scholarly journals Accurate localization of linear probe electrodes across multiple brains

2020 ◽  
Author(s):  
Liu D Liu ◽  
Susu Chen ◽  
Michael N Economo ◽  
Nuo Li ◽  
Karel Svoboda
Keyword(s):  
Ground Truth ◽  
Brain Structures ◽  
Brain Regions ◽  
Brain Atlas ◽  
3 Dimensional ◽  
Accurate Localization ◽  
Large Numbers ◽  
Linear Probe ◽  
Electrode Localization ◽  
Recording Electrodes

AbstractRecently developed silicon probes have large numbers of recording electrodes on long linear shanks. Specifically, Neuropixels probes have 960 recording electrodes distributed over 9.6 mm shanks. Because of their length, Neuropixels probe recordings in rodents naturally span multiple brain areas. Typical studies collate recordings across several recording sessions and animals. Neurons recorded in different sessions and animals have to be aligned to each other and to a standardized brain coordinate system. Here we report a workflow for accurate localization of individual electrodes in standardized coordinates and aligned across individual brains. This workflow relies on imaging brains with fluorescent probe tracks and warping 3-dimensional image stacks to standardized brain atlases. Electrophysiological features are then used to anchor particular electrodes along the reconstructed tracks to specific locations in the brain atlas and therefore to specific brain structures. We performed ground-truth experiments, in which motor cortex outputs are labelled with ChR2 and a fluorescence protein. Recording from brain regions targeted by these outputs reveals better than 100 μm accuracy for electrode localization.

scholarly journals DeepNavNet: Automated Landmark Localization for Neuronavigation

2021 ◽  
Vol 15 ◽  
Author(s):  
Christine A. Edwards ◽  
Abhinav Goyal ◽  
Aaron E. Rusheen ◽  
Abbas Z. Kouzani ◽  
Kendall H. Lee
Keyword(s):  
Deep Learning ◽  
Human Error ◽  
Ground Truth ◽  
Brain Structures ◽  
Image Resolution ◽  
Brain Atlas ◽  
Anatomical Landmarks ◽  
Posterior Commissure ◽  
3D Localization ◽  
Mri Scans

Functional neurosurgery requires neuroimaging technologies that enable precise navigation to targeted structures. Insufficient image resolution of deep brain structures necessitates alignment to a brain atlas to indirectly locate targets within preoperative magnetic resonance imaging (MRI) scans. Indirect targeting through atlas-image registration is innately imprecise, increases preoperative planning time, and requires manual identification of anterior and posterior commissure (AC and PC) reference landmarks which is subject to human error. As such, we created a deep learning-based pipeline that consistently and automatically locates, with submillimeter accuracy, the AC and PC anatomical landmarks within MRI volumes without the need for an atlas. Our novel deep learning pipeline (DeepNavNet) regresses from MRI scans to heatmap volumes centered on AC and PC anatomical landmarks to extract their three-dimensional coordinates with submillimeter accuracy. We collated and manually labeled the location of AC and PC points in 1128 publicly available MRI volumes used for training, validation, and inference experiments. Instantiations of our DeepNavNet architecture, as well as a baseline model for reference, were evaluated based on the average 3D localization errors for the AC and PC points across 311 MRI volumes. Our DeepNavNet model significantly outperformed a baseline and achieved a mean 3D localization error of 0.79 ± 0.33 mm and 0.78 ± 0.33 mm between the ground truth and the detected AC and PC points, respectively. In conclusion, the DeepNavNet model pipeline provides submillimeter accuracy for localizing AC and PC anatomical landmarks in MRI volumes, enabling improved surgical efficiency and accuracy.

scholarly journals Joint-Label Fusion Brain Atlases for Dementia Research in Down Syndrome

2020 ◽  
Author(s):  
Nazek Queder ◽  
Michael J. Phelan ◽  
Lisa Taylor ◽  
Nicholas Tustison ◽  
Eric Doran ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Ground Truth ◽  
Disease Status ◽  
Brain Regions ◽  
Motion Artifacts ◽  
Brain Atlas ◽  
Positron Emission ◽  
Dementia Research ◽  
Amyloid Load

AbstractResearch suggests a link between Alzheimer’s Disease in Down Syndrome (DS) and the overexpression of amyloid plaques. Using Positron Emission Tomography (PET) we can assess the in-vivo regional amyloid load using several available ligands. To measure amyloid distributions in specific brain regions, a brain atlas is used. A popular method of creating a brain atlas is to segment a participant’s structural Magnetic Resonance Imaging (MRI) scan. Acquiring an MRI is often challenging in intellectually-imparied populations because of contraindications or data exclusion due to significant motion artifacts or incomplete sequences related to general discomfort. When an MRI cannot be acquired, it is typically replaced with a standardized brain atlas derived from neurotypical populations which may be inappropriate for use in DS. In this project, we create a series of disease and diagnosis-specific (cognitively stable, mild cognitive impairment (MCI-DS), and dementia) probabilistic group atlases of participants with DS and evaluate their accuracy of quantifying regional amyloid load compared to our ground truth individual MRI-based segmentations. Further, we compare the diagnostic-specific atlases with a probabilistic atlas constructed from similar-aged cognitively-stable neurotypical participants. We hypothesized that regional PET signals will best match the ground truth by using DS group atlases that aligns with a participant’s disorder and disease status (e.g. DS and MCI-DS). Our results vary by brain region but generally show that using a disorder-specific atlas in DS better matches the ground truth than using an atlas constructed from cognitively-stable neurotypical participants. We found no additional benefit of using a disease-state specific atlas. All atlases are made publicly available for the research community.AbbreviationsAD, DS, Aβ, DSG, CS-DS, CS-NT, LOOCV, ROI, MSE, MRI, PET, JLF, CS, MCI-DS, DEM.

scholarly journals Exploiting Gene Expression Profiles for the Automated Prediction of Connectivity between Brain Regions

2019 ◽  
Vol 20 (8) ◽  
pp. 2035 ◽  
Author(s):  
Ilaria Roberti ◽  
Marta Lovino ◽  
Santa Di Cataldo ◽  
Elisa Ficarra ◽  
Gianvito Urgese
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Expression Profiles ◽  
Brain Area ◽  
Gene Expression Profiles ◽  
Brain Structures ◽  
Brain Regions ◽  
Brain Atlas ◽  
Data Driven Approach ◽  
Prediction Problems

The brain comprises a complex system of neurons interconnected by an intricate network of anatomical links. While recent studies demonstrated the correlation between anatomical connectivity patterns and gene expression of neurons, using transcriptomic information to automatically predict such patterns is still an open challenge. In this work, we present a completely data-driven approach relying on machine learning (i.e., neural networks) to learn the anatomical connection directly from a training set of gene expression data. To do so, we combined gene expression and connectivity data from the Allen Mouse Brain Atlas to generate thousands of gene expression profile pairs from different brain regions. To each pair, we assigned a label describing the physical connection between the corresponding brain regions. Then, we exploited these data to train neural networks, designed to predict brain area connectivity. We assessed our solution on two prediction problems (with three and two connectivity class categories) involving cortical and cerebellum regions. As demonstrated by our results, we distinguish between connected and unconnected regions with 85% prediction accuracy and good balance of precision and recall. In our future work we may extend the analysis to more complex brain structures and consider RNA-Seq data as additional input to our model.

scholarly journals An adult brain atlas reveals broad neuroanatomical changes in independently evolved populations of Mexican cavefish

2019 ◽  
Author(s):  
Cody Loomis ◽  
Robert Peuß ◽  
James Jaggard ◽  
Yongfu Wang ◽  
Sean McKinney ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Brain Structures ◽  
Brain Regions ◽  
Adult Brain ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Behavioral Regulation ◽  
Surface Population ◽  
Volumetric Reconstruction ◽  
And Behavior

AbstractA shift in environmental conditions impacts the evolution of complex developmental and behavioral traits. The Mexican cavefish, Astyanax mexicanus, is a powerful model for examining the evolution of development, physiology, and behavior because multiple cavefish populations can be compared to an extant and ancestral-like surface population of the same species. Many behaviors have diverged in cave populations of A. mexicanus, and previous studies have shown that cavefish have a loss of sleep, reduced stress, an absence of social behaviors, and hyperphagia. Despite these findings, surprisingly little is known about the changes in neuroanatomy that underlie these behavioral phenotypes. Here, we use serial sectioning to generate a brain atlas of surface fish and three independent cavefish populations. Volumetric reconstruction of serial-sectioned brains confirms convergent evolution on reduced optic tectum volume in all cavefish populations tested. In addition, we quantified volumes of specific neuroanatomical loci within several brain regions, which have previously been implicated in behavioral regulation, including the hypothalamus, thalamus, and habenula. These analyses reveal an expansion of the hypothalamus across all three cavefish populations relative to surface fish, as well as subnuclei-specific differences within the thalamus and habenulae. Taken together, these analyses support the notion that changes in environmental conditions are accompanied by neuroanatomical changes in brain structures associated with behavior. This atlas provides a resource for comparative neuroanatomy of additional brain regions and the opportunity to associate brain anatomy with evolved changes in behavior.

scholarly journals Homologous laminar organization of the mouse and human subiculum

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael S. Bienkowski ◽  
Farshid Sepehrband ◽  
Nyoman D. Kurniawan ◽  
Jim Stanis ◽  
Laura Korobkova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hippocampal Formation ◽  
Expression Patterns ◽  
Pyramidal Cells ◽  
Brain Structures ◽  
Longitudinal Axis ◽  
Brain Atlas ◽  
Fiber Density ◽  
Laminar Organization ◽  
Hybridization Data

AbstractThe subiculum is the major output component of the hippocampal formation and one of the major brain structures most affected by Alzheimer’s disease. Our previous work revealed a hidden laminar architecture within the mouse subiculum. However, the rotation of the hippocampal longitudinal axis across species makes it unclear how the laminar organization is represented in human subiculum. Using in situ hybridization data from the Allen Human Brain Atlas, we demonstrate that the human subiculum also contains complementary laminar gene expression patterns similar to the mouse. In addition, we provide evidence that the molecular domain boundaries in human subiculum correspond to microstructural differences observed in high resolution MRI and fiber density imaging. Finally, we show both similarities and differences in the gene expression profile of subiculum pyramidal cells within homologous lamina. Overall, we present a new 3D model of the anatomical organization of human subiculum and its evolution from the mouse.

scholarly journals Dissociable Mechanisms of Verbal Working Memory Revealed through Multivariate Lesion Mapping

2019 ◽  
Vol 30 (4) ◽  
pp. 2542-2554 ◽  
Author(s):  
Maryam Ghaleh ◽  
Elizabeth H Lacey ◽  
Mackenzie E Fama ◽  
Zainab Anbari ◽  
Andrew T DeMarco ◽  
...  
Keyword(s):  
Working Memory ◽  
Spatial Working Memory ◽  
Superior Temporal Gyrus ◽  
Verbal Working Memory ◽  
Brain Structures ◽  
Brain Regions ◽  
Task Demands ◽  
Auditory Information ◽  
Verbal Information ◽  
Task Conditions

Abstract Two maintenance mechanisms with separate neural systems have been suggested for verbal working memory: articulatory-rehearsal and non-articulatory maintenance. Although lesion data would be key to understanding the essential neural substrates of these systems, there is little evidence from lesion studies that the two proposed mechanisms crucially rely on different neuroanatomical substrates. We examined 39 healthy adults and 71 individuals with chronic left-hemisphere stroke to determine if verbal working memory tasks with varying demands would rely on dissociable brain structures. Multivariate lesion–symptom mapping was used to identify the brain regions involved in each task, controlling for spatial working memory scores. Maintenance of verbal information relied on distinct brain regions depending on task demands: sensorimotor cortex under higher demands and superior temporal gyrus (STG) under lower demands. Inferior parietal cortex and posterior STG were involved under both low and high demands. These results suggest that maintenance of auditory information preferentially relies on auditory-phonological storage in the STG via a nonarticulatory maintenance when demands are low. Under higher demands, sensorimotor regions are crucial for the articulatory rehearsal process, which reduces the reliance on STG for maintenance. Lesions to either of these regions impair maintenance of verbal information preferentially under the appropriate task conditions.

Sleep Duration, Sleep Apnea, and Gray Matter Volume

2021 ◽  
pp. 089198872098891
Author(s):  
Regina Eun Young Kim ◽  
Robert Douglas Abbott ◽  
Soriul Kim ◽  
Robert Joseph Thomas ◽  
Chang-Ho Yun ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Sleep Duration ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Brain Structures ◽  
Brain Regions ◽  
Population Based ◽  
Older Individuals ◽  
Bootstrap Sampling ◽  
Matter Volume

This study aimed to evaluate the effect of sleep duration on brain structures in the presence versus absence of sleep apnea in middle-aged and older individuals. The study investigated a population-based sample of 2,560 individuals, aged 49-80 years. The presence of sleep apnea and self-reported sleep duration were examined in relation to gray matter volume (GMV) in total and lobar brain regions. We identified ranges of sleep duration associated with maximal GMV using quadratic regression and bootstrap sampling. A significant quadratic association between sleep duration and GMV was observed in total and lobar brain regions of men with sleep apnea. In the fully adjusted model, optimal sleep durations associated with peak GMV between brain regions ranged from 6.7 to 7.0 hours. Shorter and longer sleep durations were associated with lower GMV in total and 4 sub-regions of the brain in men with sleep apnea.

scholarly journals Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Norio Takata ◽  
Nobuhiko Sato ◽  
Yuji Komaki ◽  
Hideyuki Okano ◽  
Kenji F. Tanaka
Keyword(s):  
Mouse Brain ◽  
Brain Structures ◽  
Brain Atlas ◽  
Resting State Functional Connectivity ◽  
Brain Science ◽  
Step Procedure ◽  
Public Resources ◽  
Large Brain ◽  
Magnetic Resonance Imaging Mri ◽  
And Function

AbstractA brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.

Intranasal administration of mitochondria improves spatial memory in olfactory bulbectomized mice

2021 ◽  
pp. 153537022110568
Author(s):  
Natalia V Bobkova ◽  
Daria Y Zhdanova ◽  
Natalia V Belosludtseva ◽  
Nikita V Penkov ◽  
Galina D Mironova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Memory ◽  
Intranasal Administration ◽  
Brain Structures ◽  
Brain Regions ◽  
Dependent Manner ◽  
Behavioral Experiments ◽  
Hippocampal Cell Culture ◽  
The Brain

Here, we found that functionally active mitochondria isolated from the brain of NMRI donor mice and administrated intranasally to recipient mice penetrated the brain structures in a dose-dependent manner. The injected mitochondria labeled with the MitoTracker Red localized in different brain regions, including the neocortex and hippocampus, which are responsible for memory and affected by degeneration in patients with Alzheimer's disease. In behavioral experiments, intranasal microinjections of brain mitochondria of native NMRI mice improved spatial memory in the olfactory bulbectomized (OBX) mice with Alzheimer’s type degeneration. Control OBX mice demonstrated loss of spatial memory tested in the Morris water maze. Immunocytochemical analysis revealed that allogeneic mitochondria colocalized with the markers of astrocytes and neurons in hippocampal cell culture. The results suggest that a non-invasive route intranasal administration of mitochondria may be a promising approach to the treatment of neurodegenerative diseases characterized, like Alzheimer's disease, by mitochondrial dysfunction.

scholarly journals Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients

2018 ◽  
Vol 11 (8) ◽  
pp. 678-687
Author(s):  
Liang Ma ◽  
Edmund T Rolls ◽  
Xiuqin Liu ◽  
Yuting Liu ◽  
Zeyu Jiao ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Brain Structures ◽  
Brain Regions ◽  
First Episode ◽  
Grey Matter Volume ◽  
Scale Analysis ◽  
Risk Genes ◽  
Multi Scale ◽  
Scale Scores ◽  
Multi Scale Analysis

AbstractAnalysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.

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