22 papers · ranked by Valyu relevance
Ruslan Masharipov, Irina Knyazeva, Yaroslav Nikolaev, Alexander Korotkov + 3 more
Classical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to ‘null effect’ assessment focusing on the Bayesian…
Daniel Spencer, David Bolin, Amanda F. Mejia
Task functional magnetic resonance imaging (fMRI) is a type of neuroimaging data used to identify areas of the brain that activate during specific tasks or stimuli. These data are conventionally modeled using a massive univariate approach across all data locations, which ignores spatial dependence at the cost of model…
Daniel I. R. Spencer, David Bolin, Mary Beth Nebel, Amanda F. Mejia
Analysis of brain imaging scans is critical to understanding the way the human brain functions, which can be leveraged to treat injuries and conditions that affect the quality of life for a significant portion of the human population. In particular, functional magnetic resonance imaging (fMRI) scans give detailed data…
Daniel Spencer, Yu Ryan Yue, David Bolin, Sarah Ryan + 1 more
'Amanda F. Mejia'] The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not explicitly leverage the…
Yuan Zhong, Gang Chen, Paul A. Taylor, Jian Kang
Bayesian spatial modeling provides a flexible framework for whole-brain fMRI analysis by explicitly incorporating spatial dependencies, overcoming the limitations of traditional massive univariate approaches that lead to information waste. In this work, we introduce SIMBA, a Scalable Image Modeling using a Bayesian…
Zhengxin Wang, Daniel B. Rowe, Xinyi Li, D. Andrew Brown
Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely…
Andrew S. Whiteman, Andreas J. Bartsch, Jian Kang, Timothy D. Johnson
'Timothy D. Johnson'] Abstract: Neuroradiologists and neurosurgeons increasingly opt to use functional magnetic resonance imaging (fMRI) to map functionally relevant brain regions for noninvasive presurgical planning and intraoperative neuronavigation. This application requires a high degree of spatial accuracy, but…
Jungin Choi, Abhirup Datta, Martin A. Lindquist
Task-based fMRI is commonly analyzed using voxel-wise general linear models, a non-spatial scalable approach that can yield fragmented activation maps. Spatial alternatives such as kernel smoothing and Bayesian models address this but either blur activation boundaries or are computationally prohibitive at modern…
Jungin Choi, Abhirup Datta, Martin A. Lindquist
Task-based fMRI is commonly analyzed using voxel-wise general linear models, a non-spatial scalable approach that can yield fragmented activation maps. Spatial alternatives such as kernel smoothing and Bayesian models address this but either blur activation boundaries or are computationally prohibitive at modern…
Michael C. Freund, Ruiqi Chen, Gang Chen, Todd S. Braver
Understanding individual differences in cognitive control is a central goal in psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures in cognitive neuroscience such as response times or task-based fMRI activity. While prior…
Niharika Shimona D’Souza
The problem of jointly analysing functional connectomics and behavioral data is extremely challenging owing to the complex interactions between the two domains. In addition, clinical rs-fMRI studies often have to contend with limited samples, especially in the case of rare disorders. This data-starved regimen can…
Tommaso Costa, Donato Liloia, Franco Cauda, Peter Fox + 3 more
Activation likelihood estimation (ALE) is among the most used algorithms to perform neuroimaging meta-analysis. Since its first implementation, several thresholding procedures had been proposed, all referred to the frequentist framework, returning a rejection criterion for the null hypothesis according to the critical…
Maëliss Jallais, Pedro Luiz Coelho Rodrigues, Alexandre Gramfort, Demián Wassermann
'Demián Wassermann'] Effective characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in diffusion MRI (dMRI). Solving the problem of relating the dMRI signal with cytoarchitectural characteristics calls for the definition of a…
Huaqing Jin, Parul Verma, Fei Jiang, Srikantan Nagarajan + 1 more
The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band…
Yuliang Xu, Timothy D. Johnson, Thomas E. Nichols, Jian Kang
Neuroimaging Data Analysis Authors: ['Yuliang Xu' 'Timothy D. Johnson' 'Thomas E. Nichols' 'Jian Kang'] Bayesian Image-on-Scalar Regression (ISR) offers significant advantages for neuroimaging data analysis, including flexibility and the ability to quantify uncertainty. However, its application to large-scale imaging…
Brandon S Coventry, Edward L Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Brandon S Coventry, Edward L Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Brandon S. Coventry, Edward L. Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Authors not listed
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and widespread neurovascular disturbances. Traditional fMRI provides valuable spatial maps of blood-oxygen-level-dependent (BOLD) changes but often lacks the capacity to quantify the interplay of cerebral blood flow…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Jonas Verhellen
In recent years, there have been considerable academic and industrial research efforts to develop novel generative models for high-performing, small molecules. Traditional, rules-based algorithms such as genetic algorithms [Jensen, Chem. Sci., 2019, 12, 3567-3572] have, however, been shown to rival deep learning…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…