Deep Learning for Functional Brain Connectivity: Are We There Yet?

RaviPrakash, Harish; Watane, Arjun; Jambawalikar, Sachin; Bağcı, Ulaş

doi:10.1007/978-3-030-13969-8_17

Cited by 6 publications

(5 citation statements)

References 42 publications

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“…In future work, we will use a further splitting of the network labels to analyze morphometric features of individual regions within the functional cluster. Moreover, with the increased dimensionality of the feature vectors, deep learning algorithms can be utilized to perform classification but the size of the dataset may prove to be a limiting factor (RaviPrakash et al, 2019). While MSN enabled us to combine structural information, as a potential extension of this work, we would combine structural information from diffusion tensor imaging based measures such as fractional anisotropy, mean diffusivity, and structural node degree into the morphometric measures to further strengthen the functional-morphometric relationship.…”

Section: Discussionmentioning

confidence: 99%

Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players

et al. 2021

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A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Toward this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.

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Section: Discussionmentioning

confidence: 99%

Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players

et al. 2021

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“…In future work, we will use a further splitting of the network labels to analyze morphometric features of individual regions within the functional cluster. Moreover, with the increased dimensionality of the feature vectors, deep learning algorithms can be utilized to perform classification but the size of the dataset may prove to be a limiting factor 41 . While MSN enabled us to combine structural information, as a potential extension of this work, we would combine structural information from diffusion tensor imaging based measures such as fractional anisotropy, mean diffusivity, and structural node degree into the morphometric measures to further strengthen the functional-morphometric relationship.…”

Section: Discussionmentioning

confidence: 99%

Morphometric and Functional Brain Connectivity Differentiates Chess Masters from Amateur Players

RaviPrakash

Anwar

Biassou

et al. 2020

Preprint

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A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Towards this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.

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“…Although scientists and engineers have attempted different ways to integrate whole-brain networks into the framework of deep neural networks, the predictive power of existing models is questioned by some researchers ( He et al, 2018 , 2020 ; Khosla et al, 2019 , 2021 ; Raviprakash et al, 2019 ). He et al (2018 , 2020) compared the prediction results of human cognitive performance using brain DTI structural connectivity data between the deep graph convolutional network model and a simple kernel regression model.…”

Section: Deep Learning In Brain Networkmentioning

confidence: 99%

Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations

Liu

Amey

Backer

et al. 2022

Front. Hum. Neurosci.

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Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.

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Deep Learning for Functional Brain Connectivity: Are We There Yet?

Cited by 6 publications

References 42 publications

Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players

Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players

Morphometric and Functional Brain Connectivity Differentiates Chess Masters from Amateur Players

Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations

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