Quantitative brain [18F]FDG PET beyond normal blood glucose levels.

Introduction SUV measurements from static brain [18F]FDG PET acquisitions are a commonly used tool in preclinical research, providing a simple alternative for kinetic modelling, which requires complex and time-consuming dynamic acquisitions. However, SUV can be severely affected by the animal handling and preconditioning protocols, primarily by those that may induce changes in blood glucose levels (BGL). Here, we aimed at developing and investigating the feasibility of SUV-based approaches for a wide range of BGL far beyond normal values, and consequently, to develop and validate a new model to generate standardized and reproducible SUV measurements for any BGL. Material and methods We performed dynamic and static brain [18F]FDG PET acquisitions in 52 male Sprague-Dawley rats sorted into control (n = 10), non-fasting (n = 14), insulin-induced hypoglycemia (n = 12) and glucagon-induced hyperglycemia (n = 16) groups. Brain [18F]FDG PET images were cropped, aligned and co-registered to a standard template to calculate whole-brain and regional SUV. Cerebral Metabolic Rate of Glucose (CMRglc) was also estimated from 2-Tissue Compartment Model (2TCM) and Patlak plot for validation purposes. Results Our results showed that BGL=100±6 mg/dL can be considered a reproducible reference value for normoglycemia. Furthermore, we successfully established a 2nd-degree polynomial model (C1=0.66E-4, C2=-0.0408 and C3=7.298) relying exclusively on BGL measures at pre-[18F]FDG injection time, that characterizes more precisely the relationship between SUV and BGL for a wide range of BGL values (from 10 to 338 mg/dL). We confirmed the ability of this model to generate corrected SUV estimations that are highly correlated to CMRglc estimations (R2= 0.54 2TCM CMRgluc and R2= 0.49 Patlak CMRgluc). Besides, slight regional differences in SUV were found in animals from extreme BGL groups, showing that [18F]FDG uptake is mostly directed toward central regions of the brain when BGLs are significantly decreased. Conclusion Our study successfully established a non-linear model that relies exclusively on pre-scan BGL measurements to characterize the relationship between [18F]FDG SUV and BGL. The extensive validation confirmed its ability to generate SUV-based surrogates of CMRglu along a wide range of BGL and it holds the potential to be adopted as a standard protocol by the preclinical neuroimaging community using brain [18F]FDG PET imaging.

Differentiation between multifocal CNS lymphoma and glioblastoma based on MRI criteria.

PURPOSE: Differentiating between glioblastoma (GB) with multiple foci (mGB) and multifocal central nervous system lymphoma (mCNSL) can be challenging because these cancers share several features at first appearance on magnetic resonance imaging (MRI). The aim of this study was to explore morphological differences in MRI findings for mGB versus mCNSL and to develop an interpretation algorithm with high diagnostic accuracy. METHODS: In this retrospective study, MRI characteristics were compared between 50 patients with mGB and 50 patients with mCNSL treated between 2015 and 2020. The following parameters were evaluated: size, morphology, lesion location and distribution, connections between the lesions on the fluid-attenuated inversion recovery sequence, patterns of contrast enhancement, and apparent diffusion coefficient (ADC) values within the tumor and the surrounding edema, as well as MR perfusion and susceptibility weighted imaging (SWI) whenever available. RESULTS: A total of 187 mCNSL lesions and 181 mGB lesions were analyzed. The mCNSL lesions demonstrated frequently a solid morphology compared to mGB lesions, which showed more often a cystic, mixed cystic/solid morphology and a cortical infiltration. The mean measured diameter was significantly smaller for mCNSL than mGB lesions (p < 0.001). Tumor ADC ratios were significantly smaller in mCNSL than in mGB (0.89 ± 0.36 vs. 1.05 ± 0.35, p < 0.001). The ADC ratio of perilesional edema was significantly higher (p < 0.001) in mCNSL than in mGB. In SWI / T2*-weighted imaging, tumor-associated susceptibility artifacts were more often found in mCNSL than in mGB (p < 0.001). CONCLUSION: The lesion size, ADC ratios of the lesions and the adjacent tissue as well as the vascularization of the lesions in the MR-perfusion were found to be significant distinctive patterns of mCNSL and mGB allowing a radiological differentiation of these two entities on initial MRI. A diagnostic algorithm based on these parameters merits a prospective validation.

Children's exposure to chemical contaminants: Demographic disparities and associations with the developing basal ganglia.

Children are regularly exposed to chemical contaminants that may influence brain development. However, relatively little is known about how these contaminants impact the developing human brain. Here, we combined silicone wristband exposure assessments with neuroimaging for the first time to examine how chemical contaminant mixtures are associated with the developing basal ganglia-a brain region key for the healthy development of emotion, reward, and motor processing, and which may be particularly susceptible to contaminant harm. Further, we examined demographic disparities in exposures to clarify which children were at highest risk for any contaminant-associated neurobiological changes. Participants included 62 community children (average age 7.00 years, 53% female, 66% White) who underwent structural neuroimaging to provide data on their basal ganglia structure and wore a silicone wristband for seven days to track their chemical contaminant exposure. 45 chemical contaminants-including phthalates and their alternatives, brominated flame retardants, organophosphate esters, pesticides, polycyclic aromatic hydrocarbons, and polychlorinated biphenyls-were detected in over 75% of wristbands. Notable demographic disparities in exposure were present, such that Non-White and lower-income children were more exposed to several contaminants. Exposure to chemical contaminant mixtures was not associated with overall basal ganglia volume; however, two organophosphate esters (2IPPDPP and 4IPPDPP) were both associated with a larger globus pallidus, a basal ganglia sub-region. Results highlight demographic disparities in exposure and suggest possible risks to a brain region key for healthy emotional development.

Differences in Grey Matter Concentrations and Functional Connectivity between Young Carriers and Non-Carriers of the APOE ε4 Genotype.

Background: The pathophysiology of Alzheimer's disease (AD) may begin developing years or even decades prior to the manifestation of its first symptoms. The APOE ε4 genotype is a prominent genetic risk for AD that has been found to be associated with brain changes across the lifespan since early adulthood. Thus, studying brain changes that may occur in young adults with an APOE ε4 status is highly relevant. Objective: Examine potential differences in grey matter (GM) and functional connectivity (FC) in brains of cognitively healthy young APOE ε4 carriers and non-carriers, denoted here as ε4(+) and ε4(-), respectively. Methods: Three Tesla magnetic resonance imaging (MRI) brain scans were acquired from cognitively healthy young participants aged approximately 20 years (n = 151). Voxel-based morphometry (VBM) analysis was employed to identify potential structural differences in GM between ε4(+) and ε4(-). In a subsequent seed-based connectivity (SBC) analysis, brain regions that structurally differed in the VBM analysis were considered as seeds and correlated with all the remaining voxels across the brains to then measure the differences in FC between groups. Results: The VBM analysis suggested that ε4(+) (n = 28) had greater GM densities relative to ε4(-) (n = 123) in the left hippocampus and the left posterior insula (puncorr < 0.001). However, the effect did not survive the correction for multiple comparisons, suggesting minimal structural differences in this age range. In contrast, the SBC analysis indicated that ε4(+) exhibited significantly decreased FC between the left hippocampus and areas of the left middle temporal gyrus (n = 27) compared to ε4(-) (n = 102). These results remained significant after multiple comparisons (pFDR < 0.05). Lastly, no statistically significant differences in FC between groups were observed for the left insular seed (pFDR > 0.05). Discussion: These results suggest early structural and functional brain changes associated with the APOE ε4 genotype on young adults. Yet, they must be cautiously interpreted and contrasted with both older adults with genetic risk for AD and patients diagnosed with AD.

Optical Transmission in Single-Layer Brain Tissues under Different Optical Source Types: Modelling and Simulation.

The human brain is a complex organ controlling daily activity. Present technique models have mostly focused on multi-layer brain tissues, which lack understanding of the propagation characteristics of various single brain tissues. To better understand the influence of different optical source types on individual brain tissues, we constructed single-layer brain models and simulated optical propagation using the Monte Carlo method. Based on the optical simulation results, sixteen optical source types had different optical energy distributions, and the distribution in cerebrospinal fluid had obvious characteristics. Five brain tissues (scalp, skull, cerebrospinal fluid, gray matter, and blood vessel) had the same set of the first three optical source types with maximum depth, while white matter had a different set of the first three optical source types with maximum depth. Each brain tissue had different optical source types with the maximum and minimum full width at half maximum. The study on single-layer brain tissues under different optical source types lays the foundation for constructing complex brain models with multiple tissue layers. It provides a theoretical reference for optimizing the selection of optical source devices for brain imaging.

Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue.

Significance: Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of living tissue. Particularly, the possibility of extracting the molecular composition of the tissue from the optical spectra deems the spectroscopy techniques as a unique diagnostic tool. Aim: No established method exists to streamline the inference of the biochemical composition from the optical spectrum for real-time applications such as surgical monitoring. We analyze a machine learning technique for inference of changes in the molecular composition of brain tissue. Approach: We propose modifications to the existing learnable methodology based on the Beer-Lambert law. We evaluate the method's applicability to linear and nonlinear formulations of this physical law. The approach is tested on data obtained from the bNIRS- and HSI-based monitoring of brain tissue. Results: The results demonstrate that the proposed method enables real-time molecular composition inference while maintaining the accuracy of traditional methods. Preliminary findings show that Beer-Lambert law-based spectral unmixing allows contrasting brain anatomy semantics such as the vessel tree and tumor area. Conclusion: We present a data-driven technique for inferring molecular composition change from diffuse spectroscopy of brain tissue, potentially enabling intra-operative monitoring.

Altered structure and functional connection of subcortical gray matter in female patients with classical trigeminal neuralgia.

The etiology of classical trigeminal neuralgia (CTN) is still unclear. A better understanding of the cerebral structural and functional changes in female patients with CTN may provide important novel insights into the pathophysiologic mechanisms of female CTN. A total 37 female CTN patients were included and referred to MRI scans, comprising with 19 left CTN and 18 right CTN patients. We analyzed the volume and shape of subcortical gray matter (GM), and the functional connectivity (FC) between the accumbens nucleus (NAc) and whole brain in right and left CTN patients respectively. We found left CTN patients had a reduced right NAc volume compared to controls, similarly, the right CTN had the decreased volume in the left NAc. Vertex-wise shapes of right NAc in left CTN patients showed significant regional shape deformation on the anterior, medial and ventroposterior aspects, in contrast, left NAc of right CTN patients showed significant regional shape deformation on the anterior and posterior aspect. Furthermore, patients with left CTN showed significantly lower FC between the right NAc and right orbitofrontal cortex than control subjects. The volume of NAc in all CTN was significantly related to the perception of present pain intensity. The CTN might be majorly caused by volume reduction in NAc. A greater understanding of the neurobiological basis of pain-related changes in NAc will provide the knowledge for the development of novel NAc based therapeutic targets for pain management or even prevention in CTN patients.

Unveiling the potential of ultrasound in brain imaging: Innovations, challenges, and prospects.

Within medical imaging, ultrasound serves as a crucial tool, particularly in the realms of brain imaging and disease diagnosis. It offers superior safety, speed, and wider applicability compared to Magnetic Resonance Imaging (MRI) and X-ray Computed Tomography (CT). Nonetheless, conventional transcranial ultrasound applications in adult brain imaging face challenges stemming from the significant acoustic impedance contrast between the skull bone and soft tissues. Recent strides in ultrasound technology encompass a spectrum of advancements spanning tissue structural imaging, blood flow imaging, functional imaging, and image enhancement techniques. Structural imaging methods include traditional transcranial ultrasound techniques and ultrasound elastography. Transcranial ultrasound assesses the structure and function of the skull and brain, while ultrasound elastography evaluates the elasticity of brain tissue. Blood flow imaging includes traditional transcranial Doppler (TCD), ultrafast Doppler (UfD), contrast-enhanced ultrasound (CEUS), and ultrasound localization microscopy (ULM), which can be used to evaluate the velocity, direction, and perfusion of cerebral blood flow. Functional ultrasound imaging (fUS) detects changes in cerebral blood flow to create images of brain activity. Image enhancement techniques include full waveform inversion (FWI) and phase aberration correction techniques, focusing on more accurate localization and analysis of brain structures, achieving more precise and reliable brain imaging results. These methods have been extensively studied in clinical animal models, neonates, and adults, showing significant potential in brain tissue structural imaging, cerebral hemodynamics monitoring, and brain disease diagnosis. They represent current hotspots and focal points of ultrasound medical research. This review provides a comprehensive summary of recent developments in brain imaging technologies and methods, discussing their advantages, limitations, and future trends, offering insights into their prospects.

Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics.

Brain imaging genetics is an evolving neuroscience topic aiming to identify genetic variations related to neuroimaging measurements of interest. Traditional linear regression methods have shown success, but their reliance on individual-level imaging and genetic data limits their applicability. Herein, we proposed S-GsMTLR, a group sparse multi-task linear regression method designed to harness summary statistics from genome-wide association studies (GWAS) of neuroimaging quantitative traits. S-GsMTLR directly employs GWAS summary statistics, bypassing the requirement for raw imaging genetic data, and applies multivariate multi-task sparse learning to these univariate GWAS results. It amalgamates the strengths of conventional sparse learning methods, including sophisticated modeling techniques and efficient feature selection. Additionally, we implemented a rapid optimization strategy to alleviate computational burdens by identifying genetic variants associated with phenotypes of interest across the entire chromosome. We first evaluated S-GsMTLR using summary statistics derived from the Alzheimer's Disease Neuroimaging Initiative. The results were remarkably encouraging, demonstrating its comparability to conventional methods in modeling and identification of risk loci. Furthermore, our method was evaluated with two additional GWAS summary statistics datasets: One focused on white matter microstructures and the other on whole brain imaging phenotypes, where the original individual-level data was unavailable. The results not only highlighted S-GsMTLR's ability to pinpoint significant loci but also revealed intriguing structures within genetic variations and loci that went unnoticed by GWAS. These findings suggest that S-GsMTLR is a promising multivariate sparse learning method in brain imaging genetics. It eliminates the need for original individual-level imaging and genetic data while demonstrating commendable modeling and feature selection capabilities.

Optimizing neuroscience data management by combining REDCap, BIDS and SQLite: a case study in Deep Brain Stimulation.

Neuroscience studies entail the generation of massive collections of heterogeneous data (e.g. demographics, clinical records, medical images). Integration and analysis of such data in research centers is pivotal for elucidating disease mechanisms and improving clinical outcomes. However, data collection in clinics often relies on non-standardized methods, such as paper-based documentation. Moreover, diverse data types are collected in different departments hindering efficient data organization, secure sharing and compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Henceforth, in this manuscript we present a specialized data management system designed to enhance research workflows in Deep Brain Stimulation (DBS), a state-of-the-art neurosurgical procedure employed to treat symptoms of movement and psychiatric disorders. The system leverages REDCap to promote accurate data capture in hospital settings and secure sharing with research institutes, Brain Imaging Data Structure (BIDS) as image storing standard and a DBS-specific SQLite database as comprehensive data store and unified interface to all data types. A self-developed Python tool automates the data flow between these three components, ensuring their full interoperability. The proposed framework has already been successfully employed for capturing and analyzing data of 107 patients from 2 medical institutions. It effectively addresses the challenges of managing, sharing and retrieving diverse data types, fostering advancements in data quality, organization, analysis, and collaboration among medical and research institutions.

A Full-wave Solution of Deep Sources in the Lossy Human Head to Accurate Electroencephalography and Magnetoencephalography.

Introduction: Currents in the brain flow inside neurons and across their boundaries into the extracellular medium, create electric and magnetic fields. These fields, which contain suitable information on brain activity, can be measured by electroencephalography (EEG), magnetoencephalography (MEG), and direct neural imaging. Methods: In this paper, we employed an electromagnetic model of the neuron activity and human head to derive electric and magnetic fields (brain waves) using a full-wave approach (ie. without any approximation). Currently, the brain waves are only derived using the quasi-static approximation (QSA) of Maxwell's equations in electromagnetic theory. Results: As a result, source localization in brain imaging will produce some errors. So far, the error rate of the QSA on the output results of electric and magnetic fields has not been investigated. This issue has become more noticeable due to the increased sensitivity of modern electroencephalography (EEG) and magnetoencephalography (MEG) devices. This work introduces issues that QSA encounters in this problem and reveals the necessity of a full-wave solution. Then, a full-wave solution of the problem in closed-form format is presented for the first time. This solution is done in two scenarios: the source (active neurons) is in the center of a sphere, and when the source is out of the center but deeply inside the sphere. The first scenario is simpler, but the second one is much more complicated and is solved using a partial-wave series expression. Conclusion: One of the significant achievements of this model is improving the interpretation of EEG and MEG measurements, resulting in more accurate source localization.

Revisiting equivalent optical properties for cerebrospinal fluid to improve diffusion-based modeling accuracy in the brain.

Significance: The diffusion approximation (DA) is used in functional near-infrared spectroscopy (fNIRS) studies despite its known limitations due to the presence of cerebrospinal fluid (CSF). Nearly all of these studies rely on a set of empirical CSF optical properties, recommended by a previous simulation study, that were not selected for the purpose of minimizing DA modeling errors. Aim: We aim to directly quantify the accuracy of DA solutions in brain models by comparing those with the gold-standard solutions produced by the mesh-based Monte Carlo (MMC), based on which we derive updated recommendations. Approach: For both a 5-layer head and Colin27 atlas models, we obtain DA solutions by independently sweeping the CSF absorption ( µ a ) and reduced scattering ( µ s ' ) coefficients. Using an MMC solution with literature CSF optical properties as reference, we compute the errors for surface fluence, total brain sensitivity and brain energy-deposition, and identify the optimized settings where the such error is minimized. Results: Our results suggest that previously recommended CSF properties can cause significant errors (8.7% to 52%) in multiple tested metrics. By simultaneously sweeping µ a and µ s ' , we can identify infinite numbers of solutions that can exactly match DA with MMC solutions for any single tested metric. Furthermore, it is also possible to simultaneously minimize multiple metrics at multiple source/detector separations, leading to our new recommendation of setting µ s ' = 0.15 mm-1 while maintaining physiological µ a for CSF in DA simulations. Conclusion: Our new recommendation of CSF equivalent optical properties can greatly reduce the model mismatches between DA and MMC solutions at multiple metrics without sacrificing computational speed. We also show that it is possible to eliminate such a mismatch for a single or a pair of metrics of interest.

Improving Prefrontal Oxygenation and Cardiac Autonomic Activity Following Meditation: A Functional Near-Infrared Spectroscopy (fNIRS) Study.

OBJECTIVE: The empirical evidence explicitly demonstrates that meditation practice enhances both brain functions and mental well-being. A meditative relaxation approach called the mind sound resonance technique (MSRT) has shown promising effects on children, adolescents, and people with psychological illnesses. This study aimed to investigate the effects of MSRT practice on brain hemodynamics, heart rate variability (HRV), mindfulness, and anxiety levels in college students. METHODS: Fifty volunteers in all genders (females, n = 30; males, n = 20) aged between 19 and 30 years were chosen from an educational institute and allocated into two groups, i.e., MSRT (n = 25) and supine rest (SR; n = 25). Enrolled participants were measured cerebral hemodynamics and HRV before, during, and after the MSRT or SR practice. The self-reported assessments including state anxiety and mindfulness were assessed before and after the intervention. RESULTS: The results demonstrated that practicing MSRT significantly improved oxygenation (p < 0.05) in the right prefrontal cortex (PFC) and increased low-frequency (LF) (p < 0.05) and decreased high-frequency (HF) (p < 0.05) component of HRV when compared to the baseline. The between-group analysis showed a significant difference between MSRT and SR in the standard deviation of the normal-to-normal (SDNN) (p < 0.05) component of HRV. CONCLUSION: These crumbs of evidence imply that MSRT sessions may foster the development of anxiety-related coping skills by elevating mindfulness, promoting PFC oxygenation, and modulating HRV in MSRT practitioners.

A Study Over Brain Connectivity Network of Parkinson's Patients, Using Nonparametric Bayesian Model.

Introduction: Parkinson disease is a neurodegenerative disease that disrupts functional brain networks. Many neurodegenerative disorders are associated with changes in brain communication patterns. Resting-state functional connectivity studies can distinguish the topological structure of Parkinson patients from healthy individuals by analyzing patterns between different regions of the brain. Accordingly, the present study aimed to determine the brain topological features and functional connectivity in patients with Parkinson disease, using a Bayesian approach. Methods: The data of this study were downloaded from the open neuro site. These data include resting-state functional magnetic resonance imaging (rs-fMRI) of 11 healthy individuals and 11 Parkinson patients with mean ages of 64.36 and 63.73, respectively. An advanced nonparametric Bayesian model was used to evaluate topological characteristics, including clustering of brain regions and correlation coefficient of the clusters. The significance of functional relationships based on each edge between the two groups was examined through false discovery rate (FDR) and network-based statistics (NBS) methods. Results: Brain connectivity results showed a major difference in terms of the number of regions in each cluster and the correlation coefficient between the patient and healthy groups. The largest clusters in the patient and control groups were 26 and 53 regions, respectively, with clustering correlation values of 0.36 and 0.26. Although there are 15 common areas across the two clusters, the intensity of the functional relationship between these areas was different in the two groups. Moreover, using NBS and FDR methods, no significant difference was observed for each edge between the patient and healthy groups (P>0.05). Conclusion: The results of this study show a different topological configuration of the brain network between the patient and healthy groups, indicating changes in the functional relationship between a set of areas of the brain.

SzCORE: Seizure Community Open-Source Research Evaluation framework for the validation of electroencephalography-based automated seizure detection algorithms.

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the EEG 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine-learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open-Source Research Evaluation) framework and benchmark are made publicly available along with an open-source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.

The effect of SGLT2 inhibition on brain-related phenotypes and aging: a drug target Mendelian randomization study.

INTRODUCTION: Observational study suggested SGLT2 inhibitors might promote healthy aging. However, whether brain-related phenotypes mediate this association. We applied Mendelian randomization (MR) to investigate the effect of SGLT2 inhibition on chronological, biological age and cognition and explore the mediation effects of brain imaging-derived phenotypes (IDPs). METHODS: We selected genetic variants associated with both expression levels of SLC5A2 (GTEx and eQTLGen data; N=129 to 31,684) and HbA1c levels (UK Biobank; N=344,182) and used them to proxy the effect of SGLT2 inhibition. Aging related outcomes, including parental longevity (N=389,166) and epigenetic clocks (N=34,710), and cognitive phenotypes, including cognitive function (N=300,486) and intelligence (N= 269,867) were derived from genome-wide association studies. Two-step MR were conducted to explore the associations between SGLT2 inhibition, IDPs, and aging outcomes, cognition. RESULTS: SGLT2 inhibition was associated with longer father's attained age (years of life increase per SD (6.75 mmol/mol) reduction in HbA1c levels = 6.21, 95%CI 1.95 to 11.15), better cognitive function (beta = 0.17, 95%CI 0.03 to 0.31) and higher intelligence (beta = 0.47, 95%CI 0.19 to 0.75). Two-step MR identified two IDPs as mediators linking SGLT2 inhibition with chronological age (total proportion of mediation = 22.6%), where four and five IDPs were mediators for SGLT2 inhibition on cognitive function and intelligence respectively (total proportion of mediation = 61.6% and 68.6% respectively). CONCLUSIONS: Our study supported that SGLT2 inhibition increases father's attained age, cognitive function and intelligence, which was mediated through brain images of different brain regions. Future studies are needed to investigate whether similar effect could be observed for users of SGLT2 inhibitors.

Intranasal delivery of imaging agents to the brain.

The potential of intranasal administered imaging agents to altogether bypass the blood-brain barrier offers a promising non-invasive approach for delivery directly to the brain. This review provides a comprehensive analysis of the advancements and challenges of delivering neuroimaging agents to the brain by way of the intranasal route, focusing on the various imaging modalities and their applications in central nervous system diagnostics and therapeutics. The various imaging modalities provide distinct insights into the pharmacokinetics, biodistribution, and specific interactions of imaging agents within the brain, facilitated by the use of tailored tracers and contrast agents. Methods: A comprehensive literature search spanned PubMed, Scopus, Embase, and Web of Science, covering publications from 1989 to 2024 inclusive. Starting with advancements in tracer development, we going to explore the rationale for integration of imaging techniques, and the critical role novel formulations such as nanoparticles, nano- and micro-emulsions in enhancing imaging agent delivery and visualisation. Results: The review highlights the use of innovative formulations in improving intranasal administration of neuroimaging agents, showcasing their ability to navigate the complex anatomical and physiological barriers of the nose-to-brain pathway. Various imaging techniques, MRI, PET, SPECT, CT, FUS and OI, were evaluated for their effectiveness in tracking these agents. The findings indicate significant improvements in brain targeting efficiency, rapid uptake, and sustained brain presence using innovative formulations. Conclusion: Future directions involve the development of optimised tracers tailored for intranasal administration, the potential of multimodal imaging approaches, and the implications of these advancements for diagnosing and treating neurological disorders.

No evidence of altered language laterality in people who stutter across different brain imaging studies of speech and language.

A long-standing neurobiological explanation of stuttering is the incomplete cerebral dominance theory, which refers to competition between two hemispheres for 'dominance' over handedness and speech, causing altered language lateralization. Renewed interest in these ideas came from brain imaging findings in people who stutter of increased activity in the right hemisphere during speech production or of shifts in activity from right to left when fluency increased. Here, we revisited this theory using functional MRI data from children and adults who stutter, and typically fluent speakers (119 participants in total) during four different speech and language tasks: overt sentence reading, overt picture description, covert sentence reading and covert auditory naming. Laterality indices were calculated for the frontal and temporal lobes using the laterality index toolbox running in Statistical Parametric Mapping. We also repeated the analyses with more specific language regions, namely the pars opercularis (Brodmann area 44) and pars triangularis (Brodmann area 45). Laterality indices in people who stutter and typically fluent speakers did not differ, and Bayesian analyses provided moderate to anecdotal levels of support for the null hypothesis (i.e. no differences in laterality in people who stutter compared with typically fluent speakers). The proportions of the people who stutter and typically fluent speakers who were left lateralized or had atypical rightward or bilateral lateralization did not differ. We found no support for the theory that language laterality is reduced or differs in people who stutter compared with typically fluent speakers.

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET scans. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.