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Imaging Neuroscience opening editorial
In this editorial we introduce a new non-profit open access journal, Imaging Neuroscience. In April 2023, editors of the journals NeuroImage and NeuroImage:Reports resigned, and a month later launched Imaging Neuroscience. NeuroImage had long been the leading journal in the field of neuroimaging. While the move to fully open access in 2020 represented a positive step toward modern academic practices, the publication fee was set to a level that the editors found unethical and unsustainable. The publisher of NeuroImage, Elsevier, was unwilling to reduce the fee after much discussion. This led us to launch Imaging Neuroscience with MIT Press, intended to replace NeuroImage as our field’s leading journal, but with greater control by the neuroimaging academic community over publication fees and adoption of modern and ethical publishing practices.
Tensor image registration library: Deformable registration of stand-alone histology images to whole-brain post-mortem MRI data.
BACKGROUND: Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data. METHODS: Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline. RESULTS: All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2-0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks. CONCLUSIONS: Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies.
Loneliness, social isolation, and effects on cognitive decline in patients with dementia: A retrospective cohort study using natural language processing
INTRODUCTION: The study aimed to compare cognitive trajectories between patients with reports of social isolation and loneliness and those without. METHODS: Reports of social isolation, loneliness, and Montreal Cognitive Assessment (MoCA) scores were extracted from dementia patients' medical records using natural language processing models and analyed using mixed-effects models. RESULTS: Lonely patients (n = 382), compared to controls (n = 3912), showed an average MoCA score that was 0.83 points lower at diagnosis (P = 0.008) and throughout the disease. Socially isolated patients (n = 523) experienced a 0.21 MoCA point per year faster rate of cognitive decline in the 6 months before diagnosis (P = 0.029), but were comparable to controls before this period. This led to average MoCA scores that were 0.69 MoCA points lower at diagnosis (P = 0.011). DISCUSSION: Lower cognitive levels in lonely and socially isolated patients suggest that these factors may contribute to dementia progression. Highlights: Developed Natural Language Processing model to detect social isolation and loneliness in electronic health records. Patients with loneliness reports have lower Montreal Cognitive Assessment (MoCA) scores than other patients. Social isolation was related to the faster decline in MoCA scores before diagnosis. Social isolation and loneliness are promising targets for slowing cognitive decline.
Human Brain Oscillations: From Physiological Mechanisms to Analysis and Cognition
In the cognitive neuroscience community, there is a strong and growing interest in the function of oscillatory brain activity. Brain oscillations can readily be detected with MEG, which also allows for identifying the sources and networks producing the activity. The aim of this chapter is first to describe the physiological mechanisms responsible for generating brain oscillations in various frequency bands and regions. We will focus on insight gained from the animal literature and physiologically realistic computational modeling. Next, we will explain the signal processing tools typically applied to characterize oscillatory brain activity from human electrophysiological data in the context of cognitive paradigms. The final section will address the main ideas on the functional role of brain oscillations in various frequency bands. This discussion will be focused on recent findings applying MEG.