Resting-State Brain Activity for Early Prediction Outcome in Postanoxic Patients in a Coma with Indeterminate Clinical Prognosis

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.

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The impact of hyperoxia on brain activity: A resting-state and task-evoked electroencephalography (EEG) study

PLoS ONE ◽

10.1371/journal.pone.0176610 ◽

2017 ◽

Vol 12 (5) ◽

pp. e0176610 ◽

Cited By ~ 5

Author(s):

Min Sheng ◽

Peiying Liu ◽

Deng Mao ◽

Yulin Ge ◽

Hanzhang Lu

Keyword(s):

Resting State ◽

Brain Activity ◽

The Impact ◽

And Task

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Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽

10.3390/e23030286 ◽

2021 ◽

Vol 23 (3) ◽

pp. 286

Author(s):

Soheil Keshmiri

Keyword(s):

Resting State ◽

Nearest Neighbor ◽

Brain Activity ◽

Solution Concept ◽

Conditional Entropy ◽

K Nearest Neighbor ◽

Brain Response ◽

Substantial Progress ◽

Eeg Recordings ◽

The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

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Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance

Brain Topography ◽

10.1007/s10548-021-00835-3 ◽

2021 ◽

Author(s):

Adeline Jabès ◽

Giuliana Klencklen ◽

Paolo Ruggeri ◽

Christoph M. Michel ◽

Pamela Banta Lavenex ◽

...

Keyword(s):

Older Adults ◽

Working Memory ◽

Resting State ◽

Cognitive Aging ◽

Temporal Dynamics ◽

Spatial Working Memory ◽

Brain Activity ◽

Memory Performance ◽

Brain Regions ◽

Age Related

AbstractAlterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.

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Intranasal vasopressin modulates resting state brain activity across multiple neural systems: evidence from a brain imaging machine learning study

Neuropharmacology ◽

10.1016/j.neuropharm.2021.108561 ◽

2021 ◽

pp. 108561

Author(s):

Xinling Chen ◽

Yongbo Xu ◽

Bingjie Li ◽

Xiaoyan Wu ◽

Ting Li ◽

...

Keyword(s):

Machine Learning ◽

Brain Imaging ◽

Resting State ◽

Brain Activity ◽

Neural Systems ◽

Learning Study

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Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

Nature Communications ◽

10.1038/s41467-021-22960-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Luke Baxter ◽

Fiona Moultrie ◽

Sean Fitzgibbon ◽

Marianne Aspbury ◽

Roshni Mansfield ◽

...

Keyword(s):

White Matter ◽

Prediction Model ◽

Resting State ◽

Early Life ◽

Diffusion Mri ◽

Brain Activity ◽

Noxious Stimulus ◽

Noxious Stimulation ◽

Human Connectome Project ◽

And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

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Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis

Acta Neuropsychiatrica ◽

10.1017/neu.2021.9 ◽

2021 ◽

pp. 1-29

Author(s):

Kangyu Jin ◽

Zhe Shen ◽

Guoxun Feng ◽

Zhiyong Zhao ◽

Jing Lu ◽

...

Keyword(s):

Cognitive Function ◽

Resting State ◽

Parietal Lobe ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Regions ◽

Regional Homogeneity ◽

Middle Temporal Gyrus ◽

Support Vector ◽

Fmri Study

Abstract Objective: A few former studies suggested there are partial overlaps in abnormal brain structure and cognitive function between Hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear. Methods: 21 HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent Resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ. Results: Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL) and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG and left paracentral lobule but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MCCB in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients. Conclusion: our results suggested the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.

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