Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.

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Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective

10.1101/223784 ◽

2017 ◽

Cited By ~ 1

Author(s):

Matthieu Gilson

Keyword(s):

Resting State ◽

Effective Connectivity ◽

Network Models ◽

Resting State Fmri ◽

Brain Regions ◽

Brain Network ◽

Fmri Data ◽

Second Order Statistics ◽

Proposed Model ◽

The Many

AbstractSince the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping - determined by EC - for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data - movie viewing versus resting state - illustrates that changes in excitability and changes in brain coordination go hand in hand.

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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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Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data

Studies in Neural Data Science - Springer Proceedings in Mathematics & Statistics ◽

10.1007/978-3-030-00039-4_7 ◽

2018 ◽

pp. 111-130 ◽

Cited By ~ 1

Author(s):

Alessia Caponera ◽

Francesco Denti ◽

Tommaso Rigon ◽

Andrea Sottosanti ◽

Alan Gelfand

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Temporal Modeling ◽

Spatio Temporal

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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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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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