How Artificial Intelligence is Supporting Neuroscience Research: A Discussion About Foundations, Methods and Applications

Gonzalez, Rafael; Riascos, Jaime A.; Barone, Dante Augusto Couto

doi:10.1007/978-3-319-71011-2_6

Cited by 12 publications

(8 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neuroscience can help to validate already existing DL techniques and provide rich inspiration for new types of algorithms and architectures. Also, some new techniques developed with DL algorithms can be used in neuroscience to help to understand neuropsychiatric disorders [ 8 ].…”

Section: Deep Learning Concepts and Architecturesmentioning

confidence: 99%

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Güney

Yigin

Guven

et al. 2021

Clin Psychopharmacol Neurosci

View full text Add to dashboard Cite

Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.

show abstract

Section: Deep Learning Concepts and Architecturesmentioning

confidence: 99%

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Güney

Yigin

Guven

et al. 2021

Clin Psychopharmacol Neurosci

View full text Add to dashboard Cite

show abstract

“…Deep learning, which has shown considerable potential in the last decade as a powerful tool for data analysis, can help explore new possibilities of understanding of brain behavior (Gonzalez et al, 2017). As a major family of deep learning techniques, Recurrent Neural Network (RNN) is particularly suitable for temporal signals, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…fMRI, because it models temporal correlation among data explicitly with its recurrent structure. Recently, there is growing interest in using RNN to model fMRI signal (Gonzalez et al, 2017)(Barak, 2017). Specifically, Güçlü et al (Güçlü and van Gerven, 2016) used RNNs to predict brain activity in response to natural movies to elucidate how complex visual and audio sensory information was represented in the brain.…”

Section: Introductionmentioning

confidence: 99%

Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis

Wang

Lui

2018

NeuroImage

View full text Add to dashboard Cite

Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals. There is growing interest in using Recurrent Neural Networks (RNNs), a major family of deep learning techniques, in fMRI modeling. However, the generic RNNs used in existing studies work as black boxes, making the interpretation of results in a neuroscience context difficult and obscure. In this paper, we propose a new biophysically interpretable RNN built on DCM, DCM-RNN. We generalize the vanilla RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. DCM-RNN uses back propagation for parameter estimation. We believe DCM-RNN is a promising tool for neuroscience. It can fit seamlessly into classical DCM studies. We demonstrate face validity of DCM-RNN in two principal applications of DCM: causal brain architecture hypotheses testing and effective connectivity estimation. We also demonstrate construct validity of DCM-RNN in an attention-visual experiment. Moreover, DCM-RNN enables end-to-end training of DCM and representation learning deep neural networks, extending DCM studies to complex tasks.

show abstract

“…Deep learning, which has shown considerable potential in the last decades as a powerful tool for data analysis, can help explore new possibilities of understanding of brain behavior [1]. Recurrent Neural Network (RNN) is particularly suitable for temporal signals, because it models temporal correlation among data explicitly with the recurrent structure.…”

Section: Introductionmentioning

confidence: 99%

“…Recurrent Neural Network (RNN) is particularly suitable for temporal signals, because it models temporal correlation among data explicitly with the recurrent structure. Many efforts have been made to analyze functional Magnetic Resonance Imaging (fMRI) signals with state-of-the-art RNNs [1]. However, the used generic RNNs lack biophysical meaning, making the interpretation of results in a neuroscience context difficult.…”

Section: Introductionmentioning

confidence: 99%

Biophysically interpretable recurrent neural network for functional magnetic resonance imaging analysis and sparsity based causal architecture discovery

Wang¹,

Wang²,

Lui

2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Recent efforts use state-of-the-art Recurrent Neural Networks (RNN) to gain insight into neuroscience. A limitation of these works is that the used generic RNNs lack biophysical meaning, making the interpretation of the results in a neuroscience context difficult. In this paper, we propose a biophysically interpretable RNN built on the Dynamic Causal Modelling (DCM). DCM is an advanced nonlinear generative model typically used to test hypotheses of brain causal architectures and associated effective connectivities. We show that DCM can be cast faithfully as a special form of a new generalized RNN. In the resulting DCM-RNN, the hidden states are neural activity, blood flow, blood volume, and deoxyhemoglobin content and the parameters are biological quantities such as effective connectivity, oxygen extraction fraction and vessel wall elasticity. DCM-RNN is a versatile tool for neuroscience with great potential especially when combined with deep learning networks. In this study, we explore sparsity-based causal architecture discovery with DCM-RNN. In the experiments, we demonstrate that DCM-RNN equipped with l1 connectivity regulation is more robust to noise and more powerful at discovering sparse architectures than classic DCM with l2 connectivity regulation.

show abstract

How Artificial Intelligence is Supporting Neuroscience Research: A Discussion About Foundations, Methods and Applications

Cited by 12 publications

References 52 publications

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis

Biophysically interpretable recurrent neural network for functional magnetic resonance imaging analysis and sparsity based causal architecture discovery

Contact Info

Product

Resources

About