Elevated levels of brain iron, particularly within the basal ganglia, have been associated with cognitive and motor impairment in normal aging and neurodegenerative conditions. The subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN), despite their high iron content and contribution to motor and cognitive processes, are less frequently studied. This oversight can largely be attributed to the challenges posed by in-vivo assessments of these small, deep-seated midbrain structures. We developed and validated an automated tool for the segmentation of the STN, SN, and RN. Multi-sequence magnetic resonance imaging (MRI) data, including T1-weighted, FLAIR, Quantitative Susceptibility Mapping (QSM) andalongside manual delineation on QSM images of 40 individuals, were used to train segmentation models based on the nnU-Net deep-learning framework. A combination of QSM and FLAIR sequences was found to be optimal for structure segmentation (mean Dice scores of 0.84, 0.91, and 0.94 for STN, SN, and RN, respectively). We next applied the automated segmentation method to an independent 3-year longitudinal dataset, including 175 healthy adults (age range at baseline: 20–79 years old). Structural equation modelling was used to assess iron accumulation over time using age, sex, baseline iron, and regional volume as factors of interest. Cross-sectionally, older age was linearly associated with higher iron load in SN and STN; the association was non-linear in RN. Longitudinally, results indicated significant iron accumulation in the STN (Mean increase = 0.02,p= 0.005) and SN (Mean increase = 0.035,p= 0.001), but not in the RN (Mean increase = 0.015,p= 0.2). Our findings demonstrated high performance of nnU-Net in automated segmentation, and advanced our understanding of iron accumulation in midbrain nuclei in aging.

Lactate has increasingly been recognized as both an important fuel source and a signaling molecule within the brain. Alterations in brain lactate levels are associated with various neurological diseases. Thus, there is great interest in thein vivodetection and measurement of cerebral lactate levels in animals used for investigation of normal brain function and models of disease. Proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive technique used to measure lactate and other metabolites within the brain. However, lactate can be difficult to detect with conventional1H-MRS due to its low abundance and spectral overlap with lipids. In addition, volatile anesthetics used during image acquisition increase lactate production, potentially masking any subtle physiological changes in lactate levels. Here, we made use of a transgenic mouse model in which expression of lactate dehydrogenase A (Ldha), the rate-limiting enzyme of lactate production, was induced within cortical and hippocampal neurons. Unexpectedly,1H-MRS analysis, under typical isoflurane-induced anesthesia of 4% induction followed by 1.6–2% maintenance, revealed no significant elevation of hippocampal lactate levels in neuronalLdhainduction mice compared to control mice. In contrast,1H-MRS analysis, using an isoflurane titration protocol in which mice were sequentially exposed to 1.6%, 2%, and then finally 3% isoflurane, revealed significantly higher hippocampal lactate levels inLdhatransgenic mice compared to controls. In addition, significantly fewer mice were required to detect differences in lactate levels using the isoflurane titration protocol compared to conventional isoflurane-induced anesthesia. Our findings highlight the importance of controlling for the effects of anesthesia when detecting changes in hippocampal lactate levelsin vivoand offer a novel protocol for enhanced cerebral lactate detection.

Diffusion MRI (dMRI) has become a crucial imaging technique in the field of neuroscience, with a growing number of clinical applications. Although most studies still focus on the brain, there is a growing interest in utilizing dMRI to investigate the healthy or injured spinal cord. The past decade has also seen the development of biophysical models that link MR-based diffusion measures to underlying microscopic tissue characteristics, which necessitates validation through ex vivo dMRI measurements. Building upon 13 years of research and development, we present an open-source, MATLAB-based academic software toolkit dubbed ACID:A Comprehensive Toolbox forImage Processing and Modeling of Brain, Spinal Cord, and Ex VivoDiffusion MRI Data. ACID is an extension to the Statistical Parametric Mapping (SPM) software, designed to process and model dMRI data of the brain, spinal cord, and ex vivo specimens by incorporating state-of-the-art artifact correction tools, diffusion and kurtosis tensor imaging, and biophysical models that enable the estimation of microstructural properties in white matter. Additionally, the software includes an array of linear and nonlinear fitting algorithms for accurate diffusion parameter estimation. By adhering to the Brain Imaging Data Structure (BIDS) data organization principles, ACID facilitates standardized analysis, ensures compatibility with other BIDS-compliant software, and aligns with the growing availability of large databases utilizing the BIDS format. Furthermore, being integrated into the popular SPM framework, ACID benefits from a wide range of segmentation, spatial processing, and statistical analysis tools as well as a large and growing number of SPM extensions. As such, this comprehensive toolbox covers the entire processing chain from raw DICOM data to group-level statistics, all within a single software package.

Interpretation of cortical laminar functional magnetic resonance imaging (fMRI) activity requires detailed knowledge of the spatiotemporal haemodynamic response across vascular compartments due to the well-known vascular biases (e.g., the draining veins). Further complications arise from the fact that the spatiotemporal haemodynamic response differs depending on the duration of stimulation. Information about haemodynamic response characteristics across different stimulus durations, cortical depth, and vascular compartments is crucial for future studies using depth-dependent cerebral blood volume (CBV) measurements, which promise higher specificity for the cortical microvasculature than the blood oxygenation level dependent (BOLD) contrast. To date, direct information about CBV dynamics with respect to stimulus duration, cortical depth, and vasculature is missing in humans. Therefore, we characterised the cortical depth-dependent CBV-haemodynamic responses across a wide set of stimulus durations with 0.9 mm isotropic spatial and 0.785 seconds effective temporal resolution in humans using slice-selective slab-inversion vascular space occupancy (SS-SI VASO). Additionally, we investigated signal contributions from macrovascular compartments using fine-scale vascular information from multi-echo gradient-echo (ME-GRE) data at 0.35 mm isotropic resolution. In total, this resulted in7.5 hours of scanning per participant (n = 5). We have three major findings: (I) While we could demonstrate that 1 second stimulation is viable using VASO, more than 12 seconds stimulation provides better CBV responses in terms of specificity to the microvasculature, but durations beyond 24 seconds of stimulation may be wasteful for certain applications. (II) We observed that CBV responses were slightly delayed for superficial compared deeper layers for stimuli4 seconds. (III) While we found increasingly strong BOLD signal responses in vessel-dominated voxels with longer stimulation durations, we found increasingly strong CBV signal responses in vessel-dominated voxels only until 4 second stimulation durations. After 4 seconds, only the signal from non-vessel-dominated voxels kept increasing. This might explain why CBV responses are more specific to the underlying neuronal activity for long stimulus durations.

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that safely modulates neural activity in vivo. Its precision in targeting specific brain networks makes TMS invaluable in diverse clinical applications. For example, TMS is used to treat depression by targeting prefrontal brain networks and their connection to other brain regions. Despite its widespread use, however, the underlying neural mechanisms of TMS are not completely understood. Non-human primates (NHPs) offer an ideal model to study TMS mechanisms through invasive electrophysiological recordings. As such, bridging the gap between NHP experiments and human applications is imperative to ensure translational relevance. Here, we systematically compare the TMS-targeted functional networks in the prefrontal cortex in humans and NHPs. We combine TMS electric field modeling in humans and macaques with resting-state functional magnetic resonance imaging (fMRI) data to compare the functional networks targeted via TMS across species. Distinct stimulation zones in macaque and human models arose, each exhibiting differences in impacted networks (macaque: Frontoparietal Network, Somatomotor Network; human: Frontoparietal Network, Default Network). We identified differences in brain gyrification and functional organization across species as the underlying cause of found network differences. The TMS-network profiles we identified will allow researchers to establish consistency in network stimulation across species, aiding in the translational efforts to develop improved TMS functional network targeting approaches.

Physiological signals such as pulse and respiration strongly contribute to non-neuronal signal change of the blood oxygenation level-dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI). This has been observed not only during task-based but also during resting-state fMRI measurements, where the confounding influence of physiological signals is most pronounced. Over the last decades, a variety of techniques evolved, aiming at detecting and removing physiological artifacts in fMRI time series. These follow either a solely data-driven approach or rely on externally recorded physiological data. To record cardiac and respiratory signals, typically pulse oximetry or electrocardiography (ECG) and a respiration belt are used, respectively. New technologies allow to capture respiratory signal directly with a sensor placed within the spine coil in the patient table, eliminating the need of a respiration belt, which considerably increases participants’ comfort. However, little is known about the effectiveness of these new technologies and how they compare to the standard respiration belt recording. In the current study, we compared the two devices, respiration belt and spine coil sensor, in their suitability for physiological noise removal during a visual perception task and during rest. We did not find any differences in resting-state functional connectivity (RSFC), stimulus-related activity, or residual noise between data corrected with the two recording devices. Our results show that spine coil-derived respiration recordings are as good as belt respiration recordings for physiological noise removal in task-induced activity, with spine coil recordings having an additional advantage in terms of participants’ comfort and artifact susceptibility.

Synthetic MRI offers the advantage of reducing acquisition time and enhancing flexibility through the reconstruction of various contrast weightings from a single set of MRI scans. However, the use of synthetic T1-weighted (synT1-w) MRI can lead to potentially biased measurements of the cross-sectional area (CSA) in the spinal cord when compared to conventionally acquired T1-weighted MRI. This disparity can have implications for comparability and sensitivity of MRI in assessing disease progression or treatment effects in neurodegenerative spinal cord disorders. Thus, this study aimed at improving the accuracy (i.e., difference between synthetic and acquired MRI) of cervical cord CSA measurements (C1-C3 level) based on synT1-w MRI implementing a longitudinal data set acquired from 23 acute spinal cord injury (SCI) patients and 21 healthy controls over 2 years. Moreover, the validity of using synT1-w MRI for tracking cervical cord atrophy following SCI over 2 years was verified. SynT1-w images were reconstructed from quantitative maps of proton density, longitudinal, and effective transverse relaxation rates derived from a multi-parameter mapping protocol. The results showed a minimal bias of -0.31 mm2(-0.5%) in CSA measurements based on synT1-w compared to acquired MRI. Estimates of atrophy rates and average CSA were comparable between synthetic and acquired MRI. A sample size estimation for detecting treatment effects on CSA atrophy after 2 years following SCI revealed that the required sample size is reduced by 13.5% using synT1-w instead of acquired MRI. This study shows high accuracy of synT1-w MRI and demonstrates its applicability in clinical studies for optimizing long MRI protocols.

There is a growing interest in using machine learning (ML) models to perform automatic diagnosis of psychiatric conditions; however, generalising the prediction of ML models to completely independent data can lead to sharp decrease in performance. Patients with different psychiatric diagnoses have traditionally been studied independently, yet there is a growing recognition of neuroimaging signatures shared across them as well as rare genetic copy number variants (CNVs). In this work, we assess the potential of multi-task learning (MTL) to improve accuracy by characterising multiple related conditions with a single model, making use of information shared across diagnostic categories and exposing the model to a larger and more diverse dataset. As a proof of concept, we first established the efficacy of MTL in a context where there is clearly information shared across tasks: the same target (age or sex) is predicted at different sites of data collection in a large functional magnetic resonance imaging (fMRI) dataset compiled from multiple studies. MTL generally led to substantial gains relative to independent prediction at each site. Performing scaling experiments on the UK Biobank, we observed that performance was highly dependent on sample size: for large sample sizes (N > 6000) sex prediction was better using MTL across three sites (N = K per site) than prediction at a single site (N = 3K), but for small samples (N < 500) MTL was actually detrimental for age prediction. We then used established machine-learning methods to benchmark the diagnostic accuracy of each of the 7 CNVs (N = 19–103) and 4 psychiatric conditions (N = 44–472) independently, replicating the accuracy previously reported in the literature on psychiatric conditions. We observed that MTL hurt performance when applied across the full set of diagnoses, and complementary analyses failed to identify pairs of conditions which would benefit from MTL. Taken together, our results show that if a successful multi-task diagnostic model of psychiatric conditions were to be developed with resting-state fMRI, it would likely require datasets with thousands of patients across different diagnoses.

The superficial white matter, the layer of white matter immediately deep to the cortical grey matter, is a highly complex, heterogeneous tissue region comprising dense meshes of neural fibres, a robust population of interstitial neurons, and ongoing glial activity and myelination. It originates from the histologically distinct, developmentally vital subplate in the foetal brain, maintains thalamo-cortical connections throughout adult life, and is a necessary passage for all axons passing between the grey and white matter. Despite these features, the superficial white matter is among the most poorly understood regions of the brain, in part due to its complex makeup and the resulting difficulty of its study. In this review, we present our current knowledge of superficial white matter (SWM) anatomy, development, and response to disease. We discuss the unique challenges encountered in the neuroimaging of this region, including the lack of standard definition and the non-specificity of neuroimaging markers amplified by the complexity of the tissue. We discuss recent innovations and offer potential pathways forward.

Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively turning the MRI scanner into a noninvasive in vivo microscope. In brain white matter, the Standard Model (SM) interprets the dMRI signal in terms of axon dispersion, intra- and extra-axonal water fractions, and diffusivities. However, for SM to be fully applicable and correctly interpreted, it needs to be carefully evaluated using histology. Here, we perform a comprehensive histological validation of the SM parameters, by characterizing white matter (WM) microstructure in sham and injured rat brains using volume electron microscopy and ex vivo dMRI. Sensitivity is evaluated by how well each SM metric correlates with its histological counterpart, and specificity by the lack of correlation with other, non-corresponding histological features. Compared to previously developed SM estimators with constraints, our results show that SMI is the most sensitive and specific. Furthermore, we derive the functional form of the fiber orientation distribution based on its exponentially decreasing rotational invariants. This comprehensive comparison with histology may facilitate the clinical adoption of in vivo dMRI-derived SM parameters as biomarkers for neurological disorders.