Faculty Opinions recommendation of Spatio-temporal transcriptome of the human brain.

Author(s):  
Genevieve Konopka
Keyword(s):  
1995 ◽  
Vol 7 (3) ◽  
pp. 193-200 ◽  
Author(s):  
Jia -Zhu Wang ◽  
Samuel J. Williamson ◽  
Lloyd Kaufman

2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.


Neuroscience ◽  
2010 ◽  
Vol 167 (3) ◽  
pp. 700-708 ◽  
Author(s):  
A.M. Lascano ◽  
T. Hummel ◽  
J.-S. Lacroix ◽  
B.N. Landis ◽  
C.M. Michel

2019 ◽  
Author(s):  
Talia L. Retter ◽  
Fang Jiang ◽  
Michael A. Webster ◽  
Bruno Rossion

AbstractWhether visual categorization, i.e., specific responses to a certain class of visual events across a wide range of exemplars, is graded or all-or-none in the human brain is largely unknown. We address this issue with an original frequency-sweep paradigm probing the evolution of responses between the minimum and optimal presentation times required to elicit both neural and behavioral face categorization responses. In a first experiment, widely variable natural images of nonface objects are progressively swept from 120 to 3 Hz (8.33 to 333 ms duration) in rapid serial visual presentation sequences; variable face exemplars appear every 1 s, enabling an implicit frequency-tagged face-categorization electroencephalographic (EEG) response at 1 Hz. In a second experiment, faces appear non-periodically throughout such sequences at fixed presentation rates, while participants explicitly categorize faces. Face-categorization activity emerges with stimulus durations as brief as 17 ms for both neural and behavioral measures (17 – 83 ms across individual participants neurally; 33 ms at the group level). The face-categorization response amplitude increases until 83 ms stimulus duration (12 Hz), implying graded categorization responses. However, a strong correlation with behavioral accuracy suggests instead that dilution from missed categorizations, rather than a decreased response to each face stimulus, may be responsible. This is supported in the second experiment by the absence of neural responses to behaviorally uncategorized faces, and equivalent amplitudes of isolated neural responses to only behaviorally categorized faces across presentation rates, consistent with the otherwise stable spatio-temporal signatures of face-categorization responses in both experiments. Overall, these observations provide original evidence that visual categorization of faces, while being widely variable across human observers, occurs in an all-or-none fashion in the human brain.


Genes ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 535
Author(s):  
Yujin Kim ◽  
Joon-Yong An

Transcriptional regulation of the genome arguably provides the basis for the anatomical elaboration and dynamic operation of the human brain. It logically follows that genetic variations affecting gene transcription contribute to mental health disorders, including autism spectrum disorder (ASD). A number of recent studies have shown the role of de novo variants (DNVs) in disrupting early neurodevelopment. However, there is limited knowledge concerning the role of inherited variants during the early brain development of ASD. In this study, we investigate the role of rare inherited variations in neurodevelopment. We conducted co-expression network analyses using an anatomically comprehensive atlas of the developing human brain and examined whether rare coding and regulatory variants, identified from our genetic screening of Australian families with ASD, work in different spatio-temporal functions.


2014 ◽  
Vol 10 (3) ◽  
pp. 253-267 ◽  
Author(s):  
Yiwen Wang ◽  
Liang Huang ◽  
Wei Zhang ◽  
Zhen Zhang ◽  
Stephanie Cacioppo

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Joan Rué-Queralt ◽  
Angus Stevner ◽  
Enzo Tagliazucchi ◽  
Helmut Laufs ◽  
Morten L. Kringelbach ◽  
...  

AbstractCurrent state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.


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