Functional MRI (fMRI) remains one of the primary tools for non-invasive studies of brain activity and organization in humans. Recently, multi-echo imaging has generated interest due to the improved signal-to-noise and potential for superior denoising. In parallel, developments in MRI processing techniques, such as the use of phase data and advanced denoising methods, have continued to advance the field. An important step towards adopting these methodologies is directly comparing them to existing techniques using real-world data. We present a neuroimaging dataset that allows for within-subject comparison of single-echo vs multi-echo imaging with cutting-edge imaging acquisition parameters such as magnitude and phase reconstruction and no-excitation (noise only) volumes. The sample includes test-retest data from eight young adults. Each session includes a T1-weighted anatomical scan, four resting-state functional MRI scans (two each for single-echo and complex multi-echo), and a multi-echo task-based functional MRI scan from a fractal n-back working memory task. Raw imaging files are released, as well as derivatives from our open-source processing pipelines. These rich data provide opportunities for direct comparison of single-echo and multi-echo methodologies. Moving forward, the data facilitate studies of evaluating how advanced image acquisition and processing impact test-retest reliability.

Perfusion magnetic resonance imaging (MRI) is a noninvasive technique for quantifying tissue blood flow. The widely adopted Arterial Spin Labeling (ASL) method uses magnetically labeled arterial blood as an endogenous tracer, thereby eliminating the need for exogenous contrast agents. Although dedicated RF coil systems can enhance labeling efficiency and reduce coupling artifacts, they impose stringent requirements on the associated radio-frequency (RF) amplifiers. In particular, continuous ASL (CASL) demands higher operating frequencies, increased transmit power, and rapid switching capability.In this paper, we present an MRI-compatible RF power amplifier designed to address these requirements. The amplifier operates at 64 MHz with a 2 MHz bandwidth, delivers up to 9 W of output power, and achieves a fast transition time of less than 40 ns. The system was validated through testing under a 50 Ω load as well as in a real MRI environment using a labeling coil to modulate MRI signals.

Functional MRI (fMRI) remains one of the primary tools for non-invasive studies of brain activity and organization in humans. Recently, multi-echo imaging has generated interest due to the improved signal-to-noise and potential for superior denoising. In parallel, developments in MRI processing techniques, such as the use of phase data and advanced denoising methods, have continued to advance the field. An important step towards adopting these methodologies is directly comparing them to existing techniques using real-world data. We present a neuroimaging dataset that allows for within-subject comparison of single-echo vs multi-echo imaging with cutting-edge imaging acquisition parameters such as magnitude and phase reconstruction and no-excitation (noise only) volumes. The sample includes test-retest data from eight young adults. Each session includes a T1-weighted anatomical scan, four resting-state functional MRI scans (two each for single-echo and complex multi-echo), and a multi-echo task-based functional MRI scan from a fractal n-back working memory task. Raw imaging files are released, as well as derivatives from our open-source processing pipelines. These rich data provide opportunities for direct comparison of single-echo and multi-echo methodologies. Moving forward, the data facilitate studies of evaluating how advanced image acquisition and processing impact test-retest reliability.

Abstract Background Developmental trajectories of low-concentration neurometabolites such as the neurotransmitter γ-aminobutyric acid (GABA), and the antioxidants glutathione (GSH) and ascorbate (Asc) across early infancy remain unexplored. Advances in spectral editing enabled the measurement of these key molecules together with high-concentration metabolites like N-acetylaspartate (NAA) and glutamate (Glu) in the HEALthy Brain and Child Development (HBCD) study, the largest longitudinal study of early brain development in the United States. Purpose To determine the age-associated trajectories of 14 key neurometabolites during early infancy from a cross-sectional 1 H-MRS dataset. Materials and Methods HBCD utilizes ISTHMUS, an integrated MRS sequence that includes both an unedited short-echo-time PRESS acquisition and an advanced 4-step Hadamard-encoded sequence, HERCULES to enable measurement of both high- and low-concentration metabolites. Metabolite quantification was carried out by the HBCD Data Coordinating Center (HDCC) using an automated Osprey pipeline, with data from 201 infants ages 0 – 10 weeks adjusted age included in the tabular imaging results in HBCD data release 1.0. After excluding preterm born infants and data with poor linewidth or model quality metric, we tested for linear associations with adjusted age for each metabolite. Results Concentration estimates of total NAA (tNAA), total creatine (tCr) and glutamate (Glu) as well as the combined sum of glutamate and glutamine (Glx) significantly increased across ages 0 – 10 weeks, while myo-inositol (mI) decreased. GABA and GSH showed age-related trends, but did not reach significance. Levels of the lipid precursor phosphorylethanolamine (PE) and Asc are higher in the first months than established adult values. Conclusion Multiple metabolites showed significant age-related changes during early infancy. While GABA and GSH did not, future work will establish whether the trends suggested here contribute to linear or non-linear patterns across the first years of life. Summary Statement HBCD MRS Data Release 1.0 cross-sectional analysis of tabulated results shows significant changes of neurometabolite levels in the thalamus from birth to 10-week-old infants. Key Results This study provides the first ever concentration level reports of multiple low-concentration metabolites such as GABA+, GSH, PE, Lac, Asc as well as their cross-sectional trajectory in early postnatal period of brain development.

Abstract Background Perinatal mood and anxiety disorders (PMADs) are among the most common and consequential complications of pregnancy. The perinatal period is also characterized by profound hormonal fluctuations and large-scale brain plasticity. However, the mechanisms linking these neurobiological changes to psychiatric risk are poorly understood. Prospective, clinically informed studies are needed to identify quantitative biomarkers and clarify pathways linking perinatal neurobiology to PMADs risk. Methods This report describes the design of a prospective, longitudinal cohort study integrating multimodal neuroimaging, biofluid sampling, and deep clinical phenotyping to enable precision characterization of neurobiological trajectories of PMADs risk. Twenty-five individuals at elevated risk for PMADs will be recruited prior to conception and followed across six in-person timepoints spanning the menstrual cycle, pregnancy, and early postpartum, with additional remote follow-ups through the first postpartum year. Data collection includes high-resolution structural MRI, functional brain mapping using multi-echo resting-state fMRI, diffusion MRI, arterial spin labeling, ultra-high field MR-based techniques for measuring glutamate (GluCEST and 1 HMRS), biofluid sampling, and comprehensive clinical, behavioral, and cognitive assessments. Structured clinical interviews assess categorical diagnoses while dimensional symptom measures capture heterogeneity and transdiagnostic features of perinatal psychopathology. Longitudinal analyses will model nonlinear trajectories of brain and symptom change across the perinatal period as well as evaluate whether preconception network features and menstrual cycle-related brain changes are associated with subsequent perinatal symptom emergence. Discussion This cohort study establishes a longitudinal, multimodal framework for investigating neurobiological changes across the transition to pregnancy in individuals at elevated risk for PMADs. By anchoring pregnancy-related brain changes to preconception and menstrual cycle-related variability within the same individuals, this study is designed to evaluate associations between preconception hormone sensitivity, pregnancy-induced neuroplasticity, and PMADs risk. The resulting dataset will provide a deeply phenotyped longitudinal resource for investigating brain-behavior relationships across the perinatal period. Findings are expected to inform future larger-scale studies aimed at advancing mechanistic understanding of PMADs, improving individualized risk stratification, and supporting development of personalized preventive and neuromodulatory interventions.

Brain development during adolescence and early adulthood coincides with shifts in emotion regulation and sleep. Despite this, few existing datasets simultaneously characterize affective dynamics, sleep variation, and multimodal measures of brain development. Here, we describe the study protocol and initial release (n = 10) of an open data resource of neuroimaging paired with densely sampled behavioral measures in adolescents and young adults. All participants complete multi-echo functional MRI, compressed-sensing diffusion MRI, and advanced arterial spin-labeled MRI. Behavioral measures include ecological momentary assessment, actigraphy, extensive cognitive assessments, and detailed clinical phenotyping focused on emotion regulation. Raw and processed data are openly available without a data use agreement and will be regularly updated as accrual continues. Together, this resource will accelerate research on the links between mood, sleep, and brain development.

Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities, which currently must be corrected using separately acquired field map data. However, changes in the head position of a participant across fMRI frames cause changes in the B0 field, preventing accurate correction of geometric distortions. Movement during field map acquisitions corrupts field maps, preventing distortion correction altogether. In this study, we use multi-echo (ME) fMRI data to dynamically sample and correct for magnetic field image distortions caused by head motion. Our distortion correction pipeline, MEDIC (Multi-Echo DIstortion Correction), leverages magnetic field inhomogeneity information found in the difference between echoes and uses it to correct for distortion on a frame-by-frame basis. Here, we demonstrate that MEDIC's frame-wise distortion correction decreases the impact of head motion on resting-state functional connectivity (RSFC) maps and improves alignment to anatomy when compared with the prior gold standard approach (i.e., FSL TOPUP). Enhanced frame-wise distortion correction with MEDIC, without the requirement for field map collection, furthers the benefit of cutting-edge multi-echo fMRI imaging over single-echo fMRI.

Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource – the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) – that combines longitudinal multimodal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8–16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, n-back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.

Globular glial tauopathy is a 4-repeat tauopathy associated with heterogenous clinical syndromes, including primary progressive aphasia. Iron-reactive gliosis in mid-to-deep cortical layers has previously been reported in this disorder, but detailed anatomic localization and its relationship to clinical symptoms is understudied, particularly within the anatomic framework of primary progressive aphasia. In a series of five autopsy-confirmed patients with globular glial tauopathy and one healthy control, we utilize ultra-high-resolution whole-hemisphere ex vivo 7 Telsa MRI and digital pathology to study whole-hemisphere and local laminar/cellular patterns of pathology within affected cortex. We find signature laminar patterns of iron-rich gliosis localized to brain regions implicated in distinct clinical aphasia syndromes between patients: patients who presented with non-fluent aphasia had iron-rich gliosis pathology localized to inferior and superior frontal and motor regions while iron-rich pathology was largely localized to the anterior temporal lobe in a patient with the semantic variant. Moreover, in one patient with non-fluent aphasia and additional iron-sensitive 7 Telsa MRI during life, we find evidence of antemortem iron-rich pathology in the same frontal regions observed post-mortem. These data suggest that focal neuroinflammation and iron dysregulation may contribute to the clinical expression of tauopathies and be detectable during life to improve diagnosis.

INTRODUCTION:The impact of different neuropathologies on deep brain structures remains to be understood. We examine subcortical and limbic volumetry in neurodegenerative diseases involving phosphorylated tau (p-tau), α-synuclein, and transactive response DNA binding protein 43 (TDP-43). METHODS:We acquired neuropathological measures and brain segmentations from postmortem analysis of 132 donors with Alzheimer's disease (AD), Lewy body disease (LBD), frontotemporal lobar degeneration with TDP-43 (FTLD-TDP), and FTLD-tau. RESULTS:LBD had the least subcortical, limbic, and cortical atrophy compared to AD, FTLD-TDP, and FTLD-tau. In donors with both AD and LBD pathologies, primary LBD was associated with less atrophy than primary AD. While AD had cortico-subcortical and cortico-limbic morphometric associations, LBD had more limited parieto-occipital cortico-limbic associations. FTLD-TDP had cortico-subcortical while FTLD-tau had cortico-subcortical and cortico-limbic associations. In AD and FTLD-tau, hippocampal volumes correlated with p-tau burden, neuron loss, and gliosis. In LBD, thalamic α-synuclein severity was associated with subcortical/limbic volumes. DISCUSSION:Postmortem neuroimaging reveals disease- and region-specific structure-pathology relationships.

Volumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer's disease. However, poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a multi-modality segmentation model using both 7T and 3T structural MRI to obtain robust segmentation in poor-quality images. MRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimer's Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. The multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A + MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images. The multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.

The medial temporal lobe (MTL) is the epicenter of both primary and concomitant molecular pathologies in Alzheimer’s disease (AD). The intricate anatomy of the MTL has been the subject of extensive study over the past two centuries. However, current PET and MRI AD biomarkers use often crude parcellations of the MTL that have not been sufficiently validated vis-à-vis anatomical ground truth. Here we use a unique dataset of finely annotated serial histology, ex vivo MRI and antemortem 3T in vivo MRI of the MTL from 17 brain donors to train and evaluate an automatic MTL subregion segmentation algorithm. To our knowledge, this is the most comprehensive attempt to infuse MRI biomarkers with cytoarchitectural reference annotations . A team of neuroanatomists annotated the boundaries of 27 MTL subregions on over 2000 serial Nissl histological sections from 17 brain donors. These boundaries were mapped to 9.4T ex vivo MRI (proton density, 0.2x0.2x0.2mm 3 ) and, subsequently, to 3T in vivo MRI (T2-weighted, ∼0.4x0.4x2.6mm 3 ) using deformable registration, with extensive manual editing after each registration step to correct for registration errors and ensure 3D continuity and smoothness (Figure 1). Deep learning method nnU-Net was trained on the resulting in vivo MRI annotations to automatically segment a set of major subregions (formed by merging smaller subregions). Surface-based registration between an MTL template and the output of nnU-Net was used to parcellate major subregions into 27 smaller subregions. Segmentation accuracy was assessed by five-fold cross-validation. Figure 2 compares nnU-Net and surface-based automatic segmentations with corresponding cytoarchitectural reference segmentations. Table 1 reports segmentation accuracy for major subregions in terms of Dice coefficient. While accuracy is not as high as in some previous MTL subregion segmentation approaches, this is not surprising because the anatomical variability of cytoarchitecture-based ground truth annotations in our approach is likely much higher than in prior approaches where ground truth was generated by applying heuristic/geometric rules. It is feasible to leverage cytoarchitecturally defined anatomical boundaries for automatic in vivo MRI segmentation. However, high variability in the location of cytoarchitectural borders poses clear limitations on MTL segmentation accuracy.

The anterior portion of the MTL is one of the first regions targeted by pathology in sporadic Alzheimer’s disease (AD) indicating the potential for imaging metrics from this region to serve as valuable imaging biomarkers. However, most existing automated approaches for MTL segmentation do not incorporate anterior MTL subregions, and the few that do fail to account for its complex anatomical variability. Leveraging a unique postmortem dataset consisting of histology and structural MRI scans we aimed to develop an anatomically valid segmentation protocol for anterior entorhinal cortex (ERC), Brodmann Area (BA) 35, and BA36 and apply it for automated MTL segmentation of in vivo 3 tesla (T) MRI. We included 20 cases between 61 to 97 years of age (50% females) with and without neurodegenerative diseases (11 vs. 9 cases) to ensure broad generalizability of the developed protocol. Postmortem digitized MTL Nissl-stained coronal histology serial sections from these cases were registered to same-subject 0.2×0.2×0.2-mm 3 9.4T postmortem MRI and annotated by an expert neuroanatomist. To develop the segmentation protocol, we determined the location of the histological borders of interest in relation to anatomical landmarks observable on in vivo MRI. The protocol was first applied manually to 29 3T in vivo MRI scans and then used to train an automatic segmentation method T1-ASHS (Automatic Segmentation of Hippocampal Subfields). Intra-rater reliability of a manual rater and five-fold cross-validation accuracy of T1-ASHS were assessed with the Dice Similarity Index (DSI). Segmentation rules for the borders of ERC, BA35 and BA36 based on systematic analysis of inter-landmark distances on histological sections are shown in Figure 1. Intra-rater reliability for the manual rater applying these rules to 15 in vivo 3T MRI scans was high (Table-1; Figure-2). Comparing manual segmentations with the automated ones generated by T1-ASHS showed moderate reliability, reflecting the challenging anatomy of this region. However, segmentation accuracy for the whole MTL including the newly added region was comparable to the previously reported accuracy for MTL without this region (Table-1). Future work will examine trhe utility of morphometric measures of anterior MTL regions enabled by this protocol for early AD.

We recently found region-specific patterns of iron-rich gliosis in frontotemporal lobar degeneration (FTLD) groups with tau (FTLD-Tau; PSP) and TDP-43 (FTLD-TDP) pathology using iron-sensitive MRI of whole-hemispheres. These patterns largely corresponded to regions of early pathology reported in previous traditional histopathologic staging schemes of protein inclusions for FTLD-Tau and FTLD-TDP. Ferritin light chain (FLC) reactivity highlights activated glia but has not been studied extensively in FTLD. We hypothesize neuroinflammation marked by FLC-reactive glia may relate to protein aggregation and contribute to partially distinct regional patterns of Tau vs TDP-43 propagation in FTLD. We used digital histopathology to measure FLC % area occupied (%AO) in a pilot cohort of 48 FLTD-Tau and 83 FTLD-TDP patients in three frontal cortical regions implicated early in FTLD-Tau (i.e., motor cortex [MOT]) and FTLD-TDP (orbitofrontal cortex [OFC]) staging and an intermediate region for both staging schemes (medial frontal cortex [MFC]). We used linear mixed effect (LME) models to test the effect of region*group on FLC %AO while adjusting for age, sex, disease duration, and hemisphere sampled; individual was included as random-intercept. Similarly, within-group LME models tested for the association of %AO of Tau/TDP inclusions on FLC %AO. Between- and within-group t-tests were used to further examine FLC %AO levels. In the total cohort, we found lower FLC%AO in MFC (Beta = -.008, SE = .002, p = <.001) and OFC (Beta = -.008 SE = .002, p = <.001) than MOT of FTLD-Tau compared to FTLD-TDP (Figure 1). T-tests showed OFC FLC%AO was higher in FTLD-TDP than FTLD-Tau (p = <.001; Figure 1) and MOT FLC%AO was higher than OFC FLC%AO in FTLD-Tau (p = 0.01). Separate LME models for Tau and TDP %AO was positively associated with FLC%AO across regions (Tau; Beta = 0.1 SE = .06, p = .03; TDP; Beta = 0.5 SE = .2, p = .02). In our preliminary sampling, iron-rich gliosis is associated with protein aggregation in partially-dissociated regional patterns in FLTD-Tau vs FTLD-TDP, recapitulating previous histopathological staging schemes. Future work will more comprehensively model the association of iron-rich gliosis and protein aggregation across the brain.

The presence of interictal epileptiform discharges on electroencephalography (EEG) may indicate increased epileptic seizure risk and on invasive EEG are the signature of the irritative zone. In highly epileptogenic lesions – such as cortical tubers in tuberous sclerosis – these discharges can be recorded with intracranial stereotactic EEG as part of the evaluation for epilepsy surgery. Yet the network mechanisms that underwrite the generation and spread of these discharges remain poorly understood, limiting their current diagnostic use. Here, we investigate the dynamics of interictal epileptiform discharges using a combination of quantitative analysis of invasive EEG recordings and mesoscale neural mass modelling of cortical dynamics. We first characterise spatially organised local dynamics of discharges recorded from 36 separate tubers in 8 patients with tuberous sclerosis. We characterise these dynamics with a set of competing explanatory network models using dynamic causal modelling. Bayesian model comparison of plausible network architectures suggests that the recurrent coupling between neuronal populations within – and adjacent to – the tuber core explains the travelling wave dynamics observed in these patient recordings. Our results – based on interictal activity – unify competing theories about the pathological organisation of epileptic foci and surrounding cortex in patients with tuberous sclerosis. Coupled oscillator dynamics have previously been used to describe ictal activity, where fast travelling ictal discharges are commonly observed within the recruited seizure network. The interictal data analysed here add the insight that this functional architecture is already established in the interictal state. This links observations of interictal EEG abnormalities directly to pathological network coupling in epilepsy, with possible implications for epilepsy surgery approaches in tuberous sclerosis. Significance Statement Interictal epileptiform discharges (IEDs) are clinically important markers of an epileptic brain. Here we link local IED spread to network coupling through a combination of clinical recordings in paediatric patients with tuberous sclerosis complex, quantitative EEG analysis of interictal discharges spread, and Bayesian inference on coupled neural mass model parameters. We show that the kinds of interictal discharges seen in our patients require recurrent local network coupling extending beyond the putative seizure focus and that in fact only those recurrent coupled networks can support seizure-like and interictal dynamics when run in simulation. Our findings provide a novel integrated perspective on emergent epileptic dynamics in human patients.

Postmortem MRI allows brain anatomy to be examined at high-resolution linking pathology with morphometric measurements. However, automated methods for analyzing postmortem MRI are not well developed. We present a deep learning-based framework for automated segmentation of cortical mantle, subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities (WMH), and normal appearing white matter in (n=135) postmortem human brain tissue specimens (Table 1) imaged at 0.3 mm 3 T2w 7T spanning Alzheimer’s disease and related dementias. We show generalizing capabilities across unseen images acquired at 0.28 mm 3 and 0.16 mm 3 T2*w 7T FLASH sequence. We report associations between localized cortical thickness and volumetric measurements across key regions and semi-quantitative neuropathological ratings. A deep learning model was trained on manually segmented images to produce automated whole-brain hemisphere segmentations (Figure 1) with a post-hoc topological correction step to delineate buried sulcus. We report regional patterns of association between localized cortical thickness at 16 anatomical locations and neuropathology ratings of regional measures of p-tau, neuronal loss; global amyloid-β, Braak staging, and CERAD ratings obtained from histology data in a subset (n=82) with AD continuum diagnoses. We correlate subcortical volumetry and regional cortical thickness with WMH burden (Figure 2) for the entire cohort (n=135). All analyses include age, sex, and postmortem interval as covariates. Tau pathology in Braak regions play an important role in cortical atrophy and cognitive decline in AD. Significant negative correlations (Figure 2) between p-tau and cortical thickness were found in angular gyrus and midfrontal regions. Cortical thickness showed significant negative correlation with neuronal loss in Brodmann area (BA) 35 and entorhinal cortex (ERC), and with Braak staging in midfrontal, ERC and BA35, regions consistent with high p-tau uptake in PET imaging with cortical thickness on MRI. High WMH volume disrupts structural and functional connectivity impacting memory. Significant negative correlation of WMH volume with thickness in posterior cingulate and superior temporal regions was observed. Our automated postmortem MRI framework provides geometrically accurate segmentations of several key brain regions. Our analysis linking morphometry and pathology measurements demonstrated that automated segmentation and analysis of postmortem MRI can complement and inform antemortem neuroimaging studies.

Background:Clinical intracranial vessel wall imaging (VWI) requires high spatial resolution leading to long scan times and artifacts. Purpose:To accelerate standard-of-care (SOC) 3D T1-weighted variable-flip-angle turbo-spin-echo (VFA-TSE) sequence with parallel imaging (Generalized Autocalibrating Partially Parallel Acquisitions, GRAPPA) using compressed sensing (CS) or Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA, CAIPI) with either standard or large field-of-view (FOV) configurations to reduce scan time, artifacts and accommodate head sizes. Study Type:Prospective study. Subjects:Ten healthy volunteers. Field Strength/Sequence:3 Telsa, 20-channel head coil, T1-weighted VFA-TSE. Assessment:Accelerated sequences were compared to SOC GRAPPA (R=2), including standard FOV CAIPI (SFCAIPI, R=4), CS (SFCS7, R=7), and large FOV CS (LFCS7, R=7; LFCS10, R=10). Four neuroradiologists rated image quality (IQ) and signal-to-noise ratio (SNR) using a 4-point Likert scale. Scores of 3-4 were categorized as clinically interpretable. Lumen and wall diameters were measured. Statistical Analysis:Descriptive statistics are reported. McNemar's test compared proportions of IQ- and SNR-based clinically interpretable scans between relevant sequences of interest. Inter- and intra-rater reliabilities were calculated with Fleiss Kappa and weighted Cohen's Kappa, respectively. Lumen and wall diameters of the CS- and CAIPI-accelerated sequences were compared to SOC using paired t-tests. Results:SFCAIPI showed the lowest mean IQ and SNR scores. SFCS7 showed no significant difference in the proportion of IQ-based clinically interpretable scans compared to SFGRAPPA. When testing FOV, LFCS7 (35/40 scans; time of acquisition (TA)=3:45) showed a significantly higher proportion of IQ-based clinically interpretable scans compared to SFCS7 (27/40, p=0.03; TA=6:37). Upon increasing acceleration (R=10), there was no difference in the proportion of IQ-based clinically interpretable scans between LFCS7 and LFCS10 (36/40, p=0.65). Large FOV eliminated aliasing artifacts compared standard FOV (aliasing in 7 of 10 subjects). LFCS10 (TA=4:55) achieved a 50.6% reduction in TA relative to SFGRAPPA (TA=9:57). Conclusion:Large FOV CS VWI sequence with 10x acceleration achieved a 50.6% reduction in scan time while delivering image quality comparable to SOC standard FOV GRAPPA.

The anterior portion of the medial temporal lobe (MTL) is one of the first regions targeted by pathology in sporadic Alzheimer's disease (AD) and limbic-predominant age-related TDP-43 encephalopathy (LATE) indicating a potential for metrics from this region to serve as imaging biomarkers. Leveraging a unique post-mortem dataset of histology and magnetic resonance imaging (MRI) scans, we aimed to (1) develop an anatomically valid segmentation protocol for anterior entorhinal cortex (ERC), Brodmann area (BA) 35, and BA36 for in vivo 3 T MRI and (2) incorporate this protocol in an automated approach. We included 20 cases (61-97 years old, 50% females) with and without neurodegenerative diseases (11 vs. 9 cases) to ensure generalizability of the developed protocol. Digitized MTL Nissl-stained coronal histology sections from these cases were annotated and registered to same-subject post-mortem MRI. The protocol was developed by determining the location of histological borders of the MTL cortices in relation to anatomical landmarks. Subsequently, the protocol was applied to 15 cases twice, with a 2-week interval, to assess intra-rater reliability with the Dice Similarity Index (DSI). Thereafter, it was implemented in our in-house Automatic Segmentation of Hippocampal Subfields (ASHS)-T1 approach and evaluated with DSIs. The anterior histological border distances of ERC, BA35 and BA36 were evaluated with respect to various anatomical landmarks, and the distance relative to the beginning of the hippocampus was chosen. To formulate segmentation rules, we examined the histological sections for the location of borders in relationship to anatomical landmarks in the coronal sections. The DSI for the anterior MTL cortices for the intra-rater reliability was 0.85-0.88, and for the ASHS-T1 against the manual segmentation, it was 0.62-0.65. We developed a reliable segmentation protocol and incorporated it in an automated approach. Given the vulnerability of the anterior MTL cortices to tau deposition in AD and LATE, the updated approach is expected to improve imaging biomarkers for these diseases.

Postmortem neuropathological examination, while the gold standard for diagnosing neurodegenerative diseases, often relies on limited regional sampling that may miss critical areas affected by Alzheimer’s disease and related disorders. Ultra-high resolution postmortem MRI can help identify regions that fall outside the diagnostic sampling criteria for additional histopathologic evaluation. However, there are no standardized guidelines for integrating histology and MRI in a traditional brain bank. We developed a comprehensive protocol for whole hemisphere postmortem 7T MRI-guided histopathological sampling with whole-slide digital imaging and histopathological analysis, providing a reliable pipeline for high-volume brain banking in heterogeneous brain tissue. Our method uses patient-specific 3D printed molds built from postmortem MRI, allowing standardized tissue processing with a permanent spatial reference frame. To facilitate pathology-MRI association studies, we created a semi-automated MRI to histology registration pipeline and developed a quantitative pathology scoring system using weakly supervised deep learning. We validated this protocol on a cohort of 29 brains with diagnosis on the AD spectrum that revealed correlations between cortical thickness and phosphorylated tau accumulation. This pipeline has broad applicability across neuropathological research and brain banking, facilitating large-scale studies that integrate histology with neuroimaging. The innovations presented here provide a scalable and reproducible approach to studying postmortem brain pathology, with implications for advancing diagnostic and therapeutic strategies for Alzheimer’s disease and related disorders.