Functional Brain Research Articles

Genistein-3′-sodium sulfonate promotes brain functional rehabilitation in ischemic stroke rats by regulating astrocytes polarization through NF-κB signaling pathway

The activation and polarization of astrocytes are involved in neuroinflammation and brain functional rehabilitation after ischemic stroke. Our previous studies display the neuroprotective effect of genistein-3′-sodium sulfonate (GSS) in the acute phase of cerebral ischemia-reperfusion injury (CI/RI). This study aimed to investigate the brain function improvement of GSS during the recovery period after CI/RI in rats and to explore the potential mechanism from the perspective of astrocyte activation and polarization. The transient middle cerebral artery occlusion (tMCAO) rats were treated with GSS (1 mg/kg) continuously for 28 days. The behavior tests were measured to assess neurological function. The mRNA and protein expression in affected cerebral cortex were detected on day 29 after tMCAO. Our results demonstrated that GSS treatment significantly improved the spatial and temporal gait parameters in the Catwalk gait test, prolonged the time on the stick and increased the rotation speed in the rotarod test, and decreased the time to find the hidden platform and increased the time in the target quadrant in the Morris water maze test. In addition, GFAP, GBP2, C3, IL-1β protein expressions and Nos2A mRNA level were decreased, while Nrf2, BDNF, IL-10 protein expressions and Sphk1 and Nef2l2 mRNA levels increased after GSS treatment. Interestingly, GSS presented a strong binding affinity to TLR4 and suppressed the activation of NF-κB signaling. In conclusion, GSS can promote brain function recovery by inhibiting astrocyte activation and polarization to A1 phenotype, and enhancing astrocyte polarization to A2 phenotype via inactivating TLR4/NF-κB signaling, which provide a candidate compound for clinical rehabilitation therapy in the recovery period after ischemic stroke.

Association of plasma metals with resting-state functional connectivity in ischemic stroke

BackgroundMetal exposure has long been considered a significant risk factor for ischemic stroke. However, existing data on the effects of metal exposure on brain function in ischemic stroke are limited. Therefore, this study aimed to explore the correlation between exposure to various metals and changes in resting-state functional connectivity (rs-FC) in ischemic stroke patients. MethodsThis study included 28 acute ischemic stroke patients with hemiplegia and 28 matched healthy controls (HCs). All participants underwent T1-weighted MRI and 3.0 T resting-state functional magnetic resonance imaging (fMRI). After MRI acquisition, the rs-FC between 137 cortical and subcortical regions was extracted and preprocessed. Plasma levels of 19 metals were measured using inductively coupled plasma mass spectrometry (ICP-MS). The Bayesian kernel machine regression (BKMR) model and the weighted quantile sum regression (WQS) model were used to assess the overall effect of metal mixture exposure. The severity of neurological deficits in each acute ischemic stroke patient was evaluated using the National Institutes of Health Stroke Scale (NIHSS). Additionally, the associations between exposure to various metals and modifications in brain functional connectivity were determined using Pearson or Spearman correlation analysis. ResultsBilateral brain connectivity was significantly decreased compared to controls and was associated with neurological impairment in ischemic stroke. In patients with ischemic stroke, the plasma concentrations of Cr (p < 0.001), Cu (p = 0.004), As (p = 0.010), Cs (p = 0.046), Rb (p = 0.041), and Sb (p = 0.001) were significantly higher than those in the HCs, whereas the plasma Tl concentrations (p = 0.022) were significantly lower. The results of the BKMR and WQS models showed that combined exposure to metal mixtures was linked to a higher risk of ischemic stroke. Cr was positively correlated with the rs-FC between the left Rolandic_Oper and the left Supp_Motor_Area (r = 0.414, p = 0.029), while negatively correlated with the rs-FC between the right Parietal_Inf and the left supramarginal (r = −0.398, p = 0.037). Cu was negatively correlated with the rs-FC between the left paracentral lobule and the left thalamus (r = −0.409, p = 0.031). Tl was positively correlated with the rs-FC between the right Parietal_Inf and the left supramarginal cortex (r = 0.590, p = 0.001). A negative correlation was observed between Cs and rs-FC between the right Cingulate_Mid and left Occipital_Sup (r = −0.429, p = 0.024). Sb was negatively correlated with the rs-FC between the left Parietal_Inf and the right SupraMarginal (r = −0.384, p = 0.044), the right Parietal_Inf and the left SupraMarginal (r = −0.583, p = 0.001), and the left SupraMarginal and the right SupraMarginal (r = −0.377, p = 0.048). ConclusionPlasma levels of Cr, Cu, Tl, Cs, and Sb were associated with altered rs-FC in brain regions related to motor control, sensory integration, executive function, language processing, and emotional regulation in ischemic stroke patients with basal ganglia infarction.

Imbalanced temporal states of cortical blood-oxygen-level-dependent signal variability during rest in episodic migraine

BackgroundMigraine has been associated with functional brain changes including altered connectivity and activity both during and between headache attacks. Recent studies established that the variability of the blood-oxygen-level-dependent (BOLD) signal is an important attribute of brain activity, which has so far been understudied in migraine. In this study, we investigate how time-varying measures of BOLD variability change interictally in episodic migraine patients.MethodsTwo independent resting state functional MRI datasets acquired on 3T (discovery cohort) and 1.5T MRI scanners (replication cohort) including 99 episodic migraine patients (n3T = 42, n1.5T=57) and 78 healthy controls (n3T = 46, n1.5T=32) were analyzed in this cross-sectional study. A framework using time-varying measures of BOLD variability was applied to derive BOLD variability states. Descriptors of BOLD variability states such as dwell time and fractional occupancy were calculated, then compared between migraine patients and healthy controls using Mann-Whitney U-tests. Spearman’s rank correlation was calculated to test associations with clinical parameters.ResultsResting-state activity was characterized by states of high and low BOLD signal variability. Migraine patients in the discovery cohort spent more time in the low variability state (mean dwell time: p = 0.014, median dwell time: p = 0.022, maximum dwell time: p = 0.013, fractional occupancy: p = 0.013) and less time in the high variability state (mean dwell time: p = 0.021, median dwell time: p = 0.021, maximum dwell time: p = 0.025, fractional occupancy: p = 0.013). Higher uptime of the low variability state was associated with greater disability as measured by MIDAS scores (maximum dwell time: R = 0.45, p = 0.007; fractional occupancy: R = 0.36, p = 0.035). Similar results were observed in the replication cohort.ConclusionEpisodic migraine patients spend more time in a state of low BOLD variability during rest in headache-free periods, which is associated with greater disability. BOLD variability states show potential as a replicable functional imaging marker in episodic migraine.

Epilepsy lesion localization method based on brain function network.

In the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning. To overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification. The proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others. Our method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

Relationships between the gut microbiome and brain functional alterations in first-episode, drug-naïve patients with major depressive disorder

ObjectiveIncreasing evidence has shown that the microbiota-gut-brain axis (MGB) is involved in the mechanism of major depressive disorder (MDD). However, the relationship between the gut microbiome and brain function in MDD patients has not been determined. Here, we intend to identify specific changes in the gut microbiome and brain function in first-episode, drug-naïve MDD patients and then explore the associations between the two omics to elucidate how the MGB axis plays a role in MDD development. MethodsWe recruited 38 first-episode, drug-naïve MDD patients and 37 healthy controls (HC). The composition of the fecal microbiome and neural spontaneous activity alterations were examined using 16S rRNA gene amplicon sequencing analysis and regional homogeneity (ReHo). Spearman correlation analyses were conducted to assess the associations between the gut microbiome and brain function. ResultsCompared with HC, MDD patients exhibited distinct alterations in the gut microbiota and elevated ReHo in the frontal regions. In the MDD group, a positive relationship was noted between the relative abundance of Blautia and the HAMD-17 and HAMA scores, as well as between the relative abundance of Oxalobacteraceae and the HAMD-17 score. The relative abundances of Porphyromonadaceae and Parabacteroides were negatively correlated with the ReHo values of frontal regions. LimitationsOur study utilized a cross-sectional design, and the number of subjects was relatively small. ConclusionWe found that some specific gut microbiomes were associated with frontal function, and others were associated with clinical symptoms in MDD patients, which may support the role of the MGB axis underlying MDD.

Inter-modality source coupling: a fully-automated whole-brain data-driven structure-function fingerprint shows replicable links to reading in a large-scale (N∼8K) analysis.

Both structural and functional brain changes have been individually associated with developing cognitive processes such as reading. However, there is limited research about the combined influence of resting-state functional and structural magnetic resonance imaging (rs-fMRI and sMRI) features in reading development, which could provide insights into the interplay between brain structure and function in shaping cognitive growth. We propose a method called inter-modality source coupling (IMSC) to study the coupling between the rs-fMRI and sMRI and its relationship to reading ability in school-age children. This approach is applied to baseline data from four thousand participants (9-11 years) and replicated in a second group. Our analysis focused on the relationship of IMSC to overall reading score. Our findings indicate that higher reading ability was linked with increased function-structure coupling among higher-level cortical regions, particularly those links between the inferior parietal lobule and inferior frontal areas, and conversely, lower reading ability was associated with enhanced function-structure coupling among the fusiform and lingual gyrus. Our study found evidence of spatial correspondence between the data indicating an interplay between brain structure and function in our participants. Our approach revealed a linked pattern of whole brain structure to the corresponding functional connectivity pattern that correlated with reading ability. This novel IMSC analysis method provides a new approach to study the multimodal relationship between brain function and structure. These findings have interesting implications for understanding the multimodal complexity underlying the development of the neural basis for reading ability in school-aged children.

The impact of illness duration on brain activity in goal-directed and habit-learning systems in obsessive-compulsive disorder progression: A resting-state functional imaging study

It is increasingly evident that structural and functional changes in brain regions associated with obsessive–compulsive disorder (OCD) are often related to the development of the disease. However, limited research has been conducted on how the progression of OCD may lead to an imbalance between goal-directed and habit-learning systems. This study employs resting-state functional imaging to examine the relationship between illness duration and abnormal brain function in goal-directed/habitual-learning systems. Demographic, clinical, and multimodal fMRI data were collected from participants. Our findings suggest that, compared to healthy controls, individuals with OCD exhibit abnormal brain functional indicators in both goal-directed and habit-learning brain regions, with a more pronounced reduction observed in the goal-directed regions. Additionally, abnormal brain activity is associated with illness duration, and the abnormalities observed in goal-directed regions are more effective in distinguishing different courses of OCD patients. Patients with different durations of OCD have functional abnormalities in the goal-directed and habitual-learning brain regions. There are differences in the degree of abnormality in different brain regions, and these abnormalities may disrupt the balance between goal-directed and habitual-learning systems, leading to increasing reliance on repetitive behaviors.

Measuring functional connectivity in frequency-domain helps to better characterize brain function.

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

The differential effects of integrase strand transfer inhibitors and efavirenz on neuropsychiatric conditions and brain imaging in HIV-positive men who have sex with men

Integrase strand transfer inhibitors (INSTIs) have emerged as the first-line choice for treating human immunodeficiency virus (HIV) infection due to their superior efficacy and safety. However, the impact of INSTIs on the development of neuropsychiatric conditions in people living with HIV (PLWH) is not fully understood due to limited data. In this study, we conducted a cross-sectional examination of PLWH receiving antiretroviral therapy, with a specific focus on HIV-positive men who have sex with men (MSM) on INSTI-based regimens (n = 61) and efavirenz (EFV)-based regimens (n = 28). Participants underwent comprehensive neuropsychiatric evaluations and multimodal magnetic resonance imaging (MRI) scans, including T1-weighted images and resting-state functional MRI. Compared to the EFV group, the INSTI group exhibited primarily reduced gray matter volume (GMV) in the right superior parietal gyrus, higher regional homogeneity (ReHo) in the left postcentral gyrus, lower ReHo in the right orbital part of the inferior frontal gyrus, and increased voxel-wise functional connectivity for the seed region in the left inferior temporal gyrus with clusters in the right cuneus. Furthermore, the analysis revealed a main effect of antiretroviral drugs on GMV changes, but no main effect of neuropsychiatric disorders or their interaction. The repeated analysis of participants who did not switch regimens confirmed the GMV changes in the INSTI group, validating the initial findings. Our study demonstrated gray matter atrophy and functional brain changes in PLWH on INSTI-based regimens compared to those on EFV-based regimens. These neuroimaging results provide valuable insights into the characteristics of brain network modifications in PLWH receiving INSTI-based regimens.

Spontaneous brain activity in patients with central retinal artery occlusion: a resting-state functional MRI study using machine learning.

Central retinal artery occlusion (CRAO) is a serious eye condition that poses a risk to vision, resulting from the blockage of the central retinal artery. Because of the anatomical connection between the ocular artery, which derives from the internal carotid artery, and the anterior middle cerebral artery, hemodynamic alterations and sudden vision loss associated with CRAO may impact brain functionality. This study aimed to examine alterations in spontaneous neural activity among patients with CRAO by resting-state functional MRI. In addition, we selected the amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuation (fALFF) values as classification features for distinguishing CRAO from healthy controls (HCs) using a support vector machine classifier. A total of 18 patients diagnosed with CRAO and 18 HCs participated in the study. Resting-state brain function images and structural images were acquired from both groups. Aberrant changes in spontaneous brain functional activity among CRAO patients were investigated utilizing ALFF and fALFF analysis methods. Group differences in ALFF/fALFF values were assessed through a two-sample t-test. Subsequently, a machine learning classifier was developed to evaluate the clinical diagnostic potential of ALFF and fALFF values. In comparison to HCs, individuals with CRAO exhibited significantly higher ALFF values in the left cerebellum_6, vermis_7, left superior frontal gyrus, and left inferior frontal gyrus, triangular part. Conversely, the CRAO group displayed notably lower ALFF values in the left precuneus and left median cingulum gyri. Furthermore, higher fALFF values were observed in the left inferior frontal gyrus, triangular part, whereas lower fALFF values were noted in the right cerebellum_Crus2, left precuneus, right angular gyrus, left angular gyrus, right supramarginal gyrus, right superior parietal gyrus, and left precuneus. Utilizing the ALFF/fALFF values, the receiver operating characteristic curves (area under the curve) yielded 0.99 and 0.94 through machine learning analysis techniques. CRAO patients exhibit atypical neural activity in the brain, characterized by ALFF and fALFF values predominantly localized in the frontal, parietal, and cerebellar regions, which are closely linked to visual cognition and motor control impairments. Furthermore, ALFF and fALFF could serve as potential neuroimaging markers beyond the orbit among CRAO.

Brain functional training: a perspective article.

Introduction: Physical exercise (PE) positively affects the nervous system, impacting morphology and physiology. It increases brain gray and white matter, improves cerebral blood flow, and stimulates neurogenesis, synaptogenesis, and angiogenesis, promoting brain function. Although exercise already affects cognition, some training modalities place greater demands on the cognitive aspects of physical exercise, such as perceptual-motor and visual-motor training. This type of approach aims to emphasize the cognitive adaptations that occur chronically. Specifically for older people, functional training, a multi-component approach, is a promising exercise modality that stimulates functionality using multi-joint, multi-planar exercises mirroring daily activities. However, applying a greater focus on cognitive adaptations in line with the functional training proposal for maximal benefits remains underexplored. Aim: Thus, this perspective article initially explores different exercise approaches emphasizing cognitive adaptations and proposes Brain Functional Training to improve older adult's functionality. Methods: Furthermore, we explain how brain functional training can be explored to emphasize cognitive aspects based on increasing complexity to stimulate the executive function and its subdomains. Conclusion: This proposal is one alternative to combining motor and cognitive stimuli to promote autonomy and health in older people.

The effect of Baduanjin exercise on cognitive function in elderly people: A systematic review based on near-infrared spectroscopy technology

To systematically evaluate the application of near-infrared spectroscopy technology in the study of the effect of Baduanjin exercise on cognitive function in elderly people. Relevant literature was retrieved from PubMed, Web of Science core database, ScienceDirect, Wanfang, VIP, and CNKI from 2014 to June 2023, and relevant data were extracted. A total of 1 valid literature was obtained, published in 2022, sourced from the medical journal FRONTIERS IN PUBLIC HEALTH, with an impact factor of 6.46. This journal belongs to JCR Q1 area, and the SCI basic version of the Chinese Academy of Sciences is in the Q3 area of medicine. The inclusion criteria for the subjects of this study were age 60 years, experience of Baduanjin exercise for 3 years, and MMSE score 24 points. This study, based on functional near-infrared spectroscopy technology, explores the effects of Baduanjin imagery and Baduanjin exercise on cognitive function in elderly people. Baduanjin exercise can increase brain connectivity, improve brain structural connections, and enhance brain function in elderly people. These three factors are also the main reasons for Baduanjin exercise to promote the improvement of cognitive function, including increasing brain gray matter volume, brain functional area connections, etc. Baduanjin exercise can improve specific cognitive functions by increasing gray matter volume. Baduanjin exercise can regulate the functional connections of the cognitive control network in elderly people and improve their memory.

NURS-13. NEUROCOGNITIVE SIDE EFFECTS OF PEDIATRIC BRAIN TUMORS: A CONTINUING CHALLENGE

Abstract BACKGROUND Pediatric brain tumors pose a considerable challenge in clinical oncology, necessitating multimodal treatment approaches. Many of these have the potential to impact cognition on vulnerable, young brains. While advancements in therapies have improved survival rates, neurocognitive outcomes continue to be one of the most challenging late effects that affect quality of life for pediatric brain tumor survivors. METHODS We conducted a literature review to identify 1) current treatment approaches for pediatric brain tumors, 2) the neurocognitive effects that survivors of pediatric brain tumors experience, and 3) appropriate interventions that can be implemented to mitigate those neurocognitive effects. RESULTS Novel chemotherapeutic agents, such as molecularly targeted therapies, are increasingly implemented in a personalized approach to treatment. New approaches to radiation therapy, particularly proton therapy, have shown promise in minimizing cognitive decline compared to traditional photon therapy. Advances in neuroimaging techniques have enabled a more detailed understanding of structural and functional brain changes associated with treatment. While these newer treatment modalities are promising, their long-term side effects are mostly unknown and bear close monitoring over time. Because neurocognitive side effects continue to underscore the impact of pediatric brain tumor treatments, recent studies often specifically evaluate the cognitive side effects as well as attempt to personalize treatments to reduce cognitive damage. CONCLUSIONS Recent research findings reinforce the enduring concern of neurocognitive side effects in pediatric brain tumor survivors. The integration of advanced therapeutic modalities and targeted supportive care interventions may hold the key to optimizing treatment outcomes while minimizing cognitive morbidity. Continued research efforts are essential for refining treatment protocols and developing personalized strategies to safeguard neurocognitive function in this vulnerable population. Additionally, studies exploring the role of supportive care interventions, such as cognitive rehabilitation and neuroprotective strategies, demonstrate potential avenues for mitigating cognitive sequelae but require ongoing research.

Neuroimaging Insights into Depressive Disorders: Structural and Functional Brain Changes

Neuroimaging Insights into Depressive Disorders: Structural and Functional Brain Changes

Effects of Tasks on Functional Brain Connectivity Derived from Inter-Individual Correlations: Insights from Regional Homogeneity of Functional MRI Data.

Research on brain functional connectivity often relies on intra-individual moment-to-moment correlations of functional brain activity, typically using techniques like functional MRI (fMRI). Inter-individual correlations are also employed on data from fMRI and positron emission tomography (PET). Many past studies have not specified tasks for participants, keeping them in an implicit "resting" condition. This lack of task specificity raises questions about how different tasks impact inter-individual correlation estimates. In our analysis of fMRI data from 100 unrelated participants, scanned during seven task conditions and in a resting state, we calculated Regional Homogeneity (ReHo) for each task as a regional measure of brain functions. We found that changes in ReHo due to different tasks were relatively small compared with the variations across brain regions. Cross-region variations of ReHo were highly correlated between different tasks. Similarly, whole-brain inter-individual correlation patterns were remarkably consistent across the tasks, showing correlations greater than 0.78. Changes in inter-individual correlations between tasks were primarily driven by connectivity in the visual, somatomotor, default mode network, and the interactions between them. The subtle yet statistically significant differences in functional connectivity may be linked to specific brain regions associated with the studied tasks. Future studies should consider task design when exploring inter-individual connectivity in specific brain systems.

Adoption of deep learning-based magnetic resonance image information diagnosis in brain function network analysis of Parkinson’s disease patients with end-of-dose wearing-off

Objectivethis study was to analyze the brain functional network of end-of-dose wearing-off (EODWO) in patients with Parkinson’s disease (PD) using a convolutional neural network (CNN)-based functional magnetic resonance imaging (fMRI) data classification model. Methodsone hundred PD patients were recruited and assigned to control (Ctrl) group (39 cases without EODWO) and experimental (Exp) group (61 cases with EODWO). The data classification model based on a CNN was employed to assist the analysis of the changes in brain functional network structure in the two groups. The CNN-based fMRI data classification model was primarily based on a CNN architecture, with improvements made to the initialization of convolutional kernel parameters. Firstly, a structure based on restricted Boltzmann machine (RBM) was constructed, followed by the initialization of convolutional kernel parameters. Subsequently, the model underwent training. Utilizing the data analysis module within the GRETNA toolbox, extracted feature sets were analyzed, including local measures such as betweenness centrality (BC) and degree centrality (DC), as well as global measures such as global efficiency (Eg) and local efficiency (Eloc). Resultsas sparsity increased, there was a gradual upward trend observed in Eg; however, the values of Eg in both brain functional networks remained relatively stable within the range of 0.2–0.5. The Eg value of the Ctrl group’s whole-brain functional network was 0.17 ± 0.02, while that of the Exp group’s whole-brain functional network was 0.17 ± 0.03, with no significant difference between them (P>0.05). The functional DC value of the superior frontal gyrus in the Exp group (8.71 ± 2.56) was significantly lower than that of the Ctrl group (13.32 ± 3.22), whereas the functional DC value of the anterior cingulate gyrus in the Exp group (19.33 ± 4.78) was significantly higher than that of the Ctrl group (15.21 ± 4.02) (P<0.05). There was no significant correlation observed between the functional DC value and levodopa or dopamine agonist therapy (DDT) in the Exp group, whereas the Ctrl group exhibited a significant positive correlation. Conclusionanalysis conducted via a CNN-based fMRI data classification model revealed a correlation between the occurrence of EODWO in PD patients and functional impairments in the left precuneus. Additionally, the occurrence of EODWO may potentially diminish the plasticity of the central prefrontal dopamine.

Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning.

The early stages of human development are increasingly acknowledged as pivotal in laying the groundwork for subsequent behavioral and cognitive development. Spatiotemporal (4D) brain functional atlases are important in elucidating the development of human brain functions. However, the scarcity of such atlases for early life stages stems from two primary challenges: (1) the significant noise in functional magnetic resonance imaging (fMRI) that complicates the generation of high-quality atlases for each age group, and (2) the rapid and complex changes in the early human brain that hinder the maintenance of temporal consistency in 4D atlases. This study tackles these challenges by integrating low-rank tensor learning with spectral embedding, thereby proposing a novel, data-driven 4D functional atlas generation framework based on spectral functional network learning (SFNL). This method utilizes low-rank tensor learning to capture common functional connectivity (FC) patterns across different ages, thus optimizing FCs for each age group to improve the temporal consistency of functional networks. Incorporating spectral embedding aids in mitigating potential noise in FC networks derived from fMRI data by reconstructing networks in the spectral space. Utilizing SFNL-generated functional networks enables the creation of consistent and highly qualified spatiotemporal functional atlases. The framework was applied to the developing Human Connectome Project (dHCP) dataset, generating the first neonatal 4D functional atlases with fine-grained temporal and spatial resolutions. Experimental evaluations focusing on functional homogeneity, reliability, and temporal consistency demonstrated the superiority of our framework compared to existing methods for constructing 4D atlases. Additionally, network analysis experiments, including individual identification, functional systems development, and local efficiency assessments, further corroborate the efficacy and robustness of the generated atlases. The 4D atlases and related codes will be made publicly accessible (https://github.com/zhaoyunxi/neonate-atlases).

Topological Organization of the Brain Network in Patients with Primary Angle-closure Glaucoma Through Graph Theory Analysis.

Primary angle-closure glaucoma (PACG) is a sight-threatening eye condition that leads to irreversible blindness. While past neuroimaging research has identified abnormal brain function in PACG patients, the relationship between PACG and alterations in brain functional networks has yet to be explored. This study seeks to examine the influence of PACG on brain networks, aiming to advance knowledge of its neurobiological processes for better diagnostic and therapeutic approaches utilizing graph theory analysis. A cohort of 44 primary angle-closure glaucoma (PACG) patients and 44 healthy controls participated in this study. Functional brain networks were constructed using fMRI data and the Automated Anatomical Labeling 90 template. Subsequently, graph theory analysis was employed to evaluate global metrics, nodal metrics, modular organization, and network-based statistics (NBS), enabling a comparative analysis between PACG patients and the control group. The analysis of global metrics, including small-worldness and network efficiency, did not exhibit significant differences between the two groups. However, PACG patients displayed elevated nodal metrics, such as centrality and efficiency, in the left frontal superior medial, right frontal superior medial, and right posterior central brain regions, along with reduced values in the right temporal superior gyrus region compared to healthy controls. Furthermore, Module 5 showed notable disparities in intra-module connectivity, while Module 1 demonstrated substantial differences in inter-module connectivity with both Module 7 and Module 8. Noteworthy, the NBS analysis unveiled a significantly altered network when comparing the PACG and healthy control groups. The study proposes that PACG patients demonstrate variations in nodal metrics and modularity within functional brain networks, particularly affecting the prefrontal, occipital, and temporal lobes, along with cerebellar regions. However, an analysis of global metrics suggests that the overall connectivity patterns of the entire brain network remain unaltered in PACG patients. These results have the potential to serve as early diagnostic and differential markers for PACG, and interventions focusing on brain regions with high degree centrality and nodal efficiency could aid in optimizing therapeutic approaches.

Network and state specificity in connectivity-based predictions of individual behavior.

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Combined neuromodulation and resection for functional cortex epilepsy: a case series

OBJECTIVE Medically refractory epilepsy (MRE) often requires resection of the seizure onset zone (SOZ) for effective treatment. However, when the SOZ is in functional cortex (FC), achieving complete and safe resection becomes difficult, due to the seizure network overlap with function. The authors aimed to assess the safety and outcomes of a combined approach involving partial resection combined with focal neuromodulation for FC refractory epilepsy. METHODS The authors performed a retrospective analysis of individuals diagnosed with MRE who underwent surgical intervention from January 2015 to December 2022. Patients whose SOZ was located in FC and were treated with resection combined with simultaneous implantation of a focal neuromodulation device (responsive neurostimulation [RNS] device) with more than 12 months of follow-up data were included. All patients underwent a standard epilepsy preoperative assessment including intracranial electroencephalography and extraoperative stimulation mapping. Resections were performed under general anesthesia, followed by the concurrent implantation of an RNS device. RESULTS Seven patients (4 males, median age 32.3 years, all right-handed) were included. The median interval from seizure onset to surgery was 17.4 years. The epileptogenic network included sensorimotor areas (cases 2, 3, and 6), visual cortex (case 1), language areas (cases 4 and 7), and the insula (case 5). The median follow-up was 3 years (range 1–5.8 years). No significant changes in neuropsychological tests were reported. One permanent nondisabling planned neurological deficit (left inferior quadrantanopia) was observed. Six patients had stimulation activated at a median of 4.7 months after resection. All patients achieved good seizure outcomes (5 with Engel class I and 2 with Engel class II outcomes). CONCLUSIONS Maximal safe resection combined with focal neuromodulation presents a promising alternative to stand-alone resections for MRE epileptogenic zones overlapping with functional brain. This combined approach prioritizes the preservation of function while improving seizure outcomes.