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

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.

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.

Comparison of water exchange measurements between filter-exchange imaging and diffusion time-dependent kurtosis imaging in the human brain

PURPOSE

Filter-exchange imaging (FEXI) and diffusion time (t)-dependent kurtosis imaging (DKI(t)) are two diffusion-based methods that have been proposed for in vivo measurements of water exchange rates. Few studies have directly compared these methods. We aimed to investigate whether FEXI and DKI(t) yield comparable water exchange measurements in the human brain in vivo.

METHODS

Eight healthy volunteers underwent multiple-direction FEXI and DKI(t) acquisitions on a 3T scanner. We performed region of interest (ROI) analysis to determine correlations between FEXI-derived apparent exchange rate (AXR) and DKI(t)-derived reciprocal of exchange time (1 / τ ex $$ 1/{\tau}_{ex} $$ ).

RESULTS

In both white matter (WM) and gray matter (GM), DKI(t) revealed substantial diffusion-time dependence of diffusivity and kurtosis. However, at t ≥ 100 ms, the diffusivity showed weak time dependence. In WM, this time dependence may be due to water exchange between myelin water and "free" water with different T1 values, although other factors, such as remaining restrictive effects from microstructural barriers, cannot be excluded. We found a significant correlation between DKI(t)-derived1 / τ ex $$ 1/{\tau}_{ex} $$ and FEXI-derived AXR in the axial direction within WM. No such correlation was present in GM, although both values showed similar ranges.

CONCLUSION

These results suggest that FEXI and DKI(t) could be sensitive to the same water exchange process only when the diffusion time in DKI(t) is sufficiently long, and only in WM. In both GM and WM, the restrictive effect of microstructure is non-negligible, especially at short diffusion times (<100 ms).

Longitudinal analysis highlights structural changes in grey- and white-matter within military personnel exposed to blast

OBJECTIVE

The purpose of this study was to determine whether gray matter volume and diffusion-based metrics in associated white matter changed in breachers who had neuroimaging performed at two timepoints. A secondary purpose was to compare these changes in a group who had a one-year interval between their imaging timepoints to a group that had a two-year interval between imaging.

METHODS

Between timepoints, clusters with significantly different gray matter volume were used as seeds for reconstruction of associated structural networks using diffusion metrics.

RESULTS

Of 92 eligible participants, 62 had imaging at two timepoints, 36 with a one-year interval between scans and 26 with a two-year interval between scans. A significant effect of time was documented in the midcingulate cortex, but there was no effect of timepoint (1 versus 2 years). The associated white matter in this cluster had three regions with differences in fractional anisotropy compared to baseline, while there was no effect of timepoint (1 versus 2 years).

CONCLUSIONS

This study provides preliminary evidence that military personnel involved in repetitive exposure to sub-concussive blast overpressures may experience changes to both gray matter and white matter structures.

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 will not 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.

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.

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.

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 it 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 3.0 T 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 the root mean squared deviation) 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 root mean squared deviation: P = .0353 for the study cohort fitted; P = .0292 for the control only fitted), but no difference in standard pulsatility measures. However, a lower mean velocity (P = .0323) and lower dynamic CSF flow pulsatility (root mean squared deviation P = .0181 for the study cohort fitted; P = .0149 for the control only fitted) predicted poor inhibitory executive functional outcomes.

DISCUSSION

Although the whole CHD group exhibited higher dynamic CSF flow pulsatility compared with controls, the subset of patients with CHD 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 to cognitive impairment in CHD.

Visual Statistical Learning Alters Low-Dimensional Cortical Architecture

Our brains are in a constant state of generating predictions, implicitly extracting environmental regularities to support later cognition and behavior, a process known as statistical learning (SL). While prior work investigating the neural basis of SL has focused on the activity of single brain regions in isolation, much less is known about how distributed brain areas coordinate their activity to support such learning. Using fMRI and a classic visual SL task, we investigated changes in whole-brain functional architecture as human female and male participants implicitly learned to associate pairs of images, and later, when predictions generated from learning were violated. By projecting individuals' patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space, we found that SL was associated with changes along a single neural dimension describing covariance across the visual-parietal and perirhinal cortex (PRC). During learning, we found regions within the visual cortex expanded along this dimension, reflecting their decreased communication with other networks, whereas regions within the dorsal attention network (DAN) contracted, reflecting their increased connectivity with higher-order cortex. Notably, when SL was interrupted, we found the PRC and entorhinal cortex, which did not initially show learning-related effects, now contracted along this dimension, reflecting their increased connectivity with the default mode and DAN, and decreased covariance with visual cortex. While prior research has linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.

Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli

The Emo-FilM dataset stands for Emotion research using Films and fMRI in healthy participants. This dataset includes emotion annotations by 44 raters for 14 short films with a combined duration of over 2½ hours and recordings of respiration, heart rate, and functional magnetic resonance imaging (fMRI) from a sample of 30 individuals watching the same films. 50 items were annotated including discrete emotions and emotion components from the domains of appraisal, motivation, motor expression, physiological response, and feeling. The ratings had a mean inter-rater agreement of 0.38. The fMRI data acquired at 3 Tesla is includes high-resolution structural and resting state fMRI for each participant. Physiological recordings included heart rate, respiration, and electrodermal activity. This dataset is designed, but not limited, to studying the dynamic neural processes involved in emotion experience. It has a high temporal resolution of annotations, and includes validations of annotations by the fMRI sample. The Emo-FilM dataset is a treasure trove for researching emotion in response to naturalistic stimulation in a multimodal framework.

Liganded magnetic nanoparticles for magnetic resonance imaging of α-synuclein

Aggregation of the protein α-synuclein (α-syn) is the histopathological hallmark of neurodegenerative diseases such as Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), which are collectively known as synucleinopathies. Currently, patients with synucleinopathies are diagnosed by physical examination and medical history, often at advanced stages of disease. Because synucleinopathies are associated with α-syn aggregates, and α-syn aggregation often precedes onset of symptoms, detecting α-syn aggregates would be a valuable early diagnostic for patients with synucleinopathies. Here, we design a liganded magnetic nanoparticle (LMNP) functionalized with an α-syn-targeting peptide to be used as a magnetic resonance imaging (MRI)-based biomarker for α-syn. Our LMNPs bind to aggregates of α-syn in vitro, cross the blood-brain barrier in mice with mannitol adjuvant, and can be used as an MRI contrast agent to distinguish mice with α-synucleinopathy from age-matched, wild-type control mice in vivo. These results provide evidence for the potential of magnetic nanoparticles that target α-syn for diagnosis of synucleinopathies.

The relationships of resting-state brain entropy (BEN), ovarian hormones and behavioral inhibition and activation systems (BIS/BAS)

Brain entropy (BEN) quantifies irregularity, disorder and uncertainty of brain activity. Recent studies have linked BEN, derived from resting-state functional magnetic resonance imaging (rs-fMRI), to cognition, task activation, neuromodulation, and pharmacological interventions. However, it remains unknown whether BEN can reflect the effects of hormonal fluctuations. Furthermore, ovarian hormones are known to modulate behavioral traits, such as inhibitory control and impulsivity, as measured by the Behavioral Inhibition and Activation Systems (BIS/BAS). In this study, we investigated how ovarian hormones influence BEN and BIS/BAS in young adult women. The forty-four participants (mean age = 22.61 ± 2.14 years) were obtained from OpenNeuro in the study. Ovarian hormones including estradiol (E2), progesterone (PROG) and BIS/BAS were acquired before scanning. The voxel-wise BEN maps were calculated from the preprocessed rs-fMRI images. Pearson's correlation and mediation analyses were used to assess the relationships between BEN and ovarian hormones as well as BIS/BAS. Our results revealed a negative correlation between BEN and PROG in frontoparietal network (FPN), including the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC), as well as in the limbic network, encompassing the amygdala, hippocampus, and parahippocampal cortex. In contrast, BEN showed a positive correlation with impulsivity traits measured by the BAS-drive subscale of BAS in the left DLPFC. Additionally, PROG was negatively correlated with impulsivity traits measured by BAS-drive. Results from mediation analysis demonstrated that PROG reduces impulsivity, as measured by BAS-drive, by decreasing BEN in the left DLPFC and subsequently increasing functional connectivity (FC) within this region. These findings provide the first evidence that BEN reflects the influence of PROG on brain function and behavior. Furthermore, they elucidate the neural mechanisms through which PROG modulates impulsivity traits measured by BAS-drive: PROG enhances the temporal coherence (decreased entropy) of neural activity in the left DLPFC, which in turn increases temporal synchronization (increased FC) within this region during resting-state, and then enhances executive control functions, thereby negatively regulating impulsivity. These findings provide new insights into our understanding of the effects of ovarian hormones on the brain and behavior in women.

Combined peritumoral radiomics and clinical features predict 12-month progression free survival in glioblastoma

PURPOSE

Analyzing post-treatment MRIs from glioblastoma patients can be challenging due to similar radiological presentations of disease progression and treatment effects. Identifying radiomics features (RFs) revealing progressive glioblastoma can contribute to an improved evaluation of the response assessment.

METHODS

3 Tesla MRI data from 560 glioblastoma patients (mean age 58.1 years) after treatment according to Stupp's protocol were analyzed retrospectively. A total of 418 RFs were extracted from contrast-enhancing tumors, non-enhancing lesions, peritumoral regions (PeriCET) and normal-appearing white matter as regions of interest using PyRadiomics. Dataset was initially split into a training (70%) and a validation (30%) cohort. The training cohort was used for feature selection and model-optimization. Logistic regression was used as a machine-learning model to identify patients with progression-free survival (PFS) as defined by the RANO criteria at 6 and 12 months after treatment. Models were trained with (i) clinical features only, (ii) RFs only, and (iii) a combination of clinical and radiomics features. The performance of each model was evaluated with the validation cohort.

RESULTS

The predictive performances of the model trained with only RFs from the PeriCET were AUC = 0.61 (95%-CI: 0.51-0.70) and AUC = 0.71 (95%-CI: 0.61-0.81) for 6-months and 12-months PFS respectively. Combining clinical and RFs from PeriCET resulted in overall best performance in predicting patients with progression within 12-months AUC = 0.75 (95%-CI: 0.65-0.85).

CONCLUSION

RFs from peritumoral region combined with clinical features including age, sex, and MGMT status can identify patients with 12-months PFS, suggesting the important role of peritumoral regions for the progression of glioblastoma.

Brain network connectivity underlying neuropsychiatric symptoms in prodromal Lewy body dementia

Neuropsychiatric symptoms (NPS) are prevalent, emerge early, and are associated with poorer outcomes in Lewy body dementia (LBD). Research suggests NPS may reflect LBD-related dysfunction in distributed neuronal networks. This study investigated NPS neural correlates in prodromal LBD using resting-state functional MRI. Fifty-seven participants were included with mild cognitive impairment (MCI) with Lewy bodies (MCI-LB, n = 28) or Parkinson's disease (PD-MCI, n = 29). Functional MRI assessed connectivity within five resting-state networks: primary visual, dorsal attention, salience, limbic, and default mode networks. NPS were measured using the Neuropsychiatric Inventory. Principal component analyses identified three neuropsychiatric factors: affective disorder (apathy, depression), psychosis (delusions, hallucinations) and anxiety. Seed-to-voxel connectivity maps were analysed to determine associations between NPS and network connectivity. In PD-MCI, affective symptoms and anxiety were associated with greater connectivity between limbic orbitofrontal cortex and default mode areas, including medial prefrontal cortex, subgenual cingulate and precuneus, and weaker connectivity between limbic orbitofrontal cortex and the brainstem and between the salience network and medial prefrontal cortex (all pFWE<0.001). Psychosis severity in PD-MCI correlated with connectivity across multiple networks (all pFWE<0.001). In MCI-LB, no significant correlations were found between NPS severity and network connectivity. However, participants with anxiety demonstrated a trend towards greater connectivity within medial prefrontal areas than those without (pFWE=0.046). Altered connectivity within and between networks associated with mood disorders may explain affective and anxiety symptoms in PD-MCI. Neural correlates of NPS in MCI-LB, however, remain unclear, highlighting the need for research in larger, more diverse LBD populations to identify symptomatic treatment targets.

Individual variation in the chimpanzee arcuate fasciculus predicts vocal and gestural communication

Whether language has its evolutionary origins in vocal or gestural communication has long been a matter of debate. In humans, the arcuate fasciculus, a major fronto-temporal white matter tract, is left-lateralized, is larger than in nonhuman apes, and is linked to language. However, the extent to which the arcuate fasciculus of nonhuman apes is linked to vocal and/or manual communication is currently unknown. Here, using probabilistic tractography in 67 chimpanzees (45 female, 22 male), we report that the chimpanzee arcuate fasciculus is not left-lateralized at the population level, in marked contrast with humans. However, individual variation in the anatomy and leftward asymmetry of the chimpanzee arcuate fasciculus is associated with individual variation in the use of both communicative gestures and communicative sounds under volitional orofacial motor control. This indicates that the arcuate fasciculus likely supported both vocal and gestural communication in the chimpanzee/human last common ancestor, 6-7 million years ago.

A neurocomputational account of the link between social perception and social action

People selectively help others based on perceptions of their merit or need. Here, we develop a neurocomputational account of how these social perceptions translate into social choice. Using a novel fMRI social perception task, we show that both merit and need perceptions recruited the brain's social inference network. A behavioral computational model identified two non-exclusive mechanisms underlying variance in social perceptions: a consistent tendency to perceive others as meritorious/needy (bias) and a propensity to sample and integrate normative evidence distinguishing high from low merit/need in other people (sensitivity). Variance in people's merit (but not need) bias and sensitivity independently predicted distinct aspects of altruism in a social choice task completed months later. An individual's merit bias predicted context-independent variance in people's overall other-regard during altruistic choice, biasing people toward prosocial actions. An individual's merit sensitivity predicted context-sensitive discrimination in generosity toward high and low merit recipients by influencing other- and self-regard during altruistic decision-making. This context-sensitive perception-action link was associated with activation in the right temporoparietal junction. Together, these findings point toward stable, biologically based individual differences in perceptual processes related to abstract social concepts like merit, and suggest that these differences may have important behavioral implications for an individual's tendency toward favoritism or discrimination in social settings.

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.

The Beholder's Share: Bridging art and neuroscience to study individual differences in subjective experience

Our experience of the world is inherently subjective, shaped by individual history, knowledge, and perspective. Art offers a framework within which this subjectivity is practiced and promoted, inviting viewers to engage in interpretation. According to art theory, different forms of art-ranging from the representational to the abstract-challenge these interpretive processes in different ways. Yet, much remains unknown about how art is subjectively interpreted. In this study, we sought to elucidate the neural and cognitive mechanisms that underlie the subjective interpretation of art. Using brain imaging and written descriptions, we quantified individual variability in responses to paintings by the same artists, contrasting figurative and abstract paintings. Our findings revealed that abstract art elicited greater interindividual variability in activity within higher-order, associative brain areas, particularly those comprising the default-mode network. By contrast, no such differences were found in early visual areas, suggesting that subjective variability arises from higher cognitive processes rather than differences in sensory processing. These findings provide insight into how the brain engages with and perceives different forms of art and imbues it with subjective interpretation.

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.

Unveiling the neural network involved in mentally projecting the self through episodic autobiographical memories

Episodic autobiographical memory involves the ability to travel along the mental timeline, so that events of our own life can be recollected and re-experienced. In the present study, we tested the neural underpinnings of mental travel across past and future autobiographical events by using a spatiotemporal interference task. Participants were instructed to mentally travel across past and future personal (Episodic Autobiographical Memories; EAMs) and Public Events (PEs) during Functional Magnetic Resonance Imaging (fMRI). We found that a distributed network of brain regions (i.e., occipital, temporal, parietal, frontal, and subcortical regions) is implicated in mental projection across past and future independently from the memory category (EAMs or PEs). Interestingly, we observed that most of these regions exhibited a neural modulation as a function of the lifetime period and/or as a function of the compatibility with a back-to-front mental timeline, specifically for EAMs, indicating the key role of these regions in representing the temporal organization of personal but not public events. Present findings provide insights into how personal events are temporally organized within the human brain.

The self-supervised fitting method based on similar neighborhood information of voxels for intravoxel incoherent motion diffusion-weighted MRI

BACKGROUND

The intravoxel incoherent motion (IVIM) parameter estimation is affected by noise, while existing CNN-based fitting methods utilize neighborhood spatial features around voxels to obtain more robust parameters. However, due to the heterogeneity of tissue, neighborhood features with low similarity can lead to excessively smooth parameter maps and even loss of tissue details.

PURPOSE

To propose a novel neural network fitting approach, IVIM-CNNsimilar, which utilizes similar neighborhood information of voxels to assist in the estimation of IVIM parameters in diffusion-weighted imaging (DWI).

METHODS

The proposed fitting model is based on convolutional neural network (CNN), which first identifies the similar neighborhoods of voxels through cluster analysis and then uses CNN to learn the spatial features of similar neighborhoods to reduce the impact of noise on the parameter estimation of the voxel. To evaluate the performance of the proposed method, comparisons were conducted with the least squares (LSQ), Bayesian, PI-DNN, and IVIM-CNNunet algorithms on both simulated and in vivo brains, including 23 healthy brains and three brain tumors, in terms of root mean square error (RMSE) of IVIM parameters and the parameter contrast ratio between the tumor and normal regions.

RESULTS

The CNN-based methods, such as IVIM-CNNsimilar and IVIM-CNNunet, yield smoother parameter maps compared to voxel-based methods like nonlinear least squares, segmented nonlinear least squares, Bayesian, and PI-DNN. Additionally, the IVIM-CNNsimilar retains more local tissue details while maintaining smoothness of parameter maps compared to the IVIM-CNNunet. In simulated experiments, IVIM-CNNsimilar outperforms IVIM-CNNunet in terms of parameter estimation accuracy (SNR = 30; RMSE [ D $D$ ] = 0.0168 vs. 0.0253; RMSE ( F $F$ ) = 0.0001 vs. 0.0002; RMSE [D ∗ $D^{*}$ ] = 0.0266 vs. 0.0416). In addition, compared with other methods, the proposed IVIM-CNNsimilar is more robust to noise, which is reflected in the lower RMSE of each parameter at different SNRs. For in vivo brains, compared to other methods, IVIM-CNNsimilar achieved the highest PCR for most parameters when comparing the normal and tumor regions.

CONCLUSIONS

The IVIM-CNNsimilar method uses similar neighborhood information to assist IVIM parameter fitting by reducing the impact of noise on voxel parameter estimation, thereby improving the accuracy of parameter estimation and increasing the potential for IVIM clinical application.

Cerebral hypoperfusion, brain structural integrity, and cognitive impairment in older APOE4 carriers

Cerebral blood flow (CBF) deficits, cognitive decline, and brain structural changes have been reported in older adults with and without apolipoprotein E-e4 (APOE4)-related risk for dementia. However, it remains unclear whether brain structural changes mediate the effects of hypoperfusion on cognitive impairment in APOE4 carriers and non-carriers. We studied 166 (60-89 years) APOE4 carriers (ε3/ε4 or ε4/ε4) and APOE3 homozygotes (e3/e3) with and without cognitive impairment by clinical dementia rating (CDR) and neuropsychological testing. Pseudocontinuous arterial spin-labeling-MRI assessed regional CBF, and T1-anatomical and diffusion-MRI assessed structural integrity. Mediation analyses examined relationships among grey matter CBF, grey matter volume, and white matter integrity in regions underlying impairment in distinct cognitive ability domains. APOE4 carriers with global/memory impairment (CDR 0.5) exhibited decreased CBF in the posterior cingulate, decreased grey matter volume in the hippocampus, parahippocampal gyrus, and posterior cingulate, and decreased white matter integrity in the cingulum relative to APOE4 carriers with no impairment (CDR 0). Mediation analysis in APOE4 carriers indicated decreased posterior cingulate CBF effects on global/memory impairment were mediated by decreased cingulum integrity. In the combined APOE4 and APOE3 carriers sample, there were direct effects of frontal and inferior parietal CBF and superior longitudinal fasciculus integrity on attention/executive impairment. There were also direct effects of left inferior frontal CBF on language impairment. Findings suggest links between hypoperfusion and brain structural integrity underlying global/memory impairment in APOE4 carriers. Independent CBF relationships with structural integrity are also identified across genotypes and impairment domains.

Cognitive reserve in young-onset cognitive impairment

Cognitive reserve (CR) reflects brain's resilience to pathology, enabling to maintain function despite structural damage. This study investigates its role in young-onset cognitive impairment (<65 years) beyond brain integrity and neurodegeneration. Participants underwent neuropsychological assessment - including the Cognitive Reserve Index questionnaire (CRIq) -, magnetic resonance imaging (MRI), and blood neurofilaments light-chain (NfLs) measurement. Scores of global cognition and domain-specific cognition were derived from Principal Component Analyses of neuropsychological results. Linear regression models estimated CR's contribution to global and domain-specific cognition, alongside age, sex, MRI measures, and NfLs as predictors. Among the 115 participants, global cognition was significantly explained by CR [effect size (ES) = 0.229], grey matter volume (ES = 0.348), and NfLs (ES = -0.302). The effect of CR was prominent on language and attentional-executive functions: while the CRIq subscore related to education predicted performance in both these domains, the subscore related to leisure activities was positively associated with the language domain only. These findings highlight CR's protective role in young-onset cognitive impairment, particularly for non-amnestic cognitive domains. Since a high CR can mask or compensate for neurological cognitive disorders delaying its diagnosis, our results suggest that measures of CR, including time spent on leisure activities, should be considered when interpreting neuropsychological tests.

A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain

Magnetic resonance imaging (MRI) is a powerful modality for investigating abnormal developmental patterns in utero. However, since it is not the first-line diagnostic tool in this sensitive population, data remain scarce and heterogeneous across scanners and hospitals. To address this, we present a novel dataset of synthetic images representative of real fetal brain MRI. Our dataset comprises 594 two-dimensional, low-resolution series of T2-weighted images corresponding to 78 developing human fetal brains between 20.0 and 34.8 weeks of gestational age. Data are generated using a new version of the Fetal Brain MR Acquisition Numerical phantom (FaBiAN) to account for local white matter heterogeneities throughout maturation. Both healthy and pathological anatomies are simulated with standard clinical settings. Two independent radiologists qualitatively assessed the realism of the simulated images. A quantitative analysis confirms an enhanced fidelity compared to the original version of the software, with further validation through its applicability to fetal brain tissue segmentation. The cohort is publicly available to support the continuous endeavor of developing advanced post-processing methods as well as cutting-edge artificial intelligence models.

The human brainstem's red nucleus was upgraded to support goal-directed action

The red nucleus, a large brainstem structure, coordinates limb movement for locomotion in quadrupedal animals. In humans, its pattern of anatomical connectivity differs from that of quadrupeds, suggesting a different purpose. Here, we apply our most advanced resting-state functional connectivity based precision functional mapping in highly sampled individuals (n = 5), resting-state functional connectivity in large group-averaged datasets (combined n ~ 45,000), and task based analysis of reward, motor, and action related contrasts from group-averaged datasets (n > 1000) and meta-analyses (n > 14,000 studies) to precisely examine red nucleus function. Notably, red nucleus functional connectivity with motor-effector networks (somatomotor hand, foot, and mouth) is minimal. Instead, connectivity is strongest to the action-mode and salience networks, which are important for action/cognitive control and reward/motivated behavior. Consistent with this, the red nucleus responds to motor planning more than to actual movement, while also responding to rewards. Our results suggest the human red nucleus implements goal-directed behavior by integrating behavioral valence and action plans instead of serving a pure motor-effector function.

Dorsal-Ventral Reinforcement Learning Network Connectivity and Incentive-Driven Changes in Exploration

Probabilistic reinforcement learning (RL) tasks assay how individuals make decisions under uncertainty. The use of internal models (model-based) or direct learning from experiences (model-free), and the degree of choice stochasticity across alternatives (i.e., exploration), can all be influenced by the state space of the decision-making task. There is considerable individual variation in the balance between model-based and model-free control during decision-making, and this balance is affected by incentive motivation. The effect of variable reward incentives on the arbitration between model-based and model-free learning remains understudied, and individual differences in neural signatures and cognitive traits that moderate the effect of reward on model-free/model-based control are unknown. Here we combined a two-stage decision-making task utilizing differing reward incentives with computational modeling, neuropsychological tests, and neuroimaging to address these questions. Results showed the prospect of greater reward decreased exploration of alternative options and increased the balance toward model-based learning. These behavioral effects were replicated across two independent datasets including both sexes. Individual differences in processing speed and analytical thinking style affected how reward altered the dependence on both systems. Using a systems neuroscience-inspired approach to resting-state functional connectivity, we found reduced exploration of the options during the first stage of our task under high relative to low incentives was predicted by increased cross-network coupling between ventral and dorsal RL circuitry. These findings suggest that integrity of functional connections between stimulus valuation (ventral) and action valuation (dorsal) RL networks is associated with changes in the balance between explore-exploit decisions under changing reward incentives.

Age-related white matter alterations in children with neurofibromatosis type 1: a diffusion MRI tractography study

Neurofibromatosis type 1 (NF1) is a genetic condition affecting 1 in 3,000 children, often leading to learning challenges, including deficits in attention, executive function, and working memory. While white matter pathways play a crucial role in these cognitive processes, they are not well-characterized in NF1. In this retrospective cohort study, we used diffusion MRI tractography to examine the microstructure of major white matter pathways in 20 children with NF1 (ages 1-18 years) compared to 20 age- and sex-matched controls. An automated approach was used to identify and extract mean diffusivity (MD) and fractional anisotropy (FA) of eight cerebral white matter pathways bilaterally and the anterior and posterior part of the corpus callosum. Compared to controls, children with NF1 had significantly increased MD and significantly decreased FA in multiple white matter pathways including the anterior thalamic radiation, cingulate, uncinate fasciculus, inferior fronto-occipital fasciculus, arcuate fasciculus, and corticospinal tract. Differences in MD and FA remained significant after controlling for intracranial volume. In addition, MD and FA differences between children with NF1 and controls were greater at younger than older ages. These findings have implications for understanding the etiology of the neurocognitive deficits seen in many children with NF1.

Deep learning-based automatic segmentation of brain structures on MRI: A test-retest reproducibility analysis

Objective

The aim of our study was to assess the reproducibility of deep learning-based automatic segmentation of brain structures in MRI scans across different scanner types and magnetic field strengths, particularly focusing on the comparison between 1.5 T and 3 T MRI scanners.

Methods

Our analysis encompassed a comprehensive examination of MRI images, focusing on the consistency of volumetric segmentation. We utilized advanced deep learning techniques with human-in-the-loop as a part of the workflow for segmenting brain structures and compared results across subsequent scans using the same and different scanner types.

Results

Our findings revealed high consistency in volumetric segmentation when comparing scans conducted on the same type of scanner (1.5 T to 1.5 T or 3 T to 3 T). The study revealed slightly better segmentation results for 1.5 T scanners compared to 3 T scanners when each was used independently. However, cross-comparisons between different scanner types (1.5 T vs. 3 T) demonstrated slightly less consistency, highlighting the influence of magnetic field strength on segmentation accuracy.

Conclusion

This study emphasizes the necessity of using the same scanner type and protocol for reliable MRI studies, particularly for brain atrophy monitoring. The high repeatability of deep learning-based segmentation under these conditions confirms its efficacy for clinical and research applications.

Diffusion MRI tractography with along-tract profiling reveals subtle neurodevelopmental differences between moderate and late preterm infants

PURPOSE

Moderately preterm (MP) infants (32-33 weeks' gestation) are at increased risk for developmental problems compared to late preterm (LP) infants (34-36 weeks' gestation). Fiber bundle tractography remains an unexplored avenue to understanding this risk-difference between MP and LP infants. This study aimed to examine along-tract profile differences between MP and LP infants at term-equivalent age (TEA).

METHODS

Ninety-five infants (31 MP and 64 LP), born between November 2020 and March 2023, underwent MRI around TEA (40-44 weeks postmenstrual age). MRI included T2-weighted imaging and diffusion MRI (dMRI) with b-values 800 and 2000 s/mm2 (single shell). dMRI scans were preprocessed to reduce common artifacts. For all infants, 15 fiber bundles were reconstructed using TractSeg and along-tract profiles, expressed as fractional anisotropy (FA) and mean diffusivity (MD), and were compared between MP and LP infants using tractometry.

RESULTS

Reconstructions with TractSeg demonstrated shape, position, and orientation of fiber bundles consistent with known neuroanatomy. FA and MD profiles were not significantly different between MP and LP infants. However, alternating trends towards along-tract profile differences between MP and LP infants were observed for multiple bundles. Wide 95% confidence intervals indicated substantial variability in fiber bundle organization within groups.

CONCLUSION

Although not significant, along-tract differences between MP and LP infants suggest subtle alterations in white matter maturation. These findings indicate along-tract variability as potential focus for future research aimed at uncovering the mechanisms underlying early maturational differences and their potential role in later neurodevelopmental challenges encountered in moderate-late preterm infants.

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.

Abnormal sensory processing cortex in insomnia disorder: a degree centrality study

Insomnia disorder is a significant global health concern. This research aimed to explore the pathogenesis of insomnia disorder using static and dynamic degree centrality methods at the voxel level. A total of 29 patients diagnosed with insomnia disorder and 28 healthy controls were ultimately included to examine differences in degree centrality between the two groups. Additionally, the relationship between altered degree centrality values and various clinical indicators was analyzed. The results revealed that patients with insomnia disorder exhibited higher static degree centrality in brain regions associated with sensory processing, such as the occipital gyrus, inferior temporal gyrus, and supramarginal gyrus. In contrast, lower static degree centrality was observed in the parahippocampal gyrus, amygdala, insula, and thalamus. Changes in dynamic degree centrality were identified in regions including the parahippocampal gyrus, anterior cingulum, medial superior frontal gyrus, inferior parietal gyrus, and precuneus. Notably, a negative correlation was found between dynamic degree centrality in the inferior parietal gyrus and the Pittsburgh Sleep Quality Index, while a positive correlation was observed between static degree centrality in the inferior temporal gyrus and the Hamilton Depression Scale. These findings suggest that dysfunction in centrality within the sensory processing cortex and subcortical nuclei may be associated with the sleep-wake imbalance in individuals with insomnia disorder, contributing to our understanding of hyperarousal mechanisms in insomnia. Moreover, the abnormalities observed in the default mode network and the salience network provide insights into understanding the neuropathogenesis of insomnia from both static and dynamic centrality perspectives. The clinical trial registration number: ChiCTR2200058768. Date: 2022-04-16.

Cortical Surface Spatial Analysis Reveals Altered Brain Functional Network Topology in T2DM With Mild Cognitive Impairment

OBJECTIVE

Approximately 45.0% of patients who have type 2 diabetes mellitus (T2DM) exhibit mild cognitive impairment (MCI). However, the specific alternations in T2DM with MCI (T2DM-MCI)-related brain functional networks (BFN) remain unclear. Therefore, the present study aimed to investigate the alterations in the topological properties of BFN in T2DM patients with and without MCI, utilizing a cortical surface-based graph theory analysis of resting-state functional magnetic resonance imaging data.

METHODS

Neuropsychological performance and topological properties of BFNs were determined in 64 T2DM-MCI patients, 58 T2DM patients without MCI (T2DM-noMCI), and 78 healthy controls (HC). Moreover, we conducted the correlation and stepwise multiple linear regression analysis.

RESULTS

The T2DM-MCI group showed increased global efficiency and decreased shortest path length compared to T2DM-noMCI. In the left posterior cingulate, the T2DM-MCI group exhibited higher nodal efficiency compared to the T2DM-noMCI group. Additionally, both degree centrality and nodal efficiency in the T2DM-noMCI group were significantly lower than in the HC. Degree centrality and nodal efficiency in the left basal ganglia were elevated in both T2DM groups. Alterations in these regions were related to cognitive function scores.

CONCLUSION

The alterations in nodal properties of the left basal ganglia suggest that nodal attributes in this region may be involved in the neurophysiopathological mechanisms of brain injury in T2DM. Conversely, the alterations of nodal efficiency in the left posterior cingulate gyrus indicate its potential as a neuroimaging biomarker of cognitive impairment in T2DM patients.

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.

Trial-level Representational Similarity Analysis

Neural representation refers to the brain activity that stands in for one's cognitive experience, and in cognitive neuroscience, the principal method to studying neural representations is representational similarity analysis (RSA). The classic RSA (cRSA) approach examines the overall quality of representations across numerous items by assessing the correspondence between two representational similarity matrices (RSMs): one based on a theoretical model of stimulus similarity and the other based on similarity in measured neural data. However, because cRSA cannot model representation at the level of individual trials, it is fundamentally limited in its ability to assess subject-, stimulus-, and trial-level variances that all influence representation. Here, we formally introduce trial-level RSA (tRSA), an analytical framework that estimates the strength of neural representation for singular experimental trials and evaluates hypotheses using multi-level models. First, we verified the correspondence between tRSA and cRSA in quantifying the overall representation strength across all trials. Second, we compared the statistical inferences drawn from both approaches using simulated data that reflected a wide range of scenarios. Compared to cRSA, the multi-level framework of tRSA was both more theoretically appropriate and significantly sensitive to true effects. Third, using real fMRI datasets, we further demonstrated several issues with cRSA, to which tRSA was more robust. Finally, we presented some novel findings of neural representations that could only be assessed with tRSA and not cRSA. In summary, tRSA proves to be a robust and versatile analytical approach for cognitive neuroscience and beyond.

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.

A qBOLD-based clinical radiomics-integrated model for predicting isocitrate dehydrogenase-1 mutation in gliomas

BACKGROUND

Quantitative blood oxygenation level-dependent (qBOLD) technique can be applied to detect tissue damage and changes in hemodynamic in gliomas. It is not known whether qBOLD-based radiomics approaches can improve the prediction of isocitrate dehydrogenase-1 (IDH-1) mutation.

PURPOSE

To establish a qBOLD-based clinical radiomics-integrated model for predicting IDH-1 mutation in gliomas.

METHODS

A total of 125 patients of grade II-IV glioma (IDH1 mutation: IDH1 wild-type = 50:75) were divided into a training group (n = 87) and a validation group (n = 38). Contrast enhanced T1-weighted (CE-T1W), T2-weighted (T2W), and 3D multi-gradient-recalled-echo (MGRE) images were acquired. Radiomics features were extracted from the region of interests of each image. The feature selection and support vector machine radiomics models were established for each sequence. A clinical radiomics-integrated model was finally constructed combining the best radiomics model with age. The predictive effectiveness of the models was evaluated by area under the receiver operating characteristic curve (AUC). Brier score was used to assess overall predictive performance. Decision curve analysis and calibration curve were also conducted.

RESULTS

The best radiomics model was CE-T1W + T2W + qBOLD with AUCs of 0.823 (95% confidence interval [CI]: 0.743-0.831) in the training group and 0.751 (95% CI: 0.655-0.794) in the validation group, respectively. The clinical radiomics-integrated model, incorporating the best radiomics model with age, showed the best predictive effectiveness with AUCs of 0.851 (95% CI 0.759-0.918) in the training group and 0.786 (95% CI 0.622-0.902) in the validation group.

CONCLUSION

A clinical radiomics-integrated model that combined qBOLD parametric maps, CE-T1W, and T2W images with age achieved promising performance for predicting IDH1 mutation in glioma patients.

The effects of intermittent theta burst stimulation (iTBS) on resting-state brain entropy (BEN)

Intermittent theta burst stimulation (iTBS), a novel protocol within repetitive transcranial magnetic stimulation (rTMS), has shown superior therapeutic effects for depression compared to conventional high-frequency rTMS (HF-rTMS). However, the neural mechanisms underlying iTBS remain poorly understood. Brain entropy (BEN), a measure of the irregularity of brain activity, has recently emerged as a promising marker for regional brain function and has demonstrated sensitivity to depression and HF-rTMS. Given its potential, BEN may help elucidate the mechanisms of iTBS. In this study, we computed BEN using resting-state fMRI data from sixteen healthy participants obtained from OpenNeuro. Participants underwent iTBS over the left dorsolateral prefrontal cortex (L-DLPFC) at two different intensities (90 ​% and 120 ​% of resting motor threshold (rMT)) on separate days. We used a 2 ​× ​2 repeated measures analysis of variance (ANOVA) to analyze the interaction between iTBS stimulation intensity and the pre- vs. post-stimulation effects on BEN and paired sample t-tests to examine the specific BEN effects of iTBS at different intensities. Additionally, spatial correlation analysis was conducted to determine whether iTBS altered the baseline coupling between BEN and neurotransmitter receptors/transporters, to investigate potential neurotransmitter changes induced by iTBS. Our results indicate that subthreshold iTBS (90 ​% rMT) reduced striatal BEN, while suprathreshold iTBS (120 ​% rMT) increased it. Subthreshold iTBS led to changes in the baseline coupling between BEN and several neurotransmitter receptor/transporter maps, primarily involving serotonin (5-HT), cannabinoid (CB), acetylcholine (ACh), and glutamate (Glu). Our findings suggest that BEN is sensitive to the effects of iTBS, with different stimulation intensities having distinct effects on neural activity. Notably, subthreshold iTBS may offer more effective stimulation. This research highlights the crucial role of stimulation intensity in modulating brain activity and lays the groundwork for future clinical studies focused on optimizing therapeutic outcomes through precise stimulation intensity.

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.

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.

Uncinate Fasciculus Lesion Burden and Anxiety in Multiple Sclerosis

Importance

Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects 2.4 million people worldwide, and up to 60% experience anxiety.

Objective

To investigate whether anxiety in MS is associated with white matter lesion burden in the uncinate fasciculus (UF).

Design, Setting, and Participants

This was a retrospective case-control study of participants aged 18 years or older diagnosed with MS by an MS specialist and identified from the electronic medical record at a single-center academic medical specialty MS clinic in Pennsylvania. Participants received research-quality 3-Tesla magnetic resonance neuroimaging as part of MS clinical care from January 6, 2010, to February 14, 2018. After excluding participants with poor image quality, participants were stratified into 3 groups naturally balanced in age and sex: (1) MS without anxiety, (2) MS with mild anxiety, and (3) MS with severe anxiety. Analyses were performed from June 1 to September 30, 2024.

Exposure

Anxiety diagnosis and anxiolytic medication.

Main Outcomes and Measures

Main outcomes were whether patients with severe anxiety had greater lesion burden in the UF than those without anxiety and whether higher anxiety severity was associated with greater UF lesion burden. Generalized additive models were used, with the burden of lesions (eg, proportion of fascicle impacted) within the UF as the outcome measure and sex, spline of age, and total brain volume as covariates.

Results

Among 372 patients with MS (mean [SD] age, 47.7 [11.4] years; 296 [80%] female), after anxiety phenotype stratification, 99 (27%) had no anxiety (mean [SD] age, 49.4 [11.7] years; 74 [75%] female), 249 (67%) had mild anxiety (mean [SD] age, 47.1 [11.1] years; 203 [82%] female), and 24 (6%) had severe anxiety (mean [SD] age, 47.0 [12.2] years; 19 [79%] female). UF burden was higher in patients with severe anxiety compared with no anxiety (T = 2.01 [P = .047]; Cohen f2, 0.19 [95% CI, 0.08-0.52]). Additionally, higher mean UF burden was associated with higher severity of anxiety (T = 2.09 [P = .04]; Cohen f2, 0.10 [95% CI, 0.05-0.21]).

Conclusions and Relevance

In this case-control study of UF lesion burden and anxiety in MS, overall lesion burden in the UF was associated with the presence and severity of anxiety. Future studies linking white matter lesion burden in the UF with treatment prognosis are warranted.

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.

Preferred spatial frequency covaries with cortical magnification in human primary visual cortex

Primary visual cortex (V1) has played a key role in understanding the organization of cerebral cortex. Both structural and functional properties vary sharply throughout the human V1 map. Despite large variation, underlying constancies computed from the covariation pattern of V1 properties have been proposed. Such constancies would imply that V1 is composed of multiple identical units whose visual properties differ only due to differences in their inputs. To test this, we used fMRI to investigate how V1 cortical magnification and preferred spatial frequency covary across eccentricity and polar angle, and across individual observers (n=40). The two properties correlated across individuals, such that those with higher overall cortical magnification (i.e., larger V1 maps) had higher preferred spatial frequency (integrated across the map). Although correlated, the two properties were not proportional, and hence their ratio (mm of cortex per stimulus cycle) was not constant. Cortical magnification and preferred spatial frequency were strongly correlated across eccentricity and across polar angle, however their relation differed between these dimensions: they were proportional across eccentricity but not polar angle. The constant ratio of cortical magnification to preferred spatial frequency across eccentricity suggests a shared underlying cause of variation in the two properties, e.g., the gradient of retinal ganglion cell density across eccentricity. In contrast, the deviation from proportionality around polar angle implies that cortical variation differs from that in retina along this dimension. Thus, a constancy hypothesis is supported for one of the two spatial dimensions of V1, highlighting the importance of examining the full 2D-map, in multiple individuals, to understand how V1 is organized.

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.

Neurocognitive correlates of cerebral mitochondrial function and energy metabolism using phosphorus magnetic resonance spectroscopy in older adults

The goal of the current study was to learn about the role of cerebral mitochondrial function on cognition. Based on established cognitive neuroscience, clinical neuropsychology, and cognitive aging literature, we hypothesized mitochondrial function within a focal brain region would map onto cognitive behaviors linked to that brain region. To test this hypothesis, we used phosphorous (31P) magnetic resonance spectroscopy (MRS) to derive indirect markers of mitochondrial function and energy metabolism across two regions of the brain (bifrontal, left temporal). We administered cognitive tasks sensitive to frontal-executive or temporal-hippocampal systems to a sample of 70 cognitively unimpaired older adults with subjective memory complaints and a first-degree family history of Alzheimer's disease and predicted better executive function and recent memory performance would be related to greater frontal and temporal 31P MRS indirect markers, respectively. Results of separate hierarchical linear regressions indicated better recent memory scores were related to 31P MRS indirect markers of lower static energy and higher energy reserve within the left temporal voxel; these findings were associated with moderate effect sizes. Contrary to predictions, executive function performance was unrelated to 31P MRS indirect markers within the bilateral frontal voxel, which may reflect a combination of theoretical and/or methodological issues. Findings represent a snapshot of the relationship between cognition and 31P MRS indirect markers of mitochondrial function, providing potential avenues for future work investigating mitochondrial underpinnings of cognition. 31P MRS may provide a sensitive neuroimaging marker for differences in aspects of memory among persons at-risk for mild cognitive impairment or dementia.

Revealing the Multivariate Associations Between Autistic Traits and Principal Functional Connectome

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition characterized by a spectrum of behavioral and cognitive traits. As the characteristics of ASD are highly heterogeneous across individuals, a dimensional approach that overcomes the limitation of the categorical approach is preferred to reveal the symptomatology of ASD. Previous neuroimaging studies demonstrated strong links between large-scale brain networks and autism phenotypes. However, the existing studies have primarily focused on univariate association analysis, which limits our understanding of autism connectopathy. Using resting-state functional magnetic resonance imaging data from 309 participants (168 individuals with ASD and 141 typically developing controls) across a discovery dataset and two independent validation datasets, we identified multivariate associations between high-dimensional neuroimaging features and diverse phenotypic measures (20 or 7 measures). We generated low-dimensional representations of functional connectivity (i.e., gradients) and assessed their multivariate associations with autism-related phenotypes of social, behavioral, and cognitive problems using sparse canonical correlation analysis (SCCA). We selected three functional gradients that represented the cortical axes of the sensory-transmodal, motor-visual, and multiple demand-rests of the brain. The SCCA revealed multivariate associations between gradients and phenotypic measures, which were noted as linked dimensions. We identified three linked dimensions: the links between (1) the first gradient and social impairment, (2) the second and internalizing/externalizing problems, and (3) the third and metacognitive problems. Our findings were partially replicated in two independent validation datasets, indicating robustness. Multivariate association analysis linking high-dimensional neuroimaging and phenotypic features may offer promising avenues for establishing a dimensional approach to autism diagnosis.

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.

Quantifying brain-wide cerebrospinal fluid flow dynamics using slow-flow-sensitized phase-contrast MRI

Cerebrospinal fluid (CSF) flow is a key component of the brain's waste clearance system. However, our understanding of CSF flow in the human brain, particularly within the brain-wide subarachnoid space (SAS), is limited due to a lack of non-invasive tools for measuring slow flow. Here, we propose a CSF flowmetry technique using phase-contrast MRI combined with a slow-flow-sensitized acquisition. It achieves high sensitivity in measuring slow CSF flow (e.g., 100 μm/s), and enables quantitative measurement of the velocity and direction with whole-brain coverage, spanning from ventricles to SAS. Our proof-of-concept results demonstrate repeatable flow measurements and show that cardiac pulsation induces coherent CSF flow changes within the SAS. Our data also suggest that cardiac pulsation has a stronger driving effect on brain-wide CSF flow compared to respiration. This technique provides a valuable tool for investigating CSF dynamics and pathways to advance a holistic understanding of brain-wide CSF flow.

Two Axes of White Matter Development

Despite decades of neuroimaging research, how white matter develops along the length of major tracts in humans remains unknown. Here, we identify fundamental patterns of white matter maturation by examining developmental variation along major, long-range cortico-cortical tracts in youth ages 5-23 years using diffusion MRI from three large-scale, cross-sectional datasets (total N = 2,710). Across datasets, we delineate two replicable axes of human white matter development. First, we find a deep-to-superficial axis, in which superficial tract regions near the cortical surface exhibit greater age-related change than deep tract regions. Second, we demonstrate that the development of superficial tract regions aligns with the cortical hierarchy defined by the sensorimotor-association axis, with tract ends adjacent to sensorimotor cortices maturing earlier than those adjacent to association cortices. These results reveal developmental variation along tracts that conventional tract-average analyses have previously obscured, challenging the implicit assumption that white matter tracts mature uniformly along their length. Such developmental variation along tracts may have functional implications, including mitigating ephaptic coupling in densely packed deep tract regions and tuning neural synchrony through hierarchical development in superficial tract regions - ultimately refining neural transmission in youth.

Velocity-selective arterial spin labelling bolus duration measurements: Implications for consensus recommendations

Velocity-selective arterial spin labelling (VSASL) MRI is insensitive to prolonged arterial transit time. This is an advantage over other arterial spin labelling schemes, where long arterial transit times can lead to bias. Therefore, VSASL can be used with greater confidence to study perfusion in the presence of long arterial transit times, such as in the ageing brain, in vascular pathologies, and cancer, or where arterial transit time changes, such as during measurement of cerebrovascular reactivity (CVR). However, when calculating perfusion (cerebral blood flow, CBF, in the brain) from VSASL signal, it is assumed that a vascular crushing module, defining the duration of the bolus, is applied before the arrival of the trailing edge. The early arrival of the trailing edge of the labelled bolus of blood will cause an underestimation of perfusion. Here we measure bolus duration in adult, healthy human brains, both at rest and during elevated CBF during CO2 breathing (5% inspired CO2). Grey matter bolus duration was of 2.20 ± 0.35 s / 2.22 ± 0.53 s / 2.05 ± 0.34 s (2/3/4 cm/s vcutoff) at rest, in close agreement with a prior investigation. However, we observed a significant decrease in bolus duration during hypercapnia, and a matched reduction in CVR above a labelling delay of approximately 1.2 s. The reduction in CVR and bolus duration was spatially heterogenous, with shorter hypercapnic bolus durations observed in the frontal lobe (1.31 ± 0.54 s) and temporal lobes (1.36 ± 0.24 s), compared to the occipital lobe (1.50 ± 0.26 s). We place these results in context of recommendations from a recent consensus paper, which recommends imaging 1.4 s after the label, which could lead to CBF underestimation in conditions with fast flow or during CVR measurements. These results can be used to inform the experimental design of future VSASL studies, to avoid underestimating perfusion by imaging after the arrival of the trailing edge of the labelled bolus.