Attention Deficit/Hyperactivity Disorder


Brain wiring could be behind learning difficulties, say experts

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Learning difficulties are not linked to differences in particular brain regions, but in how the brain is wired, research suggests. According to figures from the Department for Education, 14.9% of all pupils in England – about 1.3 million children – had special educational needs in January 2019, with 271,200 having difficulties that required support beyond typical special needs provision. Dyslexia, attention deficit hyperactivity disorder (ADHD), autism and dyspraxia are among conditions linked to learning difficulties. Now experts say different learning difficulties are not specific to particular diagnoses, nor are they linked to particular regions of the brain – as has previously been thought. Instead the team, from the University of Cambridge, say learning difficulties appear to be associated with differences in the way connections in the brain are organised.


ADHD MRI: Brain Scans Improved with Artificial Intelligence

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Artificial intelligence can significantly improve the accuracy of neural models using MRI brain scans to detect attention deficit hyperactivity disorder (ADHD), according to a study recently published in Radiology: Artificial Intelligence.1 The study, conducted by researchers from Ohio's University of Cincinnati and the Cincinnati Children's Hospital Medical Center, centers on the emerging idea of using brain imaging to detect signs of ADHD in patients. Currently, there is no single, definitive test for ADHD -- diagnosis comes after a series of symptom and behavioral tests. Research, however, suggests that ADHD can potentially be detected by studying the connectome -- a map of the brain's neural connections built by layering MRI scans of the brain, known as parcellations. Some studies suggest that a disrupted or interrupted connectome is linked to ADHD.


High--Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality

arXiv.org Artificial Intelligence

High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning. The well-known phenomenon of the "curse of dimensionality" states: many problems become exponentially difficult in high dimensions. Recently, the other side of the coin, the "blessing of dimensionality", has attracted much attention. It turns out that generic high-dimensional datasets exhibit fairly simple geometric properties. Thus, there is a fundamental tradeoff between complexity and simplicity in high dimensional spaces. Here we present a brief explanatory review of recent ideas, results and hypotheses about the blessing of dimensionality and related simplifying effects relevant to machine learning and neuroscience.


Artificial Intelligence Boosts MRI Detection of ADHD

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Deep learning can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD), according to a study published in Radiology: Artificial Intelligence. Increasingly, the connectome is regarded as key to understanding brain disorders like ADHD. According to the National Survey of Children's Health, approximately 9.4% of U.S. children, ages 2 to 17 years (6.1 million) in 2016 have been diagnosed with ADHD. The disorder cannot yet be definitively diagnosed in an individual child with a single test or medical imaging exam. Instead, ADHD diagnosis is based on a series of symptoms and behavior-based tests.


A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection

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To develop a multichannel deep neural network (mcDNN) classification model based on multiscale brain functional connectome data and demonstrate the value of this model by using attention deficit hyperactivity disorder (ADHD) detection as an example. In this retrospective case-control study, existing data from the Neuro Bureau ADHD-200 dataset consisting of 973 participants were used. Multiscale functional brain connectomes based on both anatomic and functional criteria were constructed. The mcDNN model used the multiscale brain connectome data and personal characteristic data (PCD) as joint features to detect ADHD and identify the most predictive brain connectome features for ADHD diagnosis. The mcDNN model was compared with single-channel deep neural network (scDNN) models and the classification performance was evaluated through cross-validation and hold-out validation with the metrics of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).


MRI Detects ADHD With Help of Deep Learning

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Deep learning, a type of artificial intelligence, can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD), according to a study published in Radiology: Artificial Intelligence. Researchers said the approach could also have applications for other neurological conditions. The human brain is a complex set of networks. Advances in functional MRI, a type of imaging that measures brain activity by detecting changes in blood flow, have helped with the mapping of connections within and between brain networks. This comprehensive brain map is referred to as the connectome.


Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification

arXiv.org Artificial Intelligence

Graph and language embedding models are becoming commonplace in large scale analyses given their ability to represent complex sparse data densely in low-dimensional space. Integrating these models' complementary relational and communicative data may be especially helpful if predicting rare events or classifying members of hidden populations - tasks requiring huge and sparse datasets for generalizable analyses. For example, due to social stigma and comorbidities, mental health support groups often form in amorphous online groups. Predicting suicidality among individuals in these settings using standard network analyses is prohibitive due to resource limits (e.g., memory), and adding auxiliary data like text to such models exacerbates complexity- and sparsity-related issues. Here, I show how merging graph and language embedding models (metapath2vec and doc2vec) avoids these limits and extracts unsupervised clustering data without domain expertise or feature engineering. Graph and language distances to a suicide support group have little correlation (\r{ho} < 0.23), implying the two models are not embedding redundant information. When used separately to predict suicidality among individuals, graph and language data generate relatively accurate results (69% and 76%, respectively); however, when integrated, both data produce highly accurate predictions (90%, with 10% false-positives and 12% false-negatives). Visualizing graph embeddings annotated with predictions of potentially suicidal individuals shows the integrated model could classify such individuals even if they are positioned far from the support group. These results extend research on the importance of simultaneously analyzing behavior and language in massive networks and efforts to integrate embedding models for different kinds of data when predicting and classifying, particularly when they involve rare events.


Assistive Technology to Help Children with Attention Deficit/Hyperactivity Disorder (ADHD) Succeed Academically - The Edvocate

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Helping children with attention deficit hyperactivity disorder (ADHD) to focus in the classroom can be a major challenge. Educators might find themselves constantly prompting and redirecting students to perform routine tasks. It can be frustrating for the student and their classmates when their need to move around the classroom disrupts the learning experience. Gains in assistive technology could help to promote better learning for students who struggle with ADHD. Teachers who are growing weary of the constant motion and redirection required for students with ADHD might want to investigate some of these breakthroughs.


Artificial intelligence boosts MRI detection of ADHD

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Deep learning, a type of artificial intelligence, can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD), according to a study published in Radiology: Artificial Intelligence. Researchers said the approach could also have applications for other neurological conditions. The human brain is a complex set of networks. Advances in functional MRI, a type of imaging that measures brain activity by detecting changes in blood flow, have helped with the mapping of connections within and between brain networks. This comprehensive brain map is referred to as the connectome.


This AI can detect ADHD better than humans

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A team of researchers used a type of artificial intelligence to predict attention deficit hyperactivity disorder (ADHD) in patients by having it analyze magnetic resonance imaging (MRI) scans. According to a new paper published in the journal Radiology: Artificial Intelligence, their technique could also be used to spot other neurological conditions. Health care professionals have increasingly been relying on MRI scans to understand ADHD, a brain disorder that often causes patients to be restless, and makes it more difficult for them to pay attention. More than eight percent of children in the U.S. have been diagnosed with the condition according to The American Psychiatric Association (APA). Research suggests that a breakdown in the connections between the different regions of the brain, the so-called connectome, causes ADHD.