The Human Connectome Project: a data acquisition perspective

MPRAGElike: A novel approach to generate T1w images from multi-contrast gradient echo images for brain segmentation

PURPOSE

Brain segmentation and multi-parameter mapping (MPM) are important steps in neurodegenerative disease characterization. However, acquiring both a high-resolution T1w sequence like MPRAGE (standard input to brain segmentation) and an MPM in the same neuroimaging protocol increases scan time and patient discomfort, making it difficult to combine both in clinical examinations.

METHODS

A novel approach to synthesize T1w images from MPM images, named MPRAGElike, is proposed and compared to the standard technique used to produce synthetic MPRAGE images (synMPRAGE). Twenty-three healthy subjects were scanned with the same imaging protocol at three different 7T sites using universal parallel transmit RF pulses. SNR, CNR, and automatic brain segmentation results from both MPRAGElike and synMPRAGE were compared against an acquired MPRAGE.

RESULTS

The proposed MPRAGElike technique produced higher SNR values than synMPRAGE for all regions evaluated while also having higher CNR values for subcortical structures. MPRAGE was still the image with the highest SNR values overall. For automatic brain segmentation, MPRAGElike outperformed synMPRAGE when compared to MPRAGE (median Dice Similarity Coefficient of 0.90 versus 0.29 and Average Asymmetric Surface Distance of 0.33 versus 2.93 mm, respectively), in addition to being simple, flexible, and considerably more robust to low image quality than synMPRAGE.

CONCLUSION

The MPRAGElike technique can provide a better and more reliable alternative to synMPRAGE as a substitute for MPRAGE, especially when automatic brain segmentation is of interest and scan time is limited.

Neurobiologically interpretable causal connectome for predicting young adult depression: A graph neural network study

BACKGROUND

There is a surprising lack of neuroimaging studies of depression that not only identify the whole brain causal connectivity features but also explore whether these features have neurobiological correlates.

METHODS

Three graph neural networks (GNN) models were applied to three types of causal connectomes (CCs): granger causality, regression DCM (rDCM), and TwoStep, obtained from a total of 1296 young adult participants in three large-scale datasets.

RESULTS

GNN models showed better performance for predicting depression when using causal connectomes such as TwoStep (average precision score, 0.882), granger causality (0.878), or rDCM (0.853) compared with using functional connectomes like Pearson's (0.850) and partial (0.823) correlation. Notably, nodal features derived only from rDCM and TwoStep showed spatial associations with positron emission tomography measures of receptors for neurotransmitters such as dopamine and serotonin. Further analysis revealed the shared directed edges among the subject's edge features, which included cortical causal connections in networks such as the default mode, control, dorsal attention, peripheral visual, and parietofrontal networks.

LIMITATIONS

The classification performance of leave-one-site-out cross-validation did not achieve a similar level with that of 10-fold cross-validation.

CONCLUSIONS

Our findings suggest that the connectomes derived from CCs using GNN, rather than functional connectomes, provide more accurate and neurobiologically relevant information for depression. Moreover, the observed spatial heterogeneity of this relevance and subject-specific edge features emphasizes the complexity of depression. These results have the potential to advance our understanding of depression's nature and potentially contribute to precision psychiatry by aiding in its diagnosis and treatment.

Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease

Examining the altered arrangement and patterning of sulcal folds offers insights into the mechanisms of neurodevelopmental differences in psychiatric and neurological disorders. Previous sulcal pattern analysis used spectral graph matching of sulcal pit-based graph structures to assess deviations from normative sulcal patterns. However, challenges exist, including the absence of a standard criterion for defining a typical reference set, time-consuming cost of graph matching, user-defined feature weight sets, and assumptions about uniform node distribution. We developed a deep learning-based sulcal pattern analysis to address these challenges by adapting prototype-based graph neural networks to sulcal pattern graphs. Additionally, we proposed a prototype inverse-projection for better interpretability. Unlike other prototype-based models, our approach inversely projects prototypes onto individual node representations to calculate the inverse-projection weights, enabling efficient visualization of prototypes and focusing the model on selective regions. We evaluated our method through a classification task between healthy controls (n = 174, age = 15.4 ±1.9 [mean ± standard deviation, years]) and patients with congenital heart disease (n = 345, age = 15.8 ±4.7) from four cohort studies and a public dataset. Our approach demonstrated superior classification performance compared to other state-of-the-art models, supported by extensive ablative studies. Furthermore, we visualized and examined the learned prototypes to enhance understanding. We believe our method has the potential to be a sensitive and understandable tool for sulcal pattern analysis.

AutoDPS: An unsupervised diffusion model based method for multiple degradation removal in MRI

BACKGROUND AND OBJECTIVE

Diffusion models have demonstrated their ability in image generation and solving inverse problems like restoration. Unlike most existing deep-learning based image restoration techniques which rely on unpaired or paired data for degradation awareness, diffusion models offer an unsupervised degradation independent alternative. This is well-suited in the context of restoring artifact-corrupted Magnetic Resonance Images (MRI), where it is impractical to exactly model the degradations apriori. In MRI, multiple corruptions arise, for instance, from patient movement compounded by undersampling artifacts from the acquisition settings.

METHODS

To tackle this scenario, we propose AutoDPS, an unsupervised method for corruption removal in brain MRI based on Diffusion Posterior Sampling. Our method (i) performs motion-related corruption parameter estimation using a blind iterative solver, and (ii) utilizes the knowledge of the undersampling pattern when the corruption consists of both motion and undersampling artifacts. We incorporate this corruption operation during sampling to guide the generation in recovering high-quality images.

RESULTS

Despite being trained to denoise and tested on completely unseen corruptions, our method AutoDPS has shown ∼ 1.63 dB of improvement in PSNR over baselines for realistic 3D motion restoration and ∼ 0.5 dB of improvement for random motion with undersampling. Additionally, our experiments demonstrate AutoDPS's resilience to noise and its generalization capability under domain shift, showcasing its robustness and adaptability.

CONCLUSION

In this paper, we propose an unsupervised method that removes multiple corruptions, mainly motion with undersampling, in MRI images which are essential for accurate diagnosis. The experiments show promising results on realistic and composite artifacts with higher improvement margins as compared to other methods. Our code is available at https://github.com/arunima101/AutoDPS/tree/master.

Simultaneously capturing excessive variations and smooth dynamics of the underlying neural activity using spatiotemporal basis expansion and multisubject fMRI data

In the last decade, dictionary learning (DL) has gained popularity over independent component analysis (ICA) within the blind source separation (BSS) framework for functional magnetic resonance imaging (fMRI) signals. Despite its rising popularity, a primary challenge in DL remains model fitting. It is susceptible to overfitting because the conventional loss function strives to correspond too closely to the training data. However, in the case of multi-subject (MS) analysis, it becomes imperative to overfit in order to acquire the source diversities across different brains. In this paper, an attempt has been made to resolve this predicament by concurrently preserving and mitigating the effect of high variance. A novel algorithm named joint analysis and synthesis DL (JASDL) has been proposed that simultaneously learns the overfitted trends to retain the data-centric cross-subject diversities and wellfitted trends by adequately regularizing the model complexity. This fusion was achieved by benefiting from modeling each subject's data in terms of both spatiotemporal (ST) prior information (PI) and MS-ST components. The PI consisted of biological priors derived from neuroscience knowledge, such as brain network templates, and mathematical priors derived from basis functions, such as three-dimensional (3D) cubic basis splines (B-splines). In contrast, MS-ST components were estimated using the computationally most parsimonious sparse ST blind source separation (ssBSS) method. Using the proposed analysis/synthesis cost function that exploits tri and quad-factorization for matrix approximation, the JASDL algorithm can model temporal smoothness and spatial reduction of false positives while retaining MS variations. Its efficacy was evaluated by comparing it with existing DL techniques using both experimental and synthetic fMRI datasets. Overall, the mean of correlation and F-score was found to be [Formula: see text] higher for the JASDL synthesis dictionary than the state-of-the-art subject-wise sequential DL (swsDL).

Examining the unique contribution of parent anxiety sensitivity on adolescent neural responses during an emotion regulation task

Parent factors impact adolescent's emotion regulation, which has key implications for the development of internalizing psychopathology. A key transdiagnostic factor which may contribute to the development of youth internalizing pathology is parent anxiety sensitivity (fear of anxiety-related physiological sensations). In a sample of 146 adolescents (M/SDage = 12.08/.90 years old) and their parents (98% mothers) we tested whether parent anxiety sensitivity was related to their adolescent's brain activation, over and above the child's anxiety sensitivity. Adolescents completed an emotion regulation task in the scanner that required them to either regulate vs. react to negative vs. neutral stimuli. Parent anxiety sensitivity was associated with adolescent neural responses in bilateral orbitofrontal cortex (OFC), anterior cingulate, and paracingulate, and left dorsolateral prefrontal cortex, such that higher parent anxiety sensitivity was associated with greater activation when adolescents were allowed to embrace their emotional reaction(s) to stimuli. In the right OFC region only, higher parent anxiety sensitivity was also associated with decreased activation when adolescents were asked to regulate their emotional responses. The findings are consistent with the idea that at-risk adolescents may be modeling the heightened attention and responsivity to environmental stimuli that they observe in their parents.

Comprehensive evaluation of U-Net based transcranial magnetic stimulation electric field estimations

Transcranial Magnetic Stimulation (TMS) is a non-invasive method to modulate neural activity by inducing an electric field in the human brain. Computational models are an important tool for informing TMS targeting and dosing. State-of-the-art modeling techniques use numerical methods, such as the finite element method (FEM), to produce highly accurate simulation results. However, these methods operate at a high computational cost, limiting real-time integration and high throughput applications. Deep learning (DL) methods, particularly U-Nets, are being investigated for TMS electric field estimations. However, their performance across large datasets and whole-head stimulation conditions has not been systematically evaluated. Here, we develop a DL framework to estimate TMS-induced electric fields directly from an anatomical magnetic resonance image (MRI) and TMS coil parameters. We perform a comprehensive evaluation of the performance of our U-Net approach compared to the FEM gold standard. We selected a dataset of 100 MRI scans from a diverse population demographic (ethnic, gender, age) made available by the Human Connectome Project. For each MRI, we generated a FEM head model and simulated the electric fields for 13 TMS coil orientations and 1206 positions (a total of 15,678 coil configurations per participant). We trained a modified U-Net architecture to predict individual TMS-induced electric fields in the brain based on an input T1-weighted MRI scan and stimulation parameters. We characterized the model's performance according to computational efficiency and simulation accuracy compared to FEM using an independent testing dataset. The U-Net results demonstrated an accelerated electric field modeling speed at 0.8 s per simulation (×97,000 times acceleration over the FEM-based approach). Sampling stimulation conditions across the whole brain yielded an average DICE coefficient of 0.71 ± 0.06 mm and an average center of gravity deviation of 7.52 ± 4.06 mm from the FEM-based approach. Our findings indicate that while deep learning has the potential to significantly accelerate electric field predictions, the precision it achieves needs to be evaluated for the specific TMS application.

Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks

The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the mechanistic significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp electroencephalography - EEG) recordings in 36 patients with drug-resistant focal epilepsy during presurgical intracerebral electrical stimulation (iES) (323 stimulation sessions). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence on recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show an activity pattern characterized by more inter-network functional integration than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure. These findings offer new insights into how hierarchical brain organization influences cognitive functions and could inform strategies for targeted neuromodulation therapies.

Cortical structure in relation to empathy and psychopathy in 800 incarcerated men

Background

Reduced affective empathy is a hallmark of psychopathy, which incurs major interpersonal and societal costs. Advancing our neuroscientific understanding of this reduction and other psychopathic traits is crucial for improving their treatment.

Methods

In 804 incarcerated adult men, we administered the Perspective Taking (IRI-PT) and Empathic Concern (IRI-EC) subscales of the Interpersonal Reactivity Index, Hare Psychopathy Checklist-Revised (PCL-R; two factors), and T1-weighted MRI to quantify cortical thickness (CT) and surface area (SA). We also included the male sample of the Human Connectome Project (HCP; N = 501) to replicate patterns of macroscale structural organization.

Results

Factor 1 (Interpersonal/Affective) uniquely negatively related to IRI-EC, while Factor 2 (Lifestyle/Antisocial) uniquely negatively related to IRI-PT. Cortical structure did not relate to either IRI subscale, although there was effect-size differentiation by microstructural class and/or functional network. CT related to Factor 1 (mostly positively), SA related to both factors (only positively), and both cortical indices demonstrated out-of-sample predictive utility for Factor 1. The high-psychopathy group (N = 178) scored uniquely lower on IRI-EC while having increased SA (but not CT). Regionally, these SA increases localized primarily in the paralimbic class and somatomotor network, with meta-analytic task-based activations corroborating affective-sensory importance. High psychopathy also showed "compressed" global and/or network-level organization of both cortical indices, and this organization in the total sample replicated in HCP. All findings accounted for age, IQ, and/or total intracranial volume.

Conclusions

Psychopathy had negative relationships with affective empathy and positive relationships with paralimbic/somatomotor SA, highlighting the role of affect and sensation.

Subthalamic Deep Brain Stimulation: Mapping Non-Motor Outcomes to Structural Connections

In Parkinson's Disease (PD), deep brain stimulation of the subthalamic nucleus (STN-DBS) reliably improves motor symptoms, and the circuits mediating these effects have largely been identified. However, non-motor outcomes are more variable, and it remains unclear which specific brain circuits need to be modulated or avoided to improve them. Since numerous non-motor symptoms potentially respond to DBS, it is challenging to independently identify the circuits mediating each one of them. Data compression algorithms such as principal component analysis (PCA) may provide a powerful alternative. This study aimed at providing a proof of concept for this approach by mapping changes along extensive score batteries to a few anatomical fiber bundles and, in turn, estimating changes in individual scores based on stimulation of these tracts. Retrospective data from 56 patients with PD and bilateral STN-DBS was included. The patients had undergone comprehensive clinical assessments covering changes in appetitive behaviors, mood, anxiety, impulsivity, cognition, and empathy. PCA was implemented to identify the main dimensions of neuropsychiatric and neuropsychological outcomes. Using DBS fiber filtering, we identified the structural connections whose stimulation was associated with change along these dimensions. Then, estimates of individual symptom outcomes were derived based on the stimulation of these connections by inverting the PCA. Finally, changes along a specific non-motor score were estimated in an independent validation dataset (N = 68) using the tract model. Four principal components were retained, which could be interpreted to reflect (i) general non-motor improvement; (ii) improvement of mood and cognition and worsening of trait impulsivity; (iii) improvement of cognition; and (iv) improvement of empathy and worsening of impulsive-compulsive behaviors. Each component was associated with the stimulation of spatially segregated fiber bundles connecting regions of the frontal cortex with the subthalamic nucleus. The extent of stimulation of these tracts was able to explain significant amounts of variance in outcomes for individual symptoms in the original cohort (circular analysis), as well as in the rank of depression outcomes in the independent validation cohort. Our approach represents an innovative concept for mapping changes along extensive score batteries to a few anatomical fiber bundles and could pave the way toward personalized deep brain stimulation.

From Dyadic to Higher-Order Interactions: Enhanced Representation of Homotopic Functional Connectivity Through Control of Intervening Variables

Background: The brain's complex functionality emerges from network interactions that go beyond dyadic connections, with higher-order interactions significantly contributing to this complexity. Homotopic functional connectivity (HoFC) is a key neurophysiological characteristic of the human brain, reflecting synchronized activity between corresponding regions in the brain's hemispheres. Materials and Methods: Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we evaluate dyadic and higher-order interactions of three functional connectivity (FC) parameterizations-bivariate correlation, partial correlation, and tangent space embedding-in their effectiveness at capturing HoFC through the inter-hemispheric analogy test. Results: Higher-order feature vectors are generated through node2vec, a random walk-based node embedding technique applied to FC networks. Our results show that higher-order feature vectors derived from partial correlation most effectively represent HoFC, while tangent space embedding performs best for dyadic interactions. Discussion: These findings validate HoFC and underscore the importance of the FC construction method in capturing intrinsic characteristics of the human brain.

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.

Site effects in multisite fetal brain MRI: morphological insights into early brain development

OBJECTIVE

To evaluate multisite effects on fetal brain MRI. Specifically, to identify crucial acquisition factors affecting fetal brain structural measurements and developmental patterns, while assessing the effectiveness of existing harmonization methods in mitigating site effects.

MATERIALS AND METHODS

Between May 2017 and March 2022, T2-weighted fast spin-echo sequences in-utero MRI were performed on healthy fetuses from retrospectively recruited pregnant volunteers on four different scanners at four sites. A generalized additive model (GAM) was used to quantitatively assess site effects, including field strength (FS), manufacturer (M), in-plane resolution (R), and slice thickness (ST), on subcortical volume and cortical morphological measurements, including cortical thickness, curvature, and sulcal depth. Growth models were selected to elucidate the developmental trajectories of these morphological measurements. Welch's test was performed to evaluate the influence of site effects on developmental trajectories. The comBat-GAM harmonization method was applied to mitigate site-related biases.

RESULTS

The final analytic sample consisted of 340 MRI scans from 218 fetuses (mean GA, 30.1 weeks ± 4.4 [range, 21.7-40 weeks]). GAM results showed that lower FS and lower spatial resolution led to overestimations in selected brain regions of subcortical volumes and cortical morphological measurements. Only the peak cortical thickness in developmental trajectories was significantly influenced by the effects of FS and R. Notably, ComBat-GAM harmonization effectively removed site effects while preserving developmental patterns.

CONCLUSION

Our findings pinpointed the key acquisition factors in in-utero fetal brain MRI and underscored the necessity of data harmonization when pooling multisite data for fetal brain morphology investigations.

KEY POINTS

Question How do specific site MRI acquisition factors affect fetal brain imaging? Finding Lower FS and spatial resolution overestimated subcortical volumes and cortical measurements. Cortical thickness in developmental trajectories was influenced by FS and in-plane resolution. Clinical relevance This study provides important guidelines for the fetal MRI community when scanning fetal brains and underscores the necessity of data harmonization of cross-center fetal studies.

Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging

PURPOSE

The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted images (DWIs). This model addresses the challenge of prolonged data acquisition times in diffusion MRI while preserving metric accuracy.

METHODS

DiffDL was trained using data from the Human Connectome Project, including 300 training/validation subjects and 50 testing subjects. High-quality DTI and DKI metrics were generated using many DWIs and combined with subsets of DWIs to form training pairs. A UNet architecture was used for denoising, trained over 500 epochs with a linear noise schedule. Performance was evaluated against conventional DTI/DKI modeling and a reference UNet model using normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), and Pearson correlation coefficient (PCC).

RESULTS

DiffDL showed significant improvements in the quality and accuracy of fractional anisotropy (FA) and mean diffusivity (MD) maps compared to conventional methods and the baseline UNet model. For DKI metrics, DiffDL outperformed conventional DKI modeling and the UNet model across various acceleration scenarios. Quantitative analysis demonstrated superior NMAE, PSNR, and PCC values for DiffDL, capturing the full dynamic range of DTI and DKI metrics. The generative nature of DiffDL allowed for multiple predictions, enabling uncertainty quantification and enhancing performance.

CONCLUSION

The DiffDL framework demonstrated the potential to significantly reduce data acquisition times in diffusion MRI while maintaining high metric quality. Future research should focus on optimizing computational demands and validating the model with clinical cohorts and standard MRI scanners.

Contrastive machine learning reveals species -shared and -specific brain functional architecture

A deep comparative analysis of brain functional connectome across species in primates has the potential to yield valuable insights for both scientific and clinical applications. However, the interspecies commonality and differences are inherently entangled with each other and with other irrelevant factors. Here we develop a novel contrastive machine learning method, called shared-unique variation autoencoder (SU-VAE), to allow disentanglement of the species-shared and species-specific functional connectome variation between macaque and human brains on large-scale resting-state fMRI datasets. The method was validated by confirming that human-specific features are differentially related to cognitive scores, while features shared with macaque better capture sensorimotor ones. The projection of disentangled connectomes to the cortex revealed a gradient that reflected species divergence. In contrast to macaque, the introduction of human-specific connectomes to the shared ones enhanced network efficiency. We identified genes enriched on 'axon guidance' that could be related to the human-specific connectomes. The code contains the model and analysis can be found in https://github.com/BBBBrain/SU-VAE.

Deep learning-based free-water correction for single-shell diffusion MRI

Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white matter and cerebrospinal fluid. The presence of free water partial volume effects leads to biases in estimating diffusion properties. Additionally, the existing mathematical FWE model is a two-compartment model, which can be well posed for multi-shell data. However, single-shell acquisitions are more common in clinical cohorts due to time constraints. To overcome these problems, we proposed a deep-learning framework that focuses on mapping and correcting free-water partial volume contamination in DWI. It utilizes data-driven techniques to infer plausible free-water volumes across different diffusion MRI acquisition schemes, including single-shell acquisitions. In this work, we study the Human Connectome Project Young Adults (HCP-ya), the HCP Aging dataset (HCP-a) as well as Brain Tumor Connectomics Data (BTC). The evaluation demonstrates that it produces more plausible results compared to previous single-shell free water estimation approaches. The proposed method is generalizable through model fine-tuning and b-value re-mapping when dealing with new data. The results have demonstrated improved consistency of properties estimation between scan/rescan data and accuracy in identifying neural pathways, as well as enhanced clarity in the visualization of white matter tracts.

Evaluation of the glymphatic system in relapsing remitting multiple sclerosis by measuring the diffusion along the perivascular space

BACKGROUND

In the inflammatory process of multiple sclerosis (MS) several toxic waste products are generated. The clearance of these products might depend on the glymphatic system; however, it's preserved function in MS is uncertain. Recently, it was suggested that this 'waste clearance' system can be examined by measuring the diffusion along the perivascular space (ALPS) index.

METHODS

Reproducibility of the ALPS index was tested with intraclass correlation on two open-source datasets with two methods: calculating ALPS indices from the skeleton map (sk-ALPS) and via registration to the common space (ro-ALPS). ALPS indices of 66 MS patient were calculated via the reorientation method. Spearman's correlation and partial least squares regression were applied to reveal the connection between the ALPS indices and the radiological (lesion count) and clinical parameters (SDMT, BVMT, CVLT, EDSS, disease duration) of the patients.

RESULTS

Repeatability of the ALPS index calculated by the ro-ALPS method is the most reliable (ICC: 0.961). Significant correlation was found between the left ALPS index and SDMT. On the right side, significant correlation was found between the ALPS index and the number of periventricular lesions and black holes. The most important predictors of EDSS are disease duration, age, SDMT and infratentorial lesion count.

CONCLUSION

Reproducibility of the ALPS index ranges from 'good' to 'excellent'. No relationship was found between the ALPS index and clinical disability. A lateralization was observed with cognitive characteristics on the left sided ALPS index and radiological characteristics on the right sided ALPS index.

The Neural Oscillatory Basis of Perspective-Taking in Autistic and Non-Autistic Adolescents Using Magnetoencephalography

Taking another's perspective is a high-level mental skill underlying many aspects of social cognition. Perspective-taking is usually an embodied egocentric process whereby people mentally rotate themselves away from their physical location into the other's orientation. This is accompanied by increased theta-band (3-7 Hz) brain oscillations within a widespread fronto-parietal cortical network including the temporoparietal junction. Individuals with autism spectrum conditions (ASC) have been reported to experience challenges with high-level perspective-taking, particularly when adopting embodied strategies. To investigate the potential neurophysiological basis of these autism-related individual differences, we used magnetoencephalography in combination with a well-replicated perspective-taking paradigm in a group of 18 autistic and 17 age-matched non-autistic adolescents. Findings revealed that increasing the angle between self and other perspective resulted in prolonged reaction times for the autistic group during perspective-taking. This was accompanied by reduced theta power across a wide network of regions typically active during social cognitive tasks. On the other hand, the autistic group showed greater alpha power decreases in visual cortex compared with the non-autistic group across all perspective-taking conditions. These divergent theta and alpha power effects, coupled with steeper response time slopes, suggest that autistic individuals may rely more on alternative cognitive strategies, such as mental object rotation, rather than an egocentric embodied approach. Finally, no group differences were found when participants were asked to track, rather than take, another's viewpoint, suggesting that autism-related individual differences are specific to high-level perspective-taking.

The multiscale self-similarity of the weighted human brain connectome

Anatomical connectivity between different brain regions can be mapped to a network representation, the connectome, where the intensities of the links, the weights, influence resilience and functional processes. Yet, many features associated with these weights are not fully understood, particularly their multiscale organization. In this paper, we elucidate the architecture of weights, including weak ties, in multiscale human brain connectomes reconstructed from empirical data. Our findings reveal multiscale self-similarity, including the ordering of weak ties, in every individual connectome and group representative. This phenomenon is captured by a renormalization technique based on a geometric network model that replicates the observed structure of connectomes across all length scales, using the same connectivity law and weighting function for both weak and strong ties. The observed symmetry represents a signature of criticality in the weighted connectivity of the human brain and raises important questions for future research, such as the existence of symmetry breaking at some scale or whether it is preserved in cases of neurodegeneration or psychiatric disorder.

The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study

The shape of the brain's white matter connections is relatively unexplored in diffusion magnetic resonance imaging (dMRI) tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement two machine learning models (1D-CNN and Least Absolute Shrinkage and Selection Operator [LASSO]) to predict individual cognitive performance scores. We study a large-scale database from the Human Connectome Project Young Adult study (n = 1065). We apply an atlas-based fiber cluster parcellation (953 fiber clusters) to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models (using fivefold cross-validation) to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHapley Additive exPlanations (SHAP), to assess the importance of each fiber cluster for prediction. Our results demonstrate that fiber cluster shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are generally as effective for prediction as traditional microstructure and connectivity measures. The 1D-CNN model generally outperforms the LASSO method for prediction. Further interpretation and analysis using SHAP values from the 1D-CNN suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.

Exploiting Polynomial Chaos Expansion for Rapid Assessment of the Impact of Tissue Property Uncertainties in Low-Intensity Focused Ultrasound Stimulation

Neuromodulation with low-intensity focused ultrasound (LIFUS) holds significant promise for noninvasive treatment of neurological disorders, but its success relies heavily on accurately targeting specific brain regions. Computational model predictions can be used to optimize LIFUS, but uncertain acoustic tissue properties can affect prediction accuracy. The Monte Carlo method is often used to quantify the impact of uncertainties, but many iterations are generally needed for accurate estimates. We studied a surrogate model based on polynomial chaos expansion (PCE) to quantify the uncertainty in the LIFUS acoustic intensity field caused by tissue acoustic property uncertainties. The PCE approach was benchmarked against Monte Carlo method for LIFUS in three different head models. We also investigated the effect of the number of PCE samples on the accuracy of the surrogate model. Our results show that the PCE surrogate model requires only 20 simulation samples to estimate the mean and standard deviation of the acoustic intensity field with high accuracy compared to 100 samples needed for Monte Carlo method. The root mean squared percentage error (RMSPE) in the mean acoustic intensity field was less than 1.5%, with a maximum error of less than 0.5 W/cm2 (< 1% of the focus peak intensity in water), while the RMSPE in the standard deviation was less than 9%, with a maximum error of less than 0.3 W/cm2. The accuracy of the PCE surrogate model, and the limited number of iterations it requires makes it a promising tool for quantifying the uncertainty in the acoustic intensity field in LIFUS applications.

Multimodal precision MRI of the individual human brain at ultra-high fields

Multimodal neuroimaging, in particular magnetic resonance imaging (MRI), allows for non-invasive examination of human brain structure and function across multiple scales. Precision neuroimaging builds upon this foundation, enabling the mapping of brain structure, function, and connectivity patterns with high fidelity in single individuals. Highfield MRI, operating at magnetic field strengths of 7 Tesla (T) or higher, increases signal-to-noise ratio and opens up possibilities for gains spatial resolution. Here, we share a multimodal Precision Neuroimaging and Connectomics (PNI) 7 T MRI dataset. Ten healthy individuals underwent a comprehensive MRI protocol, including T1 relaxometry, magnetization transfer imaging, T2*-weighted imaging, diffusion MRI, and multi-state functional MRI paradigms, aggregated across three imaging sessions. Alongside anonymized raw MRI data, we release cortex-wide connectomes from different modalities across multiple parcellation scales, and supply "gradients" that compactly characterize spatial patterning of cortical organization. Our precision MRI dataset will advance our understanding of structure-function relationships in the individual human brain and is publicly available via the Open Science Framework.

Human head models and populational framework for simulating brain stimulations

Noninvasive brain stimulation (NIBS) is pivotal in studying human brain-behavior relations and treating brain disorders. NIBS effectiveness relies on informed targeting of specific brain regions, a challenge due to anatomical differences between humans. Computational volumetric head modeling can capture individual effects and enable comparison across a population. However, most studies implementing modeling use a single-head model, ignoring morphological variability, potentially skewing interpretation, and realistic precision. We present a comprehensive dataset of 100 realistic head models with variable tissue conductivity values, lead-field matrices, standard-space co-registrations, and quality-assured tissue segmentations to provide a large sample of healthy adult head models with anatomical and tissue variance. Leveraging the Human Connectome Project s1200 release, this dataset powers population head modeling for stimulation target optimization, MEEG source modeling simulations, and advanced meta-analysis of brain stimulation studies. We performed a quality assessment for each head mesh, which included a semi-manual segmentation accuracy correction and finite-element analysis quality measures. This dataset will facilitate brain stimulation developments in academic and clinical research.

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

The brain can be decomposed into large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. We have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and multiple widely used functional brain atlases. We provide several exemplar demonstrations to illustrate how researchers can use the NCT to report their own findings. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

Assessing measurement consistency of a diffusion tensor imaging (DTI) quality control (QC) anisotropy phantom

OBJECTIVES

We evaluated a quality control (QC) phantom designed to mimic diffusion characteristics and white matter fiber tracts in the brain. We hypothesized that acquisition of diffusion tensor imaging (DTI) data on different vendors and over multiple repeated measures would not contribute to significant variability in calculated diffusion tensor scalar metrics such as fractional anisotropy (FA) and mean diffusivity (MD).

MATERIALS AND METHODS

The DTI QC phantom was scanned using a 32-direction DTI sequence on General Electric (GE), Siemens, and Philips 3 Tesla scanners. Motion probing gradients (MPGs) were investigated as a source of variance in our statistical design, and data were acquired on GE and Siemens scanners using GE, Siemens, and Philips vendor MPGs for 32 directions. In total, 8 repeated scans were made for each GE/Siemens combination of vendor and MPGs with 8 repeated scans on a Philips machine using its stock DTI sequence. Data were analyzed using 2-way ANOVAs to investigate repeat scan and vendor variances and 3-way ANOVAs with repeat, MPG, and vendor as factors.

RESULTS

No statistical differences (i.e., P > 0.05) were found in any DTI scalar metrics (FA, MD) or for any factor, suggesting system constancy across imaging platforms and the specified phantom's reliability and reproducibility across vendors and conditions.

DISCUSSION

A DTI QC phantom demonstrates that DTI measurements maintain their consistency across different MRI systems and can contribute to a standard that is more reliable for quantitative MRI analyses.

Gyral crowns contribute to the cortical infrastructure of human face processing

Neuroanatomical features across spatial scales contribute to functional specialization and individual differences in behavior across species. Among species with gyrencephalic brains, gyral crown height, which measures a key aspect of the morphology of cortical folding, may represent an anatomical characteristic that importantly shapes neural function. Nevertheless, little is known about the relationship between functional selectivity and gyral crowns-especially in clinical populations. Here, we investigated this relationship and found that the size and gyral crown height of the middle, but not posterior, face-selective region on the fusiform gyrus (FG) was smaller in individuals with developmental prosopagnosia (DPs; N = 22, 68% female, aged 25-62) compared to neurotypical controls (NTs; N = 25, 60% females, aged 21-55), and this difference was related to face perception. Additional analyses replicated the relationship between gyral crowns and face selectivity in 1,053 NTs (55% females, aged 22-36). These results inform theoretical models of face processing while also providing a novel neuroanatomical feature contributing to the cortical infrastructure supporting face processing.

The interplay between brain and behavior during development: A multisite effort to generate and share simulated datasets

One of the challenges in the field of neuroimaging is that we often lack knowledge about the underlying truth and whether our methods can detect developmental changes. To address this gap, five research groups around the globe created simulated datasets embedded with their assumptions of the interplay between brain development, cognition, and behavior. Each group independently created the datasets, unaware of the approaches and assumptions made by the other groups. Each group simulated three datasets with the same variables, each with 10,000 participants over 7 longitudinal waves, ranging from 7 to 20 years-of-age. The independently created datasets include demographic data, brain derived variables along with behavior and cognition variables. These datasets and code that were used to generate the datasets can be downloaded and used by the research community to apply different longitudinal models to determine the underlying patterns and assumptions where the ground truth is known.

Mapping Dynamic Metabolic Energy Distribution in Brain Networks using fMRI: A Novel Dynamic Time Warping Framework

Understanding how metabolic energy is distributed across brain networks is essential for elucidating healthy brain function and neurological disorders. Research has established the link between blood flow changes and glucose metabolic processes that fuel neural activity. Here, we introduce a novel framework based on the normalized dynamic time warping algorithm robust to neural temporal variability, enabling reliable insights into metabolic energy demands using functional magnetic resonance imaging data. Our findings indicate that healthy brains maintain balanced energy distribution, whereas imbalances are more pronounced in schizophrenia with links to both positive and negative symptoms, particularly during rapid neural processes. Additionally, we identified a dynamic state that supports the brain criticality theory and is associated with higher-order cognitive abilities, demonstrating our framework's functional and clinical relevance. By linking metabolic energy distribution to neural dynamics, this framework provides a novel way to estimate and quantify the brain's maintenance of functional balance in a broadly applicable manner for studying brain health and disorders.

Translational network neuroscience: Nine roadblocks and possible solutions

Translational network neuroscience aims to integrate advanced neuroimaging and data analysis techniques into clinical practice to better understand and treat neurological disorders. Despite the promise of technologies such as functional MRI and diffusion MRI combined with network analysis tools, the field faces several challenges that hinder its swift clinical translation. We have identified nine key roadblocks that impede this process: (a) theoretical and basic science foundations; (b) network construction, data interpretation, and validation; (c) MRI access, data variability, and protocol standardization; (d) data sharing; (e) computational resources and expertise; (f) interdisciplinary collaboration; (g) industry collaboration and commercialization; (h) operational efficiency, integration, and training; and (i) ethical and legal considerations. To address these challenges, we propose several possible solution strategies. By aligning scientific goals with clinical realities and establishing a sound ethical framework, translational network neuroscience can achieve meaningful advances in personalized medicine and ultimately improve patient care. We advocate for an interdisciplinary commitment to overcoming translational hurdles in network neuroscience and integrating advanced technologies into routine clinical practice.

Gauge equivariant convolutional neural networks for diffusion MRI

Diffusion MRI (dMRI) is an imaging technique widely used in neuroimaging research, where the signal carries directional information of underlying neuronal fibres based on the diffusivity of water molecules. One of the shortcomings of dMRI is that numerous images, sampled at gradient directions on a sphere, must be acquired to achieve a reliable angular resolution for model-fitting, which translates to longer scan times, higher costs, and barriers to clinical adoption. In this work we introduce gauge equivariant convolutional neural network (gCNN) layers for dMRI that overcome the challenges associated with the signal being acquired on a sphere with antipodal points identified. This is done by noting that the domain is equivalent to the real projective plane, [Formula: see text], which is a non-euclidean and a non-orientable manifold. This is in stark contrast to a rectangular grid which typical convolutional neural networks (CNNs) are designed for. We apply our method to upsample angular resolution for predicting diffusion tensor imaging (DTI) parameters from just six diffusion gradient directions. The symmetries introduced allow gCNNs the ability to train with fewer subjects as compared to a baseline model that involves only 3D convolutions.

Brain handedness associations depend on how and when handedness is measured

Hand preference is ubiquitous, intuitive, and often simplified to right- or left-handed. Accordingly, differences between right- and left-handed individuals in the brain have been established. Nevertheless, considering handedness as a binarized construct fails to capture the variability of brain-handedness associations across different domains or activities. Further, hand-use changes across generations (e.g., letter writing vs. texting) such that individuals of different ages live in different environments. As a result, brain-handedness associations may depend on how and when handedness is measured. We used two large datasets, the Human Connectome Project-Development (HCP-D; n = 465; age = 5-21 years) and Human Connectome Project-Aging (HCP-A; n = 368; age = 36-100 years), to investigate generational differences in brain-handedness associations. Nine items from the Edinburgh Handedness Inventory were associated with resting-state functional connectomes. We show that brain-handedness associations differed across the two cohorts. Moreover, these differences depended on the way handedness was measured. Given that brain-handedness associations differ across handedness measures and datasets, we caution against a one-size-fits-all approach to neuroimaging studies of this complex trait.

Time Persistence of the FMRI Resting-State Functional Brain Networks

Time persistence is a fundamental property of many complex physical and biological systems; thus understanding the phenomenon in the brain is of high importance. Time persistence has been explored at the level of stand-alone neural time-series, but since the brain functions as an interconnected network, it is essential to examine time persistence at the network level. Changes in resting-state networks have been previously investigated using both dynamic (i.e., examining connectivity states) and static functional connectivity (i.e., test-retest reliability), but no systematic investigation of the time persistence as a network was conducted, particularly across different timescales (i.e., seconds, minutes, dozens of seconds, days) and different brain subnetworks. Additionally, individual differences in network time persistence have not been explored. Here, we devised a new framework to estimate network time persistence at both the link (i.e., connection) and node levels. In a comprehensive series analysis of three functional MRI resting-state datasets including both sexes, we established that (1) the resting-state functional brain network becomes gradually less similar to itself for the gaps up to 23 min within the run and even less similar for the gap between the days; (2) network time persistence varies across functional networks, while the sensory networks are more persistent than nonsensory networks; (3) participants show stable individual characteristic persistence, which has a genetic component; and (4) individual characteristic persistence could be linked to behavioral performance. Overall, our detailed characterization of network time persistence sheds light on the potential role of time persistence in brain functioning and cognition.

Structural covariance of early visual cortex is negatively associated with PTSD symptoms: A Mega-Analysis from the ENIGMA PTSD workgroup

Background

Identifying robust neural signatures of posttraumatic stress disorder (PTSD) symptoms is important to facilitate precision psychiatry and help in understanding and treatment of the disorder. Emergent research suggests structural covariance of early visual regions is associated with later PTSD development. However, large-scale analyses are needed - in heterogeneous samples of trauma-exposed and trauma naive individuals - to determine if such a neural signature is a robust - and potentially a pretrauma - marker of vulnerability.

Methods

We analyzed data from the ENIGMA-PTSD dataset (n = 2,814) and the Human Connectome Project - Young Adult (HCP-YA) dataset (n = 890) to investigate whether structural covariance of early visual cortex is associated with either PTSD symptoms or perceived stress. Structural covariance was derived from a multimodal pattern previously identified in recent trauma survivors, and participant loadings on the profile were included in linear mixed effects models to evaluate associations with stress.

Results

Early visual cortex covariance loadings were negatively associated with PTSD symptoms in the ENIGMA-PTSD dataset. The relationship persisted when accounting for prior childhood maltreatment; supporting PTSD symptom specificity, no relationship was observed with depressive symptoms and no association was observed between loadings and perceived stress measures in the HCP-YA dataset.

Conclusion

Structural covariance of early visual cortex was robustly associated with PTSD symptoms across an international, heterogeneous sample of trauma survivors. Future studies should aim to identify specific mechanisms that underlie structural alterations in the visual cortex to better understand posttrauma psychopathology.

Genetic Foundations of Inter-individual Neurophysiological Variability

Neurophysiological brain activity shapes cognitive functions and individual traits. Here, we investigated the extent to which individual neurophysiological properties are genetically determined and how these adult traits align with cortical gene expression patterns across development. Using task-free magnetoencephalography in monozygotic and dizygotic twins, as well as unrelated individuals, we found that neurophysiological traits were significantly more similar between monozygotic twins, indicating a strong genetic influence. These heritable brain dynamics were predominantly associated with genes involved in neurotransmission, expressed along a topographical gradient that mirrors major cognitive and psychological functions. Furthermore, the cortical expression patterns of genes that contribute to individual differentiation showed progressive changes throughout development. These findings underscore a persistent genetic influence on neurophysiological activity, supporting individual cognitive and behavioral variability.

Connectomics modeling of regional networks of white-matter fractional anisotropy to predict the severity of young adult drinking

Background

Alcohol use impacts brain structure, including white matter integrity, which can be quantified by fractional anisotropy (FA) in diffusion tensor imaging (DTI). This study explored the relationship between the severity of alcohol consumption and white matter FA changes, and its sex differences, in young adults, using data from the Human Connectome Project.

Methods

We analyzed DTI data from 949 participants (491 females) and used principal component analysis (PCA) of 15 drinking metrics to quantify drinking severity. Connectome-based predictive modeling (CPM) was employed to predict the principal component of drinking severity from network FA values in a matrix of 116×116 regions. Mediation analyses were conducted to explore the interrelationships among networks identified by CPM, drinking severity, and rule-breaking behavior.

Results

Significant correlations were found between drinking severity and network FA values. Both men and women showed significant correlations between negative network connectivity and drinking severity (men: r=0.15, P=0.001; women: r=0.30, P<0.001). Sex differences were observed in the brain regions contributing to drinking severity predictions. Mediation analyses revealed significant inter-relationships between network features, drinking severity, and rule-breaking behavior.

Conclusions

The connectomics of white matter FA can predict the severity of alcohol consumption, and by incorporating brain network pathways, identify sex differences. This approach provides new clues to the biological basis of alcohol abuse and evaluates how these regions interact in broader brain networks for understanding alcohol misuse and its comorbidities.

DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI

This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution (1 mm3) or using mono-modal data, the proposed method improves cerebellum lobule segmentation through the use of a multimodal and ultra-high resolution (0.125 mm3) training dataset. To develop the method, first, a database of semi-automatically labelled cerebellum lobules was created to train the proposed method with ultra-high resolution T1 and T2 MR images. Then, an ensemble of deep networks has been designed and developed, allowing the proposed method to excel in the complex cerebellum lobule segmentation task, improving precision while being memory efficient. Notably, our approach deviates from the traditional U-Net model by exploring alternative architectures. We have also integrated deep learning with classical machine learning methods incorporating a priori knowledge from multi-atlas segmentation which improved precision and robustness. Finally, a new online pipeline, named DeepCERES, has been developed to make available the proposed method to the scientific community requiring as input only a single T1 MR image at standard resolution.

Single-shot echo planar time-resolved imaging for multi-echo functional MRI and distortion-free diffusion imaging

PURPOSE

To develop a single-shot SNR-efficient distortion-free multi-echo imaging technique for dynamic imaging applications.

METHODS

Echo planar time-resolved imaging (EPTI) was first introduced as a multi-shot technique for distortion-free multi-echo imaging. This work aims to develop single-shot EPTI (ss-EPTI) to achieve improved robustness to motion/physiological noise, increased temporal resolution, and higher SNR efficiency. A new spatiotemporal encoding that enables reduced phase-encoding blips and minimized echo spacing under the single-shot regime was developed, which improves sampling efficiency and enhances spatiotemporal correlation in the k-TE space for improved reconstruction. A continuous readout with minimized deadtime was employed to optimize SNR efficiency. Moreover, k-TE partial Fourier and simultaneous multi-slice acquisition were integrated for further acceleration.

RESULTS

ss-EPTI provided distortion-free imaging with densely sampled multi-echo images at standard resolutions (e.g., ˜1.25 to 3 mm) in a single-shot. Improved SNR efficiency was observed in ss-EPTI due to improved motion/physiological-noise robustness and efficient continuous readout. Its ability to eliminate dynamic distortions-geometric changes across dynamics due to field changes induced by physiological variations or eddy currents-further improved the data's temporal stability. For multi-echo fMRI, ss-EPTI's multi-echo images recovered signal dropout in short-T 2 * $$ {\mathrm{T}}_2^{\ast } $$ regions and provided TE-dependent functional information to distinguish non-BOLD noise for further tSNR improvement. For diffusion MRI, it achieved shortened TEs for improved SNR and provided images free from both B0-induced and diffusion-encoding-dependent eddy-current-induced distortions with multi-TE diffusion metrics.

CONCLUSION

ss-EPTI provides SNR-efficient distortion-free multi-echo imaging with comparable temporal resolutions to ss-EPI, offering a new acquisition tool for dynamic imaging.

Identifying influential nodes in brain networks via self-supervised graph-transformer

BACKGROUND

Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood. In contrast, self-supervised deep learning dispenses with manual features, allowing it to learn meaningful representations directly from the data. This approach enables the exploration of I-nodes for brain networks, which is also lacking in current studies.

METHOD

This paper proposes a Self-Supervised Graph Reconstruction framework based on Graph-Transformer (SSGR-GT) to identify I-nodes, which has three main characteristics. First, as a self-supervised model, SSGR-GT extracts the importance of brain nodes to the reconstruction. Second, SSGR-GT uses Graph-Transformer, which is well-suited for extracting features from brain graphs, combining both local and global characteristics. Third, multimodal analysis of I-nodes uses graph-based fusion technology, combining functional and structural brain information.

RESULTS

The I-nodes we obtained are distributed in critical areas such as the superior frontal lobe, lateral parietal lobe, and lateral occipital lobe, with a total of 56 identified across different experiments. These I-nodes are involved in more brain networks than other regions, have longer fiber connections, and occupy more central positions in structural connectivity. They also exhibit strong connectivity and high node efficiency in both functional and structural networks. Furthermore, there is a significant overlap between the I-nodes and both the structural and functional rich-club.

CONCLUSIONS

Experimental results verify the effectiveness of the proposed method, and I-nodes are obtained and discussed. These findings enhance our understanding of the I-nodes within the brain network, and provide new insights for future research in further understanding the brain working mechanisms.

Neurosurgical Breakthroughs of the Last 50 Years: A Historical Journey Through the Past and Present

This article presented the author's historical perspective on 25 of the most significant neurosurgical breakthrough events of the last 50 years. These breakthroughs have advanced neurosurgical patient care and management. They have improved the management of aneurysms, arteriovenous malformations, tumors, stroke, traumatic brain injury, movement disorders, epilepsy, hydrocephalus, and spine pathologies. Neurosurgery has evolved through research, innovation, and technology. Several neurosurgical breakthroughs were achieved using neuroendoscopy, neuronavigation, radiosurgery, endovascular techniques, and refinements in computer technology. With these breakthroughs, neurosurgery did not change; it just progressed. Neurosurgery should continue its progress through research to obtain new knowledge for the benefit of our patients.

Infraslow Dynamic Patterns in Human Cortical Networks Track a Spectrum of External to Internal Attention

Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.

The role of structural connectivity on brain function through a Markov model of signal transmission

Structure determines function. However, this universal theme in biology has been surprisingly difficult to observe in human brain neuroimaging data. Here, we link structure to function by hypothesizing that brain signals propagate as a Markovian process on an underlying structure. We focus on a metric called commute time: the average number of steps for a random walker to go from region A to B and then back to A. Commute times based on white matter tracts from diffusion MRI exhibit an average ± standard deviation Spearman correlation of -0.26 ± 0.08 with functional MRI connectivity data across 434 UK Biobank individuals and -0.24 ± 0.06 across 400 HCP Young Adult brain scans. The correlation increases to -0.36 ± 0.14 and to -0.32 ± 0.12 when the principal contributions of both commute time and functional connectivity are compared for both datasets. The observed weak but robust correlations provide evidence of a relationship, albeit restricted, between neuronal connectivity and brain function. The correlations are stronger by 33% compared to broadly used communication measures such as search information and communicability. The difference further widens to a factor of 5 when commute times are correlated to the principal mode of functional connectivity from its eigenvalue decomposition. Overall, the study points to the utility of commute time to account for the role of polysynaptic (indirect) connectivity underlying brain function.

Connectome-based Predictive Models of General and Specific Executive Functions

Executive functions, the set of cognitive control processes that facilitate adaptive thoughts and actions, are composed primarily of three distinct yet interrelated cognitive components: Inhibition, Shifting, and Updating. While prior research has examined the nature of different components as well as their inter-relationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated tasks. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2-back tasks after confirming superior predictive performance over resting-state and volumetric data. High cross-task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default-mode networks, suggest the existence of a common executive function factor. To investigate the relationships among the three executive function components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared executive function component can be predicted from functional connectivity patterns densely located around the frontoparietal, default-mode and dorsal attention networks. The Updating-specific component showed significant cross-prediction with the general executive function factor, suggesting a relatively stronger role than the other components. In contrast, the Shifting-specific and Inhibition-specific components exhibited lower cross-prediction performance, indicating more distinct and specialized roles. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates a novel approach to infer common and specific components of executive function.

Lesion network mapping of REM Sleep Behaviour Disorder

REM Sleep Behaviour Disorder (RBD) is a parasomnia characterised by dream enactment behaviour due to loss of sleep atonia during REM sleep. It is of considerable interest as idiopathic RBD is strongly associated with a high risk of future α-synuclein disorders. Whilst candidate brainstem structures for sleep atonia have been identified in animal studies, the precise mechanisms underpinning RBD in humans remain unclear. Here, we set out to empirically define a candidate anatomical RBD network using lesion network mapping. Our objective was to test the hypothesis that RBD is either due to damage to canonical RBD nodes previously identified in the animal literature, or disruption to the white matter connections between these nodes, or as a consequence of damage to some other brains regions. All published cases of secondary RBD arising due to discrete brain lesions were reviewed and those providing sufficient detail to estimate the original lesion selected. This resulted in lesion masks for 25 unique RBD cases. These were combined to create a lesion probability map, demonstrating the area of maximal overlap. We also obtained MRI lesion masks for 15 pontine strokes that had undergone sleep polysomnography investigations confirming the absence of RBD. We subsequently used these as an exclusion mask and removed any intersecting voxels from the aforementioned region of maximal overlap, creating a single candidate region-of-interest (ROIs) within the pons. This remaining region overlapped directly with the locus coeruleus. As sleep atonia is unlikely to be lateralized, a contralateral ROI was created via a left-right flip, and both were warped to the 100 healthy adult Human Connectome dataset. Probabilistic tractography was run from each ROI to map and characterize the white-matter tracts and connectivity properties. All reported lesions were within the brainstem but there was significant variability in location. Only half of these intersected with at least one of the six a priori RBD anatomical nodes assessed, however 72 % directly intersected with the white matter tracts created from the region of maximum overlap pontine ROIs, and the remainder were within 3.05 mm (±1.51 mm) of these tracts. 92 % of lesions were either at the level of region of maximum overlap or caudal to it. These results suggest that RBD is a brainstem disconnection syndrome, where damage anywhere along the tract connecting the rostral locus coeruleus and medulla may result in failure of sleep atonia, in line with the animal literature. This implies idiopathic disease may emerge through different patterns of damage across this brainstem circuit. This observation may account for the both the paucity of brainstem neuroimaging results reported to date and the observed phenotypic variability seen in idiopathic RBD.

Myelination across cortical hierarchies and depths in humans and macaques

Myelination is fundamental to brain function, enabling rapid neural communication and supporting neuroplasticity throughout the lifespan. While hierarchical patterns of myelin maturation across the cortical surface are well-documented in humans, it remains unclear which features reflect evolutionarily conserved developmental processes versus human-characteristic adaptations. Moreover, the laminar development of myelin across the primate cortical surface, which shapes hierarchies and supports functions ranging from sensory integration to network communication, has been largely unexplored. Using neuroimaging to measure the T1-weighted/T2-weighted ratio in tissue contrast as a proxy for myelin content, we systematically compared depth-dependent trajectories of myelination across the cortical surface in humans and macaques. We identified a conserved "inside-out" pattern, with deeper layers exhibiting steeper increases in myelination and earlier plateaus than superficial layers. This depth-dependent organization followed a hierarchical gradient across the cortical surface, progressing from early maturation in sensorimotor regions to prolonged development in association areas. Humans exhibited a markedly extended timeline of myelination across both cortical regions and depths compared to macaques, allowing for prolonged postnatal plasticity across the entire cortical hierarchy - from sensory and motor processing to higher-order association networks. This extended potential for plasticity may facilitate the shaping of cortical circuits through postnatal experience in ways that support human-characteristic perceptual and cognitive capabilities.

Consistent cerebellar pathway-cognition associations across pre-adolescents & young adults: a diffusion MRI study of 9000+ participants

The cerebellum, long implicated in movement, is now recognized as a contributor to higher-order cognition. The cerebellar pathways provide key structural links between the cerebellum and cerebral regions integral to language, memory, and executive function. Here, we present a large-scale, cross-sectional diffusion MRI (dMRI) analysis investigating the relationships between cerebellar pathway microstructure and cognitive performance in over 9,000 participants spanning pre-adolescence (n>8,000 from the ABCD dataset) and young adulthood (n>900 from the HCP-YA dataset). We assessed the microstructure of five cerebellar pathways-the inferior, middle, and superior cerebellar peduncles; the parallel fibers; and input/Purkinje fibers-using three dMRI measures of fractional anisotropy, mean diffusivity, and number of streamlines. Cognitive performance was evaluated using seven NIH Toolbox assessments of language, executive function, and memory. In both datasets, we found numerous significant associations between cerebellar pathway microstructure and cognitive performance. These associations showed a strong correlation across the two datasets (r = 0.47, p < 0.0001), underscoring the reliability of cerebellar dMRI-cognition relationships in pre-adolescents and young adults. In both datasets, the strongest associations were found between the superior cerebellar peduncle and performance on language assessments, suggesting this pathway plays an important role in language function across age groups. In young adults, but not pre-adolescents, parallel fiber microstructure was linked to inhibitory control, suggesting that contributions to attentional processes may emerge or strengthen with maturation. Overall, our findings highlight the important role of cerebellar pathways in cognition and the utility of large-scale datasets for advancing our understanding of brain-cognition relationships.

Cortical modulation of BOLD signals in white matter

The relationship of BOLD signals in white matter to cortical neural activity remains unclear. We quantified the degree to which spontaneous neural activities in the cortex, which are reflected in low frequency fluctuations in cortical BOLD signals, modulate BOLD signals in white matter. From measurements of resting state correlations we find cortical networks of more basic level functions tend to contribute more to correlated fluctuations in white matter than those of higher level functions. In addition, each cortical network exhibits distinct, structurally interpretable spatial distribution patterns of white matter projections. Moreover, the myelination level of cortical networks is found to be strongly correlated with the white matter projection of cortical BOLD signals. Our findings confirm that BOLD signals in white matter encode neural activity in proportion to the spontaneous activity of individual cortical networks, and with network-specific spatial distribution patterns, which could be mediated by the microstructure of the brain cortex.

Leveraging Input-Level Feature Deformation With Guided-Attention for Sulcal Labeling

The identification of cortical sulci is key for understanding functional and structural development of the cortex. While large, consistent sulci (or primary/secondary sulci) receive significant attention in most studies, the exploration of smaller and more variable sulci (or putative tertiary sulci) remains relatively under-investigated. Despite its importance, automatic labeling of cortical sulci is challenging due to (1) the presence of substantial anatomical variability, (2) the relatively small size of the regions of interest (ROIs) compared to unlabeled regions, and (3) the scarcity of annotated labels. In this paper, we propose a novel end-to-end learning framework using a spherical convolutional neural network (CNN). Specifically, the proposed method learns to effectively warp geometric features in a direction that facilitates the labeling of sulci while mitigating the impact of anatomical variability. Moreover, we introduce a guided-attention mechanism that takes into account the extent of deformation induced by the learned warping. This extracts discriminative features that emphasize sulcal ROIs, while suppressing irrelevant information of unlabeled regions. In the experiments, we evaluate the proposed method on 8 sulci of the posterior medial cortex. Our method outperforms existing methods particularly in the putative tertiary sulci. The code is publicly available at https://github.com/Shape-Lab/DSPHARM-Net.

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.

Deciphering the Neural Effects of Emotional, Motivational, and Cognitive Challenges on Inhibitory Control Processes

Converging lines of research indicate that inhibitory control is likely to be compromised in contexts that place competing demands on emotional, motivational, and cognitive systems, potentially leading to damaging impulsive behavior. The objective of this study was to identify the neural impact of three challenging contexts that typically compromise self-regulation and weaken impulse control. Participants included 66 healthy adults (M/SDage = 29.82/10.21 years old, 63.6% female) who were free of psychiatric disorders and psychotropic medication use. Participants completed a set of novel Go/NoGo (GNG) paradigms in the scanner, which manipulated contextual factors to induce (i) aversive emotions, (ii) appetitive drive, or (iii) concurrent working memory load. Voxelwise analysis of neural activation during each of these tasks was compared to that of a neutral GNG task. Findings revealed differential inhibition-related activation in the aversive emotions and appetitive drive GNG tasks relative to the neutral task in frontal, parietal and temporal cortices, suggesting emotional and motivational contexts may suppress activation of these cortical regions during inhibitory control. In contrast, the GNG task with a concurrent working memory load showed widespread increased activation across the cortex compared to the neutral task, indicative of enhanced recruitment of executive control regions. Results suggest the neural circuitry recruited for inhibitory control varies depending on the concomitant emotional, motivational, and cognitive demands of a given context. This battery of GNG tasks can be used by researchers interested in studying unique patterns of neural activation associated with inhibitory control across three clinically relevant contexts that challenge self-regulation and confer risk for impulsive behavior.

Fast EEG/MEG BEM-based forward problem solution for high-resolution head models

A fast BEM (boundary element method) based approach is developed to solve an EEG/MEG forward problem for a modern high-resolution head model. The method utilizes a charge-based BEM accelerated by the fast multipole method (BEM-FMM) with an adaptive mesh pre-refinement method (called b-refinement) close to the singular dipole source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models within 90 s after initial model assembly using a regular workstation. The forward method is validated by comparison against an analytical solution on a spherical shell model as well as comparison against a full h-refinement method on realistic 1M facet human head models, both of which yield agreement to within 5 % for the EEG skin potential and MEG magnetic fields. The method is further applied to an EEG source localization (inverse) problem for real human data, and a reasonable source dipole distribution is found.