scholarly journals Visual Explanation for Identification of the Brain Bases for Developmental Dyslexia on fMRI Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Laura Tomaz Da Silva ◽  
Nathalia Bianchini Esper ◽  
Duncan D. Ruiz ◽  
Felipe Meneguzzi ◽  
Augusto Buchweitz
Keyword(s):  
Deep Learning ◽  
Brain Imaging ◽  
Developmental Dyslexia ◽  
Brain Regions ◽  
Data Sets ◽  
Learning Approaches ◽  
Imaging Data ◽  
Learning Techniques ◽  
Brain Imaging Data

Problem: Brain imaging studies of mental health and neurodevelopmental disorders have recently included machine learning approaches to identify patients based solely on their brain activation. The goal is to identify brain-related features that generalize from smaller samples of data to larger ones; in the case of neurodevelopmental disorders, finding these patterns can help understand differences in brain function and development that underpin early signs of risk for developmental dyslexia. The success of machine learning classification algorithms on neurofunctional data has been limited to typically homogeneous data sets of few dozens of participants. More recently, larger brain imaging data sets have allowed for deep learning techniques to classify brain states and clinical groups solely from neurofunctional features. Indeed, deep learning techniques can provide helpful tools for classification in healthcare applications, including classification of structural 3D brain images. The adoption of deep learning approaches allows for incremental improvements in classification performance of larger functional brain imaging data sets, but still lacks diagnostic insights about the underlying brain mechanisms associated with disorders; moreover, a related challenge involves providing more clinically-relevant explanations from the neural features that inform classification.Methods: We target this challenge by leveraging two network visualization techniques in convolutional neural network layers responsible for learning high-level features. Using such techniques, we are able to provide meaningful images for expert-backed insights into the condition being classified. We address this challenge using a dataset that includes children diagnosed with developmental dyslexia, and typical reader children.Results: Our results show accurate classification of developmental dyslexia (94.8%) from the brain imaging alone, while providing automatic visualizations of the features involved that match contemporary neuroscientific knowledge (brain regions involved in the reading process for the dyslexic reader group and brain regions associated with strategic control and attention processes for the typical reader group).Conclusions: Our visual explanations of deep learning models turn the accurate yet opaque conclusions from the models into evidence to the condition being studied.

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

2017 ◽  
pp. 265-275 ◽  
Author(s):  
Tewodros Mulugeta Dagnew ◽  
Letizia Squarcina ◽  
Massimo W. Rivolta ◽  
Paolo Brambilla ◽  
Roberto Sassi
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

2022 ◽  
pp. 27-50
Author(s):  
Rajalaxmi Prabhu B. ◽  
Seema S.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Model Building ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Important Challenge

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.

scholarly journals Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Min Song ◽  
Minseok Kang ◽  
Hyeonsu Lee ◽  
Yong Jeong ◽  
Se-Bum Paik
Keyword(s):  
Brain Imaging ◽  
Neural Activity ◽  
Activity Patterns ◽  
Imaging Data ◽  
Brain Imaging Data

Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study (Preprint)

2021 ◽  
Author(s):  
Alycia Noel Carey ◽  
William Baker ◽  
Jason B. Colditz ◽  
Huy Mai ◽  
Shyam Visweswaran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Data Sets ◽  
Learning Approaches ◽  
Sensitive Data ◽  
Twitter Data ◽  
Traditional Natural

BACKGROUND Twitter provides a valuable platform for the surveillance and monitoring of public health topics; however, manually categorizing large quantities of Twitter data is labor intensive and presents barriers to identify major trends and sentiments. Additionally, while machine and deep learning approaches have been proposed with high accuracy, they require large, annotated data sets. Public pre-trained deep learning classification models, such as BERTweet, produce higher quality models while using smaller annotated training sets. OBJECTIVE This study aims to derive and evaluate a pre-trained deep learning model based on BERTweet that can identify tweets relevant to vaping, tweets (related to vaping) of commercial nature, and tweets with pro-vape sentiment. Additionally, the performance of the BERTweet classifier will be compared against a long short-term memory (LSTM) model to show the improvements a pre-trained model has over traditional deep learning approaches. METHODS Twitter data were collected from August – October 2019 using vaping related search terms. From this set, a random subsample of 2,401 English tweets was manually annotated for relevance (vaping related or not), commercial nature (commercial or not), and sentiment (positive, negative, neutral). Using the annotated data, three separate classifiers were built using BERTweet with the default parameters defined by the Simple Transformer API. Each model was trained for 20 iterations and evaluated with a random split of the annotate tweets, reserving 10% of tweets for evaluations. RESULTS The relevance, commercial, and sentiment classifiers achieved an area under the receiver operating characteristic curve (AUROC) of 94.5%, 99.3%, and 81.7%, respectively. Additionally, the weighted F1 scores of each were 97.6%, 99.0%, and 86.1%. We found that BERTweet outperformed the LSTM model in classification of all categories. CONCLUSIONS Large, open-source deep learning classifiers, such as BERTweet, can provide researchers the ability to reliably determine if tweets are relevant to vaping, include commercial content, and include positive, negative, or neutral content about vaping with a higher accuracy than traditional Natural Language Processing deep learning models. Such enhancement to the utilization of Twitter data can allow for faster exploration and dissemination of time-sensitive data than traditional methodologies (e.g., surveys, polling research).

scholarly journals Hype versus hope: Deep learning encodes more predictive and robust brain imaging representations than standard machine learning

2020 ◽  
Author(s):  
Anees Abrol ◽  
Zening Fu ◽  
Mustafa Salman ◽  
Rogers Silva ◽  
Yuhui Du ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Imaging ◽  
Large Scale ◽  
Representation Learning ◽  
Training Sample ◽  
Superior Performance ◽  
Computational Time ◽  
Imaging Data ◽  
Brain Imaging Data

AbstractPrevious successes of deep learning (DL) approaches on several complex tasks have hugely inflated expectations of their power to learn subtle properties of complex brain imaging data, and scale to large datasets. Perhaps as a reaction to this inflation, recent critical commentaries unfavorably compare DL with standard machine learning (SML) approaches for the analysis of brain imaging data. Yet, their conclusions are based on pre-engineered features which deprives DL of its main advantage: representation learning. Here we evaluate this and show the importance of representation learning for DL performance on brain imaging data. We report our findings from a large-scale systematic comparison of SML approaches versus DL profiled in a ten-way age and gender-based classification task on 12,314 structural MRI images. Results show that DL methods, if implemented and trained following the prevalent DL practices, have the potential to substantially improve compared to SML approaches. We also show that DL approaches scale particularly well presenting a lower asymptotic complexity in relative computational time, despite being more complex. Our analysis reveals that the performance improvement saturates as the training sample size grows, but shows significantly higher performance throughout. We also show evidence that the superior performance of DL is primarily due to the excellent representation learning capabilities and that SML methods can perform equally well when operating on representations produced by the trained DL models. Finally, we demonstrate that DL embeddings span a comprehensible projection spectrum and that DL consistently localizes discriminative brain biomarkers, providing an example of the robustness of prediction relevance estimates. Our findings highlight the presence of non-linearities in brain imaging data that DL frameworks can exploit to generate superior predictive representations for characterizing the human brain, even with currently available data sizes.

Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data

2017 ◽  
pp. 185-202 ◽  
Author(s):  
Annamária Szenkovits ◽  
Regina Meszlényi ◽  
Krisztian Buza ◽  
Noémi Gaskó ◽  
Rodica Ioana Lung ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals Exploratory Factor Analysis with Structured Residuals for Brain Imaging Data

2020 ◽  
Author(s):  
Erik-Jan van Kesteren ◽  
Rogier A. Kievit
Keyword(s):  
Factor Analysis ◽  
Dimension Reduction ◽  
Brain Imaging ◽  
Exploratory Factor Analysis ◽  
A Priori ◽  
Brain Regions ◽  
Imaging Data ◽  
Number Of Factors ◽  
Brain Imaging Data ◽  
The Brain

AbstractDimension reduction is widely used and often necessary to reduce high dimensional data to a small number of underlying variables, making subsequent analyses and their interpretation tractable. One popular technique is Exploratory Factor Analysis (EFA), used by cognitive neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA using structured residuals (EFAST), and (c) apply this technique to three large and varied brain imaging datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.

scholarly journals Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0250755
Author(s):  
Gregory Kiar ◽  
Yohan Chatelain ◽  
Pablo de Oliveira Castro ◽  
Eric Petit ◽  
Ariel Rokem ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Brain Connectivity ◽  
Random Noise ◽  
Imaging Data ◽  
Brain Organization ◽  
Computational Sciences ◽  
Extreme Variability ◽  
Brain Imaging Data ◽  
The Stability

The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 − 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.

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