scholarly journals Light-sheet imaging and graph analysis of antidepressant action in the larval zebrafish brain network

2019 ◽  
Author(s):  
Jessica Burgstaller ◽  
Elena Hindinger ◽  
Joseph Donovan ◽  
Marco Dal Maschio ◽  
Andreas M. Kist ◽  
...  

AbstractThe zebrafish is increasingly being employed as an experimental platform to model neuropsychiatric diseases and to screen for novel neuro-active compounds. While the superb genetic and optical access that this system offers has long been recognized, these features have not been fully exploited to investigate disease mechanisms and possible therapeutic interventions. Here we introduce a light-sheet imaging and graph-theoretical analysis pipeline to determine the effects of the known or suspected antidepressant compounds fluoxetine, ketamine and cycloserine on brain-wide neural activity patterns. We imaged the brains of both wildtype fish and grs357 mutants, which harbor a missense mutation that abolishes glucocorticoid receptor transcriptional activity. The grs357 mutation results in a chronically elevated stress axis together with behavioral endophenotypes of depression. Consistent with broad expression of the glucocorticoid receptor throughout the brain, we show that the mutant fish exhibit an altered correlational structure of resting-state brain activity. Intriguingly, in grs357 mutant fish, an increased ‘modularity’, which represents the degree of segregation of the network into highly clustered modules, was restored by acute fluoxetine administration to wildtype levels. Ketamine and cycloserine also normalized specific parameters of the graph. Fluoxetine altered network function in the same direction in mutant and wildtype, while ketamine and cycloserine had effects that were opposite for the two genotypes. We propose that light-sheet imaging, followed by graph analysis, is a content-rich and scalable first-pass approach for studying the neural consequences of drug effects and drug x genotype interactions in zebrafish models of psychiatric disorders.

2019 ◽  
Author(s):  
Sophie Arana ◽  
André Marquand ◽  
Annika Hultén ◽  
Peter Hagoort ◽  
Jan-Mathijs Schoffelen

AbstractThe meaning of a sentence can be understood, whether presented in written or spoken form. Therefore it is highly probable that brain processes supporting language comprehension are at least partly independent of sensory modality. To identify where and when in the brain language processing is independent of sensory modality, we directly compared neuromagnetic brain signals of 200 human subjects (102 males) either reading or listening to sentences. We used multiset canonical correlation analysis to align individual subject data in a way that boosts those aspects of the signal that are common to all, allowing us to capture word-by-word signal variations, consistent across subjects and at a fine temporal scale. Quantifying this consistency in activation across both reading and listening tasks revealed a mostly left hemispheric cortical network. Areas showing consistent activity patterns include not only areas previously implicated in higher-level language processing, such as left prefrontal, superior & middle temporal areas and anterior temporal lobe, but also parts of the control-network as well as subcentral and more posterior temporal-parietal areas. Activity in this supramodal sentence processing network starts in temporal areas and rapidly spreads to the other regions involved. The findings do not only indicate the involvement of a large network of brain areas in supramodal language processing, but also indicate that the linguistic information contained in the unfolding sentences modulates brain activity in a word-specific manner across subjects.


2021 ◽  
Vol 14 (11) ◽  
pp. 1741-1747
Author(s):  
Wen-Jia Dong ◽  
◽  
Chu-Qi Li ◽  
Yong-Qiang Shu ◽  
Wen-Qing Shi ◽  
...  

AIM: To explore the intrinsic brain activity variations in retinal vein occlusion (RVO) subjects by using the voxel-wise degree centrality (DC) technique. METHODS: Twenty-one subjects with RVO and twenty-one healthy controls (HCs) were enlisted and underwent the resting-state functional magnetic resonance imaging (rs-fMRI) examination. The spontaneous cerebrum activity variations were inspected using the DC technology. The receiver operating characteristic (ROC) curve was implemented to distinguish the DC values of RVOs from HCs. The relationships between DC signal of definite regions of interest and the clinical characteristics in RVO group were evaluated by Pearson’s correlation analysis. RESULTS: RVOs showed notably higher DC signals in right superior parietal lobule, middle frontal gyrus and left precuneus, but decreased DC signals in left middle temporal gyrus and bilateral anterior cingulated (BAC) when comparing with HCs. The mean DC value of RVOs in the BAC were negatively correlated with the anxiety and depression scale. CONCLUSION: RVO is associated aberrant intrinsic brain activity patterns in several brain areas including pain-related as well as visual-related regions, which might assist to reveal the latent neural mechanisms.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meir Meshulam ◽  
Liat Hasenfratz ◽  
Hanna Hillman ◽  
Yun-Fei Liu ◽  
Mai Nguyen ◽  
...  

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.


SLEEP ◽  
2021 ◽  
Author(s):  
Ernesto Sanz-Arigita ◽  
Yannick Daviaux ◽  
Marc Joliot ◽  
Bixente Dilharreguy ◽  
Jean-Arthur Micoulaud-Franchi ◽  
...  

Abstract Study objectives Emotional reactivity to negative stimuli has been investigated in insomnia, but little is known about emotional reactivity to positive stimuli and its neural representation. Methods We used 3T fMRI to determine neural reactivity during the presentation of standardized short, 10-40-s, humorous films in insomnia patients (n=20, 18 females, aged 27.7 +/- 8.6 years) and age-matched individuals without insomnia (n=20, 19 females, aged 26.7 +/- 7.0 years), and assessed humour ratings through a visual analogue scale (VAS). Seed-based functional connectivity was analysed for left and right amygdala networks: group-level mixed-effects analysis (FLAME; FSL) was used to compare amygdala connectivity maps between groups. Results fMRI seed-based analysis of the amygdala revealed stronger neural reactivity in insomnia patients than in controls in several brain network clusters within the reward brain network, without humour rating differences between groups (p = 0.6). For left amygdala connectivity, cluster maxima were in the left caudate (Z=3.88), left putamen (Z=3.79) and left anterior cingulate gyrus (Z=4.11), while for right amygdala connectivity, cluster maxima were in the left caudate (Z=4.05), right insula (Z=3.83) and left anterior cingulate gyrus (Z=4.29). Cluster maxima of the right amygdala network were correlated with hyperarousal scores in insomnia patients only. Conclusions Presentation of humorous films leads to increased brain activity in the neural reward network for insomnia patients compared to controls, related to hyperarousal features in insomnia patients, in the absence of humor rating group differences. These novel findings may benefit insomnia treatment interventions.


Author(s):  
Lorenzo Chicchi ◽  
Gloria Cecchini ◽  
Ihusan Adam ◽  
Giuseppe de Vito ◽  
Roberto Livi ◽  
...  

AbstractAn inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.


Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 226
Author(s):  
Lisa-Marie Vortmann ◽  
Leonid Schwenke ◽  
Felix Putze

Augmented reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG data windows from 20 participants in a person-dependent manner with an average accuracy above 70% if the testing data and training data came from different trials. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a brain–computer interface is high enough for it to be treated as a useful input mechanism for augmented reality applications.


2011 ◽  
Vol 228 (2) ◽  
pp. 200-205 ◽  
Author(s):  
Naim Haddad ◽  
Rathinaswamy B. Govindan ◽  
Srinivasan Vairavan ◽  
Eric Siegel ◽  
Jessica Temple ◽  
...  

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

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