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Dynamic Causal Modeling (DCM) is a crucial tool for studying brain effective connectivity, offering valuable insights into brain network dynamics through functional magnetic resonance imaging (fMRI) and electrophysiology (EEG and MEG). However, its high computational complexity limits its applicability in large-scale network analysis. To address this issue, we propose a regression algorithm that integrates the Generalized Linear Model (GLM) with Sparse DCM, termed GSD. This algorithm enhances computational performance through three key optimizations: (1) utilizing the symmetry of the Fourier transform to convert complex frequency domain calculations into real number operations, thereby reducing computational complexity; (2) applying GLM and filtering techniques to minimize the effects of noise and confounds, enhancing parameter estimation accuracy; and (3) defining a new cost function to optimize variational inference and filter parameters, further improving parameter estimation accuracy. We validated the GSD algorithm using three public fMRI datasets: simulated Smith small-world network data, attention and motion measured data, and face recognition repetition effect measured data. The experimental results demonstrate that the GSD algorithm reduces computation time by over 50 % while maintaining parameter estimation performance comparable to traditional methods. These findings offer a new perspective on balancing model interpretability and computational efficiency, potentially broadening the application of DCM across various fields., Competing Interests: Declaration of competing interest The authors declare no competing financial interests or personal relationships that could have influenced the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: Accurately localizing brain motor areas is vital for protecting motor function during neurosurgical procedures. Magnetoencephalography (MEG) based on optically pumped magnetometer (OPM) improves the availability of MEG in clinical applications. The aim of this study is to evaluate the availability, accuracy and precision of "OPM-MEG" for localizing motor areas in brain tumor patients and healthy individuals., Methods: Participants were enrolled and subjected to primary motor area localization by both 3T-fMRI and 128-channel OPM-MEG examinations. The localization accuracy (ability of mapping on the anatomical location) and precision (activation signal centralization) were compared between the two methods, and accuracy was further validated by intraoperative direct cortical electrical stimulation (DCS) on the localized area with assistance of neuro-navigation system., Result: A total of 12 participants (7 brain tumor patients and 5 healthy individuals) were enrolled and all had successful localization for motor areas by both methods. The average time of OPM-MEG examination for each limb function was approximately 9 min. The localizations by both methods mainly covered the anatomical location of primary motor cortex and were partially overlapped. The motor activation signal identified by OPM-MEG was more centralized than fMRI did. The centroid of motor area localized by the OPM-MEG deviated from it by fMRI, with a mean distance of 19.7 mm and 27.48 mm for hand or foot localization, respectively. Furthermore, the OPM-MEG centroid for hand movement successfully triggered corresponding hand response by DCS., Conclusions: In this first-in-human study exploring the potential of OPM-MEG in functional localization of motor areas, we revealed its availability and reliability in mapping motor areas, demonstrating it as a promising tool in assisting neurosurgical practice and neuroscience research., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ming Ding reports a relationship with Beijing X-Magtech Technology Limited that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: The rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain-computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders., Objective: This study aimed to analyze brain activity patterns in patients undergoing awake craniotomy during actual movements and motor imagery, mainly focusing on the motor control processes of the bilateral limbs., Methods: We conducted detailed observations of patients undergoing awake craniotomies. The experimenter requested participants to perform and imagine a series of motor tasks involving their hands and tongues. Brain activity during these tasks was recorded using functional magnetic resonance imaging (fMRI) and intraoperative electrocorticography (ECoG). The study included left and right finger tapping, tongue protrusion, hand clenching, and imagined movements corresponding to these actions., Results: fMRI revealed significant activation in the brain's motor areas during task performance, mainly involving bilateral brain regions during imagined movement. ECoG data demonstrated a marked desynchronization pattern in the ipsilateral motor cortex during bilateral motor imagination, especially in bilateral coordination tasks. This finding suggests a potential controlling role of the unilateral cerebral cortex in bilateral motor imagination., Conclusion: Our study highlights the unilateral cerebral cortex's significance in controlling bilateral limb motor imagination, offering new insights into future brain network remodeling in patients with hemiplegia. Additionally, these findings provide important insights into understanding motor imagination and its impact on BCI and neurorehabilitation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Neuroimaging studies have identified functional and structural brain circuits that support reading. However, much less is known about how reading-related functional dynamics are constrained by white matter structure. Network control theory proposes that cortical brain dynamics are linearly determined by the white matter connectome, using control energy to evaluate the difficulty of the transition from one cognitive state to another. Here we apply this approach to linking brain dynamics with reading ability and disability in school-age children. A total of 51 children ages 8.25 -14.6 years performed an in-scanner rhyming task in visual and auditory modalities, with orthographic (spelling) and phonological (rhyming) similarity manipulated across trials. White matter structure and fMRI activation were used conjointly to compute the control energy of the reading network in each condition relative to a null fixation state. We then tested differences in control energy across trial types, finding higher control energy during non-word trials than word trials, and during incongruent trials than congruent trials. ROI analyses further showed a dissociation between control energy of the left fusiform and superior temporal gyrus depending on stimulus modality, with higher control energy for visual modalities in fusiform and higher control energy for auditory modalities in STG. Together, this study highlights that control theory can explain variations on cognitive demands in higher-level abilities such as reading, beyond what can be inferred from either functional or structural MRI measures alone., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Introduction: Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this extent, SynthSeg is a robust deep learning model designed for automatic brain segmentation across various contrasts and resolutions. This study validates the SynthSeg robust brain segmentation model on computed tomography (CT), using a multi-center dataset., Methods: An open access dataset of 260 paired CT and magnetic resonance imaging (MRI) from radiotherapy patients treated in 5 centers was collected. Brain segmentations from CT and MRI were obtained with SynthSeg model, a component of the Freesurfer imaging suite. These segmentations were compared and evaluated using Dice scores and Hausdorff 95 distance (HD95), treating MRI-based segmentations as the ground truth. Brain regions that failed to meet performance criteria were excluded based on automated quality control (QC) scores., Results: Dice scores indicate a median overlap of 0.76 (IQR: 0.65-0.83). The mean volume difference is 7.79% (CI: 6.41%-9.18%), with CT segmentations typically smaller than MRI-based. The median HD95 is 2.95 mm (IQR: 1.73-5.39). QC score based thresholding improves median dice by 0.1 and median HD95 by 0.05 mm. Morphological differences related to sex and age, as detected by MRI, were also replicated with CT, with an approximate 17% difference between the CT and MRI results for sex and 10% difference between the results for age., Conclusion: SynthSeg can be utilized for CT-based automatic brain segmentation, but only in applications where precision is not essential. CT performance is lower than MRI based on the integrated QC scores, but low-quality segmentations can be excluded with QC-based thresholding. Additionally, performing CT-based neuroanatomical studies is encouraged, as the results show correlations in sex- and age-based analyses similar to those found with MRI., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Multi-contrast magnetic resonance (MR) imaging is an advanced technology used in medical diagnosis, but the long acquisition process can lead to patient discomfort and limit its broader application. Shortening acquisition time by undersampling k-space data introduces noticeable aliasing artifacts. To address this, we propose a method that reconstructs multi-contrast MR images from zero-filled data by utilizing a fully-sampled auxiliary contrast MR image as a prior to learn an adjacency complementary graph. This graph is then combined with a residual hybrid attention network, forming the adjacency complementary graph assisted residual hybrid attention network (ACGRHA-Net) for multi-contrast MR image reconstruction. Specifically, the optimal structural similarity is represented by a graph learned from the fully sampled auxiliary image, where the node features and adjacency matrices are designed to precisely capture structural information among different contrast images. This structural similarity enables effective fusion with the target image, improving the detail reconstruction. Additionally, a residual hybrid attention module is designed in parallel with the graph convolution network, allowing it to effectively capture key features and adaptively emphasize these important features in target contrast MR images. This strategy prioritizes crucial information while preserving shallow features, thereby achieving comprehensive feature fusion at deeper levels to enhance multi-contrast MR image reconstruction. Extensive experiments on the different datasets, using various sampling patterns and accelerated factors demonstrate that the proposed method outperforms the current state-of-the-art reconstruction methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Impaired cerebral waste clearance (i.e., glymphatics) is evident in aging and neurodegenerative disorders, such as Alzheimer's disease, where an impaired waste clearance system could be related to the accumulation of pathological proteins (e.g., tau). One marker of impaired cerebral clearance is the abundance of enlarged perivascular spaces (PVS). Preclinical studies propose a similar clearance system in the eye, driven by intraocular pressure (IOP). This cross-sectional pilot study explores the link between ocular and cerebral waste clearance by examining the association between MRI-visible PVS, tear fluid total-tau, and IOP. Thirty cognitively healthy participants, all aged over 55 years, underwent 7 Tesla MRI, with PVS visually rated in the centrum semiovale (CSO) and basal ganglia. Tear fluid was collected using paper Schirmer's strips and analyzed for total-tau using enzyme-linked immunosorbent assay. IOP was measured using non-contact tonometry. Partial Spearman's correlation coefficients of eye and brain markers were calculated, adjusted for age, sex, tear fluid-wetting length, and hemispheric region of interest volume. Higher CSO PVS scores in the left and right hemisphere were associated with higher levels of tear fluid total-tau. Higher CSO PVS scores in both hemispheres were related to lower ipsilateral IOP. The exploratory results suggest that higher tear fluid total-tau and a reduced driving force of ocular waste clearance are connected to impaired cerebral waste clearance in cognitive healthy individuals. This study connects the potential ocular glymphatic system to the cerebral waste clearance system. Clarifying waste clearance biology and validating eye biomarkers for cerebral waste clearance could provide treatment targets and diagnostic opportunities for neurological diseases., Competing Interests: Declaration of competing interest The authors report no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The ongoing brain activity serves as a baseline that supports both internal and external cognitive processes. However, its precise nature remains unclear. Considering that people display various patterns of brain activity even when engaging in the same task, it is reasonable to believe that individuals possess their unique brain baseline pattern. Using spatial independent component analysis on a large sample of fMRI data from the Human Connectome Project (HCP), we found an individual-specific component which can be consistently extracted from either resting-state or different task states and is reliable over months. Compared to functional connectome fingerprinting, it is much more stable across different fMRI modalities. Its stability is closely related to high explained variance and is minimally influenced by factors such as noise, scan duration, and scan interval. We propose that this component underlying the ongoing activity represents an individual-specific baseline pattern of brain activity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Cognitive reserve (CR) explains the varying trajectories of cognitive decline in healthy and pathological ageing. CR is often operationalized in terms of socio-behavioural proxies that modulate cognitive performance. Individuals with higher CR are known to maintain better cognitive functions, but evidence on the underlying brain activity remains scattered. Here we review CR studies using functional MRI in young, healthy and pathologically elderly individuals. We focus on the two potential neural mechanisms of CR, neural reserve (efficiency of brain networks) and neural compensation (recruitment of additional brain regions), and the effect of different proxies on them. The results suggest increased task-related activity in different cognitive domains with age and compensation in case of difficult task and pathology. The effects of proxies lead to increased neural reserve (reduced brain activity) in both older and younger individuals. Their relationship with compensation remains unclear, largely due to the lack of young adult samples, particularly in clinical studies. These findings underscore the critical role of lifelong engagement in mentally enriching activities for preserving cognitive function during aging. New studies are encouraged to refine the CR theoretical and empirical framework, particularly regarding the measurement of socio-behavioral proxies and their relationship with cognitive decline and neural underpinning., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Procrastination, an irrational delay of intended action, leads to numerous adverse effects in many life domains, such as low academic performance, poor mental health, and financial distress. Previous studies have revealed a substantial negative correlation between emotional regulation and procrastination. However, the neural basis for the association between emotion regulation and procrastination remains unclear. Therefore, we employed the voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to explore the neural substrates underlying how emotion regulation is responsible for procrastination (N = 243). In line with our hypothesis, the results showed a significant negative correlation between emotion regulation ability and procrastination. Additionally, the VBM analysis showed that emotion regulation ability was positively correlated with gray matter (GM) volumes in the right dorsal-lateral prefrontal cortex (dlPFC). The mediation analysis revealed that emotion regulation ability mediated the relationship between the GM volumes of the right dlPFC and procrastination. Furthermore, the RSFC results indicated that right dlPFC-left insula functional connectivity was positively associated with emotion regulation ability. Emotion regulation ability further mediated the relationship between the right dlPFC-left insula functional connectivity and procrastination. The current findings suggest that the neural pathway related to cognitive control over aversive emotion may be responsible for the close relationship between emotion regulation and procrastination, which provides a novel perspective for explaining the tight association between emotion regulation and procrastination., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Brain vascular health worsens with age, as is made evident by resting grey matter cerebral blood flow (CBF GM ) reductions and lengthening arterial transit time (ATT GM ). Exercise training can improve aspects of brain health in older adults, yet its effects on CBF GM and ATT GM remain unclear. This randomised controlled trial assessed responses of CBF GM and ATT GM to a 26 week exercise intervention in 65 healthy older adults (control: n = 33, exercise: n = 32, aged 60-81 years), including whether changes in CBF GM or ATT GM were associated with changes in cognitive functions. Multiple-delay pseudo-continuous arterial spin labelling data were used to estimate resting global and regional CBF GM and ATT GM . Results showed no between-group differences in CBF GM or ATT GM following the intervention. However, exercise participants with the greatest cardiorespiratory gains (n = 17; ∆V̇O 2peak >2 mL/kg/min) experienced global CBF GM reductions (-4.0 [-7.3, -0.8] mL/100 g/min). Cognitive functions did not change in either group and changes were not associated with changes in CBF GM or ATT GM . Our findings indicate that exercise training in older adults may induce global CBF GM reductions when high cardiorespiratory fitness gains are induced, but this does not appear to affect cognitive functions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Katrien Segaert reports financial support was provided by the Research Council of Norway. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: Status epilepticus (SE) is a severe neurological condition that might lead to long-term consequences such as neuronal death. This study investigated whether SE leads to brain volume loss by characterizing the dynamic of peri-ictal MRI abnormalities (PMA) through follow-up MRIs and assessing whether SE duration and specific outcome characteristics are associated with brain atrophy., Methods: A prospective single-center cohort study enrolled 590 adult patients with definitive or possible SE. MRI in an acute setting was performed in 353/590 (60 %) patients. Follow-up MRIs at one week and one month were conducted to assess the reversibility of PMA. Measurements of diffuse brain volume were performed by employing a voxel-based morphometry with FreeSurfer, comparing an initial MRI with a follow-up test done four weeks after the initial one. The study analyzed the correlation between brain volume loss, SE duration, and clinical outcomes., Results: PMA were observed in 156/353 (44 %) patients in at least one MRI sequence. In 44/83 (53 %) patients, PMA were reversible in one week. PMA persisted in 39/83 (47 %) patients. A second follow-up MRI was performed four weeks after the initial MRI in 33/39 (85 %) patients. In 14/33 (42 %), the MRI showed signs of focal atrophy, mostly in hippocampus. Volumetric analysis performed in patients who underwent two follow-up MRIs, indicated that 85 % of patients (28/33) had a decreased diffuse brain volume, with a median volume reduction of 16 %. A moderate negative correlation was found between diffuse brain volume and SE duration (Spearman correlation: -0.57) as well as hospitalization length (Spearman correlation: -0.60). This indicates that longer SE duration and extended hospitalization were associated with a greater brain volume loss., Conclusion: In this prospective study, a proportion of patients displayed cerebral volume loss following a SE. These patients had longer duration and worse outcome of SE. However, the findings should be interpreted with caution due to several limitations, including the lack of consideration for underlying etiologies that may contribute to volume loss., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: The occurrence of comorbid psychiatric disorders (PD) in patients with pharmacoresistant temporal lobe epilepsy (TLE) associated to mesial temporal sclerosis (MTS) can be considered as a result of the complex interaction between biological and psychosocial factors, as well as the effects of antiseizure medications (ASM). Regarding biological aspects, despite the growing amount of knowledge, there is still a scarcity of data in literature clarifying whether a more precise definition of the seizure onset zone (SOZ) could be associated with a more favorable post-surgical psychiatric outcome. In the present study, the clinical and sociodemographic pre-surgical variables, including the results of neurophysiological and neuroimaging exams, were evaluated in patients with pharmacoresistant TLE-MTS aiming to investigate possible risk factors for the presence of PD after cortico-amygdalohippocampectomy (CAH)., Methods: A retrospective cohort analysis of medical records from initially 106 pre-surgical patients with pharmacoresistant TLE-MTS with PD (n = 51; 48.1 %) and without PD (n = 55; 51.9 %) proceeded. Pre-surgical clinical and sociodemographic data were compared between both groups and the predictors for the presence of post-surgical PD were characterized up to one and two years after CAH., Results: Seventeen patients (16 %) had lost their follow-up in the first year after surgery, and 89 (84 %) had completed the study. No clinical and sociodemographic differences were observed between both groups of patients (p > 0.05), except for a history of previous psychiatric treatment (p = 0.001). Eighteen patients (35.29 %) with pre-surgical history of PD had remission of PD after CAH, while eight (14.5 %) developed de novo PD. The previous history of PD was directly associated with the development of post-surgical PD one year after CAH (p < 0.0001). Previous psychiatric treatment (p < 0.01), previous history of mood (p = 0.002) and anxiety (p = 0.03) disorder, as well as discordance in lateralization between MRI, SPECT, and EEG (p = 0.02), were predictors for the development of PD two years after CAH. Post-surgical psychiatric outcomes were associated to seizure outcome based on the Engel classification (p < 0,0001)., Conclusion: The present data observed an association between lateralization concordance of results of pre-surgical investigative exams and positive postoperative psychiatric outcomes in patients with pharmacoresistant TLE-MTS. These results could suggest that a more precise definition of the SOZ could be associated with a more favorable post-surgical psychiatric outcome after CAH., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The human brain works as a hierarchical organization that is a continuous axis spanning sensorimotor cortex to transmodal cortex (referring to cortex that integrates multimodal sensory information and participates in complex cognitive functions). Previous studies have demonstrated abnormalities in several specific networks that may account for their multiple behavioral deficits in patients with bulimia nervosa (BN), but whether and how the network hierarchical organization changes in BN remain unknown. This study aimed to investigate alterations of the hierarchy organization in BN network and their clinical relevance. Connectome gradient analyses were applied to depict the network hierarchy patterns of fifty-nine patients with BN and thirty-nine healthy controls (HCs). Then, we evaluated the network- and voxel-level gradient alterations of BN by comparing gradient values in each network and each voxel between patients with BN and HCs. Finally, the association between altered gradient values and clinical variables was explored. In the principal gradient, patients with BN exhibited reduced gradient values in dorsal attention network and increased gradient values in subcortical regions compared to HCs. In the secondary gradient, patients with BN showed decreased gradient values in ventral attention network and increased gradient values in limbic network. Regionally, the areas with altered principal or secondary gradient values in BN group were mainly located in transmodal networks, i.e., the default-mode and frontoparietal network. In BN group, the principal gradient values of right inferior frontal gyrus were negatively associated with external eating behavior. This study revealed the disordered network hierarchy patterns in patients with BN, which suggested a disturbance of brain information integration from attention network and subcortical regions to transmodal networks in these patients. These findings may provide insight into the neurobiological underpinnings of BN., Competing Interests: Declaration of competing interest The authors declare that there are no conflicts of interests regarding the publication of this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The relationship between the brain's structural wiring and its dynamic activity is thought to vary regionally, implying that the mechanisms underlying structure-function coupling may differ depending on a region's position within the brain's hierarchy. To better bridge the gap between structure and function, it is crucial to identify the factors shaping this regionality, not only in terms of how static functional connectivity aligns with structure, but also regarding the time-domain variability of this interplay. Here we map structure - function coupling and its time-domain variability and relate them to the heterogeneity of the cortex. We show that these two properties split the cortical landscape into two districts anchored to the opposite ends of the brain's hierarchy. By looking at statistical relationships with layer-specific gene transcription, T1w/T2 w ratio, and synaptic density, we show that macro-scale structure-function coupling may be rooted in the brain's microstructure and meso‑scale laminar specialization. Finally, we demonstrate that a lower and more variable alignment of function and structure may bestow the emergence of unique functional dynamics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Brain development is characterized by an increase in structural and functional segregation, which supports the specialization of cognitive processes within the context of network neuroscience. In this study, we investigated age-related changes in morphological segregation using individual Regional Radiomics Similarity Networks (R2SNs) constructed with a longitudinal dataset of 494 T1-weighted MR scans from 309 typically developing children aged 6.2 to 13 years at baseline. Segertation indices were defined as the relative difference in connectivity strengths within and between modules and cacluated at the global, system and local levels. Linear mixed-effect models revealed longitudinal increases in both global and system segregation indices, particularly within the limbic and dorsal attention network, and decreases within the ventral attention network. Superior performance in working memory and inhibitory control was associated with higher system-level segregation indices in default, frontoparietal, ventral attention, somatomotor and subcortical systems, and lower local segregation indices in visual network regions, regardless of age. Furthermore, gene enrichment analysis revealed correlations between age-related changes in local segregation indices and regional expression levels of genes related to developmental processes. These findings provide novel insights into typical brain developmental changes using R2SN-derived segregation indices, offering a valuable tool for understanding human brain structural and cognitive maturation., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shuyu Li reports financial support was provided by National Natural Science Foundation of China. Yong He reports financial support was provided by National Natural Science Foundation of China. Qiongling Li, Qi Dong, Sha Tao, Yanpei Wang, Daoyang Wang, Mingming Hu, and Zhiying Pan reports financial support was provided by National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: Functional brain alterations in post-Covid-19 condition have been minimally explored to date. Here, we investigate differences in resting-state thalamic functional connectivity among post-Covid patients with and without fatigue, alongside structural brain changes and cognition., Methods: Thirty-nine post-Covid patients (n = 15 fatigued, n = 24 non-fatigued) participated in our study, undergoing comprehensive cognitive assessments, as well as functional and structural neuroimaging. We conducted a seed-based functional connectivity analysis using the thalamus as a seed region, exploring its connectivity with the entire brain. To further elucidate our findings, correlation analyses were performed using the functional coupling between the thalamus and regions showing different connectivity between the two patient groups., Results: Our results reveal that patients experiencing fatigue exhibit anti-correlated functional coupling between the thalamus and motor-associated regions, including the motor cortex (M1), supplementary motor area (SMA), and anterior cingulate cortex (ACC), compared to non-fatigued patients, who are showing positive functional coupling. Furthermore, this observed coupling was found to correlate with both the fatigue scores obtained from a fatigue questionnaire and performance on the Trail Making Test, Part A, which represents a measure of processing speed., Conclusions: Our study highlights significant differences in resting-state functional connectivity between post-Covid patients with and without fatigue, particularly within motor-associated brain regions. These findings suggest a potential neural mechanism underlying post-Covid fatigue and underscore the importance of considering both functional and structural brain changes in understanding the symptomatic sequelae of post-Covid-19 condition. Further research is warranted to provide insight into the longitudinal trajectories of these neural alterations., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Marisa Koini reports financial support was provided by Austrian Research Promotion Agency. The authors declare that there are no conflicts of interest. The co-authors affiliated with Probando GmbH and digitAAL life GmbH were not involved in the conception, design, execution, data analysis, or interpretation of the study. Their role was limited to patient recruitment (Probando GmbH) and designing a cognitive training app (not part of this manuscript; digitAAL life GmbH). All other authors contributed to the study in compliance with the journal's ethical and research guidelines. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: This study aims to explore the impact of smoking on intrinsic brain activity among high-altitude (HA) populations. Smoking is associated with various neural alterations, but it remains unclear whether smokers in HA environments exhibit specific neural characteristics., Methods: We employed ALFF and fALFF methods across different frequency bands to investigate differences in brain functional activity between high-altitude smokers and non-smokers. 31 smokers and 31 non-smokers from HA regions participated, undergoing resting-state functional magnetic resonance imaging (rs-fMRI) scans. ALFF/fALFF values were compared between the two groups. Correlation analyses explored relationships between brain activity and clinical data., Results: Smokers showed increased ALFF values in the right superior frontal gyrus (R-SFG), right middle frontal gyrus (R-MFG), right anterior cingulate cortex (R-ACC), right inferior frontal gyrus (R-IFG), right superior/medial frontal gyrus (R-MSFG), and left SFG compared to non-smokers in HA. In sub-frequency bands (0.01-0.027 Hz and 0.027-0.073 Hz), smokers showed increased ALFF values in R-SFG, R-MFG, right middle cingulate cortex (R-MCC), R-MSFG, Right precentral gyrus and L-SFG while decreased fALFF values were noted in the right postcentral and precentral gyrus in the 0.01-0.027 Hz band. Negative correlations were found between ALFF values in the R-SFG and smoking years., Conclusion: Our study reveals the neural characteristics of smokers in high-altitude environments, highlighting the potential impact of smoking on brain function. These results provide new insights into the neural mechanisms of high-altitude smoking addiction and may inform the development of relevant intervention measures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Corticostriatal connections are essential for motivation, cognition, and behavioral flexibility. There is broad interest in using resting-state functional magnetic resonance imaging (rs-fMRI) to link circuit dysfunction in these connections with neuropsychiatric disorders. In this paper, we used tract-tracing data from non-human primates (NHPs) to assess the likelihood of monosynaptic connections being represented in rs-fMRI data of NHPs and humans. We also demonstrated that existing hub locations in the anatomical data can be identified in the rs-fMRI data from both species. To characterize this in detail, we mapped the complete striatal projection zones from 27 tract-tracer injections located in the orbitofrontal cortex (OFC), dorsal anterior cingulate cortex (dACC), ventromedial prefrontal cortex (vmPFC), ventrolateral PFC (vlPFC), and dorsal PFC (dPFC) of macaque monkeys. Rs-fMRI seeds at the same regions of NHP and homologous regions of human brains showed connectivity maps in the striatum mostly consistent with those observed in the tracer data. We then examined the location of overlap in striatal projection zones. The medial rostral dorsal caudate connected with all five frontocortical regions evaluated in this study in both modalities (tract-tracing and rs-fMRI) and species (NHP and human). Other locations in the caudate also presented an overlap of four frontocortical regions, suggesting the existence of different locations with lower levels of input diversity. Small retrograde tracer injections and rs-fMRI seeds in the striatum confirmed these cortical input patterns. This study sets the ground for future studies evaluating rs-fMRI in clinical samples to measure anatomical corticostriatal circuit dysfunction and identify connectional hubs to provide more specific treatment targets for neurological and psychiatric disorders., Competing Interests: Declaration of competing interest The authors have nothing to disclose., (Copyright © 2024. Published by Elsevier Inc.)

We present a comprehensive methodology for measuring heterogeneous interstitial fluid flow in murine brain tumors using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) coupled with the computational tool, Lymph4D. This four-part protocol encompasses glioma cell preparation, tumor inoculation, MRI imaging protocol, and histological verification using Evans Blue. While conventional DCE-MRI analysis primarily focuses on vascular perfusion, our methods reveal untapped potential to extract crucial information about interstitial fluid dynamics, including directions, velocities, and diffusion coefficients. This methodology extends beyond glioma research, with applicability to conditions routinely imaged with DCE-MRI, thereby offering a versatile tool for investigating interstitial fluid dynamics across a wide range of diseases and conditions. Our methodology holds promise for accelerating discoveries and advancements in biomedical research, ultimately enhancing diagnostic and therapeutic strategies for a wide range of diseases and conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Status epilepticus (SE) carries risks of morbidity and mortality. Experimental studies have implicated the entorhinal cortex in prolonged seizures; however, studies in large human cohorts are limited. We hypothesised that individuals with temporal lobe epilepsy (TLE) and a history of SE would have more severe entorhinal atrophy compared to others with TLE and no history of SE. 357 individuals with drug resistant temporal lobe epilepsy (TLE) and 100 healthy controls were scanned on a 3T MRI. For all subjects, the cortex was segmented, parcellated, and the thickness calculated from the T1-weighted anatomical scan. Subcortical volumes were derived similarly. Cohen's d and Wilcoxon rank-sum tests respectively were used to capture effect sizes and significance. Individuals with TLE and SE had reduced entorhinal thickness compared to those with TLE and no history of SE. The entorhinal cortex was more atrophic ipsilaterally (d = 0.51, p < 0.001) than contralaterally (d = 0.37, p = 0.01). Reductions in ipsilateral entorhinal thickness were present in both left TLE (n = 22:176, d = 0.78, p < 0.001), and right TLE (n = 19:140, d = 0.31, p = 0.04), albeit with a smaller effect size in right TLE. Several other regions exhibited atrophy in individuals with TLE, but these did not relate to a history of SE. These findings suggest potential involvement or susceptibility of the entorhinal cortex in prolonged seizures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Pain is a highly subjective and multidimensional experience, significantly influenced by various psychological factors. Placebo analgesia and nocebo hyperalgesia exemplify this influence, where inert treatments result in pain relief or exacerbation, respectively. While extensive research has elucidated the psychological and neural mechanisms behind these effects, most studies have focused on transient pain stimuli. To explore these mechanisms in the context of tonic pain, we conducted a study using a 15-minute tonic muscle pain induction procedure, where hypertonic saline was infused into the left masseter of healthy participants. We collected real-time Visual Analogue Scale (VAS) scores and functional magnetic resonance imaging (fMRI) data during the induction of placebo analgesia and nocebo hyperalgesia via conditioned learning. Our findings revealed that placebo analgesia was more pronounced and lasted longer than nocebo hyperalgesia. Real-time pain ratings correlated significantly with neural activity in several brain regions. Notably, the putamen was implicated in both effects, while the caudate and other regions were differentially involved in placebo and nocebo effects. These findings confirm that the tonic muscle pain paradigm can be used to investigate the mechanisms of placebo and nocebo effects and indicate that placebo analgesia and nocebo hyperalgesia may have more distinct than common neural bases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging. MGA-Net is designed to extract the brain from other structures and reconstruct high-quality brain images. The network employs a common encoder and two decoders: one for brain mask extraction and the other for brain region reconstruction. A key feature of MGA-Net is its high-level mask-guided attention module, which leverages features from the brain mask decoder to enhance image reconstruction. To enable the same encoder and decoder to process both MRI and ultrasound (US) images, MGA-Net integrates sinusoidal positional encoding. This encoding assigns distinct positional values to MRI and US images, allowing the model to effectively learn from both modalities. Consequently, features learned from a single modality can aid in learning a modality with less available data, such as US. We extensively validated the proposed MGA-Net on diverse and independent datasets from varied clinical settings and neonatal age groups. The metrics used for assessment included the DICE similarity coefficient, recall, and accuracy for image segmentation; structural similarity for image reconstruction; and root mean squared error for total brain volume estimation from 3D ultrasound images. Our results demonstrate that MGA-Net significantly outperforms traditional methods, offering superior performance in brain extraction and segmentation while achieving high precision in image reconstruction and volumetric analysis. Thus, MGA-Net represents a robust and effective preprocessing tool for MRI and 3D ultrasound images, marking a significant advance in neuroimaging that enhances both research and clinical diagnostics in the neonatal period and beyond., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Recent technical developments have led to the invention of multiband functional magnetic resonance imaging (fMRI) sequences that allow for faster sampling rates. However, some studies have highlighted problems with these sequences, leading to a decreased temporal signal-to-noise ratio (tSNR). In addition, this temporal noise may interfere with detecting reward-related responses in mesolimbic regions. The blood-oxygen-level-dependent signal utilized in the majority of fMRI measurements is relatively slow. Furthermore, the cerebral response to gustatory stimuli would also be relatively slow. Therefore, given the temporal noise issues with multiband sequences, it is unclear whether multiband sequences are necessary for fMRI studies using gustatory stimuli. We thus conducted an fMRI experiment using a gustatory stimulus to investigate the effects of multiband sequences and increased sampling rates on statistical outcome measures. A single-band sequence with a repetition time (TR) of 2 s of phantom fMRI data and gustatory fMRI data from the gustatory regions exhibited the highest tSNR, although the tSNR of this sequence of gustatory fMRI was not statistically different from tSNR of multiband sequences with a TR of 2 s in any of the selected region of interests. Conventional general linear model analysis of fMRI showed that single-band sequences are more advantageous than multiband sequences for detecting brain responses to gustatory stimuli in the primary gustatory cortex. In addition, a Bayesian data comparison showed that data derived from a single-band sequence with a TR of 2 s was optimal for inferring neuronal connectivity in gustatory processing. Therefore, a conventional single-band sequence with a TR of 2 s is more appropriate for fMRI with gustatory stimuli. Image acquisition sequences should be selected aligned with the study objectives and target brain regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates l 2,1 sparse regularization that enables selecting a sparse and noise-robust feature subset. Experimental evaluations on eight public datasets with diverse properties demonstrate the superior performance of our method over nine popular feature selection methods in identifying discriminative features, with average classification accuracy gains ranging from 1.30 % to 9.11 %. Furthermore, the LRFS method demonstrates superior discriminability in four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls (HCs) and 537 SZ patients, with an average increase in classification accuracy ranging from 1.89 % to 9.24 % compared to other nine methods. Notably, our method reveals reproducible and significant changes in SZ patients relative to HCs across the four datasets, predominantly in the thalamus-related functional network connectivity, which exhibit a significant correlation with clinical symptoms. Convergence analysis, parameter sensitivity analysis, and ablation studies further demonstrate the effectiveness and robustness of our method. In short, our proposed feature selection method effectively identifies discriminative and reliable features that hold the potential to be biomarkers, paving the way for the elucidation of brain abnormalities and the advancement of precise diagnosis of mental disorders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data., Competing Interests: Declaration of competing interest The authors have no conflict of interest to report., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease diagnosis. However, MRI scans acquired at different sites can have different appearances due to the difference in the hardware, pulse sequence, and imaging parameter. It is important to reduce or eliminate such cross-site variations with brain MRI harmonization so that downstream image processing and analysis is performed consistently. Previous works on the harmonization problem require the data acquired from the sites of interest for model training. But in real-world scenarios there can be test data from a new site of interest after the model is trained, and training data from the new site is unavailable when the model is trained. In this case, previous methods cannot optimally handle the test data from the new unseen site. To address the problem, in this work we explore domain generalization for brain MRI harmonization and propose Site Mix (SiMix). We assume that images of travelling subjects are acquired at a few existing sites for model training. To allow the training data to better represent the test data from unseen sites, we first propose to mix the training images belonging to different sites stochastically, which substantially increases the diversity of the training data while preserving the authenticity of the mixed training images. Second, at test time, when a test image from an unseen site is given, we propose a multiview strategy that perturbs the test image with preserved authenticity and ensembles the harmonization results of the perturbed images for improved harmonization quality. To validate SiMix, we performed experiments on the publicly available SRPBS dataset and MUSHAC dataset that comprised brain MRI acquired at nine and two different sites, respectively. The results indicate that SiMix improves brain MRI harmonization for unseen sites, and it is also beneficial to the harmonization of existing sites., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Focal brain injuries, such as stroke, cause local structural damage as well as alteration of neuronal activity in distant brain regions. Experimental evidence suggests that one of these changes is the appearance of sleep-like slow waves in the otherwise awake individual. This pattern is prominent in areas surrounding the damaged region and can extend to connected brain regions in a way consistent with the individual's specific long-range connectivity patterns. In this paper we present a generative whole-brain model based on (f)MRI data that, in combination with the disconnection mask associated with a given patient, explains the effects of the sleep-like slow waves originated in the vicinity of the lesion area on the distant brain activity. Our model reveals new aspects of their interaction, being able to reproduce functional connectivity patterns of stroke patients and offering a detailed, causal understanding of how stroke-related effects, in particular slow waves, spread throughout the brain. The presented findings demonstrate that the model effectively captures the links between stroke occurrences, sleep-like slow waves, and their subsequent spread across the human brain., Competing Interests: Declaration of competing interest Marcello Massimini is co-founder of Intrinsic Powers, a spin-off of the University of Milan., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

In adults, individual differences in procedural learning (PL) are associated with white matter organization within the basal ganglia-cerebellar circuit. However, no research has examined whether this circuitry is related to individual differences in PL during childhood. Here, 28 children (M age  = 10.00 ± 2.31, 10 female) completed the serial reaction time (SRT) task to measure PL, and underwent structural magnetic resonance imaging (MRI). Fixel-Based Analysis was performed to extract specific measures of white matter fiber density (FD) and fiber cross-section (FC) from the superior cerebellar peduncles (SCP) and the striatal premotor tracts (STPMT), which underlie the fronto-basal ganglia-cerebellar system. These fixel metrics were correlated with the 'rebound effect' from the SRT task - a measure of PL proficiency which compares reaction times associated with generating a sequence, to random trials. While no significant associations were observed at the fixel level, a significant positive association was observed between average FD in the right SCP and the rebound effect, with a similar trend observed in the left SCP. No significant effects were detected in the STPMT. Our results indicate that, like in adults, microstructure of the basal ganglia-cerebellar circuit may explain individual differences in childhood PL., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: There is paucity of information about status epilepticus (SE) in tuberculous meningitis (TBM). In this communication, we report SE semiology, response to antiseizure medication (ASM) and outcome of the TBM patients with SE., Methods: The diagnosis of TBM was based on clinical, cerebrospinal fluid and MRI findings. The clinical details, severity of meningitis, and MRI and electroencephalography findings were noted. The type of SE, onset from the meningitis symptoms, number of ASMs required to control SE and outcomes were noted., Results: During study period from august 2015 to march 2023, 143 TBM patients were admitted and 10 (6.9 %) had SE, whose age ranged between 12 and 45 years. MRI revealed exudates in six, hydrocephalus in three, infarctions in seven and tuberculoma in six patients. Median (interquartile range) duration of SE after meningitis symptoms was 65 (43.7-100.5) days. Three had generalized convulsive SE, three epileptia partialis continua (EPC), three focal convulsive SE with bilateral convulsion, and one had non-convulsive SE. Two (20 %) patients responded to two ASMs, six (60 %) had refractory SE whose seizure continued after benzodiazepine and one ASM, and two (20 %) had super-refractory SE having seizures for ≥ 24 h despite use of intravenous anesthetic agent. Four (40 %) patients died; uncontrolled SE resulted death in one, and the remaining patients died due to primary disease. Only 2 (20 %) patients had good recovery and 4 (40 %) had poor recovery at 6 months., Conclusion: Status epilepticus in TBM is uncommon and can be refractory or super-refractory resulting in poor outcome., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Glaucoma is a chronic progressive disease leading to irreversible visual impairment and blindness. High intraocular pressure (IOP) resulting from abnormally high outflow resistance is a major risk factor for glaucoma development, however, it is unclear how IOP elevation influences the structure and function of the retina and the optic nerve via vitreous humor located between the lens and retina in the eye. To understand vitreous biomechanical and stimulus response toward IOP elevation, we developed a novel near-infrared (NIR)/MRI dual-modal nanoprobe, DTA/P-NCA/17F@Co, which is composed of N, N-dimethyl-4(thien-2-yl)-aniline group (DTA) as NIR fluorophore and the fluorine-based polyamino acid cobalt nanoparticles (P-NCA/17F@Co) as T 2 contrast agent. These nanoprobes exhibit good biocompatibility, low surface energy characteristics, and viscosity-responsive NIR emission and T 2 relaxation values. The intrinsic viscosity-sensitivemechanismof nanoprobes was ascribed to constrained molecular motion in high-viscosity vitreous chamber, which causes enhanced fluorescence emission and shortened T 2 relaxation times. By using its ability for dual-modal visualization of viscosity, we achieved non-invasive in vivo monitoring the changes in vitreous viscosity during elevated IOP in a glaucoma rat model. In vivo experiments validated that vitreous viscosity is very strongly correlated with IOP elevation induced by glaucoma, much earlier than structural and functional change in the retina. Our findings revealed that IOP elevation induced the increase of vitreous viscosity, indicating that monitoring vitreous viscosity is key to the glaucoma model. This study not only provides versatile nanoprobes for dual-modal visualization of biomechanical properties of the vitreous humor in its native environment, but also shows great potential in the early diagnosis of glaucoma., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Synergistic photothermal/immunotherapy has garnered significant attention for its potential to enhance tumor therapeutic outcomes. However, the fabrication of an intelligent system with a simple composition that simultaneously exerts photothermal/immunotherapy effect and imaging guidance function still remains a challenge. Herein, a glutathione (GSH)-responsive theranostic nanoprobe, named HA-MnO 2 /ICG, was elaborately constructed by loading photothermal agent (PTA) indocyanine green (ICG) onto the surface of hyaluronic acid (HA)-modified manganese dioxide nanosheets (HA-MnO 2 ) for magnetic resonance (MR) imaging-guided synergetic photothermal/immuno-enhanced therapy. In this strategy, HA-MnO 2 nanosheets were triggered by the endogenous GSH in tumor microenvironment to generate Mn 2+ for MR imaging, where the longitudinal relaxation rate of HA-MnO 2 /ICG was up to 14.97 mM -1 s -1 (∼24 times than that found in a natural environment), demonstrating excellent intratumoral MR imaging. Moreover, the HA-MnO 2 /ICG nanoprobe demonstrates remarkable photothermal therapy (PTT) efficacy, generating sufficient heat to induce immunogenic cell death (ICD) within tumor cells. Meanwhile the released Mn 2+ ions from the nanosheets function as potent immune adjuvants, amplifying the immune response against cancer. In vivo experiments validated that HA-MnO 2 /ICG-mediated PTT was highly effective in eradicating primary tumors, while simultaneously enhancing immunogenicity to prevent the growth of distal metastasis. This hybrid HA-MnO 2 /ICG nanoprobe opened new avenues in the design of MR imaging-monitored PTT/immuno-enhanced synergistic therapy for advanced cancer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: To identify newly diagnosed patients with drug-resistant epilepsy (DRE) based on radiomics and clinical features., Methods: A radiomics approach was used to combine clinical features with magnetic resonance imaging (MRI) features extracted by the ResNet-18 deep learning model to predict DRE. Three machine learning classifiers were built, and k-fold cross-validation was used to assess the classifier outcomes, and other evaluation metrics of accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) were used to evaluate the performance of these models., Results: One hundred and thirty-four newly diagnosed epilepsy patients with 13 available clinical features and 1394 MRI features extracted by the ResNet-18 model were included in our study. Then three machine learning classifiers were built based on5 clinical features and 8 MRI features, including Support Vector Machine (SVM), Gradient-Boosted Decision Tree (GBDT) and Random Forest. After internally validation, the GBDT model performed the best, with an average accuracy of 0.85 [95% confidence interval (CI) 0.77-0.91], sensitivity of 0.97 [95% CI 0.85-1.00], specificity of 0.96 [95% CI 0.83-1.00], F1 score of 0.81 [95% CI 0.77-0.89], AUC of 0.95 [95% CI 0.82-0.99], and ten-fold cross validation avg score of 0.96 [95% CI 0.89-0.99] in test set., Significance: This study offers a novel approach for early diagnosis of DRE. Radiomics can provide potential diagnostic and predictive information to support personalized treatment decisions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Manganese oxide nanoparticles (MONs)-based contrast agents have attracted increasing attention for magnetic resonance imaging (MRI), attributed to their good biocompatibility and advantageous paramagnetism. However, conventional MONs have poor imaging performance due to low T 1 relaxivity. Additionally, their lack of tumor-targeting theranostics capabilities and complex synthesis pathways have impeded clinical applications. Rutin (Ru) is an ideal tumor-targeted ligand that targets glucose transporters (GLUTs) overexpressed in various malignant tumors, and exhibits photothermal effects upon chelation with metal ions. Herein, a series of Ru-coated MONs (Ru/MnO 2 ) were synthesized using a straightforward, rapid one-step process. Specifically, Ru/MnO 2 -5, with the smallest crystal size of approximately 4 nm, exhibits the highest T 1 relaxivity (33.3 mM -1 s -1 at 1.5 T, surpassing prior MONs) along with notable stability, photothermal efficacy, and tumor-targeting ability. Furthermore, Ru/MnO 2 -5 shows promise in MRI and photothermal therapy of H22 tumors owing to its superior GLUTs-mediated tumor-targeting capability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine. We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures. We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data. In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Spinal cord (SC) atrophy obtained from structural magnetic resonance imaging has gained relevance as an indicator of neurodegeneration in various neurological disorders. The common method to assess SC atrophy is by comparing numerical differences of the cross-sectional spinal cord area (CSA) between time points. However, this indirect approach leads to considerable variability in the obtained results. Studies showed that this limitation can be overcome by using a registration-based technique. The present study introduces the Structural Image Evaluation using Normalization of Atrophy on the Spinal Cord (SIENA-SC), which is an adapted version of the original SIENA method, designed to directly calculate the percentage of SC volume change over time from clinical brain MRI acquired with an extended field of view to cover the superior part of the cervical SC. In this work, we compared SIENA-SC with the Generalized Boundary Shift Integral (GBSI) and the CSA change. On a scan-rescan dataset, SIENA-SC was shown to have the lowest measurement error than the other two methods. When comparing a group of 190 Healthy Controls with a group of 65 Multiple Sclerosis patients, SIENA-SC provided significantly higher yearly rates of atrophy in patients than in controls and a lower sample size when measured for treatment effect sizes of 50%, 30% and 10%. Our findings indicate that SIENA-SC is a robust, reproducible, and sensitive approach for assessing longitudinal changes in spinal cord volume, providing neuroscientists with an accessible and automated tool able to reduce the need for manual intervention and minimize variability in measurements., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nicola De Stefano reports financial support was provided by National Recovery and Resilience Plan (PNRR), and also relationship with Biogen, Merck, Novartis, Sanofi, Roche, Teva, FISM that includes: consulting or advisory, funding grants, speaking and lecture fees, and travel reimbursement. Rosa Cortese reports a relationship with Roche, Merck Serono, Janssen, Novartis, Sanofi that includes: speaking and lecture fees and travel reimbursement. Ferran Prados reports a relationship with National Institute for Health and Care Research (NIHR) Biomedical Research Centres (BRC) at University College London (UCL) that includes: employment, funding grants, speaking and lecture fees, and travel reimbursement. Massimiliano Calabrese reports a relationship with Roche, Sanofi Genzyme, Merck Serono, Biogen Idec, Teva, and Novartis Pharma that includes: consulting or advisory, funding grants, speaking and lecture fees, and travel reimbursement. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Objective: This study aimed to identify the factors associated with insomnia in MRI-negative epilepsy and uncover the underlying pathological mechanism driving insomnia within the context of epilepsy., Methods: We conducted a retrospective study of patients with MRI-negative epilepsy recruited consecutively from December 2021 to December 2022. All subjects completed the Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS). Additionally, some subjects underwent the three-dimensional pseudo continuous arterial spin labeling(3D-pCASL) imaging examination. Bilateral frontal lobe, temporal lobe, hippocampus, thalamus, amygdala, caudate nucleus and lenticular nucleus were selected as regions of interest(ROI) and cerebral blood flow(CBF) values were measured in these regions. Subjects were classified into insomnia (ISI ≥ 10) or non-insomnia (ISI < 10) groups, and univariate and stepwise logistic regression analyses were employed to identify the factors associated with insomnia. Furthermore, CBF values in each ROI were compared between the two groups to identify the brain regions potentially related to the underlying pathological mechanism of insomnia in epilepsy., Results: A total of 73 patients with MRI-negative epilepsy were recruited in this study(men, 49.3 %). Among them, 14 patients(19.2 %) had insomnia. Univariate regression revealed that nocturnal seizures, number of anti-seizure medication(ASM), anxiety, use of valproic acid(VPA), depression, and excessive daytime sleepiness(EDS) may be associated with insomnia in MRI-negative epilepsy (all p＜0.05). Stepwise regression demonstrated that nocturnal seizures, anxiety, and EDS were independently associated with insomnia in MRI-negative epilepsy (OR[95 %CI]P: 14.64[2.02-106.27]0.008,49.35[3.06-796.61]0.006, 13.28[1.25-140.66]0.032, respectively). Furthermore, CBF values in the left amygdala were significantly lower in patients with MRI- negative epilepsy who had insomnia., Conclusion: The prevalence of insomnia in MRI-negative epilepsy is 19.2%. Nocturnal seizures, anxiety, and EDS were independently associated with insomnia in MRI-negative epilepsy. The noteworthy decrease in CBF values in the left amygdala might be connected to the underlying pathological mechanism of insomnia in epilepsy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

In this study, we describe a comprehensive 3D magnetic resonance imaging (MRI) protocol designed to assess major tissue and fluid components in the brain. The protocol comprises four different sequences: 1) magnetization transfer prepared Cones (MT-Cones) for two-pool MT modeling to quantify macromolecular content; 2) short-TR adiabatic inversion-recovery prepared Cones (STAIR-Cones) for myelin water imaging; 3) proton-density weighted Cones (PDw-Cones) for total water imaging; and 4) highly T 2 weighted Cones (T 2 w-Cones) for free water imaging. By integrating these techniques, we successfully mapped key brain components-namely macromolecules, myelin water, intra/extracellular water, and free water-in ten healthy volunteers and five patients with multiple sclerosis (MS) using a 3T clinical scanner. Brain macromolecular proton fraction (MMPF), myelin water proton fraction (MWPF), intra/extracellular water proton fraction (IEWPF), and free water proton fraction (FWPF) values were generated in white matter (WM), grey matter (GM), and MS lesions. Excellent repeatability of the protocol was demonstrated with high intra-class correlation coefficient (ICC) values. In MS patients, the MMPF and MWPF values of the lesions and normal-appearing WM (NAWM) were significantly lower than those in normal WM (NWM) in healthy volunteers. Moreover, we observed significantly higher FWPF values in MS lesions compared to those in NWM and NAWM regions. This study demonstrates the capability of our technique to volumetrically map major brain components. The technique may have particular value in providing a comprehensive assessment of neuroinflammatory and neurodegenerative diseases of the brain., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Skull stripping is a crucial preprocessing step in magnetic resonance imaging (MRI), where experts manually create brain masks. This labor-intensive process heavily relies on the annotator's expertise, as automation faces challenges such as low tissue contrast, significant variations in image resolution, and blurred boundaries between the brain and surrounding tissues, particularly in rodents. In this study, we have developed a lightweight framework based on Swin-UNETR to automate the skull stripping process in MRI scans of mice and rats. The primary objective of this framework is to eliminate the need for preprocessing, reduce the workload, and provide an out-of-the-box solution capable of adapting to various MRI image resolutions. By employing a lightweight neural network, we aim to lower the performance requirements of the framework. To validate the effectiveness of our approach, we trained and evaluated the network using publicly available multi-center data, encompassing 1,037 rodents and 1,142 images from 89 centers, resulting in a preliminary mean Dice coefficient of 0.9914. The framework, data, and pre-trained models can be found on the following link: https://github.com/VitoLin21/Rodent-Skull-Stripping., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering DEC. The presented dynamic DAG enables us to discover direct feedback loop edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process., Competing Interests: Declaration of competing interest The authors declares that they have no financial or personal relationships with other people or organizations that could inappropriately influence (bias) our work. They have no competing interests to declare., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Modular dynamic graph theory metrics effectively capture the patterns of dynamic information interaction during human brain development. While existing research has employed modular algorithms to examine the overall impact of dynamic changes in community structure throughout development, there is a notable gap in understanding the cross-community dynamic changes within different functional networks during early childhood and their potential contributions to the efficiency of brain information transmission. This study seeks to address this gap by tracing the trajectories of cross-community structural changes within early childhood functional networks and modeling their contributions to information transmission efficiency. We analyzed 194 functional imaging scans from 83 children aged 2 to 8 years, who participated in passive viewing functional magnetic resonance imaging sessions. Utilizing sliding windows and modular algorithms, we evaluated three spatiotemporal metrics-temporal flexibility, spatiotemporal diversity, and within-community spatiotemporal diversity-and four centrality metrics: within-community degree centrality, eigenvector centrality, between-community degree centrality, and between-community eigenvector centrality. Mixed-effects linear models revealed significant age-related increases in the temporal flexibility of the default mode network (DMN), executive control network (ECN), and salience network (SN), indicating frequent adjustments in community structure within these networks during early childhood. Additionally, the spatiotemporal diversity of the SN also displayed significant age-related increases, highlighting its broad pattern of cross-community dynamic interactions. Conversely, within-community spatiotemporal diversity in the language network exhibited significant age-related decreases, reflecting the network's gradual functional specialization. Furthermore, our findings indicated significant age-related increases in between-community degree centrality across the DMN, ECN, SN, language network, and dorsal attention network, while between-community eigenvector centrality also increased significantly for the DMN, ECN, and SN. However, within-community eigenvector centrality remained stable across all functional networks during early childhood. These results suggest that while centrality of cross-community interactions in early childhood functional networks increases, centrality within communities remains stable. Finally, mediation analysis was conducted to explore the relationships between age, brain dynamic graph metrics, and both global and local efficiency based on community structure. The results indicated that the dynamic graph metrics of the SN primarily mediated the relationship between age and the decrease in global efficiency, while those of the DMN, language network, ECN, dorsal attention network, and SN primarily mediated the relationship between age and the increase in local efficiency. This pattern suggests a developmental trajectory in early childhood from global information integration to local information segregation, with the SN playing a pivotal role in this transformation. This study provides novel insights into the mechanisms by which early childhood brain functional development impacts information transmission efficiency through cross-community adjustments in functional networks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Acting as a central hub in regulating brain functions, the thalamus plays a pivotal role in controlling high-order brain functions. Considering the impact of preterm birth on infant brain development, traditional studies focused on the overall development of thalamus other than its subregions. In this study, we compared the volumetric growth and shape development of the thalamic hemispheres between the infants born preterm and full-term (Left volume: P = 0.027, Left normalized volume: P < 0.0001; Right volume: P = 0.070, Right normalized volume: P < 0.0001). The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus exhibit higher vulnerability to alterations induced by preterm birth. The structural covariance (SC) between the thickness of thalamus and insula in preterm infants (Left: corrected P = 0.0091, Right: corrected P = 0.0119) showed significant increase as compared to full-term controls. Current findings suggest that preterm birth affects the development of the thalamus and has differential effects on its subregions. The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus are more susceptible to the impacts of preterm birth., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

N, N-dimethyltryptamine (DMT) is a psychedelic tryptamine acting on 5-HT2A serotonin receptors, which is associated with intense visual hallucinatory phenomena and perceptual changes such as distortions in visual space. The neural underpinnings of these effects remain unknown. We hypothesised that changes in population receptive field (pRF) properties in the primary visual cortex (V1) might underlie visual perceptual experience. We tested this hypothesis using magnetic resonance imaging (MRI) in a within-subject design. We used a technique called pRF mapping, which measures neural population visual response properties and retinotopic maps in early visual areas. We show that in the presence of visual effects, as documented by the Hallucinogen Rating Scale (HRS), the mean pRF sizes in V1 significantly increase in the peripheral visual field for active condition (inhaled DMT) compared to the control. Eye and head movement differences were absent across conditions. This evidence for short-term effects of DMT in pRF may explain perceptual distortions induced by psychedelics such as field blurring, tunnel vision (peripheral vision becoming blurred while central vision remains sharp) and the enlargement of nearby visual space, particularly at the visual locations surrounding the fovea. Our findings are also consistent with a mechanistic framework whereby gain control of ongoing and evoked activity in the visual cortex is controlled by activation of 5-HT2A receptors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Lasting thalamus volume reduction after preterm birth is a prominent finding. However, whether thalamic nuclei volumes are affected differentially by preterm birth and whether nuclei aberrations are relevant for cognitive functioning remains unknown. Using T1-weighted MR-images of 83 adults born very preterm (≤ 32 weeks' gestation; VP) and/or with very low body weight (≤ 1,500 g; VLBW) as well as of 92 full-term born (≥ 37 weeks' gestation) controls, we compared thalamic nuclei volumes of six subregions (anterior, lateral, ventral, intralaminar, medial, and pulvinar) across groups at the age of 26 years. To characterize the functional relevance of volume aberrations, cognitive performance was assessed by full-scale intelligence quotient using the Wechsler Adult Intelligence Scale and linked to volume reductions using multiple linear regression analyses. Thalamic volumes were significantly lower across all examined nuclei in VP/VLBW adults compared to controls, suggesting an overall rather than focal impairment. Lower nuclei volumes were linked to higher intensity of neonatal treatment, indicating vulnerability to stress exposure after birth. Furthermore, we found that single results for lateral, medial, and pulvinar nuclei volumes were associated with full-scale intelligence quotient in preterm adults, albeit not surviving correction for multiple hypotheses testing. These findings provide evidence that lower thalamic volume in preterm adults is observable across all subregions rather than focused on single nuclei. Data suggest the same mechanisms of aberrant thalamus development across all nuclei after premature birth., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Melissa Thalhammer reports financial support was provided by Studienstiftung des deutschen Volkes. Christian Sorg reports financial support was provided by Deutsche Forschungsgemeinschaft (DFG). Peter Bartmann reports financial support was provided by German Federal Ministry of Education and Science. Dieter Wolke reports financial support was provided by German Federal Ministry of Education and Science. Dieter Wolke reports financial support was provided by EU Horizon 2020. Peter Bartmann reports financial support was provided by EU Horizon 2020. Christian Sorg reports financial support was provided by Commission for Clinical Research, Technical University of Munich. Benita Schmitz-Koep reports financial support was provided by Commission for Clinical Research, Technical University of Munich. Dennis Hedderich reports financial support was provided by Commission for Clinical Research, Technical University of Munich. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Quantitative susceptibility mapping (QSM) is a rising MRI-based technology and quite a few QSM-related algorithms have been proposed to reconstruct maps of tissue susceptibility distribution from phase images. In this paper, we develop a comprehensive susceptibility imaging process and analysis studio (SIPAS) that can accomplish reliable QSM processing and offer a standardized evaluation system. Specifically, SIPAS integrates multiple methods for each step, enabling users to select algorithm combinations according to data conditions, and QSM maps could be evaluated by two aspects, including image quality indicators within all voxels and region-of-interest (ROI) analysis. Through a sophisticated design of user-friendly interfaces, the results of each procedure are able to be exhibited in axial, coronal, and sagittal views in real-time, meanwhile ROIs can be displayed in 3D rendering visualization. The accuracy and compatibility of SIPAS are demonstrated by experiments on multiple in vivo human brain datasets acquired from 3T, 5T, and 7T MRI scanners of different manufacturers. We also validate the QSM maps obtained by various algorithm combinations in SIPAS, among which the combination of iRSHARP and SFCR achieves the best results on its evaluation system. SIPAS is a comprehensive, sophisticated, and reliable toolkit that may prompt the QSM application in scientific research and clinical practice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Brain disorders are often associated with changes in brain structure and function, where functional changes may be due to underlying structural variations. Gray matter (GM) volume segmentation from 3D structural MRI offers vital structural information for brain disorders like schizophrenia, as it encompasses essential brain tissues such as neuronal cell bodies, dendrites, and synapses, which are crucial for neural signal processing and transmission; changes in GM volume can thus indicate alterations in these tissues, reflecting underlying pathological conditions. In addition, the use of the ICA algorithm to transform high-dimensional fMRI data into functional network connectivity (FNC) matrices serves as an effective carrier of functional information. In our study, we introduce a new generative deep learning architecture, the conditional efficient vision transformer generative adversarial network (cEViT-GAN), which adeptly generates FNC matrices conditioned on GM to facilitate the exploration of potential connections between brain structure and function. We developed a new, lightweight self-attention mechanism for our ViT-based generator, enhancing the generation of refined attention maps critical for identifying structural biomarkers based on GM. Our approach not only generates high quality FNC matrices with a Pearson correlation of 0.74 compared to real FNC data, but also uses attention map technology to identify potential biomarkers in GM structure that could lead to functional abnormalities in schizophrenia patients. Visualization experiments within our study have highlighted these structural biomarkers, including the medial prefrontal cortex (mPFC), dorsolateral prefrontal cortex (DL-PFC), and cerebellum. In addition, through cross-domain analysis comparing generated and real FNC matrices, we have identified functional connections with the highest correlations to structural information, further validating the structure-function connections. This comprehensive analysis helps to understand the intricate relationship between brain structure and its functional manifestations, providing a more refined insight into the neurobiological research of schizophrenia., Competing Interests: Declaration of competing interest None of the authors have any conflicts of interest to declare in relation to this manuscript. This includes any financial, personal, or professional interests that could be construed to influence the work reported in this paper. We confirm that the content of the manuscript has not been influenced by any external interests, and all research was conducted in accordance with ethical standards. This statement is true to the best of our knowledge and belief, and any potential conflicts will be disclosed promptly should they arise in the future., (Copyright © 2024. Published by Elsevier Inc.)

Background: Hippocampal sclerosis (HS) is a prevalent cause of temporal lobe epilepsy (TLE). However, up to 30% of individuals with TLE present negative magnetic resonance imaging (MRI) findings. A comprehensive grasp of the similarities and differences in brain activity among distinct TLE subtypes holds significant clinical and scientific importance., Objective: To comprehensively examine the similarities and differences between TLE with HS (TLE-HS) and MRI-negative TLE (TLE-N) regarding static and dynamic abnormalities in spontaneous brain activity (SBA). Furthermore, we aimed to determine whether these alterations correlate with epilepsy duration and cognition, and to determine a potential differential diagnostic index for clinical utility., Methods: We measured 12 SBA metrics in 38 patients with TLE-HS, 51 with TLE-N, and 53 healthy volunteers. Voxel-wise analysis of variance (ANOVA) and post-hoc comparisons were employed to compare these metrics. The six static metrics included amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), degree centrality (DC), and global signal correlation (GSCorr). Additionally, six corresponding dynamic metrics were assessed: dynamic ALFF (dALFF), dynamic fALFF (dfALFF), dynamic ReHo (dReHo), dynamic DC (dDC), dynamic VMHC (dVMHC), and dynamic GSCorr (dGSCorr). Receiver operating characteristic (ROC) curve analysis of abnormal indices was employed. Spearman correlation analyses were also conducted to examine the relationship between the abnormal indices, epilepsy duration and cognition scores., Results: Both TLE-HS and TLE-N presented as extensive neural network disorders, sharing similar patterns of SBA alterations. The regions with increased fALFF, dALFF, and dfALFF levels were predominantly observed in the mesial temporal lobe, thalamus, basal ganglia, pons, and cerebellum, forming a previously proposed mesial temporal epilepsy network. Conversely, decreased SBA metrics (fALFF, ReHo, dReHo, DC, GSCorr, and VMHC) consistently appeared in the lateral temporal lobe ipsilateral to the epileptic foci. Notably, SBA alterations were more obvious in patients with TLE-HS than in those with TLE-N. Additionally, patients with TLE-HS exhibited reduced VMHC in both mesial and lateral temporal lobes compared with patients with TLE-N, with the hippocampus displaying moderate discriminatory power (AUC = 0.759). Correlation analysis suggested that alterations in SBA indicators may be associated with epilepsy duration and cognitive scores., Conclusions: The simultaneous use of static and dynamic SBA metrics provides evidence supporting the characterisation of both TLE-HS and TLE-N as complex network diseases, facilitating the exploration of mechanisms underlying epileptic activity and cognitive impairment. Overall, SBA abnormality patterns were generally similar between the TLE-HS and TLE-N groups, encompassing networks related to TLE and auditory and occipital visual functions. These changes were more pronounced in the TLE-HS group, particularly within the mesial and lateral temporal lobes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Children reared in institutional settings experience early deprivation that has lasting implications for multiple aspects of neurocognitive functioning, including executive function (EF). Changes in brain development are thought to contribute to these persistent EF challenges, but little research has used fMRI to investigate EF-related brain activity in children with a history of early deprivation. This study examined behavioral and neural data from a response conflict task in 12-14-year-olds who spent varying lengths of time in institutional care prior to adoption (N = 84; age at adoption - mean: 15.85 months, median: 12 months, range: 4-60 months). In initial analyses, earlier- and later-adopted (EA, LA) youth were compared to a group of children raised in their biological families (non-adopted, NA). NA youth performed significantly more accurately than LA youth, with EA youth falling in between. Imaging data suggested that previously institutionalized (PI) youth activated additional frontoparietal regions, including dorsolateral prefrontal cortex, as compared to NA youth. In addition, EA youth uniquely activated medial prefrontal regions, and LA uniquely activated parietal regions during this task. A separate analysis in a larger group of PI youth examined whether behavioral or brain measures of EF varied with the duration of deprivation experienced. Duration of deprivation was negatively associated with activation of default mode network (DMN) regions. Overall, results suggest that there are lasting effects of deprivation on EF, but that those who are removed from institutional care earlier may be able to recruit additional neural resources as a compensatory mechanism., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)