scholarly journals BrainIAK: The Brain Imaging Analysis Kit

2020 ◽  
Author(s):  
Manoj Kumar ◽  
Michael Anderson ◽  
James Antony ◽  
Christopher Baldassano ◽  
Paula Pacheco Brooks ◽  
...  
Keyword(s):  
Brain Imaging ◽  
High Performance ◽  
Markov Models ◽  
Real Data ◽  
Matrix Analysis ◽  
Imaging Analysis ◽  
Neural Basis ◽  
Noise Characteristics ◽  
Fmri Analysis ◽  
The Brain

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally-optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEM), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve, and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.

scholarly journals BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis

2019 ◽  
Author(s):  
Manoj Kumar ◽  
Cameron Thomas Ellis ◽  
Qihong Lu ◽  
Hejia Zhang ◽  
Mihai Capota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Open Source ◽  
Programming Languages ◽  
High Performance ◽  
Large Scale ◽  
Markov Models ◽  
Matrix Analysis ◽  
Fmri Analysis ◽  
User Friendly

Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data, and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computationally efficient techniques with other Python packages (e.g., Nilearn, Scikit-learn) for file handling, visualization, and machine learning. To disseminate these powerful tools, we developed user-friendly tutorials (in Jupyter format; https://brainiak.org/tutorials/) for learning BrainIAK and advanced fMRI analysis in Python more generally. These materials cover techniques including: MVPA (pattern classification and representational similarity analysis); parallelized searchlight analysis; background connectivity; full correlation matrix analysis; inter-subject correlation; inter-subject functional connectivity; shared response modeling; event segmentation using hidden Markov models; and real-time fMRI. For long-running jobs or large memory needs we provide detailed guidance on high-performance computing clusters. These notebooks were successfully tested at multiple sites, including as problem sets for courses at Yale and Princeton universities and at various workshops and hackathons. These materials are freely shared, with the hope that they become part of a pool of open-source software and educational materials for large-scale, reproducible fMRI analysis and accelerated discovery.

scholarly journals Microwave Tomography for Brain Imaging: Feasibility Assessment for Stroke Detection

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Serguei Y. Semenov ◽  
Douglas R. Corfield
Keyword(s):  
Brain Imaging ◽  
High Performance ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Head Model ◽  
Microwave Tomography ◽  
Feasibility Assessment ◽  
Imaging Results ◽  
High Dielectric ◽  
The Brain

There is a need for a medical imaging technology, that supplements current clinical brain imaging techniques, for the near-patient and mobile assessment of cerebral vascular disease. Microwave tomography (MWT) is a novel imaging modality that has this potential. The aim of the study was to assess the feasibility, and potential performance characteristics, of MWT for brain imaging with particular focus on stroke detection. The study was conducted using MWT computer simulations and 2D head model with stroke. A nonlinear Newton reconstruction approach was used. The MWT imaging of deep brain tissues presents a significant challenge, as the brain is an object of interest that is located inside a high dielectric contrast shield, comprising the skull and CSF. However, high performance, nonlinear MWT inversion methods produced biologically meaningful images of the brain including images of stroke. It is suggested that multifrequency MWT has the potential to significantly improve imaging results.

Pharmacokinetics and Tissue Distribution Study of Ferruginol in Wistar Rat by High-performance Liquid Chromatography

2018 ◽  
Vol 15 (1) ◽  
pp. 67-73 ◽  
Author(s):  
Guiyun Cao ◽  
Suqiao Han ◽  
Keke Li ◽  
Li Shen ◽  
Xiaohong Wang ◽  
...  
Keyword(s):  
Tissue Distribution ◽  
Mobile Phase ◽  
High Performance ◽  
Wistar Rat ◽  
Freezing And Thawing ◽  
Promising Candidate ◽  
Distribution Study ◽  
Substance Loss ◽  
Tissue Distribution Study ◽  
The Brain

Background: Ferruginol (FRGN) exhibits a broad range of pharmacological properties which make it a promising candidate for chemoprevention. However, little is known about its absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Methods: A rapid, sensitive and specific HPLC-DAD method was established to quantify FRGN in the plasma and tissues of Wistar rats. After extraction of FRGN with ethyl acetate (EtOAc), chromatographic separation was performed on a YMC ODS C18 column (250 × 4.6 mm I.D., 5 µm) with a mobile phase consisting of methanol-water (92:8, v/v) at a flow rate of 0.9 mL/min. Detection was conducted with a wavelength of 273 nm at 25 °C. Results: The calibration curves for FRGN were linear in the concentration range of 0.5-20 µg/mL for plasma, 0.5-10 µg/mL for heart, liver, spleen, lung, kidney, stomach, intestine, brain and muscle. After three cycles of freezing and thawing, the concentration variations were within ± 7% of nominal concentrations, indicating no significant substance loss during repeated thawing and freezing. The assay was applied to pharmacokinetic and tissue distribution study in rats. Results suggested that lung, heart, liver, spleen and kidney were the major distribution tissues of FRGN in rats, and FRGN could permeate the blood-brain barrier to distribute in the brain of rats. Conclusion: The information provided by this research is very useful for gaining knowledge of the pharmacokinetic process and tissue distribution of FRGN.

scholarly journals Alternative Method to the Replication of Wind Effects into the Buildings Thermal Simulation

Buildings ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 237
Author(s):  
Aiman Albatayneh ◽  
Dariusz Alterman ◽  
Adrian Page ◽  
Behdad Moghtaderi
Keyword(s):  
Air Temperature ◽  
High Performance ◽  
Real Data ◽  
Wind Effect ◽  
Wind Effects ◽  
Energy Assessment ◽  
Wind Simulation ◽  
Brick Veneer ◽  
Equal Capacity ◽  
Simulation Time

To design energy-efficient buildings, energy assessment programs need to be developed for determining the inside air temperature, so that thermal comfort of the occupant can be sustained. The internal temperatures could be calculated through computational fluid dynamics (CFD) analysis; however, miniscule time steps (seconds and milliseconds) are used by a long-term simulation (i.e., weeks, months) that require excessive time for computing wind effects results even for high-performance personal computers. This paper examines a new method, wherein the wind effect surrounding the buildings is integrated with the external air temperature to facilitate wind simulation in building analysis over long periods. This was done with the help of an equivalent temperature (known as Tnatural), where the convection heat loss is produced in an equal capacity by this air temperature and by the built-in wind effects. Subsequently, this new external air temperature Tnatural can be used to calculate the internal air temperature. Upon inclusion of wind effects, above 90% of the results were found to be within 0–3 °C of the perceived temperatures compared to the real data (99% for insulated cavity brick (InsCB), 91% for cavity brick (CB), 93% for insulated reverse brick veneer (InsRBV) and 94% for insulated brick veneer (InsBV) modules). However, a decline of 83–88% was observed in the results after ignoring the wind effects. Hence, the presence of wind effects holds greater importance in correct simulation of the thermal performance of the modules. Moreover, the simulation time will expectedly reduce to below 1% of the original simulation time.

scholarly journals A novel PET tracer 18F-deoxy-thiamine: synthesis, metabolic kinetics, and evaluation on cerebral thiamine metabolism status

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Changpeng Wang ◽  
Siwei Zhang ◽  
Yuefei Zou ◽  
Hongzhao Ma ◽  
Donglang Jiang ◽  
...  
Keyword(s):  
High Performance ◽  
Specific Activity ◽  
High Accumulation ◽  
Metabolic Kinetics ◽  
Thiamine Metabolism ◽  
Pet Tracer ◽  
The Status ◽  
The Brain

Abstract Background Some neuropsychological diseases are associated with abnormal thiamine metabolism, including Korsakoff–Wernicke syndrome and Alzheimer’s disease. However, in vivo detection of the status of brain thiamine metabolism is still unavailable and needs to be developed. Methods A novel PET tracer of 18F-deoxy-thiamine was synthesized using an automated module via a two-step route. The main quality control parameters, such as specific activity and radiochemical purity, were evaluated by high-performance liquid chromatography (HPLC). Radiochemical concentration was determined by radioactivity calibrator. Metabolic kinetics and the level of 18F-deoxy-thiamine in brains of mice and marmosets were studied by micro-positron emission tomography/computed tomography (PET/CT). In vivo stability, renal excretion rate, and biodistribution of 18F-deoxy-thiamine in the mice were assayed using HPLC and γ-counter, respectively. Also, the correlation between the retention of cerebral 18F-deoxy-thiamine in 60 min after injection as represented by the area under the curve (AUC) and blood thiamine levels was investigated. Results The 18F-deoxy-thiamine was stable both in vitro and in vivo. The uptake and clearance of 18F-deoxy-thiamine were quick in the mice. It reached the max standard uptake value (SUVmax) of 4.61 ± 0.53 in the liver within 1 min, 18.67 ± 7.04 in the kidney within half a minute. The SUV dropped to 0.72 ± 0.05 and 0.77 ± 0.35 after 60 min of injection in the liver and kidney, respectively. After injection, kidney, liver, and pancreas exhibited high accumulation level of 18F-deoxy-thiamine, while brain, muscle, fat, and gonad showed low accumulation concentration, consistent with previous reports on thiamine distribution in mice. Within 90 min after injection, the level of 18F-deoxy-thiamine in the brain of C57BL/6 mice with thiamine deficiency (TD) was 1.9 times higher than that in control mice, and was 3.1 times higher in ICR mice with TD than that in control mice. The AUC of the tracer in the brain of marmosets within 60 min was 29.33 ± 5.15 and negatively correlated with blood thiamine diphosphate levels (r = − 0.985, p = 0.015). Conclusion The 18F-deoxy-thiamine meets the requirements for ideal PET tracer for in vivo detecting the status of cerebral thiamine metabolism.

scholarly journals Transcriptomic, peptidomic and mass spectrometry imaging analysis of the brain in the ant Cataglyphis nodus

2021 ◽  
Author(s):  
Jens Habenstein ◽  
Franziska Schmitt ◽  
Sander Liessem ◽  
Alice Ly ◽  
Dennis Trede ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Imaging Analysis ◽  
The Brain

scholarly journals Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽  
2003 ◽  
Vol 165 (4) ◽  
pp. 2269-2282
Author(s):  
D Mester ◽  
Y Ronin ◽  
D Minkov ◽  
E Nevo ◽  
A Korol
Keyword(s):  
Discrete Optimization ◽  
High Performance ◽  
Large Scale ◽  
Simulated Data ◽  
Real Data ◽  
Genetic Maps ◽  
Chromosome 1 ◽  
Evolutionary Strategy ◽  
Group A ◽  
The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

scholarly journals Delivery of Thyronamines (TAMs) to the Brain: A Preliminary Study

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1616
Author(s):  
Nicoletta di Leo ◽  
Stefania Moscato ◽  
Marco Borso' ◽  
Simona Sestito ◽  
Beatrice Polini ◽  
...  
Keyword(s):  
High Performance ◽  
In Vitro Model ◽  
New Drugs ◽  
Therapeutic Approaches ◽  
Neuroprotective Effects ◽  
Pharmacological Response ◽  
Preliminary Study ◽  
Vitro Model ◽  
The Brain

Recent reports highlighted the significant neuroprotective effects of thyronamines (TAMs), a class of endogenous thyroid hormone derivatives. In particular, 3-iodothyronamine (T1AM) has been shown to play a pleiotropic role in neurodegeneration by modulating energy metabolism and neurological functions in mice. However, the pharmacological response to T1AM might be influenced by tissue metabolism, which is known to convert T1AM into its catabolite 3-iodothyroacetic acid (TA1). Currently, several research groups are investigating the pharmacological effects of T1AM systemic administration in the search of novel therapeutic approaches for the treatment of interlinked pathologies, such as metabolic and neurodegenerative diseases (NDDs). A critical aspect in the development of new drugs for NDDs is to know their distribution in the brain, which is fundamentally related to their ability to cross the blood–brain barrier (BBB). To this end, in the present study we used the immortalized mouse brain endothelial cell line bEnd.3 to develop an in vitro model of BBB and evaluate T1AM and TA1 permeability. Both drugs, administered at 1 µM dose, were assayed by high-performance liquid chromatography coupled to mass spectrometry. Our results indicate that T1AM is able to efficiently cross the BBB, whereas TA1 is almost completely devoid of this property.

scholarly journals Markov-Modulated Continuous-Time Markov Chains to Identify Site- and Branch-Specific Evolutionary Variation in BEAST

2020 ◽  
Vol 70 (1) ◽  
pp. 181-189
Author(s):  
Guy Baele ◽  
Mandev S Gill ◽  
Paul Bastide ◽  
Philippe Lemey ◽  
Marc A Suchard
Keyword(s):  
High Performance ◽  
Markov Models ◽  
Marginal Likelihood ◽  
Likelihood Estimation ◽  
Phylogenetic Inference ◽  
Time Variability ◽  
Substitution Process ◽  
Wide Range ◽  
Markov Modulated ◽  
Over Time

Abstract Markov models of character substitution on phylogenies form the foundation of phylogenetic inference frameworks. Early models made the simplifying assumption that the substitution process is homogeneous over time and across sites in the molecular sequence alignment. While standard practice adopts extensions that accommodate heterogeneity of substitution rates across sites, heterogeneity in the process over time in a site-specific manner remains frequently overlooked. This is problematic, as evolutionary processes that act at the molecular level are highly variable, subjecting different sites to different selective constraints over time, impacting their substitution behavior. We propose incorporating time variability through Markov-modulated models (MMMs), which extend covarion-like models and allow the substitution process (including relative character exchange rates as well as the overall substitution rate) at individual sites to vary across lineages. We implement a general MMM framework in BEAST, a popular Bayesian phylogenetic inference software package, allowing researchers to compose a wide range of MMMs through flexible XML specification. Using examples from bacterial, viral, and plastid genome evolution, we show that MMMs impact phylogenetic tree estimation and can substantially improve model fit compared to standard substitution models. Through simulations, we show that marginal likelihood estimation accurately identifies the generative model and does not systematically prefer the more parameter-rich MMMs. To mitigate the increased computational demands associated with MMMs, our implementation exploits recent developments in BEAGLE, a high-performance computational library for phylogenetic inference. [Bayesian inference; BEAGLE; BEAST; covarion, heterotachy; Markov-modulated models; phylogenetics.]

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