fMRI: Advances and Challenges in Big Data Analysis
Edited by Prof Russell Poldrack
GigaScience is proud to present this cutting-edge series on Functional MRI (fMRI). fMRI is a commonly used technique in the field of neuroscience, and the explosion of big imaging data using this technique highlights new challenges, such as data sharing, management, and processing, as well as reproducibility, novel analysis techniques, and new tools for managing complex analysis workflows and provenance. This cutting-edge series aims to explore and highlight new advances and ongoing challenges and to improve data sharing and repoducibility with fMRI data.
This collection of articles has not been sponsored and articles have undergone the journal's standard peer-review process. The Guest Editors declare no competing interests.
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Research
Resting-state functional magnetic resonance imaging (RS-fMRI) has frequently been used to investigate local spontaneous brain activity in Parkinson's disease (PD) in a whole-brain, voxel-wise manner. To quantitatively integrate these studies, we conducted a coordinate-based (CB) meta-analysis using the signed differential mapping method on 15 studies that used amplitude of low-frequency fluctuation (ALFF) and 11 studies that used regional homogeneity (ReHo). All ALFF and ReHo studies compared PD patients with healthy controls. We also performed a validation RS-fMRI study of ALFF and ReHo in a frequency-dependent manner for a novel dataset consisting of 49 PD and 49 healthy controls.
Jue Wang; Jia-Rong Zhang; Yu-Feng Zang; and et. al
GigaScience
Published on: 18 June 2018
Technical Note
Advanced lesion symptom mapping analyses and implementation as BCBtoolkit
Patients with brain lesions provide a unique opportunity to understand the functioning of the human mind. However, even when focal, brain lesions have local and remote effects that impact functionally and structurally connected circuits. Similarly, function emerges from the interaction between brain areas rather than their sole activity. For instance, category fluency requires the associations between executive, semantic, and language production functions.
Chris Foulon; Leonardo Cerliani; Serge Kinkingnéhun; and et. al
GigaScience
Published on: 8 February 2018
Research
Science in the cloud (SIC); A use case in MRI connectomics
Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often making reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries.
Gregory Kiar; Krzysztof J. Gorgolewski; Dean Kleissas
GigaScience
Published on: 7 March 2017
Review
Multilayer modeling and analysis of human brain networks
Understanding how the human brain is structured, and how its architecture is related to function, is of paramount importance for a variety of applications, including but not limited to new ways to prevent, deal with, and cure brain diseases, such as Alzheimer's or Parkinson's, and psychiatric disorders, such as schizophrenia. The recent advances in structural and functional neuroimaging, together with the increasing attitude toward interdisciplinary approaches involving computer science, mathematics, and physics, are fostering interesting results from computational neuroscience that are quite often based on the analysis of complex network representation of the human brain.
Manlio De Domenico
GigaScience
Published on: 6 February 2017
Commentary
Four aspects to make science open "by design" and not as an after-thought
Unrestricted dissemination of methodological developments in neuroimaging became the propelling force in advancing out understanding of brain function. However, despite such a rich legacy, it remains not uncommon to encounter software and datasets that are distributed under unnecessarily restricted terms, or that violate terms of third-party products (software or data). With this brief correspondence we would like to recapitulate four important aspects of scientific research practice, which should be taken into consideration as early as possible in the course of any project. Keeping these in check will help neuroimaging to stay at the forefront of the open science moment.
Yaroslav O. Halchenko; Michael Hanke
GigaScience
Published on: 18 July 2015
Commentary
Improving functional magnetic resonance imaging reproducibility
The ability to replicate an entire experiment is crucial to the scientific method. With the development of more and more complex paradigms, and the variety of analysis techniques available, fMRI studies are becoming harder to reproduce. In this article, we aim to provide practical advice to fMRI researchers to move towards a more open science, in which all aspects of the experimental method are documented and shared.
Cyril Pernet; Jean-Baptiste Poline
GigaScience
Published on: 31 March 2015
Review
Connectomics and new approaches for analyzing human brain functional connectivity
Estimating the functional interactions between brain regions and mapping those connections to corresponding inter-individual differences in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. The number and complexity of functional interactions within the coonnectome and the large amounts of data required to study them position functional connectivity research as a "big data" problem. Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimagining analysis algorithms and tools. This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems.
R Cameron Craddock; Rosalia L Tungaraza; Michael P Milham
GigaScience
Published on: 25 March 2015
Review
A spectrum of sharing: maximization of information content for brain imaging data
Efforts to expand sharing of neuroimaging data have been growing exponentially in recent years. There are several different types of data sharing which can be considered to fall along a pectrum, ranging from simpler and less informative to more complex and more informative. In this paper we consider this spectrum for three domains: data capture, data density, and data analysis. Here the focus is on the right end of the spectrum, that is, how to maximize the information content while addressing the challenges.
Vince D Calhoun
GigaScience
Published on: 29 January 2015
Review
The rise of large-scale imaging studies in psychiatry
From the initial arguments over whether 12 to 20 subjects were sufficient for an fMRI study, sample sizes in psychiatric neuroimaging studies have expanded into the tens of thousands. These large-scale imaging studies fall into several categories, each of which has specific advantages and challenges. The different study types can be grouped based on their level of control" meta-analyses, at one extreme of the spectrum, control nothing about the imaging protocol or subject selection criteria in the datasets they include. On the other hand, planned multi-site mega studies pour intense efforts into strictly having the same protocols. However, there are several other combinations possible, each of which is best used to address certain questions.
Jessica A Turner
GigaScience
Published on: 25 November 2014
Review
How machine learning is shaping cognitive neuroimaging
Functional brain images are rich and noisy data that can capture indirect signatures of neural activity underlying cognition in a given experimental setting. Can data mining leverage hem to build models of cognition? Only if it is applied to well-posed questions, crafted to reveal cognitive mechanism. Here we review how predictive models have been used on neurimaging data to ask new questions, i.e., to uncover new aspects of cognitive organization. We also give a statistical learning perspective on these progresses and on the remaining gaping holes.
Gael Varoquaux; Bertrand Thirion
GigaScience
Published on: 17 November 2014
Data Note
A test-retest fMRI dataset for motor, language and spatial attention functions
Since its inception over twenty years ago, functional magnetic resonance imaging (fMRI) has been used in numerous studies probing neural underpinnings of human cognition. However, the between session variance of many tasks used in fMRI remains understudied. Such information is especially important in context of clinical applications. A test-retest dataset was acquired to validate fMRI tasks used in pre-surgical planning. In particular, five task-related fMRI time series (finger, food and lip movement, overt verb generation, covert very generation, overt word repetition, and landmark tasks) were used to investigate which protocols gave reliable single-subject results. Ten healthy participants in their fifties were scanned twice using an identical protocol 2-3 days apart. In addition to the fMRI sessions, high-angular resolution diffusion tensor MRI (DTI), and high resolution 3D T1-weighted volume scans were acquired.
Krzysztof J Gorgolewski; Amos Storkey; Mark E Bastin; and et. al
GigaScience
Published on: 29 April 2013
