Work Stress and Depressive Symptoms in Fishermen With a Smoking Habit: A Mediator Role of Nicotine Dependence and Possible Moderator Role of Expressive Suppression and Cognitive Reappraisal

Ginsenoside Rg1 ameliorates stress-exacerbated Parkinson's disease in mice by eliminating RTP801 and α-synuclein autophagic degradation obstacle

Emerging evidence shows that psychological stress promotes the progression of Parkinson's disease (PD) and the onset of dyskinesia in non-PD individuals, highlighting a potential avenue for therapeutic intervention. We previously reported that chronic restraint-induced psychological stress precipitated the onset of parkinsonism in 10-month-old transgenic mice expressing mutant human α-synuclein (αSyn) (hαSyn A53T). We refer to these as chronic stress-genetic susceptibility (CSGS) PD model mice. In this study we investigated whether ginsenoside Rg1, a principal compound in ginseng notable for soothing the mind, could alleviate PD deterioration induced by psychological stress. Ten-month-old transgenic hαSyn A53T mice were subjected to 4 weeks' restraint stress to simulate chronic stress conditions that worsen PD, meanwhile the mice were treated with Rg1 (40 mg· kg-1 ·d-1, i.g.), and followed by functional magnetic resonance imaging (fMRI) and a variety of neurobehavioral tests. We showed that treatment with Rg1 significantly alleviated both motor and non-motor symptoms associated with PD. Functional MRI revealed that Rg1 treatment enhanced connectivity between brain regions implicated in PD, and in vivo multi-channel electrophysiological assay showed improvements in dyskinesia-related electrical activity. In addition, Rg1 treatment significantly attenuated the degeneration of dopaminergic neurons and reduced the pathological aggregation of αSyn in the striatum and SNc. We revealed that Rg1 treatment selectively reduced the level of the stress-sensitive protein RTP801 in SNc under chronic stress conditions, without impacting the acute stress response. HPLC-MS/MS analysis coupled with site-directed mutation showed that Rg1 promoted the ubiquitination and subsequent degradation of RTP801 at residues K188 and K218, a process mediated by the Parkin RING2 domain. Utilizing αSyn A53T+; RTP801-/- mice, we confirmed the critical role of RTP801 in stress-aggravated PD and its necessity for Rg1's protective effects. Moreover, Rg1 alleviated obstacles in αSyn autophagic degradation by ameliorating the RTP801-TXNIP-mediated deficiency of ATP13A2. Collectively, our results suggest that ginsenoside Rg1 holds promise as a therapeutic choice for treating PD-sensitive individuals who especially experience high levels of stress and self-imposed expectations.

Transcriptional patterns of amygdala functional connectivity in first-episode, drug-naïve major depressive disorder

Previous research has established associations between amygdala functional connectivity abnormalities and major depressive disorder (MDD). However, inconsistencies persist due to limited sample sizes and poorly elucidated transcriptional patterns. In this study, we aimed to address these gaps by analyzing a multicenter magnetic resonance imaging (MRI) dataset consisting of 210 first-episode, drug-naïve MDD patients and 363 age- and sex-matched healthy controls (HC). Using Pearson correlation analysis, we established individualized amygdala functional connectivity patterns based on the Automated Anatomical Labeling (AAL) atlas. Subsequently, machine learning techniques were employed to evaluate the diagnostic utility of amygdala functional connectivity for identifying MDD at the individual level. Additionally, we investigated the spatial correlation between MDD-related amygdala functional connectivity alterations and gene expression through Pearson correlation analysis. Our findings revealed reduced functional connectivity between the amygdala and specific brain regions, such as frontal, orbital, and temporal regions, in MDD patients compared to HC. Importantly, amygdala functional connectivity exhibited robust discriminatory capability for characterizing MDD at the individual level. Furthermore, we observed spatial correlations between MDD-related amygdala functional connectivity alterations and genes enriched for metal ion transport and modulation of chemical synaptic transmission. These results underscore the significance of amygdala functional connectivity alterations in MDD and suggest potential neurobiological mechanisms and markers for these alterations.

Altered individual-level morphological similarity network in children with growth hormone deficiency

BACKGROUND

Accumulating evidences indicate regional grey matter (GM) morphology alterations in pediatric growth hormone deficiency (GHD); however, large-scale morphological brain networks (MBNs) undergo these patients remains unclear.

OBJECTIVE

To investigate the topological organization of individual-level MBNs in pediatric GHD.

METHODS

Sixty-one GHD and 42 typically developing controls (TDs) were enrolled. Inter-regional morphological similarity of GM was taken to construct individual-level MBNs. Between-group differences of topological parameters and network-based statistics analysis were compared. Finally, association relationship between network properties and clinical variables was analyzed.

RESULTS

Compared to TDs, GHD indicated a disturbance in the normal small-world organization, reflected by increased Lp, γ, λ, σ and decreased Cp, Eglob (all PFDR < 0.017). Regarding nodal properties, GHD exhibited increased nodal profiles at cerebellum 4-5, central executive network-related left inferior frontal gyrus, limbic regions-related right posterior cingulate gyrus, left hippocampus, and bilateral pallidum, thalamus (all PFDR < 0.05). Meanwhile, GHD exhibited decreased nodal profiles at sensorimotor network -related bilateral paracentral lobule, default-mode network-related left superior frontal gyrus, visual network -related right lingual gyrus, auditory network-related right superior temporal gyrus and bilateral amygdala, right cerebellum 3, bilateral cerebellum 10, vermis 1-2, 3, 4-5, 6 (all PFDR < 0.05). Furthermore, serum markers and behavior scores in GHD group were correlated with altered nodal profiles (P ≤ 0.046, uncorrected).

CONCLUSION

GHD undergo an extensive reorganization in large-scale individual-level MBNs, probably due to abnormal cortico-striatal-thalamo-cerebellum loops, cortico-limbic-cerebellum, dorsal visual-sensorimotor-striatal, and auditory-cerebellum circuitry. This study highlights the crucial role of abnormal morphological connectivity underlying GHD, which might result in their relatively slower development in motor, cognitive, and linguistic functional within behavior problem performance.

Cortical Morphological Networks Differ Between Gyri and Sulci

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.

Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application

Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.

Where Top-Down Meets Bottom-Up: Cell-Type Specific Connectivity Map of the Whisker System

Sensorimotor computation integrates bottom-up world state information with top-down knowledge and task goals to form action plans. In the rodent whisker system, a prime model of active sensing, evidence shows neuromodulatory neurotransmitters shape whisker control, affecting whisking frequency and amplitude. Since neuromodulatory neurotransmitters are mostly released from subcortical nuclei and have long-range projections that reach the rest of the central nervous system, mapping the circuits of top-down neuromodulatory control of sensorimotor nuclei will help to systematically address the mechanisms of active sensing. Therefore, we developed a neuroinformatic target discovery pipeline to mine the Allen Institute's Mouse Brain Connectivity Atlas. Using network connectivity analysis, we identified new putative connections along the whisker system and anatomically confirmed the existence of 42 previously unknown monosynaptic connections. Using this data, we updated the sensorimotor connectivity map of the mouse whisker system and developed the first cell-type-specific map of the network. The map includes 157 projections across 18 principal nuclei of the whisker system and neuromodulatory neurotransmitter-releasing. Performing a graph network analysis of this connectome, we identified cell-type specific hubs, sources, and sinks, provided anatomical evidence for monosynaptic inhibitory projections into all stages of the ascending pathway, and showed that neuromodulatory projections improve network-wide connectivity. These results argue that beyond the modulatory chemical contributions to information processing and transfer in the whisker system, the circuit connectivity features of the neuromodulatory networks position them as nodes of sensory and motor integration.

s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI

OBJECTIVE

To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD.

METHODS

A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy.

RESULTS

The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation.

CONCLUSION

The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.

Topological alterations in white matter anatomical networks in cervical dystonia

BACKGROUND

Accumulating neuroimaging evidence indicates that patients with cervical dystonia (CD) have changes in the cortico-subcortical white matter (WM) bundle. However, whether these patients' WM structural networks undergo reorganization remains largely unclear. We aimed to investigate topological changes in large-scale WM structural networks in patients with CD compared to healthy controls (HCs), and explore the network changes associated with clinical manifestations.

METHODS

Diffusion tensor imaging (DTI) was conducted in 30 patients with CD and 30 HCs, and WM network construction was based on the BNA-246 atlas and deterministic tractography. Based on the graph theoretical analysis, global and local topological properties were calculated and compared between patients with CD and HCs. Then, the AAL-90 atlas was used for the reproducibility analyses. In addition, the relationship between abnormal topological properties and clinical characteristics was analyzed.

RESULTS

Compared with HCs, patients with CD showed changes in network segregation and resilience, characterized by increased local efficiency and assortativity, respectively. In addition, a significant decrease of network strength was also found in patients with CD relative to HCs. Validation analyses using the AAL-90 atlas similarly showed increased assortativity and network strength in patients with CD. No significant correlations were found between altered network properties and clinical characteristics in patients with CD.

CONCLUSION

Our findings show that reorganization of the large-scale WM structural network exists in patients with CD. However, this reorganization is attributed to dystonia-specific abnormalities or hyperkinetic movements that need further identification.

Altered static and dynamic functional network connectivity in primary angle-closure glaucoma patients

To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3 T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.

White matter structural changes before and after microvascular decompression for hemifacial spasm

Hemifacial spasm (HFS) is a syndrome characterized by involuntary contractions of the facial muscles innervated by the ipsilateral facial nerve. Currently, microvascular decompression (MVD) is an effective treatment for HFS. Diffusion weighted imaging (DWI) is a non-invasive advanced magnetic resonance technique that allows us to reconstruct white matter (WM) virtually based on water diffusion direction. This enables us to model the human brain as a complex network using graph theory. In our study, we recruited 32 patients with HFS and 32 healthy controls to analyze and compare the topological organization of whole-brain white matter networks between the groups. We also explored the potential relationships between altered topological properties and clinical outcomes. Compared to the HC group, the white matter network was disrupted in both preoperative and postoperative groups of HFS patients, mainly located in the somatomotor network, limbic network, and default network (All P < 0.05, FDR corrected). There was no significant difference between the preoperative and postoperative groups (P > 0.05, FDR corrected). There was a correlation between the altered topological properties and clinical outcomes in the postoperative group of patients (All P < 0.05, FDR corrected). Our findings indicate that in HFS, the white matter structural network was disrupted before and after MVD, and that these alterations in the postoperative group were correlated with the clinical outcomes. White matter alteration here described may subserve as potential biomarkers for HFS and may help us identify patients with HFS who can benefit from MVD and thus can help us make a proper surgical patient selection.

Different brain functional network abnormalities between attention-deficit/hyperactivity disorder youth with and without familial risk for bipolar disorder

Attention-deficit/hyperactivity disorder (ADHD) commonly precedes the initial onset of mania in youth with familial risk for bipolar disorder (BD). Although ADHD youth with and without BD familial risk exhibit different clinical features, associated neuropathophysiological mechanisms remain poorly understood. This study aimed to identify brain functional network abnormalities associated with ADHD in youth with and without familial risk for BD. Resting-state functional magnetic resonance imaging scans were acquired from 37 ADHD youth with a family history of BD (high-risk), 45 ADHD youth without a family history of BD (low-risk), and 32 healthy controls (HC). Individual whole-brain functional networks were constructed, and graph theory analysis was applied to estimate network topological metrics. Topological metrics, including network efficiency, small-worldness and nodal centrality, were compared across groups, and associations between topological metrics and clinical ratings were evaluated. Compared to HC, low-risk ADHD youth exhibited weaker global integration (i.e., decreased global efficiency and increased characteristic path length), while high-risk ADHD youth showed a disruption of localized network components with decreased frontoparietal and frontolimbic connectivity. Common topological deficits were observed in the medial superior frontal gyrus between low- and high-risk ADHD. Distinct network deficits were found in the inferior parietal lobule and corticostriatal circuitry. Associations between global topological metrics and externalizing symptoms differed significantly between the two ADHD groups. Different patterns of functional network topological abnormalities were found in high- as compared to low-risk ADHD, suggesting that ADHD in youth with BD familial risk may represent a phenotype that is different from ADHD alone.

The Relationship Between Work-Related Stress and Depression: A Scoping Review

Objectives

Work-related stress is highly prevalent. Recent systematic reviews concluded on a significant association between common work-related stress measures and depression. Our scoping review aims to explore whether work-related psychosocial stress is generally associated with depression or depressiveness, the extent and methodology of the primary research undertaken on this topic and to elucidate inconsistencies or gaps in knowledge.

Methods

We searched for literature in Pubmed, PsycInfo and Web of Science including full reports in seven languages published between 1999 and 2022 and applied the PRISMA statement for scoping reviews criteria.

Results

Of 463 primarily identified articles, 125 were retained after abstract and full-text screening. The majority report significant associations between work-related stress and depression. Cross-sectional studies are most prevalent. Sufficient evidence exists only for job strain and effort-reward imbalance. Most studies are from Asia, North America and Europe. The health sector is the most studied. Several research gaps such as the lack of interventional studies were identified.

Conclusion

The consistency of most studies on the significant association between work-related stress and depression is remarkable. More studies are needed to improve evidence and to close research gaps.

The characteristic patterns of individual brain susceptibility networks underlie Alzheimer's disease and white matter hyperintensity-related cognitive impairment

Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer's disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective.

Structural connectome combining DTI features predicts postoperative language decline and its recovery in glioma patients

OBJECTIVES

A decline in language function is a common complication after glioma surgery, affecting patients' quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network.

MATERIALS AND METHODS

Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features.

RESULTS

Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively.

CONCLUSION

We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas.

CLINICAL RELEVANCE STATEMENT

Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function.

KEY POINTS

• Structural connectome combining diffusion tensor imaging features predicted glioma patients' language decline after surgery. • Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder. • Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.

Longitudinal Functional Connectome in Pediatric Concussion: An Advancing Concussion Assessment in Pediatrics Study

Advanced magnetic resonance imaging (MRI) techniques indicate that concussion (i.e., mild traumatic brain injury) disrupts brain structure and function in children. However, the functional connectivity of brain regions within global and local networks (i.e., functional connectome) is poorly understood in pediatric concussion. This prospective, longitudinal study addressed this gap using data from the largest neuroimaging study of pediatric concussion to date to study the functional connectome longitudinally after concussion as compared with mild orthopedic injury (OI). Children and adolescents (n = 967) 8-16.99 years with concussion or mild OI were recruited from pediatric emergency departments within 48 h post-injury. Pre-injury and 1-month post-injury symptom ratings were used to classify concussion with or without persistent symptoms based on reliable change. Subjects completed a post-acute (2-33 days) and chronic (3 or 6 months via random assignment) MRI scan. Graph theory metrics were derived from 918 resting-state functional MRI scans in 585 children (386 concussion/199 OI). Linear mixed-effects modeling was performed to assess group differences over time, correcting for multiple comparisons. Relative to OI, the global clustering coefficient was reduced at 3 months post-injury in older children with concussion and in females with concussion and persistent symptoms. Time post-injury and sex moderated group differences in local (regional) network metrics of several brain regions, including degree centrality, efficiency, and clustering coefficient of the angular gyrus, calcarine fissure, cuneus, and inferior occipital, lingual, middle occipital, post-central, and superior occipital gyrus. Relative to OI, degree centrality and nodal efficiency were reduced post-acutely, and nodal efficiency and clustering coefficient were reduced chronically after concussion (i.e., at 3 and 6 months post-injury in females; at 6 months post-injury in males). Functional network alterations were more robust and widespread chronically as opposed to post-acutely after concussion, and varied by sex, age, and symptom recovery at 1-month post-injury. Local network segregation reductions emerged globally (across the whole brain network) in older children and in females with poor recovery chronically after concussion. Reduced functioning between neighboring regions could negatively disrupt specialized information processing. Local network metric alterations were demonstrated in several posterior regions that are involved in vision and attention after concussion relative to OI. This indicates that functioning of superior parietal and occipital regions could be particularly susceptibile to the effects of concussion. Moreover, those regional alterations were especially apparent at later time periods post-injury, emerging after post-concussive symptoms resolved in most and persisted up to 6 months post-injury, and differed by biological sex. This indicates that neurobiological changes continue to occur up to 6 months after pediatric concussion, although changes emerge earlier in females than in males. Changes could reflect neural compensation mechanisms.

Longitudinal monitoring of the mouse brain reveals heterogenous network trajectories during aging

The human aging brain is characterized by changes in network efficiency that are currently best captured through longitudinal resting-state functional MRI (rs-fMRI). These studies however are challenging due to the long human lifespan. Here we show that the mouse animal model with a much shorter lifespan allows us to follow the functional network organization over most of the animal's adult lifetime. We used a longitudinal study of the functional connectivity of different brain regions with rs-fMRI under anesthesia. Our analysis uncovers network modules similar to those reported in younger mice and in humans (i.e., prefrontal/default mode network (DMN), somatomotor and somatosensory networks). Statistical analysis reveals different patterns of network reorganization during aging. Female mice showed a pattern akin to human aging, with de-differentiation of the connectome, mainly due to increases in connectivity of the prefrontal/DMN cortical networks to other modules. Our male cohorts revealed heterogenous aging patterns with only one group confirming the de- differentiation, while the majority showed an increase in connectivity of the somatomotor cortex to the Nucleus accumbens. In summary, in line with human work, our analysis in mice supports the concept of de-differentiation in the aging mammalian brain and reveals additional trajectories in aging mice networks.

Clinical study protocol on electronic cigarettes and nicotine pouches for smoking cessation in Pakistan: a randomized controlled trial

BACKGROUND

Pakistan is one of most vulnerable low- and middle-income countries with 29 million adult active tobacco users. Smoking cessation services are lacking as the tobacco control initiatives have largely failed to address the smoking endemic. Over the last 5 years, Pakistan has witnessed the use of innovative tobacco harm reduction (THR) products such as e-cigarettes and nicotine pouches. However, their use remains limited. THR products are imported legally as consumer goods and are taxable. The lack of sufficient data for THR and its application is a challenge in gauging their effectiveness in assisting smokers quit combustible smoking. Evidence-based studies can help in measuring the effectiveness of e-cigarettes and nicotine pouches as smoking cessation aids.

METHOD

Keeping in view the study objectives, a sample size of 600 participants will be sufficient to assess the effectiveness of e-cigarettes and nicotine pouches for smoking cessation in Pakistan. Of these, 200 participants each will receive e-cigarettes and nicotine pouches along with basic care counselling, while the remaining 200 participants will only receive basic care counselling for 48 weeks. The association of participants' characteristics with smoking and health status will be based on the bivariate and multivariate analysis. The simple t-test and variance analysis will assess the differences in intervention indicators between the control and treatment groups. For the inferential analysis, the average treatment impact will be based on the quasi-experimental techniques such as difference in difference (DID) or propensity score matching (PMS).

DISCUSSION

The study will evaluate the participants at the baseline as they decide the quit date. After every 12 weeks, a follow-up survey with the participants will be conducted. Results are anticipated to inform the public, decision-makers, and researchers about the effects of using e-cigarettes and nicotine pouches in the short- and medium-term periods. Critically, the potential of e-cigarettes and other alternative nicotine delivery systems as smoking cessation aid will be assessed.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05715164 . Registered on February 6, 2023.

PROTOCOL VERSION

Protocol version 1.0, 14-12-2022 Trial in progress and not yet recruiting participants. Estimated primary data collection date-April 2024.

White matter-based brain network topological properties associated with individual impulsivity

Delay discounting (DD), a parameter derived from the intertemporal choice task, is a representative behavioral indicator of choice impulsivity. Previous research reported not only an association between DD and impulsive control disorders and negative health outcomes but also the neural correlates of DD. However, to date, there are few studies investigating the structural brain network topologies associated with individual differences in DD and whether self-reported measures (BIS-11) of impulsivity associated with DD share the same or distinct neural mechanisms is still unclear. To address these issues, here, we combined graph theoretical analysis with diffusion tensor imaging to investigate the associations between DD and the topological properties of the structural connectivity network and BIS-11 scores. Results revealed that people with a steep DD (greater impatience) had decreased small-worldness (a shift toward weaker small-worldnization) and increased degree centrality in the medial superior prefrontal cortex, associated with subjective value in the task. Though DD was associated with the BIS-11 motor impulsiveness subscale, this subscale was linked to topological properties different from DD; that is, high motor impulsiveness was associated with decreased local efficiency (less segregation) and decreased degree centrality in the precentral gyrus, involved in motor control. These findings provide insights into the systemic brain characteristics underlying individual differences in impulsivity and potential neural markers which could predict susceptibility to impulsive behaviors.

Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.

Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals

An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.

Evaluation of individual-based morphological brain network alterations in children with attention-deficit/hyperactivity disorder: a multi-method investigation

To investigate the topological organization of individual-based morphological brain networks (MBNs) in attention-deficit/hyperactivity disorder (ADHD) children with different methods. A total of 60 ADHD children and 60 typically developing (TD) controls matched for age and gender were enrolled. Each participant underwent a structural 3D T1-weighted scan. Based on the inter-regional morphological similarity of GM regions, Kullback-Leibler-based similarity (KLS), Multivariate Euclidean Distance (MED), and Tijms's method were used to construct individual-based MBNs, respectively. The between-group difference of global and nodal network topological profiles was estimated, and partial correlation analysis was used for further analysis. According to KLS and MED-based network, ADHD showed a decreased global efficiency (Eglob) and increased characteristic path length (Lp) compared to the TD group, while Tijms's method-based network showed no between-group difference in global and nodal profiles. Nodal profiles were significantly decreased in the bilateral caudate, and nodal efficiency of the bilateral caudate was negatively correlated with clinical symptom severity of ADHD (P < 0.05, FDR-corrected) by the KLS-based network. Nodal betweenness was significantly decreased in the left inferior occipital gyrus and correlated with clinical symptom severity of ADHD (P < 0.05, FDR-corrected) by the MED-based network. ADHD was found to have a significantly less integrated organization and a shift to a "weaker small-worldness" pattern, while abnormal nodal profiles were mainly in the corpus striatum and default-mode networks. Our study highlights the crucial role of abnormal morphological connectivity patterns in understanding the brain maturational effects in ADHD and enriching the insights into MBNs at an individual level.

Decreased Functional and Structural Connectivity is Associated with Core Symptom Improvement in Children with Autism Spectrum Disorder After Mini-basketball Training Program

Exercise intervention has been proven helpful to ameliorate core symptoms of Autism Spectrum Disorder (ASD). However, the underlying mechanisms are not fully understood. In this study, we carried out a 12-week mini-basketball training program (MBTP) on ASD children and examined the changes of brain functional and structural networks before and after exercise intervention. We applied individual-based method to construct functional network and structural morphological network, and investigated their alterations following MBTP as well as their associations with the change in core symptom. Structural MRI and resting-state functional MRI data were obtained from 58 ASD children aged 3-12 years (experiment group: n = 32, control group: n = 26). ASD children who received MBTP intervention showed several distinguishable alternations compared to the control without special intervention. These included decreased functional connectivity within the sensorimotor network (SM) and between SM and the salience network, decreased morphological connectivity strength in a cortical-cortical network centered on the left inferior temporal gyrus, and a subcortical-cortical network centered on the left caudate. Particularly, the aforementioned functional and structural changes induced by MBTP were associated with core symptoms of ASD. Our findings suggested that MBTP intervention could be an effective approach to improve core symptoms in ASD children, decrease connectivity in both structure and function networks, and may drive the brain change towards normal-like neuroanatomy.

Abnormal intrinsic brain functional network dynamics in patients with cervical spondylotic myelopathy

The specific topological changes in dynamic functional networks and their role in cervical spondylotic myelopathy (CSM) brain function reorganization remain unclear. This study aimed to investigate the dynamic functional connection (dFC) of patients with CSM, focusing on the temporal characteristics of the functional connection state patterns and the variability of network topological organization. Eighty-eight patients with CSM and 77 healthy controls (HCs) were recruited for resting-state functional magnetic resonance imaging. We applied the sliding time window analysis method and K-means clustering analysis to capture the dFC variability patterns of the two groups. The graph-theoretical approach was used to investigate the variance in the topological organization of whole-brain functional networks. All participants showed four types of dynamic functional connection states. The mean dwell time in state 2 was significantly different between the two groups. Particularly, the mean dwell time in state 2 was significantly longer in the CSM group than in the healthy control group. Among the four states, switching of relative brain networks mainly included the executive control network (ECN), salience network (SN), default mode network (DMN), language network (LN), visual network (VN), auditory network (AN), precuneus network (PN), and sensorimotor network (SMN). Additionally, the topological properties of the dynamic network were variable in patients with CSM. Dynamic functional connection states may offer new insights into intrinsic functional activities in CSM brain networks. The variance of topological organization may suggest instability of the brain networks in patients with CSM.

Magnetic Resonance-Guided Focused Ultrasound Thalamotomy Rebalances Atypical Functional Hierarchy in Patients with Essential Tremor

Magnetic resonance-guided focused ultrasound (MRgFUS) has brought thalamotomy back to the frontline for essential tremor (ET). As functional organization of human brain strictly follows hierarchical principles which are frequently deficient in neurological diseases, whether additional damage from MRgFUS thalamotomy induces further disruptions of ET functional scaffolds are still controversial. This study was to examine the alteration features of brain functional frameworks following MRgFUS thalamotomy in patients with ET. We retrospectively obtained preoperative (ETpre) and postoperative 6-month (ET6m) data of 30 ET patients underwent MRgFUS thalamotomy from 2018 to 2020. Their archived functional MR images were used to functional gradient comparison. Both supervised pattern learning and stepwise linear regression were conducted to associate gradient features to tremor symptoms with additional neuropathophysiological analysis. MRgFUS thalamotomy relieved 78.19% of hand tremor symptoms and induced vast global framework alteration (ET6m vs. ETpre: Cohen d = - 0.80, P < 0.001). Multiple robust alterations were identified especially in posterior cingulate cortex ([Formula: see text] ET6m vs. [Formula: see text] ETpre: Cohen d = 0.87, P = 0.048). Compared with matched health controls (HCs), its gradient distances to primary communities were significantly increased in [Formula: see text] ETpre patients with anomalous stepwise connectivity (P < 0.05 in ETpre vs. HCs), which were restored after MRgFUS thalamotomy. Both global and regional gradient features could be used for tremor symptom prediction and were linked to neuropathophysiological features of Parkinson disease and oxidative phosphorylation. MRgFUS thalamotomy not only suppress tremor symptoms but also rebalances atypical functional hierarchical architecture of ET patients.

Effects of computerized cognitive training on structure‒function coupling and topology of multiple brain networks in people with mild cognitive impairment: a randomized controlled trial

BACKGROUND

People with mild cognitive impairment (MCI) experience a loss of cognitive functions, whose mechanism is characterized by aberrant structure‒function (SC-FC) coupling and topological attributes of multiple networks. This study aimed to reveal the network-level SC-FC coupling and internal topological changes triggered by computerized cognitive training (CCT) to explain the therapeutic effects of this training in individuals with MCI.

METHODS

In this randomized block experiment, we recruited 60 MCI individuals and randomly divided them into an 8-week multidomain CCT group and a health education control group. The neuropsychological outcome measures were the Montreal Cognitive Assessment (MoCA), Chinese Auditory Verbal Learning Test (CAVLT), Chinese Stroop Color-Word Test (SCWT), and Rey-Osterrieth Complex Figure Test (Rey CFT). The brain imaging outcome measures were SC-FC coupling and topological attributes using functional MRI and diffusion tensor imaging methods. We applied linear model analysis to assess the differences in the outcome measures and identify the correspondence between the changes in the brain networks and cognitive functions before and after the CCT.

RESULTS

Fifty participants were included in the analyses after the exclusion of three dropouts and seven participants with low-quality MRI scans. Significant group × time effects were found on the changes in the MoCA, CAVLT, and Rey CFT recall scores. The changes in the SC-FC coupling values of the default mode network (DMN) and somatomotor network (SOM) were higher in the CCT group than in the control group (P(unc.) = 0.033, P(unc.) = 0.019), but opposite effects were found on the coupling values of the visual network (VIS) (P(unc.) = 0.039). Increasing clustering coefficients in the functional DMN and SOM and subtle changes in the nodal degree centrality and nodal efficiency of the right dorsal medial prefrontal cortex, posterior cingulate cortex, left parietal lobe, somatomotor area, and visual cortex were observed in the CCT group (P < 0.05, Bonferroni correction). Significant correspondences were found between global cognitive function and DMN coupling values (P(unc.) = 0.007), between immediate memory and SOM as well as FPC coupling values (P(unc.) = 0.037, P(unc.) = 0.030), between delayed memory and SOM coupling values (P(unc.) = 0.030), and between visual memory and VIS coupling values (P(unc.) = 0.007).

CONCLUSIONS

Eight weeks of CCT effectively improved global cognitive and memory functions; these changes were correlated with increases in SC-FC coupling and changes in the topography of the DMN and SOM in individuals with MCI. The CCT regimen also modulated the clustering coefficient and the capacity for information transformation in functional networks; these effects appeared to underlie the cognitive improvement associated with CCT.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR2000034012. Registered on 21 June 2020.

Network biomarkers in recovered psychosis patients who discontinued antipsychotics

There are no studies investigating topological properties of resting-state fMRI (rs-fMRI) in patients who have recovered from psychosis and discontinued medication (hereafter, recovered patients [RP]). This study aimed to explore topological organization of the functional brain connectome in the RP using graph theory approach. We recruited 30 RP and 50 age and sex-matched healthy controls (HC). The RP were further divided into the subjects who were relapsed after discontinuation of antipsychotics (RP-R) and who maintained recovered state without relapse (RP-M). Using graph-based network analysis of rs-fMRI signals, global and local metrics and hub information were obtained. The robustness of the network was tested with random failure and targeted attack. As an ancillary analysis, Network-Based Statistic (NBS) was performed. Association of significant findings with psychopathology and cognitive functioning was also explored. The RP showed intact network properties in terms of global and local metrics. However, higher global functional connectivity strength and hyperconnectivity in the interconnected component were observed in the RP compared to HC. In the subgroup analysis, the RP-R were found to have lower global efficiency, longer characteristic path length and lower robustness whereas no such abnormalities were identified in the RP-M. Associations of the degree centrality of some hubs with cognitive functioning were identified in the RP-M. Even though network properties of the RP were intact, subgroup analysis revealed more altered topological organizations in the RP-R. The findings in the RP-R and RP-M may serve as network biomarkers for predicting relapse or maintained recovery after the discontinuation of antipsychotics.

Leading basic modes of spontaneous activity drive individual functional connectivity organization in the resting human brain

Spontaneous activity of the human brain provides a window to explore intrinsic principles of functional organization. However, most studies have focused on interregional functional connectivity. The principles underlying rich repertoires of instantaneous activity remain largely unknown. We apply a recently proposed eigen-microstate analysis to three resting-state functional MRI datasets to identify basic modes that represent fundamental activity patterns that coexist over time. We identify five leading basic modes that dominate activity fluctuations. Each mode exhibits a distinct functional system-dependent coactivation pattern and corresponds to specific cognitive profiles. In particular, the spatial pattern of the first leading basis mode shows the separation of activity between the default-mode and primary and attention regions. Based on theoretical modelling, we further reconstruct individual functional connectivity as the weighted superposition of coactivation patterns corresponding to these leading basic modes. Moreover, these leading basic modes capture sleep deprivation-induced changes in brain activity and interregional connectivity, primarily involving the default-mode and task-positive regions. Our findings reveal a dominant set of basic modes of spontaneous activity that reflect multiplexed interregional coordination and drive conventional functional connectivity, furthering the understanding of the functional significance of spontaneous brain activity.

Does emotion regulation network mediate the effect of social network on psychological distress among older adults?

Socio-emotional interactions are integral for regulating emotions and buffering psychological distress. Social neuroscience perspectives on aging suggest that empathetic interpersonal interactions are supported by the activation of brain regions involved in regulating negative affect. The current study tested whether resting state functional connectivity of a network of brain regions activated during cognitive emotion regulation, i.e., emotion regulation network (ERN), statistically mediates the frequency of social contact with friends or family on psychological distress. Here, a 10-min resting-state functional MRI scan was collected along with self-reported anxiety/depressive, somatic, and thought problems and social networking from 90 community-dwelling older adults (aged 65-85 years). The frequency of social interactions with family, but not friends and neighbors, was associated with lower psychological distress. The magnitude of this effect was reduced by 33.34% to non-significant upon adding resting state ERN connectivity as a mediator. Follow-up whole-brain graph network analyses revealed that efficiency and centrality of the left inferior frontal gyrus and the right middle temporal gyrus relate to greater family interactions and lower distress. These hubs may help to buffer psychological problems in older adults through interactions involving empathetic and cognitive emotion regulation with close family.

Structural connectome alterations in anxious dogs: a DTI-based study

Anxiety and fear are dysfunctional behaviors commonly observed in domesticated dogs. Although dogs and humans share psychopathological similarities, little is known about how dysfunctional fear behaviors are represented in brain networks in dogs diagnosed with anxiety disorders. A combination of diffusion tensor imaging (DTI) and graph theory was used to investigate the underlying structural connections of dysfunctional anxiety in anxious dogs and compared with healthy dogs with normal behavior. The degree of anxiety was assessed using the Canine Behavioral Assessment & Research Questionnaire (C-BARQ), a widely used, validated questionnaire for abnormal behaviors in dogs. Anxious dogs showed significantly decreased clustering coefficient ([Formula: see text]), decreased global efficiency ([Formula: see text]), and increased small-worldness (σ) when compared with healthy dogs. The nodal parameters that differed between the anxious dogs and healthy dogs were mainly located in the posterior part of the brain, including the occipital lobe, posterior cingulate gyrus, hippocampus, mesencephalon, and cerebellum. Furthermore, the nodal degree ([Formula: see text]) of the left cerebellum was significantly negatively correlated with "excitability" in the C-BARQ of anxious dogs. These findings could contribute to the understanding of a disrupted brain structural connectome underlying the pathological mechanisms of anxiety-related disorders in dogs.

Spatial navigation is associated with subcortical alterations and progression risk in subjective cognitive decline

BACKGROUND

Subjective cognitive decline (SCD) may serve as a symptomatic indicator for preclinical Alzheimer's disease; however, SCD is a heterogeneous entity regarding clinical progression. We aimed to investigate whether spatial navigation could reveal subcortical structural alterations and the risk of progression to objective cognitive impairment in SCD individuals.

METHODS

One hundred and eighty participants were enrolled: those with SCD (n = 80), normal controls (NCs, n = 77), and mild cognitive impairment (MCI, n = 23). SCD participants were further divided into the SCD-good (G-SCD, n = 40) group and the SCD-bad (B-SCD, n = 40) group according to their spatial navigation performance. Volumes of subcortical structures were calculated and compared among the four groups, including basal forebrain, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Topological properties of the subcortical structural covariance network were also calculated. With an interval of 1.5 years ± 12 months of follow-up, the progression rate to MCI was compared between the G-SCD and B-SCD groups.

RESULTS

Volumes of the basal forebrain, the right hippocampus, and their respective subfields differed significantly among the four groups (p < 0.05, false discovery rate corrected). The B-SCD group showed lower volumes in the basal forebrain than the G-SCD group, especially in the Ch4p and Ch4a-i subfields. Furthermore, the structural covariance network of the basal forebrain and right hippocampal subfields showed that the B-SCD group had a larger Lambda than the G-SCD group, which suggested weakened network integration in the B-SCD group. At follow-up, the B-SCD group had a significantly higher conversion rate to MCI than the G-SCD group.

CONCLUSION

Compared to SCD participants with good spatial navigation performance, SCD participants with bad performance showed lower volumes in the basal forebrain, a reorganized structural covariance network of subcortical nuclei, and an increased risk of progression to MCI. Our findings indicated that spatial navigation may have great potential to identify SCD subjects at higher risk of clinical progression, which may contribute to making more precise clinical decisions for SCD individuals who seek medical help.

Enlarged choroid plexus related to cortical atrophy in multiple sclerosis

OBJECTIVES

To investigate the correlation between choroid plexus volume and whole brain morphology in patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD).

METHODS

Fifty-one patients with MS, 42 patients with NMOSD, and 56 healthy controls (HC) were recruited. The morphological changes in choroid plexus and whole brain tissue were compared between three groups and the correlations between choroid plexus volume and brain atrophy were further investigated. The longitudinal alterations of brain morphology in 25 MS and 20 NMOSD patients were compared.

RESULTS

Compared to the HC group, the choroid plexus volumes were increased in the MS group (p < 0.001) but not in the NMOSD group (p > 0.05). Compared to the HC group, the MS group showed reduced cortex thickness, deep gray matter volume, and increased ventricle system volume, and the NMOSD group showed increased third ventricle volume (all p < 0.05, false discovery rate corrected). In the MS group, there were widespread correlations between enlarged choroid plexus volume and reduced cerebral cortex thickness (p < 0.05, r = -0.292~-0.538, false discovery rate corrected). The interval time was not significantly different between the MS (median: 1.37 years) and NMOSD group (median: 1.25 years) (p > 0.05). In MS, compared with the baseline, the right hippocampus and nucleus accumbens volumes were decreased in long follow-up, and bilateral lateral ventricle volumes were increased both in short and long follow-up (all p < 0.05, false discovery rate corrected).

CONCLUSIONS

The enlarged choroid plexus related to reduced cortical thickness and progressive local brain atrophy are shown in MS patients, but not obvious in NMOSD patients.

KEY POINTS

• MS and NMOSD have different altered patterns in choroid plexus volume and brain atrophy. • The enlarged choroid plexus related to brain atrophy is shown in MS patients, but not obvious in NMOSD patients. • Progressive local brain atrophy is shown in MS patients, but not obvious in NMOSD patients.

Mapping the long-term delayed recall-based cortex-hippocampus network constrained by the structural and functional connectome: a case-control multimodal MRI study

Background

Connectome mapping may reveal new treatment targets for patients with neurological and psychiatric diseases. However, the long-term delayed recall based-network with structural and functional connectome is still largely unknown. Our objectives were to (1) identify the long-term delayed recall-based cortex-hippocampus network with structural and functional connectome and (2) investigate its relationships with various cognitive functions, age, and activities of daily living.

Methods

This case-control study enrolled 131 subjects (73 amnestic mild cognitive impairment [aMCI] patients and 58 age- and education-matched healthy controls [HCs]). All subjects completed a neuropsychological battery, activities of daily living assessment, and multimodal magnetic resonance imaging. Nodes of the cortical-hippocampal network related to long-term delayed recall were identified by probabilistic fiber tracking and functional connectivity (FC) analysis. Then, the main and interaction effects of the network on cognitive functions were assessed by a generalized linear model. Finally, the moderating effects of the network on the relationships between long-term delayed recall and clinical features were analyzed by multiple regression and Hayes’ bootstrap method. All the effects of cortex-hippocampus network were analyzed at the connectivity and network levels.

Results

The result of a generalized linear model showed that the bilateral hippocampus, left dorsolateral superior frontal gyrus, right supplementary motor area, left lingual gyrus, left superior occipital gyrus, left superior parietal gyrus, left precuneus, and right temporal pole (superior temporal gyrus) are the left and right cortex-hippocampus network nodes related to long-term delayed recall (P < 0.05). Significant interaction effects were found between the Auditory Verbal Learning Test Part 5 (AVLT 5) scores and global properties of the left cortex-hippocampus network [hierarchy, clustering coefficient, characteristic path length, global efficiency, local efficiency, Sigma and synchronization (P < 0.05 Bonferroni corrected)]. Significant interaction effects were found between the general cognitive function/executive function/language and global properties of the left cortex-hippocampus network [Sigma and synchronization (P < 0.05 Bonferroni corrected)].

Conclusion

This study introduces a novel symptom-based network and describes relationships among cognitive functions, brain function, and age. The cortex–hippocampus network constrained by the structural and functional connectome is closely related to long-term delayed recall.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-023-01197-7.

Disrupted resting-state brain functional network properties in non-neuropsychiatric systemic lupus erythematosus patients

INTRODUCTION

Previous fMRI studies revealed that the abnormal functional connectivity (FC) was related to cognitive impairment in patients with SLE. However, it remains unclear how the disease severity affects the functional topological organization of the whole-brain network in SLE patients without neuropsychiatric symptoms (non-NPSLE).

OBJECTIVE

We aim to examine the impairment of the whole-brain functional network in SLE patients without neuropsychiatric symptoms (non-NPSLE), which may improve the understanding of neural mechanism in SLE.

METHODS

We acquired resting-state fMRI data from 32 non-NPSLE patients and 32 healthy controls (HC), constructed their whole-brain functional network, and then estimated the topological properties including global and nodal parameters by using graph theory. Meanwhile, we also investigated the differences in intra- and inter-network FC between the non-NPSLE patients and the HC.

RESULTS

The non-NPSLE patients showed significantly lower clustering coefficient, global and local efficiency, but higher characteristic path length than the HC. The non-NPSLE patients had significantly lower nodal strength in two regions, ventromedial prefrontal cortex (vmPFC) and anterior PFC (aPFC) than the HC. We found the non-NPSLE patients had significantly lower intra-network FC within frontal-parietal network (FPN) and within default mode network (DMN), and significantly lower inter-network FC between DMN and FPN than the HC. The intra-network FC within DMN was negatively correlated with systemic lupus erythematosus disease activity index (SLEDAI).

CONCLUSION

Abnormal whole-brain functional network properties and abnormal intra- and inter-network FC may be related to cognitive impairment and disease degree in the non-NPSLE patients. Our findings provide a network perspective to understand the neural mechanisms of SLE.

Cortical response induced by task-oriented training of the upper limb in subacute stroke patients as assessed by functional near-infrared spectroscopy

Despite the popularity of task-oriented training for stroke, the cortical reorganization associated with this type of therapy remains to be fully elucidated due to the lack of dynamic assessment tools. A good tolerance for motion artifacts makes functional near-infrared spectroscopy (fNIRS) suitable for investigating task-induced cortical responses in stroke patients. Here, patients were randomly assigned to receive task oriented (n = 25) or cyclic rotary training (n = 25) with simultaneous cortical activation and effective connectivity network analysis between prefrontal and motor cortices (PFC/MC). Compared with cyclic rotary training, task-oriented training induced significantly increased activation in both hemispheres and enhanced influence of PFC on MC. In addition, significantly decreased activation lateralization and increased betweenness centrality of the contralesional MC suggested widespread involvement of the contralesional hemisphere during task-oriented training. This study verifies the feasibility of fNIRS combined with motor paradigms for assessing neural responses associated with stroke rehabilitation in real time.

Neuroinflammation is independently associated with brain network dysfunction in Alzheimer's disease

Brain network dysfunction is increasingly recognised in Alzheimer's disease (AD). However, the causes of brain connectivity disruption are still poorly understood. Recently, neuroinflammation has been identified as an important factor in AD pathogenesis. Microglia participate in the construction and maintenance of healthy neuronal networks, but pro-inflammatory microglia can also damage these circuits. We hypothesised that microglial activation is independently associated with brain connectivity disruption in AD. We performed a cross-sectional multimodal imaging study and interrogated the relationship between imaging biomarkers of neuroinflammation, Aβ deposition, brain connectivity and cognition. 42 participants (12 Aβ-positive MCI, 14 Aβ-positive AD and 16 Aβ-negative healthy controls) were recruited. Participants had ¹¹C-PBR28 and ¹⁸F-flutemetamol PET to quantify Aβ deposition and microglial activation, T1-weighted, diffusion tensor and resting-state functional MRI to assess structural network and functional network. ¹¹C-PBR28 uptake, structural network integrity and functional network orgnisation were compared across diagnostic groups and the relationship between neuroinflammation and brain network was tested in 26 Aβ-positive patients. Increased ¹¹C-PBR28 uptake, decreased FA, network small-worldness and local efficiency were observed in AD patients. Cortical ¹¹C-PBR28 uptake correlated negatively with structural integrity (standardised β = -0.375, p = 0.037) and network local efficiency (standardised β = -0.468, p < 0.001), independent of cortical thickness and Aβ deposition, while Aβ was not. Network structural integrity, small-worldness and local efficiency, and cortical thickness were positively associated with cognition. Our findings suggest cortical neuroinflammation coincide with structural and functional network disruption independent of Aβ and cortical atrophy. These findings link the brain connectivity change and pathological process in Alzheimer's disease, and suggest a pathway from neuroinflammation to systemic brain dysfunction.

Enhanced brain parcellation via abnormality inpainting for neuroimage-based consciousness evaluation of hydrocephalus patients by lumbar drainage

Brain network analysis based on structural and functional magnetic resonance imaging (MRI) is considered as an effective method for consciousness evaluation of hydrocephalus patients, which can also be applied to facilitate the ameliorative effect of lumbar cerebrospinal fluid drainage (LCFD). Automatic brain parcellation is a prerequisite for brain network construction. However, hydrocephalus images usually have large deformations and lesion erosions, which becomes challenging for ensuring effective brain parcellation works. In this paper, we develop a novel and robust method for segmenting brain regions of hydrocephalus images. Our main contribution is to design an innovative inpainting method that can amend the large deformations and lesion erosions in hydrocephalus images, and synthesize the normal brain version without injury. The synthesized images can effectively support brain parcellation tasks and lay the foundation for the subsequent brain network construction work. Specifically, the novelty of the inpainting method is that it can utilize the symmetric properties of the brain structure to ensure the quality of the synthesized results. Experiments show that the proposed brain abnormality inpainting method can effectively aid the brain network construction, and improve the CRS-R score estimation which represents the patient's consciousness states. Furthermore, the brain network analysis based on our enhanced brain parcellation method has demonstrated potential imaging biomarkers for better interpreting and understanding the recovery of consciousness in patients with secondary hydrocephalus.

Addictive brain-network identification by spatial attention recurrent network with feature selection

Addiction in the brain is associated with adaptive changes that reshape addiction-related brain regions and lead to functional abnormalities that cause a range of behavioral changes, and functional magnetic resonance imaging (fMRI) studies can reveal complex dynamic patterns of brain functional change. However, it is still a challenge to identify functional brain networks and discover region-level biomarkers between nicotine addiction (NA) and healthy control (HC) groups. To tackle it, we transform the fMRI of the rat brain into a network with biological attributes and propose a novel feature-selected framework to extract and select the features of addictive brain regions and identify these graph-level networks. In this framework, spatial attention recurrent network (SARN) is designed to capture the features with spatial and time-sequential information. And the Bayesian feature selection(BFS) strategy is adopted to optimize the model and improve classification tasks by restricting features. Our experiments on the addiction brain imaging dataset obtain superior identification performance and interpretable biomarkers associated with addiction-relevant brain regions.

Greater functional connectivity between the ventral frontal cortex and occipital cortex in herpes zoster patients than post-herpetic neuralgia patients

OBJECTIVE

This study aimed to compare whole brain network between herpes zoster (HZ) patients and post-herpetic neuralgia (PHN) patients, as well as to investigate the associations between whole brain network changes and pain intensity and the accuracy of classifying between different types of pain.

METHODS

PHN patients (n = 50) and HZ patients (n = 50) and healthy controls (HCs) (n = 50) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Functional connectivity and global and local graph theory metrics were calculated by using Dosenbach-160 atlas. The relationship between neuroimaging indicators and clinical scales was evaluated using correlation analysis, and receiver operating characteristic (ROC) curves evaluated the feasibility of classifying PHN and HZ patients using specific neuroimaging indicators.

RESULTS

(1) 10 greater average connectivities were found in HZ group among the default mode, frontoparietal, cingulo-opercular, sensorimotor, occipital networks (ONs), and cerebellum (p < 0.001). (2) HZ patients exhibited higher global efficiency than those in the PHN and HCs (t = 2.178, p = 0.038). (3) Multiple linear regression analyses indicated that functional connectivity between the ventral frontal cortex in the cingulo-opercular network and the occipital gyrus in the ON influenced the visual analog score pain scores (β = 4.273; p = 0.004).

CONCLUSION

The variation of functional connectivity between ventral frontal cortex in the cingulo-opercular network and occipital gyrus in the ON may be a robust neuroimaging marker of the transition from HZ to PHN patients.

ADVANCES IN KNOWLEDGE

Whole-brain network analysis may be effective in distinguishing HZ and PHN patients and predicting pain intensity.

Disrupted topological organization of functional brain networks in Alzheimer's disease patients with depressive symptoms

BACKGROUND

Depression is a common symptom of Alzheimer's disease (AD), but the underlying neural mechanism is unknown. The aim of this study was to explore the topological properties of AD patients with depressive symptoms (D-AD) using graph theoretical analysis.

METHODS

We obtained 3-Tesla rsfMRI data from 24 D-AD patients, 20 non-depressed AD patients (nD-AD), and 20 normal controls (NC). Resting state networks were identified using graph theory analysis. ANOVA with a two-sample t-test post hoc analysis in GRETNA was used to assess the topological measurements.

RESULTS

Our results demonstrate that the three groups show characteristic properties of a small-world network. NCs showed significantly larger global and local efficiency than D-AD and nD-AD patients. Compared with nD-AD patients, D-AD patients showed decreased nodal centrality in the pallidum, putamen, and right superior temporal gyrus. They also showed increased nodal centrality in the right superior parietal gyrus, the medial orbital portion of the right superior frontal gyrus, and the orbital portion of the right superior frontal gyrus. Compared with nD-AD patients, NC showed decreased nodal betweenness in the right superior temporal gyrus, and increased nodal betweenness in medial orbital part of the right superior frontal gyrus.

CONCLUSIONS

These results indicate that D-AD is associated with alterations of topological structure. Our study provides new insights into the brain mechanisms underlying D-AD.

Superior temporal gyrus and cerebellar loops predict nonsuicidal self-injury in major depressive disorder patients by multimodal neuroimaging

In major depressive disorder (MDD) patients, nonsuicidal self-injury (NSSI) is a common comorbidity, and it is important to clarify the underlying neurobiology. Here, we investigated the association of NSSI with brain function and structure in MDD patients. A total of 260 MDD patients and 132 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging and three-dimensional T1-weighted structural scans. NSSI behaviour was assessed through interviews. Voxel-based morphometry analysis (VBM), regional homogeneity analysis (ReHo), functional connectome topology properties and network-based statistics were used to detect the differences in neuroimaging characteristics. Finally, the random forest method was used to evaluate whether these factors could predict NSSI in MDD. Compared with HCs, MDD patients with a history of NSSI showed significant right putamen grey matter volume (GMV), right superior orbital frontal cortex ReHo, left pallidum degree centrality, and putamen-centre function network differences. Compared to MDD subjects without NSSI, those with past NSSI showed significant right superior temporal gyrus (STG) GMV, right lingual gyrus ReHo, sigma and global efficiency, and cerebellum-centre function network differences. The right STG GMV and cerebellum-centre function network were more important than other factors in predicting NSSI behaviour in MDD. MDD patients with a history of NSSI have dysregulated spontaneous brain activity and structure in regions related to emotions, pain regulation, and the somatosensory system. Importantly, right STG GMV and cerebellar loops may play important roles in NSSI in MDD patients.

Public Health Practitioners’ Knowledge towards Nicotine and Other Cigarette Components on Various Human Diseases in Pakistan: A Contribution to Smoking Cessation Policies

Smoking cessation seems to be a weak link in the struggle against tobacco epidemic in Pakistan. Awareness regarding nicotine is lacking not only in the general population but also among public health practitioners. This lack of knowledge is one of the key barriers to bringing down the prevalence of smoking. Using primary survey data and nonparametric econometric techniques, this study assesses the knowledge of nicotine and harm reduction among public health practitioners in Pakistan. Results indicate physicians have misconceptions about nicotine. The majority of the medical professionals associate nicotine use with birth defects, cancer, cardiovascular illness, and chronic obstructive pulmonary disease (COPD). More than two-thirds of doctors (70%) strongly agreed and 17.9% somewhat agreed with the statement that nicotine causes cancer. This study suggests physicians need to be better informed about the fact that nicotine in tobacco products is addictive while chemicals, particularly those causing combustion, are the primary risk sources for tobacco-related illnesses. Misconceptions regarding nicotine can be quickly remedied with communication interventions. This study also suggests that alternative nicotine delivery systems can help smoking cessation and reduce the consumption of combustible tobacco in Pakistan.

Reading-related Brain Function Restored to Normal After Articulation Training in Patients with Cleft Lip and Palate: An fMRI Study

Cleft lip and/or palate (CLP) are the most common craniofacial malformations in humans. Speech problems often persist even after cleft repair, such that follow-up articulation training is usually required. However, the neural mechanism behind effective articulation training remains largely unknown. We used fMRI to investigate the differences in brain activation, functional connectivity, and effective connectivity across CLP patients with and without articulation training and matched normal participants. We found that training promoted task-related brain activation among the articulation-related brain networks, as well as the global attributes and nodal efficiency in the functional-connectivity-based graph of the network. Our results reveal the neural correlates of effective articulation training in CLP patients, and this could contribute to the future improvement of the post-repair articulation training program.

Treatment-Specific Network Modulation of MRI-Guided Focused Ultrasound Thalamotomy in Essential Tremor : Modulation of ET-Related Network by MRgFUS Thalamotomy

MRI-guided focused ultrasound (MRgFUS) thalamotomy is a novel, effective, and non-invasive treatment for essential tremor (ET). However, the network mediating MRgFUS in treating ET is not precisely known. This study aimed to identify the disease-specific network associated with the therapeutic effects of MRgFUS thalamotomy on ET and investigate its regional characteristics and genetic signatures to gain insights into the neurobiological mechanism of ET and MRgFUS thalamotomy. Twenty-four ET patients treated with MRgFUS thalamotomy underwent resting-state functional MRI at baseline and postoperative 6 months to measure the fractional amplitude of low-frequency fluctuation (fALFF). Ordinal trends canonical variates analysis (OrT/CVA) was performed on the within-subject fALFF data to identify the ET-related network. Genetic functional enrichment analysis was conducted to study the genetic signatures of this ET-related network using brain-wide gene expression data. OrT/CVA analysis revealed a significant ET-related network for which subject expression showed consistent increases after surgery. The treatment-induced increases in subject expression were significantly correlated with concurrent tremor improvement. This network was characterized by increased activity in the sensorimotor cortex and decreased activity in the posterior cingulate cortex. It was correlated with an expression map of a weighted combination genes enriched for mitochondria relevant ontology terms. This study demonstrates that the therapeutic effects of MRgFUS thalamotomy on ET are associated with modulating a distinct ET-related network which may be driven by mitochondria relevant neurobiological mechanism. Quantification of treatment-induced modulation on the ET-related network can provide an objective marker for evaluating the efficacy of MRgFUS thalamotomy.

EEG based functional brain networks analysis in dyslexic children during arithmetic task

Developmental Dyslexia is a neuro-developmental disorder that often refers to a phonological processing deficit regardless of average IQ. The present study investigated the distinct functional changes in brain networks of dyslexic children during arithmetic task performance using an electroencephalogram. Fifteen dyslexic children and fifteen normally developing children (NDC) were recruited and performed an arithmetic task. Brain functional network measures such as node strength, clustering coefficient, characteristic pathlength and small-world were calculated using graph theory methods for both groups. Task performance showed significantly less performance accuracy in dyslexics against NDC. The neural findings showed increased connectivity in the delta band and reduced connectivity in theta, alpha, and beta band at temporoparietal, and prefrontal regions in dyslexic group while performing the task. The node strengths were found to be significantly high in delta band (T3, O1, F8 regions) and low in theta (T5, P3, Pz regions), beta (Pz) and gamma band (T4 and prefrontal regions) during the task in dyslexics compared to the NDC. The clustering coefficient was found to be significantly low in the dyslexic group (theta and alpha band) and characteristic pathlength was found to be significantly high in the dyslexic group (theta and alpha band) compared to the NDC group while performing task. In conclusion, the present study shows evidence for poor fact-retrieval mechanism and altered network topology in dyslexic brain networks during arithmetic task performance.

Alterations of static and dynamic functional network connectivity in acute ischemic brainstem stroke

Background

Prior studies have shown abnormal brain functional network changes in patients with acute ischemic stroke. However, the alterations of dynamic functional network connectivity (FNC) in brainstem strokes have not been elucidated.

Purpose

To assess alterations of static and dynamic FNCs and determine the relationships between these and upper limb movement performance in patients with acute brainstem ischemic stroke.

Material and Methods

In total, 50 patients with acute brainstem ischemic stroke and 50 age- and sex-matched healthy controls were enrolled in the present study and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Independent component analysis was conducted to assess static and dynamic FNC patterns based on seven resting-state networks, namely, the default mode network (DMN), executive control network (ECN), attention network (AN), somatomotor network (SMN), visual network (VN), auditory network (AUN), and cerebellum network (CN).

Results

Compared with controls, patients with acute brainstem ischemic stroke exhibited wide aberrations of static FNC, including increased FNC in DMN–ECN, DMN–VN, ECN–VN, ECN–AN and AN–AUN pairs. Patients with acute brainstem ischemic stroke showed aberrant dynamic FNC in State 1, involving increased FNC aberrance in the DMN with AN, DMN with ECN, and reduced FNC in SMN–VN pairs. In State 5, patients with acute brainstem ischemic stroke showed increased FNC in DMN–VN and AN–AUN, and decreased FNC in AN–SMN pairs.

Conclusion

This study suggests that static and dynamic FNC impairment and aberrant connections exist in acute brainstem ischemic stroke, which expands what is known regarding the relationship between stroke and FNC from static and dynamic perspectives.

Brain connectomes in youth at risk for serious mental illness: an exploratory analysis

BACKGROUND

Identifying early biomarkers of serious mental illness (SMI)-such as changes in brain structure and function-can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI.

METHODS

Participants were classified as healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data.

RESULTS

Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections.

CONCLUSIONS

Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset.

A cognitive neurogenetic approach to uncovering the structure of executive functions

One central mission of cognitive neuroscience is to understand the ontology of complex cognitive functions. We addressed this question with a cognitive neurogenetic approach using a large-scale dataset of executive functions (EFs), whole-brain resting-state functional connectivity, and genetic polymorphisms. We found that the bifactor model with common and shifting-specific components not only was parsimonious but also showed maximal dissociations among the EF components at behavioral, neural, and genetic levels. In particular, the genes with enhanced expression in the middle frontal gyrus (MFG) and the subcallosal cingulate gyrus (SCG) showed enrichment for the common and shifting-specific component, respectively. Finally, High-dimensional mediation models further revealed that the functional connectivity patterns significantly mediated the genetic effect on the common EF component. Our study not only reveals insights into the ontology of EFs and their neurogenetic basis, but also provides useful tools to uncover the structure of complex constructs of human cognition.

Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms

Objectives

To investigate the relationship between diffusion tensor imaging (DTI) indicators and cerebral small vessel disease (CSVD) with depressive states, and to explore the underlying mechanisms of white matter damage in CSVD with depression.

Method

A total of 115 elderly subjects were consecutively recruited from the neurology clinic, including 36 CSVD patients with depressive state (CSVD+D), 34 CSVD patients without depressive state (CSVD-D), and 45 controls. A detailed neuropsychological assessment and multimodal magnetic resonance imaging (MRI) were performed. Based on tract-based spatial statistics (TBSS) analysis and structural network analysis, differences between groups were compared, including white matter fiber indicators (fractional anisotropy and mean diffusivity) and structural brain network indicators (global efficiency, local efficiency and network strength), in order to explore the differences and correlations of DTI parameters among the three groups.

Results

There were no significant differences in terms of CSVD burden scores and conventional imaging findings between the CSVD-D and CSVD+D groups. Group differences were found in DTI indicators (p <  0.05), after adjusting for age, gender, education level, and vascular risk factors (VRF), there were significant correlations between TBSS analysis indicators and depression, including: fractional anisotropy (FA) (r = − 0.291, p <  0.05), mean diffusivity (MD) (r = 0.297, p < 0.05), at the same time, between structural network indicators and depression also show significant correlations, including: local efficiency (E_Local) (r = − 0.278, p < 0.01) and network strength (r = − 0.403, p < 0.001).

Conclusions

Changes in FA, MD values and structural network indicators in DTI parameters can predict the depressive state of CSVD to a certain extent, providing a more direct structural basis for the hypothesis of abnormal neural circuits in the pathogenesis of vascular-related depression. In addition, abnormal white matter alterations in subcortical neural circuits probably affect the microstructural function of brain connections, which may be a mechanism for the concomitant depressive symptoms in CSVD patients.

Psychological distress and smoking behaviors of Chinese college students: mediating effects of the dimensions of learning burnout

Objectives

Smokers or never smokers exposed to environmental tobacco use are usually associated with various diseases and cancers. In order to better help college students prevent the tobacco use and thus lower the incidence of avoidable diseases, this study explored the predictive power of different variables including demographic and psychological variables in relation to smoking behaviors.

Methods

Maslach Burnout Inventory-Student Survey and Kessler Psychological Distress Scale (K10) were used in this study.

Results

There were 1449 college students participating in the study with 1340 pieces of valid data left, the effective ratio was 92.48%. The valid data included 37.1% male and 62.9% female aged 18.83 on average with 1.55 standard deviation. The multivariate logistic regression indicated that college students who were male (versus female, OR = 9.55), majoring in medicine and sports (versus nursing, OR_medicine = 2.19, OR_sports = 2.81), born in the non-singleton family (versus singleton family, OR = 0.63) with higher family income (versus lower family income, OR = 0.45), surrounded with smoking friends (versus without smoking friends, OR = 0.18), were more vulnerable to smoke. In addition, combined with the theory of planned behavior, the dimensions of learning burnout had full mediation effects between psychological distress and smoking behavior.

Conclusions

Psychological distress can only indirectly affect smoking behavior via learning efficacy, cynicism and emotional exhaustion. Adjustments from different dimensions of learning burnout such as avoiding cynicism about learning, enhancing learning efficacy and emotion exhaustion will help college students better prevent the tobacco use.