Publications by authors named "I Lopez Diez"

The human brain is organized as a hierarchical global network. Functional connectivity research reveals that sensory cortices are connected to corresponding association cortices via a series of intermediate nodes linked by synchronous neural activity. These sensory pathways and relay stations converge onto central cortical hubs such as the default-mode network (DMN).

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Chiral optical forces exhibit opposite signs for the two enantiomeric versions of a chiral molecule or particle. If large enough, these forces might be able to separate enantiomers all optically, which would find numerous applications in different fields, from pharmacology to chemistry. Longitudinal chiral forces are especially promising for tackling the challenging scenario of separating particles of realistically small chiralities.

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Objective: Research suggests that disrupted interoception contributes to the development and maintenance of functional neurological disorder (FND); however, no functional neuroimaging studies have examined the processing of interoceptive signals in patients with FND.

Methods: The authors examined univariate and multivariate functional MRI neural responses of 38 patients with mixed FND and 38 healthy control individuals (HCs) during a task exploring goal-directed attention to cardiac interoception-versus-control (exteroception or rest) conditions. The relationships between interoception-related neural responses, heartbeat-counting accuracy, and interoceptive trait prediction error (ITPE) were also investigated for FND patients.

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The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals.

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Article Synopsis
  • Epileptic seizures are dangerous neurological events that require early detection for effective treatment, leading to the development of advanced artificial intelligence methods for improved detection.
  • This study introduces a new ensemble approach, combining fast independent component analysis random forest (FIR) and prediction probability, using EEG data to enhance the early detection of seizures.
  • Experimental results show that the FIR model, particularly when combined with support vector machine (FIR + SVM), achieves a high detection accuracy of 98.4%, indicating its potential for early diagnosis and improved patient outcomes in the medical field.
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