NeuroADaS Lab
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Where Science Meets Impact

At NeuroADaS Lab, we tackle scientific challenges through three key research areas: Cognitive Neuroscience, Imaging Biomarkers, and Applied Neuroscience & Clinical Neuropsychology.

COGNITIVE NEUROSCIENCE

Decoding the neural basis of cognitive processes.

We aim to uncover how memory, attention, and executive functions are rooted in neural activity. Our research lines include:

We are studying the functional dissociation of the dorsal and ventral neural system related to different aspects of cognitive control: decision-making, working memory, numerical cognition, and information processing.
Understanding the neural mechanisms behind that is key to advancing our knowledge of disorders where this function is severely impaired, such as addiction, mood disorders, and some developmental conditions.
This research could pave the way for more effective interventions and treatments for these challenges.

We are studying the functional brain connectivity of bilingual individuals to determine whether there are specific differences compared to monolinguals or within different kinds of bilinguals regarding their competence and language use and exposure.
To this end, we use public neuroimaging datasets, and we apply machine learning methods.
We aim to identify the specific characteristics that connect functional brain differences with language processing and use.
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IMAGING BIOMARKERS

Advancing Diagnostics with braing Imaging Techniques

We aim to study the patterns of brain connectivity in brain disorders and development of predictive analytical solutions using Magnetic Resonance Imaging, next generation biomarkers and the design Artificial Intelligence-based tools.
Our research lines include:

We want to explore the potential of applying multidimensional data analysis over brain connectivity networks using the latest advances in graph theory to enhance our understanding of brain mechanism complexity. Our aim is to combine the morphological, structural and functional information of brain connectivity in order to define a new multi-layer network, which can be used to analyze all information at once by using graph-mining techniques.

The main objective of this project is to design a new database system able to accept images as part of the query. This change will introduce a revolution in the way that clinicians analyze and diagnose images and patients. Moreover, the outcome of this project goes beyond medical imaging, and it could be applied in other clinical disciplines, like dermatology for identifying skin disease, and it is a solution that can be translated to other environments. Latest advances in data science will allow us to build an efficient retrieval system for clinical images containing multiple pathologies using shape, texture, edge histogram features and abnormalities detection. All these features, among many others, could be very relevant to look for similar images in the database.

A number of studies have already shown that magnetic resonance imaging (MRI) can be used to measure biophysically meaningful features, i.e. quantitative metrics that can be associated with brain tissue characteristics, which are showing a clear dependency on age and pathology. There is a pressing need to explain the largely explored brain tissues in terms of imaging biomarkers. Our goal is to help to address this problem, thanks to the availability of specific MRI software and a uniquely placed team of multidisciplinary experts in image analysis and computational modeling, essential for the intended development of new image-based measurements.

Individuals with mental health disorders, such as depression, show specific brain differences compared to healthy individuals. In collaboration with Dr. Joan Camprodon (Laboratory for Neuropsychiatry and Neuromodulation, Massachusetts General Hospital), we try to characterize those differences in order to offer better therapeutic approaches based on non-invasive brain stimulation. In particular, we are studying the differences in connectivity between the nucleus accumbens and the subgenual cingulate cortex and its relationship with symptomatology. And, also, the brain connectivity of the limbic regions, the changes produced by brain stimulation treatments and its relationship with the patients’ symptoms.

APPLIED NEUROSCIENCE & CLINICAL NEUROPSYCHOLOGY

From Lab to Clinic: Applying Neuroscience for Health Solutions

Our objectives in this area is to study of the efficacy of neuromodulation techniques and other non-pharmacological interventions in neurological disorders and addictions.
Our research lines include:

Nicotine addiction is often characterized by dysfunctional cognitive control, an uncontrolled reward impulse and an altered decision-making process. Transcranial direct current stimulation (tDCS) can be used to increase self-control in habitual tobacco users, reducing anxiety caused by abstinence and giving up nicotine consumption. This technique has already been successfully used to reduce craving and tobacco consumption, but the optimal parameters to implement it as a common treatment are yet to be established. Our main goal is to improve tDCS treatment parameters used to help smoking cessation.

We leverage image processing, data analysis, artificial intelligence, and neuromodulation to improve people's well-being and support the work of healthcare professionals.

This research in funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER) under the project ‘MEM-COG’ (PID2020-118672RB-I0) for the next three years. So, we will carry out several studies as planned in the project by using music, in collaboration with the Hospital de la Santa Creu i Sant Pau. We also aim to include new collaborators in the project and to extend the research to theater-based interventions. There is an ongoing pilot study on the benefits of theater on cognition and emotion in collaboration with the ‘Teatre Lliure’.

We develop predictive models based on artificial intelligence to anticipate the appearance and evolution of neurodegenerative diseases. Based on clinical, demographic and/or genetic data, we create algorithms capable of detecting patterns and predicting future scenarios. These tools help improve diagnosis, personalise treatments and optimise health planning.
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