Publications by authors named "Sarrias-Arrabal E"

The aim of this study was to describe the spectral features of pre-stimulus event-related potential (ERP) components elicited in visual tasks such as the Bereitschaftspotential (BP), prefrontal negativity (pN) and visual negativity (vN). ERPs are considered time-locked and phase-locked (evoked) activity, but we have also analyzed the non-phase but time-locked (induced) activity in the same interval by applying the temporal spectral evolution (TSE) method. Participants (N = 26) were tested in a passive task, a simple response task (SRT) and a discriminative response task (DRT), where EEG activity was recorded with 64 scalp electrodes.

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Background And Objectives: Intermittent theta-burst stimulation (iTBS) is a patterned form of excitatory transcranial magnetic stimulation that has yielded encouraging results as an adjunctive therapeutic option to alleviate the emergence of clinical deficits in Parkinson's disease (PD) patients. Although it has been demonstrated that iTBS influences dopamine-dependent corticostriatal plasticity, little research has examined the neurobiological mechanisms underlying iTBS-induced clinical enhancement. Here, our primary goal is to verify whether iTBS bilaterally delivered over the primary motor cortex (M1) is effective as an add-on treatment at reducing scores for both motor functional impairment and nonmotor symptoms in PD.

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Recent studies have suggested that nonphase-locked activity can reveal cognitive mechanisms that cannot be observed in phase-locked activity. In fact, we describe a concomitant decrease in nonphase-locked alpha activity (desynchronization) when stimuli were processed (alpha phase-locked modulation). This desynchronization may represent a reduction in "background activity" in the visual cortex that facilitates stimulus processing.

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Introduction: The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

Objective: We present a systematic review of the application of ML algorithms in MS.

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Introduction: Understanding alterations to brain anatomy and cognitive function associated with neurodegenerative diseases remains a challenge for neuroscience today. In experimental neuroscience, several computerised tests have been developed to contribute to our understanding of neural networks involved in cognition. The Attention Network Test (ANT) enables us to measure the activity of 3 attentional networks (alertness, orienting, and executive function).

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Currently, there is scarce knowledge about the relation between spectral bands modulations and the basis of cognitive impairment in multiple sclerosis (MS). In this sense, analyzing the evoked or phase activity can confirm results from traditional event-related potential (ERP) studies. However, studying the induced or nonphase activity may be necessary to elucidate hidden compensatory or affected cognitive mechanisms.

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Introduction: The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

Objective: We present a systematic review of the application of ML algorithms in MS.

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Some of the anatomical and functional basis of cognitive impairment in multiple sclerosis (MS) currently remains unknown. In particular, there is scarce knowledge about modulations in induced EEG (nonphase activity) for diverse frequency bands related to attentional deficits in this pathology. The present study analyzes phase and nonphase alpha and gamma modulations in 26 remitting-relapsing multiple sclerosis patients during their participation in the attention network test compared with twenty-six healthy controls (HCs) matched in sociodemographic variables.

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Introduction: Understanding alterations to brain anatomy and cognitive function associated with neurodegenerative diseases remains a challenge for neuroscience today. In experimental neuroscience, several computerised tests have been developed to contribute to our understanding of neural networks involved in cognition. The Attention Network Test (ANT) enables us to measure the activity of 3 attentional networks (alertness, orienting, and executive function).

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Introduction: The Attention Network Test (ANT) has been applied to the study of potential attentional impairments in diverse neuropathologies in the last years. This test allows analyzing of different networks involved in attentional processing (alerting, orientation and executive system).

Development: A specific application of ANT in ADHD patients shows that it is possible to find diverse impairments in the three attentional networks and even some studies revealed no alterations.

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