Andrea Insabato

IDIBAPS August Pi i Sunyer Biomedical Research Institute | IDIBAPS · Division of Clinical and Experimental Neuroscience

Recent resting-state functional MRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity between homotopic regions of the same network, and an abnormal increase of ipsi-lesional functional connectivity between task-negative and task-positive resting-sta...

Traditional gender roles that define what is feminine and masculine also imply that men have higher social status than women. These stereotypes still influence how people interact with each other and with computers. Touch behaviour, essential in social interactions, is an interesting example of such social behaviours. The Midas touch effect describ...

Recent resting-state fMRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity (FC) between homotopic regions of the same network, and an abnormal increase of ipsilesional FC between task-negative and task-positive resting-state networks (RSNs). Whole-b...

Learning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing. Implementations have been inspired by stereotypical neuronal responses like tuning curves in the visual system, where, for example, ON/OFF cells fire or not depending on the contrast in their recep...

Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of...

Neuroimaging-based personalized medicine is emerging to characterize brain disorders and their evolution at the patient level. In this chapter, we present the most classic methods used to infer large-scale brain connectivity based on functional MRI. We adopt a modeling perspective where every connectivity measure is linked to a specific model that...

Learning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing. Implementations have been inspired by stereotypical neuronal responses like tuning curves in the visual system, where, for example, ON/OFF cells fire or not depending on the contrast in their recep...

The concept of brain states, functionally relevant large-scale patterns, has become popular in neuroimaging. Not all components of such patterns are equally characteristic for each brain state, but machine learning provides a possibility of extracting the structure of brain states from functional data. However, the characterization in terms of func...

Estimation of reliable whole-brain connectivity is a crucial step towards the use of connectivity information in quantitative approaches to the study of neuropsychiatric disorders. When estimating brain connectivity a challenge is imposed by the paucity of time samples and the large dimensionality of the measurements. Bayesian estimation methods fo...

The study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aim...

The study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aim...

Abstract of poster presentation at OCNS-2017

http://www.investigacionyciencia.es/revistas/investigacion-y-ciencia/numero/485/saben-los-animales-que-piensan-lo-que-piensan-14926

The uncertain option task has been recently adopted to investigate the neural systems underlying the decision confidence. Latterly single neurons activity has been recorded in lateral intraparietal cortex of monkeys performing an uncertain option task, where the subject is allowed to opt for a small but sure reward instead of making a risky percept...

Humans have a remarkable ability to reflect upon their behavior and mental processes, a capacity known as metacognition. Recent neurophysiological experiments have attempted to elucidate the neural correlates of metacognition in other species. Despite this increased attention, there is still no operational definition of metacognition and the abilit...

Models that integrate sensory evidence to a threshold can explain task accuracy, response times and confidence, yet it is still unclear how confidence is encoded in the brain. Classic models assume that confidence is encoded in some form of balance between the evidence integrated in favor and against the selected option. However, recent experiments...

Author Summary Understanding how the brain produces complex cognitive functions has been a crucial question since ancient philosophical inquiries. The encoding of decision difficulty in the brain is fundamental for complex and adaptive behaviour, and can provide valuable information in uncertain environments where the future outcome of a choice mus...

Decision making is a process of utmost importance in our daily lives, the study of which has been receiving notable attention for decades. Nevertheless, the neural mechanisms underlying decision making are still not fully understood. Computational modeling has revealed itself as a valuable asset to address some of the fundamental questions. Biophys...

Decision confidence, the degree of certainty to which a subject believes his choice is correct, is an emerging subject in neuroscience. On one hand it is a fundamental component of our subjective conscious experience. On the other hand it is crucial to many cognitive functions like action planning and learning. In the past the speed accuracy trade-...

Neurons have been recorded that reflect in their firing rates the confidence in a decision. Here we show how this could arise as an emergent property in an integrate-and-fire attractor network model of decision making. The attractor network has populations of neurons that respond to each of the possible choices, each biased by the evidence for that...