Robust brain extraction across datasets and comparison with publicly available methods

Investigating the association between the GAP-43 concentration with diffusion tensor imaging indices in Alzheimer's dementia continuum

BACKGROUND

Synaptic degeneration, axonal injury, and white matter disintegration are among the pathological events in Alzheimer's disease (AD), for which growth-associated protein 43 (GAP-43) and diffusion tensor imaging (DTI) could be an indicator. In this study, the cerebrospinal fluid (CSF) GAP-43 clinical trajectories and their association with progression and AD hallmarks with white matter microstructural changes were evaluated.

METHODS

A total number of 133 participants were enrolled in GAP-43 and DTI values were compared between groups, both cross-sectionally and longitudinally with two and four-year follow-ups. Subsequently, the correlation between GAP-43 levels in the CSF and DTI values was investigated using Spearman's correlation.

RESULTS

The CSF level of GAP-43 is negatively correlated with the mean diffusivity measures in Fornix (Cres)/Stria terminals in early and late MCI (rs=-0.478 p = 0.021 and rs=-0.425 p = 0.038). Additionally, the CSF level of GAP-43 is negatively correlated with fractional anisotropy in the cingulum in late MCI (rs=-0.437 p = 0.033). Moreover, the axial diffusivity in superior corona radiate (rs=-0.562 p = 0.005 and rs=-0.484 p = 0.036) and radial diffusivity in superior fronto-occipital fasciculus was negatively correlated with GAP-43 level in the early and mid-MCI participants (rs=-0.520 p = 0.011 and rs=-0.498 p = 0.030).

CONCLUSIONS

Presynaptic marker GAP-43 in combination with DTI can be used as a novel biomarker to identify microstructural synaptic degeneration in the early MCI. In addition, it can be used as a biomarker for tracking the progression of AD and monitoring treatment efficacy.

Anterior and posterior thalamic volumes differentially correlate with memory, attention, and motor processes in HIV infection and alcohol use disorder comorbidity

The thalamus, with its reciprocal connections to and from cortical, subcortical, and cerebellar regions, is a central active participant in multiple functional brain networks. Structural MRI studies measuring the entire thalamus without respect to its regional or nuclear divisions report volume shrinkage in diseases including HIV infection, alcohol use disorder (AUD), and their comorbidity (HIV+AUD). Here, we examined relations between thalamic subregions (anterior, ventral, medial, and posterior) and neuropsychological functions (attention/working memory, executive functioning, episodic memory, and motor skills). Volumes of thalamic subregions were derived from automatic segmentations of standard T1 weighted MRIs of 65 individuals with HIV, 189 with AUD, 80 with HIV+AUD comorbidity, and 141 healthy controls (CTRL). Total thalamic volume was smaller and cognitive and motor composite scores were lower in the three diagnostic groups relative to the CTRL group. The AUD and HIV+AUD groups had significantly smaller thalamic subregional volumes than the CTRL group. The HIV+AUD group had smaller anterior thalamic volume than the HIV-only group and smaller ventral thalamic volume than the AUD-only group. In the HIV+AUD group, memory scores correlated with anterior thalamic volumes, attention/working memory scores correlated with posterior and medial thalamic volumes, and motor skill scores correlated with posterior thalamic volumes. Exploratory analyses focused on the HIV+AUD group indicated that within the posterior thalamic region, the pulvinar and medial geniculate nuclei were related to attention/working memory scores, and the pulvinar was related to motor skills scores. This study is novel in locating volume deficits in specific thalamic subregions, in addition to the thalamus as a whole, in HIV, AUD, and their comorbidity and in identifying functional ramifications of these deficits. Taken together, this study highlights the relevance of thalamic subregional volume deficits to dissociable cognitive and motor processes.

Aging, HIV infection, and alcohol exert synergist effects on regional thalamic volumes resulting in functional impairment

OBJECTIVE

Pharmacologically-treated people living with HIV infection have near-normal life spans with more than 50 % living into at-risk age for dementia and a disproportionate number relative to uninfected people engaging in unhealthy drinking. Accelerated aging in HIV occurs in some brain structures including the multinucleated thalamus. Unknown is whether aging with HIV affects thalamic nuclei and associated functions differentially and whether the common comorbidity of alcohol use disorder (AUD) + HIV accelerates aging.

METHODS

This mixed cross-sectional/longitudinal design examined 216 control, 69 HIV, and 74 HIV + AUD participants, age 25-75 years old at initial visit, examined 1-8 times. MRI thalamic volumetry, parcellated using THalamus Optimized Multi-Atlas Segmentation (THOMAS), identified 10 nuclei grouped into 4 functional regions for correlation with age and measures of neuropsychological, clinical, and hematological status.

RESULTS

Aging in the control group was best modeled with quadratic functions in the Anterior and Ventral regions and with linear functions in the Medial and Posterior regions. Relative to controls, age-related decline was even steeper in the Anterior and Ventral regions of the HIV group and in the Anterior region of the comorbid group. Anterior volumes of each HIV group declined significantly faster after age 50 (HIV = -2.4 %/year; HIV + AUD = -2.8 %/year) than that of controls (-1.8 %/year). Anterior and Ventral volumes were significantly smaller in the HIV + AUD than HIV-only group when controlling for infection factors. Although compared with controls HIV + AUD declined faster than HIV alone, the two HIV groups did not differ significantly from each other in aging rates. Declining Attention/Working Memory and Motor Skills performance correlated with Anterior and Posterior volume declines in the HIV + AUD group.

CONCLUSIONS

Regional thalamic volumetry detected normal aging declines, differential and accelerated volume losses in HIV, relations between age-related nuclear and performance declines, and exacerbation of volume declines in comorbid AUD contributing to functional deficits.

Parasagittal dural volume correlates with cerebrospinal fluid volume and developmental delay in children with autism spectrum disorder

BACKGROUND

The parasagittal dura, a tissue that lines the walls of the superior sagittal sinus, acts as an active site for immune-surveillance, promotes the reabsorption of cerebrospinal fluid, and facilitates the removal of metabolic waste products from the brain. Cerebrospinal fluid is important for the distribution of growth factors that signal immature neurons to proliferate and migrate. Autism spectrum disorder is characterized by altered cerebrospinal fluid dynamics.

METHODS

In this retrospective study, we investigated potential correlations between parasagittal dura volume, brain structure volumes, and clinical severity scales in young children with autism spectrum disorder. We employed a semi-supervised two step pipeline to extract parasagittal dura volume from 3D-T2 Fluid Attenuated Inversion Recovery sequences, based on U-Net followed by manual refinement of the extracted parasagittal dura masks.

RESULTS

Here we show that the parasagittal dura volume does not change with age but is significantly correlated with cerebrospinal fluid (p-value = 0.002), extra-axial cerebrospinal fluid volume (p-value = 0.0003) and severity of developmental delay (p-value = 0.024).

CONCLUSIONS

These findings suggest that autism spectrum disorder children with severe developmental delay may have a maldeveloped parasagittal dura that potentially perturbs cerebrospinal fluid dynamics.

Contributions of Cerebral White Matter Hyperintensities to Postural Instability in Aging With and Without Alcohol Use Disorder

BACKGROUND

Both postural instability and brain white matter hyperintensities (WMHs) are noted markers of normal aging and alcohol use disorder (AUD). Here, we questioned what variables contribute to the sway path-WMH relationship in individuals with AUD and healthy control participants.

METHODS

The data comprised 404 balance platform sessions, yielding sway path length and magnetic resonance imaging data acquired cross-sectionally or longitudinally in 102 control participants and 158 participants with AUD ages 25 to 80 years. Balance sessions were typically conducted on the same day as magnetic resonance imaging fluid-attenuated inversion recovery acquisitions, permitting WMH volume quantification. Factors considered in multiple regression analyses as potential contributors to the relationship between WMH volumes and postural instability were age, sex, socioeconomic status, education, pedal 2-point discrimination, systolic and diastolic blood pressure, body mass index, depressive symptoms, total alcohol consumed in the past year, and race.

RESULTS

Initial analysis identified diagnosis, age, sex, and race as significant contributors to observed sway path-WMH relationships. Inclusion of these factors as predictors in multiple regression analyses substantially attenuated the sway path-WMH relationships in both AUD and healthy control groups. Women, irrespective of diagnosis or race, had shorter sway paths than men. Black participants, irrespective of diagnosis or sex, had shorter sway paths than non-Black participants despite having modestly larger WMH volumes than non-Black participants, which is possibly a reflection of the younger age of the Black sample.

CONCLUSIONS

Longer sway paths were related to larger WMH volumes in healthy men and women with and without AUD. Critically, however, age almost fully accounted for these associations.

Decreased inter-hemispheric connectivity predicts a coherent retrieval of auditory symbolic material

Investigating the transmission of information between individuals is essential to better understand how humans communicate. Coherent information transmission (i.e., transmission without significant modifications or loss of fidelity) helps preserving cultural traits and traditions over time, while innovation may lead to new cultural variants. Although much research has focused on the cognitive mechanisms underlying cultural transmission, little is known on the brain features which correlates with coherent transmission of information. To address this gap, we combined structural (from high-resolution diffusion imaging) and functional connectivity (from resting-state functional magnetic resonance imaging [fMRI]) with a laboratory model of cultural transmission, the signalling games, implemented outside the MRI scanner. We found that individuals who exhibited more coherence in the transmission of auditory symbolic information were characterized by lower levels of both structural and functional inter-hemispheric connectivity. Specifically, higher coherence negatively correlated with the strength of bilateral structural connections between frontal and subcortical, insular and temporal brain regions. Similarly, we observed increased inter-hemispheric functional connectivity between inferior frontal brain regions derived from structural connectivity analysis in individuals who exhibited lower transmission coherence. Our results suggest that lateralization of cognitive processes involved in semantic mappings in the brain may be related to the stability over time of auditory symbolic systems.

deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks

BACKGROUND

Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods.

METHOD

Here, we used a unique dataset compilation comprising 7837 T1-weighted (T1w) MR images from 191 different OpenNeuro datasets in combination with advanced deep learning methods to build a fast, high-precision brain extraction tool called deepbet.

RESULTS

deepbet sets a novel state-of-the-art performance during cross-dataset validation with a median Dice score (DSC) of 99.0 on unseen datasets, outperforming the current best performing deep learning (DSC=97.9) and classic (DSC=96.5) methods. While current methods are more sensitive to outliers, deepbet achieves a Dice score of >97.4 across all 7837 images from 191 different datasets. This robustness was additionally tested in 5 external datasets, which included challenging clinical MR images. During visual exploration of each method's output which resulted in the lowest Dice score, major errors could be found for all of the tested tools except deepbet. Finally, deepbet uses a compute efficient variant of the UNet architecture, which accelerates brain extraction by a factor of ≈10 compared to current methods, enabling the processing of one image in ≈2 s on low level hardware.

CONCLUSIONS

In conclusion, deepbet demonstrates superior performance and reliability in brain extraction across a wide range of T1w MR images of adults, outperforming existing top tools. Its high minimal Dice score and minimal objective errors, even in challenging conditions, validate deepbet as a highly dependable tool for accurate brain extraction. deepbet can be conveniently installed via "pip install deepbet" and is publicly accessible at https://github.com/wwu-mmll/deepbet.

Optimization and validation of multi-echo, multi-contrast SAGE acquisition in fMRI

The purpose of this study was to optimize and validate a multi-contrast, multi-echo fMRI method using a combined spin- and gradient-echo (SAGE) acquisition. It was hypothesized that SAGE-based blood oxygen level-dependent (BOLD) functional MRI (fMRI) will improve sensitivity and spatial specificity while reducing signal dropout. SAGE-fMRI data were acquired with five echoes (2 gradient-echoes, 2 asymmetric spin-echoes, and 1 spin-echo) across 12 protocols with varying acceleration factors, and temporal SNR (tSNR) was assessed. The optimized protocol was then implemented in working memory and vision tasks in 15 healthy subjects. Task-based analysis was performed using individual echoes, quantitative dynamic relaxation times T2 * and T2, and echo time-dependent weighted combinations of dynamic signals. These methods were compared to determine the optimal analysis method for SAGE-fMRI. Implementation of a multiband factor of 2 and sensitivity encoding (SENSE) factor of 2.5 yielded adequate spatiotemporal resolution while minimizing artifacts and loss in tSNR. Higher BOLD contrast-to-noise ratio (CNR) and tSNR were observed for SAGE-fMRI relative to single-echo fMRI, especially in regions with large susceptibility effects and for T2-dominant analyses. Using a working memory task, the extent of activation was highest with T2 *-weighting, while smaller clusters were observed with quantitative T2 * and T2. SAGE-fMRI couples the high BOLD sensitivity from multi-gradient-echo acquisitions with improved spatial localization from spin-echo acquisitions, providing two contrasts for analysis. SAGE-fMRI provides substantial advantages, including improving CNR and tSNR for more accurate analysis.

Anatomy-aware and acquisition-agnostic joint registration with SynthMorph

Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as the resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing. First, we leverage a strategy that trains networks with widely varying images synthesized from label maps, yielding robust performance across acquisition specifics unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content which would impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. This framework is applicable to learning anatomy-aware, acquisition-agnostic registration of any anatomy with any architecture, as long as label maps are available for training. We analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates high accuracy and is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain magnetic resonance imaging (MRI) data.

Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm

BACKGROUND

Manual brain extraction from magnetic resonance (MR) images is time-consuming and prone to intra- and inter-rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipelines. However, these methods tend to reduce their performance in unseen magnetic resonance imaging (MRI) scanner vendors and different imaging protocols.

PURPOSE

To present and evaluate for clinical use PARIETAL, a pre-trained deep learning brain extraction method. We compare its reproducibility in a scan/rescan analysis and its robustness among scanners of different manufacturers.

STUDY TYPE

Retrospective.

POPULATION

Twenty-one subjects (12 women) with age range 22-48 years acquired using three different MRI scanner machines including scan/rescan in each of them.

FIELD STRENGTH/SEQUENCE

T1-weighted images acquired in a 3-T Siemens with magnetization prepared rapid gradient-echo sequence and two 1.5 T scanners, Philips and GE, with spin-echo and spoiled gradient-recalled (SPGR) sequences, respectively.

ASSESSMENT

Analysis of the intracranial cavity volumes obtained for each subject on the three different scanners and the scan/rescan acquisitions.

STATISTICAL TESTS

Parametric permutation tests of the differences in volumes to rank and statistically evaluate the performance of PARIETAL compared to state-of-the-art methods.

RESULTS

The mean absolute intracranial volume differences obtained by PARIETAL in the scan/rescan analysis were 1.88 mL, 3.91 mL, and 4.71 mL for Siemens, GE, and Philips scanners, respectively. PARIETAL was the best-ranked method on Siemens and GE scanners, while decreasing to Rank 2 on the Philips images. Intracranial differences for the same subject between scanners were 5.46 mL, 27.16 mL, and 30.44 mL for GE/Philips, Siemens/Philips, and Siemens/GE comparison, respectively. The permutation tests revealed that PARIETAL was always in Rank 1, obtaining the most similar volumetric results between scanners.

DATA CONCLUSION

PARIETAL accurately segments the brain and it generalizes to images acquired at different sites without the need of training or fine-tuning it again. PARIETAL is publicly available.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 2.

Serum albumin and white matter hyperintensities

People living with HIV and those diagnosed with alcohol use disorders (AUD) relative to healthy individuals commonly have low levels of serum albumin, substantiated as an independent predictor of cardiovascular events. White matter hyperintensities (WMH)-a neuroimaging feature of cerebral small vessel disease-are also related to cardiovascular disease. Despite consensus regarding associations between high levels of urine albumin and WMH prevalence, and low serum albumin levels and impaired cognitive functioning, relations between serum albumin and WMH burdens have rarely been evaluated. Here, a sample including 160 individuals with AUD, 142 living with HIV, and 102 healthy controls was used to test the hypothesis that serum albumin would be inversely related to WMH volumes and directly related to cognitive performance in the two diagnostic groups. Although serum albumin and periventricular WMH volumes showed an inverse relationship in both AUD and HIV groups, this relationship persisted only in the HIV group after consideration of traditional cardiovascular (i.e., age, sex, body mass index (BMI), nicotine use, hypertension, diabetes), study-relevant (i.e., race, socioeconomic status, hepatitis C virus status), and disease-specific (i.e., CD4 nadir, HIV viral load, HIV duration) factors. Further, serum albumin contributed more significantly than periventricular WMH volume to variance in performance on a verbal learning and memory composite score in the HIV group only. Relations in both HIV and AUD groups between albumin and hematological red blood cell markers (e.g., hemoglobin, hematocrit) suggest that in this sample, serum albumin reflects hematological abnormalities. Albumin, a simple serum biomarker available in most clinical settings, may therefore help identify periventricular WMH burden and performance levels in specific cognitive domains in people living with HIV. Whether serum albumin contributes mechanistically to periventricular WMH in HIV will require additional investigation.

BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES

Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.

Synthesized 7T MPRAGE From 3T MPRAGE Using Generative Adversarial Network and Validation in Clinical Brain Imaging: A Feasibility Study

BACKGROUND

Ultra-high field 7T MRI can provide excellent tissue contrast and anatomical details, but is often cost prohibitive, and is not widely accessible in clinical practice.

PURPOSE

To generate synthetic 7T images from widely acquired 3T images with deep learning and to evaluate the feasibility of this approach for brain imaging.

STUDY TYPE

Prospective.

POPULATION

33 healthy volunteers and 89 patients with brain diseases, divided into training, and evaluation datasets in the ratio 4:1.

SEQUENCE AND FIELD STRENGTH

T1-weighted nonenhanced or contrast-enhanced magnetization-prepared rapid acquisition gradient-echo sequence at both 3T and 7T.

ASSESSMENT

A generative adversarial network (SynGAN) was developed to produce synthetic 7T images from 3T images as input. SynGAN training and evaluation were performed separately for nonenhanced and contrast-enhanced paired acquisitions. Qualitative image quality of acquired 3T and 7T images and of synthesized 7T images was evaluated by three radiologists in terms of overall image quality, artifacts, sharpness, contrast, and visualization of vessel using 5-point Likert scales.

STATISTICAL TESTS

Wilcoxon signed rank tests to compare synthetic 7T images with acquired 7T and 3T images and intraclass correlation coefficients to evaluate interobserver variability. P < 0.05 was considered significant.

RESULTS

Of the 122 paired 3T and 7T MRI scans, 66 were acquired without contrast agent and 56 with contrast agent. The average time to generate synthetic images was ~11.4 msec per slice (2.95 sec per participant). The synthetic 7T images achieved significantly improved tissue contrast and sharpness in comparison to 3T images in both nonenhanced and contrast-enhanced subgroups. Meanwhile, there was no significant difference between acquired 7T and synthetic 7T images in terms of all the evaluation criteria for both nonenhanced and contrast-enhanced subgroups (P ≥ 0.180).

DATA CONCLUSION

The deep learning model has potential to generate synthetic 7T images with similar image quality to acquired 7T images.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging

BACKGROUND

Neuroimaging studies often quantify tau burden in standardized brain regions to assess Alzheimer disease (AD) progression. However, this method ignores another key biological process in which tau spreads to additional brain regions. We have developed a metric for calculating the extent tau pathology has spread throughout the brain and evaluate the relationship between this metric and tau burden across early stages of AD.

METHODS

445 cross-sectional participants (aged ≥ 50) who had MRI, amyloid PET, tau PET, and clinical testing were separated into disease-stage groups based on amyloid positivity and cognitive status (older cognitively normal control, preclinical AD, and symptomatic AD). Tau burden and tau spatial spread were calculated for all participants.

FINDINGS

We found both tau metrics significantly elevated across increasing disease stages (p < 0.0001) and as a function of increasing amyloid burden for participants with preclinical (p < 0.0001, p = 0.0056) and symptomatic (p = 0.010, p = 0.0021) AD. An interaction was found between tau burden and tau spatial spread when predicting amyloid burden (p = 0.00013). Analyses of slope between tau metrics demonstrated more spread than burden in preclinical AD (β = 0.59), but then tau burden elevated relative to spread (β = 0.42) once participants had symptomatic AD, when the tau metrics became highly correlated (R = 0.83).

INTERPRETATION

Tau burden and tau spatial spread are both strong biomarkers for early AD but provide unique information, particularly at the preclinical stage. Tau spatial spread may demonstrate earlier changes than tau burden which could have broad impact in clinical trial design.

FUNDING

This research was supported by the Knight Alzheimer Disease Research Center (Knight ADRC, NIH grants P30AG066444, P01AG026276, P01AG003991), Dominantly Inherited Alzheimer Network (DIAN, NIH grants U01AG042791, U19AG03243808, R01AG052550-01A1, R01AG05255003), and the Barnes-Jewish Hospital Foundation Willman Scholar Fund.

Additive Effects of Monetary Loss and Positive Emotion in the Human Brain

In many real-life scenarios, our decisions could lead to multiple outcomes that conflict with value. Hence, an appropriate neural representation of the net experienced value of conflicting outcomes, which play a crucial role in guiding future decisions, is critical for adaptive behavior. As some recent functional neuroimaging work has primarily focused on the concurrent processing of monetary gains and aversive information, very little is known regarding the integration of conflicting value signals involving monetary losses and appetitive information in the human brain. To address this critical gap, we conducted a functional MRI study involving healthy human male participants to examine the nature of integrating positive emotion and monetary losses. We employed a novel experimental design where the valence (positive or neutral) of an emotional stimulus indicated the type of outcome (loss or no loss) in a choice task. Specifically, we probed two plausible integration patterns while processing conflicting value signals involving positive emotion and monetary losses: interactive versus additive. We found overlapping main effects of positive (vs neutral) emotion and loss (vs no loss) in multiple brain regions, including the ventromedial prefrontal cortex, striatum, and amygdala, notably with a lack of evidence for interaction. Thus, our findings revealed the additive integration pattern of monetary loss and positive emotion outcomes, suggesting that the experienced value of the monetary loss was not modulated by the valence of the image signaling those outcomes. These findings contribute to our limited understanding of the nature of integrating conflicting outcomes in the healthy human brain with potential clinical relevance.

Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI

Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.

Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset

Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.

Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space

Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human frontoparietal dorsal stream regions, but not in MT+, tracks the number of competing options, as preferred options are selectively maintained on the map, while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior β1/α oscillations desynchronize within 0.4 to 0.7 s, consistent with option encoding by competing β1-stabilized subpopulations. Together, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.

Multi-scale V-net architecture with deep feature CRF layers for brain extraction

BACKGROUND

Brain extraction is a computational necessity for researchers using brain imaging data. However, the complex structure of the interfaces between the brain, meninges and human skull have not allowed a highly robust solution to emerge. While previous methods have used machine learning with structural and geometric priors in mind, with the development of Deep Learning (DL), there has been an increase in Neural Network based methods. Most proposed DL models focus on improving the training data despite the clear gap between groups in the amount and quality of accessible training data between.

METHODS

We propose an architecture we call Efficient V-net with Additional Conditional Random Field Layers (EVAC+). EVAC+ has 3 major characteristics: (1) a smart augmentation strategy that improves training efficiency, (2) a unique way of using a Conditional Random Fields Recurrent Layer that improves accuracy and (3) an additional loss function that fine-tunes the segmentation output. We compare our model to state-of-the-art non-DL and DL methods.

RESULTS

Results show that even with limited training resources, EVAC+ outperforms in most cases, achieving a high and stable Dice Coefficient and Jaccard Index along with a desirable lower Surface (Hausdorff) Distance. More importantly, our approach accurately segmented clinical and pediatric data, despite the fact that the training dataset only contains healthy adults.

CONCLUSIONS

Ultimately, our model provides a reliable way of accurately reducing segmentation errors in complex multi-tissue interfacing areas of the brain. We expect our method, which is publicly available and open-source, to be beneficial to a wide range of researchers.

Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention

Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.

ezBIDS: Guided standardization of neuroimaging data interoperable with major data archives and platforms

Data standardization promotes a common framework through which researchers can utilize others' data and is one of the leading methods neuroimaging researchers use to share and replicate findings. As of today, standardizing datasets requires technical expertise such as coding and knowledge of file formats. We present ezBIDS, a tool for converting neuroimaging data and associated metadata to the Brain Imaging Data Structure (BIDS) standard. ezBIDS contains four major features: (1) No installation or programming requirements. (2) Handling of both imaging and task events data and metadata. (3) Semi-automated inference and guidance for adherence to BIDS. (4) Multiple data management options: download BIDS data to local system, or transfer to OpenNeuro.org or to brainlife.io. In sum, ezBIDS requires neither coding proficiency nor knowledge of BIDS, and is the first BIDS tool to offer guided standardization, support for task events conversion, and interoperability with OpenNeuro.org and brainlife.io.

Automated, fast, robust brain extraction on contrast-enhanced T1-weighted MRI in presence of brain tumors: an optimized model based on multi-center datasets

OBJECTIVES

Existing brain extraction models should be further optimized to provide more information for oncological analysis. We aimed to develop an nnU-Net-based deep learning model for automated brain extraction on contrast-enhanced T1-weighted (T1CE) images in presence of brain tumors.

METHODS

This is a multi-center, retrospective study involving 920 patients. A total of 720 cases with four types of intracranial tumors from private institutions were collected and set as the training group and the internal test group. Mann-Whitney U test (U test) was used to investigate if the model performance was associated with pathological types and tumor characteristics. Then, the generalization of model was independently tested on public datasets consisting of 100 glioma and 100 vestibular schwannoma cases.

RESULTS

In the internal test, the model achieved promising performance with median Dice similarity coefficient (DSC) of 0.989 (interquartile range (IQR), 0.988-0.991), and Hausdorff distance (HD) of 6.403 mm (IQR, 5.099-8.426 mm). U test suggested a slightly descending performance in meningioma and vestibular schwannoma group. The results of U test also suggested that there was a significant difference in peritumoral edema group, with median DSC of 0.990 (IQR, 0.989-0.991, p = 0.002), and median HD of 5.916 mm (IQR, 5.000-8.000 mm, p = 0.049). In the external test, our model also showed to be robust performance, with median DSC of 0.991 (IQR, 0.983-0.998) and HD of 8.972 mm (IQR, 6.164-13.710 mm).

CONCLUSIONS

For automated processing of MRI neuroimaging data presence of brain tumors, the proposed model can perform brain extraction including important superficial structures for oncological analysis.

CLINICAL RELEVANCE STATEMENT

The proposed model serves as a radiological tool for image preprocessing in tumor cases, focusing on superficial brain structures, which could streamline the workflow and enhance the efficiency of subsequent radiological assessments.

KEY POINTS

• The nnU-Net-based model is capable of segmenting significant superficial structures in brain extraction. • The proposed model showed feasible performance, regardless of pathological types or tumor characteristics. • The model showed generalization in the public datasets.

[WITHDRAWN] Serum biomarkers of liver fibrosis identify changes in striatal metabolite levels

The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

Serum albumin and white matter hyperintensities

Urine albumin, high in kidney disease, predicts cardiovascular incidents and CNS white matter hyperintensity (WMH) burdens. Serum albumin - a more general biomarker which can be low in several disorders - including kidney and liver disease, malnutrition, and inflammation - also predicts cardiovascular events and is associated with cognitive impairment in several clinical populations; relations between serum albumin and WMH prevalence, however, have rarely been evaluated. In a sample of 160 individuals with alcohol use disorder (AUD), 142 infected with HIV, and 102 healthy controls, the hypothesis was tested that lower serum albumin levels would predict larger WMH volumes and worse cognitive performance irrespective of diagnosis. After considering traditional cardiovascular risk factors (e.g., age, sex, body mass index (BMI), nicotine use, hypertension, diabetes) and study-relevant variables (i.e., primary diagnoses, race, socioeconomic status, hepatitis C virus status), serum albumin survived false discovery rate (FDR)-correction in contributing variance to larger periventricular but not deep WMH volumes. This relationship was salient in the AUD and HIV groups, but not the control group. In secondary analyses, serum albumin and periventricular WMH along with age, sex, diagnoses, BMI, and hypertension were considered for hierarchical contribution to variance in performance in 4 cognitive domains. Albumin survived FDR-correction for significantly contributing to visual and verbal learning and memory performance after accounting for diagnosis. Relations between albumin and markers of liver integrity [e.g., aspartate transaminase (AST)] and blood status (e.g., hemoglobin, red blood cell count, red cell distribution width) suggest that in this sample, albumin reflects both liver dysfunction and hematological abnormalities. The current results suggest that albumin, a simple serum biomarker available in most clinical settings, can predict variance in periventricular WMH volumes and performance in visual and verbal learning and memory cognitive domains. Whether serum albumin contributes mechanistically to periventricular WMH prevalence will require additional investigation.

SEA-NET: medical image segmentation network based on spiral squeeze-and-excitation and attention modules

BACKGROUND

Medical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are required for them. The current deep neural network based medical segmentation methods have good effect on image with balanced foreground and background, but it will loss the characteristics of small targets on image with imbalanced foreground and background after multiple convolutions.

METHODS

In order to retain the features of small targets in the deep network, we proposed a new medical image segmentation model based on the U-Net with squeeze-and-excitation and attention modules which form a spiral closed path,callled as Spiral Squeeze-and-Excitation and Attention NET (SEA-NET) in this paper. The segmentation model used squeeze-and-extraction modules to adjust the channel information to enhance the useful information and used attention modules to adjust the spatial information of the feature map to highlight the target area for small target segmentation when up-sampling. The deep semantic information is integrated into the shallow feature map by the attention model. Therefore, the deep semantic information cannot be scattered by continuous up-sampling. We used cross entropy loss + Tversky loss function for fast convergence and well processing the imbalanced data sets. Our proposed SEA-NET was tested on the brain MRI dataset LPBA40 and peripheral blood smear images.

CONCLUSIONS

On brain MRI data, the average value of the Dice coefficient we obtained reached 98.1[Formula: see text]. On the peripheral blood smear dataset, our proposed model has a good segmentation effect on adhesion cells.

RESULTS

The experimental results proved that the proposed SEA-Net performed better than U-Net, U-Net++, etc. in medical image segmentation.

Increased task-relevant fMRI responsiveness in comatose cardiac arrest patients is associated with improved neurologic outcomes

Early prediction of the recovery of consciousness in comatose cardiac arrest patients remains challenging. We prospectively studied task-relevant fMRI responses in 19 comatose cardiac arrest patients and five healthy controls to assess the fMRI's utility for neuroprognostication. Tasks involved instrumental music listening, forward and backward language listening, and motor imagery. Task-specific reference images were created from group-level fMRI responses from the healthy controls. Dice scores measured the overlap of individual subject-level fMRI responses with the reference images. Task-relevant responsiveness index (Rindex) was calculated as the maximum Dice score across the four tasks. Correlation analyses showed that increased Dice scores were significantly associated with arousal recovery (P < 0.05) and emergence from the minimally conscious state (EMCS) by one year (P < 0.001) for all tasks except motor imagery. Greater Rindex was significantly correlated with improved arousal recovery (P = 0.002) and consciousness (P = 0.001). For patients who survived to discharge (n = 6), the Rindex's sensitivity was 75% for predicting EMCS (n = 4). Task-based fMRI holds promise for detecting covert consciousness in comatose cardiac arrest patients, but further studies are needed to confirm these findings. Caution is necessary when interpreting the absence of task-relevant fMRI responses as a surrogate for inevitable poor neurological prognosis.

Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation

Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting on brain tumor segmentation algorithms were surveyed for the reported quality evaluation. Additionally, ratings of segmentation quality on a four-point scale were collected from medical professionals for 60 brain tumor segmentation cases. Results Of the surveyed articles, Dice score, sensitivity, and Hausdorff distance were the most popular metrics to report segmentation performance. Notably, only 2.8% of the articles included clinical experts' evaluation of segmentation quality. The experimental results revealed a low interrater agreement (Krippendorff α, 0.34) in experts' segmentation quality perception. Furthermore, the correlations between the ratings and commonly used quantitative quality metrics were low (Kendall tau between Dice score and mean rating, 0.23; Kendall tau between Hausdorff distance and mean rating, 0.51), with large variability among the experts. Conclusion The results demonstrate that quality ratings are prone to variability due to the ambiguity of tumor boundaries and individual perceptual differences, and existing metrics do not capture the clinical perception of segmentation quality. Keywords: Brain Tumor Segmentation, Deep Learning Algorithms, Glioblastoma, Cancer, Machine Learning Clinical trial registration nos. NCT00756106 and NCT00662506 Supplemental material is available for this article. © RSNA, 2023.

Deformation equivariant cross-modality image synthesis with paired non-aligned training data

Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method consists of joint training of an image synthesis network together with separate registration networks and allows adversarial training conditioned on the input even with misaligned data. The work lowers the bar for new clinical applications by allowing effortless training of cross-modality image synthesis networks for more difficult data sets.

MRI and CT Fusion in Stereotactic Electroencephalography (SEEG)

Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures. While 20% to 30% of epilepsy cases are untreatable with Anti-Epileptic Drugs, some of these cases can be addressed through surgical intervention. The success of such interventions greatly depends on accurately locating the epileptogenic tissue, a task achieved using diagnostic techniques like Stereotactic Electroencephalography (SEEG). SEEG utilizes multi-modal fusion to aid in electrode localization, using pre-surgical resonance and post-surgical computer tomography images as inputs. To ensure the absence of artifacts or misregistrations in the resultant images, a fusion method that accounts for electrode presence is required. We proposed an image fusion method in SEEG that incorporates electrode segmentation from computed tomography as a sampling mask during registration to address the fusion problem in SEEG. The method was validated using eight image pairs from the Retrospective Image Registration Evaluation Project (RIRE). After establishing a reference registration for the MRI and identifying eight points, we assessed the method's efficacy by comparing the Euclidean distances between these reference points and those derived using registration with a sampling mask. The results showed that the proposed method yielded a similar average error to the registration without a sampling mask, but reduced the dispersion of the error, with a standard deviation of 0.86 when a mask was used and 5.25 when no mask was used.

Dimensionality Reduction Hybrid U-Net for Brain Extraction in Magnetic Resonance Imaging

In various applications, such as disease diagnosis, surgical navigation, human brain atlas analysis, and other neuroimage processing scenarios, brain extraction is typically regarded as the initial stage in MRI image processing. Whole-brain semantic segmentation algorithms, such as U-Net, have demonstrated the ability to achieve relatively satisfactory results even with a limited number of training samples. In order to enhance the precision of brain semantic segmentation, various frameworks have been developed, including 3D U-Net, slice U-Net, and auto-context U-Net. However, the processing methods employed in these models are relatively complex when applied to 3D data models. In this article, we aim to reduce the complexity of the model while maintaining appropriate performance. As an initial step to enhance segmentation accuracy, the preprocessing extraction of full-scale information from magnetic resonance images is performed with a cluster tool. Subsequently, three multi-input hybrid U-Net model frameworks are tested and compared. Finally, we propose utilizing a fusion of two-dimensional segmentation outcomes from different planes to attain improved results. The performance of the proposed framework was tested using publicly accessible benchmark datasets, namely LPBA40, in which we obtained Dice overlap coefficients of 98.05%. Improvement was achieved via our algorithm against several previous studies.

Effect of Direction and Frequency of Skull Motion on Mechanical Vulnerability of the Human Brain

Strain energy and kinetic energy in the human brain were estimated by magnetic resonance elastography (MRE) during harmonic excitation of the head, and compared to characterize the effect of loading direction and frequency on brain deformation. In brain MRE, shear waves are induced by external vibration of the skull and imaged by a modified MR imaging sequence; the resulting harmonic displacement fields are typically "inverted" to estimate mechanical properties, like stiffness or damping. However, measurements of tissue motion from MRE also illuminate key features of the response of the brain to skull loading. In this study, harmonic excitation was applied in two different directions and at five different frequencies from 20 to 90 Hz. Lateral loading induced primarily left-right head motion and rotation in the axial plane; occipital loading induced anterior-posterior head motion and rotation in the sagittal plane. The ratio of strain energy to kinetic energy (SE/KE) depended strongly on both direction and frequency. The ratio of SE/KE was approximately four times larger for lateral excitation than for occipital excitation and was largest at the lowest excitation frequencies studied. These results are consistent with clinical observations that suggest lateral impacts are more likely to cause injury than occipital or frontal impacts, and also with observations that the brain has low-frequency (∼10 Hz) natural modes of oscillation. The SE/KE ratio from brain MRE is potentially a simple and powerful dimensionless metric of brain vulnerability to deformation and injury.

Locus coeruleus activity while awake is associated with REM sleep quality in older individuals

BACKGROUNDThe locus coeruleus (LC) is the primary source of norepinephrine in the brain and regulates arousal and sleep. Animal research shows that it plays important roles in the transition between sleep and wakefulness, and between slow wave sleep and rapid eye movement sleep (REMS). It is unclear, however, whether the activity of the LC predicts sleep variability in humans.METHODSWe used 7-Tesla functional MRI, sleep electroencephalography (EEG), and a sleep questionnaire to test whether the LC activity during wakefulness was associated with sleep quality in 33 healthy younger (~22 years old; 28 women, 5 men) and 19 older (~61 years old; 14 women, 5 men) individuals.RESULTSWe found that, in older but not in younger participants, higher LC activity, as probed during an auditory attentional task, was associated with worse subjective sleep quality and with lower power over the EEG theta band during REMS. The results remained robust even when accounting for the age-related changes in the integrity of the LC.CONCLUSIONThese findings suggest that LC activity correlates with the perception of the sleep quality and an essential oscillatory mode of REMS, and we found that the LC may be an important target in the treatment of sleep- and age-related diseases.FUNDINGThis work was supported by Fonds National de la Recherche Scientifique (FRS-FNRS, T.0242.19 & J. 0222.20), Action de Recherche Concertée - Fédération Wallonie-Bruxelles (ARC SLEEPDEM 17/27-09), Fondation Recherche Alzheimer (SAO-FRA 2019/0025), ULiège, and European Regional Development Fund (Radiomed & Biomed-Hub).

SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform

Multimodal neuroimaging data of various brain disorders provides valuable information to understand brain function in health and disease. Various neuroimaging-based databases have been developed that mainly consist of volumetric magnetic resonance imaging (MRI) data. We present the comprehensive web-based neuroimaging platform "SWADESH" for hosting multi-disease, multimodal neuroimaging, and neuropsychological data along with analytical pipelines. This novel initiative includes neurochemical and magnetic susceptibility data for healthy and diseased conditions, acquired using MR spectroscopy (MRS) and quantitative susceptibility mapping (QSM) respectively. The SWADESH architecture also provides a neuroimaging database which includes MRI, MRS, functional MRI (fMRI), diffusion weighted imaging (DWI), QSM, neuropsychological data and associated data analysis pipelines. Our final objective is to provide a master database of major brain disease states (neurodegenerative, neuropsychiatric, neurodevelopmental, and others) and to identify characteristic features and biomarkers associated with such disorders.

Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg

Background and purpose

To develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg.

Materials and methods

This retrospective study included 60 subjects [30 Alzheimer's disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference.

Results

The single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects.

Conclusion

Our deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.

An MRI Study Combining Virtual Brain Grafting and Surface-Based Morphometry Analysis to Investigate Contralateral Alterations in Cortical Morphology in Patients With Diffuse Low-Grade Glioma

BACKGROUND

The human brain has ability to reorganize itself in response to glioma. However, the mechanism of cortical reorganization remains unclear.

PURPOSE

To investigate alterations in cortical thickness and local gyration index (LGI) in patients with unilateral frontal lobe diffuse low-grade glioma (DLGG).

STUDY TYPE

Retrospective.

SUBJECTS

Ninety-nine patients with histopathologically proven DLGG invading the left frontal lobe (LF; N = 56) or the right frontal lobe (RF; N = 43), and healthy controls (HC; N = 53).

FIELD STRENGTH/SEQUENCE

3.0 T, 3D T1-weighted images and gadolinium enhanced T1-weighted images using magnetization-prepared rapid gradient echo sequence, T2-weighted images, and fluid-attenuated inversion recovery using turbo spin echo sequence.

ASSESSMENT

In patients with DLGG, virtual brain grafting combined with Freesurfer was utilized to enable automated cortical thickness and LGI calculation. In HC, standard FreeSurfer pipeline was applied to calculate these measures. Radiomic features were extracted from glioma using Pyradiomic software.

STATISTICAL TESTS

General linear model and Pearson's correlation analysis. A P value <0.05 was considered statistically significant.

RESULTS

For LF patients, there was significantly increased cortical thickness in the rostral middle frontal gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual and medial orbitofrontal (MOF) gyrus in contralateral hemisphere. For RF patients, there was significantly increased cortical thickness in the middle temporal, lateral occipital extending to isthmus cingulate gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual gyrus in the contralateral hemisphere. A negative association between four textural features of DLGG and LGI in the right MOF gyrus of LF group was found (r = -0.609, -0.442, -0.545, and -0.417, respectively).

DATA CONCLUSION

Cortical thickness compensation was shown in contralateral homotopic location and some distant contralateral regions. Additionally, there was decreased cortical thickness in the contralateral precentral gyrus and hypogyrification in contralateral lingual gyrus.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

The longitudinal progression of MRI changes in pre-ataxic carriers of SCA3/MJD

BACKGROUND

The natural history of magnetic resonance imaging (MRI) in pre-ataxic stages of spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is not well known. We report cross-sectional and longitudinal data obtained at this stage.

METHODS

Baseline (follow-up) observations included 32 (17) pre-ataxic carriers (SARA < 3) and 20 (12) related controls. The mutation length was used to estimate the time to onset (TimeTo) of gait ataxia. Clinical scales and MRIs were performed at baseline and after a median (IQR) of 30 (7) months. Cerebellar volumetries (ACAPULCO), deep gray-matter (T1-Multiatlas), cortical thickness (FreeSurfer), cervical spinal cord area (SCT) and white matter (DTI-Multiatlas) were assessed. Baseline differences between groups were described; variables that presented a p < 0.1 after Bonferroni correction were assessed longitudinally, using TimeTo and study time. For TimeTo strategy, corrections for age, sex and intracranial volume were done with Z-score progression. A significance level of 5% was adopted.

RESULTS

SCT at C1 level distinguished pre-ataxic carriers from controls. DTI measures of the right inferior cerebellar peduncle (ICP), bilateral middle cerebellar peduncles (MCP) and bilateral medial lemniscus (ML), also distinguished pre-ataxic carriers from controls, and progressed over TimeTo, with effect sizes varying from 0.11 to 0.20, larger than those of the clinical scales. No MRI variable showed progression over study time.

DISCUSSION

DTI parameters of the right ICP, left MCP and right ML were the best biomarkers for the pre-ataxic stage of SCA3/MJD. TimeTo is an interesting timescale, since it captured the longitudinal worsening of these structures.

Deep Learning Segmentation of the Nucleus Basalis of Meynert on 3T MRI

BACKGROUND AND PURPOSE

The nucleus basalis of Meynert is a key subcortical structure that is important in arousal and cognition and has been explored as a deep brain stimulation target but is difficult to study due to its small size, variability among patients, and lack of contrast on 3T MR imaging. Thus, our goal was to establish and evaluate a deep learning network for automatic, accurate, and patient-specific segmentations with 3T MR imaging.

MATERIALS AND METHODS

Patient-specific segmentations can be produced manually; however, the nucleus basalis of Meynert is difficult to accurately segment on 3T MR imaging, with 7T being preferred. Thus, paired 3T and 7T MR imaging data sets of 21 healthy subjects were obtained. A test data set of 6 subjects was completely withheld. The nucleus was expertly segmented on 7T, providing accurate labels for the paired 3T MR imaging. An external data set of 14 patients with temporal lobe epilepsy was used to test the model on brains with neurologic disorders. A 3D-Unet convolutional neural network was constructed, and a 5-fold cross-validation was performed.

RESULTS

The novel segmentation model demonstrated significantly improved Dice coefficients over the standard probabilistic atlas for both healthy subjects (mean, 0.68 [SD, 0.10] versus 0.45 [SD, 0.11], P = .002, t test) and patients (0.64 [SD, 0.10] versus 0.37 [SD, 0.22], P < .001). Additionally, the model demonstrated significantly decreased centroid distance in patients (1.18 [SD, 0.43] mm, 3.09 [SD, 2.56] mm, P = .007).

CONCLUSIONS

We developed the first model, to our knowledge, for automatic and accurate patient-specific segmentation of the nucleus basalis of Meynert. This model may enable further study into the nucleus, impacting new treatments such as deep brain stimulation.

Robotic transcranial magnetic stimulation in the treatment of depression: a pilot study

There has been an increasing demand for robotic coil positioning during repetitive transcranial magnetic stimulation (rTMS) treatment. Accurate coil positioning is crucial because rTMS generally targets specific brain regions for both research and clinical application with other reasons such as safety, consistency and reliability and individual variablity. Some previous studies have employed industrial robots or co-robots and showed they can more precisely stimulate the target cortical regions than traditional manual methods. In this study, we not only developed a custom-TMS robot for better TMS coil placement but also analyzed the therapeutic effects on depression. Treatment effects were evaluated by measuring regional cerebral blood flow (rCBF) using single-photon emission computed tomography and depression severity before and after rTMS for the two positioning methods. The rTMS preparation time with our robotic coil placement was reduced by 53% compared with that of the manual method. The position and orientation errors were also significantly reduced from 11.17 mm and 4.06° to 0.94 mm and 0.11°, respectively, confirming the superiority of robotic positioning. The results from clinical and neuroimaging assessments indicated comparable improvements in depression severity and rCBF in the left dorsolateral prefrontal cortex between the robotic and manual rTMS groups. A questionnaire was used to determine the patients' feelings about the robotic system, including the safety and preparation time. A high safety score indicated good acceptability of robotic rTMS at the clinical site.

Disrupted excitation-inhibition balance in cognitively normal individuals at risk of Alzheimer's disease

Background

Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology.

Objective

Examine how AD risk factors (age, APOE-ɛ4, amyloid-β) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures.

Methods

Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231).

Results

In absence of AD risk factors (APOE-ɛ4/Aβ+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, β = -0.007). Regression modeling including APOE-ɛ4 allele carriers (Aβ-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, β = 0.014), persisting with inclusion of Aβ+ individuals (p = 0.012, β = 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the trail-making test (p < 0.05).

Conclusion

Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOE-ɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOE-ɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.

ImUnity: A generalizable VAE-GAN solution for multicenter MR image harmonization

ImUnity is an original 2.5D deep-learning model designed for efficient and flexible MR image harmonization. A VAE-GAN network, coupled with a confusion module and an optional biological preservation module, uses multiple 2D slices taken from different anatomical locations in each subject of the training database, as well as image contrast transformations for its training. It eventually generates 'corrected' MR images that can be used for various multi-center population studies. Using 3 open source databases (ABIDE, OASIS and SRPBS), which contain MR images from multiple acquisition scanner types or vendors and a large range of subjects ages, we show that ImUnity: (1) outperforms state-of-the-art methods in terms of quality of images generated using traveling subjects; (2) removes sites or scanner biases while improving patients classification; (3) harmonizes data coming from new sites or scanners without the need for an additional fine-tuning and (4) allows the selection of multiple MR reconstructed images according to the desired applications. Tested here on T1-weighted images, ImUnity could be used to harmonize other types of medical images.

The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.

Evaluation of the PaCER Algorithm for Postoperative Subthalamic Nucleus Deep Brain Stimulation Electrode Localization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083396 DOI: 10.1109/embc40787.2023.10340555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Deep Brain Stimulation (DBS) is an established therapy for many movement disorders. DBS entails electrical stimulation of precise brain structures using permanently implanted electrodes. Following implantation, locating the electrodes relative to the target brain structure assists patient outcome optimization. Here we evaluated an open-source automatic algorithm (PaCER) to localize individual electrodes on Computed Tomography imaging (co-registered to Magnetic Resonance Imaging). In a dataset of 111 participants, we found a modified version of the algorithm matched manual-markups with median error less than 0.191 mm (interquartile range 0.698 mm). Given the error is less than the voxel resolution (1 mm3) of the images, we conclude that the automatic algorithm is suitable for DBS electrode localizations.Clinical Relevance- Automated DBS electrode localization identifies the closest electrode to the target brain structure; allowing the neurologist to direct electrical stimulation to maximize patient outcomes. Further, if none of the electrodes are deemed suitable, localization will guide re-implantation.

A Deep Learning Framework for Skull Stripping in Brain MRI

Skull-stripping, an important pre-processing step in neuroimage computing, involves the automated removal of non-brain anatomy (such as the skull, eyes, and ears) from brain images to facilitate brain segmentation and analysis. Manual segmentation is still practiced, but it is time-consuming and highly dependent on the expertise of clinicians or image analysts. Prior studies have developed various skull-stripping algorithms that perform well on brains with mild or no structural abnormalities. Nonetheless, they were not able to address the issue for brains with significant morphological changes, such as those caused by brain tumors, particularly when the tumors are located near the skull's border. In such cases, a portion of the normal brain may be stripped, or the reverse may occur during skull stripping. To address this limitation, we propose to use a novel deep learning framework based on nnUNet for skull stripping in brain MRI. Two publicly available datasets were used to evaluate the proposed method, including a normal brain MRI dataset - The Neurofeedback Skull-stripped Repository (NFBS), and a brain tumor MRI dataset - The Cancer Genome Atlas (TCGA). The method proposed in the study performed better than six other current methods, namely BSE, ROBEX, UNet, SC-UNet, MV-UNet, and 3D U-Net. The proposed method achieved an average Dice coefficient of 0.9960, a sensitivity of 0.9999, and a specificity of 0.9996 on the NFBS dataset, and an average Dice coefficient of 0.9296, a sensitivity of 0.9288, a specificity of 0.9866 and an accuracy of 0.9762 on the TCGA brain tumor dataset.

Structural and functional characterization of the locus coeruleus in young and late middle-aged individuals

INTRODUCTION

The brainstem locus coeruleus (LC) influences a broad range of brain processes, including cognition. The so-called LC contrast is an accepted marker of the integrity of the LC that consists of a local hyperintensity on specific Magnetic Resonance Imaging (MRI) structural images. The small size of the LC has, however, rendered its functional characterization difficult in humans, including in aging. A full characterization of the structural and functional characteristics of the LC in healthy young and late middle-aged individuals is needed to determine the potential roles of the LC in different medical conditions. Here, we wanted to determine whether the activation of the LC in a mismatch negativity task changes in aging and whether the LC functional response was associated to the LC contrast.

METHODS

We used Ultra-High Field (UHF) 7-Tesla functional MRI (fMRI) to record brain response during an auditory oddball task in 53 healthy volunteers, including 34 younger (age: 22.15y ± 3.27; 29 women) and 19 late middle-aged (age: 61.05y ± 5.3; 14 women) individuals.

RESULTS

Whole-brain analyses confirmed brain responses in the typical cortical and subcortical regions previously associated with mismatch negativity. When focusing on the brainstem, we found a significant response in the rostral part of the LC probability mask generated based on individual LC images. Although bilateral, the activation was more extensive in the left LC. Individual LC activity was not significantly different between young and late middle-aged individuals. Importantly, while the LC contrast was higher in older individuals, the functional response of the LC was not significantly associated with its contrast.

DISCUSSION

These findings may suggest that the age-related alterations of the LC structural integrity may not be related to changes in its functional response. The results further suggest that LC responses may remain stable in healthy individuals aged 20 to 70.

Discordant and Converting Receptor Expressions in Brain Metastases from Breast Cancer: MRI-Based Non-Invasive Receptor Status Tracking

Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Therefore, personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. Radiological in vivo techniques may allow receptor status tracking at high frequencies at low risk and cost. The present study aims to investigate the potential of receptor status prediction through machine-learning-based analysis of radiomic MR image features. The analysis is based on 412 brain metastases samples from 106 patients acquired between 09/2007 and 09/2021. Inclusion criteria were as follows: diagnosed cerebral metastases from breast cancer; histopathology reports on progesterone (PR), estrogen (ER), and human epidermal growth factor 2 (HER2) receptor status; and availability of MR imaging data. In total, 3367 quantitative features of T1 contrast-enhanced, T1 non-enhanced, and FLAIR images and corresponding patient age were evaluated utilizing random forest algorithms. Feature importance was assessed using Gini impurity measures. Predictive performance was tested using 10 permuted 5-fold cross-validation sets employing the 30 most important features of each training set. Receiver operating characteristic areas under the curves of the validation sets were 0.82 (95% confidence interval [0.78; 0.85]) for ER+, 0.73 [0.69; 0.77] for PR+, and 0.74 [0.70; 0.78] for HER2+. Observations indicate that MR image features employed in a machine learning classifier could provide high discriminatory accuracy in predicting the receptor status of brain metastases from breast cancer.

Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO)

PURPOSE

Efforts to use growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling, owing to data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements.

MATERIALS AND METHODS

Our end-to-end framework (1) classifies MRI sequences using an ensemble classifier, (2) preprocesses the data in a reproducible manner, (3) delineates tumor tissue subtypes using convolutional neural networks, and (4) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach in which the segmentation results may be manually refined by radiologists. After the implementation of the framework in Docker containers, it was applied to two retrospective glioma data sets collected from the Washington University School of Medicine (WUSM; n = 384) and The University of Texas MD Anderson Cancer Center (MDA; n = 30), comprising preoperative MRI scans from patients with pathologically confirmed gliomas.

RESULTS

The scan-type classifier yielded an accuracy of >99%, correctly identifying sequences from 380 of 384 and 30 of 30 sessions from the WUSM and MDA data sets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. The mean Dice scores were 0.882 (±0.244) and 0.977 (±0.04) for whole-tumor segmentation for WUSM and MDA, respectively.

CONCLUSION

This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology data sets and demonstrating high potential for integration as an assistive tool in clinical practice.

Episodic memory deficit in HIV infection: common phenotype with Parkinson's disease, different neural substrates

Episodic memory deficits occur in people living with HIV (PLWH) and individuals with Parkinson's disease (PD). Given known effects of HIV and PD on frontolimbic systems, episodic memory deficits are often attributed to executive dysfunction. Although executive dysfunction, evidenced as retrieval deficits, is relevant to mnemonic deficits, learning deficits may also contribute. Here, the California Verbal Learning Test-II, administered to 42 PLWH, 41 PD participants, and 37 controls, assessed learning and retrieval using measures of free recall, cued recall, and recognition. Executive function was assessed with a composite score comprising Stroop Color-Word Reading and Backward Digit Spans. Neurostructural correlates were examined with MRI of frontal (precentral, superior, orbital, middle, inferior, supplemental motor, medial) and limbic (hippocampus, thalamus) volumes. HIV and PD groups were impaired relative to controls on learning and free and cued recall trials but did not differ on recognition or retention of learned material. In no case did executive functioning solely account for the observed mnemonic deficits or brain-performance relations. Critically, the shared learning and retrieval deficits in HIV and PD were related to different substrates of frontolimbic mnemonic neurocircuitry. Specifically, diminished learning and poorer free and cued recall were related to smaller orbitofrontal volume in PLWH but not PD, whereas diminished learning in PD but not PLWH was related to smaller frontal superior volume. In PD, poorer recognition correlated with smaller thalamic volume and poorer retention to hippocampal volume. Although memory deficits were similar, the neural correlates in HIV and PD suggest different pathogenic mechanisms.

Threat and Reward Imminence Processing in the Human Brain

In the human brain, aversive and appetitive processing have been studied with controlled stimuli in rather static settings. In addition, the extent to which aversive-related and appetitive-related processing engage distinct or overlapping circuits remains poorly understood. Here, we sought to investigate the dynamics of aversive and appetitive processing while male and female participants engaged in comparable trials involving threat avoidance or reward seeking. A central goal was to characterize the temporal evolution of responses during periods of threat or reward imminence. For example, in the aversive domain, we predicted that the bed nucleus of the stria terminalis (BST), but not the amygdala, would exhibit anticipatory responses given the role of the former in anxious apprehension. We also predicted that the periaqueductal gray (PAG) would exhibit threat-proximity responses based on its involvement in proximal-threat processes, and that the ventral striatum would exhibit threat-imminence responses given its role in threat escape in rodents. Overall, we uncovered imminence-related temporally increasing ("ramping") responses in multiple brain regions, including the BST, PAG, and ventral striatum, subcortically, and dorsal anterior insula and anterior midcingulate, cortically. Whereas the ventral striatum generated anticipatory responses in the proximity of reward as expected, it also exhibited threat-related imminence responses. In fact, across multiple brain regions, we observed a main effect of arousal. In other words, we uncovered extensive temporally evolving, imminence-related processing in both the aversive and appetitive domain, suggesting that distributed brain circuits are dynamically engaged during the processing of biologically relevant information regardless of valence, findings further supported by network analysis.SIGNIFICANCE STATEMENT In the human brain, aversive and appetitive processing have been studied with controlled stimuli in rather static settings. Here, we sought to investigate the dynamics of aversive/appetitive processing while participants engaged in trials involving threat avoidance or reward seeking. A central goal was to characterize the temporal evolution of responses during periods of threat or reward imminence. We uncovered imminence-related temporally increasing ("ramping") responses in multiple brain regions, including the bed nucleus of the stria terminalis, periaqueductal gray, and ventral striatum, subcortically, and dorsal anterior insula and anterior midcingulate, cortically. Overall, we uncovered extensive temporally evolving, imminence-related processing in both the aversive and appetitive domain, suggesting that distributed brain circuits are dynamically engaged during the processing of biologically relevant information regardless of valence.

Simultaneous Acquisition of Diffusion Tensor and Dynamic Diffusion MRI

BACKGROUND

Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time hindering its clinical application.

PURPOSE

To determine whether a diffusion gradient table can simultaneously acquire data to estimate dynamic and diffusion tensor imaging (DTI) metrics.

STUDY TYPE

Prospective.

SUBJECTS

Seven healthy subjects, 39 epilepsy patients (15 female, 31 male, age ± 15).

FIELD STRENGTH/SEQUENCE

Two-dimensional diffusion MRI (b = 1000 s/mm2 ) at a field strength of 3 T. Sessions in healthy subjects-standard ddMRI (30 directions), standard DTI (15 and 30 directions), and nested cubes scans (15 and 30 directions). Sessions in epilepsy patients-two 30 direction (standard ddMRI, 10 nested cubes) or two 15 direction scans (standard DTI, 5 nested cubes).

ASSESSMENT

Fifteen direction DTI was repeated twice for within-session test-retest measurements in healthy subjects. Bland-Altman analysis computed bias and limits of agreement for DTI metrics using test-retest scans and standard 15 direction vs. 5 nested cubes scans. Intraclass correlation (ICC) analysis compared tensor metrics between 15 direction DTI scans (standard vs. 5 nested cubes) and the coefficients of variation (CoV) of trace and apparent diffusion coefficient (ADC) between 30 direction ddMRI scans (standard vs. 10 nested cubes).

STATISTICAL TESTS

Bland-Altman and ICC analysis using a P-value of 0.05 for statistical significance.

RESULTS

Correlations of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were strong and significant in gray (ICC > 0.95) and white matter (ICC > 0.95) between standard vs. nested cubes DTI acquisitions. Correlation of white matter fractional anisotropy was also strong (ICC > 0.95) and significant. ICCs of the CoV of dynamic ADC measured using repeated cubes and nested cubes acquisitions were modest (ICC >0.60), but significant in gray matter.

CONCLUSION

A nested cubes diffusion gradient table produces tensor-based and dynamic diffusion measurements in a single acquisition.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

In vivo Locus Coeruleus activity while awake is associated with REM sleep quality in healthy older individuals

The locus coeruleus (LC) is the primary source of norepinephrine (NE) in the brain, and the LC-NE system is involved in regulating arousal and sleep. It plays key roles in the transition between sleep and wakefulness, and between slow wave sleep (SWS) and rapid eye movement sleep (REMS). However, it is not clear whether the LC activity during the day predicts sleep quality and sleep properties during the night, and how this varies as a function of age. Here, we used 7 Tesla functional Magnetic Resonance Imaging (7T fMRI), sleep electroencephalography (EEG) and a sleep questionnaire to test whether the LC activity during wakefulness was associated with sleep quality in 52 healthy younger (N=33; ~22y; 28 women) and older (N=19; ~61y; 14 women) individuals. We find that, in older, but not in younger participants, higher LC activity, as probed during an auditory mismatch negativity task, is associated with worse subjective sleep quality and with lower power over the EEG theta band during REMS (4-8Hz), which are two sleep parameters significantly correlated in our sample of older individuals. The results remain robust even when accounting for the age-related changes in the integrity of the LC. These findings suggest that the activity of the LC may contribute to the perception of the sleep quality and to an essential oscillatory mode of REMS, and that the LC may be an important target in the treatment of sleep disorders and age-related diseases.