scholarly journals Resting-State FMRI Single Subject Cortical Parcellation Based on Region Growing

2012 ◽  
pp. 188-195 ◽  
Author(s):  
Thomas Blumensath ◽  
Timothy E. J. Behrens ◽  
Stephen M. Smith
Keyword(s):  
Resting State ◽  
Region Growing ◽  
Resting State Fmri ◽  
Single Subject ◽  
Cortical Parcellation

scholarly journals The resting-state fMRI arterial signal predicts differential blood transit time through the brain

2018 ◽  
Vol 39 (6) ◽  
pp. 1148-1160 ◽  
Author(s):  
Yunjie Tong ◽  
Jinxia (Fiona) Yao ◽  
J Jean Chen ◽  
Blaise deB Frederick
Keyword(s):  
Transit Time ◽  
Resting State ◽  
Time Delays ◽  
Carotid Arteries ◽  
Near Infrared ◽  
Low Frequency ◽  
Hemoglobin Concentration ◽  
Resting State Fmri ◽  
Single Subject ◽  
The Brain

Previous studies have found that aperiodic, systemic low-frequency oscillations (sLFOs) are present in blood-oxygen-level-dependent (BOLD) data. These signals are in the same low frequency band as the “resting state” signal; however, they are distinct signals which represent non-neuronal, physiological oscillations. The same sLFOs are found in the periphery (i.e. finger tips) as changes in oxy/deoxy-hemoglobin concentration using concurrent near-infrared spectroscopy. Together, this evidence points toward an extra-cerebral origin of these sLFOs. If this is the case, it is expected that these sLFO signals would be found in the carotid arteries with time delays that precede the signals found in the brain. To test this hypothesis, we employed the publicly available MyConnectome dataset (a two-year longitudinal study of a single subject) to extract the sLFOs in the internal carotid arteries (ICAs) with the help of the T1/T2-weighted images. Significant, but negative, correlations were found between the LFO BOLD signals from the ICAs and (1) the global signal (GS), (2) the superior sagittal sinus, and (3) the jugulars. We found the consistent time delays between the sLFO signals from ICAs, GS and veins which coincide with the blood transit time through the cerebral vascular tree.

scholarly journals Functional imaging to guide network-based TMS treatments: toward a tailored medicine approach in Alzheimer’s disease

2020 ◽  
Author(s):  
Chiara Bagattini ◽  
Debora Brignani ◽  
Sonia Bonnì ◽  
Roberto Gasparotti ◽  
Michela Pievani
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Imaging ◽  
Resting State ◽  
Therapeutic Strategy ◽  
Resting State Fmri ◽  
Single Subject ◽  
Proof Of Concept ◽  
Group Level ◽  
Traditional Group

AbstractINTRODUCTIONTranscranial magnetic stimulation (TMS) has gained increasing attention as a potential therapeutic strategy in Alzheimer’s disease (AD). Among factors determining a clinical response, the choice of the stimulation site represents a key point. In this proof of concept study, we prove the feasibility of a tailored TMS targeting approach for AD, which stems from a network-based perspective. Based on functional imaging, the procedure allows to extract individual optimal targets meanwhile accounting for functional variability.METHODSSingle-subject resting-state fMRI was used to extract individual target coordinates of two networks primarily affected in AD, the default mode and the fronto-parietal network. The localization of these targets was compared to that of traditional group-level approaches and tested against varying degrees of TMS focality.RESULTSThe distance between individual fMRI-derived coordinates and traditionally-defined targets was significant for a focality <12mm, but not for >20mm. Comparison with anatomical labels confirmed a lack of 1:1 correspondence between anatomical and functional targets.DISCUSSIONThe proposed network-based fMRI-guided TMS approach allows targeting disorder-specific networks meanwhile accounting for inter-individual functional variability in Alzheimer’s disease. This approach might represent a step toward tailored TMS interventions for AD.

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.

Resting-state fMRI used for intraoperative neuronavigation in children

2013 ◽  
Vol 44 (S 01) ◽  
Author(s):  
C Dorfer ◽  
T Czech ◽  
G Kasprian ◽  
A Azizi ◽  
J Furtner ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri

Altered functional connectivity of right inferior frontal gyrus subregions in bipolar disorder: a resting state fMRI study

2020 ◽  
Vol 272 ◽  
pp. 58-65 ◽  
Author(s):  
Li Zhang ◽  
Wenfei Li ◽  
Long Wang ◽  
Tongjian Bai ◽  
Gong-Jun Ji ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Inferior Frontal Gyrus ◽  
Resting State Fmri ◽  
Fmri Study
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