11 papers · ranked by Valyu relevance
Jiayuan Ding, Zhongyu Xing, Yixin Wang, Renming Liu + 9 more
Preprocessing is a critical step in single-cell data analysis, yet current practices remain largely a black-box, trial-and-error process driven by user intuition, legacy defaults, and ad hoc heuristics. The optimal combination of steps such as normalization, gene selection, and dimensionality reduction varies across…
Cristina Gil Ávila, Felix S. Bott, Laura Tiemann, Vanessa D. Hohn + 5 more
Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing and analysis of EEG data is time-consuming and mostly…
Jianxun Ren, Ning An, Cong Lin, Youjia Zhang + 13 more
Neuroimaging has entered the era of big data. However, the advancement of preprocessing pipelines falls behind the rapid expansion of data volume, causing significant computational challenges. Here, we present DeepPrep, a pipeline empowered by deep learning and workflow manager. Evaluated on over 55,000 scans, DeepPrep…
Daniele Scanzi, Dylan A. Taylor, Katie A. McNair, Rohan O. C. King + 2 more
Electroencephalography (EEG) data are inherently contaminated by non-neuronal noise, including eye movements, muscle activity, cardiac signals, electrical interference, and technical issues such as poorly connected electrodes. Preprocessing to remove these artefacts is essential, yet the optimal method remains unclear…
Nadine S. J. Jacobsen, Daniel Kristanto, Suong Welp, Yusuf Cosku Inceler + 1 more
Preprocessing is necessary to extract meaningful results from electroencephalography (EEG) data. With many possible preprocessing choices, their impact on outcomes is fundamental. While previous studies have explored the effects of preprocessing on stationary EEG data, this research delves into mobile EEG, where…
Xingyu Liu, Yijun Zhang, Zi Yin, Zonglei Zhen + 1 more
Macaque MRI bridges non-invasive systems neuroscience with cellular and circuit-level mechanisms, but preprocessing tools remain difficult to integrate and deploy reproducibly. We present Brainana, an automated, BIDS-compatible preprocessing and visualization framework for macaque neuroimaging. Brainana integrates…
M.E. Hoeppli, M.A. Garenfeld, C.K. Mortensen, H. Nahman-Averbuch + 2 more
Preprocessing fMRI data requires striking a fine balance between conserving signals of interest and removing noise. Typical steps of preprocessing include motion correction, slice timing correction, spatial smoothing, and high-pass filtering. However, these standard steps do not remove many sources of noise. Thus…
Mark M. McAvoy, Lei Liu, Ruiwen Zhou, Benjamin A. Philip
Numerous methods exist to analyze functional MRI (fMRI) data, but no software currently exists to integrate the commonly-used FSL statistical analysis software with alternative preprocessing methods from the Human Connectome Project. Here we developed the Connectome Operations For FSL ExEcution (COFFEE) pipeline to…
Scott Huberty, James Desjardins, Tyler Collins, Mayada Elsabbagh + 1 more
EEG recordings are typically long and contain large amounts of data, making manual cleaning a time-consuming and error-prone task. Automated pre-processing pipelines can facilitate the efficient and objective extraction of artifacts, enabling standardized and reproducible analyses. However, automated pre-processing…
Lara Dular, Franjo Pernuš, Žiga Špiclin
Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI) and represents a simple diagnostic biomarker of brain ageing and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years…
Alain de Cheveigné
Predictive coding is an influential concept in sensory and cognitive neuroscience. It is often understood as involving top-down prediction of bottom-up sensory patterns, but the term also applies to feed-forward predictive mechanisms, for example in the retina. Here, I discuss a recent model of low-level predictive…