Silent Pauses and Speech Indices as Biomarkers for Primary Progressive Aphasia.

Background and Objectives: Recent studies highlight the importance of investigating biomarkers for diagnosing and classifying patients with primary progressive aphasia (PPA). Even though there is ongoing research on pathophysiological indices in this field, the use of behavioral variables, and especially speech-derived factors, has drawn little attention in the relevant literature. The present study aims to investigate the possible utility of speech-derived indices, particularly silent pauses, as biomarkers for primary progressive aphasia (PPA). Materials and Methods: We recruited 22 PPA patients and 17 healthy controls, from whom we obtained speech samples based on two elicitation tasks, i.e., cookie theft picture description (CTP) and the patients' personal narration of the disease onset and course. Results: Four main indices were derived from these speech samples: speech rate, articulation rate, pause frequency, and pause duration. In order to investigate whether these indices could be used to discriminate between the four groups of participants (healthy individuals and the three patient subgroups corresponding to the three variants of PPA), we conducted three sets of analyses: a series of ANOVAs, two principal component analyses (PCAs), and two hierarchical cluster analyses (HCAs). The ANOVAs revealed significant differences between the four subgroups for all four variables, with the CTP results being more robust. The subsequent PCAs and HCAs were in accordance with the initial statistical comparisons, revealing that the speech-derived indices for CTP provided a clearer classification and were especially useful for distinguishing the non-fluent variant from healthy participants as well as from the two other PPA taxonomic categories. Conclusions: In sum, we argue that speech-derived indices, and especially silent pauses, could be used as complementary biomarkers to efficiently discriminate between PPA and healthy speakers, as well as between the three variants of the disease.

GEVI cell-type specific labelling and a manifold learning approach provide evidence for lateral inhibition at the population level in the mouse hippocampal CA1 area.

The CA1 area in the mammalian hippocampus is essential for spatial learning. Pyramidal cells are the hippocampus output neurons and their activities are regulated by inhibition exerted by a diversified population of interneurons. Lateral inhibition has been suggested as the mechanism enabling the reconfiguration of pyramidal cell assembly activity observed during spatial learning tasks in rodents. However, lateral inhibition in the CA1 lacks the overwhelming evidence reported in other hippocampal areas such as the CA3 and the dentate gyrus. The use of genetically encoded voltage indicators and fast optical recordings permits the construction of cell-type specific response maps of neuronal activity. Here, we labelled mouse CA1 pyramidal neurons with the genetically encoded voltage indicator ArcLight and optically recorded their response to Schaffer Collaterals stimulation in vitro. By undertaking a manifold learning approach, we report a hyperpolarization-dominated area focused in the perisomatic region of pyramidal cells receiving late excitatory synaptic input. Functional network organization metrics revealed that information transfer was higher in this area. The localized hyperpolarization disappeared when GABAA receptors were pharmacologically blocked. This is the first report where the spatiotemporal pattern of lateral inhibition is visualized in the CA1 by expressing a genetically encoded voltage indicator selectively in principal neurons. Our analysis suggests a fundamental role of lateral inhibition in CA1 information processing.

Fluid biomarker agreement and interrelation in dementia due to Alzheimer's disease.

The cerebrospinal fluid (CSF) levels of ß-amyloid 42, total tau, and phosphorylated tau 181 are supposed to be all continuously abnormal in dementia due to Alzheimer's disease (AD), being the most advanced disease stage. The aim of the present study, which included a monocentric and a multicentric sample (N = 119 and 178, respectively), was to investigate the degree of CSF biomarker agreement and interrelation in AD dementia. Based on previously published cut-off values, biomarker values were categorized as positive or negative for AD (dichotomization strategy) and as either positive, negative, or borderline (trichotomization strategy). The statistical analyses relied on distance correlation analysis and kappa (k) statistics. Poor agreement (k < 0.4) and low interrelations between the studied biomarkers were detected in all cases with the exception of the interrelation between the markers total tau and phosphorylated tau 181, especially in the monocentric sample. Interestingly, lower interrelation and agreement degrees were observed in carriers of the Apolipoprotein E ε4 allele compared to non-carriers. The clinical phenotype currently referred to as "AD dementia" is characterized by an inhomogeneous CSF biomarker profile, possibly mirroring the complex genesis of AD-typical dementia symptoms and pointing to the necessity of shedding more light on the hypothesis of biomarker stability over time in symptomatic AD.

Mapping CSF biomarker profiles onto NIA-AA guidelines for Alzheimer's disease.

The National Institute on Aging-Alzheimer's Association (NIA-AA) guidelines for Alzheimer's disease (AD) propose the categorization of individuals according to their biomarker constellation. Though the NIA-AA criteria for preclinical AD and AD dementia have already been applied in conjunction with imaging AD biomarkers, the application of the criteria using comprehensive cerebrospinal fluid (CSF) biomarker information has not been thoroughly studied yet. The study included a monocentric cohort with healthy (N = 41) and disease (N = 22) controls and patients with AD dementia (N = 119), and a multicentric sample with healthy controls (N = 116) and patients with AD dementia (N = 102). The CSF biomarkers ß-amyloid 1-42, total tau, and phosphorylated tau at threonine 181 were measured with commercially available assays. Biomarker values were trichotomized into positive for AD, negative, or borderline. In controls the presence of normal CSF profiles varied between 13.6 and 25.4 % across the studied groups, while up to 8.6 % of them had abnormal CSF biomarkers. In 40.3-52.9 % of patients with AD dementia, a typical CSF profile for AD was detected. Approximately 40 % of the potential biomarker constellations are not considered in the NIA-AA guidelines, and more than 40 % of participants could not be classified into the NIA-AA categories with distinct biomarker constellations. Here, a refined scheme covering all potential biomarker constellations is proposed. These results enrich the discussion on the NIA-AA guidelines and point to a discordance between clinical symptomatology and CSF biomarkers even in patients with full-blown AD dementia, who are supposed to have a clearly positive for AD neurochemical profile.

Limited agreement between biomarkers of neuronal injury at different stages of Alzheimer's disease.

New diagnostic criteria for Alzheimer's disease (AD) treat different biomarkers of neuronal injury as equivalent. Here, we quantified the degree of agreement between hippocampal volume on structural magnetic resonance imaging, regional glucose metabolism on positron emission tomography, and levels of phosphorylated tau in cerebrospinal fluid (CSF) in 585 subjects from all phases of the AD Neuroimaging Initiative. The overall chance-corrected agreement was poor (Cohen κ, 0.24-0.34), in accord with a high rate of conflicting findings (26%-41%). Neither diagnosis nor APOE ε4 status significantly influenced the distribution of agreement between the biomarkers. The degree of agreement tended to be higher in individuals with abnormal versus normal CSF ß-amyloid (Aß1-42) levels. Prospective diagnostic criteria for AD should address the relative importance of markers of neuronal injury and elaborate a way of dealing with conflicting biomarker findings.

EEGminer: discovering interpretable features of brain activity with learnable filters.

Objective. The patterns of brain activity associated with different brain processes can be used to identify different brain states and make behavioural predictions. However, the relevant features are not readily apparent and accessible. Our aim is to design a system for learning informative latent representations from multichannel recordings of ongoing EEG activity.Approach: We propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Specifically, we introduce filters parameterized by generalized Gaussian functions that offer a smooth derivative for stable end-to-end model training and allow for learning interpretable features. For the feature module, we use signal magnitude and functional connectivity estimates.Main results.We demonstrate the utility of our model on a new EEG dataset of unprecedented size (i.e. 721 subjects), where we identify consistent trends of music perception and related individual differences. Furthermore, we train and apply our model in two additional datasets, specifically for emotion recognition on SEED and workload classification on simultaneous task EEG workload. The discovered features align well with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening. This agrees with the specialisation of the temporal lobes regarding music perception proposed in the literature.Significance. The proposed method offers strong interpretability of learned features while reaching similar levels of accuracy achieved by black box deep learning models. This improved trustworthiness may promote the use of deep learning models in real world applications. The model code is available athttps://github.com/SMLudwig/EEGminer/.

A causal perspective on brainwave modeling for brain-computer interfaces.

Objective. Machine learning (ML) models have opened up enormous opportunities in the field of brain-computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting.Approach. Mixing causal reasoning, identifying causal relationships between variables of interest, with brainwave modeling can change one's viewpoint on some of these major challenges which can be found in various stages in the ML pipeline, ranging from data collection and data pre-processing to training methods and techniques.Main results. In this work, we employ causal reasoning and present a framework aiming to breakdown and analyze important challenges of brainwave modeling for BCIs.Significance. Furthermore, we present how general ML practices as well as brainwave-specific techniques can be utilized and solve some of these identified challenges. And finally, we discuss appropriate evaluation schemes in order to measure these techniques' performance and efficiently compare them with other methods that will be developed in the future.

Unraveling the Thread of Aphasia Rehabilitation: A Translational Cognitive Perspective.

Translational neuroscience is a multidisciplinary field that aims to bridge the gap between basic science and clinical practice. Regarding aphasia rehabilitation, there are still several unresolved issues related to the neural mechanisms that optimize language treatment. Although there are studies providing indications toward a translational approach to the remediation of acquired language disorders, the incorporation of fundamental neuroplasticity principles into this field is still in progress. From that aspect, in this narrative review, we discuss some key neuroplasticity principles, which have been elucidated through animal studies and which could eventually be applied in the context of aphasia treatment. This translational approach could be further strengthened by the implementation of intervention strategies that incorporate the idea that language is supported by domain-general mechanisms, which highlights the impact of non-linguistic factors in post-stroke language recovery. Here, we highlight that translational research in aphasia has the potential to advance our knowledge of brain-language relationships. We further argue that advances in this field could lead to improvement in the remediation of acquired language disturbances by remodeling the rationale of aphasia-therapy approaches. Arguably, the complex anatomy and phenomenology of aphasia dictate the need for a multidisciplinary approach with one of its main pillars being translational research.

BrainWave-Scattering Net: a lightweight network for EEG-based motor imagery recognition.

Objective.Brain-computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one's neural activity, measured by electroencephalography (EEG) signals. In recent years, convolutional neural networks (CNNs) have been widely used to perform automatic feature extraction and classification in various EEG-based tasks. However, their undeniable benefits are counterbalanced by the lack of interpretability properties as well as the inability to perform sufficiently when only limited amount of training data is available.Approach.In this work, we introduce a novel, lightweight, fully-learnable neural network architecture that relies on Gabor filters to delocalize EEG signal information into scattering decomposition paths along frequency and slow-varying temporal modulations.Main results.We utilize our network in two distinct modeling settings, for building either a generic (training across subjects) or a personalized (training within a subject) classifier.Significance.In both cases, using two different publicly available datasets and one in-house collected dataset, we demonstrate high performance for our model with considerably less number of trainable parameters as well as shorter training time compared to other state-of-the-art deep architectures. Moreover, our network demonstrates enhanced interpretability properties emerging at the level of the temporal filtering operation and enables us to train efficient personalized BCI models with limited amount of training data.

Improving Generalization of CNN-Based Motor-Imagery EEG Decoders via Dynamic Convolutions.

Deep Convolutional Neural Networks (CNNs) have recently demonstrated impressive results in electroencephalogram (EEG) decoding for several Brain-Computer Interface (BCI) paradigms, including Motor-Imagery (MI). However, neurophysiological processes underpinning EEG signals vary across subjects causing covariate shifts in data distributions and hence hindering the generalization of deep models across subjects. In this paper, we aim to address the challenge of inter-subject variability in MI. To this end, we employ causal reasoning to characterize all possible distribution shifts in the MI task and propose a dynamic convolution framework to account for shifts caused by the inter-subject variability. Using publicly available MI datasets, we demonstrate improved generalization performance (up to 5%) across subjects in various MI tasks for four well-established deep architectures.

Exploring Pragmatic Deficits in Relation to Theory of Mind and Executive Functions: Evidence from Individuals with Right Hemisphere Stroke.

Research investigating pragmatic deficits in individuals with right hemisphere damage focuses on identifying the potential mechanisms responsible for the nature of these impairments. Nonetheless, the presumed shared cognitive mechanisms that could account for these deficits have not yet been established through data-based evidence from lesion studies. This study aimed to examine the co-occurrence of pragmatic language deficits, Theory of Mind impairments, and executive functions while also exploring their associations with brain lesion sites. Twenty-five patients suffering from unilateral right hemisphere stroke and thirty-seven healthy participants were recruited for this study. The two groups were tested in pragmatics, Theory of Mind, and executive function tasks. Structural imaging data were also obtained for the identification of the lesion sites. The findings of this study suggest a potential convergence among the three aforementioned cognitive mechanisms. Moreover, we postulate a hypothesis for a neural circuitry for communication impairments observed in individuals with right hemisphere damage.

Investigating Aphasia Recovery: Demographic and Clinical Factors.

Post-stroke language recovery remains one of the main unresolved topics in the field of aphasia. In recent years, there have been efforts to identify specific factors that could potentially lead to improved language recovery. However, the exact relationship between the recovery of particular language functions and possible predictors, such as demographic or lesion variables, is yet to be fully understood. In the present study, we attempted to investigate such relationships in 42 patients with aphasia after left hemisphere stroke, focusing on three language domains: auditory comprehension, naming and speech fluency. Structural imaging data were also obtained for the identification of the lesion sites. According to our findings, patients demonstrated an overall improvement in all three language domains, while no demographic factor significantly contributed to aphasia recovery. Interestingly, specific lesion loci seemed to have a differential effect on language performance, depending on the time of testing (i.e., acute/subacute vs. chronic phase). We argue that this variability concerning lesion-deficit associations reflects the dynamic nature of aphasia and further discuss possible explanations in the framework of neuroplastic changes during aphasia recovery.

Surface EEG shows that functional segregation via phase coupling contributes to the neural substrate of mental calculations.

Multichannel EEG traces from healthy subjects are used to investigate the brain's self-organisation tendencies during two different mental arithmetic tasks. By making a comparison with a control-state in the form of a classification problem, we can detect and quantify the changes in coordinated brain activity in terms of functional connectivity. The interactions are quantified at the level of EEG sensors through descriptors that differ over the nature of functional dependencies sought (linear vs. nonlinear) and over the specific form of the measures employed (amplitude/phase covariation). Functional connectivity graphs (FCGs) are analysed with a novel clustering algorithm, and the resulting segregations enter an appropriate discriminant function. The magnitude of the contrast function depends on the frequency-band (θ, α(1), α(2), ß and γ) and the neural synchrony descriptor. We first show that the maximal-contrast corresponds to a phase coupling descriptor and then identify the corresponding spatial patterns that represent best the task-induced changes for each frequency band. The principal finding of this study is that, during mental calculations, phase synchrony plays a crucial role in the segregation into distinct functional domains, and this segregation is the most prominent feature of the brain's self-organisation as this is reflected in sensor space.

An EEG study of brain connectivity dynamics at the resting state.

We investigated the dynamical behavior of resting state functional connectivity using EEG signals. Employing a recently introduced methodology that considers the time variations of phase coupling among signals from different channels, a sequence of functional connectivity graphs (FCGs) was constructed for different frequency bands and analyzed based on graph theoretic tools. In the first stage of analysis, hubs were detected in the FCGs based on local and global efficiency. The probability of each node to be identified as a hub was estimated. This defined a topographic function that showed widespread distribution with prominence over the frontal brain regions for both local and global efficiency. Hubs consistent across time were identified via a summarization technique and found to locate over forehead. In the second stage of analysis, the modular structure of each single FCG was delineated. The derived time-dependent signatures of functional structure were compared in a systematic way revealing fluctuations modulated by frequency. Interestingly, the evolution of functional connectivity can be described via abrupt transitions between states, best described as short-lasting bimodal functional segregations. Based on a distance function that compares clusterings, we discovered that these segregations are recurrent. Entropic measures further revealed that the apparent fluctuations are subject to intrinsic constraints and that order emerges from spatially extended interactions.

Common Neuroanatomical Substrate of Cholinergic Pathways and Language-Related Brain Regions as an Explanatory Framework for Evaluating the Efficacy of Cholinergic Pharmacotherapy in Post-Stroke Aphasia: A Review.

Despite the relative scarcity of studies focusing on pharmacotherapy in aphasia, there is evidence in the literature indicating that remediation of language disorders via pharmaceutical agents could be a promising aphasia treatment option. Among the various agents used to treat chronic aphasic deficits, cholinergic drugs have provided meaningful results. In the current review, we focused on published reports investigating the impact of acetylcholine on language and other cognitive disturbances. It has been suggested that acetylcholine plays an important role in neuroplasticity and is related to several aspects of cognition, such as memory and attention. Moreover, cholinergic input is diffused to a wide network of cortical areas, which have been associated with language sub-processes. This could be a possible explanation for the positive reported outcomes of cholinergic drugs in aphasia recovery, and specifically in distinct language processes, such as naming and comprehension, as well as overall communication competence. However, evidence with regard to functional alterations in specific brain areas after pharmacotherapy is rather limited. Finally, despite the positive results derived from the relevant studies, cholinergic pharmacotherapy treatment in post-stroke aphasia has not been widely implemented. The present review aims to provide an overview of the existing literature in the common neuroanatomical substrate of cholinergic pathways and language related brain areas as a framework for interpreting the efficacy of cholinergic pharmacotherapy interventions in post-stroke aphasia, following an integrated approach by converging evidence from neuroanatomy, neurophysiology, and neuropsychology.

The role of the aggregate shape on the compressive strength of concrete using a new micro geo-informatics methodology.

In this paper the results of an experimental study on the behavior of aggregate shape on the compressive concrete strength was described. The main scope of that work is to answer whether there is a low-cost, low-energy methodology for predicting the behavior of an aggregate within a concrete and therefore its ultimate strength. This was achieved by using a combination of petrographic methods with GIS and MatLab software in a variety of lithologies when simultaneously producing a new micropetrographic index (Mshape) for the first time. For this reason, variable rocks such as sandstones, ultramafic, mafic and volcanic have been collected from Greece which are used as aggregates. Their petrographic characteristics as well as their geometrical properties were studied and hence their influence on concrete production. In the present study, a new micro-petrographic index is proposed based on the present proposed methodology which is able to act as a predictor of the aggregates shape and therefore of their behavior and suitability. Mshape index is strongly correlated with the geometrical indices of shape IE and IF as well as with the concrete strength.

Manufacturing Zero-Waste COVID-19 Personal Protection Equipment: a Case Study of Utilizing 3D Printing While Employing Waste Material Recycling.

COVID-19 pandemic outbreak dictated the extensive use of personal protective equipment (PPE) by the majority of the population and mostly by frontline professionals. This need triggered a sudden demand that led to a global shortage of available PPEs threatening to have an immense contribution to the virus contamination spread. In these conditions, the need for a local, flexible, and rapid manufacturing method that would be able to cope with the increased demand for PPE fabrication arose. 3D printing proved to be such a manufacturing technique since its working principles make it an ideal technology for local, decentralized production of PPEs meeting the local demands. While considered to be more environmentally friendly than conventional fabrication techniques and aligning well with the principles of sustainability and circular economy, 3D printing can produce waste as the result of potential failed prints and material used for the fabrication of support structures. This paper describes the case of utilizing pre-existing FDM 3D printing equipment in an academic facility for the production of PPEs (face shields) and their distribution according to local demands. The plastic wastes produced were forwarded to a recycling process that led to their conversion to 3D filament that would be returned to the academic facility as raw material for future 3D printing operations. The followed procedure minimized 3D printing waste and led to a zero-waste fabrication case that was initiated in a pandemic for a greater-good cause (production of COVID-19 fighting PPEs) while assimilating the values of sustainability and circular economy.

Correction to: Manufacturing Zero-Waste COVID-19 Personal Protection Equipment: a Case Study of Utilizing 3D Printing while Employing Waste Material Recycling.

[This corrects the article DOI: 10.1007/s43615-021-00047-8.].

On the estimate of music appraisal from surface EEG: a dynamic-network approach based on cross-sensor PAC measurements.

Objective.The aesthetic evaluation of music is strongly dependent on the listener and reflects manifold brain processes that go well beyond the perception of incident sound. Being a high-level cognitive reaction, it is difficult to predict merely from the acoustic features of the audio signal and this poses serious challenges to contemporary music recommendation systems. We attempted to decode music appraisal from brain activity, recorded via wearable EEG, during music listening.Approach.To comply with the dynamic nature of music stimuli, cross-frequency coupling measurements were employed in a time-evolving manner to capture the evolving interactions between distinct brain-rhythms during music listening. Brain response to music was first represented as a continuous flow of functional couplings referring to both regional and inter-regional brain dynamics and then modelled as an ensemble of time-varying (sub)networks. Dynamic graph centrality measures were derived, next, as the final feature-engineering step and, lastly, a support-vector machine was trained to decode the subjective music appraisal. A carefully designed experimental paradigm provided the labeled brain signals.Main results.Using data from 20 subjects, dynamic programming to tailor the decoder to each subject individually and cross-validation, we demonstrated highly satisfactory performance (MAE= 0.948,R2= 0.63) that can be attributed, mostly, to interactions of left frontal gamma rhythm. In addition, our music-appraisal decoder was also employed in a part of the DEAP dataset with similar success. Finally, even a generic version of the decoder (common for all subjects) was found to perform sufficiently.Significance.A novel brain signal decoding scheme was introduced and validated empirically on suitable experimental data. It requires simple operations and leaves room for real-time implementation. Both the code and the experimental data are publicly available.

Characterizing dynamic functional connectivity across sleep stages from EEG.

Following a nonlinear dynamics approach, we investigated the emergence of functional clusters which are related with spontaneous brain activity during sleep. Based on multichannel EEG traces from 10 healthy subjects, we compared the functional connectivity across different sleep stages. Our exploration commences with the conjecture of a small-world patterning, present in the scalp topography of the measured electrical activity. The existence of such a communication pattern is first confirmed for our data and then precisely determined by means of two distinct measures of non-linear interdependence between time-series. A graph encapsulating the small-world network structure along with the relative interdependence strength is formed for each sleep stage and subsequently fed to a suitable clustering procedure. Finally the delineated graph components are comparatively presented for all stages revealing novel attributes of sleep architecture. Our results suggest a pivotal role for the functional coupling during the different stages and indicate interesting dynamic characteristics like its variable hemispheric asymmetry and the isolation between anterior and posterior cortical areas during REM.