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Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts., Competing Interests: Declaration of competing interest None, (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Augmented reality (AR) apps, in which the virtual and real world are combined, can recreate instrumental activities of daily living (IADL) and are therefore promising to measure cognition needed for IADL in early Alzheimer's disease (AD) both in the clinic and in the home settings. The primary aim of this study was to distinguish and classify healthy controls (HC) from participants with AD pathology in an early AD stage using an AR app. The secondary aims were to test the association of the app with clinical cognitive and functional tests and investigate the feasibility of at-home testing using AR. We furthermore investigated the test-retest reliability and potential learning effects of the task. The digital score from the AR app could significantly distinguish HC from preclinical AD (preAD) and prodromal AD (proAD), and preAD from proAD, both with in-clinic and at-home tests. For the classification of the proAD group, the digital score (AUC clinic&#95;visit  = 0.84 [0.75-0.93], AUC at&#95;home  = 0.77 [0.61-0.93]) was as good as the cognitive score (AUC = 0.85 [0.78-0.93]), while for classifying the preAD group, the digital score (AUC clinic&#95;visit  = 0.66 [0.53-0.78], AUC at&#95;home  = 0.76 [0.61-0.91]) was superior to the cognitive score (AUC = 0.55 [0.42-0.68]). In-clinic and at-home tests moderately correlated (rho = 0.57, p < 0.001). The digital score was associated with the clinical cognitive score (rho = 0.56, p < 0.001). No learning effects were found. Here we report the AR app distinguishes HC from otherwise healthy Aβ-positive individuals, both in the outpatient setting and at home, which is currently not possible with standard cognitive tests., (© 2023. The Author(s).)

The underlying pathophysiology of paediatric mild traumatic brain injury and the time-course for biological recovery remains widely debated, with clinical care principally informed by subjective self-report. Similarly, clinical evidence indicates that adolescence is a risk factor for prolonged recovery, but the impact of age-at-injury on biomarkers has not been determined in large, homogeneous samples. The current study collected diffusion MRI data in consecutively recruited patients (n = 203; 8-18 years old) and age and sex-matched healthy controls (n = 170) in a prospective cohort design. Patients were evaluated subacutely (1-11 days post-injury) as well as at 4 months post-injury (early chronic phase). Healthy participants were evaluated at similar times to control for neurodevelopment and practice effects. Clinical findings indicated persistent symptoms at 4 months for a significant minority of patients (22%), along with residual executive dysfunction and verbal memory deficits. Results indicated increased fractional anisotropy and reduced mean diffusivity for patients, with abnormalities persisting up to 4 months post-injury. Multicompartmental geometric models indicated that estimates of intracellular volume fractions were increased in patients, whereas estimates of free water fractions were decreased. Critically, unique areas of white matter pathology (increased free water fractions or increased neurite dispersion) were observed when standard assumptions regarding parallel diffusivity were altered in multicompartmental models to be more biologically plausible. Cross-validation analyses indicated that some diffusion findings were more reproducible when ∼70% of the total sample (142 patients, 119 controls) were used in analyses, highlighting the need for large-sample sizes to detect abnormalities. Supervised machine learning approaches (random forests) indicated that diffusion abnormalities increased overall diagnostic accuracy (patients versus controls) by ∼10% after controlling for current clinical gold standards, with each diffusion metric accounting for only a few unique percentage points. In summary, current results suggest that novel multicompartmental models are more sensitive to paediatric mild traumatic brain injury pathology, and that this sensitivity is increased when using parameters that more accurately reflect diffusion in healthy tissue. Results also indicate that diffusion data may be insufficient to achieve a high degree of objective diagnostic accuracy in patients when used in isolation, which is to be expected given known heterogeneities in pathophysiology, mechanism of injury and even criteria for diagnoses. Finally, current results indicate ongoing clinical and physiological recovery at 4 months post-injury., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Mixed results in the predictive ability of traditional biomarkers to determine cognitive functioning and changes in older adults have led to misdiagnosis and inappropriate treatment plans to address mild cognitive impairment and dementia among older adults. To address this critical gap, the primary goal of the current study is to investigate whether a digital neuro signature (DNS-br) biomarker predicted global cognitive functioning and change over time relative among cognitively impaired and cognitive healthy older adults. The secondary goal is to compare the effect size of the DNS-br biomarker on global cognitive functioning compared to traditional imaging and genomic biomarkers. The tertiary goal is to investigate which demographic and clinical factors predicted DNS-br in cognitively impaired and cognitively healthy older adults., Methods: We conducted two experiments (Study A and Study B) to assess DNS for brain resilience (DNS-br) against the established FDG-PET brain imaging signature for brain resilience, based on a 10 min digital cognitive assessment tool. Study A was a semi-naturalistic observational study that included 29 participants, age 65+, with mild to moderate mild cognitive impairment and AD diagnosis. Study B was also a semi-naturalistic observational multicenter study which included 496 participants (213 mild cognitive impairment (MCI) and 283 cognitively healthy controls (HC), a total of 525 participants-cognitively healthy ( n = 283) or diagnosed with MCI ( n = 213) or AD ( n = 29)., Results: DNS-br total score and majority of the 11 DNS-br neurocognitive subdomain scores were significantly associated with FDG-PET resilience signature, PIB ratio, cerebral gray matter and white matter volume after adjusting for multiple testing. DNS-br total score predicts cognitive impairment for the 80+ individuals in the Altoida large cohort study. We identified a significant interaction between the DNS-br total score and time, indicating that participants with higher DNS-br total score or FDG-PET in the resilience signature would show less cognitive decline over time., Conclusion: Our findings highlight that a digital biomarker predicted cognitive functioning and change, which established biomarkers are unable to reliably do. Our findings also offer possible etiologies of MCI and AD, where education did not protect against cognitive decline., Competing Interests: IT was receiving reimbursements, fees, funding, or salary from Altoida Inc., that holds or has applied for patents relating to the content of the manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Seixas, Rajabli, Pericak-Vance, Jean-Louis, Harms and Tarnanas.)

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer's, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida's digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida's application, achieved a 75% ROC-AUC (receiver operating characteristic - area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline., Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00284-3., Competing Interests: Conflict of interestES is Director of AI and Machine Learning at Bayer. A.S.C. is an official employee of Biogen International and works as Head of Stakeholder Engagement for Alzheimer disease at Biogen. She is also co-founder and CEO of the Women’s Brain Project., (© The Author(s) 2022.)

Diffusion MRI (dMRI) in ex vivo human brain specimens is an important research tool for neuroanatomical investigations and the validation of dMRI techniques. Many ex vivo dMRI applications have benefited from very high dMRI resolutions achievable on small-bore preclinical or animal MRI scanners for small tissue samples. However, the investigation of entire human brains post mortem provides the important context of entire white matter (WM) network systems and entire gray matter (GM) areas connected through these systems. The investigation of intact ex vivo human brains in large bore systems creates challenges due to the limited gradient performance and transmit radio-frequency (B 1 +) inhomogeneities, specially at ultra-high field (UHF, 7T and higher). To overcome these issues, it is necessary to tailor ex vivo diffusion-weighted sequences specifically for high resolution and high diffusion-weighting. Here, we present k T -dSTEAM, which achieves B 1 + homogenization across whole human brain specimens using parallel transmit (pTx) on a 9.4T MR system. We use k T -dSTEAM to obtain multi-shell high b-value and high resolution diffusion-weighted data in ex vivo whole human brains. Isotropic whole brain data can be acquired at high b-value (6000-8000 s/mm 2 ) at high resolution (1000 μm) and at moderate b-value (3000 s/mm 2 ) at ultra-high isotropic resolution (400 μm). As an illustration of the advantages of the ultra-high resolution, tractography across the WM/GM border shows less of the unwanted gyral crown bias, and more high-curvature paths connecting the sulcal wall than at lower resolution. The k T -dSTEAM also allows for acquisition of T 1 and T 2 weighted images suitable for estimating quantitative T 1 and T 2 maps. Finally, multi-shell analysis of k T -dSTEAM data at variable mixing time (TM) is shown as an approach for ex vivo data analysis which is adapted to the strengths of STEAM diffusion-weighting. Here, we use this gain for multi-orientation modelling and crossing-fiber tractography. We show that multi-shell data allows superior multiple orientation tractography of known crossing fiber structures in the brain stem., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.

In diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200.

Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b -value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.

Several magnetic resonance imaging (MRI) contrasts are sensitive to myelin content in gray matter in vivo which has ignited ambitions of MRI-based in vivo cortical histology. Ultra-high field (UHF) MRI, at fields of 7T and beyond, is crucial to provide the resolution and contrast needed to sample contrasts over the depth of the cortex and get closer to layer resolved imaging. Ex vivo MRI of human post mortem samples is an important stepping stone to investigate MRI contrast in the cortex, validate it against histology techniques applied in situ to the same tissue, and investigate the resolutions needed to translate ex vivo findings to in vivo UHF MRI. Here, we investigate key technology to extend such UHF studies to large human brain samples while maintaining high resolution, which allows investigation of the layered architecture of several cortical areas over their entire 3D extent and their complete borders where architecture changes. A 16 channel cylindrical phased array radiofrequency (RF) receive coil was constructed to image a large post mortem occipital lobe sample (~80×80×80mm 3 ) in a wide-bore 9.4T human scanner with the aim of achieving high-resolution anatomical and quantitative MR images. Compared with a human head coil at 9.4T, the maximum Signal-to-Noise ratio (SNR) was increased by a factor of about five in the peripheral cortex. Although the transmit profile with a circularly polarized transmit mode at 9.4T is relatively inhomogeneous over the large sample, this challenge was successfully resolved with parallel transmit using the kT-points method. Using this setup, we achieved 60μm anatomical images for the entire occipital lobe showing increased spatial definition of cortical details compared to lower resolutions. In addition, we were able to achieve sufficient control over SNR, B 0 and B 1 homogeneity and multi-contrast sampling to perform quantitative T 2 * mapping over the same volume at 200μm. Markov Chain Monte Carlo sampling provided maximum posterior estimates of quantitative T 2 * and their uncertainty, allowing delineation of the stria of Gennari over the entire length and width of the calcarine sulcus. We discuss how custom RF receive coil arrays built to specific large post mortem sample sizes can provide a platform for UHF cortical layer-specific quantitative MRI over large fields of view., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Starting in the 1991-1992 school year, Wisconsin was the first state to have a mandatory minimum weight established for high school wrestlers, along with a comprehensive standardized nutrition education program. The minimum weight project was phased in over 2 years. The goal of the project is to scientifically predict a healthy minimum weight for high school wrestlers. No guidelines were previously set and unhealthy weight loss practices are sometimes used to achieve desired weights. A wrestler's weight is often determined by the need to fill a wrestling class and not on a good scientific basis. Many wrestling coaches across the state have tried to implement individual programs, but until this project there had been no coordinated, collective effort.

Aliquotes of human amniotic fluid (AF), fetal serum (FS), and cord blood (CB) were obtained as by-products of routine clinical diagnostic procedures at term or in the second trimester of pregnancy. When samples of CB were applied to a pH 5.5-4 chromatofocusing gradient, three isoforms of AFP could be resolved; a pl 4.57 form (isoform IA, 52% AFP), a pl 4.27 form (isoform IB, 43% AFP), and one species that was bound to the column but could be eluted with 1.0 M NaCl (isoform II, pl less than 4.00, 5% AFP). Term AF displayed a profile similar to that observed in term CB. When samples of 15-20-week gestation AF were chromatofocused, the immunoreactive AFP recovered was distributed between isoform IA and IB (60%) and isoform II (40%). FS and AF obtained from same pregnancy (23-26 weeks) displayed an identical chromatofocusing profile. Aliquotes of AF subjected to conA revealed 83% reactive variants compared with greater than 95% reactive variants for CB. FS displayed a conA profile identical to CB. When individual CB charge isoforms were isolated and subjected to conA analysis, greater than 97% of the AFP bound to conA. In contrast, when AFP isoform IA and IB were isolated from midgestation AF, approximately 22% of the AFP did not bind to the lectin while 100% of isolated AFP isoform II eluted as the reactive variant. These data suggest that human AFP exists as at least three charge and two lectin variants and that the charge profile may change during fetal development.

The naturally occurring acidic forms of ovine pituitary LH were examined by chromatofocusing. Immunoreactive oLH eluted as six variants (elutions pHs of greater than 7.4, 6.83, 6.59, 6.23, 5.5-4.0, and less than 4.0, designated as "basic forms" and variants II-VI, respectively). In rams, the percentage in each variant averaged 46, 9, 28, 10, 5 and 2, respectively, of the total LH recovered. oLH from non-implanted wethers or wethers implanted with dihydrotestosterone, E2 or dihydrotestosterone + E2 eluted as six similar variants but differences were noted in the distribution of oLH among the variants. Compared to rams, pituitary extracts from non-implanted and dihydrotestosterone-implanted wethers contained significantly higher percentages of oLH eluting as basic forms. In contrast, E2- or dihydrotestosterone-implanted wethers exhibited significantly higher percentages of oLH in the acidic variants. Each of these variants was demonstrated to contain biologically active oLH using an in vitro bioassay. Furthermore, the basic forms of oLH were the most abundant biologically active forms present in pituitary extracts. These results suggest that in addition to the seven basic forms of oLH previously described, there are at least five naturally occurring acidic forms of oLH in pituitary extracts which are biologically active, and the distribution of pituitary oLH among its isohormones, including the acidic forms, appears to be modified by steroids.

Chromatofocusing was used to characterize the charge microheterogeneity of crude pituitary, highly purified native bovine (b) and deglycosylated (dg) thyrotropin (TSH) preparations. Greater than 90% of crude pituitary TSH and native bTSH-I-1 bound to concanavalin-A (conA) columns while dgbTSH was excluded from the column. Extracts of ovine (o) pituitaries contained at least nine species (isohormones) of immunoreactive TSH when chromatofocused on pH 7.5-4 gradients. Highly purified native bTSH-I-1 displayed a similar elution profile. In contrast, dgbTSH eluted as a single homogeneous species with an apparent pI greater than 7.5. When subjected to chromatofocusing on a pH 10.5-7 gradient, 68% of crude pituitary oTSH and 96% of native bTSH-I-1 was bound to the column but could be eluted with NaCl indicating acidic species, while at least three peaks of dgbTSH could be resolved having apparent pI's of 9.12, 9.03 and 8.98. These data suggest that although removal of the carbohydrate moieties markedly alters the isohormone pattern of TSH, chemical deglycosylation does not completely eliminate the charge microheterogeneity of bTSH.

It was found by using spectrophotometric, spectrofluorometric, and high-pressure liquid chromatography that four subspecies of Bacillus thuringiensis produce coproporphyrin. The porphyrin isomer was identified as coproporphyrin I for B. thuringiensis subsp. kurstaki (HD1). The porphyrin was isolated both from spores and from a variety of spent growth media. The quantity of porphyrin released by each Bacillus subspecies differed. The rank order of porphyrin production follows: B. thuringiensis subsp. kurstaki HD1 > B. thuringiensis subsp. thuringiensis HD27 > B. thuringiensis subsp. thuringiensis HD41 > B. thuringiensis subsp. darmstadiensis HD199.