A mixture model demonstrates use of distinct strategies in a global motion direction task

Mixture models are well known in cognitive psychology, less so in vision. Are there cases where the data allow clear testing as to whether different strategies are employed in a task? Most psychophysical measurements manipulate a single staircase variable to map out a monotonic increasing function, but if performance is limited by different mechanisms over the range of the variable, classical fitting could be inappropriate. We present a data set and analyses that confirm the presence of two visual strategies addressing the same task, with the choice of strategies depending on the staircase variable. In a net-motion discrimination task, stimuli were random-dot kinematograms with a fixed percent coherence, and the contrast ratio between signal and noise dots was the staircase variable. When signal and noise dots have similar contrast, the most effective strategy is to average all local motion vectors across the display. However, low contrast ratios enable a selective-tracking strategy in which attending to the dim signal dots makes them easier to detect, which creates a positive feedback loop driving the signal dot contrast further down until a second threshold is reached

Generalized Diffusion MRI Denoising and Super-Resolution using Swin Transformers

Diffusion MRI is a non-invasive, in-vivo medical imaging method able to map tissue microstructure and structural connectivity of the human brain, as well as detect changes, such as brain development and injury, not visible by other clinical neuroimaging techniques. However, acquiring high signal-to-noise ratio (SNR) datasets with high angular and spatial sampling requires prohibitively long scan times, limiting usage in many important clinical settings, especially children, the elderly, and emergency patients with acute neurological disorders who might not be able to cooperate with the MRI scan without conscious sedation or general anesthesia. Here, we propose to use a Swin UNEt TRansformers (Swin UNETR) model, trained on augmented Human Connectome Project (HCP) data and conditioned on registered T1 scans, to perform generalized denoising and super-resolution of diffusion MRI invariant to acquisition parameters, patient populations, scanners, and sites. We qualitatively demonstrate super-resolution with artificially downsampled HCP data in normal adult volunteers. Our experiments on two other unrelated datasets, one of children with neurodevelopmental disorders and one of traumatic brain injury patients, show that our method demonstrates superior denoising despite wide data distribution shifts. Further improvement can be achieved via finetuning with just one additional subject. We apply our model to diffusion tensor (2nd order spherical harmonic) and higher-order spherical harmonic coefficient estimation and show results superior to current state-of-the-art methods. Our method can be used out-of-the-box or minimally finetuned to denoise and super-resolve a wide variety of diffusion MRI datasets. The code and model are publicly available at https://github.com/ucsfncl/dmri-swin

Hemispheric lateralization of white matter microstructure in children and its potential role in sensory processing dysfunction

Diffusion tensor imaging (DTI) studies have demonstrated white matter microstructural differences between the left and right hemispheres of the brain. However, the basis of these hemispheric asymmetries is not yet understood in terms of the biophysical properties of white matter microstructure, especially in children. There are reports of altered hemispheric white matter lateralization in ASD; however, this has not been studied in other related neurodevelopmental disorders such as sensory processing disorder (SPD). Firstly, we postulate that biophysical compartment modeling of diffusion MRI (dMRI), such as Neurite Orientation Dispersion and Density Imaging (NODDI), can elucidate the hemispheric microstructural asymmetries observed from DTI in children with neurodevelopmental concerns. Secondly, we hypothesize that sensory over-responsivity (SOR), a common type of SPD, will show altered hemispheric lateralization relative to children without SOR. Eighty-seven children (29 females, 58 males), ages 8–12 years, presenting at a community-based neurodevelopmental clinic were enrolled, 48 with SOR and 39 without. Participants were evaluated using the Sensory Processing 3 Dimensions (SP3D). Whole brain 3 T multi-shell multiband dMRI (b = 0, 1,000, 2,500 s/mm2) was performed. Tract Based Spatial Statistics were used to extract DTI and NODDI metrics from 20 bilateral tracts of the Johns Hopkins University White-Matter Tractography Atlas and the lateralization Index (LI) was calculated for each left–right tract pair. With DTI metrics, 12 of 20 tracts were left lateralized for fractional anisotropy and 17/20 tracts were right lateralized for axial diffusivity. These hemispheric asymmetries could be explained by NODDI metrics, including neurite density index (18/20 tracts left lateralized), orientation dispersion index (15/20 tracts left lateralized) and free water fraction (16/20 tracts lateralized). Children with SOR served as a test case of the utility of studying LI in neurodevelopmental disorders. Our data demonstrated increased lateralization in several tracts for both DTI and NODDI metrics in children with SOR, which were distinct for males versus females, when compared to children without SOR. Biophysical properties from NODDI can explain the hemispheric lateralization of white matter microstructure in children. As a patient-specific ratio, the lateralization index can eliminate scanner-related and inter-individual sources of variability and thus potentially serve as a clinically useful imaging biomarker for neurodevelopmental disorders

The Case for Optimized Edge-Centric Tractography at Scale.

The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population

Spatiotemporal visual statistics of aquatic environments in the natural habitats of zebrafish

Abstract Animal sensory systems are tightly adapted to the demands of their environment. In the visual domain, research has shown that many species have circuits and systems that exploit statistical regularities in natural visual signals. The zebrafish is a popular model animal in visual neuroscience, but relatively little quantitative data is available about the visual properties of the aquatic habitats where zebrafish reside, as compared to terrestrial environments. Improving our understanding of the visual demands of the aquatic habitats of zebrafish can enhance the insights about sensory neuroscience yielded by this model system. We analyzed a video dataset of zebrafish habitats captured by a stationary camera and compared this dataset to videos of terrestrial scenes in the same geographic area. Our analysis of the spatiotemporal structure in these videos suggests that zebrafish habitats are characterized by low visual contrast and strong motion when compared to terrestrial environments. Similar to terrestrial environments, zebrafish habitats tended to be dominated by dark contrasts, particularly in the lower visual field. We discuss how these properties of the visual environment can inform the study of zebrafish visual behavior and neural processing and, by extension, can inform our understanding of the vertebrate brain

Hemispheric lateralization of white matter microstructure in children and its potential role in sensory processing dysfunction

Diffusion tensor imaging (DTI) studies have demonstrated white matter microstructural differences between the left and right hemispheres of the brain. However, the basis of these hemispheric asymmetries is not yet understood in terms of the biophysical properties of white matter microstructure, especially in children. There are reports of altered hemispheric white matter lateralization in ASD; however, this has not been studied in other related neurodevelopmental disorders such as sensory processing disorder (SPD). Firstly, we postulate that biophysical compartment modeling of diffusion MRI (dMRI), such as Neurite Orientation Dispersion and Density Imaging (NODDI), can elucidate the hemispheric microstructural asymmetries observed from DTI in children with neurodevelopmental concerns. Secondly, we hypothesize that sensory over-responsivity (SOR), a common type of SPD, will show altered hemispheric lateralization relative to children without SOR. Eighty-seven children (29 females, 58 males), ages 8-12 years, presenting at a community-based neurodevelopmental clinic were enrolled, 48 with SOR and 39 without. Participants were evaluated using the Sensory Processing 3 Dimensions (SP3D). Whole brain 3 T multi-shell multiband dMRI (b = 0, 1,000, 2,500 s/mm2) was performed. Tract Based Spatial Statistics were used to extract DTI and NODDI metrics from 20 bilateral tracts of the Johns Hopkins University White-Matter Tractography Atlas and the lateralization Index (LI) was calculated for each left-right tract pair. With DTI metrics, 12 of 20 tracts were left lateralized for fractional anisotropy and 17/20 tracts were right lateralized for axial diffusivity. These hemispheric asymmetries could be explained by NODDI metrics, including neurite density index (18/20 tracts left lateralized), orientation dispersion index (15/20 tracts left lateralized) and free water fraction (16/20 tracts lateralized). Children with SOR served as a test case of the utility of studying LI in neurodevelopmental disorders. Our data demonstrated increased lateralization in several tracts for both DTI and NODDI metrics in children with SOR, which were distinct for males versus females, when compared to children without SOR. Biophysical properties from NODDI can explain the hemispheric lateralization of white matter microstructure in children. As a patient-specific ratio, the lateralization index can eliminate scanner-related and inter-individual sources of variability and thus potentially serve as a clinically useful imaging biomarker for neurodevelopmental disorders

Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation

Animals benefit from knowing if and how they are moving. Across the animal kingdom, sensory information in the form of optic flow over the visual field is used to estimate self-motion. However, different species exhibit strong spatial biases in how they use optic flow. Here, we show computationally that noisy natural environments favor visual systems that extract spatially biased samples of optic flow when estimating self-motion. The performance associated with these biases, however, depends on interactions between the environment and the animal's brain and behavior. Using the larval zebrafish as a model, we recorded natural optic flow associated with swimming trajectories in the animal's habitat with an omnidirectional camera mounted on a mechanical arm. An analysis of these flow fields suggests that lateral regions of the lower visual field are most informative about swimming speed. This pattern is consistent with the recent findings that zebrafish optomotor responses are preferentially driven by optic flow in the lateral lower visual field, which we extend with behavioral results from a high-resolution spherical arena. Spatial biases in optic-flow sampling are likely pervasive because they are an effective strategy for determining self-motion in noisy natural environments

Emotional Resilience Predicts Preserved White Matter Microstructure Following Mild Traumatic Brain Injury.

BACKGROUND: Adult patients with mild traumatic brain injury (mTBI) exhibit distinct phenotypes of emotional and cognitive functioning identified by latent profile analysis of clinical neuropsychological assessments. When discerned early after injury, these latent clinical profiles have been found to improve prediction of long-term outcomes from mTBI. The present study hypothesized that white matter (WM) microstructure is better preserved in an emotionally resilient mTBI phenotype compared with a neuropsychiatrically distressed mTBI phenotype. METHODS: The present study used diffusion magnetic resonance imaging to investigate and compare WM microstructure in major association, projection, and commissural tracts between the two phenotypes and over time. Diffusion magnetic resonance images from 172 patients with mTBI were analyzed to compute individual diffusion tensor imaging maps at 2 weeks and 6 months after injury. RESULTS: By comparing the diffusion tensor imaging parameters between the two phenotypes at global, regional, and voxel levels, emotionally resilient patients were shown to have higher axial diffusivity compared with neuropsychiatrically distressed patients early after mTBI. Longitudinal analysis revealed greater compromise of WM microstructure in neuropsychiatrically distressed patients, with greater decrease of global axial diffusivity and more widespread decrease of regional axial diffusivity during the first 6 months after injury compared with emotionally resilient patients. CONCLUSIONS: These results provide neuroimaging evidence of WM microstructural differences underpinning mTBI phenotypes identified from neuropsychological assessments and show differing longitudinal trajectories of these biological effects. These findings suggest that diffusion magnetic resonance imaging can provide short- and long-term imaging biomarkers of resilience