scholarly journals Functional imaging evidence for task-induced deactivation and disconnection of a major default mode network hub in the mouse brain

2020 ◽  
Vol 117 (26) ◽  
pp. 15270-15280 ◽  
Author(s):  
Jeremy Ferrier ◽  
Elodie Tiran ◽  
Thomas Deffieux ◽  
Mickael Tanter ◽  
Zsolt Lenkei

The default mode network (DMN) has been defined in functional brain imaging studies as a set of highly connected brain areas, which are active during wakeful rest and inactivated during task-based stimulation. DMN function is characteristically impaired in major neuropsychiatric diseases, emphasizing its interest for translational research. However, in the mouse, a major preclinical rodent model, there is still no functional imaging evidence supporting DMN deactivation and deconnection during high-demanding cognitive/sensory tasks. Here we have developed functional ultrasound (fUS) imaging to properly visualize both activation levels and functional connectivity patterns, in head-restrained awake and behaving mice, and investigated their modulation during a sensory-task, whisker stimulation. We identified reproducible and highly symmetric resting-state networks, with overall connectivity strength directly proportional to the wakefulness level of the animal. We show that unilateral whisker stimulation leads to the expected activation of the contralateral barrel cortex in lightly sedated mice, while interhemispheric inhibition reduces activity in the ipsilateral barrel cortex. Whisker stimulation also leads to elevated bilateral connectivity in the hippocampus. Importantly, in addition to functional changes in these major hubs of tactile information processing, whisker stimulation during genuine awake resting-state periods leads to highly specific reductions both in activation and interhemispheric correlation within the restrosplenial cortex, a major hub of the DMN. These results validate an imaging technique for the study of activation and connectivity in the lightly sedated awake mouse brain and provide evidence supporting an evolutionary preserved function of the DMN, putatively improving translational relevance of preclinical models of neuropsychiatric diseases.

2020 ◽  
Vol 87 (9) ◽  
pp. S457
Author(s):  
Gaelle Doucet ◽  
Delfina Janiri ◽  
Rebecca Howard ◽  
Madeline O'Brien ◽  
Jessica Andrews-Hanna ◽  
...  

2014 ◽  
Vol 45 (01) ◽  
Author(s):  
G Mingoia ◽  
K Langbein ◽  
M Dietzek ◽  
G Wagner ◽  
S Smesny ◽  
...  

NeuroImage ◽  
2021 ◽  
Vol 226 ◽  
pp. 117581
Author(s):  
Fengmei Fan ◽  
Xuhong Liao ◽  
Tianyuan Lei ◽  
Tengda Zhao ◽  
Mingrui Xia ◽  
...  

Author(s):  
Yunlong Nie ◽  
Eugene Opoku ◽  
Laila Yasmin ◽  
Yin Song ◽  
Jie Wang ◽  
...  

AbstractWe conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.


Author(s):  
Ravichandran Rajkumar ◽  
Ezequiel Farrher ◽  
Jörg Mauler ◽  
Praveen Sripad ◽  
Cláudia Régio Brambilla ◽  
...  

Author(s):  
ST Lang ◽  
B Goodyear ◽  
J Kelly ◽  
P Federico

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.


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