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 Exploratory factor analysis with structured residuals for brain network data

2020 ◽  
pp. 1-27
Author(s):  
Erik-Jan van Kesteren ◽  
Rogier A. Kievit
Keyword(s):  
Factor Analysis ◽  
Dimension Reduction ◽  
Exploratory Factor Analysis ◽  
A Priori ◽  
Brain Regions ◽  
Brain Network ◽  
R Software ◽  
Network Analyses ◽  
Number Of Factors ◽  
The Brain

Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high-dimensional data to a small number of underlying variables. Techniques such as exploratory factor analysis (EFA) are used by 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 by using structured residuals (EFAST), and (c) apply this technique to three large and varied brain-imaging network 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 Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC

GigaScience ◽  
2016 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
Daniel Clark ◽  
Krzysztof J. Gorgolewski ◽  
R. Cameron Craddock
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data ◽  
The Brain

scholarly journals Probing Brain Activation Patterns by Dissociating Semantics and Syntax in Sentences

2020 ◽  
Vol 34 (05) ◽  
pp. 9201-9208
Author(s):  
Shaonan Wang ◽  
Jiajun Zhang ◽  
Nan Lin ◽  
Chengqing Zong
Keyword(s):  
Brain Regions ◽  
Feature Representation ◽  
Semantic Feature ◽  
Imaging Data ◽  
Activation Patterns ◽  
Syntactic Feature ◽  
Syntactic Information ◽  
Brain Imaging Data ◽  
The Right ◽  
The Brain

The relation between semantics and syntax and where they are represented in the neural level has been extensively debated in neurosciences. Existing methods use manually designed stimuli to distinguish semantic and syntactic information in a sentence that may not generalize beyond the experimental setting. This paper proposes an alternative framework to study the brain representation of semantics and syntax. Specifically, we embed the highly-controlled stimuli as objective functions in learning sentence representations and propose a disentangled feature representation model (DFRM) to extract semantic and syntactic information in sentences. This model can generate one semantic and one syntactic vector for each sentence. Then we associate these disentangled feature vectors with brain imaging data to explore brain representation of semantics and syntax. Results have shown that semantic feature is represented more robustly than syntactic feature across the brain including the default-mode, frontoparietal, visual networks, etc.. The brain representations of semantics and syntax are largely overlapped, but there are brain regions only sensitive to one of them. For instance, several frontal and temporal regions are specific to the semantic feature; parts of the right superior frontal and right inferior parietal gyrus are specific to the syntactic feature.

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.

scholarly journals EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Cyril R. Pernet ◽  
Stefan Appelhoff ◽  
Krzysztof J. Gorgolewski ◽  
Guillaume Flandin ◽  
Christophe Phillips ◽  
...  
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data ◽  
The Brain

scholarly journals qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

2021 ◽  
Author(s):  
Agah Karakuzu ◽  
Stefan Appelhoff ◽  
Tibor Auer ◽  
Mathieu Boudreau ◽  
Franklin Feingold ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Structure ◽  
Magnetic Resonance ◽  
Brain Imaging ◽  
Common Ground ◽  
Quantitative Mri ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Brain Imaging Data ◽  
The Brain

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI which makes multicenter dissemination of interoperable data possible. As a result, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. Finally, our BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the practical barriers to standardization that is currently lacking in the field of neuroimaging.

scholarly journals The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data

2020 ◽  
Author(s):  
Elise Bannier ◽  
Gareth Barker ◽  
Valentina Borghesani ◽  
Nils Broeckx ◽  
Patricia Clement ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Modern Science ◽  
Imaging Data ◽  
General Data Protection Regulation ◽  
Data User ◽  
History Of ◽  
General Data ◽  
Brain Imaging Data ◽  
The Brain ◽  
The Eu

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavour is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants’ privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU General Data Protection Regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.

scholarly journals BIDS-iEEG: an extension to the brain imaging data structure (BIDS) specification for human intracranial electrophysiology

2018 ◽  
Author(s):  
Christopher Holdgraf ◽  
Stefan Appelhoff ◽  
Stephan Bickel ◽  
Kristofer Bouchard ◽  
Sasha D'Ambrosio ◽  
...  
Keyword(s):  
Data Structure ◽  
Human Brain ◽  
Brain Imaging ◽  
Common Ground ◽  
Data Transfer ◽  
Open Data ◽  
Imaging Data ◽  
Data Repositories ◽  
Brain Imaging Data ◽  
The Brain

Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measures of the living human brain. However, data collection is limited to highly specialized clinical environments. To improve internal (re)use and external sharing of these unique data, we present a structure for storing and sharing iEEG data: BIDS-iEEG, an extension of the Brain Imaging Data Structure (BIDS) specification, along with freely available examples and a bids-starter-kit. BIDS is a framework for organizing and documenting data and metadata with the aim to make datasets more transparent and reusable and to improve reproducibility of research. It is a community-driven specification with an inclusive decision-making process. As an extension of the BIDS specification, BIDS-iEEG facilitates integration with other modalities such as fMRI, MEG, and EEG. As the BIDS-iEEG extension has received input from many iEEG researchers, it provides a common ground for data transfer within labs, between labs, and in open-data repositories. It will facilitate reproducible analyses across datasets, experiments, and recording sites, allowing scientists to answer more complex questions about the human brain. Finally, the cross-modal nature of BIDS will enable efficient consolidation of data from multiple sites for addressing questions about generalized brain function.

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