network", is recruited across diverse cognitive tasks, both linguistic and non-linguistic. I show that, during naturalistic cognition, each network shows high synchronization amongst its constituent regions, whereas regions across the two networks are functionally dissociated. Thus, these two systems likely play distinct roles in comprehension, which I then characterize by demonstrating that the language network closely tracks linguistic input whereas the multiple-demand network does not. This finding critically constrains the possible contributions of the multiple-demand system to comprehension. Next, I focus on the high-level language network and examine two current hypotheses about its internal structure. In one study, I find that activity elicited by syntactic processing is not localized to focal language regions but is instead distributed throughout the network, suggesting that syntax is cognitively inseparable from other aspects of language. In another study, I estimate the timescales over which different language regions integrate linguistic information and find that they share a common profile of temporal integration. Therefore, the topographic division of linguistic labor across this network is not organized along distinct integration timescales. Collectively, these results account for crucial inconsistencies in the literature and challenge common theoretical views. By characterizing the fundamental functional architecture of comprehension mechanisms, these results provide novel insights into the ontology of linguistic mechanisms that give rise to human language.by Idan Blank.Ph. D

No evidence for differences among language regions in their temporal receptive windows

© 2020 The Authors The “core language network” consists of left frontal and temporal regions that are selectively engaged in linguistic processing. Whereas functional differences among these regions have long been debated, many accounts propose distinctions in terms of representational grain-size—e.g., words vs. phrases/sentences—or processing time-scale, i.e., operating on local linguistic features vs. larger spans of input. Indeed, the topography of language regions appears to overlap with a cortical hierarchy reported by Lerner et al. (2011) wherein mid-posterior temporal regions are sensitive to low-level features of speech, surrounding areas—to word-level information, and inferior frontal areas—to sentence-level information and beyond. However, the correspondence between the language network and this hierarchy of “temporal receptive windows” (TRWs) is difficult to establish because the precise anatomical locations of language regions vary across individuals. To directly test this correspondence, we first identified language regions in each participant with a well-validated task-based localizer, which confers high functional resolution to the study of TRWs (traditionally based on stereotactic coordinates); then, we characterized regional TRWs with the naturalistic story listening paradigm of Lerner et al. (2011), which augments task-based characterizations of the language network by more closely resembling comprehension “in the wild”. We find no region-by-TRW interactions across temporal and inferior frontal regions, which are all sensitive to both word-level and sentence-level information. Therefore, the language network as a whole constitutes a unique stage of information integration within a broader cortical hierarchy.NIH award (R00-HD057522)NIH award (R01-DC016607)NIH award (R01-DC016950

Functional reorganization of the large-scale brain networks that support high-level cognition following brain damage in aphasia

Over the last decade, a number of large-scale networks in the human cortex that support high-level cognition have been identified. Here, we focus on two of these networks: the fronto-temporal language network (e.g., Fedorenko et al., 2010), and the fronto-parietal “multiple demand (MD)” network (e.g., Duncan, 2010). These two networks are clearly distinct from one another: first, their respective regions show distinct functional profiles, with language regions showing selective responses to language stimuli (Fedorenko et al., 2011; Monti et al., 2012) and MD regions showing domain-general responses to cognitive effort across a wide range of tasks (Duncan & Owen, 2001; Fedorenko et al., 2013). Second, during “rest” and cognitive processing, each network shows strong activity synchronization among its constituent regions, whereas regions across the two networks are not synchronized (Blank et al., 2014; Lee et al., 2012; Mantini et al., 2013). In the current study, we examined how these functional characteristics of the two networks were affected following aphasia-inducing strokes. In particular, we asked whether damage to the language network would alter the involvement of the MD network in linguistic processing, and whether such damage would alter the patterns of synchronization across the two networks. Four male individuals with aphasia (age: M=53), having suffered a single left MCA CVA, were scanned in fMRI on two paradigms that enable basic functional characterization of language and MD regions: (i) a language localizer task, where they passively read sentences and sequences of pseudowords (Fedorenko et al., 2010); and (ii) a spatial working memory task, where they had to remember fewer (easy) or more (hard) locations in a grid (Fedorenko et al., 2013). Language and MD regions were defined in each individual using the sentences > pseudowords contrast and the hard > easy contrast, respectively. Subjects were also scanned while listening to naturalistic stories. We found that language regions maintained their selectivity, showing strong responses to sentences (that were reliably stronger than responses to pseudowords) and little or no response during the spatial working memory task. In contrast, MD regions showed strong responses to the spatial working memory task, with a few regions responding more strongly during the hard condition than during the easy condition. These patterns replicate previous findings in healthy individuals (Fedorenko et al., 2011, 2012). However, unlike in healthy controls – where MD regions respond more strongly to pseudowords than to sentences (presumably because processing pseudowords is more demanding) – in individuals with aphasia a few MD regions responded more strongly to sentences than to pseudowords. These regions were located in the bilateral opercular inferior frontal gyrus, precentral gyrus and supplementary motor area. Nonetheless, language and MD regions remained overall dissociated during story comprehension: regions within each network were more synchronized with each other than with regions of the other network. These results suggest that parts of the domain-general MD network, a system that has been linked to problem solving and general fluid intelligence (e.g., Duncan, 2010; Woogar et al., 2010), may alter their involvement in language processing when the language system is compromised

Does linguistic ability impact nonlinguistic learning? The neural bases of nonlinguistic learning in aphasia

Categorization is an important human skill that underlies our ability to recognize and instantly assign meaning to novel items. We focus on probabilistic categorization, a form of rule-based categorization based on the probabilistic association of cues and outcomes (Meeter et al., 2008). Probabilistic categories can be learned via implicit strategies supported by the caudate and putamen. Alternatively, learners can implement verbal and explicit rules, recruiting language regions of the brain, the hippocampus and lateral prefrontal cortex (Ashby et al., 1998). Research has demonstrated that patients with aphasia (PWA) show impaired learning of nonlinguistic probabilistic categories, and that they develop suboptimal strategies when approaching such tasks (Vallila-Rohter & Kiran, 2013, in press). In the current study, we use fMRI paradigms to examine the brain systems engaged as PWA and control individuals learn novel nonlinguistic categories. Methods Stimuli are two sets of fictional animals, organized into two categories based on the distribution of their features. One animal is selected as prototype A, and the animal that differs from that prototype by all features becomes prototype B. Animals must share a majority of their features with each prototype to be considered members of that category. Animals are presented one at a time on a screen and participants guess the animals’ category membership, receiving feedback following each trial. Participants complete 96 classification and 96 perceptual-motor baseline trials that require a button press whenever a unique set of animals appears. Participants also complete a language localizer task in which they read sentences and sequences of nonwords, a task designed to functionally locate language regions of the brain (Fedorenko et al., 2010). Functional and structural images are acquired in a block design using a 3T Siemens TimTrio scanner with a 32-channel head coil. We examine contrasts learning > baseline and baseline > learning and use Marsbar to conduct region of interest (ROI) analyses over regions associated with verbal strategies: MFG, IFG, hippocampus, and over regions associated with implicit strategies: caudate and putamen. Results We have collected data from 6 PWA and two controls and anticipate enrolling 10 PWA. Whole brain analyses reveal a set of regions activated for the learning>baseline contrast across all participants that includes bilateral middle frontal gyrus (MFG), right inferior frontal gyrus (IFG), right angular gyrus and bilateral middle temporal gyrus (MTG). ROI analyses over data for two PWA and two controls demonstrate that controls produce positive percent signal change differences in the caudate (implicit system) and negative percent signal change difference in the hippocampus (verbal, explicit system). In contrast, PWA show positive signal change in the hippocampus and negative percent signal change in the caudate. Conclusions PWA and controls engage a similar overall network of regions during probabilistic category learning. ROI analyses suggest, however, that PWA may exhibit a greater reliance on verbally mediated strategies, compared with a greater reliance on implicit strategies in controls. PWA with mild aphasia who have access to language, may be predisposed to utilize that language to learn, even if it is not a productive strategy

Activity in the fronto-parietal multiple-demand network is robustly associated with individual differences in working memory and fluid intelligence

© 2020 Elsevier Ltd Numerous brain lesion and fMRI studies have linked individual differences in executive abilities and fluid intelligence to brain regions of the fronto-parietal “multiple-demand” (MD) network. Yet, fMRI studies have yielded conflicting evidence as to whether better executive abilities are associated with stronger or weaker MD activations and whether this relationship is restricted to the MD network. Here, in a large-sample (n = 216) fMRI investigation, we found that stronger activity in MD regions – functionally defined in individual participants – was robustly associated with more accurate and faster responses on a spatial working memory task performed in the scanner, as well as fluid intelligence measured independently (n = 114). In line with some prior claims about a relationship between language and fluid intelligence, we also found a weak association between activity in the brain regions of the left fronto-temporal language network during an independent passive reading task, and performance on the working memory task. However, controlling for the level of MD activity abolished this relationship, whereas the MD activity-behavior association remained highly reliable after controlling for the level of activity in the language network. Finally, we demonstrate how unreliable MD activity measures, coupled with small sample sizes, could falsely lead to the opposite, negative, association that has been reported in some prior studies. Taken together, these results demonstrate that a core component of individual differences variance in executive abilities and fluid intelligence is selectively and robustly positively associated with the level of activity in the MD network, a result that aligns well with lesion studies

Semantic projection recovers rich human knowledge of multiple object features from word embeddings

How is knowledge about word meaning represented in the mental lexicon? Current computational models infer word meanings from lexical co-occurrence patterns. They learn to represent words as vectors in a multidimensional space, wherein words that are used in more similar linguistic contexts-that is, are more semantically related-are located closer together. However, whereas inter-word proximity captures only overall relatedness, human judgements are highly context dependent. For example, dolphins and alligators are similar in size but differ in dangerousness. Here, we use a domain-general method to extract context-dependent relationships from word embeddings: 'semantic projection' of word-vectors onto lines that represent features such as size (the line connecting the words 'small' and 'big') or danger ('safe' to 'dangerous'), analogous to 'mental scales'. This method recovers human judgements across various object categories and properties. Thus, the geometry of word embeddings explicitly represents a wealth of context-dependent world knowledge

Lack of selectivity for syntax relative to word meanings throughout the language network

© 2020 Elsevier B.V. To understand what you are reading now, your mind retrieves the meanings of words and constructions from a linguistic knowledge store (lexico-semantic processing) and identifies the relationships among them to construct a complex meaning (syntactic or combinatorial processing). Do these two sets of processes rely on distinct, specialized mechanisms or, rather, share a common pool of resources? Linguistic theorizing, empirical evidence from language acquisition and processing, and computational modeling have jointly painted a picture whereby lexico-semantic and syntactic processing are deeply inter-connected and perhaps not separable. In contrast, many current proposals of the neural architecture of language continue to endorse a view whereby certain brain regions selectively support syntactic/combinatorial processing, although the locus of such “syntactic hub”, and its nature, vary across proposals. Here, we searched for selectivity for syntactic over lexico-semantic processing using a powerful individual-subjects fMRI approach across three sentence comprehension paradigms that have been used in prior work to argue for such selectivity: responses to lexico-semantic vs. morpho-syntactic violations (Experiment 1); recovery from neural suppression across pairs of sentences differing in only lexical items vs. only syntactic structure (Experiment 2); and same/different meaning judgments on such sentence pairs (Experiment 3). Across experiments, both lexico-semantic and syntactic conditions elicited robust responses throughout the left fronto-temporal language network. Critically, however, no regions were more strongly engaged by syntactic than lexico-semantic processing, although some regions showed the opposite pattern. Thus, contra many current proposals of the neural architecture of language, syntactic/combinatorial processing is not separable from lexico-semantic processing at the level of brain regions—or even voxel subsets—within the language network, in line with strong integration between these two processes that has been consistently observed in behavioral and computational language research. The results further suggest that the language network may be generally more strongly concerned with meaning than syntactic form, in line with the primary function of language—to share meanings across minds.NIH (Awards R00-HD057522, R01-DC016607, R01-DC016950

The Small and Efficient Language Network of Polyglots and Hyper-polyglots

© 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]. Acquiring a foreign language is challenging for many adults. Yet certain individuals choose to acquire sometimes dozens of languages and often just for fun. Is there something special about the minds and brains of such polyglots? Using robust individual-level markers of language activity, measured with fMRI, we compared native language processing in polyglots versus matched controls. Polyglots (n = 17, including nine "hyper-polyglots"with proficiency in 10-55 languages) used fewer neural resources to process language: Their activations were smaller in both magnitude and extent. This difference was spatially and functionally selective: The groups were similar in their activation of two other brain networks - the multiple demand network and the default mode network. We hypothesize that the activation reduction in the language network is experientially driven, such that the acquisition and use of multiple languages makes language processing generally more efficient. However, genetic and longitudinal studies will be critical to distinguish this hypothesis from the one whereby polyglots' brains already differ at birth or early in development. This initial characterization of polyglots' language network opens the door to future investigations of the cognitive and neural architecture of individuals who gain mastery of multiple languages, including changes in this architecture with linguistic experiences.NIH (Awards R00-HD057522, R01- DC016607 and R01-DC016950