Fatty acid hydroxylase-associated neurodegeneration (FAHN/SPG35) is caused by pathogenic variants in FA2H and has been linked to a continuum of specific motor and non-motor neurological symptoms, leading to progressive disability. As an ultra-rare disease, its mutational spectrum has not been fully elucidated. Here, we present the prototypical workup of a novel FA2H variant, including clinical and in silico validation. An 18-year-old male patient presented with a history of childhood-onset progressive cognitive impairment, as well as progressive gait disturbance and lower extremity muscle cramps from the age of 15. Additional symptoms included exotropia, dystonia, and limb ataxia. Trio exome sequencing revealed a novel homozygous c.75C>G (p.Cys25Trp) missense variant in the FA2H gene, which was located in the cytochrome b5 heme-binding domain. Evolutionary conservation, prediction models, and structural protein modeling indicated a pathogenic loss of function. Brain imaging showed characteristic features, thus fulfilling the complete multisystem neurodegenerative phenotype of FAHN/SPG35. In summary, we here present a novel FA2H variant and provide prototypical clinical findings and structural analyses underpinning its pathogenicity.

Isolated evaluation of multiparametric in vivo chemical exchange saturation transfer (CEST) MRI often requires complex computational processing for both correction of B₀ and B₁ inhomogeneity and contrast generation. For that, sufficiently densely sampled Z‐spectra need to be acquired. The list of acquired frequency offsets largely determines the total CEST acquisition time, while potentially representing redundant information. In this work, a linear projection‐based multiparametric CEST evaluation method is introduced that offers fast B₀ and B₁ inhomogeneity correction, contrast generation and feature selection for CEST data, enabling reduction of the overall measurement time. To that end, CEST data acquired at 7 T in six healthy subjects and in one brain tumor patient were conventionally evaluated by interpolation‐based inhomogeneity correction and Lorentzian curve fitting. Linear regression was used to obtain coefficient vectors that directly map uncorrected data to corrected Lorentzian target parameters. L1‐regularization was applied to find subsets of the originally acquired CEST measurements that still allow for such a linear projection mapping. The linear projection method allows fast and interpretable mapping from acquired raw data to contrast parameters of interest, generalizing from healthy subject training data to unseen healthy test data and to the tumor patient dataset. The L1‐regularization method shows that a fraction of the acquired CEST measurements is sufficient to preserve tissue contrasts, offering up to a 2.8‐fold reduction of scan time. Similar observations as for the 7‐T data can be made for data from a clinical 3‐T scanner. Being a fast and interpretable computation step, the proposed method is complementary to neural networks that have recently been employed for similar purposes. The scan time acceleration offered by the L1‐regularization (“CEST‐LASSO”) constitutes a step towards better applicability of multiparametric CEST protocols in a clinical context.

Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints. Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints. Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues-and other tissues-based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.

Cryopreserving the adult brain is challenging due to damage from ice formation, and traditional freezing methods fail to maintain neural architecture and function. Vitrification offers a promising alternative but has not been surveyed in the brain. Here, we demonstrate short-term recovery of the adult murine hippocampus after vitrification of brain slices and of the whole brain in situ. Key features of the hippocampus are preserved, including structural integrity, metabolic responsiveness, neuronal excitability, and synaptic transmission and plasticity. Notably, hippocampal long-term potentiation (LTP) was well preserved, indicating that the cellular machinery of learning and memory remains operational. These findings extend known biophysical limits for cerebral hypothermic shutdown by demonstrating recovery after complete cessation of molecular mobility in the vitreous state and thus contribute to achieving the objective of structural and functional preservation of neural tissue.

Structural brain preservation (SBP) and classical cryonics are techniques aimed at preserving the human brain for potential future applications. Reluctant public discourse around these techniques may be explained with intuitive aversions identified by moral psychology. In the first part of the paper, we conjecture the existence of a self-sustaining cycle of moral condemnation of SBP and classical cryonics due to quick, affect-laden moral intuitions. In the second part, we propose an alternative framing of SBP and classical cryonics through a thought experiment featuring a time machine metaphor called "Schrödinger’s chrono-cat", which might avoid triggering aversive moral intuitions and foster public discourse. We discuss the limitations of this framing and its consequences.

Perfusion impairment is one of the major barriers to using machine perfusion to prepare brain tissue for research and clinical applications. As ischemia progresses, multiple mechanisms, including intravascular obstructions, perivascular cellular edema, mural cell contraction, and vessel wall breakdown, progressively limit the uniform delivery of preservative solutions. This problem is particularly important for connectomics, wherein successful preservation requires both widespread distribution of preservative chemicals and the maintenance of the cellular ultrastructure needed for circuit reconstruction. We conducted a narrative review of interventions used to ameliorate perfusion impairment across multiple fields, including organ transplantation, resuscitation medicine, forensic pathology, embalming, and neuroscience. We evaluated the evidence for these approaches in their original contexts and considered their potential application for brain banking aimed at preserving tissue for connectome reconstruction. We classify interventions into several categories, including anticoagulants, fibrinolytics, vasodilators, washout solutions, surfactants, osmotic agents, colloids, hypothermia, and perfusion pressure optimization. Many of these interventions have the potential to improve perfusion, but each also carries tradeoffs that may adversely affect tissue preservation. As connectomics advances toward profiling larger volumes of human brain tissue, overcoming perfusion impairment is likely to become an increasingly important challenge. This review provides a mechanistic framework for evaluating existing interventions and guiding the development of future perfusion protocols.