3,789 research outputs found
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet
In this work, we utilize T1-weighted MR images and StackNet to predict fluid
intelligence in adolescents. Our framework includes feature extraction, feature
normalization, feature denoising, feature selection, training a StackNet, and
predicting fluid intelligence. The extracted feature is the distribution of
different brain tissues in different brain parcellation regions. The proposed
StackNet consists of three layers and 11 models. Each layer uses the
predictions from all previous layers including the input layer. The proposed
StackNet is tested on a public benchmark Adolescent Brain Cognitive Development
Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of
82.42 on the combined training and validation set with 10-fold
cross-validation. In addition, the proposed StackNet also achieves a mean
squared error of 94.25 on the testing data. The source code is available on
GitHub.Comment: 8 pages, 2 figures, 3 tables, Accepted by MICCAI ABCD-NP Challenge
2019; Added ND
A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction
The ABCD Neurocognitive Prediction Challenge is a community driven
competition asking competitors to develop algorithms to predict fluid
intelligence score from T1-w MRIs. In this work, we propose a deep learning
combined with gradient boosting machine framework to solve this task. We train
a convolutional neural network to compress the high dimensional MRI data and
learn meaningful image features by predicting the 123 continuous-valued derived
data provided with each MRI. These extracted features are then used to train a
gradient boosting machine that predicts the residualized fluid intelligence
score. Our approach achieved mean square error (MSE) scores of 18.4374,
68.7868, and 96.1806 for the training, validation, and test set respectively.Comment: Challenge in Adolescent Brain Cognitive Development Neurocognitive
Predictio
A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence
A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence
Child Dev
This study used a machine learning framework in conjunction with a large battery of measures from 9,718 school-age children (ages 9-11) from the Adolescent Brain Cognitive Development| (ABCD) Study to identify factors associated with fluid cognitive functioning (FCF), or the capacity to learn, solve problems, and adapt to novel situations. The identified algorithm explained 14.74% of the variance in FCF, replicating previously reported socioeconomic and mental health contributors to FCF, and adding novel and potentially modifiable contributors, including extracurricular involvement, screen media activity, and sleep duration. Pragmatic interventions targeting these contributors may enhance cognitive performance and protect against their negative impact on FCF in children.MD/NIMHD NIH HHSUnited States/U01DA041117/NH/NIH HHSUnited States/U01 DA041156/DA/NIDA NIH HHSUnited States/U24DA041123/NH/NIH HHSUnited States/NIH Office of Research on Women's Health/U01 DA041089/DA/NIDA NIH HHSUnited States/U01 DA041117/DA/NIDA NIH HHSUnited States/National Science Foundation/NIH Office of Behavioral and Social Sciences Research/P20 GM121312/GM/NIGMS NIH HHSUnited States/Stanford's Maternal Child Health Research Institute/U24 DA041147/DA/NIDA NIH HHSUnited States/HL/NHLBI NIH HHSUnited States/U01 DA041120/DA/NIDA NIH HHSUnited States/F31 HD103340/HD/NICHD NIH HHSUnited States/U01DA041120/NH/NIH HHSUnited States/P20GM121312/GM/NIGMS NIH HHSUnited States/National Institute of Aging/U01DA041148/NH/NIH HHSUnited States/MH/NIMH NIH HHSUnited States/Allergan, and the Brain and Behavior Research Foundation/U24 DA041123/DA/NIDA NIH HHSUnited States/U01DA041028/NH/NIH HHSUnited States/U01 DA041134/DA/NIDA NIH HHSUnited States/U01 DA041022/DA/NIDA NIH HHSUnited States/U24DA041147/NH/NIH HHSUnited States/U01DA041106/NH/NIH HHSUnited States/U01 DA050989/DA/NIDA NIH HHSUnited States/U01DA041022/NH/NIH HHSUnited States/NS/NINDS NIH HHSUnited States/U01 DA041106/DA/NIDA NIH HHSUnited States/National Endowment for the Arts/U01DA041048/NH/NIH HHSUnited States/U01 DA041028/DA/NIDA NIH HHSUnited States/U01 DA041048/DA/NIDA NIH HHSUnited States/U01DA041089/NH/NIH HHSUnited States/U01 DA041148/DA/NIDA NIH HHSUnited States/PCORI/Patient-Centered Outcomes Research InstituteUnited States/Johnson and Johnson/National Institute of Justice/William K. Warrent Foundation/U01DA041174/NH/NIH HHSUnited States/U01DA041134/NH/NIH HHSUnited States/U01 DA041174/DA/NIDA NIH HHSUnited States/CC/CDC HHSUnited States/U01DA041156/NH/NIH HHSUnited States/2021-09-29T00:00:00Z33900639PMC84787981036
Mental sleep activity and disturbing dreams in the lifespan
Sleep significantly changes across the lifespan, and several studies underline its crucial role in cognitive functioning. Similarly, mental activity during sleep tends to covary with age. This review aims to analyze the characteristics of dreaming and disturbing dreams at dierent age brackets. On the one hand, dreams may be considered an expression of brain maturation and cognitive development, showing relations with memory and visuo-spatial abilities. Some investigations reveal that specific electrophysiological patterns, such as frontal theta oscillations, underlie dreams during sleep, as well as episodic memories in the waking state, both in young and older adults. On the other hand, considering the role of dreaming in emotional processing and regulation, the available literature suggests that mental sleep activity could have a beneficial role when stressful events occur at dierent age ranges. We highlight that nightmares and bad dreams might represent an attempt to cope the adverse events, and the degrees of cognitive-brain maturation could impact on these mechanisms across the lifespan. Future investigations are necessary to clarify these relations. Clinical protocols could be designed to improve cognitive functioning and emotional regulation by modifying the dream contents or the ability to recall/non-recall them
Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review
Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle-Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer's disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age
A multimodal neuroimaging classifier for alcohol dependence
With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence
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