FFCM-MRF: An accurate and generalizable cerebrovascular segmentation pipeline for humans and rhesus monkeys based on TOF-MRA.

PURPOSE: Cerebrovascular segmentation and quantification of vascular morphological features in humans and rhesus monkeys are essential for prevention, diagnosis, and treatment of brain diseases. However, current automated whole-brain vessel segmentation methods are often not generalizable to independent datasets, limiting their usefulness in real-world environments with their heterogeneity in participants, scanners, and species. MATERIALS AND METHODS: In this study, we proposed an automated, accurate and generalizable segmentation method for magnetic resonance angiography images called FFCM-MRF. This method integrated fast fuzzy c-means clustering and Markov random field optimization by vessel shape priors and spatial constraints. We used a total of 123 human and 44 macaque MRA images scanned at 1.5 T, 3 T, and 7 T MRI from 9 datasets to develop and validate the method. RESULTS: FFCM-MRF achieved average Dice similarity coefficients ranging from 69.16 % to 89.63 % across multiple independent datasets, with improvements ranging from 3.24 % to 7.3 % compared to state-of-the-art methods. Quantitative analysis showed that FFCM-MRF can accurately segment major arteries in the Circle of Willis at the base of the brain and small distal pial arteries while effectively reducing noise. Test-retest analysis showed that the model yielded high vascular volume and diameter reliability. CONCLUSIONS: Our results have demonstrated that FFCM-MRF is highly accurate and reliable and largely independent of variations in field strength, scanner platforms, acquisition parameters, and species. The macaque MRA data and user-friendly open-source toolbox are freely available at OpenNeuro and GitHub to facilitate studies of imaging biomarkers for cerebrovascular and neurodegenerative diseases.

Low-Dose Ketamine-Induced Deficits in Arbitrary Visuomotor Mapping in Monkeys.

Ketamine, an NMDA antagonist, is widely used in clinical settings. Recently, low-dose ketamine has gained attention because of its promising role as a rapid antidepressant. However, the effects of low-dose ketamine on brain function, particularly higher cognitive functions of primate brains, are not fully understood. In this study, we used two macaques as subjects and found that acute low-dose ketamine administration significantly impaired the ability for arbitrary visuomotor mapping (AVM), a form of associative learning (AL) essential for flexible behaviors, including executions of learned stimuli-response contingency or learning of new contingencies. We conducted in-depth analyses and identified intrinsic characteristics of these ketamine-induced functional deficits, including lowered accuracy, prolonged time for planning and movement execution, increased tendency to make errors when visual cues are changed from trial to trial, and stronger impact on combining associative learning and another key higher cognitive function, working memory (WM). Our results shed new light on how associative learning relies on the NMDA-mediated synaptic transmission of the brain and contribute to a better understanding of the potential acute side effects of low-dose ketamine on cognition, which can help facilitate its safe usage in medical practice.

Distributed implantation of a flexible microelectrode array for neural recording.

Flexible multichannel electrode arrays (fMEAs) with multiple filaments can be flexibly implanted in various patterns. It is necessary to develop a method for implanting the fMEA in different locations and at various depths based on the recording demands. This study proposed a strategy for reducing the microelectrode volume with integrated packaging. An implantation system was developed specifically for semiautomatic distributed implantation. The feasibility and convenience of the fMEA and implantation platform were verified in rodents. The acute and chronic recording results provied the effectiveness of the packaging and implantation methods. These methods could provide a novel strategy for developing fMEAs with more filaments and recording sites to measure functional interactions across multiple brain regions.