We study a method of optimal data-driven aggregation of classifiers in a convex combination and establish tight upper bounds on its excess risk with respect to a convex loss function under the assumption that the so- lution of optimal aggregation problem is sparse. We use a boosting type algorithm of optimal aggregation to develop aggregate classifiers of ac- tivation patterns in fMRI based on locally trained SVM classifiers. The aggregation coefficients are then used to design a "boosting map" of the brain needed to identify the regions with most significant impact on clas- sification.

Summary: A new AI algorithm can predict the onset of Alzheimer's disease with an accuracy of over 99% by analyzing fMRI brain scans. Researchers from Kaunas University, Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer's disease from brain images with an accuracy of over 99 percent. The method was developed while analyzing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity, and specificity than previously developed methods. According to World Health Organisation, Alzheimer's disease is the most frequent cause of dementia, contributing to up to 70 percent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years.

Scientists have developed a robotic 3D-printed'third thumb' that's controlled using pressure sensors on the underside of the big toes. The thumb, created by a researcher at University College London (UCL), is worn on the side of the hand opposite the actual thumb, near the little finger. In trials, researchers found the human brain can adapt to the use of an extra thumb, but that it may alter the relationship between the brain and the biological hand. Volunteers who were fitted with the third thumb effectively carried out dexterous tasks, like building a tower of blocks, with one hand, researchers found. Having a third thumb could let people carry more objects than usual, hold and open a bottle of soft drink with one hand, or even become a maestro on the guitar.

Giving yourself verbal or mental instructions to clear your mind can help make room in your brain for more productive thoughts, a new study reveals. US researchers used brain imaging scans and machine learning to investigate what happens to the brain when we try to stop thinking about something. Three instructions – to'clear' our mind, 'suppress' a thought and'replace' a thought with something else – all successfully removed and manipulated unwanted information in the'working memory'. The working memory is the mental'notepad' that contains fleeting thoughts and is responsible for the temporary holding and processing of information. Holding information in the working memory is essential for cognition, but removing unwanted thoughts is equally important, researchers say.

Music can spark emotion on the listener's face, but scientists discovered they can'see' the type of melody being played when looking at the individual's brain. Using machine learning and functional magnetic resonance imaging, researchers at the University of Turku found that the auditory and motor cortex were activated when happy or sad music is played. The auditory cortex processes the acoustic elements, such as rhythm and melody, and the motor cortex could be related to the fact that music inspires feelings of movement. The study also looked at music that induces fear, revealing it correlates with subcortical structures involved with memory, emotion and pleasure. 'Music can induce strong subjective experience of emotions, but it is debated whether these responses engage the same neural circuits as emotions elicited by biologically significant events,' researchers shared in the study published in Oxford Academic.

The average person has more than 6,000 thoughts in a single day, according to a new study. Researchers designed a new method that pinpoints the beginning and ending of a thought, allowing them to calculate how many we have per day. This was done by isolating specific moments when an individual is focused on a single idea, which has been described as a'thought worm.' The team also notes that measuring a person's thought measures can predict aspects of their personality. Researchers designed a new method that pinpoints the beginning and ending of a thought, allowing them to calculate how many we have per day.

This is a crazy idea," the review read. Closing my laptop lid, I added in my mind "and ... it will never work," as a lump welled in my throat. What we were proposing to do was simple yet ambitious. Using functional magnetic resonance imaging, we might better understand what goes on in the minds of programmers as they read and understand code. We had performed pilot experiments with a neurobiologist, had promising results, and encouraging words from colleagues and reviewers.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. Although Deep Neural Networks (DNNs) have been applied in functional magnetic resonance imaging (fMRI) to identify ASD, understanding the data-driven computational decision making procedure has not been previously explored. Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier. First, we trained an accurate DNN classifier. Then, for detecting the biomarkers, different from the DNN visualization works in computer vision, we take advantage of the anatomical structure of brain fMRI and develop a frequency-normalized sampling method to corrupt images. Furthermore, in the ASD vs. control subjects classification scenario, we provide a new approach to detect and characterize important brain features into three categories. The biomarkers we found by the proposed method are robust and consistent with previous findings in the literature. We also validate the detected biomarkers by neurological function decoding and comparing with the DNN activation maps.

A new AI could finally spell the end for the IQ test - and work out how smart you really are just by looking at a scan of your brain. Researchers from Caltech, Cedars-Sinai Medical Center, and the University of Salerno say their system can predict a person's intelligence from functional magnetic resonance imaging (fMRI) scans of their resting state brain activity. They say an individual's intelligence can be gleaned from patterns of activity in their brain when they're not doing or thinking anything in particular, without the need for quizzes, math problems of vocabulary tests. Caltech researchers say their AI can predict a person's intelligence from functional magnetic resonance imaging (fMRI) scans of their resting state brain activity After conducting the largest ever study of intelligence, researchers concluded that far from indicating how clever you are, IQ testing is actually rather'meaningless'. In a bid to investigate the value of IQ, scientists asked more than 100,000 participants to complete 12 tests that required planning, reasoning, memory and attention.

Psychologists have create a creepy machine that can peer into your mind's eye with incredible accuracy. Their AI studies electrical signals in the brain to recreate faces being looked at by volunteers. It could provide a means of communication for people who are unable to talk, as well as the development of prosthetics controlled by thoughts. The finding also opens the door to strange future scenarios, such as those portrayed in the series'Black Mirror', where anyone can record and playback their memories. Psychologists have create a creepy machine that can peer into your mind's eye with incredible accuracy.