Decoding Spontaneous Brain Activity from fMRI Using Gaussian Processes: Tracking Brain Reactivation

2012 ◽  
Author(s):  
Jessica Schrouff ◽  
Caroline Kusse ◽  
Louis Wehenkel ◽  
Pierre Maquet ◽  
Christophe Phillips
Keyword(s):  
Gaussian Processes ◽  
Brain Activity ◽  
Spontaneous Brain Activity

scholarly journals Jumping over baselines with new methods to predict activation maps from resting-state fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Lacosse ◽  
Klaus Scheffler ◽  
Gabriele Lohmann ◽  
Georg Martius
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Behavioral Differences ◽  
Large Variability ◽  
Single Vertex ◽  
Spontaneous Brain Activity ◽  
New Methods ◽  
Group Averaging ◽  
Proposed Modification

AbstractCognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

scholarly journals Using Regional Homogeneity to Reveal Altered Spontaneous Activity in Patients with Mild Cognitive Impairment

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yumei Wang ◽  
Xiaochuan Zhao ◽  
Shunjiang Xu ◽  
Lulu Yu ◽  
Lan Wang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Spontaneous Activity ◽  
Healthy Aging ◽  
Early Stage ◽  
Brain Activity ◽  
Regional Homogeneity ◽  
Whole Brain ◽  
Spontaneous Brain Activity ◽  
Regional Brain Activity

Most patients with mild cognitive impairment (MCI) are thought to be in an early stage of Alzheimer’s disease (AD). Resting-state functional magnetic resonance imaging reflects spontaneous brain activity and/or the endogenous/background neurophysiological process of the human brain. Regional homogeneity (ReHo) rapidly maps regional brain activity across the whole brain. In the present study, we used the ReHo index to explore whole brain spontaneous activity pattern in MCI. Our results showed that MCI subjects displayed an increased ReHo index in the paracentral lobe, precuneus, and postcentral and a decreased ReHo index in the medial temporal gyrus and hippocampus. Impairments in the medial temporal gyrus and hippocampus may serve as important markers distinguishing MCI from healthy aging. Moreover, the increased ReHo index observed in the postcentral and paracentral lobes might indicate compensation for the cognitive function losses in individuals with MCI.

Assessment of spontaneous brain activity patterns in patients with iridocyclitis

Neuroreport ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Yan Tong ◽  
Xin Huang ◽  
Chen-Xing Qi ◽  
Yin Shen
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Spontaneous Brain Activity
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