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

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.

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Spatiotemporal functional interactivity among large-scale brain networks

10.1101/2020.04.14.041830 ◽

2020 ◽

Author(s):

Nan Xu ◽

Peter C. Doerschuk ◽

Shella D. Keilholz ◽

R. Nathan Spreng

Keyword(s):

Human Brain ◽

Short Duration ◽

Resting State ◽

Large Scale ◽

Brain Networks ◽

Resting State Fmri ◽

Brain Regions ◽

Spatial Direction ◽

Macro Scale ◽

High Degree

AbstractThe macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting-state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N=200) we applied prediction correlation techniques to four resting state fMRI runs (total TRs = 4800). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region’s role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.

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Reconfiguration of Directed Functional Connectivity Among Neurocognitive Networks with Aging: Considering the Role of Thalamo-Cortical Interactions

Cerebral Cortex ◽

10.1093/cercor/bhaa334 ◽

2020 ◽

Author(s):

Moumita Das ◽

Vanshika Singh ◽

Lucina Q Uddin ◽

Arpan Banerjee ◽

Dipanjan Roy

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Network Connectivity ◽

Resting State Fmri ◽

Brain Network ◽

Fmri Data ◽

Independent Dataset ◽

Left And Right

Abstract A complete picture of how subcortical nodes, such as the thalamus, exert directional influence on large-scale brain network interactions across age remains elusive. Using directed functional connectivity and weighted net causal outflow on resting-state fMRI data, we provide evidence of a comprehensive reorganization within and between neurocognitive networks (default mode: DMN, salience: SN, and central executive: CEN) associated with age and thalamocortical interactions. We hypothesize that thalamus subserves both modality-specific and integrative hub role in organizing causal weighted outflow among large-scale neurocognitive networks. To this end, we observe that within-network directed functional connectivity is driven by thalamus and progressively weakens with age. Secondly, we find that age-associated increase in between CEN- and DMN-directed functional connectivity is driven by both the SN and the thalamus. Furthermore, left and right thalami act as a causal integrative hub exhibiting substantial interactions with neurocognitive networks with aging and play a crucial role in reconfiguring network outflow. Notably, these results were largely replicated on an independent dataset of matched young and old individuals. Our findings strengthen the hypothesis that the thalamus is a key causal hub balancing both within- and between-network connectivity associated with age and maintenance of cognitive functioning with aging.

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

10.1101/2020.07.16.206235 ◽

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.

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Large-Scale Granger Causal Brain Network based on Resting-State fMRI data

Neuroscience ◽

10.1016/j.neuroscience.2019.11.006 ◽

2020 ◽

Vol 425 ◽

pp. 169-180 ◽

Cited By ~ 2

Author(s):

Xuewei Wang ◽

Ru Wang ◽

Fei Li ◽

Qiang Lin ◽

Xiaohu Zhao ◽

...

Keyword(s):

Resting State ◽

Large Scale ◽

Resting State Fmri ◽

Brain Network ◽

Fmri Data

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Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project

Frontiers in Neuroscience ◽

10.3389/fnins.2015.00492 ◽

2016 ◽

Vol 9 ◽

Cited By ~ 14

Author(s):

Roland N. Boubela ◽

Klaudius Kalcher ◽

Wolfgang Huf ◽

Christian Našel ◽

Ewald Moser

Keyword(s):

Big Data ◽

Resting State ◽

Large Scale ◽

Resting State Fmri ◽

Apache Spark ◽

Fmri Data ◽

Human Connectome Project ◽

Gpu Processing

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

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2021.109084 ◽

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

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

IEEE Access ◽

10.1109/access.2021.3098453 ◽

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

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

Data in Brief ◽

10.1016/j.dib.2020.105213 ◽

2020 ◽

Vol 29 ◽

pp. 105213 ◽

Cited By ~ 1

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

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Neurophysiology (fMRI)

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2015.173 ◽

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.

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