NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation

Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily functioning and independence, with significant societal and economic implications. While neurorehabilitation represents a promising avenue for addressing these deficits, traditional rehabilitation approaches face notable limitations. First, they lack adaptability, offering one-size-fits-all solutions that may not effectively meet each patient's unique needs. Furthermore, the resource-intensive nature of these interventions, often confined to clinical settings, poses barriers to widespread, cost-effective, and sustained implementation, resulting in suboptimal outcomes in terms of intervention adaptability, intensity, and duration. In response to these challenges, this paper introduces NeuroAIreh@b, an innovative cognitive profiling and training methodology that uses an AI-driven framework to optimize neurorehabilitation prescription. NeuroAIreh@b effectively bridges the gap between neuropsychological assessment and computational modeling, thereby affording highly personalized and adaptive neurorehabilitation sessions. This approach also leverages virtual reality-based simulations of daily living activities to enhance ecological validity and efficacy. The feasibility of NeuroAIreh@b has already been demonstrated through a clinical study with stroke patients employing a tablet-based intervention. The NeuroAIreh@b methodology holds the potential for efficacy studies in large randomized controlled trials in the future.

Preventing the recurrence of depression with a Mediterranean diet supplemented with extra-virgin olive oil. The PREDI-DEP trial: study protocol

BACKGROUND

The role of dietary patterns in the prevention of unipolar depression has been analyzed in several epidemiological studies. The primary aims of this study are to determine the effectiveness of an extra-olive oil-enriched Mediterranean diet in reducing the recurrence of depression and improving the symptoms of this condition.

METHODS

Multicenter, two-arm, parallel-group clinical trial. Arm 1, extra-virgin olive oil Mediterranean diet; Arm 2, control group without nutritional intervention. Dieticians are in charge of the nutritional intervention and regular contact with the participants. Contacts are made through our web platform ( https://predidep.es/participantes/ ) or by phone. Recurrence of depression is assessed by psychiatrists and clinical psychologists through clinical evaluations (semi-structured clinical interviews: Spanish SCID-I). Depressive symptoms are assessed with the Beck Depression Inventory. Information on quality of life, level of physical activity, dietary habits, and blood, urine and stool samples are collected after the subject has agreed to participate in the study and once a year.

DISCUSSION

To the best of our knowledge, the PREDI-DEP trial is the first ongoing randomized clinical trial designed to assess the role of the Mediterranean diet in the prevention of recurrent depression. It could be a cost-effective approach to avoid recurrence and improve the quality of life of these patients.

TRIAL REGISTRATION

The study has been prospectively registered in the U.S. National Library of Medicine ( https://clinicaltrials.gov ) with NCT number: NCT03081065.

An autonomous robot for continuous tracking of millimetric-sized walkers

The precise and continuous tracking of millimetric-sized walkers-such as ants-is quite important in behavioral studies. However, due to technical limitations, most studies concentrate on trajectories within areas no more than 100 times bigger than the size of the walker or longer trajectories at the expense of either accuracy or continuity. Our work describes a scientific instrument designed to push the boundaries of precise and continuous motion analysis up to 1000 body lengths or more. It consists of a mobile robotic platform that uses digital image processing techniques to track the targets in real time by calculating their spatial position. During the experiments, all the images are stored and afterwards processed to estimate with higher precision the path traced by the walkers. Some preliminary results achieved using the proposed tracking system are presented.