scholarly journals Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Xuechun Wang ◽  
Weilin Zeng ◽  
Xiaodan Yang ◽  
Chunyu Fang ◽  
Yunyun Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Mouse Brain ◽  
Brain Regions ◽  
Training Data ◽  
Registration Accuracy ◽  
Whole Brain ◽  
Brain Data ◽  
Digital Frame ◽  
Channel Image

We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocessing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to the other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment-incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.

scholarly journals Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

2020 ◽  
Author(s):  
Xuechun Wang ◽  
Weilin Zeng ◽  
Xiaodan Yang ◽  
Chunyu Fang ◽  
Yunyun Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Mouse Brain ◽  
Training Data ◽  
Registration Accuracy ◽  
Whole Brain ◽  
Microscopy Data ◽  
Brain Data ◽  
Digital Frame ◽  
Channel Image

AbstractWe have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data of mouse brain. BIRDS features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full pipeline including image pre-processing, bi-channel registration, automatic annotation, creation of 3D digital frame, high-resolution visualization, and expandable quantitative analysis (via link with Imaris). The new bi-channel registration algorithm is adaptive to various types of whole brain data from different microscopy platforms and shows obviously improved registration accuracy. Also, the attraction of combing registration with neural network lies in that the registration procedure can readily provide training data for network, while the network can efficiently segment incomplete/defective brain data that are otherwise difficult for registration. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality whole-brain datasets, or real-time inference-based image segmentation for various brain region of interests. Jobs can be easily implemented on Fiji plugin that can be adapted for most computing environments.

scholarly journals Correction: Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Xuechun Wang ◽  
Weilin Zeng ◽  
Xiaodan Yang ◽  
Yongsheng Zhang ◽  
Chunyu Fang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Mouse Brain ◽  
3D Mapping ◽  
Channel Image

scholarly journals Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4900
Author(s):  
Maryam Doborjeh ◽  
Zohreh Doborjeh ◽  
Nikola Kasabov ◽  
Molood Barati ◽  
Grace Y. Y. Wang
Keyword(s):  
Deep Learning ◽  
Cognitive Task ◽  
Brain Regions ◽  
Feature Interaction ◽  
Control Group ◽  
Eeg Data ◽  
Healthy Control ◽  
On Line ◽  
Brain Data ◽  
Improved Accuracy

The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model’s explainability while learning from streaming spatiotemporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatiotemporal rules from SNN models that explain why a certain decision (output prediction) was made by the model. During the learning process, the SNN created dynamic neural clusters, captured as polygons, which evolved in time and continuously changed their size and shape. The dynamic patterns of the clusters were quantitatively analyzed to identify the important STBD features that correspond to the most activated brain regions. We studied the trend of dynamically created clusters and their spike-driven events that occur together in specific space and time. The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (n = 21) and opiate use (n = 18) subjects while they were performing a cognitive task. The SNN models of EEG demonstrated different trends of dynamic clusters across the groups. This suggested to select a group of marker EEG features and resulted in an improved accuracy of EEG classification to 92%, when compared with all-feature classification. During learning of EEG data, the areas of neurons in the SNN model that form adjacent clusters (corresponding to neighboring EEG channels) were detected as fuzzy boundaries that explain overlapping activity of brain regions for each group of subjects.

scholarly journals Molecular Atlas of the Adult Mouse Brain

2019 ◽  
Author(s):  
Cantin Ortiz ◽  
Jose Fernandez Navarro ◽  
Aleksandra Jurek ◽  
Antje Märtin ◽  
Joakim Lundeberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Spatial Organization ◽  
Adult Mouse ◽  
Brain Regions ◽  
Adult Brain ◽  
Adult Mouse Brain ◽  
Whole Brain ◽  
Systematic Classification ◽  
Molecular Code

AbstractBrain maps are essential for integrating information and interpreting the structure-function relationship of circuits and behavior. We aimed to generate a systematic classification of the adult mouse brain organization based on unbiased extraction of spatially-defining features. Applying whole-brain spatial transcriptomics, we captured the gene expression signatures to define the spatial organization of molecularly discrete subregions. We found that the molecular code contained sufficiently detailed information to directly deduce the complex spatial organization of the brain. This unsupervised molecular classification revealed new area- and layer-specific subregions, for example in isocortex and hippocampus, and a new division of striatum. The whole-brain molecular atlas further supports the identification of the spatial origin of single neurons using their gene expression profile, and forms the foundation to define a minimal gene set - a brain palette – that is sufficient to spatially annotate the adult brain. In summary, we have established a new molecular atlas to formally define the identity of brain regions, and a molecular code for mapping and targeting of discrete neuroanatomical domains.

Serotonergic mechanisms in the 6-Hz psychomotor seizures in mice

2018 ◽  
Vol 38 (3) ◽  
pp. 336-346 ◽  
Author(s):  
K Jahan ◽  
KK Pillai ◽  
D Vohora
Keyword(s):  
Sodium Valproate ◽  
Mouse Brain ◽  
Structural Changes ◽  
Brain Regions ◽  
Mechanisms Of Action ◽  
Behavioral Changes ◽  
Structural Effects ◽  
Whole Brain ◽  
Psychomotor Seizure

Serotonin (5-hydroxytrytamine (5-HT)) plays an important role in experimental seizures. Recently, we reported the depletion of 5-HT by parachlorophynylalanine (PCPA) in whole brain to enhance 6-Hz psychomotor seizures in mice. In the present work, we investigated the effect of 5-HT depletion in cortex and hippocampus, brain regions relevant for epilepsy, on behavioral and ultra-structural changes following 6-Hz psychomotor seizures in mice. In addition, we studied the effect of sodium valproate (SVP) on behavioral, biochemical, and ultra-structural effects induced by 6 Hz. Behavioral changes induced by 6 Hz stimulation were characterized as the increased duration of Straub’s tail, stun position, twitching of vibrissae, forelimb clonus, and increased rearing and grooming. PCPA administration further enhanced while SVP reduced these behaviors in mice. The 6-Hz psychomotor seizure induced ultra-structural changes in both cortex and hippocampus in mice treated with PCPA. Furthermore, PCPA administrations followed by 6Hz-induced seizures were accompanied by reduced hippocampal and cortical 5-HT. SVP attenuated the PCPA-induced ultra-structural changes and alterations of 5-HT content in the mouse brain. The study suggests the involvement of 5-HT in the 6 Hz psychomotor seizures and in the mechanisms of action of SVP against such seizures in mice.

scholarly journals CUBIC-Cloud: An Integrative Computational Framework Towards Community-driven Whole-Mouse-Brain Mapping

2020 ◽  
Author(s):  
Tomoyuki Mano ◽  
Ken Murata ◽  
Kazuhiro Kon ◽  
Chika Shimizu ◽  
Hiroaki Ono ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Brain Mapping ◽  
Wide Distribution ◽  
Automated System ◽  
Activity Profile ◽  
Computational Framework ◽  
Whole Brain ◽  
Plaque Deposition ◽  
Model Mouse ◽  
Brain Data

ABSTRACTRecent advancements in tissue clearing technologies have offered unparalleled opportunities for researchers to explore the whole mouse brain at cellular resolution. With the expansion of this experimental technique, however, a scalable and easy-to-use computational tool is in demand to effectively analyze and integrate whole-brain mapping datasets. To that end, here we present CUBIC-Cloud, a cloud-based framework to quantify, visualize and integrate whole mouse brain data. CUBIC-Cloud is a fully automated system where users can upload their whole-brain data, run analysis and publish the results. We demonstrate the generality of CUBIC-Cloud by a variety of applications. First, we investigated brain-wide distribution of PV, Sst, ChAT, Th and Iba1 expressing cells. Second, Aβ plaque deposition in AD model mouse brains were quantified. Third, we reconstructed neuronal activity profile under LPS-induced inflammation by c-Fos immunostaining. Last, we show brain-wide connectivity mapping by pseudo-typed Rabies virus. Together, CUBIC-Cloud provides an integrative platform to advance scalable and collaborative whole-brain mapping.

Conceptual disorganization and redistribution of resting state cortical hubs in untreated first episode psychosis: A 7T MRI study

2020 ◽  
Author(s):  
Avyarthana Dey ◽  
Kara Dempster ◽  
Michael Mackinley ◽  
Peter Jeon ◽  
Tushar Das ◽  
...  
Keyword(s):  
Negative Symptoms ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
First Episode Psychosis ◽  
Cortical Reorganization ◽  
First Episode ◽  
Formal Thought Disorder ◽  
Whole Brain ◽  
Episode Psychosis ◽  
Positive And Negative Symptoms

Background:Network level dysconnectivity has been studied in positive and negative symptoms of schizophrenia. Conceptual disorganization (CD) is a symptom subtype which predicts impaired real-world functioning in psychosis. Systematic reviews have reported aberrant connectivity in formal thought disorder, a construct related to CD. However, no studies have investigated whole-brain functional correlates of CD in psychosis. We sought to investigate brain regions explaining the severity of CD in patients with first-episode psychosis (FEPs) compared with healthy controls (HCs).Methods:We computed whole-brain binarized degree centrality maps of 31 FEPs, 25 HCs and characterized the patterns of network connectivity in the two groups. In FEPs, we related these findings to the severity of CD. We also studied the effect of positive and negative symptoms on altered network connectivity.Results:Compared to HCs, reduced hubness of a right superior temporal gyrus (rSTG) cluster was observed in the FEPs. In patients exhibiting high CD, increased hubness of a medial superior parietal (mSPL) cluster was observed, compared to patients exhibiting low CD. These two regions were strongly correlated with CD scores but not with other symptom scores.Discussion:Our observations are congruent with previous findings of reduced but not increased hubness. We observed increased hubness of mSPL suggesting that cortical reorganization occurs to provide alternate routes for information transfer.Conclusion:These findings provide insight into the underlying neural processes mediating the presentation of symptoms in untreated FEP. A longitudinal tracking of the symptom course will be useful to assess the mechanisms underlying these compensatory changes.

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