Understanding of auditory discourse in older adults: the effect of syntax

We present results from an ongoing study investigating whether older adults understand auditory discourse, consisting of syntactically complex sentences as accurately as discourse consisting of sentences with simple syntax. We tested the comprehension of young (mean age 22) and old (mean age 80) adults using simple and complex discourse. Recall of information was poorer for the older group for complex compared with simple discourse. Overall, working memory did not have much effect on discourse comprehension ability. These results point towards new avenues of research on discourse and ageing, with syntactic complexity as an important variable

Treating written verb and written sentence production in an individual with aphasia: A clinical study

We document the effectiveness of a treatment for written verb and written sentence production deficits in an individual with moderate to severe aphasia and apraxia of speech. Using a multiple-baseline experimental design, we trained our speaker on a set of intransitive verbs and consequently subject-verb sentences and on a set of transitive verbs and subject-verb-object sentences. A control task, letter spelling to dictation, evaluated the specificity of the intervention. Improvements were noted for the trained sets of verbs and sentences but not the control task. Other clinically significant quantitative and qualitative improvements are also reported

Mining time-resolved functional brain graphs to an EEG-based chronnectomic brain aged index (CBAI)

The brain at rest consists of spatially and temporal distributed but functionally connected regions that called intrinsic connectivity networks (ICNs). Resting state electroencephalography (rs-EEG) is a way to characterize brain networks without confounds associated with task EEG such as task difficulty and performance. A novel framework of how to study dynamic functional connectivity under the notion of functional connectivity microstates (FCμstates) and symbolic dynamics is further discussed. Furthermore, we introduced a way to construct a single integrated dynamic functional connectivity graph (IDFCG) that preserves both the strength of the connections between every pair of sensors but also the type of dominant intrinsic coupling modes (DICM). The whole methodology is demonstrated in a significant and unexplored task for EEG which is the definition of an objective Chronnectomic Brain Aged index (CBAI) extracted from resting-state data (N = 94 subjects) with both eyes-open and eyes-closed conditions. Novel features have been defined based on symbolic dynamics and the notion of DICM and FCμstates. The transition rate of FCμstates, the symbolic dynamics based on the evolution of FCμstates (the Markovian Entropy, the complexity index), the probability distribution of DICM, the novel Flexibility Index that captures the dynamic reconfiguration of DICM per pair of EEG sensors and the relative signal power constitute a valuable pool of features that can build the proposed CBAI. Here we applied a feature selection technique and Extreme Learning Machine (ELM) classifier to discriminate young adults from middle-aged and a Support Vector Regressor to build a linear model of the actual age based on EEG-based spatio-temporal features. The most significant type of features for both prediction of age and discrimination of young vs. adults age groups was the dynamic reconfiguration of dominant coupling modes derived from a subset of EEG sensor pairs. Specifically, our results revealed a very high prediction of age for eyes-open (R2 = 0.60; y = 0.79x + 8.03) and lower for eyes-closed (R2 = 0.48; y = 0.71x + 10.91) while we succeeded to correctly classify young vs. middle-age group with 97.8% accuracy in eyes-open and 87.2% for eyes-closed. Our results were reproduced also in a second dataset for further external validation of the whole analysis. The proposed methodology proved valuable for the characterization of the intrinsic properties of dynamic functional connectivity through the age untangling developmental differences using EEG resting-state recordings

Short-term and working memory treatments for improving sentence comprehension in aphasia: a review and a replication study

Although the roles of verbal short-term and working memory on spoken sentence comprehension skills in persons with aphasia have been debated for many years, the development of treatments to mitigate verbal short-term and working memory deficits as a way of improving spoken sentence comprehension is a new avenue in treatment research. In this article, we review and critically appraise this emerging evidence base. We also present new data from five persons with aphasia of a replication of a previously reported treatment that had resulted in some improvement of spoken sentence comprehension in a person with aphasia. The replicated treatment did not result in improvements in sentence comprehension. We forward recommendations for future research in this, admittedly weak at present, but important clinical research avenue that would help improve our understanding of the mechanisms of improvement of short-term and working memory training in relation to sentence comprehension

Assessment and treatment of short-term and working memory impairments in stroke aphasia - a practical tutorial

Background: Aphasia following stroke refers to impairments that affect the comprehension and expression of spoken and/or written language, and co‐occurring cognitive deficits are common. In this paper we focus on short‐term and working memory impairments that impact on the ability to retain and manipulate auditory–verbal information. Evidence from diverse paradigms (large group studies, case studies) report close links between short‐term/working memory and language functioning in aphasia. This evidence leads to the hypothesis that treating such memory impairments would improve language functioning. This link has only recently been acknowledged in aphasia treatment but has not been embraced widely by clinicians. Aims: To examine the association between language, and short‐term and working memory impairments in aphasia. To describe practical ways of assessing short‐term and working memory functioning that could be used in clinical practice. To discuss and critically appraise treatments of short‐term and working memory reported in the literature. Methods & Procedures: Taking a translational research approach, this paper provides clinicians with current evidence from the literature and practical information on how to assess and treat short‐term and working memory impairments in people with aphasia. Published treatments of short‐term and/or working memory in post‐stroke aphasia are discussed through a narrative review. Main Contributions: This paper provides the following. A theoretical rationale for adopting short‐term and working memory treatments in aphasia. It highlights issues in differentially diagnosing between short‐term, working memory disorders and other concomitant impairments, e.g. apraxia of speech. It describes short‐term and working memory assessments with practical considerations for use with people with aphasia. It also offers a description of published treatments in terms of participants, treatments and outcomes. Finally, it critically appraises the current evidence base relating to the treatment of short‐term and working memory treatments. Conclusions: The links between short‐term/working memory functioning and language in aphasia are generally acknowledged. These strongly indicate the need to incorporate assessment of short‐term/working memory functioning for people with aphasia. While the supportive evidence for treatment is growing and appears to highlight the benefits of including short‐term/working memory in aphasia treatment, the quality of the evidence in its current state is poor. However, because of the clinical needs of people with aphasia and the prevalence of short‐term/working memory impairments, incorporating related treatments through practice‐based evidence is advocated

A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates

Objective Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels, have long been recognized. Manual staging is resource intensive and time consuming, and considerable effort must be spent to ensure inter-rater reliability. There is thus great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC). Methods In this paper, we present a single-EEG-sensor ASSC technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5 s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database with repeat recordings from 20 healthy young adults. Results We achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively, and a high mean F1 score (92%, range 90–94%) when a multi-class Naive Bayes classifier was applied. Conclusions Our method outperformed the accuracy of previous studies not only on different datasets but also on the same database

The methodological quality of short-term/working memory treatments in post-stroke aphasia: A systematic review

Purpose: The aims of this systematic review are to provide a critical overview of short-term memory (STM) and working memory (WM) treatments in stroke aphasia and to systematically evaluate the internal and external validity of STM/WM treatments. Method: A systematic search was conducted in February 2014 and then updated in December 2016 using 13 electronic databases. We provided descriptive characteristics of the included studies and assessed their methodological quality using the Risk of Bias in N-of-1 Trials quantitative scale (Tate et al., 2015), which was completed by 2 independent raters. Results: The systematic search and inclusion/exclusion procedure yielded 17 single-case or case-series studies with 37 participants for inclusion. Nine studies targeted auditory STM consisting of repetition and/or recognition tasks, whereas 8 targeted attention and WM, such as attention process training including n-back tasks with shapes and clock faces as well as mental math tasks. In terms of their methodological quality, quality scores on the Risk of Bias in N-of-1 Trials scale ranged from 4 to 17 (M = 9.5) on a 0–30 scale, indicating a high risk of bias in the reviewed studies. Effects of treatment were most frequently assessed on STM, WM, and spoken language comprehension. Transfer effects on communication and memory in activities of daily living were tested in only 5 studies. Conclusions: Methodological limitations of the reviewed studies make it difficult, at present, to draw firm conclusions about the effects of STM/WM treatments in poststroke aphasia. Further studies with more rigorous methodology and stronger experimental control are needed to determine the beneficial effects of this type of intervention. To understand the underlying mechanisms of STM/WM treatment effects and how they relate to language functioning, a careful choice of outcome measures and specific hypotheses about potential improvements on these measures are required. Future studies need to include outcome measures of memory functioning in everyday life and psychosocial functioning more generally to demonstrate the ecological validity of STM and WM treatments

Design of a computerised treatment for short-term memory deficits in aphasia

The treatment of auditory-verbal short-term memory (STM) deficits in aphasia is a growing avenue of research (Martin & Reilly, 2012; Murray, 2012). STM treatment requires time precision, which is suited to computerised delivery. We have designed software, which provides STM treatment for aphasia. The treatment is based on matching listening span tasks (Howard & Franklin, 1990), aiming to improve the temporal maintenance of multi-word sequences (Salis, 2012). The person listens to pairs of word-lists that differ in word-order and decides if the pairs are the same or different. This approach does not require speech output and is suitable for persons with aphasia who have limited or no output. We describe the software and how its review from clinicians shaped its design

Modeling the evolution of a classic genetic switch

Abstract Background The regulatory network underlying the yeast galactose-use pathway has emerged as a model system for the study of regulatory network evolution. Evidence has recently been provided for adaptive evolution in this network following a whole genome duplication event. An ancestral gene encoding a bi-functional galactokinase and co-inducer protein molecule has become subfunctionalized as paralogous genes (GAL1 and GAL3) in Saccharomyces cerevisiae, with most fitness gains being attributable to changes in cis- regulatory elements. However, the quantitative functional implications of the evolutionary changes in this regulatory network remain unexplored. Results We develop a modeling framework to examine the evolution of the GAL regulatory network. This enables us to translate molecular changes in the regulatory network to changes in quantitative network function. We computationally reconstruct an inferred ancestral version of the network and trace the evolutionary paths in the lineage leading to S. cerevisiae. We explore the evolutionary landscape of possible regulatory networks and find that the operation of intermediate networks leading to S. cerevisiae differs substantially depending on the order in which evolutionary changes accumulate; in particular, we systematically explore evolutionary paths and find that some network features cannot be optimized simultaneously. Conclusions We find that a computational modeling approach can be used to analyze the evolution of a well-studied regulatory network. Our results are consistent with several experimental studies of the evolutionary of the GAL regulatory network, including increased fitness in Saccharomyces due to duplication and adaptive regulatory divergence. The conceptual and computational tools that we have developed may be applicable in further studies of regulatory network evolution