scholarly journals Estimation of Voxelwise Effective Connectivities: Applications to High Connectivity Sub-Regions within Hippocampal and within Corticostriatal Networks

2016 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Joseph D. Ramsey ◽  
Jackson C. Liang ◽  
Kevin Jarbo ◽  
Clark Glymour
Keyword(s):  
Temporal Lobe ◽  
Resting State ◽  
Medial Temporal Lobe ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Regions Of Interest ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
High Connectivity ◽  
Anatomical Information

Standard BOLD connectivity analyses depend on aggregating the signals of individual voxel within regions of interest (ROIs). In certain cases, this aggregation implies a loss of valuable functional and anatomical information about sub-regions of voxels that drive the ROI level connectivity. We describe a data-driven statistical search method that identifies the voxels that are chiefly responsible for exchanging signals between regions of interest that are known to be effectively connected. We apply the method to high-resolution resting state functional magnetic resonance imaging (rs-fMRI) data from medial temporal lobe regions of interest of a single healthy individual measured repeated times over a year and a half. The method successfully recovered densely connected voxels within larger ROIs of entorhinal cortex and hippocampus subfields consistent with the well-known medial temporal lobe structural connectivity. To assess the performance of our method in more common scanning protocols we apply it to resting state fMRI data of corticostriatal regions of interest for 50 healthy individuals. The method recovered densely connected voxels within the caudate nucleus and the putamen in good qualitative agreement with structural connectivity measurements. We describe related methods for estimation of effective connections at the voxel level that merit investigation.

scholarly journals Human Connectome Project resting state fMRI data organized into 60 fine-scaled retinotopically organized regions in cortical areas V1, V2 and V3

2018 ◽  
Author(s):  
Debra Ann Dawson ◽  
Zixuan Yin ◽  
Jack Lam ◽  
Amir Shmuel
Keyword(s):  
Visual Cortex ◽  
Resting State ◽  
Resting State Fmri ◽  
Regions Of Interest ◽  
Fmri Data ◽  
Cortical Areas ◽  
Human Connectome Project ◽  
Public Data ◽  
Visual Areas ◽  
Data Release

AbstractThe data comprises 60 regions of interest (ROIs) from V1, V2, and V3 of the human visual cortex. Preprocessed data from the Human Connectome Project (HCP) 900 subjects public data release were utilized: 220 subjects were randomly selected, each with 4 scans of resting state fMRI data. Given that these subjects did not have retinotopy scans performed, the visual areas were defined using an anatomical template from Benson et al. (2014). Visual areas from each hemisphere were further divided along dorsal-ventral lines into quadrants, resulting in 4 quadrants per subject. Within each quadrant, fine scaled ROIs were defined by subdividing each visual area into 5 regions according to eccentricity. These data may be useful for studying retinotopically organized functional connectivity in the visual cortex using the HCP 3 Tesla dataset.

scholarly journals Identification of Mechanisms of Functional Signaling Between Human Hippocampus Regions

2017 ◽  
Author(s):  
Ruben Sanchez-Romero ◽  
Joseph D. Ramsey ◽  
Jackson C. Liang ◽  
Clark Glymour
Keyword(s):  
Temporal Lobe ◽  
Medial Temporal Lobe ◽  
Effective Connectivity ◽  
Regions Of Interest ◽  
Field Methods ◽  
Human Hippocampus ◽  
Markov Random ◽  
Common Effect ◽  
Anatomical Information

AbstractBackgroundStandard BOLD connectivity analyses depend on aggregating the signals of individual voxels within regions of interest (ROIs). In certain cases, this spatial aggregation implies a loss of valuable functional and anatomical information about subsets of voxels that drive the ROI level connectivity.New MethodWe use the FGES algorithm, a data-driven score-based graphical search method, to identify subsets of voxels that are chiefly responsible for exchanging signals between ROIs. We apply the method to high-resolution resting state functional magnetic resonance imaging (rs-fMRI) data from medial temporal lobe regions of interest of a single healthy individual measured repeated times over a year and a half.ResultsThe FGES algorithm recovered subsets of voxels within larger medial temporal lobe ROIs of entorhinal cortex and hippocampus subfields that show spatially consistency across different scanning sessions, and are statistically significant under tests that validate the role of these subsets as main drivers of effective connectivity between hippocampal regions of interest.Comparison with Existing MethodsIn contrast to standard functional connectivity methods, the FGES algorithm is robust against false positive connections produced by transitive closures of adjacencies (correlation methods) and common effect conditioning (Markov random field methods).ConclusionsThe FGES algorithm allows for identification of communication subsets of voxels driving the connectivity between regions of interest, recovering valuable anatomical and functional information that is lost when ROIs are aggregated. The FGES algorithm is specially suited for voxelwise connectivity research, given its short running time and scalability to big data problems.

A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies

2020 ◽  
Vol 30 (10) ◽  
pp. 5544-5559 ◽  
Author(s):  
Jonathan D Power ◽  
Charles J Lynch ◽  
Babatunde Adeyemo ◽  
Steven E Petersen
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Critical Event ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Decay Properties ◽  
Signal Decay ◽  
Biological Variables ◽  
Related Approach

Abstract This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like “Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties”. Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.

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.

scholarly journals Resting State fMRI: A Valuable Tool for Studying Cognitive Dysfunction in PD

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Kai Li ◽  
Wen Su ◽  
Shu-Hua Li ◽  
Ying Jin ◽  
Hai-Bo Chen
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Dysfunction ◽  
Cognitive Deficits ◽  
Resting State ◽  
Lewy Bodies ◽  
Resting State Fmri ◽  
Underlying Mechanism ◽  
Research Field ◽  
Functional Magnetic Resonance ◽  
Positron Emission

Cognitive impairment is a common disabling symptom in PD. Unlike motor symptoms, the mechanism underlying cognitive dysfunction in Parkinson’s disease (PD) remains unclear and may involve multiple pathophysiological processes. Resting state functional magnetic resonance imaging (rs-fMRI) is a fast-developing research field, and its application in cognitive impairments in PD is rapidly growing. In this review, we summarize rs-fMRI studies on cognitive function in PD and discuss the strong potential of rs-fMRI in this area. rs-fMRI can help reveal the pathophysiology of cognitive symptoms in PD, facilitate early identification of PD patients with cognitive impairment, distinguish PD dementia from dementia with Lewy bodies, and monitor and guide treatment for cognitive impairment in PD. In particular, ongoing and future longitudinal studies would enhance the ability of rs-fMRI in predicting PD dementia. In combination with other modalities such as positron emission tomography, rs-fMRI could give us more information on the underlying mechanism of cognitive deficits in PD.

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
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