scholarly journals Significance tests for Leave-One-Out Inter-Subject Correlations in fMRI

2020 ◽  
Author(s):  
L. De Angelis ◽  
V. Gazzola ◽  
C. Keysers
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
False Positives ◽  
Multiple Comparison ◽  
Fmri Data ◽  
Parametric Methods ◽  
Significance Tests ◽  
Neural Processes ◽  
Computationally Intensive ◽  
Leave One Out

AbstractThe inter-subject correlation of fMRI data of different subjects performing the same fMRI task (ISC) is in principle a powerful way to localize and differentiate neural processes caused by a presented stimulus from those that spontaneously or idiosyncratically take place in each subject. The wider adoption of this method has however been impeded by the lack of widely available tools to assess the significance of the observed correlations. Several non-parametric approaches have been proposed, but these approaches are computationally intensive, challenging to implement, and sensitive methods to correct for multiple comparison across voxels in these approaches are not yet well established. More widely available, and computationally simple, parametric methods have been criticized on the basis that dependencies in the data could inflate false positives. Here, using three independent resting state fMRI datasets, we demonstrate that conventional parametric tests actually do provide appropriate control for false positives for inter-subject correlation analyses. This finding paves the way to a wider adoption of ISC, and empowers a wider range of neuroimagers to use ISC to tackle the challenges of naturalistic neuroscience.

scholarly journals Alternative Labeling Tool: a minimal algorithm for denoising single-subject resting-state fMRI data with ICA-MELODIC

2021 ◽  
Author(s):  
Peter Zhukovsky ◽  
Gillian Coughlan ◽  
Erin W Dickie ◽  
Colin Hawco ◽  
Aristotle N Voineskos
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Single Subject ◽  
Imaging Studies ◽  
Computationally Intensive ◽  
Statistical Library ◽  
User Friendly ◽  
High Degree

Abstract Subject-level independent component analysis (ICA) is a well-established and widely used approach in denoising of resting-state functional magnetic resonance imaging (fMRI) data. However, approaches such as ICA-FIX and ICA-AROMA require advanced setups and/or are computationally intensive. Here, we aim to introduce a user-friendly, computationally lightweight toolbox for labeling independent signal and noise components, termed Alternative Labeling Tool (ALT). ALT uses two features that require manual tuning: proportion of an independent component’s spatial map located inside gray matter and positive skew of the power spectrum. ALT is tightly integrated with the commonly used FMRIB’s statistical library (FSL). Using the Open Access Series of Imaging Studies (OASIS-3) ageing dataset (n=30), we found that ALT shows a high degree of inter-rater agreement with manual labeling (over 86% of true positives for both signal and noise components on average). Crucially, denoising using ALT-generated labels significantly reduced mean framewise displacement (p<0.001). In conclusion, ALT can be extended to small and large-scale datasets when the use of more complex tools such as ICA-FIX is not possible. ALT will thus allow for more widespread adoption of ICA-based denoising of resting-state fMRI data.

Aging effect on head motion: A Machine Learning study on resting state fMRI data

2021 ◽  
Vol 352 ◽  
pp. 109084
Author(s):  
Valeria Saccà ◽  
Alessia Sarica ◽  
Andrea Quattrone ◽  
Federico Rocca ◽  
Aldo Quattrone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Aging Effect ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Head Motion ◽  
Learning Study

scholarly journals Identifying patients with PTSD utilizing resting-state fMRI data and neural network approach

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mirza Naveed Shahzad ◽  
Haider Ali ◽  
Tanzila Saba ◽  
Amjad Rehman ◽  
Hoshang Kolivand ◽  
...  
Keyword(s):  
Neural Network ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data

Data in Brief ◽  
2020 ◽  
Vol 29 ◽  
pp. 105213 ◽  
Author(s):  
Pradyumna Lanka ◽  
D. Rangaprakash ◽  
Sai Sheshan Roy Gotoor ◽  
Michael N. Dretsch ◽  
Jeffrey S. Katz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Resting State Fmri ◽  
Clinical Diagnostics ◽  
Fmri Data ◽  
Matlab Toolbox

scholarly journals Neurophysiology (fMRI)

2015 ◽  
Vol 42 (S1) ◽  
pp. S38-S38
Author(s):  
ST Lang ◽  
B Goodyear ◽  
J Kelly ◽  
P Federico
Keyword(s):  
Cognitive Functioning ◽  
Default Mode Network ◽  
Resting State ◽  
Attentional Control ◽  
Brain Tumours ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Resting State Networks ◽  
Default Mode ◽  
Eloquent Cortex

Background: Resting state functional MRI (rs-fMRI) provides many advantages to task-based fMRI in neurosurgical populations, foremost of which is the lack of the need to perform a task. Many networks can be identified by rs-fMRI in a single period of scanning. Despite the advantages, there is a paucity of literature on rs-fMRI in neurosurgical populations. Methods: Eight patients with tumours near areas traditionally considered as eloquent cortex participated in a five minute rs-fMRI scan. Resting-state fMRI data underwent Independent Component Analysis (ICA) using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) toolbox in FSL. Resting state networks (RSNs) were identified on a visual basis. Results: Several RSNs, including language (N=7), sensorimotor (N=7), visual (N=7), default mode network (N=8) and frontoparietal attentional control (n=7) networks were readily identifiable using ICA of rs-fMRI data. Conclusion: These pilot data suggest that ICA applied to rs-fMRI data can be used to identify motor and language networks in patients with brain tumours. We have also shown that RSNs associated with cognitive functioning, including the default mode network and the frontoparietal attentional control network can be identified in individual subjects with brain tumours. While preliminary, this suggests that rs-fMRI may be used pre-operatively to localize areas of cortex important for higher order cognitive functioning.

A Deterministic and Symbolic Regression Hybrid Applied to Resting-State fMRI Data

2014 ◽  
pp. 155-173 ◽  
Author(s):  
Ilknur Icke ◽  
Nicholas A. Allgaier ◽  
Christopher M. Danforth ◽  
Robert A. Whelan ◽  
Hugh P. Garavan ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Symbolic Regression ◽  
Fmri Data

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals Primate homologs of mouse cortico-striatal circuits

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Joshua Henk Balsters ◽  
Valerio Zerbi ◽  
Jerome Sallet ◽  
Nicole Wenderoth ◽  
Rogier B Mars
Keyword(s):  
Prefrontal Cortex ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Fingerprint Matching ◽  
Motor Circuits ◽  
Connectivity Patterns ◽  
Species Comparisons ◽  
Similarities And Differences ◽  
Anterior Prefrontal Cortex

With the increasing necessity of animal models in biomedical research, there is a vital need to harmonise findings across species by establishing similarities and differences in rodent and primate neuroanatomy. Using connectivity fingerprint matching, we compared cortico-striatal circuits across humans, non-human primates, and mice using resting-state fMRI data in all species. Our results suggest that the connectivity patterns for the nucleus accumbens and cortico-striatal motor circuits (posterior/lateral putamen) were conserved across species, making them reliable targets for cross-species comparisons. However, a large number of human and macaque striatal voxels were not matched to any mouse cortico-striatal circuit (mouse->human: 85% unassigned; mouse->macaque 69% unassigned; macaque->human; 31% unassigned). These unassigned voxels were localised to the caudate nucleus and anterior putamen, overlapping with executive function and social/language regions of the striatum and connected to prefrontal-projecting cerebellar lobules and anterior prefrontal cortex, forming circuits that seem to be unique for non-human primates and humans.

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