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

Time-efficient, high-resolution 3T whole-brain relaxometry using Cartesian 3D MR Spin TomogrAphy in Time-Domain (MR-STAT) with cerebrospinal fluid suppression

PURPOSE

Current three-dimensional (3D) MR Spin TomogrAphy in Time-Domain (MR-STAT) protocols use transient-state, gradient-spoiled gradient-echo sequences that are prone to cerebrospinal fluid (CSF) pulsation artifacts when applied to the brain. This study aims to develop a 3D MR-STAT protocol for whole-brain relaxometry that overcomes the challenges posed by CSF-induced ghosting artifacts.

METHOD

We optimized the flip-angle train within the Cartesian 3D MR-STAT framework to achieve two objectives: (1) minimization of the noise level in the reconstructed quantitative maps, and (2) reduction of the CSF-to-white-matter signal ratio to suppress CSF-associated pulsation artifacts. The optimized new sequence was tested on a gel/water phantom for accuracy evaluation of the quantitative maps, and on healthy volunteers to explore the effectiveness of the CSF artifact suppression and robustness of the new protocol.

RESULTS

An optimized sequence with high parameter-encoding capability and low CSF signal response was proposed and validated in the gel/water phantom experiment. From in vivo experiments with 5 volunteers, the proposed CSF-suppressed sequence produced quantitative maps with no CSF artifacts and showed overall greatly improved image quality compared with the baseline sequence. Statistical analysis indicated low intersubject and interscan variability for quantitative parameters in gray matter and white matter (1.6%-2.4% for T1 and 2.0%-4.6% for T2), demonstrating the robustness of the new sequence.

CONCLUSION

We present a new 3D MR-STAT sequence with CSF suppression that effectively eliminates CSF pulsation artifacts. The new sequence ensures consistently high-quality, 1-mm3 whole-brain relaxometry within a rapid 5.5-min scan time.

Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction

OBJECT

Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction time using deep learning.

MATERIALS AND METHODS

This study focuses on accelerating the reconstruction of volumetric multi-axis spiral projection MRF, aiming for whole-brain T1 and T2 mapping, while ensuring a streamlined approach compatible with clinical requirements. To optimize reconstruction time, the traditional method is first revamped with a memory-efficient GPU implementation. Deep Learning Initialized Compressed Sensing (Deli-CS) is then introduced, which initiates iterative reconstruction with a DL-generated seed point, reducing the number of iterations needed for convergence.

RESULTS

The full reconstruction process for volumetric multi-axis spiral projection MRF is completed in just 20 min compared to over 2 h for the previously published implementation. Comparative analysis demonstrates Deli-CS's efficiency in expediting iterative reconstruction while maintaining high-quality results.

DISCUSSION

By offering a rapid warm start to the iterative reconstruction algorithm, this method substantially reduces processing time while preserving reconstruction quality. Its successful implementation paves the way for advanced spatio-temporal MRI techniques, addressing the challenge of extensive reconstruction times and ensuring efficient, high-quality imaging in a streamlined manner.

A quantitatively interpretable model for Alzheimer's disease prediction using deep counterfactuals

Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions. Counterfactual reasoning has recently gained increasing attention in medical research because of its ability to provide a refined visual explanatory map. However, such visual explanatory maps based on visual inspection alone are insufficient unless we intuitively demonstrate their medical or neuroscientific validity via quantitative features. In this study, we synthesize the counterfactual-labeled structural MRIs using our proposed framework and transform it into a gray matter density map to measure its volumetric changes over the parcellated region of interest (ROI). We also devised a lightweight linear classifier to boost the effectiveness of constructed ROIs, promoted quantitative interpretation, and achieved comparable predictive performance to DL methods. Throughout this, our framework produces an "AD-relatedness index" for each ROI. It offers an intuitive understanding of brain status for an individual patient and across patient groups concerning AD progression.

Moral conviction interacts with metacognitive ability in modulating neural activity during sociopolitical decision-making

The extent to which a belief is rooted in one's sense of morality has significant societal implications. While moral conviction can inspire positive collective action, it can also prompt dogmatism, intolerance, and societal divisions. Research in social psychology has documented the functional characteristics of moral conviction and shows that poor metacognition exacerbates its negative outcomes. However, the cognitive and neural mechanisms underlying moral conviction, their relationship with metacognition, and how moral conviction is integrated into the valuation and decision-making process remain unclear. This study investigated these neurocognitive processes during decision-making on sociopolitical issues varying in moral conviction. Participants (N = 44) underwent fMRI scanning while deciding, on each trial, which of two groups of political protesters they supported more. As predicted, stronger moral conviction was associated with faster decision times. Hemodynamic responses in the anterior insula (aINS), anterior cingulate cortex (ACC), and lateral prefrontal cortex (lPFC) were elevated during decisions with higher moral conviction, supporting the emotional and cognitive dimensions of moral conviction. Functional connectivity between lPFC and vmPFC was greater on trials higher in moral conviction, elucidating mechanisms through which moral conviction is incorporated into valuation. Average support for the two displayed groups of protesters was positively associated with brain activity in regions involved in valuation, particularly vmPFC and amygdala. Metacognitive sensitivity, the ability to discriminate one's correct from incorrect judgments, measured in a perceptual task, negatively correlated with parametric effects of moral conviction in the brain, providing new evidence that metacognition modulates responses to morally convicted issues.

Symmetric deformable registration of multimodal brain magnetic resonance images via appearance residuals

BACKGROUND AND OBJECTIVE

Deformable registration of multimodal brain magnetic resonance images presents significant challenges, primarily due to substantial structural variations between subjects and pronounced differences in appearance across imaging modalities.

METHODS

Here, we propose to symmetrically register images from two modalities based on appearance residuals from one modality to another. Computed with simple subtraction between modalities, the appearance residuals enhance structural details and form a common representation for simplifying multimodal deformable registration. The proposed framework consists of three serially connected modules: (i) an appearance residual module, which learns intensity residual maps between modalities with a cycle-consistent loss; (ii) a deformable registration module, which predicts deformations across subjects based on appearance residuals; and (iii) a deblurring module, which enhances the warped images to match the sharpness of the original images.

RESULTS

The proposed method, evaluated on two public datasets (HCP and LEMON), achieves the highest registration accuracy with topology preservation when compared with state-of-the-art methods.

CONCLUSIONS

Our residual space-guided registration framework, combined with GAN-based image enhancement, provides an effective solution to the challenges of multimodal deformable registration. By mitigating intensity distribution discrepancies and improving image quality, this approach improves registration accuracy and strengthens its potential for clinical application.

Effects of Aging, Estimated Fitness, and Cerebrovascular Status on White Matter Microstructural Health

White matter (WM) microstructural health declines with increasing age, with evidence suggesting that improved cardiorespiratory fitness (CRF) may mitigate this decline. Specifically, higher fit older adults tend to show preserved WM microstructural integrity compared to their lower fit counterparts. However, the extent to which fitness and aging independently impact WM integrity across the adult lifespan is still an open question, as is the extent to which cerebrovascular health mediates these relationships. In a large sample (N = 125, aged 25-72), we assessed the impact of age and estimated cardiorespiratory fitness on fractional anisotropy (FA, derived using diffusion weighted imaging, dwMRI) and probed the mediating role of cerebrovascular health (derived using diffuse optical tomography of the cerebral arterial pulse, pulse-DOT) in these relationships. After orthogonalizing age and estimated fitness and computing a PCA on whole brain WM regions, we found several WM regions impacted by age that were independent from the regions impacted by estimated fitness (hindbrain areas, including brainstem and cerebellar tracts), whereas other areas showed interactive effects of age and estimated fitness (midline areas, including fornix and corpus callosum). Critically, cerebrovascular health mediated both relationships suggesting that vascular health plays a linking role between age, fitness, and brain health. Secondarily, we assessed potential sex differences in these relationships and found that, although females and males generally showed the same age-related FA declines, males exhibited somewhat steeper declines than females. Together, these results suggest that age and fitness impact specific WM regions and highlight the mediating role of cerebrovascular health in maintaining WM health across adulthood.

Neural dynamics of semantic control underlying generative storytelling

Storytelling has been pivotal for the transmission of knowledge across human history, yet the role of semantic control and its associated neural dynamics has been poorly investigated. Here, human participants generated stories that were either appropriate (ordinary), novel (random), or balanced (creative), while recording functional magnetic resonance imaging (fMRI). Deep language models confirmed participants adherence to task instructions. At the neural level, linguistic and visual areas exhibited neural synchrony across participants regardless of the semantic control level, with parietal and frontal regions being more synchronized during random ideation. Importantly, creative stories were differentiated by a multivariate pattern of neural activity in frontal and fronto-temporo-parietal cortices compared to ordinary and random stories. Crucially, similar brain regions were also encoding the features that distinguished the stories. Moreover, we found specific spatial frequency patterns underlying the modulation of semantic control during story generation, while functional coupling in default, salience, and control networks differentiated creative stories with their controls. Remarkably, the temporal irreversibility between visual and high-level areas was higher during creative ideation, suggesting the enhanced hierarchical structure of causal interactions as a neural signature of creative storytelling. Together, our findings highlight the neural mechanisms underlying the regulation of semantic exploration during narrative ideation.

Partial information transfer from peripheral visual streams to foveal visual streams may be mediated through local primary visual circuits

Visual object recognition is driven through the what pathway, a hierarchy of visual areas processing features of increasing complexity and abstractness. The primary visual cortex (V1), this pathway's origin, exhibits retinotopic organization: neurons respond to stimuli in specific visual field regions. A neuron responding to a central stimulus won't respond to a peripheral one, and vice versa. However, despite this organization, task-relevant feedback about peripheral stimuli can be decoded in unstimulated foveal cortex, and disrupting this feedback impairs discrimination behavior. The information encoded by this feedback remains unclear, as prior studies used computer-generated objects ill-suited to dissociate different representation types. To address this knowledge gap, we investigated the nature of information encoded in periphery-to-fovea feedback using real-world stimuli. Participants performed a same/different discrimination task on peripherally displayed images of vehicles and faces. Using fMRI multivariate decoding, we found that both peripheral and foveal V1 could decode images separated by low-level perceptual models (vehicles) but not those separated by semantic models (faces). This suggests the feedback primarily carries low-level perceptual information. In contrast, higher visual areas resolved semantically distinct images. A functional connectivity analysis revealed foveal V1 connections to both peripheral V1 and later-stage visual areas. These findings indicate that while both early and late visual areas may contribute to information transfer from peripheral to foveal processing streams, higher-to-lower area transfer may involve information loss.

Ensemble network using oblique coronal MRI for Alzheimer's disease diagnosis

Alzheimer's disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mild cognitive impairment (MCI) can be considered a transitional stage from normal aging to Alzheimer's disease. Therefore, distinguishing between normal aging and disease-induced neurofunctional impairments is crucial in clinical treatment. Although deep learning methods have been widely applied in Alzheimer's diagnosis, the varying data formats used by different methods limited their clinical applicability. In this study, based on the ADNI dataset and previous clinical diagnostic experience, we propose a method using oblique coronal MRI to assist in diagnosis. We developed an algorithm to extract oblique coronal slices from 3D MRI data and used these slices to train classification networks. To achieve subject-wise classification based on 2D slices, rather than image-wise classification, we employed ensemble learning methods. This approach fused classification results from different modality images or different positions of the same modality images, constructing a more reliable ensemble classification model. The experiments introduced various decision fusion and feature fusion schemes, demonstrating the potential of oblique coronal MRI slices in assisting diagnosis. Notably, the weighted voting from decision fusion strategy trained on oblique coronal slices achieved accuracy rates of 97.5% for CN vs. AD, 100% for CN vs. MCI, and 94.83% for MCI vs. AD across the three classification tasks.

Functional brain network dynamics of brooding in depression: Insights from real-time fMRI neurofeedback

BACKGROUND

Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD.

METHODS

We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n = 18/18, HC-active/sham: n = 13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B).

RESULTS

Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r = -0.4, p = 0.002, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z = -2.09, FWE-p = 0.034).

LIMITATIONS

The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback.

CONCLUSION

We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.

Neural Patterns of Social Pain in the Brain-Wide Representations Across Social Contexts

Empathy can be elicited by physiological pain, as well as in social contexts. Although physiological and different social contexts induce a strong subjective experience of empathy, the general and context-specific neural representations remain elusive. Here, it is combined fMRI with multivariate pattern analysis (MVPA) to establish neurofunctional models for social pain triggered by observing social exclusion and separation naturistic stimuli. The findings revealed that both social contexts engaged the empathy and social function networks. Notably, the intensity of pain empathy elicited by these two social stimuli does not significantly differentiate the neural representations of social exclusion and separation, suggesting context-specific neural representations underlying these experiences. Furthermore, this study established a model that traces the progression from physiological pain to social pain empathy. In conclusion, this study revealed the neural pathological foundations and interconnectedness of empathy induced by social and physiological stimuli and provide robust neuromarkers to precisely evaluate empathy across physiological and social domains.

Altered Hemispheric Asymmetry of Functional Hierarchy in Schizophrenia

BACKGROUND/OBJECTIVES

Schizophrenia is a severe psychiatric disorder characterized by deficits in perception and advanced cognitive functions. Prior studies have reported abnormal lateralization in cortical morphology and functional connectivity in schizophrenia. However, it remains unclear whether schizophrenia affects hemispheric asymmetry in the hierarchical organization of functional connectome.

METHODS

Here, we apply a gradient mapping framework to the hemispheric functional connectome to estimate the first three gradients, which characterize unimodal-to-transmodal, visual-to-somatomotor, and somatomotor/default mode-to-multiple demand hierarchy axes. We then assess between-group differences in intra- and inter-hemispheric asymmetries of these three functional gradients.

RESULTS

We find that, compared to healthy controls, patients with schizophrenia exhibit significantly altered hemispheric asymmetry in functional gradient across multiple networks, including the dorsal attention, ventral attention, visual, and control networks. Region-level analyses further reveal that patients with schizophrenia show significantly abnormal hemispheric gradient asymmetries in several cortical regions in the dorsal prefrontal gyrus, medial superior frontal gyrus, and somatomotor areas. Lastly, we find that hemispheric asymmetries in functional gradients can differentiate between patients and healthy controls and predict the severity of positive symptoms in schizophrenia.

CONCLUSIONS

Collectively, these findings suggest that schizophrenia is associated with altered hemispheric asymmetry in functional hierarchy, providing novel perspectives for understanding the atypical brain lateralization in schizophrenia.

Variable cerebral blood flow responsiveness to acute hypoxic hypoxia

Introduction

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.

Methods

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.

Results

Hypoxic CVRreliability was poor-to-moderate (ICC = 0.42 for CVR relative to PETO2 changes, ICC = 0.56 relative to SpO2 changes) and was less reliable than hypercapnic CVR (ICC = 0.67).

Discussion

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. The data included in this article were collected as part of a randomized cross-over clinical trial, but do not assess the outcomes of this trial: Improving Human Cerebrovascular Function Using Acute Intermittent Hypoxia (NCT05164705), https://clinicaltrials.gov/study/NCT05164705.

Latent preference representation in the human brain for scented products: Effects of novelty and familiarity

Decoding latent preferences for novel products is crucial for understanding decision-making processes, especially when subjective evaluations are unclear. Brain activity in regions like the medial orbitofrontal cortex and nucleus accumbens (NAcc) correlates with subjective preferences. However, whether these regions represent preferences toward novel products and whether coding persists after familiarity remain unclear. We examined the brain coding of latent preferences for novel scented products and how they evolve with familiarity. We measured functional magnetic resonance imaging (fMRI) signals evoked by three fabric softener odors, both when novel and when familiar, in 25 previously unexposed females. To obtain reliable preferences, participants chose one softener after using all three twice at home after the first fMRI measurement (Day 1) and continued using it at home for four months until the second day of the fMRI measurement (Day 2). Subjective ratings were also obtained after each fMRI run. On Day 1, no significant differences in subjective ratings between selected and non-selected odors were found. However, the decoding analysis revealed that future odor preferences for novel products were coded in several regions, including the left superior frontal lobe (SF), right NAcc, and left piriform cortex. On Day 2, the left SF continued to encode preferences after familiarity. These results suggest that odor preferences for novel products are coded in the brain even without conscious awareness, and that the coding in the SF is robust against familiarity. These findings provide insights into a more comprehensive understanding of the brain coding of latent preferences.

Along-tract white matter abnormalities and their clinical associations in recent-onset and chronic schizophrenia

Structural impairments in white matter tracts are well-documented in schizophrenia, though their clinical implications remain limited. Most previous studies using diffusion-weighted magnetic resonance imaging (dMRI) and tractography relied on averaged diffusion indices, potentially obscuring localized changes in white matter tracts. Tractometry enables the investigation of localized changes at specific points along white matter tracts. We used dMRI and centerline tractometry to examine along-tract white matter abnormalities in 55 patients with recent-onset schizophrenia, 69 with chronic schizophrenia, and 77 healthy controls. Fractional anisotropy (FA) and peak length were measured at individual points along tract trajectories. Group differences in diffusion indices and their associations with clinical variables, including the Positive and Negative Syndrome Scale (PANSS), were analyzed using linear mixed models and Spearman's rho. In recent-onset schizophrenia, reduced FA was observed in the genu and splenium of the corpus callosum, along with deviations in peak length across multiple white matter tracts. The peak length of association tracts showed a negative correlation with antipsychotic dose. In chronic schizophrenia, widespread reductions in FA and deviations in peak length were identified across various white matter tracts. Decreased FA in commissural tracts was negatively associated with the PANSS negative score, antipsychotic dose, and illness duration. This study identified along-tract white matter abnormalities in recent-onset and chronic schizophrenia and revealed their associations with clinical symptoms. Localized measurements along tract trajectories enhance the detection of clinically relevant abnormalities compared to traditional methods relying on averaged diffusion indices.

Stereo-EEG propagating source reconstruction identifies new surgical targets for epilepsy patients

Epilepsy surgery can eliminate seizures in patients with drug-resistant focal epilepsy. Surgical intervention requires proper identification of the epileptic network and often involves implanting stereo-EEG electrodes in patients where non-invasive methods are insufficient. However, only ∼60% of patients achieve seizure-freedom following surgery. Quantitative methods have been developed to help improve surgical outcomes. However, previous quantitative methods that localized interictal spike and seizure activity using stereo-EEG recordings did not account for the propagation path encoded by the temporal dynamics of stereo-EEG recordings. Reconstructing the seizure propagation path can aid in determining whether a signal originated from the seizure onset or propagation zone, which directly informs treatment decisions. We developed a novel source reconstruction algorithm, Temporally Dependent Iterative Expansion (TEDIE), that accurately reconstructs propagating and expanding neural sources over time. TEDIE iteratively optimizes the number, location and size of neural sources to minimize the differences between the reconstructed and recorded stereo-EEG signals using temporal information to refine the reconstructions. The TEDIE output comprises a movie of seizure activity projected onto patient-specific brain anatomy. We analysed data from 46 epilepsy patients implanted with stereo-EEG electrodes at Duke Hospital (12 patients) and the Hospital of the University of Pennsylvania (34 patients). We reconstructed seizure recordings and found that TEDIE's seizure onset zone reconstructions were closer to the resected brain region for Engel 1 compared to Engel 2-4 patients, retrospectively validating the clinical utility of TEDIE. We also demonstrated that TEDIE has prospective clinical value, whereby metrics that can be determined presurgically accurately predict whether a patient would achieve seizure-freedom following surgery. Furthermore, we used TEDIE to delineate new potential surgical targets in 12/23 patients who are currently Engel 2-4. We validated TEDIE by accurately reconstructing various dynamic synthetic neural sources with known locations and sizes. TEDIE generated more accurate, focal and interpretable dynamic reconstructions of seizures compared to other algorithms (sLORETA and IRES). Our findings demonstrate that TEDIE is a promising clinical tool that can greatly improve epileptogenic zone localization and epilepsy surgery outcomes.

Functional differentiation of the default and frontoparietal control networks predicts individual differences in creative achievement: evidence from macroscale cortical gradients

Much of the research on the neural correlates of creativity has emphasized creative cognition, and growing evidence suggests that creativity is related to functional properties of the default and frontoparietal control networks. The present work expands on this body of evidence by testing associations of creative achievement with connectivity profiles of brain networks assessed using macroscale cortical gradients. Using resting-state connectivity functional magnetic resonance imaging in 2 community samples (N's = 236 and 234), we found evidence that creative achievement is positively associated with greater functional dissimilarity between core regions of the default and frontoparietal control networks. These results suggest that creative achievement is supported by the ability of these 2 networks to carry out distinct cognitive roles. This research provides further evidence, using a cortical gradient approach, that individual differences in creative achievement can be predicted from functional properties of brain networks involved in higher-order cognition, and it aligns with past research on the functional connectivity correlates of creative task performance.

3D MERMAID: 3D Multi-shot enhanced recovery motion artifact insensitive diffusion for submillimeter, multi-shell, and SNR-efficient diffusion imaging

PURPOSE

To enhance SNR per unit time of diffusion MRI to enable high spatial resolution and extensive q-sampling in a feasible scan time on clinical scanners.

METHODS

3D multi-shot enhanced recovery motion-insensitive diffusion (MERMAID) consists of a whole brain nonselective 3D multi-shot spin-echo sequence with an inversion pulse immediately before the excitation pulse to enhance the recovery of longitudinal magnetization. The excitation flip angle is reduced to the Ernst angle. The sequence includes a trajectory using radially batched internal navigator echoes (TURBINE) readout, where a 3D projection of the FOV is acquired at a different radial angle in every shot. An image-based phase-correction method combined with compressed sensing image reconstruction was developed to correct phase errors between shots. The performance of the 3D MERMAID sequence was investigated using Bloch simulations as well as phantom and human scans at 3 T and then compared to a typical multi-slice 2D spin-echo sequence.

RESULTS

Improvements in SNR per unit time of 70%-240% were observed in phantom and human scans when using 3D MERMAID compared to a single-slice 2D spin-echo sequence. This SNR per unit time improvement allowed scans to be acquired at a nominal isotropic resolution of 0.74 mm and a total of 112 directions across four shells (b = 150, 300, 1000, 2000 s/mm2) in 37 min on a clinical scanner.

CONCLUSION

The 3D MERMAID sequence was shown to significantly improve SNR per unit time compared to multi-slice 2D and 3D diffusion sequences. This SNR improvement allows for shorter scan times and higher spatial and angular resolutions on clinical scanners.

Accuracy and Longitudinal Consistency of PET/MR Attenuation Correction in Amyloid PET Imaging amid Software and Hardware Upgrades

BACKGROUND AND PURPOSE

Integrated PET/MR allows the simultaneous acquisition of PET biomarkers and structural and functional MRI to study Alzheimer disease (AD). Attenuation correction (AC), crucial for PET quantification, can be performed by using a deep learning approach, DL-Dixon, based on standard Dixon images. Longitudinal amyloid PET imaging, which provides important information about disease progression or treatment responses in AD, is usually acquired over several years. Hardware and software upgrades often occur during a multiple-year study period, resulting in data variability. This study aims to harmonize PET/MR DL-Dixon AC amid software and head coil updates and evaluate its accuracy and longitudinal consistency.

MATERIALS AND METHODS

Tri-modality PET/MR and CT images were obtained from 329 participants, with a subset of 38 undergoing tri-modality scans twice within approximately 3 years. Transfer learning was used to fine-tune DL-Dixon models on images from 2 scanner software versions (VB20P and VE11P) and 2 head coils (16-channel and 32-channel coils). The accuracy and longitudinal consistency of the DL-Dixon AC were evaluated. Power analyses were performed to estimate the sample size needed to detect various levels of longitudinal changes in the PET standardized uptake value ratio (SUVR).

RESULTS

The DL-Dixon method demonstrated high accuracy across all data, irrespective of scanner software versions and head coils. More than 95.6% of brain voxels showed less than 10% PET relative absolute error in all participants. The median [interquartile range] PET mean relative absolute error was 1.10% [0.93%, 1.26%], 1.24% [1.03%, 1.54%], 0.99% [0.86%, 1.13%] in the cortical summary region, and 1.04% [0.83%, 1.36%], 1.08% [0.84%, 1.34%], 1.05% [0.72%, 1.32%] in cerebellum by using the DL-Dixon models for the VB20P 16-channel coil, VE11P 16-channel coil, and VE11P 32-channel coil data, respectively. The within-subject coefficient of variation and intraclass correlation coefficient of PET SUVR in the cortical regions were comparable between the DL-Dixon and CT AC. Power analysis indicated that similar numbers of participants would be needed to detect the same level of PET changes by using DL-Dixon and CT AC.

CONCLUSIONS

DL-Dixon exhibited excellent accuracy and longitudinal consistency across the 2 software versions and head coils, demonstrating its robustness for longitudinal PET/MR neuroimaging studies in AD.

The control costs of human brain dynamics

The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using the network control theory, here, we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from the positron emission tomography, providing insight into the bioenergetic footing of resting-state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.

Functional connectivity-based compensation in the brains of non-demented older adults and the influence of lifestyle: A longitudinal 7-year study

INTRODUCTION

The aging brain is characterized by structural decline and functional connectivity changes towards dedifferentiation, leading to cognitive decline. To some degree, the brain can compensate for structural deterioration. In this study, we aim to answer two questions: Where can we detect longitudinal functional connectivity-based compensation in the brains of cognitively healthy older adults? Can lifestyle predict the strength of this functional compensation?

METHODS

Using longitudinal data from 228 cognitively healthy older adults, we analyzed five measurement points over 7 years. Network-based statistics and latent growth modeling were employed to examine changes in structural and functional connectivity, as well as potential functional compensation for declines in processing speed and memory. Random forest and linear regression were used to predict the amplitude of compensation based on demographic, biological, and lifestyle factors.

RESULTS

Both functional and structural connectivity showed increases and decreases over time, depending on the specific connection and measure. Increased functional connectivity of 27 connections was linked to smaller declines in cognition. Five of those connections showed simultaneous decreases in fractional anisotropy, indicating direct compensation. The degree of compensation depended on the type of compensation and the cognitive ability, with demographic, biological, and lifestyle factors explaining 3.4-8.9% of the variance.

CONCLUSIONS

There are widespread changes in structural and functional connectivity in older adults. Despite the trend of dedifferentiation in functional connectivity, we detected both direct and indirect compensatory subnetworks that mitigated the decline in cognitive performance. The degree of compensation was influenced by demographic, biological, and lifestyle factors.

Towards Clinical Diagnoses: Classifying Alzheimer's Disease Using Single fMRI, Small Datasets, and Transfer Learning

PURPOSE

Deep learning and functional magnetic resonance imaging (fMRI) are two unique methodologies that can be combined to diagnose Alzheimer's disease (AD). Multiple studies have harnessed these methods to diagnose AD with high accuracy. However, there are difficulties in adapting this research to real-world diagnoses. For example, the two key issues of data availability and model usability limit clinical applications. These two areas are concerned with problems of accessibility, generalizability, and methodology that may limit model adoption. For example, fMRI deep learning models require a large amount of training data, which is not widely available. Contemporary models are also not typically formatted for clinical data or created for use by non-specialized populations. In this study, we develop a deep-learning fMRI pipeline that addresses some of these issues.

METHOD

We use transfer learning to address problems with data availability. We also use semi-automated and single-image techniques (i.e., one fMRI volume per participant) to make a model that is usable for non-specialized populations. Our model was initially trained on 524 participants from the Autism Brain Imaging Data Exchange (ABIDE; Autism and controls). Our model was then transferred and fine-tuned to a small sample of 64 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI; AD and controls).

FINDINGS AND CONCLUSION

This transfer learning model achieved an AD classification accuracy of 77% and outperformed the same model without transfer learning by approximately 30%. Accordingly, our model showed that small AD samples can be accurately classified in a clinically friendly manner.

Detectability of white matter cerebral blood flow using arterial spin labeling MRI in patients with sickle cell disease: Relevance of flow territory, bolus arrival time and hematocrit

Sickle cell disease (SCD) is the most common genetic blood disorder, characterized by red cell hemolysis, anemia, and corresponding increased compensatory cerebral blood flow (CBF). SCD patients are at high risk for cerebral infarcts and CBF quantification is likely critical to assess infarct risk. Infarcts primarily localize to white matter (WM), yet arterial spin labeling (ASL) MRI, the most common non-invasive CBF approach, has poor WM CBF sensitivity owing to low WM CBF and long WM bolus arrival time (BAT). We hypothesize that anemia, and associated cerebral hyperemia, in SCD leads to improved WM detection with ASL. We performed 3-Tesla multi-delay pulsed ASL in SCD (n = 35; age = 30.5 ± 8.3 years) and control (n = 15; age = 28.7 ± 4.5 years) participants and applied t-tests at each inversion time within different flow territories, and determined which regions were significantly above noise floor (criteria: one-sided p < 0.05). Total WM CBF-weighted signal was primarily detectable outside of borderzone regions in SCD (CBF = 17.7 [range = 12.9-25.0] mL/100 g/min), but was largely unphysiological in control (CBF = 8.1 [range = 7.6-9.9)] mL/100 g/min) participants. WM BAT was reduced in SCD versus control participants (ΔBAT = 37 [range = 46-70] ms) and BAT directly correlated with hematocrit (Spearman's-ρ = 0.62; p < 0.001). Findings support the feasibility of WM CBF quantification using ASL in SCD participants for appropriately parameterized protocols.

fMRI Activation in Sensorimotor Regions at 6 Weeks After Anterior Cruciate Ligament Reconstruction

BACKGROUND

Brain activity during knee movements is altered throughout the sensorimotor network after anterior cruciate ligament reconstruction (ACLR). Patients at 2 to 5 years after surgery appear to require greater neural activity to perform basic knee movement patterns, but it is unclear if brain activity differences within sensorimotor regions are present early after surgery. It is also unknown whether uninvolved knee movements elicit similar or unique activity compared with involved knee movements.

PURPOSE

To examine brain activity in sensorimotor regions during involved and uninvolved knee movements in patients at 6 weeks after ACLR compared with control participants.

STUDY DESIGN

Cohort study; Level of evidence, 2.

METHODS

A total of 15 patients who underwent ACLR (mean age, 21.9 ± 4.3 years [range, 17-29 years]; 8 female) and 15 control participants performed 30-second blocks of repeated knee flexion and extension, followed by 30 seconds of rest, during functional magnetic resonance imaging. Regions of interest included the right and left primary motor cortex (M1), right and left primary somatosensory cortex (S1), supplementary motor area (SMA), precuneus, and lingual gyrus. Activity from task-relevant voxels (move > rest) was extracted, and generalized estimating equations evaluated the main effect of group and group-by-limb interaction. Effect sizes were calculated using the Cohen d.

RESULTS

Reduced brain activity during knee flexion and extension was observed in the ACLR group in the ipsilateral M1 and S1, contralateral S1, SMA, and precuneus during movements of the involved and uninvolved knees. There were no group-by-limb interaction effects, indicating no significant differences between the involved knee and uninvolved knee in the ACLR group. Medium to large effect sizes were identified for between-group differences in all regions.

CONCLUSION

At 6 weeks after ACLR, patients exhibited bilateral reductions in brain activity during knee movements in multiple sensorimotor regions. These identified regions are associated with motor planning, motor execution, somatosensory function, and sensorimotor integration. These data indicate that ACLR affected sensorimotor brain activity in both limbs during the early postoperative phase of rehabilitation.

Topographical mapping of metabolic abnormalities in multiple sclerosis using rapid echo-less 3D-MR spectroscopic imaging at 7T

OBJECTIVES

To assess topographical patterns of metabolic abnormalities in the cerebrum of multiple sclerosis (MS) patients and their relationship to clinical disability using rapid echo-less 3D-MR spectroscopic imaging (MRSI) at 7T.

MATERIALS AND METHODS

This study included 26 MS patients (13 women; median age 34) and 13 age- and sex-matched healthy controls (7 women; median age 33). Metabolic maps were obtained using echo-less 3D-MRSI at 7T with a 64 × 64 × 33 matrix and a nominal voxel size of 3.4 × 3.4 × 4 mm³ in an 8-minute scan. After spatial normalization, voxel-wise comparisons between MS and controls were conducted to identify clusters of metabolic abnormalities, while correlations with clinical disability were analyzed using Expanded Disability Status Scale (EDSS) scores.

RESULTS

Statistical mapping (FWE-corrected; P<.05) revealed elevated myo-inositol to total creatine (mI/tCr) ratios in the bilateral periventricular white matter and reduced N-acetylaspartate to total creatine (NAA/tCr) within and beyond lesions, notably near the lateral ventricles, cingulate gyrus, and superior frontal gyrus. Patients with sustained disability (EDSS≥2) showed additional reductions in the posterior parietal lobe. A strong negative association was found between NAA/tCr and EDSS in the precentral gyrus (Spearman's rank ρ=-0.58, P=.005), and a moderate positive association between mI/NAA and EDSS in the precentral and superior frontal gyri (ρ=0.47, P=.015).

CONCLUSIONS

This study highlights the ability of 3D-MRSI at 7T to map widespread metabolic abnormalities in MS, with NAA reductions in prefrontal, motor, and sensory areas, linked to neuroaxonal damage and disability progression, and elevated mI in periventricular regions, reflecting gliosis.

Cerebrovascular Function in Sporadic and Genetic Cerebral Small Vessel Disease

OBJECTIVE

Cerebral small vessel diseases (SVDs) are associated with cerebrovascular dysfunction, such as increased blood-brain barrier leakage (permeability surface area product), vascular pulsatility, and decreased cerebrovascular reactivity (CVR). No studies assessed all 3 functions concurrently. We assessed 3 key vascular functions in sporadic and genetic SVD to determine associations with SVD severity, subtype, and interrelations.

METHODS

In this prospective, cross-sectional, multicenter INVESTIGATE-SVDs study, we acquired brain magnetic resonance imaging in patients with sporadic SVD/cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), including structural, quantitative microstructural, permeability surface area product, blood plasma volume fraction, vascular pulsatility, and CVR (in response to CO2) scans. We determined vascular function and white matter hyperintensity (WMH) associations, using covariate-adjusted linear regression; normal-appearing white matter and WMH differences, interrelationships between vascular functions, using linear mixed models; and major sources of variance using principal component analyses.

RESULTS

We recruited 77 patients (45 sporadic/32 CADASIL) at 3 sites. In adjusted analyses, patients with worse WMH had lower CVR (B = -1.78, 95% CI -3.30, -0.27) and blood plasma volume fraction (B = -0.594, 95% CI -0.987, -0.202). CVR was worse in WMH than normal-appearing white matter (eg, CVR: B = -0.048, 95% CI -0.079, -0.017). Adjusting for WMH severity, SVD subtype had minimal influence on vascular function (eg, CVR in CADASIL vs sporadic: B = 0.0169, 95% CI -0.0247, 0.0584). Different vascular function mechanisms were not generally interrelated (eg, permeability surface area product~CVR: B = -0.85, 95% CI -4.72, 3.02). Principal component analyses identified WMH volume/quantitative microstructural metrics explained most variance in CADASIL and arterial pulsatility in sporadic SVD, but similar main variance sources.

INTERPRETATION

Vascular function was worse with higher WMH, and in WMH than normal-appearing white matter. Sporadic SVD-CADASIL differences largely reflect disease severity. Limited vascular function interrelations may suggest disease stage-specific differences. ANN NEUROL 2025;97:483-498.

Cognitive Deficits in Anti-LGI1 Encephalitis Are Linked to Immunotherapy-Resistant White Matter Network Changes

BACKGROUND AND OBJECTIVES

Cognitive deficits represent a major long-term complication of anti-leucine-rich, glioma-inactivated 1 encephalitis (LGI1-E). Although severely affecting patient outcomes, the structural brain changes underlying these deficits remain poorly understood. In this study, we hypothesized a link between white matter (WM) networks and cognitive outcomes in LGI1-E.

METHODS

In this cross-sectional study, we combined clinical assessments, comprehensive neuropsychological testing, diffusion tensor MRI, probabilistic WM tractography, and computational network analysis in patients with LGI1-E referred to Charité-Universitätsmedizin Berlin. Healthy individuals were recruited as control participants and matched to patients for age and sex with logistic regression propensity scores.

RESULTS

Twenty-five patients with LGI1-E (mean age = 63 ± 12 years, 76% male) and 25 healthy controls were enrolled. Eighty-eight percent of patients presented persistent cognitive symptoms at postacute follow-up (median: 12 months from onset, interquartile range: 6-23 months)-despite treatment with immunotherapy and good overall recovery (modified Rankin Scale [mRS] score at peak illness vs postacute: z = -4.1, p < 0.001, median mRS score at postacute visit: 1). Neuroimaging revealed that WM networks in LGI1-E are characterized by (1) a systematic reduction in whole-brain connectivity (t = -2.16, p = 0.036, d = -0.61), (2) a cortico-subcortical hypoconnectivity cluster affecting both limbic and extralimbic brain systems, and (3) a "topological reorganization" marked by a bidirectional shift in the relative importance of individual brain regions in the WM network. The extent of this WM reorganization was strongly associated with long-term deficits of verbal memory (r = -0.56), attention (r = -0.55), and executive functions (r = -0.60, all pFDR = 0.017).

DISCUSSION

Although traditionally viewed as a form of limbic encephalitis, our study characterizes LGI1-E as a "network disorder" that affects the whole brain. Structural reorganization of WM networks was linked to long-term and multidomain cognitive impairment, which was not prevented by immunotherapy. These findings highlight the need for closer monitoring and improved treatment strategies to mitigate long-term cognitive impairment in LGI1-E.

VCU-Net: a vascular convolutional network with feature splicing for cerebrovascular image segmentation

Cerebrovascular image segmentation is one of the crucial tasks in the field of biomedical image processing. Due to the variable morphology of cerebral blood vessels, the traditional convolutional kernel is weak in perceiving the structure of elongated blood vessels in the brain, and it is easy to lose the feature information of the elongated blood vessels during the network training process. In this paper, a vascular convolutional U-network (VCU-Net) is proposed to address these problems. This network utilizes a new convolution (vascular convolution) instead of the traditional convolution kernel, to extract features of elongated blood vessels in the brain with different morphologies and orientations by adaptive convolution. In the network encoding stage, a new feature splicing method is used to combine the feature tensor obtained through vascular convolution with the original tensor to provide richer feature information. Experiments show that the DSC and IOU of the proposed method are 53.57% and 69.74%, which are improved by 2.11% and 2.01% over the best performance of the GVC-Net among several typical models. In image visualization, the proposed network has better segmentation performance for complex cerebrovascular structures, especially in dealing with elongated blood vessels in the brain, which shows better integrity and continuity.

Association of Increased Brain Iron Levels With Anxiety and Motor Dysfunction in Cerebral Small Vessel Disease

AIMS

This study explored the relationships between brain iron levels, emotion, and cognitive and motor function in cerebral small vessel disease (CSVD) patients using quantitative susceptibility mapping (QSM).

METHODS

A total of 208 subjects were enrolled in this study. A brain QSM map was calculated from multiecho GRE data via morphology-enabled dipole inversion with an automatic uniform cerebrospinal fluid zero reference algorithm (MEDI+0). Multiple linear regression analysis was applied to explore the clinical factors influencing cerebral susceptibility in CSVD patients. Correlation analysis and pathway-specific mediation effects between brain iron levels and motor function were investigated.

RESULTS

There were significant differences in the MoCA scores, depression scores, five-repetition sit-to-stand test (5R-STS) time, and susceptibility values of the caudate nucleus and putamen among the three groups (p < 0.05, FDR correction). Age and history of diabetes played crucial roles in brain iron levels in the caudate nucleus and putamen, which may increase iron levels in the basal ganglia, associated with cognitive decline. Notably, the susceptibility values of the left caudate nucleus and putamen were positively correlated with the 5R-STS time in CSVD subjects, and there were significant mediating effects of anxiety on the prediction of motor dysfunction with respect to iron levels in the left putamen in CSVD patients.

CONCLUSION

Age, diabetes status, and anxiety may serve as effective intervention targets for individuals with CSVD, especially individuals with cognitive and motor dysfunction. A greater brain iron burden may be a quantitative imaging marker of cognitive and motor dysfunction in CSVD patients.

TRIAL REGISTRATION

ISRCTN20008650.

Does the Cortical-Depth Dependence of the Hemodynamic Response Function Differ Between Age Groups?

Functional magnetic resonance imaging (fMRI) is a widely used tool to investigate the functional brain responses in living humans. Valid comparisons of fMRI results depend on consistency of the blood-oxygen-level-dependent (BOLD) hemodynamic response function (HRF). Although common statistical approaches assume a single HRF across the entire brain, the HRF differs across individuals, regions of the brain, and cortical depth. Here, we measure HRF properties in primary visual cortex (V1) using 7 T fMRI with ultra-high spatiotemporal resolution line-scanning (250 μm in laminar direction, sampled every 105 ms). Line-scanning allowed us to investigate age-related HRF changes as a function of cortical depth. Eleven young and eleven middle-aged healthy participants participated in the experiments. We estimated the HRFs using a smooth basis function deconvolution approach. We also compared the results with conventional resolutions. From these HRFs, we extracted properties related to response magnitude and temporal dynamics. The cortical depth dependent HRFs were similar to the HRFs extracted using conventional resolutions validating the cortical depth dependent approach. We found that the properties of the HRF in the two age groups are similar across cortical depth. In other words, the variance between participants is larger than the variance between age groups. This suggests that middle-aged individuals can participate in cortical depth dependent studies free of bias in HRF properties.

Circulating biomarkers and neuroanatomical brain structures differ in older adults with and without post-traumatic stress disorder

The aim of this study was to advance post-traumatic stress disorder (PTSD) understanding in older adults (48-77 years) by determining if circulating cytokines (IL-1β, IL-2, IL-4, IL-6, IL-12p70, IL17A and TNFα), brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF-A) and neuroanatomical brain volumes (grey and white matter, hippocampus, and amygdala) significantly differed in those with versus without PTSD. While none of the tested cytokines showed a significant difference, serum BDNF and VEGF-A levels were found to be significantly higher in the PTSD cohort. The assay used for BDNF quantification was important, with differences in general BDNF detected, but not when pro- and mature BDNF were measured specifically. Additionally, BDNF genotyping revealed a significant difference in Val66Met genotype distribution by PTSD diagnosis, with Val66Met carriers generally having lower circulating levels of BDNF compared to their Val66Val counterparts, regardless of PTSD diagnosis. Neuroanatomically, an all-female subset was examined to find total grey and white matter volumes and left and right hippocampal volumes were significantly smaller in those with PTSD. Collectively, these results show that both novel (VEGF-A) and established targets (BDNF and neuroimaging) may serve as useful biomarkers for older adults with PTSD.

Omissions of threat trigger subjective relief and prediction error-like signaling in the human reward and salience systems

The unexpected absence of danger constitutes a pleasurable event that is critical for the learning of safety. Accumulating evidence points to similarities between the processing of absent threat and the well-established reward prediction error (PE). However, clear-cut evidence for this analogy in humans is scarce. In line with recent animal data, we showed that the unexpected omission of (painful) electrical stimulation triggers activations within key regions of the reward and salience pathways and that these activations correlate with the pleasantness of the reported relief. Furthermore, by parametrically violating participants' probability and intensity related expectations of the upcoming stimulation, we showed for the first time in humans that omission-related activations in the VTA/SN were stronger following omissions of more probable and intense stimulations, like a positive reward PE signal. Together, our findings provide additional support for an overlap in the neural processing of absent danger and rewards in humans.

Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health

Major mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopathology. To this end, we introduce the Reproducible Brain Charts (RBC), an open data resource that integrates data from 5 large studies of brain development in youth from three continents (N=6,346; 45% Female). Confirmatory bifactor models were used to create harmonized psychiatric phenotypes that capture major dimensions of psychopathology. Following rigorous quality assurance, neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner with DataLad, the Configurable Pipeline for the Analysis of Connectomes (C-PAC), and FreeSurfer. Initial analyses of RBC data emphasize the benefit of careful quality assurance and data harmonization in delineating developmental effects and associations with psychopathology. Critically, all RBC data - including harmonized psychiatric phenotypes, unprocessed images, and fully processed imaging derivatives - are openly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. Together, RBC facilitates large-scale, reproducible, and generalizable research in developmental and psychiatric neuroscience.

Dynamic reconfiguration of brain coactivation states associated with active and lecture-based learning of university physics

Academic institutions are increasingly adopting active learning methods to enhance educational outcomes. Using functional magnetic resonance imaging (fMRI), we investigated neurobiological differences between active learning and traditional lecture-based approaches in university physics education. Undergraduate students enrolled in an introductory physics course underwent an fMRI session before and after a 15-week semester. Coactivation pattern (CAP) analysis was used to examine the temporal dynamics of brain states across different cognitive contexts, including physics conceptual reasoning, physics knowledge retrieval, and rest. CAP results identified seven distinct brain states, with contributions from frontoparietal, somatomotor, and visuospatial networks. Among active learning students, physics learning was associated with increased engagement of a somatomotor network, supporting an embodied cognition framework, while lecture-based students demonstrated stronger engagement of a visuospatial network, consistent with observational learning. These findings suggest significant neural restructuring over a semester of physics learning, with different instructional approaches preferentially modulating distinct patterns of brain dynamics.

Longitudinal Cognitive Changes in Cerebral Small Vessel Disease: The Effect of White Matter Hyperintensity Regression and Progression

BACKGROUND AND OBJECTIVES

White matter hyperintensities (WMHs) are the commonest imaging marker of cerebral small vessel disease (SVD) and a major cause of cognitive decline and vascular dementia. WMHs typically accumulate over time, but recent studies show they can also regress, but potential clinical benefits have received little attention. We examined progressing, stable, and regressing WMH in people with stroke-related SVD and the effect on cognitive outcomes.

METHODS

We recruited patients with minor nondisabling ischemic stroke (modified Rankin score ≤2) from stroke services into our prospective longitudinal observational study. Participants underwent cognitive assessment and brain MRI within 3-month poststroke and 1 year later. We gathered information on vascular risk factors, stroke severity, global cognition (Montreal Cognitive Assessment [MoCA]), processing speed and executive functioning (Trail Making Test [TMT] A and B, and the B/A ratio with ratio ≥3 reflecting executive dysfunction), and the Letter Digit Substitution Test. We measured WMH volumes at baseline and 1 year and categorized net WMH volume change into quintiles: Q1 (most regression), Q3 (stable), and Q5 (most progression). We applied repeated-measures linear mixed models to analyze longitudinal WMH and cognitive changes, adjusting for age, sex, premorbid intelligence, stroke severity, disability, white matter structural integrity, and baseline WMH volume.

RESULTS

One hundred ninety-eight of 229 participants had WMH volumes available at both time-points. At baseline, the mean age was 67.5 years (SD = 10.9), with 33% female. Mean net WMH volume change per quintile was Q1 -1.79 mL (SD = 1.54), Q2 -0.27 mL (0.20), Q3 0.35 mL (0.18), Q4 1.43 mL (0.48), and Q5 5.31 mL (3.07). MoCA deteriorated the most in participants with most WMH progression (Q5) (estimated β -0.428 [95% CI -0.750 to -0.106]), compared with stable WMH (Q3), with no clear deterioration in those with most WMH regression (Q1). TMT B/A ratio improved in participants with most WMH regression (Q1; -0.385 [-0.758 to -0.012]).

DISCUSSION

WMH regression was associated with preserved global cognition and improved executive function, compared with stable WMH, while WMH progression was associated with global cognitive decline. Cognitive benefits of WMH regression suggest that WMH-affected tissue can recover, may explain variance in cognitive outcomes, offer an important intervention target, and should be assessed in other populations and longer follow-up times.

Multivariate links between the developmental timing of adversity exposure and white matter tract connectivity in adulthood

BACKGROUND

Early-life adversity is pervasive worldwide and represents a potent risk factor for increased mental health burden across the lifespan. However, there is substantial individual heterogeneity in associations between adversity exposure, neurobiological changes, and mental health problems. Accounting for key features of adversity such as the developmental timing of exposure may clarify associations between adversity, neurodevelopment, and mental health.

METHODS

The present study leverages sparse canonical correlation analysis to characterize modes of covariation between adversity exposure across development and the connectivity of white matter tracts throughout the brain in a sample of 107 adults.

RESULTS

We found that adversity exposure during preschool-age and middle childhood (ages 4-5 and 8 in particular) were consistently linked across diffusion metrics with alterations in white matter tract connectivity. Whereas tracts supporting sensorimotor functions displayed higher connectivity with higher preschool-age and middle childhood adversity exposure, tracts supporting cortico-cortical communication displayed lower connectivity. Further, latent patterns of tract connectivity linked with adversity experienced across preschool-age and middle childhood (ages 3-8) were associated with post-traumatic stress symptoms in adulthood.

CONCLUSIONS

Our findings underscore that adversity exposure may differentially affect white matter in a function- and developmental-timing specific manner and suggest that adversity experienced between ages 3-8 may shape the development of white matter tracts across the brain in ways that are relevant for mental health in adulthood.

SpiDa-MRI: behavioral and (f)MRI data of adults with fear of spiders

Neuroimaging has greatly improved our understanding of phobic mechanisms. To expand on these advancements, we present data on the heterogeneity of neural patterns in spider phobia combined with various psychological dimensions of spider phobia, using spider-relevant stimuli of various intensities. Specifically, we have created a database in which 49 spider-fearful individuals viewed 225 spider-relevant images in the fMRI scanner and performed behavioral avoidance tasks before and after the fMRI scan. For each participant, the database consists of the neuroimaging part, which includes an anatomical scan, five passive-viewing, and two resting-state functional runs in both raw and pre-processed form along with associated quality control reports. Additionally, the behavioral section includes self-report questionnaires and avoidance tasks collected in pre- and post-sessions. The dataset is well suited for investigating neural mechanisms of phobias, brain-behavior correlations, and also contributes to the existing phobic neuroimaging datasets with spider-fearful samples.

The subcortical correlates of self-reported sleep quality

Study objectives

To assess the association between self-reported measures of sleep quality and cortical and subcortical local morphometry.

Methods

Sleep quality, operationalized with the Pittsburgh Sleep Quality Index (PSQI), and neuroanatomical data from the full release of the young adult Human Connectome Project dataset were analyzed (N=1,112; 46% female; mean age: 28.8 years old). Local cortical and subcortical morphometry was measured with subject-specific segmentations resulting in voxelwise gray matter difference (i.e., voxel based morephometry) measurements for cortex and local shape measurements for subcortical regions. Associations between the total score of PSQI, two statistical groupings of its subcomponents (obtained with a principal component analysis), and their interaction with demographic (i.e., sex, age, handedness, years of education) and biometric (i.e., BMI) variables were assessed using a general linear model and a nonparametric permutation approach.

Results

Sleep quality-related variance was significantly associated with subcortical morphometry, particularly in the bilateral caudate, putamen, and left pallidum, where smaller shape measures correlated with worse sleep quality. Notably, these associations were independent of demographic and biometric factors. In contrast, cortical morphometry, along with additional subcortical sites, showed no direct associations with sleep quality but demonstrated interactions with demographic and biometric variables.

Conclusions

This study reveals a specific link between self-reported sleep quality and subcortical morphometry, particularly within the striatum and pallidum, reinforcing the role of these regions in sleep regulation. These findings underscore the importance of considering subcortical morphology in sleep research and highlight potential neuromodulatory targets for sleep-related interventions.

Linguistic coupling between neural systems for speech production and comprehension during real-time dyadic conversations

The core use of human language is communicating complex ideas from one mind to another in everyday conversations. In conversations, comprehension and production processes are intertwined, as speakers soon become listeners, and listeners become speakers. Nonetheless, the neural systems underlying these faculties are typically studied in isolation using paradigms that cannot fully engage our capacity for interactive communication. Here, we used an fMRI hyperscanning paradigm to measure neural activity simultaneously in pairs of subjects engaged in real-time, interactive conversations. We used contextual word embeddings from a large language model to quantify the linguistic coupling between production and comprehension systems within and across individual brains. We found a highly overlapping network of regions involved in both production and comprehension spanning much of the cortical language network. Our findings reveal that shared representations for both processes extend beyond the language network into areas associated with social cognition. Together, these results suggest that the specialized neural systems for speech perception and production align on a common set of linguistic features encoded in a broad cortical network for language and communication.

Neural codes track prior events in a narrative and predict subsequent memory for details

Throughout our lives, we learn schemas that specify what types of events to expect in particular contexts and the temporal order in which these events usually occur. Here, our first goal was to investigate how such context-dependent temporal structures are represented in the brain during processing of temporally extended events. To accomplish this, we ran a 2-day fMRI study (N = 40) in which we exposed participants to many unique animated videos of weddings composed of sequences of rituals; each sequence originated from one of two fictional cultures (North and South), where rituals were shared across cultures, but the transition structure between these rituals differed across cultures. The results, obtained using representational similarity analysis, revealed that context-dependent temporal structure is represented in multiple ways in parallel, including distinct neural representations for the culture, for particular sequences, and for past and current events within the sequence. Our second goal was to test the hypothesis that neural schema representations scaffold memory for specific details. In keeping with this hypothesis, we found that the strength of the neural representation of the North/South schema for a particular wedding predicted subsequent episodic memory for the details of that wedding.

Alterations in structural and functional connectivity of thalamic subregions in patients with uremic pruritus undergoing peritoneal dialysis

Uremic pruritus (UP) significantly compromises the quality of life in patients with end-stage renal disease undergoing peritoneal dialysis. Although the precise pathophysiological mechanisms of UP remain elusive, the thalamus, which is integral to processing sensory information, is potentially implicated in its development. This study aimed to investigate alterations in the structure and resting-state functional connectivity (rsFC) of thalamic subregions in patients with UP. A total of 42 UP patients and 50 healthy controls (HCs) were included in the study. Volumetric and seed-based rsFC analyses were employed to assess structural and functional changes in the thalamic subregions. Correlation and mediation effect analyses were used to explore the relationships among neuroimaging structural features, functional features, and UP severity. Compared to HCs, patients with UP showed a significant reduction in volume in the right medial prefrontal thalamus, bilateral Stha (Sensory thalamus), and right posterior parietal thalamus. In UP patients, regions of reduced rsFC between the bilateral Stha and the whole brain were primarily localized within the sensory motor network. The decreased volume of thalamic subregions and rsFC were closely associated with UP severity. It was found that the volume of R_Stha directly influences the severity of pruritus in UP patients, but this effect does not manifest through rsFC between R_Stha and left supplementary motor area or left paracentral lobule. Patients with UP exhibited changes in structural and functional connectivity within specific thalamic subregions, providing neuroimaging insights into the neural mechanisms of UP.

Mapping vascular network architecture in primate brain using ferumoxytol-weighted laminar MRI

Mapping the vascular organization of the brain is of great importance across various domains of basic neuroimaging research, diagnostic radiology, and neurology. However, the intricate task of precisely mapping vasculature across brain regions and cortical layers presents formidable challenges, resulting in a limited understanding of neurometabolic factors influencing the brain's microvasculature. Addressing this gap, our study investigates whole-brain vascular volume using ferumoxytol-weighted laminar-resolution multi-echo gradient-echo imaging in macaque monkeys. We validate the results with published data for vascular densities and compare them with cytoarchitecture, neuron and synaptic densities. The ferumoxytol-induced change in transverse relaxation rate ( Δ R 2 * ), an indirect proxy measure of cerebral blood volume (CBV), was mapped onto twelve equivolumetric laminar cortical surfaces. Our findings reveal that CBV varies 3-fold across the brain, with the highest vascular volume observed in the inferior colliculus and lowest in the corpus callosum. In the cerebral cortex, CBV is notably high in early primary sensory areas and low in association areas responsible for higher cognitive functions. Classification of CBV into distinct groups unveils extensive replication of translaminar vascular network motifs, suggesting distinct computational energy supply requirements in areas with varying cytoarchitecture types. Regionally, baselineR 2 * and CBV exhibit positive correlations with neuron density and negative correlations with receptor densities. Adjusting image resolution based on the critical sampling frequency of penetrating cortical vessels allows us to delineate approximately 30% of the arterial-venous vessels. Collectively, these results mark significant methodological and conceptual advancements, contributing to the refinement of cerebrovascular MRI. Furthermore, our study establishes a linkage between neurometabolic factors and the vascular network architecture in the primate brain.

Proactive and reactive construction of memory-based preferences

We are often faced with decisions we have never encountered before, requiring us to infer possible outcomes before making a choice. Computational theories suggest that one way to make these types of decisions is by accessing and linking related experiences stored in memory. Past work has shown that such memory-based preference construction can occur at a number of different timepoints relative to the moment a decision is made. Some studies have found that memories are integrated at the time a decision is faced (reactively) while others found that memory integration happens earlier, when memories were initially encoded (proactively). Here we offer a resolution to this inconsistency, demonstrating that these two strategies tradeoff rationally as a function of the associative structure of memory. We use fMRI to decode patterns of brain responses unique to categories of images in memory and find that proactive memory access is more common and allows more efficient inference. However, we also find that participants use reactive access when choice options are linked to a larger number of memory associations. Together, these results indicate that the brain judiciously conducts proactive inference by accessing memories ahead of time when conditions make this strategy more favorable.

Coordinating multiple mental faculties during learning

Complex behavior is supported by the coordination of multiple brain regions. How do brain regions coordinate absent a homunculus? We propose coordination is achieved by a controller-peripheral architecture in which peripherals (e.g., the ventral visual stream) aim to supply needed inputs to their controllers (e.g., the hippocampus and prefrontal cortex) while expending minimal resources. We developed a formal model within this framework to address how multiple brain regions coordinate to support rapid learning from a few example images. The model captured how higher-level activity in the controller shaped lower-level visual representations, affecting their precision and sparsity in a manner that paralleled brain measures. In particular, the peripheral encoded visual information to the extent needed to support the smooth operation of the controller. Alternative models optimized by gradient descent irrespective of architectural constraints could not account for human behavior or brain responses, and, typical of standard deep learning approaches, were unstable trial-by-trial learners. While previous work offered accounts of specific faculties, such as perception, attention, and learning, the controller-peripheral approach is a step toward addressing next generation questions concerning how multiple faculties coordinate.

Magnetic resonance imaging signatures of neuroinflammation in major depressive disorder with religious and spiritual problems

Religious and spiritual (R/S) struggles, such as questioning of faith, existential and ethical concerns, and interpersonal conflicts, are associated with depressive symptoms. Neuroinflammation is critical in major depressive disorder (MDD) and is linked to stress associated with R/S problems. This study aimed to investigate whether the presence of DSM-5 R/S problems contributes to neuroinflammation. We recruited 93 MDD patients and 93 healthy controls with and without R/S problems. MRI-based restricted fraction (RF) values, an index of neuroinflammation, were measured in the hippocampus, amygdala, and neocortex. Depression and anxiety were assessed using the Hamilton Depression and Anxiety Rating Scales (HAM-D, HAM-A), while R/S problems were quantified using the Religious and Spiritual Struggles Scale (RSS-14). Results revealed elevated RF values in the amygdala and hippocampus of healthy individuals and MDD patients with R/S problems relative to those without R/S problems, with the highest values in MDD patients with R/S problems. Importantly, R/S problems and depressive symptoms were independent predictors of RF values in the amygdala and hippocampus but not in the cortex. Elevated cortical RF values were associated with MDD. These findings indicate that R/S struggles are not secondary manifestations of depression but may independently contribute to neurobiological changes.

Understanding depression in autism: the role of subjective perception and anterior cingulate cortex volume

BACKGROUND

The prevalence of depression is elevated in individuals with autism spectrum disorder (ASD) compared to the general population, yet the reasons for this disparity remain unclear. While social deficits central to ASD may contribute to depression, it is uncertain whether social interaction behavior themselves or individuals' introspection about their social behaviors are more impactful. Although the anterior cingulate cortex (ACC) is frequently implicated in ASD, depression, and social functioning, it is unknown if it explains differences between ASD adults with and without co-occurring depression.

METHODS

The present study contrasted observed vs. subjective perception of autism symptoms and social interaction assessed with both standardized measures and a lab task, in 65 sex-balanced (52.24% male) autistic young adults. We also quantified ACC and amygdala volume with 7-Tesla structural neuroimaging to examine correlations with self-reported depression and social functioning.

RESULTS

We found that ASD individuals with self-reported depression exhibited differences in subjective evaluations including heightened self-awareness of ASD symptoms, lower subjective satisfaction with social relations, and less perceived affiliation during the social interaction task, yet no differences in corresponding observed measures, compared to those without depression. Larger ACC volume was related to depression, greater self-awareness of ASD symptoms, and worse subjective satisfaction with social relations. In contrast, amygdala volume, despite its association with clinician-rated ASD symptoms, was not related to depression.

LIMITATIONS

Due to the cross-sectional nature of our study, we cannot determine the directionality of the observed relationships. Additionally, we included only individuals with an IQ over 60 to ensure participants could complete the social task. We also utilized self-reported depression indices instead of clinically diagnosed depression, which may limit the comprehensiveness of the findings.

CONCLUSIONS

Our approach highlights the unique role of subjective perception of autism symptoms and social interactions, beyond the observable manifestation of social impairment in ASD, in contributing to self-reported depression, with the ACC playing a crucial role. These findings imply possible heterogeneity of ASD concerning co-occurring depression. Using neuroimaging, we were able to demarcate depressive phenotypes co-occurring alongside autistic phenotypes.

Cognitive maps for hierarchical spaces in the human brain

Many of the environments that we navigate through every day are hierarchically organized-they consist of spaces nested within other spaces. How do our mind/brains represent such environments? To address this question, we familiarized participants with a virtual environment consisting of a building within a courtyard, with objects distributed throughout the courtyard and building interior. We then scanned them with fMRI while they performed a memory task that required them to think about spatial relationships within and across the subspaces. Behavioral responses were less accurate and response times were longer on trials requiring integration across the subspaces compared to trials not requiring integration. fMRI response differences between integration and non-integration trials were observed in scene-responsive and medial temporal lobe brain regions, which were correlated the behavioral integration effects in retrosplenial complex, occipital place area, and hippocampus. Multivoxel pattern analyses provided additional evidence for representations in these brain regions that reflected the hierarchical organization of the environment. These results indicate that people form cognitive maps of nested spaces by dividing them into subspaces and using an active cognitive process to integrate the subspaces. Similar mechanisms might be used to support hierarchical coding in memory more broadly.

Cerebellar-hippocampal volume associations with behavioral outcomes following tDCS modulation

Here, we explore the relationship between transcranial direct current stimulation (tDCS) and brain-behavior interactions. We propose that tDCS perturbation allows for the investigation of relationships between brain volume and behavior. We focused on the hippocampus (HPC) and cerebellum (CB) regions that are implicated in our understanding of memory and motor skill acquisition. Seventy-four young adults (mean age: 22 ± 0.42 years, mean education: 14.7 ± 0.25 years) were randomly assigned to receive either anodal, cathodal, or sham stimulation. Following stimulation, participants completed computerized tasks assessing working memory and sequence learning in a magnetic resonance imaging (MRI) environment. We investigated the statistical interaction between CB and HPC volumes. Our findings showed that individuals with larger cerebellar volumes had shorter reaction times (RT) on a high-load working memory task in the sham stimulation group. In contrast, the anodal stimulation group exhibited faster RTs during the low-load working memory condition. These RT differences were associated with the cortical volumetric interaction between CB-HPC. Literature suggests that anodal stimulation down-regulates the CB and here, those with larger volumes perform more quickly, suggesting the potential need for additional cognitive resources to compensate for cerebellar downregulation or perturbation. This new insight suggests that tDCS can aid in revealing structure-function relationships, due to greater performance variability, especially in young adults. It may also reveal new targets of interest in the study of aging or in diseases where there is also greater behavioral variability.

Quantitative Magnetic Resonance Cerebrospinal Fluid Flow Properties and Neurocognitive Outcomes in Congenital Heart Disease

OBJECTIVES

To determine whether there are differences in pulsatile cerebrospinal fluid (CSF) flow between children and adolescents with congenital heart disease (CHD) and healthy, age- matched peers, and to determine if abnormal CSF flow is associated with abnormal CSF volumes and whether such predicts executive function outcomes.

STUDY DESIGN

CSF flow was measured across the lumen of the aqueduct of Sylvius using cardiac-gated phase-contrast MRI at 3.0T on 60 children and adolescents (CHD=22, healthy controls=38). CSF flow modeled as standard pulsatility characteristics (anterograde and retrograde peak velocities, mean velocity, and velocity variance measurements) and dynamic pulsatility characteristics (each participant's CSF flow deviation from study cohort's consensus flow quantified using root mean squared deviation [RMSD]) were measured. Participants underwent neurocognitive assessments for executive function, focused on inhibition, cognitive flexibility, and working memory domains.

RESULTS

Compared with controls, the CHD group demonstrated greater dynamic pulsatility over the entire cardiac cycle (higher overall flow RMSD: p=0.0353 study-cohort-fitted; p=0.0292 control-only-fitted), but no difference in standard pulsatility measures. However, lower mean velocity (p=0.0323) and lower dynamic CSF flow pulsatility (RMSD p=0.0181 study-cohort-fitted; p=0.0149 control-only-fitted) predicted poor inhibitory executive function outcome.

DISCUSSION

Although the whole CHD group exhibited higher dynamic CSF flow pulsatility compared with controls, the subset of CHD subjects with relatively reduced static and dynamic CSF flow pulsatility had the worst inhibitory domain executive functioning. These findings suggest that altered CSF flow pulsatility may be related to not only brain compensatory mechanisms but also cognitive impairment in CHD.

LF-SynthSeg: Label-Free Brain Tissue-Assisted Tumor Synthesis and Segmentation

Unsupervised brain tumor segmentation is pivotal in realms of disease diagnosis, surgical planning, and treatment response monitoring, with the distinct advantage of obviating the need for labeled data. Traditional methodologies in this domain, however, often fall short in fully capitalizing on the extensive prior knowledge of brain tissue, typically approaching the task merely as an anomaly detection challenge. In our research, we present an innovative strategy that effectively integrates brain tissues' prior knowledge into both the synthesis and segmentation of brain tumor from T2-weighted Magnetic Resonance Imaging scans. Central to our method is the tumor synthesis mechanism, employing randomly generated ellipsoids in conjunction with the intensity profiles of brain tissues. This methodology not only fosters a significant degree of variation in the tumor presentations within the synthesized images but also facilitates the creation of an essentially unlimited pool of abnormal T2-weighted images. These synthetic images closely replicate the characteristics of real tumor-bearing scans. Our training protocol extends beyond mere tumor segmentation; it also encompasses the segmentation of brain tissues, thereby directing the network's attention to the boundary relationship between brain tumor and brain tissue, thus improving the robustness of our method. We evaluate our approach across five widely recognized public datasets (BRATS 2019, BRATS 2020, BRATS 2021, PED and SSA), and the results show that our method outperforms state-of-the-art unsupervised tumor segmentation methods by large margins. Moreover, the proposed method achieves more than 92 of the fully supervised performance on the same testing datasets.