Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning

Zhang, Changqing; Adeli, Ehsan; Wu, Zhengwang; Li, Gang; Lin, Weili; Shen, Dinggang

doi:10.1109/tmi.2018.2874964

Cited by 18 publications

(3 citation statements)

References 50 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future studies may include diffusion and functional MRIs to further improve model performances. Third, among the 290 T1-weighted brain MRIs from 274 monkeys, there were 16 longitudinal scans or 6% of the monkeys had longitudinal scans, which is a much lower percentage of longitudinal scans compared to human brain age prediction studies with only dozen to lower hundreds of subjects (Zhang et al, 2018;Hu et al, 2019). It is possible that the addition of more longitudinal scans or using longitudinal information as a prior may improve model predictions.…”

Section: Discussionmentioning

confidence: 99%

Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age

He,

Guan,

Cheng

et al. 2023

Front. Aging Neurosci.

View full text Add to dashboard Cite

The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective “brain age” metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.

show abstract

Section: Discussionmentioning

confidence: 99%

Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age

He,

Guan,

Cheng

et al. 2023

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…93 Similar findings are also reported for MSEL-based future (at 4 years of age) cognitive score prediction using sMRI brain features at birth such as cortical thickness, mean curvature, local gyrification index, vertex area, vertex volume, sulcal depth in string distance and sulcal depth in Euclidean distance with a mean root square error of 0.067-0.18. [94][95][96] Better FSIQ level has also been reported for thinner parietal association cortices, especially left/right inferior parietal (BAs 39, 40) and left/right superior parietal (BA 7) cortices. 97 Overall FSIQ has been found [98][99][100] 102 allows the correlation of MRI volume or surface metrics at the voxel or surface vertex level.…”

Section: Structural Mri To Infer Intelligence and Neurocognitionmentioning

confidence: 93%

Inferring Neurocognition and Intelligence using Brain MRI

Hussain¹,

Grant²,

Ou³

2023

Preprint

View full text Add to dashboard Cite

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition and intelligence. A core mystery is the MRI explanation of individual differences in neurocognition and intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation need to be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on machine intelligence inferring human intelligence.

show abstract

“…Finally, an MRI of the two network parameters learned from the source was shared with target imaging CT (computed tomography). In Zhang et al ( 2018 ), their method treated data at various points in time as different perspectives and built an overarching representation to collect complementary data from the entire time period. The potential representation investigates the complementarity between various time points in order to increase prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network

Ahammed

Niu

Ahmed

et al. 2021

Front. Neuroinform.

View full text Add to dashboard Cite

Non-invasive whole-brain scans aid the diagnosis of neuropsychiatric disorder diseases such as autism, dementia, and brain cancer. The assessable analysis for autism spectrum disorders (ASD) is rationally challenging due to the limitations of publicly available datasets. For diagnostic or prognostic tools, functional Magnetic Resonance Imaging (fMRI) exposed affirmation to the biomarkers in neuroimaging research because of fMRI pickup inherent connectivity between the brain and regions. There are profound studies in ASD with introducing machine learning or deep learning methods that have manifested advanced steps for ASD predictions based on fMRI data. However, utmost antecedent models have an inadequacy in their capacity to manipulate performance metrics such as accuracy, precision, recall, and F1-score. To overcome these problems, we proposed an avant-garde DarkASDNet, which has the competence to extract features from a lower level to a higher level and bring out promising results. In this work, we considered 3D fMRI data to predict binary classification between ASD and typical control (TC). Firstly, we pre-processed the 3D fMRI data by adopting proper slice time correction and normalization. Then, we introduced a novel DarkASDNet which surpassed the benchmark accuracy for the classification of ASD. Our model's outcomes unveil that our proposed method established state-of-the-art accuracy of 94.70% to classify ASD vs. TC in ABIDE-I, NYU dataset. Finally, we contemplated our model by performing evaluation metrics including precision, recall, F1-score, ROC curve, and AUC score, and legitimize by distinguishing with recent literature descriptions to vindicate our outcomes. The proposed DarkASDNet architecture provides a novel benchmark approach for ASD classification using fMRI processed data.

show abstract

Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning

Cited by 18 publications

References 50 publications

Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age

Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age

Inferring Neurocognition and Intelligence using Brain MRI

DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network

Contact Info

Product

Resources

About