Advances in functional magnetic resonance imaging (fMRI) have led to the ability to study the brain across many contexts. However, the large number of features generated by functional connectivity approaches may overfit the data. These problems can be overcome with either feature selection (FS) or dimensionality reduction (DR), which can be applied to less complex models. We utilize two open source datasets to compare the performance of DR/FS methods on cognitive task decoding using a suite of ML classifiers. While FS and DR methods have been used previously in decoding research, no systematic comparison of their performance has been undertaken. Here, we compare available methods using commonly utilized machine learning libraries to establish which methods provide the best predictive performance. We then conduct statistical tests to examine the relative contributions of DR and FS methods and classifiers on decoding accuracy. Neither DR or FS was found to be superior. However, differences were identified across datasets and tasks. In the majority of methods and datasets, a peak in predictive performance was found using a small percentage (005-.10%) of the total number of original features. Some methods perform better than the baseline method of prediction with all available features or selecting features randomly. Decoding performance with some datasets with some method exceeds that of deep learning approaches. These results suggest a "sweet spot" for the tradeoff between the retention of features and predictive accuracy.

Cross-bispectral analysis identifies higher-order neural interactions, but the resulting high-dimensional tensors are difficult to interpret at the source level. Standard dimensionality reduction often lacks a physical link to the underlying neural generators. We introduce a low-rank tensor decomposition framework explicitly grounded in the physics of EEG signal generation. The method represents the cross-bispectrum using a single spatial mixing matrix and a compact source-interaction tensor, yielding a structured and physically interpretable model. This formulation enables a direct source-space interpretation and supports subsequent spatial demixing using MOCA to recover distinct source maps. Through extensive simulations, we show that the method robustly captures the dominant bispectral structure across a range of conditions, including varying signal-to-noise ratio, dipole orientation, amplitude balance, and source complexity. Applied to resting-state EEG, the approach identifies anatomically plausible parietal generators associated with prominent alpha-band bispectral interactions. In contrast to standard tensor decompositions such as Tucker and PARAFAC, which rely on unconstrained cores or rank-one assumptions, the proposed framework enforces a compact and interpretable source-interaction structure. This structure significantly improves interpretability and spatial localization while suppressing spurious high-dimensional variability. The proposed low-rank decomposition offers a principled and computationally efficient approach for reducing and interpreting cross-bispectral EEG data. By bridging tensor decomposition with biophysical source models, it enables insights into complex neural coupling that are inaccessible with standard matrix or tensor techniques.

Topological data analysis (TDA) has exploded as a tool for analyzing and making sense of high dimensional datasets across a variety of fields. Mapper is a tool from TDA that captures low-dimensional structure from high-dimensional data, precisely the approach needed to capture relevant information from high-dimensional neural time series. Electrical potential scalp recording, or electroencephalography (EEG), is routinely used in clinical applications and research studies thanks to its noninvasive nature, relatively inexpensive equipment, and high temporal resolution. But, it is prone to contamination, exhibits low spatial resolution, and has a non-stationary nature. Thus, it requires advanced signal processing and mathematical analysis methods for tasks requiring unsupervised brain state clustering. We introduce MapperEEG, an approach to unsupervised brain state clustering that uses tools from classical EEG analysis combined with Mapper to cluster and connect brain states. We show that MapperEEG can serve as a clustering algorithm in the spectral domain and provide additional information about the underlying brain state connectivity in a tapping task. Additionally, we use a go/no-go shooting task to explore how MapperEEG can still provide insight into the underlying structure and clusters of brain states even when it and other clustering methods fail. We demonstrate that it outperforms six other clustering algorithms such as hierarchical clustering, Hidden Markov Models, and basic autoencoders on identifying states in a tapping task. MapperEEG offers a novel and effective approach to analyzing EEG data, showing promise for brain state clustering and analysis.

Intracranial atherosclerotic stenosis (ICAS) is a major global cause of stroke with high recurrence risk. However, research on its pathophysiology is limited by difficulties in obtaining histopathological samples and the absence of adequate experimental models. The rising incidence of ICAS imposes a significant burden on families and healthcare systems, highlighting the urgent need for affordable, robust, and reliable animal models to study its prevention and treatment. To better understand the disease, researchers frequently employ animal models to replicate the progression of ICAS in humans. In recent years, many researchers have provided a variety of solutions for modeling methods and intervention protocols in animal models of ICAS, which has greatly improved the success rate of atherosclerosis models. Nevertheless, variations in intervention factors, material selection, and treatment methods have resulted in no consensus on the optimal animal model for ICAS. This review evaluates the strengths and limitations of existing in vivo、in vitro model and computational models of ICAS and the atherosclerotic process, aiming to inform better future models and drive novel stroke-reducing treatment strategies.