Biology subjects

Bradley, S.

Publications and source records attributed to Bradley, S..

4 recordsLinked to original sources

bioRxiv2025

Anisotropic light propagation in human brain white matter

SignificanceAccurate modeling of light diffusion in the human brain is crucial for applications in optogenetics and spectroscopy diagnostic techniques. White matter tissue is composed of myelinated axon bundles, suggesting the occurrence of enhanced light diffusion along their local orientation direction, which however has never been characterized experimentally. Existing diffuse optics models assume isotropic properties, limiting their accuracy. AimWe aim to characterize the anisotropic scattering properties of human white matter tissue by directly measuring its tensor scattering components along different directions, and to correlate them with the local axon fiber orientation. ApproachUsing a time- and space-resolved setup, we image the transverse propagation of diffusely reflected light across two perpendicular directions in a ex vivo human brain sample. Local fiber orientation is independently determined using light sheet fluorescence microscopy (LSFM). ResultsThe directional dependence of light propagation in organized myelinated axon bundles is characterized via Monte Carlo (MC) simulations accounting for a tensor scattering coefficient, revealing a lower scattering rate parallel to the fiber orientation. The effects of white matter anisotropy are further assessed by simulating a typical time-domain near-infrared spectroscopy measurement in a four-layer human head model. ConclusionsThis study provides a first characterization of the anisotropic scattering properties in ex vivo human white matter, highlighting its direct correlation with axon fiber orientation, and opening to the realization of quantitatively accurate anisotropy-aware human head 3D meshes for diffuse optics applications.

neuroscience↗

bioRxiv2025

Myelinated fiber labeling and orientation mapping of the human brain with light-sheet fluorescence microscopy

The convoluted network of myelinated fibers that supports behavior, cognition, and sensory processing in the human brain is the source of its extraordinary complexity. Advancements in tissue optical clearing, 3D fluorescence microscopy, and automated image analysis have enabled unprecedented insights into the architecture of these networks. Here, we investigate the multiscale organization of myelinated fibers in human brain tissue from the brainstem, Brocas area, hippocampus, and primary visual cortex by exploiting a specific fiber staining method, light-sheet fluorescence microscopy (LSFM), and an advanced spatial orientation analysis tool. Using an optimized protocol that integrates tissue clearing with the lipophilic DiD probe to achieve uniform and deep myelinated fiber labeling, we generate micrometerresolution volumetric reconstructions of multiple brain regions through an inverted LSFM. Automated image processing, employing unsupervised 3D multiscale Frangi filters, provides orientation distribution functions and local orientation dispersion maps. This enables precise characterization of the directionality of white matter bundles, linking mesoscopic structural properties to orientation details computed at the native micrometric resolution of the LSFM apparatus. The presented workflow illustrates a robust platform for large-scale, high-resolution brain mapping, which may facilitate the investigation of pathological alterations with unparalleled spatial resolution and, furthermore, the validation of other neuroimaging modalities.

neuroscience↗

bioRxiv2024

Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues

Advanced 3D imaging techniques and image segmentation and classification methods can profoundly transform biomedical research by offering deep insights into the cytoarchitecture of the human brain in relation to pathological conditions. Here, we propose a comprehensive pipeline for performing 3D imaging and automated quantitative cellular phenotyping on Formalin-Fixed Paraffin-Embedded (FFPE) human brain specimens, a valuable yet underutilized resource. We exploited the versatility of our method by applying it to different human specimens from both adult and pediatric, normal and abnormal brain regions. Quantitative data on neuronal volume, ellipticity, local density, and spatial clustering level were obtained from a machine learning-based analysis of the 3D cytoarchitectural organization of cells identified by different molecular markers in two subjects with malformations of cortical development (MCD). This approach will grant access to a wide range of physiological and pathological paraffin-embedded clinical specimens, allowing for volumetric imaging and quantitative analysis of human brain samples at cellular resolution. Possible genotype-phenotype correlations can be unveiled, providing new insights into the pathogenesis of various brain diseases and enlarging treatment opportunities.

neuroscience↗

bioRxiv2020

Deep learning classification of canine behavior using a single collar-mounted accelerometer: Real-world validation

Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from over 5,000 videos of over 2,500 dogs and ran the algorithms in production on over 11 million days of device data. We then surveyed project participants representing 10,550 dogs, they provided us 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking, petting, rubbing, scratching, and sniffing. We show that the devices position on the collar has no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1,514 users), and of 94.9% (among 1,491 users) for drinking. The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.

animal behavior and cognition↗