scholarly journals Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain

2022 ◽  
Author(s):  
Victor Nozais ◽  
Stephanie J Forkel ◽  
Laurent Petit ◽  
Michel Thiebaut de Schotten ◽  
marc joliot
Keyword(s):  
White Matter ◽  
Resting State ◽  
Grey Matter ◽  
Functional Neuroimaging ◽  
White Matter Lesions ◽  
Blood Oxygen Level Dependent ◽  
Resting State Networks ◽  
Multiple Networks ◽  
Human Connectome Project ◽  
The Impact

Over the past two decades, the study of resting-state functional magnetic resonance imaging (fMRI) has revealed the existence of multiple brain areas displaying synchronous functional blood oxygen level-dependent signals (BOLD) - resting-state networks (RSNs). The variation in functional connectivity between the different areas of a resting-state network or between multiple networks, have been extensively studied and linked to cognitive states and pathologies. However, the white matter connections supporting each network remain only partially described. In this work, we developed a data-driven method to systematically map the white and grey matter contributing to resting-state networks. Using the Human Connectome Project, we generated an atlas of 30 resting-state networks, each with two maps: white matter and grey matter. By integrating structural and functional neuroimaging data, this method builds an atlas that unlocks the joint anatomical exploration of white and grey matter to resting-state networks. The method also allows highlighting the overlap between networks, which revealed that most (89%) of the brain's white matter is shared amongst multiple networks, with 16% shared by at least 7 resting-state networks. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the correlations and the communication within resting-state networks. We provide an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage on RSNs.

scholarly journals Altered connectivity patterns among resting state networks in patients with ischemic white matter lesions

2017 ◽  
Vol 12 (5) ◽  
pp. 1239-1250 ◽  
Author(s):  
Ju-Rong Ding ◽  
Xin Ding ◽  
Bo Hua ◽  
Xingzhong Xiong ◽  
Yuqiao Wen ◽  
...  
Keyword(s):  
White Matter ◽  
Resting State ◽  
White Matter Lesions ◽  
Resting State Networks ◽  
Connectivity Patterns

scholarly journals Heterogeneity of Cerebral White Matter Lesions and Clinical Correlates in Older Adults

Stroke ◽  
2021 ◽  
Author(s):  
Keun-Hwa Jung ◽  
Kimberly A. Stephens ◽  
Kathryn M. Yochim ◽  
Joost M. Riphagen ◽  
Chan Mi Kim ◽  
...  
Keyword(s):  
White Matter ◽  
White Matter Lesions ◽  
Class Ii ◽  
Class I ◽  
Cerebral White Matter ◽  
Imaging Features ◽  
Volume Number ◽  
Human Connectome Project ◽  
Cerebral White Matter Lesions ◽  
The Impact

Background and Purpose: Cerebral white matter signal abnormalities (WMSAs) are a significant radiological marker associated with brain and vascular aging. However, understanding their clinical impact is limited because of their pathobiological heterogeneity. We determined whether use of robust reliable automated procedures can distinguish WMSA classes with different clinical consequences. Methods: Data from generally healthy participants aged >50 years with moderate or greater WMSA were selected from the Human Connectome Project-Aging (n=130). WMSAs were segmented on T1 imaging. Features extracted from WMSA included total and regional volume, number of discontinuous clusters, size of noncontiguous lesion, contrast of lesion intensity relative to surrounding normal appearing tissue using a fully automated procedure. Hierarchical clustering was used to classify individuals into distinct classes of WMSA. Radiological and clinical variability was evaluated across the individual WMSA classes. Results: Class I was characterized by multiple, small, lower-contrast lesions predominantly in the deep WM; class II by large, confluent lesions in the periventricular WM; and class III by higher-contrast lesions restricted to the juxtaventricular WM. Class II was associated with lower myelin content than the other 2 classes. Class II was more prevalent in older subjects and was associated with a higher prevalence of hypertension and lower physical activity levels. Poor sleep quality was associated with a greater risk of class I. Conclusions: We classified heterogeneous subsets of cerebral white matter lesions into distinct classes that have different clinical risk factors. This new method for identifying classes of WMSA will be important in understanding the underlying pathophysiology and in determining the impact on clinical outcomes.

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

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.

scholarly journals Resting State Functional Connectivity is Directly Related to Clinical Presentation of Mild Traumatic Brain Injury

Neurology ◽  
2019 ◽  
Vol 93 (14 Supplement 1) ◽  
pp. S26.2-S27
Author(s):  
Teena Shetty ◽  
Joseph Nguyen ◽  
Esther Kim ◽  
George Skulikidis ◽  
Matthew Garvey ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
White Matter ◽  
Functional Connectivity ◽  
Mild Traumatic Brain Injury ◽  
Resting State ◽  
Symptom Severity ◽  
Grey Matter ◽  
Clinical Presentation ◽  
Severity Scores

ObjectiveTo determine the utility of fractional amplitude of low frequency fluctuations (fALFF) during resting state fMRI (rs-fMRI) as an advanced neuroimaging biomarker for Mild Traumatic Brain Injury (mTBI).BackgroundmTBI is defined by a constellation of functional rather than structural deficits. As a measure of functional connectivity, fALFF has been implicated in long-term outcomes post-mTBI. It is unclear however, how longitudinal changes in fALFF may relate to the clinical presentation of mTBI.Design/Methods111 patients and 32 controls (15–50 years old) were enrolled acutely after mTBI and followed with up to 4 standardized serial assessments. Patients were enrolled at either Encounter 1 (E1), within 72 hours, or Encounter 2 (E2), 5–10 days post-injury, and returned for Encounter 3 (E3) at 15–29 days and Encounter 4 (E4) at 83–97 days. Each encounter included a clinical exam, neuropsychological assessment, as well as rs-fMRI imaging. fALFF was analyzed independently in 14 functional networks and, in grey and white matter as a function of symptom severity. Symptom severity scores (SSS) ranged from 0–132 as defined by the SCAT2 symptom evaluation.ResultsIn mTBI patients, fALFF scores across 5 functional brain networks (language, sensorimotor, visuospatial, higher-order visual, and posterior salience) differed between mTBI patients with low versus high SSS (SSS <5 and >30, respectively). Overall, greater SSS were indexed by reduced connectivity (p < 0.03, Bonferroni corrected). Further analysis also identified corresponding network pairs which were most predictive of increased SSS. White matter fALFF was not correlated with symptom severity, however, decreased grey matter fALFF was significantly correlated with greater SSS (r = −0.25, p = 0.002).ConclusionsGrey matter fALFF was correlated with mTBI symptom burden suggesting that patterns of neural connectivity relate directly to the clinical presentation of mTBI. Furthermore, differences in functional network connectivity as a function of SSS may reflect which networks are implicated in recovery of mTBI.

scholarly journals Education Modulates the Impact of White Matter Lesions on the Risk of Mild Cognitive Impairment and Dementia

2014 ◽  
Vol 22 (11) ◽  
pp. 1336-1345 ◽  
Author(s):  
Marion Mortamais ◽  
Florence Portet ◽  
Adam M. Brickman ◽  
Frank A. Provenzano ◽  
Jordan Muraskin ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
White Matter Lesions ◽  
The Impact

scholarly journals Increased soluble amyloid-beta causes early aberrant brain network hypersynchronisation in a mature-onset mouse model of amyloidosis

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Inès R. H. Ben-Nejma ◽  
Aneta J. Keliris ◽  
Jasmijn Daans ◽  
Peter Ponsaerts ◽  
Marleen Verhoye ◽  
...  
Keyword(s):  
Mouse Model ◽  
Postnatal Development ◽  
Amyloid Beta ◽  
Resting State ◽  
Inflammatory Responses ◽  
Ex Vivo ◽  
Fold Increase ◽  
Brain Network ◽  
Resting State Networks ◽  
The Impact

AbstractAlzheimer’s disease (AD) is the most common form of dementia in the elderly. According to the amyloid hypothesis, the accumulation and deposition of amyloid-beta (Aβ) peptides play a key role in AD. Soluble Aβ (sAβ) oligomers were shown to be involved in pathological hypersynchronisation of brain resting-state networks in different transgenic developmental-onset mouse models of amyloidosis. However, the impact of protein overexpression during brain postnatal development may cause additional phenotypes unrelated to AD. To address this concern, we investigated sAβ effects on functional resting-state networks in transgenic mature-onset amyloidosis Tet-Off APP (TG) mice. TG mice and control littermates were raised on doxycycline (DOX) diet from 3d up to 3 m of age to suppress transgenic Aβ production. Thereafter, longitudinal resting-state functional MRI was performed on a 9.4 T MR-system starting from week 0 (3 m old mice) up to 28w post DOX treatment. Ex-vivo immunohistochemistry and ELISA analysis was performed to assess the development of amyloid pathology. Functional Connectivity (FC) analysis demonstrated early abnormal hypersynchronisation in the TG mice compared to the controls at 8w post DOX treatment, particularly across regions of the default mode-like network, known to be affected in AD. Ex-vivo analyses performed at this time point confirmed a 20-fold increase in total sAβ levels preceding the apparition of Aβ plaques and inflammatory responses in the TG mice compared to the controls. On the contrary at week 28, TG mice showed an overall hypoconnectivity, coinciding with a widespread deposition of Aβ plaques in the brain. By preventing developmental influence of APP and/or sAβ during brain postnatal development, we demonstrated FC abnormalities potentially driven by sAβ neurotoxicity on resting-state neuronal networks in mature-induced TG mice. Thus, the Tet-Off APP mouse model could be a powerful tool while used as a mature-onset model to shed light into amyloidosis mechanisms in AD.

scholarly journals Test–Retest Reliability of Synchrony and Metastability in Resting State fMRI

2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Lan Yang ◽  
Jing Wei ◽  
Ying Li ◽  
Bin Wang ◽  
Hao Guo ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Resting State Networks ◽  
Functional Magnetic Resonance ◽  
Intra Class Correlation ◽  
Retest Reliability ◽  
Brain Signals ◽  
Human Connectome Project ◽  
Test Retest Reliability ◽  
The Brain

In recent years, interest has been growing in dynamic characteristic of brain signals from resting-state functional magnetic resonance imaging (rs-fMRI). Synchrony and metastability, as neurodynamic indexes, are considered as one of methods for analyzing dynamic characteristics. Although much research has studied the analysis of neurodynamic indices, few have investigated its reliability. In this paper, the datasets from the Human Connectome Project have been used to explore the test–retest reliabilities of synchrony and metastability from multiple angles through intra-class correlation (ICC). The results showed that both of these indexes had fair test–retest reliability, but they are strongly affected by the field strength, the spatial resolution, and scanning interval, less affected by the temporal resolution. Denoising processing can help improve their ICC values. In addition, the reliability of neurodynamic indexes was affected by the node definition strategy, but these effects were not apparent. In particular, by comparing the test–retest reliability of different resting-state networks, we found that synchrony of different networks was basically stable, but the metastability varied considerably. Among these, DMN and LIM had a relatively higher test–retest reliability of metastability than other networks. This paper provides a methodological reference for exploring the brain dynamic neural activity by using synchrony and metastability in fMRI signals.

scholarly journals Sex classification using long-range temporal dependence of resting-state functional MRI time series

2019 ◽  
Author(s):  
Elvisha Dhamala ◽  
Keith W. Jamison ◽  
Mert R. Sabuncu ◽  
Amy Kuceyeski
Keyword(s):  
Sex Differences ◽  
Long Range ◽  
Resting State ◽  
Grey Matter ◽  
Machine Learning Algorithms ◽  
Grey Matter Volume ◽  
Temporal Dependence ◽  
Matter Volume ◽  
Human Connectome Project ◽  
Males And Females

AbstractA thorough understanding of sex differences, if any, that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit differences in clinical and behavioural phenotypes between males and females. In this work, we evaluate sex differences in regional temporal dependence of resting-state brain activity using 195 male-female pairs (aged 22-37) from the Human Connectome Project. Male-female pairs are strictly matched for total grey matter volume. We find that males have more persistent long-range temporal dependence than females in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies of up to 81%. Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, frontal cortex, and occipital cortex. Additionally, we find that even after males and females are strictly matched on total grey matter volume, significant regional volumetric sex differences persist in many cortical and subcortical regions. Our results indicate males have larger cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger cingulates, precunei, frontal cortices, and parietal cortices. Sex classification based on regional volume achieves accuracies of up to 85%; cerebellum, cingulate cortex, and temporal cortex are the most important features. These findings highlight the important role of strict volume matching when studying brain-based sex differences. Differential patterns in regional temporal dependence between males and females identifies a potential neurobiological substrate underlying sex differences in functional brain activation patterns and the behaviours with which they correlate.

Sign in / Sign up

Export Citation Format

Share Document