Automatic Landmark-guided Bijective Brain Image Registration by Composing Region-based Locally Diffeomorphic Warpings

Mapping Intimacies ◽

10.1101/2020.04.16.045609 ◽

2020 ◽

Author(s):

Hengda He ◽

Qolamreza R. Razlighi

Keyword(s):

Human Brain ◽

Cognitive Aging ◽

Brain Morphology ◽

Spatial Normalization ◽

Weighted Interpolation ◽

Brain Images ◽

Whole Brain ◽

Normalization Methods ◽

Aging Populations ◽

Inverse Distance Weighted Interpolation

AbstractAs the size of the neuroimaging cohorts being increased to address key questions in the field of cognitive neuroscience, cognitive aging, and neurodegenerative diseases, the accuracy of the spatial normalization as an essential pre-processing step becomes extremely important in the neuroimaging processing pipeline. Existing spatial normalization methods have poor accuracy particularly when dealing with the highly convoluted human cerebral cortex and when brain morphology is severely altered (e.g. clinical and aging populations). To address this shortcoming, we propose to implement and evaluate a novel landmark-guided region-based spatial normalization technique that takes advantage of the existing surface-based human brain parcellation to automatically identify and match regional landmarks. To simplify the non-linear whole brain registration, the identified landmarks of each region and their counterparts are registered independently with large diffeomorphic (topology preserving) deformation via geodesic shooting. The regional diffeomorphic warping fields were combined by an inverse distance weighted interpolation technique to have a smooth global warping field for the whole brain. To ensure that the final warping field is diffeomorphic, we used simultaneously forward and reverse maps with certain symmetric constraints to yield bijectivity. We have evaluated our proposed method using both simulated and real (structural and functional) human brain images. Our evaluation shows that our method can enhance structural correspondence up to around 86%, a 67% improvement compared to the existing state-of-the-art method. Such improvement also increases the sensitivity and specificity of the functional imaging studies by about 17%, reducing the required number of subjects and subsequent costs. We conclude that our proposed method can effectively substitute existing substandard spatial normalization methods to deal with the demand of large cohorts and the need for investigating clinical and aging populations.

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Evaluating Spatial Normalization Methods for the Human Brain

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference ◽

10.1109/iembs.2005.1615685 ◽

2005 ◽

Cited By ~ 1

Author(s):

V.S. Smith ◽

L.G. Shapiro ◽

D. Hanlon ◽

R.F. Martin ◽

J.F. Brinkley ◽

...

Keyword(s):

Human Brain ◽

Spatial Normalization ◽

Normalization Methods

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Novel Multimodal Atlas Template for Spatial Normalization of Whole-Brain Images of Newborns

IRBM ◽

10.1016/j.irbm.2016.06.001 ◽

2016 ◽

Vol 37 (5-6) ◽

pp. 254-263 ◽

Cited By ~ 1

Author(s):

O.M. Dastjerdi ◽

H.A. Moghaddam ◽

F. Wallois ◽

R. Grebe ◽

S. Ghadimi

Keyword(s):

Spatial Normalization ◽

Brain Images ◽

Whole Brain

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The Right Hemisphere Is Responsible for the Greatest Differences in Human Brain Response to High-Arousing Emotional versus Neutral Stimuli: A MEG Study

Brain Sciences ◽

10.3390/brainsci11080960 ◽

2021 ◽

Vol 11 (8) ◽

pp. 960

Author(s):

Mina Kheirkhah ◽

Philipp Baumbach ◽

Lutz Leistritz ◽

Otto W. Witte ◽

Martin Walter ◽

...

Keyword(s):

Human Brain ◽

Right Hemisphere ◽

Emotional Stimuli ◽

Brain Response ◽

Whole Brain ◽

Brain Responses ◽

Cortical Regions ◽

The Right ◽

Healthy Participants

Studies investigating human brain response to emotional stimuli—particularly high-arousing versus neutral stimuli—have obtained inconsistent results. The present study was the first to combine magnetoencephalography (MEG) with the bootstrapping method to examine the whole brain and identify the cortical regions involved in this differential response. Seventeen healthy participants (11 females, aged 19 to 33 years; mean age, 26.9 years) were presented with high-arousing emotional (pleasant and unpleasant) and neutral pictures, and their brain responses were measured using MEG. When random resampling bootstrapping was performed for each participant, the greatest differences between high-arousing emotional and neutral stimuli during M300 (270–320 ms) were found to occur in the right temporo-parietal region. This finding was observed in response to both pleasant and unpleasant stimuli. The results, which may be more robust than previous studies because of bootstrapping and examination of the whole brain, reinforce the essential role of the right hemisphere in emotion processing.

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Capturing the non-stationarity of whole-brain dynamics underlying human brain states

NeuroImage ◽

10.1016/j.neuroimage.2021.118551 ◽

2021 ◽

pp. 118551

Author(s):

J.A. Galadí ◽

S. Silva Pereira ◽

Y. Sanz Perl ◽

M.L. Kringelbach ◽

I. Gayte ◽

...

Keyword(s):

Human Brain ◽

Brain Dynamics ◽

Whole Brain ◽

Brain States

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Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2021-0070 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Ahana Priyanka ◽

Kavitha Ganesan

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Network Models ◽

Curve Evolution ◽

Neural Network Models ◽

Brain Images ◽

Whole Brain ◽

Shape Constraint ◽

Mr Brain Images ◽

Trained Neural Network

Abstract The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.

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Scatterometry Measurements With Scattered Light Imaging Enable New Insights Into the Nerve Fiber Architecture of the Brain

Frontiers in Neuroanatomy ◽

10.3389/fnana.2021.767223 ◽

2021 ◽

Vol 15 ◽

Author(s):

Miriam Menzel ◽

Marouan Ritzkowski ◽

Jan A. Reuter ◽

David Gräßel ◽

Katrin Amunts ◽

...

Keyword(s):

Human Brain ◽

Nerve Fiber ◽

Brain Tissue ◽

Scattered Light ◽

Polar Angle ◽

Nerve Fibers ◽

Fiber Architecture ◽

Whole Brain ◽

Tissue Samples ◽

The Brain

The correct reconstruction of individual (crossing) nerve fibers is a prerequisite when constructing a detailed network model of the brain. The recently developed technique Scattered Light Imaging (SLI) allows the reconstruction of crossing nerve fiber pathways in whole brain tissue samples with micrometer resolution: the individual fiber orientations are determined by illuminating unstained histological brain sections from different directions, measuring the transmitted scattered light under normal incidence, and studying the light intensity profiles of each pixel in the resulting image series. So far, SLI measurements were performed with a fixed polar angle of illumination and a small number of illumination directions, providing only an estimate of the nerve fiber directions and limited information about the underlying tissue structure. Here, we use a display with individually controllable light-emitting diodes to measure the full distribution of scattered light behind the sample (scattering pattern) for each image pixel at once, enabling scatterometry measurements of whole brain tissue samples. We compare our results to coherent Fourier scatterometry (raster-scanning the sample with a non-focused laser beam) and previous SLI measurements with fixed polar angle of illumination, using sections from a vervet monkey brain and human optic tracts. Finally, we present SLI scatterometry measurements of a human brain section with 3 μm in-plane resolution, demonstrating that the technique is a powerful approach to gain new insights into the nerve fiber architecture of the human brain.

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A series of five population-specific Indian brain templates and atlases spanning ages 6 to 60 years

10.1101/2020.05.08.077172 ◽

2020 ◽

Author(s):

Bharath Holla ◽

Paul A. Taylor ◽

Daniel R. Glen ◽

John A. Lee ◽

Nilakshi Vaidya ◽

...

Keyword(s):

Brain Morphology ◽

Good Representation ◽

List Type ◽

Spatial Normalization ◽

Maximum Probability ◽

Total N ◽

Brain Scans ◽

Native Space ◽

Probability Map ◽

Population Average

AbstractAnatomical brain templates are commonly used as references in neurological MRI studies, for bringing data into a common space for group-level statistics and coordinate reporting. Given the inherent variability in brain morphology across age and geography, it is important to have templates that are as representative as possible for both age and population. A representative-template increases the accuracy of alignment, decreases distortions as well as potential biases in final coordinate reports. In this study, we developed and validated a new set of T1w Indian brain templates (IBT) from a large number of brain scans (total n=466) acquired across different locations and multiple 3T MRI scanners in India. A new tool in AFNI, make_template_dask.py, was created to efficiently make five age-specific IBTs (ages 6-60 years) as well as maximum probability map (MPM) atlases for each template; for each age-group’s template-atlas pair, there is both a “population-average” and a “typical” version. Validation experiments on an independent Indian structural and functional-MRI dataset show the appropriateness of IBTs for spatial normalization of Indian brains. The results indicate significant structural differences when comparing the IBTs and MNI template, with these differences being maximal along the Anterior-Posterior and Inferior-Superior axes, but minimal Left-Right. For each age-group, the MPM brain atlases provide reasonably good representation of the native-space volumes in the IBT space, except in a few regions with high inter-subject variability. These findings provide evidence to support the use of age and population-specific templates in human brain mapping studies. This dataset is made publicly available (https://hollabharath.github.io/IndiaBrainTemplates).HighlightsA new set of age-specific T1w Indian brain templates for ages 6-60 yr are developed and validated.A new AFNI tool, make_template_dask.py, for the creation of group-based templates.Maximum probability map atlases are also provided for each template.Results indicate the appropriateness of Indian templates for spatial normalization of Indian brains

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Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

10.1101/059618 ◽

2016 ◽

Cited By ~ 4

Author(s):

Timothy N. Rubin ◽

Oluwasanmi Koyejo ◽

Krzysztof J. Gorgolewski ◽

Michael N. Jones ◽

Russell A. Poldrack ◽

...

Keyword(s):

Large Scale ◽

Latent Dirichlet Allocation ◽

Topic Model ◽

Brain Activity ◽

Human Cognition ◽

Brain Images ◽

Whole Brain ◽

Context Sensitive ◽

Cognitive States ◽

Small Set

AbstractA central goal of cognitive neuroscience is to decode human brain activity--i.e., to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive--i.e., capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a Bayesian decoding framework based on a novel topic model---Generalized Correspondence Latent Dirichlet Allocation---that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text--enabling researchers, for the first time, to generative quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

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Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2020.599112 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yifei Zhang ◽

Xiaodan Chen ◽

Xinyuan Liang ◽

Zhijiang Wang ◽

Teng Xie ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Human Brain ◽

Cognitive Performance ◽

Power Law ◽

Resting State ◽

Brain Networks ◽

Functional Networks ◽

Whole Brain ◽

Connectivity Degree

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

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