scholarly journals Lag Analysis of Fast fMRI Reveals Delayed Information Flow Between the Default Mode and Other Networks in Narcolepsy

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
M Järvelä ◽  
V Raatikainen ◽  
A Kotila ◽  
J Kananen ◽  
V Korhonen ◽  
...  
Keyword(s):  
Information Flow ◽  
Resting State ◽  
Signal Propagation ◽  
Fmri Data ◽  
Familywise Error Rate ◽  
Functional Magnetic Resonance ◽  
Default Mode ◽  
Information Signaling ◽  
Dynamic Connectivity

Abstract Narcolepsy is a chronic neurological disease characterized by dysfunction of the hypocretin system in brain causing disruption in the wake-promoting system. In addition to sleep attacks and cataplexy, patients with narcolepsy commonly report cognitive symptoms while objective deficits in sustained attention and executive function have been observed. Prior resting-state functional magnetic resonance imaging (fMRI) studies in narcolepsy have reported decreased inter/intranetwork connectivity regarding the default mode network (DMN). Recently developed fast fMRI data acquisition allows more precise detection of brain signal propagation with a novel dynamic lag analysis. In this study, we used fast fMRI data to analyze dynamics of inter resting-state network (RSN) information signaling between narcolepsy type 1 patients (NT1, n = 23) and age- and sex-matched healthy controls (HC, n = 23). We investigated dynamic connectivity properties between positive and negative peaks and, furthermore, their anticorrelative (pos-neg) counterparts. The lag distributions were significantly (P < 0.005, familywise error rate corrected) altered in 24 RSN pairs in NT1. The DMN was involved in 83% of the altered RSN pairs. We conclude that narcolepsy type 1 is characterized with delayed and monotonic inter-RSN information flow especially involving anticorrelations, which are known to be characteristic behavior of the DMN regarding neurocognition.

scholarly journals Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network

2021 ◽  
Vol 11 (13) ◽  
pp. 6216
Author(s):  
Aikaterini S. Karampasi ◽  
Antonis D. Savva ◽  
Vasileios Ch. Korfiatis ◽  
Ioannis Kakkos ◽  
George K. Matsopoulos
Keyword(s):  
Magnetic Resonance Imaging ◽  
Autism Spectrum Disorder ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Default Mode Network ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Dynamic Connectivity

Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment.

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 A pilot study of the effects of running training on visuospatial memory in MS: A stronger functional embedding of the hippocampus in the default-mode network?

2019 ◽  
Vol 26 (12) ◽  
pp. 1594-1598 ◽  
Author(s):  
Marijn Huiskamp ◽  
Lousin Moumdjian ◽  
Paul van Asch ◽  
Veronica Popescu ◽  
Menno Michiel Schoonheim ◽  
...  
Keyword(s):  
Endurance Exercise ◽  
Default Mode Network ◽  
Resting State ◽  
Memory Function ◽  
Intervention Group ◽  
Visuospatial Memory ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Running Training ◽  
Default Mode

Background/objective: Endurance exercise can improve memory function in persons with multiple sclerosis (pwMS), but the effects on hippocampal functioning are currently unknown. We investigated the effects of a running intervention on memory and hippocampal functional connectivity in pwMS. Methods/results: Memory and resting-state functional magnetic resonance imaging (fMRI) data were collected in a running intervention ( n = 15) and waitlist group ( n = 14). Visuospatial memory improvement was correlated to increased connectivity between the hippocampus and the default-mode network (DMN) in the intervention group only. Conclusion: As a result of endurance exercise, improvements in visuospatial memory may be mediated by a stronger functional embedding of the hippocampus in the DMN.

scholarly journals Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates

2016 ◽  
Vol 113 (28) ◽  
pp. 7900-7905 ◽  
Author(s):  
Anders Eklund ◽  
Thomas E. Nichols ◽  
Hans Knutsson
Keyword(s):  
Statistical Methods ◽  
Resting State ◽  
Permutation Test ◽  
Error Rates ◽  
Familywise Error Rate ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Gaussian Shape ◽  
Familywise Error Rates ◽  
Software Packages

The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.

scholarly journals The Assessment of Brain Functional Changes in the Temporal Lobe Epilepsy Patient with Cognitive Impairment by Resting-state Functional Magnetic Resonance Imaging

2020 ◽  
Vol 10 ◽  
pp. 50
Author(s):  
Tanoj Bahadur Singh ◽  
Aikedan Aisikaer ◽  
Che He ◽  
Yalin Wu ◽  
Hong Chen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cognitive Impairment ◽  
Data Analysis ◽  
Resting State ◽  
Fmri Data ◽  
Posterior Lobe ◽  
Cingulate Gyrus ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Functional Changes

Objectives: The objective of the study was to detect functional changes in the brain of cognitive impairment-temporal lobe epilepsy (CI-TLE) patient and to sort out the possible mechanism involved in CI in CI-TLE patients using resting-state functional magnetic resonance imaging (RS-fMRI). Material and Methods: Fifty-eight TLE cases were included, which was divided into 44 TLE patients without CI (cognitive not impairment [CNI]-TLE) and 14 TLE patients with CI (CI-TLE). The normal control (NC) group consisted of 40 participants. RS-fMRI data preprocessing was carried out in statistical parametric mapping (SPM) software. The data were realigned, coregistered, normalized, and finally smoothened and then were taken for amplitude of low-frequency fluctuation (ALFF) calculation in RS-fMRI data analysis toolkit (REST) software. For data analysis, voxel-wise two-sample t-test was carried out between TLE group and NC group; CI-TLE group and cognitive not impairment-TLE (CNI-TLE) group in SPM software, a cluster >10 voxels and P < 0.01 was considered to be significant. Results: Compared to NC, the TLE patients showed increased ALFF activation mostly in parahippocampal gyrus (PG), frontal lobe, midbrain, pons, insula, inferior temporal gyrus, and anterior cingulate gyrus (ACG) while decreased ALFF value was seen in posterior cingulate gyrus, cuneus, cerebellum posterior lobe, inferior parietal lobule (IPL), and superior temporal gyrus. Compared to CNI-TLE, CI-TLE patients showed increased ALFF in middle temporal gyrus (MTG), cuneus, ACG, IPL, middle frontal gyrus (MFG), superior frontal gyrus (SFG), cerebellum posterior lobe, and decreased ALFF cluster in the corpus callosum and MFG. Conclusion: Between TLE and NC, we found increased ALFF activation in PG, frontal lobe, thalamus, insula, midbrain, and pons in TLE patient. Between CI and CNI TLE, area of executive control network and default model network, especially in MTG, ACG, IPL, MFG, and SFG, had increased ALFF value in CI-TLE patient. Activation of these areas should be because of the decompensation mechanism.

Improved estimation of dynamic connectivity from resting-state fMRI data

2019 ◽  
Author(s):  
Biao Cai ◽  
Julia M. Stephen ◽  
Tony W. Wilson ◽  
Vince D. Calhoun ◽  
Yu-ping Wang
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Dynamic Connectivity

scholarly journals Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data

2016 ◽  
Vol 10 ◽  
Author(s):  
Maksim G. Sharaev ◽  
Viktoria V. Zavyalova ◽  
Vadim L. Ushakov ◽  
Sergey I. Kartashov ◽  
Boris M. Velichkovsky
Keyword(s):  
Default Mode Network ◽  
Resting State ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Fmri Data ◽  
Network Dynamic ◽  
Dynamic Causal Modeling ◽  
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