Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

Assessing the Relationship of Brain Metabolites to Cortical Thickness and Dementia Symptoms in Adults with Down Syndrome: A Pilot Study

BACKGROUND/OBJECTIVES

Those with the genetic disorder Down syndrome are at high risk of developing Alzheimer's disease. Previous work shows group differences in magnetic resonance spectroscopy metabolite measures in adults with Down syndrome who have Alzheimer's disease-related dementia compared to those who do not. In this pilot study, we assess relationships between metabolites and measures related to dementia status in a sample of adults with Down syndrome.

METHODS

Seventeen adults with Down syndrome were scanned using a 3 tesla MRI scanner. Magnetic resonance spectroscopy scans focused on the hippocampus and dorsal lateral prefrontal cortex. Metabolites of interest, including myo-inositol and N-acetyl-aspartate, were correlated with scores on the Dementia Questionnaire for People with Learning Disabilities, cortical thickness, and a measure of cognitive ability. In addition, cortical thickness was compared to an age- and sex-matched cohort of 17 previously scanned adults without Down syndrome.

RESULTS

Metabolite measures were not significantly related to cognitive/behavioral measures or to cortical thickness in this small cohort. Participants with Down syndrome showed widespread increases in cortical thickness compared to controls, even after accounting for potential differences in grey matter/white matter contrast.

CONCLUSIONS

Metabolite values were not related to two continuous measures that have previously been associated with dementia status in those with Down syndrome.

Association of Cerebrovascular Reactivity With 1-Year Imaging and Clinical Outcomes in Small Vessel Disease: An Observational Cohort Study

BACKGROUND AND OBJECTIVES

In patients with cerebral small vessel disease (SVD), impaired cerebrovascular reactivity (CVR) is related to worse concurrent SVD burden, but less is known about cerebrovascular reactivity and long-term SVD lesion progression and clinical outcomes. We investigated associations between cerebrovascular reactivity and 1-year progression of SVD features and clinical outcomes.

METHODS

Between 2018 and 2021, we recruited patients from the Edinburgh/Lothian stroke services presenting with minor ischemic stroke and SVD features as part of the Mild Stroke Study 3, a prospective observational cohort study (ISRCTN 12113543). We acquired 3T brain MRI at baseline and 1 year. At baseline, we measured cerebrovascular reactivity to 6% inhaled CO2 in subcortical gray matter, normal-appearing white matter, and white matter hyperintensities (WMH). At baseline and 1 year, we quantified SVD MRI features, incident infarcts, assessed stroke severity (NIH Stroke Scale), recurrent stroke, functional outcome (modified Rankin Scale), and cognition (Montreal Cognitive Assessment). We performed linear and logistic regressions adjusted for age, sex, and vascular risk factors, reporting the regression coefficients and odds ratios with 95% CIs.

RESULTS

We recruited 208 patients of whom 163 (mean age and SD: 65.8 ± 11.2 years, 32% female) had adequate baseline CVR and completed the follow-up structural MRI. The median increase in WMH volume was 0.32 mL with (Q1, Q3) = (-0.48, 1.78) mL; 29% had a recurrent stroke or incident infarct on MRI. At 1 year, patients with lower baseline cerebrovascular reactivity in normal-appearing tissues had increased WMH (regression coefficient: B = -1.14 [-2.13, -0.14] log10 (%ICV) per %/mm Hg) and perivascular space volumes (B = -1.90 [-3.21, -0.60] log10 (%ROIV) per %/mm Hg), with a similar trend in WMH. CVR was not associated with clinical outcomes at 1 year.

DISCUSSION

Lower baseline cerebrovascular reactivity predicted an increase in WMH and perivascular space volumes after 1 year. CVR should be considered in SVD future research and intervention studies.

A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks

In structural magnetic resonance (MR) imaging, motion artefacts, low resolution, imaging noise and variability in acquisition protocols frequently degrade image quality and confound downstream analyses. Here we report a foundation model for the motion correction, resolution enhancement, denoising and harmonization of MR images. Specifically, we trained a tissue-classification neural network to predict tissue labels, which are then leveraged by a 'tissue-aware' enhancement network to generate high-quality MR images. We validated the model's effectiveness on a large and diverse dataset comprising 2,448 deliberately corrupted images and 10,963 images spanning a wide age range (from foetuses to elderly individuals) acquired using a variety of clinical scanners across 19 public datasets. The model consistently outperformed state-of-the-art algorithms in improving the quality of MR images, handling pathological brains with multiple sclerosis or gliomas, generating 7-T-like images from 3 T scans and harmonizing images acquired from different scanners. The high-quality, high-resolution and harmonized images generated by the model can be used to enhance the performance of models for tissue segmentation, registration, diagnosis and other downstream tasks.

The interactive effects of divided attention and semantic elaboration on associative recognition memory: an fMRI study

The present study explored the opposing effects on memory of semantic elaboration and division of attention on learning and recognition of verbal paired associates. Previous work had found that levels of recollection were reduced under divided attention conditions, even after equating expressed elaboration levels between full and divided attention. The present experiments not only confirmed this finding but also found that participants based their expressed levels of elaboration largely on normative values rather than on subjectively achieved levels of elaboration. In terms of related brain processes, experiment 2 used functional magnetic resonance to show that division of attention was associated with reduced levels of both prefrontal and hippocampal activity and with a reduction in connectivity between the anterior hippocampus and medial-orbital regions of the prefrontal cortex. Increased levels of elaboration were associated with increased activity in prefrontal regions immediately after stimulus presentation. Additionally, connectivity between the hippocampus and medial-prefrontal cortex was enhanced by increases in elaboration under full attention but reduced by increases in elaboration under conditions of divided attention. Our results therefore show that two factors influencing memory-elaboration and attention-are mediated largely by processes in the prefrontal cortex, the hippocampus, and the functional connectivity between these two structures.

Structure of subcortico-cortical tracts in middle-aged and older adults with autism spectrum disorder

Middle-aged and older adults with autism spectrum disorder may be susceptible to accelerated neurobiological changes in striato- and thalamo-cortical tracts due to combined effects of typical aging and existing disparities present from early neurodevelopment. Using magnetic resonance imaging, we employed diffusion-weighted imaging and automated tract-segmentation to explore striato- and thalamo-cortical tract microstructure and volume differences between autistic (n = 29) and typical comparison (n = 33) adults (40 to 70 years old). Fractional anisotropy, mean diffusivity, and tract volumes were measured for 14 striato-cortical and 12 thalamo-cortical tract bundles. Data were examined using linear regressions for group by age effects and group plus age effects, and false discovery rate correction was applied. Following false discovery rate correction, volumes of thalamocortical tracts to premotor, pericentral, and parietal regions were significantly reduced in autism spectrum disorder compared to thalamo-cortical groups, but no group by age interactions were found. Uncorrected results suggested additional main effects of group and age might be present for both tract volume and mean diffusivity across multiple subcortico-cortical tracts. Results indicate parallel rather than accelerated changes during adulthood in striato-cortical and thalamo-cortical tract volume and microstructure in those with autism spectrum disorder relative to thalamo-cortical peers though thalamo-cortical tract volume effects are the most reliable.

Distributed associations among white matter hyperintensities and structural brain networks with fluid cognition in healthy aging

White matter hyperintensities (WMHs) are associated with age-related cognitive impairment and increased risk of Alzheimer's disease. However, the manner by which WMHs contribute to cognitive impairment is unclear. Using a combination of predictive modeling and network neuroscience, we investigated the relationship between structural white matter connectivity and age, fluid cognition, and WMHs in 68 healthy adults (18-78 years). Consistent with previous work, WMHs were increased in older adults and exhibited a strong negative association with fluid cognition. Extending previous work, using predictive modeling, we demonstrated that age, WMHs, and fluid cognition were jointly associated with widespread alterations in structural connectivity. Subcortical-cortical connections between the thalamus/basal ganglia and frontal and parietal regions of the default mode and frontoparietal networks were most prominent. At the network level, both age and WMHs were negatively associated with network density and communicability, and positively associated with modularity. Spatially, WMHs were most prominent in arterial zones served by the middle cerebral artery and associated lenticulostriate branches that supply subcortical regions. Finally, WMHs overlapped with all major white matter tracts, most prominently in tracts that facilitate subcortical-cortical communication and are implicated in fluid cognition, including the anterior thalamic-radiations and forceps minor. Finally, results of mediation analyses suggest that whole-brain WMH load influences age-related decline in fluid cognition. Thus, across multiple levels of analysis, we showed that WMHs were increased in older adults and associated with altered structural white matter connectivity and network topology involving subcortical-cortical pathways critical for fluid cognition.

Brain activity during working memory in patients with autoimmune Addison's disease

Autoimmune Addison's disease (AAD) is treated with daily oral hormone replacements for cortisol and aldosterone. The current treatment is sub-optimal, and frequently results in supra- and infra-physiological cortisol levels that might negatively affect the brain and cognitive functioning. It is currently unclear if the brains of these patients need to be better protected. The present study investigates brain function during working memory in young adults with AAD compared to healthy controls. All participants (56 AAD (33 females), 62 controls (39 females), 19-43 years), underwent MRI brain scanning while performing a visuo-spatial and verbal working memory task. No main group differences in accuracy, reaction time or brain activity during the tasks were found. These findings suggest that patients perform equal to controls, and achieve similar levels of brain activity during working memory. However, variations in the patient population may have confounded this outcome. Controlled studies on larger cohorts are therefore needed to confirm these findings and test if having AAD affects the brain on the long term.

Unveiling Glutamate Dynamics: Cognitive Demands in Human Short-Term Memory Learning Across Frontal and Parieto-Occipital Cortex: A Functional MRS Study

Background

Acquiring new knowledge necessitates alterations at the synaptic level within the brain. Glutamate, a pivotal neurotransmitter, plays a critical role in these processes, particularly in learning and memory formation. Although previous research has explored glutamate's involvement in cognitive functions, a comprehensive understanding of its real-time dynamics remains elusive during memory tasks.

Objective

This study aimed to investigate glutamate modulation during memory tasks in the right Dorsolateral Prefrontal Cortex (DLPFC) and parieto-occipital regions using functional Magnetic Resonance Spectroscopy (fMRS).

Material and Methods

This experimental research applied fMRS acquisition concurrently with a modified Sternberg's verbal working memory task for fourteen healthy right-handed participants (5 females, mean age=30.64±4.49). The glutamate/total-creatine (Glu/tCr) ratio was quantified by LCModel in the DLPFC and parieto-occipital voxels while applying the tissue corrections.

Results

The significantly higher Glu/tCr modulation was observed during the task with a trend of increased modulation with memory load in both the DLPFC (19.9% higher, P-value=0.018) and parieto-occipital (33% higher, P-value=0.046) regions compared to the rest.

Conclusion

Our pioneering fMRS study has yielded groundbreaking insights into brain functions during S-term Memory (STM) and learning. This research provides valuable methodological advancements for investigating the metabolic functions of both healthy and disordered brains. Based on the findings, cognitive demands directly correlate with glutamate levels, highlighting the neurochemical underpinnings of cognitive processing. Additionally, the obtained results potentially challenge the traditional left-hemisphere-centric model of verbal working memory, leading to the deep vision of hemispheric contributions to cognitive functions.

Functional conductivity imaging: quantitative mapping of brain activity

Theory and modelling suggest that detection of neuronal activity may be feasible using phase sensitive MRI methods. Successful detection of neuronal activity both in vitro and in vivo has been described while others have reported negative results. Magnetic resonance electrical properties tomography may be a route by which signal changes can be identified. Here, we report successful and repeatable detection at 3 Tesla of human brain activation in response to visual and somatosensory stimuli using a functional version of tissue conductivity imaging (funCI). This detects activation in both white and grey matter with apparent tissue conductivity changes of 0.1 S/m (17-20%, depending on the tissue baseline conductivity measure) allowing visualization of complete system circuitry. The degree of activation scales with the degree of the stimulus (duration or contrast). The conductivity response functions show a distinct timecourse from that of traditional fMRI haemodynamic (BOLD or Blood Oxygenation Level Dependent) response functions, peaking within milliseconds of stimulus cessation and returning to baseline within 3-4 s. We demonstrate the utility of the funCI approach by showing robust activation of the lateral somatosensory circuitry on stimulation of an index finger, on stimulation of a big toe or of noxious (heat) stimulation of the face as well as activation of visual circuitry on visual stimulation in up to five different individuals. The sensitivity and repeatability of this approach provides further evidence that magnetic resonance imaging approaches can detect brain activation beyond changes in blood supply.

IVIM parameters mapping with artificial neural network based on mean deviation prior

BACKGROUND

The diffusion and perfusion parameters derived from intravoxel incoherent motion (IVIM) imaging provide promising biomarkers for noninvasively quantifying and managing various diseases. Nevertheless, due to the distribution gap between simulated and real datasets, the out-of-distribution (OOD) problem occurred in supervised learning-based methods degrades their performance and hinders their real applications.

PURPOSE

To address the OOD problem in supervised methods and to further improve the accuracy and stability of IVIM parameter estimation, this work proposes a novel learning framework called IterANN, based on mean deviation prior (MDP) between training and estimated IVIM parameters on the test set.

METHODS

Specifically, MDP indicates that the mean of the estimated IVIM parameters always locates between the mean of IVIM parameters in the test and train sets. In IterANN, we adopt a very simple artificial neural network (ANN) architecture of two hidden layers with 12 neurons per hidden layer, an input layer containing the signals acquired at multiple b-values and an output layer composed of three IVIM parameters ( D $D$ , F $F$ andD S t a r $DStar$ ). Inspired by MDP, the distribution of IVIM parameters in the training set (simulated data) is iteratively updated so that their mean gradually approaches the predicted values of the real data. This aims to achieve a strong correlation between the simulated data and the real data. To validate the effectiveness of IterANN, we compare it with several methods on both simulation and real acquisition datasets, including 21 healthy and 3 tumor subjects, in terms of residual errors of IVIM parameters or DW signals, the coefficients of variation (CV) of IVIM parameters, and the parameter contrast-to-noise ratio (PCNR) between normal and tumor tissues.

RESULTS

On two simulation datasets, the proposed IterANN achieves the lowest residual error in IVIM parameters, especially in the case of low signal-to-noise ratio (SNR = 10), the residual error of D $D$ , F $F$ andD S t a r $DStar$ is decreased by15.82 % / 14.92 % , 81.19 % / 74.04 % , 50.77 % / 1.549 % $15.82\%/14.92\%, 81.19\%/74.04\%, 50.77\%/1.549\%$ (Gaussian distribution /realistic distribution) respectively comparing to the suboptimal method. On real dataset, the IterANN achieves the highest PCNR when comparing the normal and tumor regions. Additionally, the proposed IterANN demonstrated better stability, with its CV being significantly lower than that of other methods in the vast majority of cases (p < 0.01 $p<0.01$ , paired-sample Student's t-test).

CONCLUSIONS

The superior performance of IterANN demonstrates that updating the distribution of the train set based on MDP can effectively solve the OOD problem, which allows us not only to improve the accuracy and stability of the estimated IVIM parameters, but also to increase the potential of IVIM in disease diagnosis.

Proof of concept: Portable ultra-low-field magnetic resonance imaging for the diagnosis of epileptogenic brain pathologies

OBJECTIVE

High-field magnetic resonance imaging (MRI) is a standard in the diagnosis of epilepsy. However, high costs and technical barriers have limited adoption in low- and middle-income countries. Even in high-income nations, many individuals with epilepsy face delays in undergoing MRI. Recent advancements in ultra-low-field (ULF) MRI technology, particularly the development of portable scanners, offer a promising solution to the limited accessibility of MRI. In this study, we present and evaluate the imaging capability of ULF MRI in detecting structural abnormalities typically associated with epilepsy and compare it to high-field MRI at 3 T.

METHODS

Data collection was conducted within 3 consecutive weeks at the University Hospital Bonn. Inclusion criteria were a minimum age of 18 years, diagnosed epilepsy, and clinical high-field MRI with abnormalities. We used a .064 T Swoop portable MR Imaging System. Both high-field MRI and ULF MRI scans were evaluated independently by two experienced neuroradiologists as part of their clinical routine, comparing pathology detection and diagnosis completeness.

RESULTS

Twenty-three individuals with epilepsy were recruited. One subject presented with a dual pathology. Across the entire cohort, in 17 of 24 (71%) pathologies, an anomaly colocalizing with the actual lesion was observed on ULF MRI. For 11 of 24 (46%) pathologies, the full diagnosis could be made based on ULF MRI. Tumors and posttraumatic lesions could be diagnosed best on ULF MRI, whereas cortical dysplasia and other focal pathologies were the least well diagnosed.

SIGNIFICANCE

This single-center series of individuals with epilepsy demonstrates the feasibility and utility of ULF MRI for the field of epileptology. Its integration into epilepsy care offers transformative potential, particularly in resource-limited settings. Further research is needed to position ULF MRI within imaging modalities in the diagnosis of epilepsy.

Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma

PURPOSE

To develop a self-supervised learning method to retrospectively estimate T1 and T2 values from clinical weighted MRI.

METHODS

A self-supervised learning approach was constructed to estimate T1, T2, and proton density maps from conventional T1- and T2-weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset.

RESULTS

For T1 and T2 estimation from three weighted images (T1 MPRAGE, T1 gradient echo sequences, and T2 turbo spin echo), the deep learning method achieved global voxel-wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T2 values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel-wise classification task between normal and abnormal regions.

CONCLUSION

The self-supervised learning method is promising for retrospective T1 and T2 quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization.

Distinct patterns of connectivity with the motor cortex reflect different components of sensorimotor learning

Sensorimotor learning is supported by multiple competing processes that operate concurrently, making it a challenge to elucidate their neural underpinnings. Here, using human functional MRI, we identify 3 distinct axes of connectivity between the motor cortex and other brain regions during sensorimotor adaptation. These 3 axes uniquely correspond to subjects' degree of implicit learning, performance errors and explicit strategy use, and involve different brain networks situated at increasing levels of the cortical hierarchy. We test the generalizability of these neural axes to a separate form of motor learning known to rely mainly on explicit processes and show that it is only the Explicit neural axis, composed of higher-order areas in transmodal cortex, that predicts learning in this task. Together, our study uncovers multiple distinct patterns of functional connectivity with motor cortex during sensorimotor adaptation, the component processes that these patterns support, and how they generalize to other forms of motor learning.

Lifestyle, biological, and genetic factors related to brain iron accumulation across adulthood

Iron is necessary for many neurobiological mechanisms, but its overaccumulation can be harmful. Factors triggering age-related brain iron accumulation remain largely unknown and longitudinal data are insufficient. We examined associations between brain iron load and accumulation and, blood markers of iron metabolism, cardiovascular health, lifestyle factors (smoking, alcohol use, physical activity, diet), and ApoE status using longitudinal data from the IronAge study (n = 208, age = 20-79, mean follow-up time = 2.75 years). Iron in cortex and basal ganglia was estimated with magnetic resonance imaging using quantitative susceptibility mapping (QSM). Our results showed that (1) higher peripheral iron levels (i.e., composite score of blood iron markers) were related to greater iron load in the basal ganglia; (2) healthier diet was related to higher iron levels in the cortex and basal ganglia, although for the latter the association was significant only in younger adults (age = 20-39); (3) worsening cardiovascular health was related to increased iron accumulation; (4) younger ApoE ε4 carriers accumulated more iron in basal ganglia than younger non-carriers. Our results demonstrate that modifiable factors, including lifestyle, cardiovascular, and physiological ones, are linked to age-related brain iron content and accumulation, contributing novel information on potential targets for interventions in preventing brain iron-overload.

How sturdy is your memory palace? Reliable room representations predict subsequent reinstatement of placed objects

Our autobiographical experiences typically occur within the context of familiar spatial locations. When we encode these experiences into memory, we can use our spatial map of the world to help organize these memories and later retrieve their episodic details. However, it is still not well understood what psychological and neural factors make spatial contexts an effective scaffold for storing and accessing memories. We hypothesized that spatial locations with distinctive and stable neural representations would best support the encoding and robust reinstatement of new episodic memories. We developed a novel paradigm that allowed us to quantify the within-participant reliability of a spatial context ("room reliability") prior to memory encoding, which could then be used to predict the degree of successful re-activation of item memories. To do this, we constructed a virtual reality (VR) "memory palace", a custom-built environment made up of 23 distinct rooms that participants explored using a head-mounted VR display. The day after learning the layout of the environment, participants underwent whole-brain fMRI while being presented with videos of the rooms in the memory palace, allowing us to measure the reliability of the neural activity pattern associated with each room. Participants were taken back to VR and asked to memorize the locations of 23 distinct objects randomly placed within each of the 23 rooms, and then returned to the scanner as they recalled the objects and the rooms in which they appeared. We found that our room reliability measure was predictive of object reinstatement across cortex, and further showed that this was driven not only by the group-level reliability of a room across participants, but also the idiosyncratic reliability of rooms within each participant. Together, these results showcase how the quality of the neural representation of a spatial context can be quantified and used to 'audit' its utility as a memory scaffold for future experiences.

Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision

We effortlessly extract behaviorally relevant information from dynamic visual input in order to understand the actions of others. In the current study, we develop and test a number of models to better understand the neural representational geometries supporting action understanding. Using fMRI, we measured brain activity as participants viewed a diverse set of 90 different video clips depicting social and nonsocial actions in real-world contexts. We developed five behavioral models using arrangement tasks: two models reflecting behavioral judgments of the purpose (transitivity) and the social content (sociality) of the actions depicted in the video stimuli; and three models reflecting behavioral judgments of the visual content (people, objects, and scene) depicted in still frames of the stimuli. We evaluated how well these models predict neural representational geometry and tested them against semantic models based on verb and nonverb embeddings and visual models based on gaze and motion energy. Our results revealed that behavioral judgments of similarity better reflect neural representational geometry than semantic or visual models throughout much of cortex. The sociality and transitivity models in particular captured a large portion of unique variance throughout the action observation network, extending into regions not typically associated with action perception, like ventral temporal cortex. Overall, our findings expand the action observation network and indicate that the social content and purpose of observed actions are predominant in cortical representation.

Reduced neurovascular coupling is associated with increased cardiovascular risk without established cerebrovascular disease: A cross-sectional analysis in UK biobank

Mid-life vascular risk factors predict late-life cerebrovascular diseases and poor global brain health. Although endothelial dysfunction is hypothesized to contribute to this process, evidence of impaired neurovascular function in early stages remains limited. In this cross-sectional study of 31,934 middle-aged individuals from UK Biobank without established cerebrovascular disease, the overall 10-year risk of cardiovascular events was associated with reduced neurovascular coupling (p < 2 × 10-16) during a visual task with functional MRI, including in participants with no clinically apparent brain injury on MRI. Diabetes, smoking, waist-hip ratio, and hypertension were each strongly associated with decreased neurovascular coupling with the strongest relationships for diabetes and smoking, whilst in older adults there was an inverted U-shaped relationship with DBP, peaking at 70-80 mmHg DBP. These findings indicate that mid-life vascular risk factors are associated with impaired cerebral endothelial-dependent neurovascular function in the absence of overt brain injury. Neurovascular dysfunction, measured by neurovascular coupling, may play a role in the development of late-life cerebrovascular disease, underscoring the need for further longitudinal studies to explore its potential as a mediator of long-term cerebrovascular risk.

Strong connectivity to the sensorimotor cortex predicts clinical effectiveness of thalamic deep brain stimulation in essential tremor

INTRODUCTION

The outcome of thalamic deep brain stimulation (DBS) for essential tremor (ET) varies, probably due to the difficulty in identifying the optimal target for DBS placement. Recent approaches compared the clinical response with a connectivity-based segmentation of the target area. However, studies are contradictory by indicating the connectivity to the primary motor cortex (M1) or to the premotor/supplementary motor cortex (SMA) to be therapeutically relevant.

OBJECTIVE

To identify the connectivity profile that corresponds to clinical effective targeting of DBS for ET.

METHODS

Patient-specific probabilistic diffusion tensor imaging was performed in 20 ET patients with bilateral thalamic DBS. Following monopolar review, the stimulation response was classified for the most effective contact in each hemisphere as complete vs. incomplete upper limb tremor suppression (40 assessments). Finally, the connectivity profiles of these contacts within the cortical and cerebellar tremor network were estimated and compared between groups.

RESULTS

The active contacts that led to complete (n = 25) vs. incomplete (n = 15) tremor suppression showed significantly higher connectivity to M1 (p < 0.001), somatosensory cortex (p = 0.008), anterior lobe of the cerebellum (p = 0.026) and SMA (p = 0.05); with Cohen's (d) effect sizes of 0.53, 0.42, 0.25 and 0.10, respectively. The clinical benefits were achieved without requiring higher stimulation intensities or causing additional side effects.

CONCLUSION

Clinical effectiveness of DBS for ET corresponded to a distributed connectivity profile, with the connection to the sensorimotor cortex being most relevant. Long-term follow-up in larger cohorts and replication in out-of-sample data are necessary to confirm the robustness of these findings.

Humans can use positive and negative spectrotemporal correlations to detect rising and falling pitch

To discern speech or appreciate music, the human auditory system detects how pitch increases or decreases over time. However, the algorithms used to detect changes in pitch, or pitch motion, are incompletely understood. Here, using psychophysics, computational modeling, functional neuroimaging, and analysis of recorded speech, we ask if humans can detect pitch motion using computations analogous to those used by the visual system. We adapted stimuli from studies of vision to create novel auditory correlated noise stimuli that elicited robust pitch motion percepts. Crucially, these stimuli are inharmonic and possess no persistent features across frequency or time, but do possess positive or negative local spectrotemporal correlations in intensity. In psychophysical experiments, we found clear evidence that humans can judge pitch direction based only on positive or negative spectrotemporal intensity correlations. The key behavioral result-robust sensitivity to the negative spectrotemporal correlations-is a direct analogue of illusory "reverse-phi" motion in vision, and thus constitutes a new auditory illusion. Our behavioral results and computational modeling led us to hypothesize that human auditory processing may employ pitch direction opponency. fMRI measurements in auditory cortex supported this hypothesis. To link our psychophysical findings to real-world pitch perception, we analyzed recordings of English and Mandarin speech and found that pitch direction was robustly signaled by both positive and negative spectrotemporal correlations, suggesting that sensitivity to both types of correlations confers ecological benefits. Overall, this work reveals how motion detection algorithms sensitive to local correlations are deployed by the central nervous system across disparate modalities (vision and audition) and dimensions (space and frequency).

MRI-based whole-brain elastography and volumetric measurements to predict brain age

Brain age, as a correlate of an individual's chronological age obtained from structural and functional neuroimaging data, enables assessing developmental or neurodegenerative pathology relative to the overall population. Accurately inferring brain age from brain magnetic resonance imaging (MRI) data requires imaging methods sensitive to tissue health and sophisticated statistical models to identify the underlying age-related brain changes. Magnetic resonance elastography (MRE) is a specialized MRI technique which has emerged as a reliable, non-invasive method to measure the brain's mechanical properties, such as the viscoelastic shear stiffness and damping ratio. These mechanical properties have been shown to change across the life span, reflect neurodegenerative diseases, and are associated with individual differences in cognitive function. Here, we aim to develop a machine learning framework to accurately predict a healthy individual's chronological age from maps of brain mechanical properties. This framework can later be applied to understand neurostructural deviations from normal in individuals with neurodevelopmental or neurodegenerative conditions. Using 3D convolutional networks as deep learning models and more traditional statistical models, we relate chronological age as a function of multiple modalities of whole-brain measurements: stiffness, damping ratio, and volume. Evaluations on held-out subjects show that combining stiffness and volume in a multimodal approach achieves the most accurate predictions. Interpretation of the different models highlights important regions that are distinct between the modalities. The results demonstrate the complementary value of MRE measurements in brain age models, which, in future studies, could improve model sensitivity to brain integrity differences in individuals with neuropathology.

MRI-Negative Temporal Lobe Epilepsy: A Study of Brain Structure in Adults Using Surface-Based Morphological Features

BACKGROUND

This research aimed to delve into the cortical morphological transformations in patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE-N), seeking to uncover the neuroimaging mechanisms behind these changes.

METHODS

A total of 29 individuals diagnosed with TLE-N and 30 healthy control participants matched by age and sex were selected for the study. Using the surface-based morphometry (SBM) technique, the study analyzed the three-dimensional-T1-weighted MRI scans of the participants' brains. Various cortical structure characteristics, such as thickness, surface area, volume, curvature, and sulcal depth, among other parameters, were measured.

RESULTS

When compared with the healthy control group, the TLE-N patients exhibited increased insular cortex thickness in both brain hemispheres. Additionally, there was a notable reduction in the curvature of the piriform cortex (PC) and the insular granular complex within the right hemisphere. In the left hemisphere, the volume of the secondary sensory cortex (OP1/SII) and the third visual area was significantly reduced in the TLE-N group. However, no significant differences were found between the groups regarding cortical surface area and sulcal depth (p < 0.025 for all, corrected by threshold-free cluster enhancement).

CONCLUSIONS

The study's initial findings suggest subtle morphological changes in the cerebral cortex of TLE-N patients. The SBM technique proved effective in identifying brain regions impacted by epileptic activity. Understanding the microstructural morphology of the cerebral cortex offers insights into the pathophysiological mechanisms underlying TLE.

Neuroimaging Markers of Brain Reserve and Associations with Delirium in Patients with Intracerebral Hemorrhage

BACKGROUND

Delirium occurs frequently in patients with stroke, but the role of preexisting neural substrates in delirium pathogenesis remains unclear. We sought to explore associations between acute and chronic neural substrates of delirium in patients with intracerebral hemorrhage (ICH).

METHODS

Using data from a single-center ICH registry, we identified consecutive patients with acute nontraumatic ICH and available magnetic resonance imaging scans. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria were used to classify each patient as delirious or nondelirious during their hospitalization. Magnetic resonance imaging scans were processed and analyzed using semiautomated software, with volumetric measurement of acute ICH volume as well as white matter hyperintensity volume (WMHV) and gray and white matter volumes from the contralateral hemisphere. We tested associations between WMHV and incident delirium using multivariable regression models, and then determined the predictive accuracy of these neuroimaging models via area under the curve (AUC) analysis.

RESULTS

Of 139 patients in our cohort (mean [standard deviation] age 67.3 [17.3] years, 53% male), 58 (42%) patients experienced delirium. In our primary analyses, WMHV was significantly associated with delirium after adjusting for ICH features (odds ratio 1.56 per 10 cm3, 95% confidence interval 1.13-2.13), and this association was strengthened after further adjustment for segmented brain volume in patients with high-resolution scans (odds ratio 1.89 per 10 cm3, 95% confidence interval 1.24-2.86). Neuroimaging-based models predicted delirium with high accuracy (AUC 0.81), especially in patients with Glasgow Coma Scale score > 13 (AUC 0.85) and smaller ICH (AUC 0.91).

CONCLUSIONS

Chronic white matter disease is independently associated with delirium in patients with acute ICH, and neuroimaging biomarkers may have utility in predicting delirium occurrence.

Resting state functional connectivity in adolescents with substance use disorder and their unaffected siblings

We aimed to examine resting-state functional connectivity (rsFC) in adolescents with substance use disorder (SUD) and their unaffected biological siblings (SIB), relative to typically-developing controls (TDC) in order to identify alterations in functional network organization that may be associated with the familial risk for SUD. Resting-state functional magnetic resonance imaging analysis included 20 adolescents with SUD, 20 SIB, and 18 TDC. Network-based analysis revealed that adolescents with SUD had significantly both weaker and higher rsFC compared to TDC mainly within the default-mode network (DMN) and between the DMN, fronto-parietal (FPN) and salience networks. In addition, adolescents with SUD showed lower rsFC between the visual network and other functional networks. Although the SIB group did not differ from TDC in the whole brain analysis, they showed lower rsFC within DMN and also between the visual network and other large-scale networks as well as higher rsFC between DMN and FPN compared to TDC in connections found to be abnormal in SUD group. Our results suggest that lower rsFC within DMN and higher rsFC between the DMN with FPN which were evident both in SUD and in SIB groups, and might be related to the familial predisposition for SUD.

Variable Cerebral Blood Flow Responsiveness to Acute Hypoxic Hypoxia

Cerebrovascular reactivity (CVR) to changes in blood carbon dioxide and oxygen levels is a robust indicator of vascular health. Although CVR is typically assessed with hypercapnia, the interplay between carbon dioxide and oxygen, and their ultimate roles in dictating vascular tone, can vary with pathology. Methods to characterize vasoreactivity to oxygen changes, particularly hypoxia, would provide important complementary information to established hypercapnia techniques. However, existing methods to study hypoxic CVR, typically with arterial spin labeling (ASL) MRI, demonstrate high variability and paradoxical responses. To understand whether these responses are real or due to methodological confounds of ASL, we used phase-contrast MRI to quantify whole-brain blood flow in 21 participants during baseline, hypoxic, and hypercapnic respiratory states in three scan sessions. Hypoxic CVR reliability was poor-to-moderate (ICC=0.42 for CVR relative to P ET O 2 changes, ICC=0.56 relative to SpO 2 changes) and was less reliable than hypercapnic CVR (ICC=0.67). Without the uncertainty from ASL-related confounds, we still observed paradoxical responses at each timepoint. Concurrent changes in blood carbon dioxide levels did not account for paradoxical responses. Hypoxic CVR and hypercapnic CVR shared approximately 40% of variance across the dataset, indicating that the two effects may indeed reflect distinct, complementary elements of vascular regulation.

Medical comorbidities and lower myelin content are associated with poor cognition in young adults with perinatally acquired HIV

OBJECTIVE

Approximately 40% of adults living with HIV experience cognitive deficits. Little is known about the risk factors for cognitive impairment and its association with myelin content in young adults living with perinatally acquired HIV (YApHIV), which is assessed in our cross-sectional study.

DESIGN

A prospective, observational cohort study.

METHODS

All participants underwent an 11-test cognitive battery and completed medical and social history surveys. Cognitive impairment was defined as Z scores falling at least 1.5 SD below the mean in at least two domains. Twelve participants underwent myelin water imaging. Neuroimaging data were compared to age and sex-matched HIV-uninfected controls. Regression analyses were used to evaluate for risk factors of lower cognitive domain scores and association between myelin content and cognition in YApHIV.

RESULTS

We enrolled 21 virally suppressed YApHIV across two sites in the United States. Ten participants (48%) met criteria for cognitive impairment. Participants with any non-HIV related medical comorbidity scored lower across multiple cognitive domains compared to participants without comorbidities. Myelin content did not differ between YApHIV and controls after adjusting for years of education. Lower cognitive scores were associated with lower myelin content in the cingulum and corticospinal tract in YApHIV participants after correcting for multiple comparisons.

CONCLUSION

Poor cognition in YApHIV may be exacerbated by non-HIV related comorbidities as noted in older adults with horizontally acquired HIV. The corticospinal tract and cingulum may be vulnerable to the legacy effect of untreated HIV in infancy. Myelin content may be a marker of cognitive reserve in YApHIV.

Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

Charting cortical-layer specific area boundaries using Gibbs' ringing attenuated T1w/T2w-FLAIR myelin MRI

Cortical areas have traditionally been defined by their distinctive layer cyto- and/or myelo- architecture using postmortem histology. Recent studies have delineated many areas by measuring overall cortical myelin content and its spatial gradients using the T1w/T2w ratio MRI in living primates, including humans. While T1w/T2w studies of areal transitions might benefit from using the layer profile of this myelin-related contrast, a significant confound is Gibbs' ringing artefact, which produces signal fluctuations resembling cortical layers. Here, we address these issues with a novel approach using cortical layer thickness-adjusted T1w/T2w-FLAIR imaging, which effectively cancels out Gibbs' ringing artefacts while enhancing intra-cortical myelin contrast. Whole-brain MRI measures were mapped onto twelve equivolumetric layers, and layer-specific sharp myeloarchitectonic transitions were identified using spatial gradients resulting in a putative 182 area/subarea partition of the macaque cerebral cortex. The myelin maps exhibit notably high homology with those in humans, suggesting cortical myelin shares a similar developmental program across species. Comparison with histological Gallyas myelin stains explains over 80% of the variance in the laminar T1w/T2w-FLAIR profiles, substantiating the validity of the method. Altogether, our approach provides a novel, noninvasive means for precision mapping layer myeloarchitecture in the primate cerebral cortex, advancing the pioneering work of classical neuroanatomists.

Shared orbitofrontal dynamics to a drug-themed movie track craving and recovery in heroin addiction

Movies captivate groups of individuals (the audience), especially if they contain themes of common motivational interest to the group. In drug addiction, a key mechanism is maladaptive motivational salience attribution whereby drug cues outcompete other reinforcers within the same environment or context. We predicted that while watching a drug-themed movie, where cues for drugs and other stimuli share a continuous narrative context, fMRI responses in individuals with heroin use disorder (iHUD) will preferentially synchronize during drug scenes. Thirty inpatient iHUD (24 male) and 25 healthy controls (16 male) watched a drug-themed movie at baseline and at follow-up after 15 weeks. Results revealed such drug-biased synchronization in the orbitofrontal cortex (OFC), ventromedial and ventrolateral prefrontal cortex, and insula. After 15 weeks during ongoing inpatient treatment, there was a significant reduction in this drug-biased shared response in the OFC, which correlated with a concomitant reduction in dynamically-measured craving, suggesting synchronized OFC responses to a drug-themed movie as a neural marker of craving and recovery in iHUD.

Functional and vascular neuroimaging in maritime pilots with long-term sleep disruption

The mechanism underlying the possible causal association between long-term sleep disruption and Alzheimer's disease remains unclear Musiek et al. 2015. A hypothesised pathway through increased brain amyloid load was not confirmed in previous work in our cohort of maritime pilots with long-term work-related sleep disruption Thomas et al. Alzheimer's Res Ther 2020;12:101. Here, using functional MRI, T2-FLAIR, and arterial spin labeling MRI scans, we explored alternative neuroimaging biomarkers related to both sleep disruption and AD: resting-state network co-activation and between-network connectivity of the default mode network (DMN), salience network (SAL) and frontoparietal network (FPN), vascular damage and cerebral blood flow (CBF). We acquired data of 16 maritime pilots (56 ± 2.3 years old) with work-related long-term sleep disruption (23 ± 4.8 working years) and 16 healthy controls (59 ± 3.3 years old), with normal sleep patterns (Pittsburgh Sleep Quality Index ≤ 5). Maritime pilots did not show altered co-activation in either the DMN, FPN, or SAL and no differences in between-network connectivity. We did not detect increased markers of vascular damage in maritime pilots, and additionally, maritime pilots did not show altered CBF-patterns compared to healthy controls. In summary, maritime pilots with long-term sleep disruption did not show neuroimaging markers indicative of preclinical AD compared to healthy controls. These findings do not resemble those of short-term sleep deprivation studies. This could be due to resiliency to sleep disruption or selection bias, as participants have already been exposed to and were able to deal with sleep disruption for multiple years, or to compensatory mechanisms Mentink et al. PLoS ONE. 2021;15(12):e0237622. This suggests the relationship between sleep disruption and AD is not as strong as previously implied in studies on short-term sleep deprivation, which would be beneficial for all shift workers suffering from work-related sleep disruptions.

Impact of Harmonization on MRI Radiomics Feature Variability Across Preprocessing Methods for Parkinson's Disease Motor Subtype Classification

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple preprocessing methods for classifying Parkinson's disease (PD) motor subtypes and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance. T1-weighted MRI scans from 140 PD patients (70 tremor-dominant and 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's Progression Markers Initiative (PPMI) database, acquired using different scanner models. Radiomic features were extracted from 16 brain regions using various preprocessing pipelines. ComBat harmonization was applied using a combined batch variable incorporating both scanner models and preprocessing methods. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Feature selection was performed using Linear Support Vector Classifier with L1 regularization. Support vector machine classifiers were used for PD subtype classification. ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC ≥ 0.90) increased from 40.2 to 56.3% after harmonization. First-order statistic features showed the highest robustness, with 71.11% demonstrating excellent ICC after harmonization. The proportion of features significantly affected by preprocessing methods was reduced following harmonization. Classification accuracy improved dramatically, from a range of 34-75% before harmonization to 89-96% after harmonization across all preprocessing methods. AUC values similarly increased from 0.28-0.87 to 0.95-0.99 after harmonization. ComBat harmonization significantly enhanced the reproducibility of radiomic features across preprocessing methods and improved PD motor subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness

A primary aim of computational psychiatry is to establish predictive models linking individual differences in brain functioning with symptoms. In particular, cognitive impairments are transdiagnostic, treatment resistant, and associated with poor outcomes. Recent work suggests that thousands of participants may be necessary for the accurate and reliable prediction of cognition, questioning the utility of most patient collection efforts. Here, using a transfer learning framework, we train a model on functional neuroimaging data from the UK Biobank to predict cognitive functioning in three transdiagnostic samples (ns = 101 to 224). We demonstrate prediction performance in all three samples comparable to that reported in larger prediction studies and a boost of up to 116% relative to classical models trained directly in the smaller samples. Critically, the model generalizes across datasets, maintaining performance when trained and tested across independent samples. This work establishes that predictive models derived in large population-level datasets can boost the prediction of cognition across clinical studies.

Clinical and Pathophysiologic Correlates of Basilar Artery Measurements in Fabry Disease

BACKGROUND AND PURPOSE

Alterations of the basilar artery (BA) anatomy have been suggested as a possible MRA feature of Fabry disease (FD). Nonetheless, no information about their clinical or pathophysiologic correlates is available, limiting our comprehension of the real impact of vessel remodeling in FD.

MATERIALS AND METHODS

Brain MRIs of 53 subjects with FD (mean age, 40.7 [SD, 12.4] years; male/female ratio = 23:30) were collected in this single-center study. Mean BA diameter and its tortuosity index were calculated on MRA. Possible correlations between these metrics and clinical, laboratory, and advanced imaging variables of the posterior circulation were tested. In a subgroup of 20 subjects, a 2-year clinical and imaging follow-up was available, and possible longitudinal changes of these metrics and their ability to predict clinical scores were also probed.

RESULTS

No significant association was found between MRA metrics and any clinical, laboratory, or advanced imaging variable (P values ranging from -0.006 to 0.32). At the follow-up examination, no changes were observed with time for the mean BA diameter (P = .84) and the tortuosity index (P = .70). Finally, baseline MRA variables failed to predict the clinical status of patients with FD at follow-up (P = .42 and 0.66, respectively).

CONCLUSIONS

Alterations of the BA in FD lack of any meaningful association with clinical, laboratory, or advanced imaging findings collected in this study. Furthermore, this lack of correlation seems constant across time, suggesting stability over time. Taken together, these results suggest that the role of BA dolichoectasia in FD should be reconsidered.

High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets

Objective. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets.Approach. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality.Main results. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.Significance. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.

Sensory representations in primary visual cortex are not sufficient for subjective imagery

The contemporary definition of mental imagery is characterized by two aspects: a sensory representation that resembles, but does not result from, perception, and an associated subjective experience. Neuroimaging demonstrated imagery-related sensory representations in primary visual cortex (V1) that show striking parallels to perception. However, it remains unclear whether these representations always reflect subjective experience or if they can be dissociated from it. We addressed this question by comparing sensory representations and subjective imagery among visualizers and aphantasics, the latter with an impaired ability to experience imagery. Importantly, to test for the presence of sensory representations independently of the ability to generate imagery on demand, we examined both spontaneous and voluntary imagery forms. Using multivariate fMRI, we tested for decodable sensory representations in V1 and subjective visual imagery reports that occurred either spontaneously (during passive listening of evocative sounds) or in response to the instruction to voluntarily generate imagery of the sound content (always while blindfolded inside the scanner). Among aphantasics, V1 decoding of sound content was at chance during voluntary imagery, and lower than in visualizers, but it succeeded during passive listening, despite them reporting no imagery. In contrast, in visualizers, decoding accuracy in V1 was greater in voluntary than spontaneous imagery (while being positively associated with the reported vividness of both imagery types). Finally, for both conditions, decoding in precuneus was successful in visualizers but at chance for aphantasics. Together, our findings show that V1 representations can be dissociated from subjective imagery, while implicating a key role of precuneus in the latter.

The role of REM sleep in neural differentiation of memories in the hippocampus

When faced with a familiar situation, we can use memory to make predictions about what will happen next. If such predictions turn out to be erroneous, the brain can adapt by differentiating the representations of the cues that generated the prediction from the mispredicted item itself, reducing the likelihood of future prediction errors. Prior work by Kim et al. (2017) found that violating a sequential association in a statistical learning paradigm triggered differentiation of the neural representations of the associated items in the hippocampus. Here, we used fMRI to test the preregistered hypothesis that this hippocampal differentiation occurs only when violations are followed by rapid eye movement (REM) sleep. In the morning, participants first learned that some items predict others (e.g., A predicts B) then encountered a violation in which a predicted item (B) failed to appear when expected after its associated item (A); the predicted item later appeared on its own after an unrelated item. Participants were then randomly assigned to one of three conditions: remain awake, take a nap containing non-REM sleep only, or take a nap with both non-REM and REM sleep. While the predicted results were not observed in the preregistered left CA2/3/DG ROI, we did observe evidence for our hypothesis in closely related hippocampal ROIs, uncorrected for multiple comparisons: In right CA2/3/DG, differentiation in the group with REM sleep was greater than in the groups without REM sleep (wake and non-REM nap); this differentiation was item-specific and concentrated in right DG. Differentiation effects were also greater in bilateral DG when the predicted item was more strongly reactivated during the violation. Overall, the results presented here provide initial evidence linking REM sleep to changes in the hippocampal representations of memories in humans.

Working memory related functional connectivity in adult ADHD and its amenability to training: A randomized controlled trial

BACKGROUND

Working memory (WM) deficits are among the most prominent cognitive impairments in attention deficit hyperactivity disorder (ADHD). While functional connectivity is a prevailing approach in brain imaging of ADHD, alterations in WM-related functional brain networks and their malleability by cognitive training are not well known. We examined whole-brain functional connectivity differences between adults with and without ADHD during n-back WM tasks and rest at pretest, as well as the effects of WM training on functional and structural brain connectivity in the ADHD group.

METHODS

Forty-two adults with ADHD and 36 neurotypical controls performed visuospatial and verbal n-back tasks during functional magnetic resonance imaging (fMRI). In addition, seven-minute resting state fMRI data and diffusion-weighted MR images were collected from all participants. The adults with ADHD continued into a 5-week randomized controlled WM training trial (experimental group training on a dual n-back task, n = 21; active control group training on Bejeweled II video game, n = 21), followed by a posttraining MRI. Brain connectivity was examined with Network-Based Statistic.

RESULTS

At the pretest, adults with ADHD had decreased functional connectivity compared with the neurotypical controls during both n-back tasks in networks encompassing fronto-parietal, temporal, occipital, cerebellar, and subcortical brain regions. Furthermore, WM-related connectivity in widespread networks was associated with performance accuracy in a continuous performance test. Regarding resting state connectivity, no group differences or associations with task performance were observed. WM training did not modulate functional or structural connectivity compared with the active controls.

CONCLUSION

Our results indicate large-scale abnormalities in functional brain networks underlying deficits in verbal and visuospatial WM commonly faced in ADHD. Training-induced plasticity in these networks may be limited.

Optimization and validation of low-field MP2RAGE T1 mapping on 0.35T MR-Linac: Toward adaptive dose painting with hypoxia biomarkers

BACKGROUND

Stereotactic MR-guided Adaptive Radiation Therapy (SMART) dose painting for hypoxia has potential to improve treatment outcomes, but clinical implementation on low-field MR-Linac faces substantial challenges due to dramatically lower signal-to-noise ratio (SNR) characteristics. While quantitative MRI and T1 mapping of hypoxia biomarkers show promise, T1-to-noise ratio (T1NR) optimization at low fields is paramount, particularly for the clinical implementation of oxygen-enhanced (OE)-MRI. The 3D Magnetization Prepared (2) Rapid Gradient Echo (MP2RAGE) sequence stands out for its ability to acquire homogeneous T1-weighted contrast images with simultaneous T1 mapping.

PURPOSE

To optimize MP2RAGE for low-field T1 mapping; conduct experimental validation in a ground-truth phantom; establish feasibility and reproducibility of low-field MP2RAGE acquisition and T1 mapping in healthy volunteers.

METHODS

The MP2RAGE optimization was performed to maximize the contrast-to-noise ratio (CNR) of T1 values in white matter (WM) and gray matter (GM) brain tissues at 0.35T. Low-field MP2RAGE images were acquired on a 0.35T MR-Linac (ViewRay MRIdian) using a multi-channel head coil. Validation of T1 mapping was performed with a ground-truth Eurospin phantom, containing inserts of known T1 values (400-850 ms), with one and two average (1A and 2A) MP2RAGE scans across four acquisition sessions, resulting in eight T1 maps. Mean (± SD) T1 relative error, T1NR, and intersession coefficient of variation (CV) were determined. Whole-brain MP2RAGE scans were acquired in 5 healthy volunteers across two sessions (A and B) and T1 maps were generated. Mean (± SD) T1 values for WM and GM were determined. Whole-brain T1 histogram analysis was performed, and reproducibility was determined with the CV between sessions. Voxel-by-voxel T1 difference maps were generated to evaluate 3D spatial variation.

RESULTS

Low-field MP2RAGE optimization resulted in parameters: MP2RAGETR of 3250 ms, inversion times (TI1/TI2) of 500/1200 ms, and flip angles (α1/α2) of 7/5°. Eurospin T1 maps exhibited a mean (± SD) relative error of 3.45% ± 1.30%, T1NR of 20.13 ± 5.31, and CV of 2.22% ± 0.67% across all inserts. Whole-brain MP2RAGE images showed high anatomical quality with clear tissue differentiation, resulting in mean (± SD) T1 values: 435.36 ± 10.01 ms for WM and 623.29 ± 14.64 ms for GM across subjects, showing excellent concordance with literature. Whole-brain T1 histograms showed high intrapatient and intersession reproducibility with characteristic intensity peaks consistent with voxel-level WM and GM T1 values. Reproducibility analysis revealed a CV of 0.46% ± 0.31% and 0.35% ± 0.18% for WM and GM, respectively. Voxel-by-voxel T1 difference maps show a normal 3D spatial distribution of noise in WM and GM.

CONCLUSIONS

Low-field MP2RAGE proved effective in generating accurate, reliable, and reproducible T1 maps with high T1NR in phantom studies and in vivo feasibility established in healthy volunteers. While current work is focused on refining the MP2RAGE protocol to enable clinically efficient OE-MRI, this study establishes a foundation for TOLD T1 mapping for hypoxia biomarkers. This advancement holds the potential to facilitate a paradigm shift toward MR-guided biological adaptation and dose painting by leveraging 3D hypoxic spatial distributions and improving outcomes in conventionally challenging-to-treat cancers.

Salience network resting state functional connectivity during airway inflammation in asthma: A feature of mental health resilience?

BACKGROUND

Inflammation is an established contributor to the pathophysiology of depression and the prevalence of depression in those with chronic inflammatory disease is two- to four-fold higher than the general population. Yet little is known about the neurobiological changes that confer depression or resilience to depression, that occur when episodes of heightened inflammation are frequent or span many years.

METHODS

We used an innovative combination of longitudinal resting state functional magnetic resonance imaging coupled to segmental bronchial provocation with allergen (SBP-Ag) to assess changes in resting state functional connectivity (rsFC) of the salience network (SN) caused by an acute inflammatory exacerbation in twenty-six adults (15 female) with asthma and varying levels of depressive symptoms. Eosinophils measured in bronchoalveolar lavage fluid and blood provided an index of allergic inflammation and the Beck Depression Inventory provided an index of depressive symptoms.

RESULTS

We found that in those with the highest symptoms of depression at baseline, SN rsFC declined most from pre- to post-SBP-Ag in the context of a robust eosinophilic response to challenge, but in those with low depressive symptoms SN rsFC was maintained or increased, even in those with the most pronounced SBP-Ag response.

CONCLUSIONS

Thus, the maintenance of SN rsFC during inflammation may be a biomarker of resilience to depression, perhaps via more effective orchestration of large-scale brain network dynamics by the SN. These findings advance our understanding of the functional role of the SN during inflammation and inform treatment recommendations for those with comorbid inflammatory disease and depression.

Improving MR axon radius estimation in human white matter using spiral acquisition and field monitoring

PURPOSE

To compare MR axon radius estimation in human white matter using a multiband spiral sequence combined with field monitoring to the current state-of-the-art echo-planar imaging (EPI)-based approach.

METHODS

A custom multiband spiral sequence was used for diffusion-weighted imaging at ultra-highb $$ b $$ -values. Field monitoring and higher order image reconstruction were employed to greatly reduce artifacts in spiral images. Diffusion weighting parameters were chosen to match a state-of-the art EPI-based axon radius mapping protocol. The spiral approach was compared to the EPI approach by comparing the image signal-to-noise ratio (SNR) and performing a test-retest study to assess the respective variability and repeatability of axon radius mapping. Effective axon radius estimates were compared over white matter voxels and along the left corticospinal tract.

RESULTS

Increased SNR and reduced artifacts in spiral images led to reduced variability in resulting axon radius maps, especially in low-SNR regions. Test-retest variability was reduced by a factor of approximately 1.5 using the spiral approach. Reduced repeatability due to significant bias was found for some subjects in both spiral and EPI approaches, and attributed to scanner instability, pointing to a previously unknown limitation of the state-of-the-art approach.

CONCLUSION

Combining spiral readouts with field monitoring improved mapping of the effective axon radius compared to the conventional EPI approach.

Delineating the neural substrates of autobiographical memory impairment in Huntington's disease

Emerging evidence suggests that autobiographical memory (ABM) is altered in Huntington's disease (HD). While these impairments are typically attributed to frontostriatal dysfunction, the neural substrates of ABM impairment in HD remain unexplored. To this end, we assessed ABM in 30 participants with genetically confirmed HD (18 premanifest, 12 manifest) and 24 age-matched healthy controls. Participants completed the Autobiographical Interview to assess free and probed ABM recall and underwent structural brain imaging. Whole-brain voxel-based morphometry (VBM) was used to explore voxel-wise associations between ABM performance and grey matter intensity (False Discovery Rate corrected at q = 0.05). Relative to controls, HD participants displayed significantly less detailed ABM retrieval across free and probed recall conditions, irrespective of disease stage. Recall performance did not differ significantly between manifest and premanifest HD groups. VBM analyses indicated that poorer ABM performance was associated with atrophy of a distributed cortico-subcortical network. Key regions implicated irrespective of ABM condition included the bilateral occipital cortex, left precuneus, right parahippocampal gyrus and right caudate nucleus. In addition, probed ABM recall was associated with the superior and inferior frontal gyri, frontal pole, right hippocampus, nucleus accumbens, paracingulate gyrus and cerebellum. Overall, our findings indicate that ABM impairments in HD reflect the progressive degeneration of a distributed cortico-subcortical brain network comprising medial temporal, frontal, striatal and posterior parietal cortices. Our findings advance our understanding of the neurocognitive profile of HD, providing an important foundation for future interventions to support memory function in this population.

Probing diffusion of water and metabolites to assess white matter microstructure in Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a progressive X-linked neuromuscular disorder caused by the absence of functional dystrophin protein. In addition to muscle, dystrophin is expressed in the brain in both neurons and glial cells. Previous studies have shown altered white matter microstructure in patients with DMD using diffusion tensor imaging (DTI). However, DTI measures the diffusion properties of water, a ubiquitous molecule, making it difficult to unravel the underlying pathology. Diffusion-weighted spectroscopy (DWS) is a complementary technique which measures diffusion properties of cell-specific intracellular metabolites. Here we performed both DWS and DTI measurements to disentangle intra- and extracellular contributions to white matter changes in patients with DMD. Scans were conducted in patients with DMD (15.5 ± 4.6 y/o) and age- and sex-matched healthy controls (16.3 ± 3.3 y/o). DWS measurements were obtained in a volume of interest (VOI) positioned in the left parietal white matter. Apparent diffusion coefficients (ADCs) were calculated for total N-acetylaspartate (tNAA), choline compounds (tCho), and total creatine (tCr). The tNAA/tCr and tCho/tCr ratios were calculated from the non-diffusion-weighted spectrum. Mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and fractional anisotropy of water within the VOI were extracted from DTI measurements. DWS and DTI data from patients with DMD (respectively n = 20 and n = 18) and n = 10 healthy controls were included. No differences in metabolite ADC or in concentration ratios were found between patients with DMD and controls. In contrast, water diffusion (MD, t = -2.727, p = 0.011; RD, t = -2.720, p = 0.011; AD, t = -2.715, p = 0.012) within the VOI was significantly higher in patients compared with healthy controls. Taken together, our study illustrates the potential of combining DTI and DWS to gain a better understanding of microstructural changes and their association with disease mechanisms in a clinical setting.

Anatomical and volumetric description of the guiana dolphin (Sotalia guianensis) brain from an ultra-high-field magnetic resonance imaging

The Guiana dolphin (Sotalia guianensis) is a common species along Central and South American coastal waters. Although much effort has been made to understand its behavioral ecology and evolution, very little is known about its brain. The use of ultra-high field MRI in anatomical descriptions of cetacean brains is a very promising approach that is still uncommon. In this study, we present for the first time a full anatomical description of the Guiana dolphin's brain based on high-resolution ultra-high-field magnetic resonance imaging, providing an exceptional level of brain anatomical details, and enriching our understanding of the species. Brain structures were labeled and volumetric measurements were delineated for many distinguishable structures, including the gray matter and white matter of the cerebral cortex, amygdala, hippocampus, superior and inferior colliculi, thalamus, corpus callosum, ventricles, brainstem and cerebellum. Additionally, we provide the surface anatomy of the Guiana dolphin brain, including the labeling of main sulci and gyri as well as the calculation of its gyrification index. These neuroanatomical data, absent from the literature to date, will help disentangle the history behind cetacean brain evolution and consequently, mammalian evolution, representing a significant new source for future comparative studies.

Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture

PURPOSE

The aim of this study was to develop a model-based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion-weighted images (DWIs), facilitating accurate analysis with reduced acquisition times.

METHODS

Our proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI-consistency blocks within the network architecture, and a fixel-classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD-Net, and multishell multitissue constrained spherical deconvolution.

RESULTS

SDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel-classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels.

CONCLUSION

Inclusion of DWI-consistency blocks improved reconstruction performance, and the fixel-classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

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.

Multiparametric Characterization of Focal Cortical Dysplasia Using 3D MR Fingerprinting

OBJECTIVE

To develop a multiparametric machine-learning (ML) framework using high-resolution 3 dimensional (3D) magnetic resonance (MR) fingerprinting (MRF) data for quantitative characterization of focal cortical dysplasia (FCD).

MATERIALS

We included 119 subjects, 33 patients with focal epilepsy and histopathologically confirmed FCD, 60 age- and gender-matched healthy controls (HCs), and 26 disease controls (DCs). Subjects underwent whole-brain 3 Tesla MRF acquisition, the reconstruction of which generated T1 and T2 relaxometry maps. A 3D region of interest was manually created for each lesion, and z-score normalization using HC data was performed. We conducted 2D classification with ensemble models using MRF T1 and T2 mean and standard deviation from gray matter and white matter for FCD versus controls. Subtype classification additionally incorporated entropy and uniformity of MRF metrics, as well as morphometric features from the morphometric analysis program (MAP). We translated 2D results to individual probabilities using the percentage of slices above an adaptive threshold. These probabilities and clinical variables were input into a support vector machine for individual-level classification. Fivefold cross-validation was performed and performance metrics were reported using receiver-operating-characteristic-curve analyses.

RESULTS

FCD versus HC classification yielded mean sensitivity, specificity, and accuracy of 0.945, 0.980, and 0.962, respectively; FCD versus DC classification achieved 0.918, 0.965, and 0.939. In comparison, visual review of the clinical magnetic resonance imaging (MRI) detected 48% (16/33) of the lesions by official radiology report. In the subgroup where both clinical MRI and MAP were negative, the MRF-ML models correctly distinguished FCD patients from HCs and DCs in 98.3% of cross-validation trials. Type II versus non-type-II classification exhibited mean sensitivity, specificity, and accuracy of 0.835, 0.823, and 0.83, respectively; type IIa versus IIb classification showed 0.85, 0.9, and 0.87. In comparison, the transmantle sign was present in 58% (7/12) of the IIb cases.

INTERPRETATION

The MRF-ML framework presented in this study demonstrated strong efficacy in noninvasively classifying FCD from normal cortex and distinguishing FCD subtypes. ANN NEUROL 2024;96:944-957.

High-level visual prediction errors in early visual cortex

Perception is shaped by both incoming sensory input and expectations derived from our prior knowledge. Numerous studies have shown stronger neural activity for surprising inputs, suggestive of predictive processing. However, it is largely unclear what predictions are made across the cortical hierarchy, and therefore what kind of surprise drives this up-regulation of activity. Here, we leveraged fMRI in human volunteers and deep neural network (DNN) models to arbitrate between 2 hypotheses: prediction errors may signal a local mismatch between input and expectation at each level of the cortical hierarchy, or prediction errors may be computed at higher levels and the resulting surprise signal is broadcast to earlier areas in the cortical hierarchy. Our results align with the latter hypothesis. Prediction errors in both low- and high-level visual cortex responded to high-level, but not low-level, visual surprise. This scaling with high-level surprise in early visual cortex strongly diverged from feedforward tuning. Combined, our results suggest that high-level predictions constrain sensory processing in earlier areas, thereby aiding perceptual inference.

Brain atrophy assessment in multiple sclerosis: technical- and subject-related barriers for translation to real-world application in individual subjects

INTRODUCTION

Brain atrophy is a well-established MRI outcome for predicting clinical progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at the group level. Despite the important progress made, the translation of brain atrophy assessment into clinical practice faces several challenges.

AREAS COVERED

In this review, the authors discuss technical- and subject-related barriers for implementing brain atrophy assessment as part of the clinical routine at the individual level. Substantial progress has been made to understand and mitigate technical barriers behind MRI acquisition. Numerous research and commercial segmentation techniques for volume estimation are available and technically validated, but their clinical value has not been fully established. A systematic assessment of subject-related barriers, which include genetic, environmental, biological, lifestyle, comorbidity, and aging confounders, is critical for the interpretation of brain atrophy measures at the individual subject level. Educating both medical providers and pwMS will help better clarify the benefits and limitations of assessing brain atrophy for disease monitoring and prognosis.

EXPERT OPINION

Integrating brain atrophy assessment into clinical practice for pwMS requires overcoming technical and subject-related challenges. Advances in MRI standardization, artificial intelligence, and clinician education will facilitate this process, improving disease management and potentially reducing long-term healthcare costs.

Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple Sclerosis

Artificial intelligence (AI) models often face performance drops after deployment to external datasets. This study evaluated the potential of a novel data augmentation framework based on generative adversarial networks (GANs) that creates synthetic patient image data for model training to improve model generalizability. Model development and external testing were performed for a given classification task, namely the detection of new fluid-attenuated inversion recovery lesions at MRI during longitudinal follow-up of patients with multiple sclerosis (MS). An internal dataset of 669 patients with MS (n = 3083 examinations) was used to develop an attention-based network, trained both with and without the inclusion of the GAN-based synthetic data augmentation framework. External testing was performed on 134 patients with MS from a different institution, with MR images acquired using different scanners and protocols than images used during training. Models trained using synthetic data augmentation showed a significant performance improvement when applied on external data (area under the receiver operating characteristic curve [AUC], 83.6% without synthetic data vs 93.3% with synthetic data augmentation; P = .03), achieving comparable results to the internal test set (AUC, 95.0%; P = .53), whereas models without synthetic data augmentation demonstrated a performance drop upon external testing (AUC, 93.8% on internal dataset vs 83.6% on external data; P = .03). Data augmentation with synthetic patient data substantially improved performance of AI models on unseen MRI data and may be extended to other clinical conditions or tasks to mitigate domain shift, limit class imbalance, and enhance the robustness of AI applications in medical imaging. Keywords: Brain, Brain Stem, Multiple Sclerosis, Synthetic Data Augmentation, Generative Adversarial Network Supplemental material is available for this article. © RSNA, 2024.

Investigating the neural basis of schematic false memories by examining schematic and lure pattern similarity

ABSTRACTSchemas allow us to make assumptions about the world based upon previous experiences and aid in memory organisation and retrieval. However, a reliance on schemas may also result in increased false memories to schematically related lures. Prior neuroimaging work has linked schematic processing in memory tasks to activity in prefrontal, visual, and temporal regions. Yet, it is unclear what type of processing in these regions underlies memory errors. The current study examined where schematic lures exhibit greater neural similarity to schematic targets, leading to this memory error, as compared to neural overlap with non-schematic lures, which, like schematic lures, are novel items at retrieval. Results showed that patterns of neural activity in ventromedial prefrontal cortex, medial frontal gyrus, middle temporal gyrus, hippocampus, and occipital cortices exhibited greater neural pattern similarity for schematic targets and schematic lures than between schematic lures and non-schematic lures. As such, results suggest that schematic membership, and not object history, may be more critical to the neural processes underlying memory retrieval in the context of a strong schema.

Amide proton transfer-weighted CEST MRI for radiotherapy target delineation of glioblastoma: a prospective pilot study

BACKGROUND

Extensive glioblastoma infiltration justifies a 15-mm margin around the gross tumor volume (GTV) to define the radiotherapy clinical target volume (CTV). Amide proton transfer (APT)-weighted imaging could enable visualization of tumor infiltration, allowing more accurate GTV delineation. We quantified the impact of integrating APT-weighted imaging into GTV delineation of glioblastoma and compared two APT-weighted quantification methods-magnetization transfer ratio asymmetry (MTRasym) and Lorentzian difference (LD) analysis-for target delineation.

METHODS

Nine glioblastoma patients underwent an extended imaging protocol prior to radiotherapy, yielding APT-weighted MTRasym and LD maps. From both maps, biological tumor volumes were generated (BTVMTRasym and BTVLD) and added to the conventional GTV to generate biological GTVs (GTVbio,MTRasym and GTVbio,LD). Wilcoxon signed-rank tests were performed for comparisons.

RESULTS

The GTVbio,MTRasym and GTVbio,LD were significantly larger than the conventional GTV (p ≤ 0.022), with a median volume increase of 9.3% and 2.1%, respectively. The GTVbio,MTRasym and GTVbio,LD were significantly smaller than the CTV (p = 0.004), with a median volume reduction of 72.1% and 70.9%, respectively. There was no significant volume difference between the BTVMTRasym and BTVLD (p = 0.074). In three patients, BTVMTRasym delineation was affected by elevated signals at the brain periphery due to residual motion artifacts; this elevation was absent on the APT-weighted LD maps.

CONCLUSION

Larger biological GTVs compared to the conventional GTV highlight the potential of APT-weighted imaging for radiotherapy target delineation of glioblastoma. APT-weighted LD mapping may be advantageous for target delineation as it may be more robust against motion artifacts.

RELEVANCE STATEMENT

The introduction of APT-weighted imaging may, ultimately, enhance visualization of tumor infiltration and eliminate the need for the substantial 15-mm safety margin for target delineation of glioblastoma. This could reduce the risk of radiation toxicity while still effectively irradiating the tumor.

TRIAL REGISTRATION

NCT05970757 (ClinicalTrials.gov).

KEY POINTS

Integration of APT-weighted imaging into target delineation for radiotherapy is feasible. The integration of APT-weighted imaging yields larger GTVs in glioblastoma. APT-weighted LD mapping may be more robust against motion artifacts than APT-weighted MTRasym.