PURPOSE:The clinical feasibility and translation of many advanced quantitative MRI (qMRI) techniques are inhibited by their restriction to 'research mode', due to resource-intensive, offline parameter estimation. This work aimed to achieve 'clinical mode' qMRI, by real-time, inline parameter estimation with a trained neural network (NN) fully integrated into a vendor's image reconstruction environment, therefore facilitating and encouraging clinical adoption of advanced qMRI techniques. METHODS:The Siemens Image Calculation Environment (ICE) pipeline was customized to deploy trained NNs for advanced diffusion MRI parameter estimation with Open Neural Network Exchange (ONNX) Runtime. Two fully-connected NNs were trained offline with data synthesized with the neurite orientation dispersion and density imaging (NODDI) model, using either conventionally estimated (NNMLE) or ground truth (NNGT) parameters as training labels. The strategy was demonstrated online in two healthy volunteers (one rescanned) and evaluated offline with synthetic data, testing two diffusion protocols. RESULTS:NNs were successfully integrated and deployed natively in ICE, performing inline, whole-brain, in vivo NODDI parameter estimation in < 10 s. The proposed workflow was reproducible across protocols, volunteers and rescans. DICOM parametric maps were exported from the scanner for further analyses. Comparisons between NNMLE and NNGT suggested NNMLE parameter estimates to be more consistent with conventional fitting, a finding supported by offline evaluations. CONCLUSION:Real-time, inline parameter estimation with the proposed generalizable framework resolves a key practical barrier to the potential clinical uptake of advanced qMRI methods, enabling their efficient integration into clinical workflows. Next steps include incorporation of pre-processing methods and evaluation in pathology.

Huntington’s disease (HD) is an autosomal dominant neurodegenerative disease with the age at which characteristic symptoms manifest strongly influenced by inherited HTT CAG length. Somatic CAG expansion occurs throughout life and understanding the impact of somatic expansion on neurodegeneration is key to developing therapeutic targets. In 57 HD gene expanded (HDGE) individuals, ~23 years before their predicted clinical motor diagnosis, no significant decline in clinical, cognitive or neuropsychiatric function was observed over 4.5 years compared with 46 controls (false discovery rate (FDR) > 0.3). However, cerebrospinal fluid (CSF) markers showed very early signs of neurodegeneration in HDGE with elevated neurofilament light (NfL) protein, an indicator of neuroaxonal damage (FDR = 3.2 × 10−12), and reduced proenkephalin (PENK), a surrogate marker for the state of striatal medium spiny neurons (FDR = 2.6 × 10−3), accompanied by brain atrophy, predominantly in the caudate (FDR = 5.5 × 10−10) and putamen (FDR = 1.2 × 10−9). Longitudinal increase in somatic CAG repeat expansion ratio (SER) in blood was a significant predictor of subsequent caudate (FDR = 0.072) and putamen (FDR = 0.148) atrophy. Atypical loss of interruption HTT repeat structures, known to predict earlier age at clinical motor diagnosis, was associated with substantially faster caudate and putamen atrophy. We provide evidence in living humans that the influence of CAG length on HD neuropathology is mediated by somatic CAG repeat expansion. These critical mechanistic insights into the earliest neurodegenerative changes will inform the design of preventative clinical trials aimed at modulating somatic expansion. ClinicalTrials.gov registration: NCT06391619 . A comprehensive longitudinal analysis of individuals with preclinical Huntington’s disease identifies biomarkers of neurodegeneration and somatic expansion in blood DNA, detectable years before symptom onset.

Diffusion MRI (dMRI) is a powerful technique for investigating tissue microstructure properties. However, advanced dMRI models are typically complex and nonlinear, requiring a large number of acquisitions in the q-space. Deep learning techniques, specifically optimization-based networks, have been proposed to improve the model fitting with limited q-space data. Previous optimization procedures relied on the empirical selection of iteration block numbers and the network structures were based on the iterative hard thresholding (IHT) algorithm, which may suffer from instability during sparse reconstruction. In this study, we introduced an extragradient and noise-tuning adaptive iterative network, a generic network for estimating dMRI model parameters. We proposed an adaptive mechanism that flexibly adjusts the sparse representation process, depending on specific dMRI models, datasets, and downsampling strategies, avoiding manual selection and accelerating inference. In addition, we proposed a noise-tuning module to assist the network in escaping from local minimum/saddle points. The network also included an additional projection of the extragradient to ensure its convergence. We evaluated the performance of the proposed network on the neurite orientation dispersion and density imaging (NODDI) model and diffusion basis spectrum imaging (DBSI) model on two 3T Human Connectome Project (HCP) datasets and a 7T HCP dataset with six different downsampling strategies. The proposed framework demonstrated superior accuracy and generalizability compared to other state-of-the-art microstructural estimation algorithms.

This paper presents NimbleReg, a light-weight deep-learning (DL) framework for diffeomorphic image registration leveraging surface representation of multiple segmented anatomical regions. Deep learning has revolutionized image registration but most methods typically rely on cumbersome gridded representations, leading to hardware-intensive models. Reliable fine-grained segmentations, that are now accessible at low cost, are often used to guide the alignment. Light-weight methods representing segmentations in terms of boundary surfaces have been proposed, but they lack mechanism to support the fusion of multiple regional mappings into an overall diffeomorphic transformation. Building on these advances, we propose a DL registration method capable of aligning surfaces from multiple segmented regions to generate an overall diffeomorphic transformation for the whole ambient space. The proposed model is light-weight thanks to a PointNet backbone. Diffeomoprhic properties are guaranteed by taking advantage of the stationary velocity field parametrization of diffeomorphisms. We demonstrate that this approach achieves alignment comparable to state-of-the-art DL-based registration techniques that consume images.

Quantitative MR imaging with self-supervised deep learning promises fast and robust parameter estimation without the need for training labels. However, previous studies have reported significant bias in self-supervised parameter estimates as the signal-to-noise ratio (SNR) decreases. A possible source of this bias may be the choice of the mean squared error (MSE) loss function for network training, which is incompatible with MR magnitude signals. To address this, we introduce the Rician likelihood loss for self-supervised learning, which explicitly accounts for the distribution of MR magnitude signals during training. We develop a stable and accurate numerical approximation of the negative log Rician (NLR) likelihood loss and compare its performance against the MSE loss using the intravoxel incoherent motion (IVIM) model as an exemplar. Parameter estimation performance was evaluated in simulated data and real data in terms of accuracy, precision and overall error by quantifying the bias, standard deviation and root mean squared error of network predictions against ground truth (or gold standard) values over a range of SNRs. Results show that self-supervised networks trained with the NLR loss have increased accuracy (reduced bias) of IVIM diffusion coefficient at low SNR, at the cost of reduced precision. As SNR increases, the performance of the NLR and MSE losses converges, resulting in estimates with higher accuracy, higher precision and lower total error. The NLR loss has potential for broad application in quantitative MR imaging by enabling more accurate parameter estimation from noisy data. The NLR loss is available as a Python package: https://pypi.org/project/RicianLoss.

To quantitatively measure the volume of white matter hyperintensities (WMHs) in different parts of the brain in patients with different types of cognitive function and analyze the relationship between WMH volume and cognitive function to obtain a threshold WMH volume for the early detection and clinical assessment of cognitive dysfunction. The clinical data and magnetic resonance imaging (MRI) data of patients with WMHs indicated by cranial MR in the Department of General Medicine of Shandong Provincial Third Hospital were collected. The FLAIR sequence images of the patients were subsequently analyzed with computer automated detection technology. Through deep learning-based 3D reconstruction, the specific volumes of the patients' WMHs were obtained. Patients were divided into three groups according to the Fazekas scale score: Fazekas score 1, Fazekas score 2, and Fazekas score 3. The WMH volumes within each group were subsequently compared, and the correlations between the WMH volumes of the patients in each group and their Montreal Cognitive Assessment (MoCA) scores, Trail Making Test A (TMT-A) scores, Trail Making Test B (TMT-B) scores, age, duration of hypertension, duration of diabetes, basic information, etc., were analyzed. The patients were subsequently divided into a normal group (MoCA > 25) and a mild cognitive impairment group (18 < MoCA ≤ 25) on the basis of their MoCA scores. The WMH volumes in each group were then calculated separately. The cutoff values of the WMH volume for differentiating between the normal group and mild cognitive impairment group were obtained through receiver operating characteristic (ROC) curve analysis. The MoCA scores significantly differed among the three Fazekas score groups (r = - 0.5716, P < 0.0001). There were also statistically significant differences in the total volume of WMHs among the three groups (r = 0.7527, P < 0.0001). WMH volume was positively correlated with the TMT-A and TMT-B scores (r = 0.2345,  P< 0.05; r = 0.2404, P < 0.05) but negatively correlated with the MoCA score (r = - 0.4789, P < 0.0001). Moreover, WMH volume was positively associated with the duration of hypertension (F = 4.743, P < 0.05) but not with the duration of diabetes (F = 1.431, P = 0.2456). The cutoff value of WMH volume between the normal group and mild cognitive impairment group was 15.474900; at this value, the sensitivity of the WMH volume in discriminating the two groups was 0.808, and the specificity was 0.556. Automated detection technology can successfully be used to obtain the volume of WMHs in different parts of patients' brains. Since WMH volume is correlated with cognitive function scores, we can use MRI to identify and assess individuals who show potential early signs of cognitive dysfunction and administer early interventions. These findings provide potential preventive and therapeutic targets for the clinical diagnosis and treatment of cognitive dysfunction.

The structure of grey matter has long been a key focus in neuroscience, as cell morphology varies by type and can be affected by neurological conditions. Understanding these variations is essential for studying brain function and disease. Diffusion-weighted MRI (dMRI) is a powerful non-invasive tool for examining cellular microstructure in vivo. However, for dMRI to accurately reflect cellular features, it is crucial to determine which aspects of morphology influence its measurements. Proper interpretation of dMRI data depends on understanding its sensitivity to different cellular characteristics. Despite growing interest in cellular morphology, there has been no systematic report on the key features defining different neural cell types. To address this, we analyzed over 11,500 three-dimensional cellular reconstructions across three species and nine cell types, establishing reference values for critical morphological traits. These traits fall into three categories: structural features that define the cell's skeletal framework, shape features that describe spatial organization, and topological features that break down cellular structure to distinguish cell types. Beyond reporting these reference values, we examine their relevance for dMRI, identifying which neural features dMRI can detect and which cell types may be distinguishable. To complement the statistical analysis, we also provide high resolution 3D surface meshes representative of each cell type and species. This work provides essential benchmarks for grey matter research, offering new guidelines on linking neuroimaging measurements to neurobiology. These reference values will be a valuable resource for neuroscientists and neuroimaging researchers, aiding in the interpretation of imaging data and the refinement of brain tissue models.

Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted memory reactivation (TMR), involving cueing learned material during posttraining sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and posttraining sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during posttraining sleep affects performance gains and delayed microstructural remodeling, using both standard diffusion tensor imaging and advanced neurite orientation dispersion and density imaging. Sixty healthy young adults participated in a 5 days protocol, undergoing five diffusion-weighted imaging sessions, pre- and post-two motor sequence training sessions, and after a posttraining night of either regular sleep (RS) or TMR. Results demonstrated rapid skill acquisition on day 1, followed by performance stabilization on day 2, and improvement on day 5, in both RS and TMR groups. (Re)training induced widespread microstructural changes in motor-related areas, initially involving the hippocampus, followed by a delayed engagement of the caudate nucleus. Mean Diffusivity changes were accompanied by increased neurite density index in the putamen, suggesting increased neurite density, while free water fraction reduction indicated glial reorganization. TMR-related structural differences emerged in the dorsolateral prefrontal cortex on day 2 and the right cuneus on day 5, suggesting unique sleep TMR-related neural reorganization patterns. Persistence of practice-related structural changes, although moderated over time, suggests a lasting neural network reorganization, partially mediated by sleep TMR.

OBJECTIVE:Amygdala enlargement can occur in temporal lobe epilepsy, and increased amygdala volume is also reported in sudden unexpected death in epilepsy (SUDEP). Apnea can be induced by amygdala stimulation, and postconvulsive central apnea (PCCA) and generalized seizures are both known SUDEP risk factors. Neurite orientation dispersion and density imaging (NODDI) has recently provided additional information on altered amygdala microstructure in SUDEP. In a series of 24 surgical temporal lobe epilepsy cases, our aim was to quantify amygdala cellular pathology parameters that could predict enlargement, NODDI changes, and ictal respiratory dysfunction. METHODS:Using whole slide scanning automated quantitative image analysis methods, parallel evaluation of myelin, axons, dendrites, oligodendroglia, microglia, astroglia, neurons, serotonergic networks, mTOR-pathway activation (pS6) and phosphorylated tau (pTau; AT8, AT100, PHF) in amygdala, periamygdala cortex, and white matter regions of interest were compared with preoperative magnetic resonance imaging data on amygdala size, and in 13 cases with NODDI and evidence of ictal-associated apnea. RESULTS:We observed significantly higher glial labeling (Iba1, glial fibrillary acidic protein, Olig2) in amygdala regions compared to cortex and a strong positive correlation between Olig2 and Iba1 in the amygdala. Larger amygdala volumes correlated with lower microtubule-associated protein (MAP2), whereas higher NODDI orientation dispersion index correlated with lower Olig2 cell densities. In the three cases with recorded PCCA, higher MAP2 and pS6-235 expression was noted than in those without. pTau did not correlate with SUDEP risk factors, including seizure frequency. SIGNIFICANCE:Histological quantitation of amygdala microstructure can shed light on enlargement and diffusion imaging alterations in epilepsy to explore possible mechanisms of amygdala dysfunction, including mTOR pathway activation, that in turn may increase the risk for SUDEP.

Quantitative magnetic resonance imaging (qMRI) is increasingly investigated for use in a variety of clinical tasks from diagnosis, through staging, to treatment monitoring. However, experiment design in qMRI, the identification of the optimal acquisition protocols, has been focused on obtaining the most precise parameter estimations, with no regard for the specific requirements of downstream tasks. Here we propose SCREENER: A general framework for task-specific experiment design in quantitative MRI. SCREENER incorporates a task-specific objective and seeks the optimal protocol with a deep-reinforcement-learning (DRL) based optimization strategy. To illustrate this framework, we employ a task of classifying the inflammation status of bone marrow using diffusion MRI data with intravoxel incoherent motion (IVIM) modelling. Results demonstrate SCREENER outperforms previous ad hoc and optimized protocols under clinical signal-to-noise ratio (SNR) conditions, achieving significant improvement, both in binary classification tasks, e.g. from 67 Additionally, we show this improvement is robust to the SNR. Lastly, we demonstrate the advantage of DRL-based optimization strategy, enabling zero-shot discovery of near-optimal protocols for a range of SNRs not used in training. In conclusion, SCREENER has the potential to enable wider uptake of qMRI in the clinic.

BACKGROUND:This study presents large-scale normative models of white matter (WM) organization across the lifespan, using diffusion magnetic resonance imaging data from over 25,000 healthy individuals ages 0 to 100 years from multiple cohorts including the Human Connectome Project (HCP) Lifespan and UK Biobank. These models capture lifespan trajectories and interindividual variation in fractional anisotropy (FA), a marker of WM integrity. METHODS:By addressing non-Gaussian data distributions, self-reported race, and site effects, the models offer reference baselines across diverse ages and scanning conditions. We applied these FA models to the HCP Early Psychosis cohort and performed a multivariate analysis to map symptoms onto deviations from multimodal normative models using multiview sparse canonical correlation analysis. RESULTS:Our results reveal extensive WM heterogeneity in psychosis, which is not captured by group-level analyses, with key regions identified, including the right uncinate fasciculus and thalami. CONCLUSIONS:These normative models offer valuable tools for individualized WM deviation identification, improving precision in psychiatric assessments. All models are publicly available for community use.

ABSTRACT:Chronic pain is common in young people and can have a major life impact. Despite the burden of chronic pain, mechanisms underlying chronic pain development and persistence are still poorly understood. Specifically, white matter (WM) connectivity has remained largely unexplored in pediatric chronic pain. Using diffusion-weighted imaging, this study examined WM microstructure in adolescents (age M = 15.8 years, SD = 2.8 years) with chronic pain (n = 44) compared with healthy controls (n = 24). Neurite orientation dispersion and density imaging modeling was applied, and voxel-based whole-white-matter analyses were used to obtain an overview of potential alterations in youth with chronic pain and tract-specific profile analyses to evaluate microstructural profiles of tracts of interest more closely. Our main findings are that (1) youth with chronic pain showed widespread elevated orientation dispersion compared with controls in several tracts, indicative of less coherence; (2) signs of neurite density tract-profile alterations were observed in several tracts of interest, with mainly higher density levels in patients; and (3) several WM microstructural alterations were associated with pain catastrophizing in the patient group. Implicated tracts include both those connecting cortical and limbic structures (uncinate fasciculus, cingulum, anterior thalamic radiation), which were associated with pain catastrophizing, as well as sensorimotor tracts (corticospinal tract). By identifying alterations in the biologically informative WM microstructural metrics orientation dispersion and neurite density, our findings provide important and novel mechanistic insights for understanding the pathophysiology underlying chronic pain. Taken together, the data support alterations in fiber organization as a meaningful characteristic, contributing process to the chronic pain state.

As part of the hypothalamic-pituitary adrenal (HPA) axis, the hypothalamus exerts pivotal influence on metabolic and endocrine homeostasis. With age, these processes are subject to considerable change, resulting in increased prevalence of physical disability and cardiac disorders. Yet, research on the aging human hypothalamus is lacking. To assess detailed hypothalamic microstructure in middle adulthood, 39 healthy participants (35–65 years) underwent comprehensive structural magnetic resonance imaging. In addition, we studied HPA axis dysfunction proxied by hair cortisol and waist circumference as potential risk factors for hypothalamic alterations. We provide first evidence of regionally different hypothalamic microstructure, with age effects in its anterior–superior subunit, a critical area for HPA axis regulation. Further, we report that waist circumference was related to increased free water and decreased iron content in this region. In age, hair cortisol was additionally associated with free water content, such that older participants with higher cortisol levels were more vulnerable to free water content increase than younger participants. Overall, our results suggest no general age-related decline in hypothalamic microstructure. Instead, older individuals could be more susceptible to risk factors of hypothalamic decline especially in the anterior–superior subregion, including HPA axis dysfunction, indicating the importance of endocrine and stress management in age.

This paper presents an efficient feature-based approach to initialize non-linear image registration. Today, nonlinear image registration is dominated by methods relying on intensity-based similarity measures. A good estimate of the initial transformation is essential, both for traditional iterative algorithms and for recent one-shot deep learning (DL)-based alternatives. The established approach to estimate this starting point is to perform affine registration, but this may be insufficient due to its parsimonious, global, and non-bending nature. We propose an improved initialization method that takes advantage of recent advances in DL-based segmentation techniques able to instantly estimate fine-grained regional delineations with state-of-the-art accuracies. Those segmentations are used to produce local, anatomically grounded, feature-based affine matchings using iteration-free closed-form expressions. Estimated local affine transformations are then fused, with the log-Euclidean polyaffine framework, into an overall dense diffeomorphic transformation. We show that, compared to its affine counterpart, the proposed initialization leads to significantly better alignment for both traditional and DL-based non-linear registration algorithms. The proposed approach is also more robust and significantly faster than commonly used affine registration algorithms such as FSL FLIRT.

Evidence for sleep-dependent changes in micro-structural neuroplasticity remains scarce, despite the fact that it is a mandatory correlate of the reorganization of learning-related functional networks. We investigated the effects of post-training sleep on structural neuroplasticity markers measuring standard diffusion tensor imaging (DTI) mean diffusivity (MD) and the revised biophysical neurite orientation dispersion and density imaging (NODDI) free water fraction (FWF) and neurite density (NDI) parameters that enable disentangling whether MD changes result from modifications in neurites or in other cellular components (e.g., glial cells). Thirty-four healthy young adults were scanned using diffusion weighted imaging [DWI] on Day1 before and after 40-minutes route learning (navigation) in a virtual environment, then were sleep deprived (SD) or slept normally (RS) for the night. After recovery sleep for 2 nights, they were scanned again (Day4) before and after 40-minutes route learning (navigation) in an extended environment. Sleep-related microstructural changes were computed on DTI (MD) and NODDI (NDI and FWF) parameters in the cortical ribbon and subcortical hippocampal and striatal regions of interest (ROIs). Results disclosed navigation learning-related decreased DWI parameters in the cortical ribbon (MD, FWF) and subcortical (MD, FWF, NDI) areas. Post-learning sleep-related changes were found at Day4 in the extended learning session (pre- to post-relearning percentage changes), suggesting a rapid sleep-related remodelling of neurites and glial cells subtending learning and memory processes in basal ganglia and hippocampal structures.

In Alzheimer’s disease, early amyloid-β and tau deposition may have differential downstream effects on synaptic function and neuronal loss in a stage-dependent manner. Amyloid-related synaptic changes are thought to precede tau accumulation, which starts in the medial temporal lobe before propagating and leading to neurodegeneration. We explored whether microstructural MRI measures can detect these different sequential cortical changes. Seventy-nine healthy, asymptomatic individuals, recruited from the Insight 46 study of the British 1946 birth cohort underwent combined PET/MR with [18F]florbetapir Aβ-PET at ∼73yrs, and [18F]MK-6240 tau-PET at ∼76yrs. Standard uptake value ratios were calculated using a whole cerebellar reference for florbetapir and an inferior cerebellar reference for MK-6240. Multi-shell diffusion MRI was acquired; neurite orientation dispersion and density imaging was used to quantify neurite density (NDI) and orientation dispersion (ODI, a proxy measure of dendritic morphology/complexity), with DTI measuring mean diffusivity (MD), a less specific measure of degenerative change. PET and microstructural MRI biomarkers were assessed in a neocortical composite ROI and across Braak stages. Thirty-six participants were amyloid positive, 18 of whom were tau positive (eight Braak stage 1-2, four Braak 3-4, six Braak 5-6). Across all participants, in the neocortical composite, significant differences between amyloid positive and amyloid negative individuals were found in both tau PET (p = 0.0092) and ODI (p = 0.030) but not for other measures. Within the amyloid positive group, in the cortical composite, significant differences in MD (p = 0.0093) but not ODI were seen when comparing those who were tau positive and negative. In the amyloid positive group, there was an association between tau PET and MD in the MTL (Braak 1-2) (r = 0.34, p = 0.040) and Braak 3-4 (r = 0.37, p = 0.025), with an association between tau and ODI (r = 0.48, p = 0.0031) in Braak 3-4 only. After adjusting for volume, only the association between tau PET and ODI in Braak 3-4 remained (p = 0.038). Our results are consistent with amyloid deposition causing dendritic/changes (as assessed by ODI) with consequent synaptic dysfunction. Later tau propogation is then associated with frank neuronal breakdown (as measured by MD). dMRI cortical imaging biomarkers have the potential to provide proxy measures of underlying AD stage/pathology prior to symptom onset.

Imaging, and particularly MRI, plays a crucial role in the assessment of inflammation in rheumatic disease, and forms a core component of the diagnostic pathway in axial spondyloarthritis. However, conventional imaging techniques are limited by image contrast being non-specific to inflammation and a reliance on subjective, qualitative reader interpretation. Quantitative MRI methods offer scope to address these limitations and improve our ability to accurately and precisely detect and characterise inflammation, potentially facilitating a more personalised approach to management. Here, we review quantitative MRI methods and emerging quantitative imaging biomarkers for imaging inflammation in axial spondyloarthritis. We discuss the potential benefits as well as the practical considerations that must be addressed in the movement toward clinical translation of quantitative imaging biomarkers.

This work shows that deep learning (DL) enables revised-NODDI parameter estimation from conventional dMRI data. Revised-NODDI is a recently proposed model which overcomes some limitations of NODDI. With conventional fitting methods, revised-NODDI parameters can be robustly estimated only in the presence of data acquired with multiple tensor-valued diffusion encodings. However, this new generation of acquisitions is not yet routinely available in clinical research. We show that revised-NODDI parameters estimated using conventional dMRI data via a DL framework are comparable with the parameters estimated fitting the model to data acquired using multiple tensor-valued diffusion encoding.

Objectives:Sudden unexpected death in epilepsy (SUDEP) is a leading cause of death for patients with epilepsy; however, the pathophysiology remains unclear. Focal-to-bilateral tonic-clonic seizures (FBTCS) are a major risk factor, and centrally-mediated respiratory depression may increase the risk further. Here, we determined volume and microstructure of the amygdala, a key structure that can trigger apnea in people with focal epilepsy, stratified by presence or absence of FBTCS, ictal central apnea (ICA) and post-ictal central apnea (PICA). Methods:73 patients with only-focal seizures and 30 with FBTCS recorded during video EEG (VEEG) with respiratory monitoring were recruited prospectively during presurgical investigations. We acquired high-resolution T1-weighted anatomical and multi-shell diffusion images, and computed neurite orientation dispersion and density imaging (NODDI) metrics in all epilepsy patients and 69 healthy controls. Amygdala volumetric and microstructure alterations were compared between healthy subjects, and patients with only-focal seizures or FBTCS The FBTCS group was further subdivided by presence of ICA and PICA, verified by VEEG. Results:Bilateral amygdala volumes were significantly increased in the FBTCS cohort compared to healthy controls and the focal cohort. Patients with recorded PICA had the highest increase in bilateral amygdala volume of the FBTCS cohort.Amygdala neurite density index (NDI) values were significantly decreased in both the focal and FBTCS groups relative to healthy controls, with values in the FBTCS group being the lowest of the two. The presence of PICA was associated with significantly lower NDI values vs the non-apnea FBTCS group (p=0.004). Significance:Individuals with FBTCS and PICA show significantly increased amygdala volumes and disrupted architecture bilaterally, with greater changes on the left side. The structural alterations reflected by NODDI and volume differences may be associated with inappropriate cardiorespiratory patterns mediated by the amygdala, particularly after FBTCS. Determination of amygdala volumetric and architectural changes may assist identification of individuals at risk.

In Alzheimer’s disease (AD), dysfunction and loss of cortical neurons occurs prior to symptom onset. Diffusion tensor imaging, measuring mean diffusivity (MD), provides a metric of microscopic change but lacks specificity for different underlying microstructural processes. We used Neurite Orientation Dispersion and Density Imaging (NODDI) to investigate specific cortical microstructural features across the disease course in autosomal dominant AD (ADAD). Sixty-three ADAD family members (36 mutation carriers) (Table 1) underwent T1w and multi-shell diffusion MRI (dMRI). Cortical thickness and cortical MD were estimated, with NODDI providing measures of tissue fraction (TF), neurite density index (NDI) and orientation dispersion index (ODI) (a proxy measure of dendritic complexity). Imaging metrics were sampled across six cortical regions of interest (ROIs) known to be particularly vulnerable to early neurodegeneration. Associations between dMRI measures and 1) disease stage as measured by estimated years to onset (EYO), 2) AD blood biomarkers, and 3) cognitive measures were assessed. Across mutation carriers, ODI (Figure 1) and TF (Figure 2) demonstrated negative associations (p<0.05) with EYO in most ROIs, while MD demonstrated positive correlations. Most associations remained after adjusting for cortical thickness. The association between ODI and EYO in the supramarginal gyrus also remained after adjustment for MD (p = 0.002). In presymptomatic carriers, EYO appeared most prominently associated with ODI (p<0.05 in 3/6 regions). ODI, TF and MD showed widespread associations with MMSE and CDR sum of boxes. TF was negatively, and MD positively, associated with serum NfL across ROIs, but for ODI this was only the case in the supramarginal gyrus. ptau181 demonstrated significant associations with ODI in the supramarginal (p = 0.0089) and inferior parietal (p = 0.033) cortices, and with MD in the entorhinal cortex (p = 0.015). Cortical dendritic complexity (modelled by ODI) and cortical tissue fraction decrease, while overall diffusivity increases as individuals approach symptom onset. ODI appears to be the metric most closely associated with presymptomatic disease stage, possibly reflecting early dendritic pruning, and is associated with ptau – a measure of early pathology – while MD and TF may be more sensitive to later larger scale changes, given their associations with serum NfL and cognitive measures.