A neuroanatomical and cognitive model of impaired social behaviour in frontotemporal dementia.

Impaired social cognition is a core deficit in frontotemporal dementia (FTD). It is most commonly associated with the behavioural-variant of FTD, with atrophy of the orbitofrontal and ventromedial prefrontal cortex. Social cognitive changes are also common in semantic dementia, with atrophy centred on the anterior temporal lobes. The impairment of social behaviour in FTD has typically been attributed to damage to the orbitofrontal cortex and/or temporal poles and/or the uncinate fasciculus that connects them. However, the relative contributions of each region are unresolved. In this review, we present a unified neurocognitive model of controlled social behaviour that not only explains the observed impairment of social behaviours in FTD, but also assimilates both consistent and potentially contradictory findings from other patient groups, comparative neurology and normative cognitive neuroscience. We propose that impaired social behaviour results from damage to two cognitively- and anatomically-distinct components. The first component is social-semantic knowledge, a part of the general semantic-conceptual system supported by the anterior temporal lobes bilaterally. The second component is social control, supported by the orbitofrontal cortex, medial frontal cortex and ventrolateral frontal cortex, which interacts with social-semantic knowledge to guide and shape social behaviour.

The graded multidimensional geometry of phenotypic variation and progression in neurodegenerative syndromes.

Clinical variants of Alzheimer's disease and frontotemporal lobar degeneration display a spectrum of cognitive-behavioural changes varying between individuals and over time. Understanding the landscape of these graded individual-/group-level longitudinal variations is critical for precise phenotyping; however, this remains challenging to model. Addressing this challenge, we leverage the National Alzheimer's Coordinating Center database to derive a unified geometric framework of graded longitudinal phenotypic variation in Alzheimer's disease and frontotemporal lobar degeneration. We included three time-point, cognitive-behavioural and clinical data from 390 typical, atypical and intermediate Alzheimer's disease and frontotemporal lobar degeneration variants (114 typical Alzheimer's disease; 107 behavioural variant frontotemporal dementia; 42 motor variants of frontotemporal lobar degeneration; and 103 primary progressive aphasia patients). On this data, we applied advanced data-science approaches to derive low-dimensional geometric spaces capturing core features underpinning clinical progression of Alzheimer's disease and frontotemporal lobar degeneration syndromes. To do so, we first used principal component analysis to derive six axes of graded longitudinal phenotypic variation capturing patient-specific movement along and across these axes. Then, we distilled these axes into a visualisable 2D manifold of longitudinal phenotypic variation using Uniform Manifold Approximation and Projection. Both geometries together enabled the assimilation and inter-relation of paradigmatic and mixed cases, capturing dynamic individual trajectories, and linking syndromic variability to neuropathology and key clinical end-points such as survival. Through these low-dimensional geometries, we show that (i) specific syndromes (Alzheimer's disease and primary progressive aphasia) converge over time into a de-differentiated pooled phenotype, while others (frontotemporal dementia variants) diverge to look different from this generic phenotype; (ii) phenotypic diversification is predicted by simultaneous progression along multiple axes, varying in a graded manner between individuals and syndromes; and (iii) movement along specific principal axes predicts survival at 36 months in a syndrome-specific manner and in individual pathological groupings. The resultant mapping of dynamics underlying cognitive-behavioural evolution potentially holds paradigm-changing implications to predicting phenotypic diversification and phenotype-neurobiological mapping in Alzheimer's disease and frontotemporal lobar degeneration.

Using in vivo functional and structural connectivity to predict chronic stroke aphasia deficits.

Focal brain damage caused by stroke can result in aphasia and advances in cognitive neuroscience suggest that impairment may be associated with network-level disorder rather than just circumscribed cortical damage. Several studies have shown meaningful relationships between brain-behaviour using lesions; however, only a handful of studies have incorporated in vivo structural and functional connectivity. Patients with chronic post-stroke aphasia were assessed with structural (n = 68) and functional (n = 39) MRI to assess whether predicting performance can be improved with multiple modalities and if additional variance can be explained compared to lesion models alone. These neural measurements were used to construct models to predict four key language-cognitive factors: (i) phonology; (ii) semantics; (iii) executive function; and (iv) fluency. Our results showed that each factor (except executive ability) could be significantly related to each neural measurement alone; however, structural and functional connectivity models did not explain additional variance above the lesion models. We did find evidence that the structural and functional predictors may be linked to the core lesion sites. First, the predictive functional connectivity features were found to be located within functional resting-state networks identified in healthy controls, suggesting that the result might reflect functionally specific reorganization (damage to a node within a network can result in disruption to the entire network). Second, predictive structural connectivity features were located within core lesion sites, suggesting that multimodal information may be redundant in prediction modelling. In addition, we observed that the optimum sparsity within the regularized regression models differed for each behavioural component and across different imaging features, suggesting that future studies should consider optimizing hyperparameters related to sparsity per target. Together, the results indicate that the observed network-level disruption was predicted by the lesion alone and does not significantly improve model performance in predicting the profile of language impairment.

Distinct but cooperating brain networks supporting semantic cognition.

Semantic cognition is a complex multifaceted brain function involving multiple processes including sensory, semantic, and domain-general cognitive systems. However, it remains unclear how these systems cooperate with each other to achieve effective semantic cognition. Here, we used independent component analysis (ICA) to investigate the functional brain networks that support semantic cognition. We used a semantic judgment task and a pattern-matching control task, each with 2 levels of difficulty, to disentangle task-specific networks from domain-general networks. ICA revealed 2 task-specific networks (the left-lateralized semantic network [SN] and a bilateral, extended semantic network [ESN]) and domain-general networks including the frontoparietal network (FPN) and default mode network (DMN). SN was coupled with the ESN and FPN but decoupled from the DMN, whereas the ESN was synchronized with the FPN alone and did not show a decoupling with the DMN. The degree of decoupling between the SN and DMN was associated with semantic task performance, with the strongest decoupling for the poorest performing participants. Our findings suggest that human higher cognition is achieved by the multiple brain networks, serving distinct and shared cognitive functions depending on task demands, and that the neural dynamics between these networks may be crucial for efficient semantic cognition.

Bipartite functional fractionation within the neural system for social cognition supports the psychological continuity of self versus other.

Research of social neuroscience establishes that regions in the brain's default-mode network (DN) and semantic network (SN) are engaged by socio-cognitive tasks. Research of the human connectome shows that DN and SN regions are both situated at the transmodal end of a cortical gradient but differ in their loci along this gradient. Here we integrated these 2 bodies of research, used the psychological continuity of self versus other as a "test-case," and used functional magnetic resonance imaging to investigate whether these 2 networks would encode social concepts differently. We found a robust dissociation between the DN and SN-while both networks contained sufficient information for decoding broad-stroke distinction of social categories, the DN carried more generalizable information for cross-classifying across social distance and emotive valence than did the SN. We also found that the overarching distinction of self versus other was a principal divider of the representational space while social distance was an auxiliary factor (subdivision, nested within the principal dimension), and this representational landscape was more manifested in the DN than in the SN. Taken together, our findings demonstrate how insights from connectome research can benefit social neuroscience and have implications for clarifying the 2 networks' differential contributions to social cognition.

The cross-domain functional organization of posterior lateral temporal cortex: insights from ALE meta-analyses of 7 cognitive domains spanning 12,000 participants.

The posterior lateral temporal cortex is implicated in many verbal, nonverbal, and social cognitive domains and processes. Yet without directly comparing these disparate domains, the region's organization remains unclear; do distinct processes engage discrete subregions, or could different domains engage shared neural correlates and processes? Here, using activation likelihood estimation meta-analyses, the bilateral posterior lateral temporal cortex subregions engaged in 7 domains were directly compared. These domains comprised semantics, semantic control, phonology, biological motion, face processing, theory of mind, and representation of tools. Although phonology and biological motion were predominantly associated with distinct regions, other domains implicated overlapping areas, perhaps due to shared underlying processes. Theory of mind recruited regions implicated in semantic representation, tools engaged semantic control areas, and faces engaged subregions for biological motion and theory of mind. This cross-domain approach provides insight into how posterior lateral temporal cortex is organized and why.

A middle ground where executive control meets semantics: the neural substrates of semantic control are topographically sandwiched between the multiple-demand and default-mode systems.

Semantic control is the capability to operate on meaningful representations, selectively focusing on certain aspects of meaning while purposefully ignoring other aspects based on one's behavioral aim. This ability is especially vital for comprehending figurative/ambiguous language. It remains unclear why and how regions involved in semantic control seem reliably juxtaposed alongside other functionally specialized regions in the association cortex, prompting speculation about the relationship between topography and function. We investigated this issue by characterizing how semantic control regions topographically relate to the default-mode network (associated with memory and abstract cognition) and multiple-demand network (associated with executive control). Topographically, we established that semantic control areas were sandwiched by the default-mode and multi-demand networks, forming an orderly arrangement observed both at the individual and group level. Functionally, semantic control regions exhibited "hybrid" responses, fusing generic preferences for cognitively demanding operation (multiple-demand) and for meaningful representations (default-mode) into a domain-specific preference for difficult operation on meaningful representations. When projected onto the principal gradient of human connectome, the neural activity of semantic control showed a robustly dissociable trajectory from visuospatial control, implying different roles in the functional transition from sensation to cognition. We discuss why the hybrid functional profile of semantic control regions might result from their intermediate topographical positions on the cortex.

Subregions of DLPFC Display Graded yet Distinct Structural and Functional Connectivity.

The human dorsolateral prefrontal cortex (DLPFC; approximately corresponding to Brodmann areas 9 and 46) has demonstrable roles in diverse executive functions such as working memory, cognitive flexibility, planning, inhibition, and abstract reasoning. However, it remains unclear whether this is the result of one functionally homogeneous region or whether there are functional subdivisions within the DLPFC. Here, we divided the DLPFC into seven areas along rostral-caudal and dorsal-ventral axes anatomically and explored their respective patterns of structural and functional connectivity. In vivo probabilistic tractography (11 females and 13 males) and resting-state functional magnetic resonance imaging (fMRI; 57 females and 21 males) were employed to map out the patterns of connectivity from each DLPFC subregion. Structural connectivity demonstrated graded intraregional connectivity within the DLPFC. The patterns of structural connectivity between the DLPFC subregions and other cortical areas revealed that the dorsal-rostral subregions connections were restricted to other frontal and limbic areas, whereas the ventral-caudal region was widely connected to frontal, parietal, and limbic cortex. Functional connectivity analyses demonstrated that subregions of DLPFC were strongly interconnected to each other. The dorsal subregions were associated with the default mode network (DMN), while middle dorsal-rostral subregions were linked with the multiple demand network (MDN). The ventral-caudal subregion showed increased functional coupling with both DMN and MDN. Our results suggest that the connectivity of the DLPFC may be subdivided along a dorsorostral-ventrocaudal axis with differing (albeit graded) patterns of connectivity reflecting the integrative executive function of the DLPFC.SIGNIFICANCE STATEMENT Research has shown that the dorsolateral prefrontal cortex (DLPFC) plays a role in various executive functions. By dividing the DLPFC into seven areas along rostral-caudal and dorsal-ventral axes anatomically, we explored their patterns of structural and functional connectivity. The patterns of connectivity within DLPFC subregions demonstrated graded intraregional connectivity. There were distinctive patterns of connectivity with other cortical areas in dorsal-rostral and ventral-caudal DLPFC subregions. Divisions across DLPFC subregions seem to align with their structural and functional connectivity. Our results suggest that DLPFC may be subdivided by the diagonal axis of the dorsal-ventral axis and rostral-caudal axis, supporting the framework of a functional organization along the anterior-posterior axis in the lateral PFC.

Semantic-specific and domain-general mechanisms for integration and update of contextual information.

Recent research has highlighted the importance of domain-general processes and brain regions for language and semantic cognition. Yet, this has been mainly observed in executively demanding tasks, leaving open the question of the contribution of domain-general processes to natural language and semantic cognition. Using fMRI, we investigated whether neural processes reflecting context integration and context update-two key aspects of naturalistic language and semantic processing-are domain-specific versus domain-general. Thus, we compared neural responses during the integration of contextual information across semantic and non-semantic tasks. Whole-brain results revealed both shared (left posterior-dorsal inferior frontal gyrus, left posterior inferior temporal gyrus, and left dorsal angular gyrus/intraparietal sulcus) and distinct (left anterior-ventral inferior frontal gyrus, left anterior ventral angular gyrus, left posterior middle temporal gyrus for semantic control only) regions involved in context integration and update. Furthermore, data-driven functional connectivity analysis clustered domain-specific versus domain-general brain regions into distinct but interacting functional neural networks. These results provide a first characterisation of the neural processes required for context-dependent integration during language processing along the domain-specificity dimension, and at the same time, they bring new insights into the role of left posterior lateral temporal cortex and left angular gyrus for semantic cognition.

Distance-dependent distribution thresholding in probabilistic tractography.

Tractography is widely used in human studies of connectivity with respect to every brain region, function, and is explored developmentally, in adulthood, ageing, and in disease. However, the core issue of how to systematically threshold, taking into account the inherent differences in connectivity values for different track lengths, and to do this in a comparable way across studies has not been solved. By utilising 54 healthy individuals' diffusion-weighted image data taken from HCP, this study adopted Monte Carlo derived distance-dependent distributions (DDDs) to generate distance-dependent thresholds with various levels of alpha for connections of varying lengths. As a test case, we applied the DDD approach to generate a language connectome. The resulting connectome showed both short- and long-distance structural connectivity in the close and distant regions as expected for the dorsal and ventral language pathways, consistent with the literature. The finding demonstrates that the DDD approach is feasible to generate data-driven DDDs for common thresholding and can be used for both individual and group thresholding. Critically, it offers a standard method that can be applied to various probabilistic tracking datasets.

The multidimensional nature of aphasia recovery post-stroke.

Language is not a single function, but instead results from interactions between neural representations and computations that can be damaged independently of each other. Although there is now clear evidence that the language profile in post-stroke aphasia reflects graded variations along multiple underlying dimensions ('components'), it is still entirely unknown if these distinct language components have different recovery trajectories and rely on the same, or different, neural regions during aphasia recovery. Accordingly, this study examined whether language components in the subacute stage: (i) mirror those observed in the chronic stage; (ii) recover together in a homogeneous manner; and (iii) have recovery trajectories that relate to changing activation in distinct or overlapping underlying brain regions. We analysed longitudinal data from 26 individuals with mild-moderate aphasia following left hemispheric infarct who underwent functional MRI and behavioural testing at ∼2 weeks and ∼4 months post-stroke. The language profiles in early post-stroke aphasia reflected three orthogonal principal components consisting of fluency, semantic/executive function and phonology. These components did not recover in a singular, homogeneous manner; rather, their longitudinal trajectories were uncorrelated, suggesting that aphasia recovery is heterogeneous and multidimensional. Mean regional brain activation during overt speech production in unlesioned areas was compared with patient scores on the three principal components of language at both the early and late time points. In addition, the change in brain activation over time was compared with the change on each of the principal component scores, both before and after controlling for baseline scores. We found that different language components were associated with changing activation in multiple, non-overlapping bilateral brain regions during aphasia recovery. Specifically, fluency recovery was associated with increasing activation in bilateral middle frontal gyri and right temporo-occipital middle temporal gyrus; semantic/executive recovery was associated with reducing activation in bilateral anterior temporal lobes; while phonology recovery was associated with reducing activation in bilateral precentral gyri, dorso-medial frontal poles and the precuneus. Overlapping clusters in the ventromedial prefrontal cortex were positively associated with fluency recovery but negatively associated with semantic/executive and phonology recovery. This combination of detailed behavioural and functional MRI data provides novel insights into the neural basis of aphasia recovery. Because different aspects of language seem to rely on different neural regions for recovery, treatment strategies that target the same neural region in all stroke survivors with aphasia might be entirely ineffective or even impair recovery, depending on the specific language profile of each individual patient.

Understanding the multidimensional cognitive deficits of logopenic variant primary progressive aphasia.

The logopenic variant of primary progressive aphasia is characterized by early deficits in language production and phonological short-term memory, attributed to left-lateralized temporoparietal, inferior parietal and posterior temporal neurodegeneration. Despite patients primarily complaining of language difficulties, emerging evidence points to performance deficits in non-linguistic domains. Temporoparietal cortex, and functional brain networks anchored to this region, are implicated as putative neural substrates of non-linguistic cognitive deficits in logopenic variant primary progressive aphasia, suggesting that degeneration of a shared set of brain regions may result in co-occurring linguistic and non-linguistic dysfunction early in the disease course. Here, we provide a Review aimed at broadening the understanding of logopenic variant primary progressive aphasia beyond the lens of an exclusive language disorder. By considering behavioural and neuroimaging research on non-linguistic dysfunction in logopenic variant primary progressive aphasia, we propose that a significant portion of multidimensional cognitive features can be explained by degeneration of temporal/inferior parietal cortices and connected regions. Drawing on insights from normative cognitive neuroscience, we propose that these regions underpin a combination of domain-general and domain-selective cognitive processes, whose disruption results in multifaceted cognitive deficits including aphasia. This account explains the common emergence of linguistic and non-linguistic cognitive difficulties in logopenic variant primary progressive aphasia, and predicts phenotypic diversification associated with progression of pathology in posterior neocortex.

The convergence and divergence of episodic and semantic functions across lateral parietal cortex.

Decades of research have highlighted the importance of lateral parietal cortex (LPC) across a myriad of cognitive domains. Yet, the underlying function of LPC remains unclear. Two domains that have emphasized LPC involvement are semantic memory and episodic memory retrieval. From each domain, sophisticated functional models have been proposed, as well as the more domain-general assumption that LPC is engaged by any form of internally directed cognition (episodic/semantic retrieval being examples). Here we used a combination of functional magnetic resonance imaging, functional connectivity, and diffusion tensor imaging white-matter connectivity to show that (i) ventral LPC (angular gyrus [AG]) was positively engaged during episodic retrieval but disengaged during semantic memory retrieval and (ii) activity negatively varied with task difficulty in the semantic task whereas episodic activation was independent of difficulty. In contrast, dorsal LPC (intraparietal sulcus) showed domain general activation that was positively correlated with task difficulty. Finally, (iii) a dorsal-ventral and anterior-posterior gradient of functional and structural connectivity was found across the AG (e.g. mid-AG connected with episodic retrieval). We propose a unifying model in which LPC as a whole might share a common underlying neurocomputation (multimodal buffering) with variations in the emergent cognitive functions across subregions arising from differences in the underlying connectivity.

Direct Neural Evidence for the Contrastive Roles of the Complementary Learning Systems in Adult Acquisition of Native Vocabulary.

The Complementary Learning Systems (CLS) theory provides a powerful framework for considering the acquisition, consolidation, and generalization of new knowledge. We tested this proposed neural division of labor in adults through an investigation of the consolidation and long-term retention of newly learned native vocabulary with post-learning functional neuroimaging. Newly learned items were compared with two conditions: 1) previously known items to highlight the similarities and differences with established vocabulary and 2) unknown/untrained items to provide a control for non-specific perceptual and motor speech output. Consistent with the CLS, retrieval of newly learned items was supported by a combination of regions associated with episodic memory (including left hippocampus) and the language-semantic areas that support established vocabulary (left inferior frontal gyrus and left anterior temporal lobe). Furthermore, there was a shifting division of labor across these two networks in line with the items' consolidation status; faster naming was associated with more activation of language-semantic areas and lesser activation of episodic memory regions. Hippocampal activity during naming predicted more than half the variation in naming retention 6 months later.

A unified neurocomputational bilateral model of spoken language production in healthy participants and recovery in poststroke aphasia.

Understanding the processes underlying normal, impaired, and recovered language performance has been a long-standing goal for cognitive and clinical neuroscience. Many verbally described hypotheses about language lateralization and recovery have been generated. However, they have not been considered within a single, unified, and implemented computational framework, and the literatures on healthy participants and patients are largely separated. These investigations also span different types of data, including behavioral results and functional MRI brain activations, which augment the challenge for any unified theory. Consequently, many key issues, apparent contradictions, and puzzles remain to be solved. We developed a neurocomputational, bilateral pathway model of spoken language production, designed to provide a unified framework to simulate different types of data from healthy participants and aphasic patients. The model encapsulates key computational principles (differential computational capacity, emergent division of labor across pathways, experience-dependent plasticity-related recovery) and provides an explanation for the bilateral yet asymmetric lateralization of language in healthy participants, chronic aphasia after left rather than right hemisphere lesions, and the basis of partial recovery in patients. The model provides a formal basis for understanding the relationship between behavioral performance and brain activation. The unified model is consistent with the degeneracy and variable neurodisplacement theories of language recovery, and adds computational insights to these hypotheses regarding the neural machinery underlying language processing and plasticity-related recovery following damage.

Neurochemical profiles of the anterior temporal lobe predict response of repetitive transcranial magnetic stimulation on semantic processing.

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive technique used to modulate cortical excitability in the human brain. However, one major challenge with rTMS is that the responses to stimulation are highly variable across individuals. The underlying reasons why responses to rTMS are highly variable between individuals still remain unclear. Here, we investigated whether the response to continuous theta-burst stimulation (cTBS) - an effective rTMS protocol for decreasing cortical excitability - is related to individual differences in glutamate and GABA neurotransmission. We acquired resting-state magnetic resonance spectroscopy (MRS) and functional magnetic resonance imaging (fMRI) during semantic processing. Then, we applied cTBS over the anterior temporal lobe (ATL), a hub for semantic representation, to explore the relationship between the baseline neurochemical profiles in this region and the response to cTBS. We found that the baseline excitation-inhibition balance (glutamate + glutamine/GABA ratio) in the ATL was associated with individual cTBS responsiveness during semantic processing. Specifically, individuals with lower excitation-inhibition balance showed stronger inhibitory effect - poorer semantic performance. Our results revealed that non-responders (subjects who did not show an inhibitory effect of cTBS on subsequent semantic performance) had higher excitatory-inhibitory balance in the ATL, which led to up-regulated task-induced regional activity as well as increased ATL-connectivity with other semantic regions compared to responders. These results disclose that the baseline neurochemical state of a cortical region can be a significant factor in predicting responses to cTBS.

Subgenual activation and the finger of blame: individual differences and depression vulnerability.

BACKGROUND: Subgenual cingulate cortex (SCC) responses to self-blaming emotion-evoking stimuli were previously found in individuals prone to self-blame with and without a history of major depressive disorder (MDD). This suggested SCC activation reflects self-blaming emotions such as guilt, which are central to models of MDD vulnerability. METHOD: Here, we re-examined these hypotheses in an independent larger sample. A total of 109 medication-free participants (70 with remitted MDD and 39 healthy controls) underwent fMRI whilst judging self- and other-blaming emotion-evoking statements. They also completed validated questionnaires of proneness to self-blaming emotions including those related to internal (autonomy) and external (sociotropy) evaluation, which were subjected to factor analysis. RESULTS: An interaction between group (remitted MDD v. Control) and condition (self- v. other-blame) was observed in the right SCC (BA24). This was due to higher SCC signal for self-blame in remitted MDD and higher other-blame-selective activation in Control participants. Across the whole sample, extracted SCC activation cluster averages for self- v. other-blame were predicted by a regression model which included the reliable components derived from our factor analysis of measures of proneness to self-blaming emotions. Interestingly, this prediction was solely driven by autonomy/self-criticism, and adaptive guilt factors, with no effect of sociotropy/dependency. CONCLUSIONS: Despite confirming the prediction of SCC activation in self-blame-prone individuals and those vulnerable to MDD, our results suggest that SCC activation reflects blame irrespective of where it is directed rather than selective for self. We speculate that self-critical individuals have more extended SCC representations for blame in the context of self-agency.

Training flexible conceptual retrieval in post-stroke aphasia.

Semantic therapy in post-stroke aphasia typically focusses on strengthening links between conceptual representations and their lexical-articulatory forms to aid word retrieval. However, research has shown that semantic deficits in this group can affect both verbal and non-verbal tasks, particularly in patients with deregulated retrieval as opposed to degraded knowledge. This study, therefore, aimed to facilitate semantic cognition in a sample of such patients with post-stroke semantic aphasia (SA) by training the identification of both strong and weak semantic associations and providing explicit pictorial feedback that demonstrated both common and more unusual ways of linking concepts together. We assessed the effects of this training on (i) trained and untrained items; and (ii) trained and untrained tasks in eleven individuals with SA. In the training task, the SA group showed improvement with practice, particularly for trained items. A similar untrained task using pictorial stimuli (Camel and Cactus Test) also improved. Together, these results suggest that semantic training can be beneficial in patients with SA and may show some degree of generalization to untrained situations. Future research should seek to understand which patients are most likely to benefit from this type of training.

Semantic diversity is best measured with unscaled vectors: Reply to Cevoli, Watkins and Rastle (2020).

Semantic diversity refers to the degree of semantic variability in the contexts in which a particular word is used. We have previously proposed a method for measuring semantic diversity based on latent semantic analysis (LSA). In a recent paper, Cevoli et al. (2020) attempted to replicate our method and obtained different semantic diversity values. They suggested that this discrepancy occurred because they scaled their LSA vectors by their singular values, while we did not. Using their new results, they argued that semantic diversity is not related to ambiguity in word meaning, as we originally proposed. In this reply, we demonstrate that the use of unscaled vectors provides better fits to human semantic judgements than scaled ones. Thus we argue that our original semantic diversity measure should be preferred over the Cevoli et al. version. We replicate Cevoli et al.'s analysis using the original semantic diversity measure and find (a) our original measure is a better predictor of word recognition latencies than the Cevoli et al. equivalent and (b) that, unlike Cevoli et al.'s measure, our semantic diversity is reliably associated with a measure of polysemy based on dictionary definitions. We conclude that the Hoffman et al. semantic diversity measure is better-suited to capturing the contextual variability among words and that words appearing in a more diverse set of contexts have more variable semantic representations. However, we found that homonyms did not have higher semantic diversity values than non-homonyms, suggesting that the measure does not capture this special case of ambiguity.

Content Word Production during Discourse in Aphasia: Deficits in Word Quantity, Not Lexical-Semantic Complexity.

Although limited and reduced connected speech production is one, if not the most, prominent feature of aphasia, few studies have examined the properties of content words produced during discourse in aphasia, in comparison to the many investigations of single-word production. In this study, we used a distributional analysis approach to investigate the properties of content word production during discourse by 46 participants spanning a wide range of chronic poststroke aphasia and 20 neurotypical adults, using different stimuli that elicited three discourse genres (descriptive, narrative, and procedural). Initially, we inspected the discourse data with respect to the quantity of production, lexical-semantic diversity, and psycholinguistic features (frequency and imageability) of content words. Subsequently, we created a "lexical-semantic landscape," which is sensitive to subtle changes and allowed us to evaluate the pattern of changes in discourse production across groups. Relative to neurotypical adults, all persons with aphasia (both fluent and nonfluent) showed significant reduction in the quantity and diversity of production, but the lexical-semantic complexity of word production directly mirrored neurotypical performance. Specifically, persons with aphasia produced the same rate of nouns/verbs, and their discourse samples covered the full range of word frequency and imageability, albeit with reduced word quantity. These findings provide novel evidence that, unlike in other disorders (e.g., semantic dementia), discourse production in poststroke aphasia has relatively preserved lexical-semantic complexity but demonstrates significantly compromised quantity of content word production. Voxel-wise lesion-symptom mapping using both univariate and multivariate approaches revealed left frontal regions particularly the pars opercularis, insular cortex, and central and frontal opercular cortices supporting word retrieval during connected speech, irrespective of their word class or lexical-semantic complexity.