Executive functions in primary progressive aphasia: A meta-analysis

Executive functions (EFs) refer to a set of cognitive processes, specifically shifting, inhibition, updating of working memory, and are involved in the cognitive control of behavior. Conflicting results have been reported regarding impairments of EFs in Primary Progressive Aphasia (PPA). We performed a multi-level meta-analysis to confirm whether deficits of EFs exist in this population, focusing on a common EFs composite, and the components shifting, inhibition and updating separately. We included 141 studies that report on 294 EFs tasks. The overall mean weighted effect size was large (d = -1,28), indicating poorer EFs in PPA as compared to age-matched cognitively healthy controls. Differences between effect sizes of the EFs components were not significant, indicating all components are affected similarly. Overall, moderator analysis revealed that PPA variant and disease duration were significant moderators of performance, while task modality and years of education were not. The non-fluent/agrammatic PPA and the logopenic PPA variants were similarly affected, but the semantic variant was affected to a lesser extent. We discuss implications for clinical and research settings, and future research.

Neural correlates of semantic number: A cross-linguistic investigation

One aspect of natural language comprehension is understanding how many of what or whom a speaker is referring to. While previous work has documented the neural correlates of number comprehension and quantity comparison, this study investigates semantic number from a cross-linguistic perspective with the goal of identifying cortical regions involved in distinguishing plural from singular nouns. Three fMRI datasets are used in which Chinese, French, and English native speakers listen to an audiobook of a children's story in their native language. These languages are selected because they differ in their number semantics. Across these languages, several well-known language regions manifest a contrast between plural and singular, including the pars orbitalis, pars triangularis, posterior temporal lobe, and dorsomedial prefrontal cortex. This is consistent with a common brain network supporting comprehension across languages with overt as well as covert number-marking.

History of aphasia: A broad overview

This chapter gives a broad overview of the description and theorizing of a wide range of language disorders resulting from brain damage, commonly classified under the umbrella term "aphasia." It covers works written in Antiquity up to the 20th century. Moreover, it looks at disturbances in various language modalities such as speech, language comprehension, reading, writing, and sign language. In addition, also forms of the more recently discovered primary progressive aphasia are discussed. Finally, important developments in the history of assessment and rehabilitation of language disorders are described. To properly characterize disorders of language, these developments are discussed from the perspectives of neurology, psychology, and linguistics.

Predicting Alzheimer's Disease from Spoken and Written Language Using Fusion-Based Stacked Generalization

The importance of automating the diagnosis of Alzheimer disease (AD) towards facilitating its early prediction has long been emphasized, hampered in part by lack of empirical support. Given the evident association of AD with age and the increasing aging population owing to the general well-being of individuals, there have been unprecedented estimated economic complications. Consequently, many recent studies have attempted to employ the language deficiency caused by cognitive decline in automating the diagnostic task via training machine learning (ML) algorithms with linguistic patterns and deficits. In this study, we aim to develop multiple heterogeneous stacked fusion models that harness the advantages of several base learning algorithms to improve the overall generalizability and robustness of AD diagnostic ML models, where we parallelly utilized two different written and spoken-based datasets to train our stacked fusion models. Further, we examined the effect of linking these two datasets to develop a hybrid stacked fusion model that can predict AD from written and spoken languages. Our feature spaces involved two widely used linguistic patterns: lexicosyntactics and character n-gram spaces. We firstly investigated lexicosyntactics of AD alongside healthy controls (HC), where we explored a few new lexicosyntactic features, then optimized the lexicosyntactic feature space by proposing a correlation feature selection technique that eliminates features based on their feature-feature inter-correlations and feature-target correlations according to a certain threshold. Our stacked fusion models establish benchmarks on both datasets with AUC of 98.1% and 99.47% for the spoken and written-based datasets, respectively, and corresponding accuracy and F1 score values around 95% on spoken-based dataset and around 97% on the written-based dataset. Likewise, the hybrid stacked fusion model on linked data presents an optimal performance with 99.2% AUC as well as accuracy and F1 score falling around 97%. In view of the achieved performance and enhanced generalizability of such fusion models over single classifiers, this study suggests replacing the initial traditional screening test with such models that can be embedded into an online format for a fully automated remote diagnosis.

Processing neutral tone under the non-attentional condition: a mismatch negativity study

The neutral tone is a unique tone form in Mandarin as it distinguishes from four canonical tones or full tones on the one hand and integrates phonetic, morphological, syntactical and prosodic information on the other hand. Research to date has been focusing on its unique and variant acoustic features. However, little is known about how native Mandarin speakers process such a unique tone. In the present study, the mismatch negativity was used to explore the comparison-based pre-attentive change detection of Mandarin neutral tone. The mismatch negativity at the time window of 400-800 ms post-first-tone onset was obtained by subtracting event-related potentials to standard neutral tone from event-related potentials to a deviant natural tone. The source analysis of mismatch negativity showed the cortex generator was located at the left temporal lobe. The data suggest that Chinese native speakers process neutral tone automatically under non-attentional conditions, as revealed by the mismatch negativity data aligned with a neutral tone, and that neutral tone does exist as an automatically recognizable one in native Mandarin speakers' tone system.

Magnetoencephalography and Language

This article provides an overview of research that uses magnetoencephalography to understand the brain basis of human language. The cognitive processes and brain networks that have been implicated in written and spoken language comprehension and production are discussed in relation to different methodologies: we review event-related brain responses, research on the coupling of neural oscillations to speech, oscillatory coupling between brain regions (eg, auditory-motor coupling), and neural decoding approaches in naturalistic language comprehension.

Investigating the fit between phonological feature systems and brain responses to speech using EEG

This paper describes a technique to assess the correspondence between patterns of similarity in the brain's response to speech sounds and the patterns of similarity encoded in phonological feature systems, by quantifying the recoverability of phonological features from the neural data using supervised learning. The technique is applied to EEG recordings collected during passive listening to consonant-vowel syllables. Three published phonological feature systems are compared, and are shown to differ in their ability to recover certain speech sound contrasts from the neural data. For the phonological feature system that best reflects patterns of similarity in the neural data, a leave-one-out analysis indicates some consistency across subjects in which features have greatest impact on the fit, but considerable across-subject heterogeneity remains in the rank ordering of features in this regard.

The language-ready head: Evolutionary considerations

This article offers a succinct overview of the hypothesis that the evolution of cognition could benefit from a close examination of brain changes reflected in the shape of the neurocranium. I provide both neurological and genetic evidence in support of this hypothesis, and conclude that the study of language evolution need not be regarded as a mystery.

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BACKGROUND

The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.

RESULTS

Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).

CONCLUSIONS

Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.

Proceedings of the Seventh International Workshop on Advances in Electrocorticography

The Seventh International Workshop on Advances in Electrocorticography (ECoG) convened in Washington, DC, on November 13-14, 2014. Electrocorticography-based research continues to proliferate widely across basic science and clinical disciplines. The 2014 workshop highlighted advances in neurolinguistics, brain-computer interface, functional mapping, and seizure termination facilitated by advances in the recording and analysis of the ECoG signal. The following proceedings document summarizes the content of this successful multidisciplinary gathering.

Neural bases of syntax-semantics interface processing

The binding problem-question of how information between the modules of the linguistic system is integrated during language processing-is as yet unresolved. The remarkable speed of language processing and comprehension (Pulvermüller et al. 2009) suggests that at least coarse semantic information (e.g. noun animacy) and syntactically-relevant information (e.g. verbal template) are integrated rapidly to allow for coarse comprehension. This EEG study investigated syntax-semantics interface processing during word-by-word sentence reading. As alpha-band neural activity serves as an inhibition mechanism for local networks, we used topographical distribution of alpha power to help identify the timecourse of the binding process. We manipulated the syntactic parameter of verbal event structure, and semantic parameter of noun animacy in reduced relative clauses (RRCs, e.g. "The witness/mansion seized/protected by the agent was in danger"), to investigate the neural bases of interaction between syntactic and semantic networks during sentence processing. The word-by-word stimulus presentation method in the present experiment required manipulation of both syntactic structure and semantic features in the working memory. The results demonstrated a gradient distribution of early components (biphasic posterior P1-N2 and anterior N1-P2) over function words "by" and "the", and the verb, corresponding to facilitation or conflict resulting from the syntactic (telicity) and semantic (animacy) cues in the preceding portion of the sentence. This was followed by assimilation of power distribution in the α band at the second noun. The flattened distribution of α power during the mental manipulation with high demand on working memory-thematic role re-assignment-demonstrates a state of α equilibrium with strong functional coupling between posterior and anterior regions. These results demonstrate that the processing of semantic and syntactic features during sentence comprehension proceeds in highly integrated fashion using gating of attentional resources to facilitate rapid comprehension, with attentional suppression of global alpha power to facilitate interaction of local networks.

Toward a computational framework for cognitive biology: unifying approaches from cognitive neuroscience and comparative cognition

Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity "dendrophilia", and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a neurally-grounded framework for theoretical cognitive science is within reach that can move beyond polarized debates and provide a more adequate theoretical future for cognitive biology.