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

. 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.

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

. 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.

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.

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.

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.

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

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.

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.

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

: 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).

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.

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.

Neurocognitive Profiles of Caucasian Moyamoya Disease Patients in Greece: A Case Series.

The impact of Moyamoya Disease (MMD) on cognition inadult Caucasian patients has not yet been thoroughly investigated. The current study is the first to present detailed neuropsychological data on a series of Greek patients with MMD. A group of eight patients was assessed with an extensive neuropsychological battery, including measures of episodic memory, working memory, executive functions, language, and social cognition.

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.].

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.

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

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.

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.

Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening.

Objective: Music, being a multifaceted stimulus evolving at multiple timescales, modulates brain function in a manifold way that encompasses not only the distinct stages of auditory perception, but also higher cognitive processes like memory and appraisal. Network theory is apparently a promising approach to describe the functional reorganization of brain oscillatory dynamics during music listening. However, the music induced changes have so far been examined within the functional boundaries of isolated brain rhythms.

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

The cerebrospinal fluid (CSF) levels of β-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).

TMS combined with EEG in genetic generalized epilepsy: A phase II diagnostic accuracy study.

Objectives: (A) To develop a TMS-EEG stimulation and data analysis protocol in genetic generalized epilepsy (GGE). (B) To investigate the diagnostic accuracy of TMS-EEG in GGE.

Methods: Pilot experiments resulted in the development and optimization of a paired-pulse TMS-EEG protocol at rest, during hyperventilation (HV), and post-HV combined with multi-level data analysis.

Conflicting cerebrospinal fluid biomarkers and progression to dementia due to Alzheimer's disease.

Background: According to new diagnostic guidelines for Alzheimer's disease (AD), biomarkers enable estimation of the individual likelihood of underlying AD pathophysiology and the associated risk of progression to AD dementia for patients with mild cognitive impairment (MCI). Nonetheless, how conflicting biomarker constellations affect the progression risk is still elusive. The present study explored the impact of different cerebrospinal fluid (CSF) biomarker constellations on the progression risk of MCI patients.

Greater Repertoire and Temporal Variability of Cross-Frequency Coupling (CFC) Modes in Resting-State Neuromagnetic Recordings among Children with Reading Difficulties.

Cross-frequency, phase-to-amplitude coupling (PAC) between neuronal oscillations at rest may serve as the substrate that supports information exchange between functionally specialized neuronal populations both within and between cortical regions. The study utilizes novel algorithms to identify prominent instantaneous modes of cross-frequency coupling and their temporal stability in resting state magnetoencephalography (MEG) data from 25 students experiencing severe reading difficulties (RD) and 27 age-matched non-impaired readers (NI). Phase coherence estimates were computed in order to identify the prominent mode of PAC interaction for each sensor, sensor pair, and pair of frequency bands (from δ to γ) at successive time windows of the continuous MEG record.

Revealing Cross-Frequency Causal Interactions During a Mental Arithmetic Task Through Symbolic Transfer Entropy: A Novel Vector-Quantization Approach.

Working memory (WM) is a distributed cognitive process that employs communication between prefrontal cortex and posterior brain regions in the form of cross-frequency coupling between theta ( θ) and high-alpha ( α2) brain waves. A novel method for deriving causal interactions between brain waves of different frequencies is essential for a better understanding of the neural dynamics of such complex cognitive process. Here, we proposed a novel method to estimate transfer entropy ( TE) through a symbolization scheme, which is based on neural-gas algorithm (NG) and encodes a bivariate time series in the form of two symbolic sequences.

A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses.

The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test.

Spontaneous Up states in vitro: a single-metric index of the functional maturation and regional differentiation of the cerebral cortex.

Understanding the development and differentiation of the neocortex remains a central focus of neuroscience. While previous studies have examined isolated aspects of cellular and synaptic organization, an integrated functional index of the cortical microcircuit is still lacking. Here we aimed to provide such an index, in the form of spontaneously recurring periods of persistent network activity -or Up states- recorded in mouse cortical slices.

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).

Functional aspects of the EGF-induced MAP kinase cascade: a complex self-organizing system approach.

The EGF-induced MAP kinase cascade is one of the most important and best characterized networks in intracellular signalling. It has a vital role in the development and maturation of living organisms. However, when deregulated, it is involved in the onset of a number of diseases.