scholarly journals Computationally Efficient Hybrid Interpolation and Baseline Restoration of the Brain-PET Pulses

2020 ◽  
Author(s):  
Saeed Mian Qaisar
Keyword(s):  
Computationally Efficient ◽  
Brain Pet ◽  
The Brain

scholarly journals Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ronald de Vlaming ◽  
Eric A. W. Slob ◽  
Philip R. Jansen ◽  
Alain Dagher ◽  
Philipp D. Koellinger ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Gray Matter Volume ◽  
Genetic Correlations ◽  
Brain Morphology ◽  
Computationally Efficient ◽  
Individual Level ◽  
Level Data ◽  
The Uk ◽  
And Behavior ◽  
The Brain

AbstractHuman variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.

Energy spectra analysis of the four-layer DOI detector for the brain PET scanner: jPET-D4

2006 ◽  
Vol 557 (2) ◽  
pp. 664-669 ◽  
Author(s):  
Eiji Yoshida ◽  
Keishi Kitamura ◽  
Tomoaki Tsuda ◽  
Kengo Shibuya ◽  
Taiga Yamaya ◽  
...  
Keyword(s):  
Energy Spectra ◽  
Spectra Analysis ◽  
Brain Pet ◽  
The Brain

scholarly journals Constructing Compact Signatures for Individual Fingerprinting of Brain Connectomes

2021 ◽  
Vol 15 ◽  
Author(s):  
Vikram Ravindra ◽  
Petros Drineas ◽  
Ananth Grama
Keyword(s):  
State Of The Art ◽  
Statistical Tests ◽  
Functional Characterization ◽  
Sampling Technique ◽  
Computational Techniques ◽  
Test Accuracy ◽  
Computationally Efficient ◽  
Matrix Sampling ◽  
The Brain ◽  
And Task

Recent neuroimaging studies have shown that functional connectomes are unique to individuals, i.e., two distinct fMRIs taken over different sessions of the same subject are more similar in terms of their connectomes than those from two different subjects. In this study, we present new results that identify specific parts of resting state and task-specific connectomes that are responsible for the unique signatures. We show that a very small part of the connectome can be used to derive features for discriminating between individuals. A network of these features is shown to achieve excellent training and test accuracy in matching imaging datasets. We show that these features are statistically significant, robust to perturbations, invariant across populations, and are localized to a small number of structural regions of the brain. Furthermore, we show that for task-specific connectomes, the regions identified by our method are consistent with their known functional characterization. We present a new matrix sampling technique to derive computationally efficient and accurate methods for identifying the discriminating sub-connectome and support all of our claims using state-of-the-art statistical tests and computational techniques.

scholarly journals Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging

2020 ◽  
Author(s):  
Joao M. Sousa ◽  
Lieuwe Appel ◽  
Inés Merida ◽  
Rolf A. Heckemann ◽  
Nicolas Costes ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Reference Method ◽  
Binding Potential ◽  
Time Activity ◽  
Brain Pet ◽  
Significant Bias ◽  
Accuracy And Precision ◽  
Echo Time ◽  
The Brain ◽  
Over Time

Abstract Background: A valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions. In this study we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain.Methods: Nine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain.Results: For BPND, ZTE-MRAC showed the highest accuracy (bias < 2%). Atlas-MRAC exhibited a significant bias in caudate nucleus (-12%) while MaxProb-MRAC revealed a substantial, non-significant bias in putamen (9%). R1 estimates had a marginal bias for all MRAC methods (-1.0-3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~ 10%), although constant over time and with the smallest inter-subject variability. Atlas-MRAC had highest variation in bias over time (+ 10 to -10%), followed by MaxProb-MRAC (+ 5 to -5%), but MaxProb showed the lowest mean bias. For cerebellum, MaxProb-MRAC showed highest variability while bias was constant over time for Atlas- and ZTE-MRAC.Conclusions: Both Maxprob and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.

scholarly journals 18F-FDG brain PET metabolism in post-SARS-CoV-2 infection: substrate for persistent/delayed disorders?

2020 ◽  
Author(s):  
Eric Guedj ◽  
Matthieu Million ◽  
Pierre Dudouet ◽  
Hervé Tissot-Dupont ◽  
Fabienne Bregeon ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Propagation Mechanism ◽  
Ethmoid Bone ◽  
Limbic Structures ◽  
Delayed Onset ◽  
Brain Pet ◽  
Painful Syndrome ◽  
18F Fdg ◽  
Exact Relationship ◽  
The Brain

Abstract Purpose: Several brain complications of SARS-CoV-2 infection have been reported. It has been moreover speculated that this neurotropism could potentially cause a delayed outbreak of neuropsychiatric and neurodegenerative diseases of neuroinflammatory origin. A propagation mechanism has been proposed across the cribriform plate of the ethmoid bone, from the nose to the olfactory epithelium, and possibly afterwards to other limbic structures, and deeper parts of the brain including the brainstem. Methods: Review of clinical examination, and whole-brain voxel-based analysis of 18F-FDG PET metabolism in comparison to healthy subjects (p-voxel<0.001, p-cluster<0.05), of two patients with confirmed diagnosis of SARS-CoV-2 pneumonia explored at the post-viral stage of the disease.Results: Hypometabolism of the olfactory/rectus gyrus was found on the two patients, especially one with 4 weeks prolonged anosmia. Additional hypometabolisms were found within bilateral amygdala, hippocampus, cingulate cortex, thalamus, pons and medulla brainstem in the other patient who complained of delayed onset of an atypical painful syndrome.Conclusion: These preliminary findings reinforce the hypotheses of SARS-CoV-2 neurotropism through the olfactory bulb, and the possible extension of this impairment to other limbic structures and to the brainstem. 18F-FDG PET hypometabolism could constitute a cerebral quantitative biomarker of this involvement. Post-viral cohort studies are required to specify the exact relationship between limbic/brainstem hypometabolisms and the possible persistent disorders, especially involving cognitive or emotion disturbances, residual respiratory symptoms or painful complaints.

scholarly journals Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging 

2020 ◽  
Author(s):  
Joao M. Sousa ◽  
Lieuwe Appel ◽  
Inés Merida ◽  
Rolf A. Heckemann ◽  
Nicolas Costes ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Reference Method ◽  
Binding Potential ◽  
Time Activity ◽  
Brain Pet ◽  
Significant Bias ◽  
Accuracy And Precision ◽  
Echo Time ◽  
The Brain ◽  
Over Time

Abstract Background: A valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions. In this study we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain. Methods: Nine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain. Results: For BPND, ZTE-MRAC showed the highest accuracy (bias < 2%) in striatal regions[JMSS1] . Atlas-MRAC exhibited a significant bias in caudate nucleus (-12%) while MaxProb-MRAC revealed a substantial, non-significant bias in putamen (9%). R1 estimates had a marginal bias for all MRAC methods (-1.0-3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~10%), although constant over time and with the smallest inter-subject variability. Atlas-MRAC had highest variation in bias over time (+10 to -10%), followed by MaxProb-MRAC (+5 to -5%), but MaxProb showed the lowest mean bias. For cerebellum, MaxProb-MRAC showed highest variability while bias was constant over time for Atlas- and ZTE-MRAC.Conclusions: Both Maxprob and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.

scholarly journals Remembrance of Inferences Past

2017 ◽  
Author(s):  
Ishita Dasgupta ◽  
Eric Schulz ◽  
Noah D. Goodman ◽  
Samuel J. Gershman
Keyword(s):  
Probabilistic Reasoning ◽  
Probability Distributions ◽  
Computational Cost ◽  
Limited Resources ◽  
Cognitive Resources ◽  
Natural Scenes ◽  
Computationally Efficient ◽  
Bayesian Models Of Cognition ◽  
The Brain ◽  
Entire Distribution

AbstractBayesian models of cognition assume that people compute probability distributions over hypotheses. However, the required computations are frequently intractable or prohibitively expensive. Since people often encounter many closely related distributions, selective reuse of computations (amortized inference) is a computationally efficient use of the brain’s limited resources. We present three experiments that provide evidence for amortization in human probabilistic reasoning. When sequentially answering two related queries about natural scenes, participants’ responses to the second query systematically depend on the structure of the first query. This influence is sensitive to the content of the queries, only appearing when the queries are related. Using a cognitive load manipulation, we find evidence that people amortize summary statistics of previous inferences, rather than storing the entire distribution. These findings support the view that the brain trades off accuracy and computational cost, to make efficient use of its limited cognitive resources to approximate probabilistic inference.

scholarly journals Amortized Hypothesis Generation

2017 ◽  
Author(s):  
Ishita Dasgupta ◽  
Eric Schulz ◽  
Noah D. Goodman ◽  
Samuel J. Gershman
Keyword(s):  
Cognitive Load ◽  
Probability Distributions ◽  
Hypothesis Generation ◽  
Natural Scenes ◽  
Summary Statistics ◽  
Summary Statistic ◽  
Computationally Efficient ◽  
Present Evidence ◽  
Bayesian Models Of Cognition ◽  
The Brain

AbstractBayesian models of cognition posit that people compute probability distributions over hypotheses, possibly by constructing a sample-based approximation. Since people encounter many closely related distributions, a computationally efficient strategy is to selectively reuse computations – either the samples themselves or some summary statistic. We refer to these reuse strategies as amortized inference. In two experiments, we present evidence consistent with amortization. When sequentially answering two related queries about natural scenes, we show that answers to the second query vary systematically depending on the structure of the first query. Using a cognitive load manipulation, we find evidence that people cache summary statistics rather than raw sample sets. These results enrich our notions of how the brain approximates probabilistic inference.

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