A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques.

Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation.

Age-related differences in network controllability are mitigated by redundancy in large-scale brain networks.

The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we use diffusion MRI data from cognitively intact participants (n = 480, ages 40-90) to study age-associated differences in the average controllability of structural brain networks, topological features that could mitigate these differences, and the overall effect on cognitive function.

Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.

Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for registration or parcellation of a single cortical surface. When applying to longitudinal studies, these methods independently register/parcellate each surface from longitudinal scans, thus often generating longitudinally inconsistent and inaccurate results, especially in small or ambiguous cortical regions.

nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species.

Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI datasets at various developmental stages and from various imaging sites/scanners, existing computational tools designed for human MRI typically perform poor on NHP data, due to huge differences in brain sizes, morphologies, and imaging appearances across species, sites, and ages, highlighting the imperative for NHP-specialized MRI processing tools. To address this issue, in this paper, we present a robust, generic, and fully automated computational pipeline, called non-human primates Brain Extraction and Segmentation Toolbox (nBEST), whose main functionality includes brain extraction, non-cerebrum removal, and tissue segmentation.

Chelator boosted tumor-retention and pharmacokinetic properties: development of Cu labeled radiopharmaceuticals targeting neurotensin receptor.

Purpose: Accumulating evidence suggests that neurotensin (NTS) and neurotensin receptors (NTSRs) play key roles in lung cancer progression by triggering multiple oncogenic signaling pathways. This study aims to develop Cu-labeled neurotensin receptor 1 (NTSR1)-targeting agents with the potential for both imaging and therapeutic applications.

Method: A series of neurotensin receptor antagonists (NRAs) with variable propylamine (PA) linker length and different chelators were synthesized, including [Cu]Cu-CB-TE2A-iPA-NRA ([Cu]Cu-4a-c, i = 1, 2, 3), [Cu]Cu-NOTA-2PA-NRA ([Cu]Cu-4d), [Cu]Cu-DOTA-2PA-NRA ([Cu]Cu-4e, also known as [Cu]Cu-3BP-227), and [Cu]Cu-DOTA-VS-2PA-NRA ([Cu]Cu-4f).

Manufacturing 6-[F]Fluoro--DOPA via Flow Chemistry-Enhanced Photoredox Radiofluorination.

In this study, we introduce a practical methodology for the synthesis of PET probes by seamlessly combining flow chemistry with photoredox radiofluorination. The clinical PET tracer 6-[F]FDOPA was smoothly prepared in a 24.3% non-decay-corrected yield with over 99.

Artificial intelligence as a prediction tool for orthognathic surgery assessment.

Introduction: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age.

From learned value to sustained bias: how reward conditioning changes attentional priority.

Introduction: Attentional bias to reward-associated stimuli can occur even when it interferes with goal-driven behavior. One theory posits that dopaminergic signaling in the striatum during reward conditioning leads to changes in visual cortical and parietal representations of the stimulus used, and this, in turn, sustains attentional bias even when reward is discontinued. However, only a few studies have examined neural activity during both rewarded and unrewarded task phases.

Optimal shrinkage denoising breaks the noise floor in high-resolution diffusion MRI.

The spatial resolution attainable in diffusion magnetic resonance (MR) imaging is inherently limited by noise. The weaker signal associated with a smaller voxel size, especially at a high level of diffusion sensitization, is often buried under the noise floor owing to the non-Gaussian nature of the MR magnitude signal. Here, we show how the noise floor can be suppressed remarkably via optimal shrinkage of singular values associated with noise in complex-valued k-space data from multiple receiver channels.

Improving Image Segmentation with Contextual and Structural Similarity.

Deep learning models for medical image segmentation are usually trained with voxel-wise losses, e.g., cross-entropy loss, focusing on unary supervision without considering inter-voxel relationships.

Federated learning for medical image analysis: A survey.

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons.

Space wandering in the rodent default mode network.

The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats.

FPLS-DC: functional partial least squares through distance covariance for imaging genetics.

Motivation: Imaging genetics integrates imaging and genetic techniques to examine how genetic variations influence the function and structure of organs like the brain or heart, providing insights into their impact on behavior and disease phenotypes. The use of organ-wide imaging endophenotypes has increasingly been used to identify potential genes associated with complex disorders. However, analyzing organ-wide imaging data alongside genetic data presents two significant challenges: high dimensionality and complex relationships.

Co-detection of azoxystrobin and thiabendazole fungicides in mold and mildew resistant wallboards and in children.

The study measured the levels of azoxystrobin (AZ) and thiabendazole (TBZ) in wallboards and metabolite levels of these fungicides in children. The paper covering of wallboard samples contained a higher concentration of AZ and TBZ than the gypsum core, and similar amounts (w/w) of these two fungicides were present in the samples. These data suggest that commercial products containing a 1:1 (w/w) amount of AZ and TBZ, such as Sporgard® WB or Azo Tech™, were applied to the wallboard paper.

Source-free unsupervised domain adaptation: A survey.

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to the unlabeled target domain with source data inaccessible.

A multimodal submillimeter MRI atlas of the human cerebellum.

The human cerebellum is engaged in a broad array of tasks related to motor coordination, cognition, language, attention, memory, and emotional regulation. A detailed cerebellar atlas can facilitate the investigation of the structural and functional organization of the cerebellum. However, existing cerebellar atlases are typically limited to a single imaging modality with insufficient characterization of tissue properties.

Distinct neurochemical influences on fMRI response polarity in the striatum.

The striatum, known as the input nucleus of the basal ganglia, is extensively studied for its diverse behavioral roles. However, the relationship between its neuronal and vascular activity, vital for interpreting functional magnetic resonance imaging (fMRI) signals, has not received comprehensive examination within the striatum. Here, we demonstrate that optogenetic stimulation of dorsal striatal neurons or their afferents from various cortical and subcortical regions induces negative striatal fMRI responses in rats, manifesting as vasoconstriction.

Language access differentially alters functional connectivity during emotion perception across cultures.

Introduction: It is often assumed that the ability to recognize the emotions of others is reflexive and automatic, driven only by observable facial muscle configurations. However, research suggests that accumulated emotion concept knowledge shapes the way people perceive the emotional meaning of others' facial muscle movements. Cultural upbringing can shape an individual's concept knowledge, such as expectations about which facial muscle configurations convey anger, disgust, or sadness.

Acute and chronic alcohol modulation of extended amygdala calcium dynamics.

The central amygdala (CeA) and bed nucleus of the stria terminalis (BNST) are reciprocally connected nodes of the extended amygdala thought to play an important role in alcohol consumption. Studies of immediate-early genes indicate that BNST and CeA are acutely activated following alcohol drinking and may signal alcohol reward in nondependent drinkers, while stress signaling in the extended amygdala following chronic alcohol exposure drives increased drinking via negative reinforcement. However, the temporal dynamics of neuronal activation in these regions during drinking behavior are poorly understood.

Hexafluoroisopropanol as a Bioconjugation Medium of Ultrafast, Tryptophan-Selective Catalysis.

The past decade has seen a remarkable growth in the number of bioconjugation techniques in chemistry, biology, material science, and biomedical fields. A core design element in bioconjugation technology is a chemical reaction that can form a covalent bond between the protein of interest and the labeling reagent. Achieving chemoselective protein bioconjugation in aqueous media is challenging, especially for generally less reactive amino acid residues, such as tryptophan.

Learning multi-site harmonization of magnetic resonance images without traveling human phantoms.

Harmonization improves Magn. Reson. Imaging (MRI) data consistency and is central to effective integration of diverse imaging data acquired across multiple sites.

Radiolabeling Diaminosarcophagine with Cyclotron-Produced Cobalt-55 and [Co]Co-NT-Sarcage as a Proof of Concept in a Murine Xenograft Model.

Cobalt-sarcophagine complexes exhibit high kinetic inertness under various stringent conditions, but there is limited literature on radiolabeling and in vivo positron emission tomography (PET) imaging using no carrier added Co. To fill this gap, this study first investigates the radiolabeling of DiAmSar (DSar) with Co, followed by stability evaluation in human serum and EDTA, pharmacokinetics in mice, and a direct comparison with [Co]CoCl to assess differences in pharmacokinetics. Furthermore, the radiolabeling process was successfully used to generate the NTSR1-targeted PET agent [Co]Co-NT-Sarcage (a DSar-functionalized SR142948 derivative) and administered to HT29 tumor xenografted mice.

Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI.

Brain structural MRI has been widely used for assessing future progression of cognitive impairment (CI) based on learning-based methods. Previous studies generally suffer from the limited number of labeled training data, while there exists a huge amount of MRIs in large-scale public databases. Even without task-specific label information, brain anatomical structures provided by these MRIs can be used to boost learning performance intuitively.