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Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight 1 . Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories 2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones 3 , showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes. MRI data from more than 100 studies have been aggregated to yield new insights about brain development and ageing, and create an interactive open resource for comparison of brain structures throughout the human lifespan, including those associated with neurological and psychiatric disorders.

Prostate-cancer biopsy directed at areas of MRI abnormality was compared with standard transrectal ultrasonographic biopsy for diagnostic specificity and sensitivity. MRI-targeted biopsy identified more high-risk cancers and fewer clinically insignificant tumors.

It is not known exactly where amyloid-β (Aβ) fibrils begin to accumulate in individuals with Alzheimer’s disease (AD). Recently, we showed that abnormal levels of Aβ42 in cerebrospinal fluid (CSF) can be detected before abnormal amyloid can be detected using PET in individuals with preclinical AD. Using these approaches, here we identify the earliest preclinical AD stage in subjects from the ADNI and BioFINDER cohorts. We show that Aβ accumulation preferentially starts in the precuneus, medial orbitofrontal, and posterior cingulate cortices, i.e., several of the core regions of the default mode network (DMN). This early pattern of Aβ accumulation is already evident in individuals with normal Aβ42 in the CSF and normal amyloid PET who subsequently convert to having abnormal CSF Aβ42. The earliest Aβ accumulation is further associated with hypoconnectivity within the DMN and between the DMN and the frontoparietal network, but not with brain atrophy or glucose hypometabolism. Our results suggest that Aβ fibrils start to accumulate predominantly within certain parts of the DMN in preclinical AD and already then affect brain connectivity. Abnormal levels of Aβ42 in the cerebrospinal fluid occur prior to a positive amyloid PET scan in the brain of individuals with Alzheimer’s disease and here the authors use this temporal pattern to identify individuals with very early stage AD. They show that Aβ fibrils start to accumulate in some of the regions of the default mode network and affect brain connectivity before neurodegeneration occurs.

The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.

To elucidate the relationship between cerebrospinal fluid (CSF) total-tau (T-tau) and phosphorylated tau (P-tau) with the tau PET ligand (18)F-AV-1451 in Alzheimer's disease (AD), we examined 30 cognitively healthy elderly (15 with preclinical AD), 14 prodromal AD, and 39 AD dementia patients. CSF T-tau and P-tau were highly correlated (R=0.92, P<0.001), but they were only moderately associated with retention of (18)F-AV-1451, and mainly in demented AD patients. (18)F-AV-1451, but not CSF T-tau or P-tau, was strongly associated with atrophy and cognitive impairment. CSF tau was increased in preclinical AD, despite normal (18)F-AV-1451 retention. However, not all dementia AD patients exhibited increased CSF tau, even though (18)F-AV-1451 retention was always increased at this disease stage. We conclude that CSF T-tau and P-tau mainly behave as biomarkers of \"disease state\", since they appear to be increased in many cases of AD at all disease stages, already before the emergence of tau aggregates. In contrast, (18)F-AV-1451 is a biomarker of \"disease stage\", since it is increased in clinical stages of the disease, and is associated with brain atrophy and cognitive decline.

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research. [Display omitted]

The purpose of the EANM Dosimetry Committee is to provide recommendations and guidance to scientists and clinicians on patient-specific dosimetry. Radiopharmaceuticals labelled with lutetium-177 (177Lu) are increasingly used for therapeutic applications, in particular for the treatment of metastatic neuroendocrine tumours using ligands for somatostatin receptors and prostate adenocarcinoma with small-molecule PSMA-targeting ligands. This paper provides an overview of reported dosimetry data for these therapies and summarises current knowledge about radiation-induced side effects on normal tissues and dose-effect relationships for tumours. Dosimetry methods and data are summarised for kidneys, bone marrow, salivary glands, lacrimal glands, pituitary glands, tumours, and the skin in case of radiopharmaceutical extravasation. Where applicable, taking into account the present status of the field and recent evidence in the literature, guidance is provided. The purpose of these recommendations is to encourage the practice of patient-specific dosimetry in therapy with 177Lu-labelled compounds. The proposed methods should be within the scope of centres offering therapy with 177Lu-labelled ligands for somatostatin receptors or small-molecule PSMA.

Magnetization Transfer Contrast (MTC) and Chemical Exchange Saturation Transfer (CEST) experiments measure the transfer of magnetization from molecular protons to the solvent water protons, an effect that becomes apparent as an MRI signal loss (“saturation”). This allows molecular information to be accessed with the enhanced sensitivity of MRI. In analogy to Magnetic Resonance Spectroscopy (MRS), these saturation data are presented as a function of the chemical shift of participating proton groups, e.g. OH, NH, NH2, which is called a Z-spectrum. In tissue, these Z-spectra contain the convolution of multiple saturation transfer effects, including nuclear Overhauser enhancements (NOEs) and chemical exchange contributions from protons in semi-solid and mobile macromolecules or tissue metabolites. As a consequence, their appearance depends on the magnetic field strength (B0) and pulse sequence parameters such as B1 strength, pulse shape and length, and interpulse delay, which presents a major problem for quantification and reproducibility of MTC and CEST effects. The use of higher B0 can bring several advantages. In addition to higher detection sensitivity (signal-to-noise ratio, SNR), both MTC and CEST studies benefit from longer water T1 allowing the saturation transferred to water to be retained longer. While MTC studies are non-specific at any field strength, CEST specificity is expected to increase at higher field because of a larger chemical shift dispersion of the resonances of interest (similar to MRS). In addition, shifting to a slower exchange regime at higher B0 facilitates improved detection of the guanidinium protons of creatine and the inherently broad resonances of the amine protons in glutamate and the hydroxyl protons in myoinositol, glycogen, and glucosaminoglycans. Finally, due to the higher mobility of the contributing protons in CEST versus MTC, many new pulse sequences can be designed to more specifically edit for CEST signals and to remove MTC contributions. •Basics of nuclear Overhauser enhancement (NOE) and chemical exchange saturation transfer (CEST).•Comprehensive description of the features of the endogenous saturation spectrum (Z-spectrum) in MRI.•Explanation of advantages of using higher magnetic field for CEST MRI.•Critical assessment of CEST data analysis approaches.•Critical assessment of early CEST applications for the brain.

Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence–enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning–based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100 Hz.A deep learning–based algorithm enables efficient reconstruction of light-field microscopy data at video rate. In addition, concurrently acquired light-sheet microscopy data provide ground truth data for training, validation and refinement of the algorithm.