Processing level of meat modulates functional brain network connectivity during visual evaluation.

The identification of post-traumatic stress disorder (PTSD) among natural disaster survivors is remarkably challenging, and there are no reliable objective signatures that can be used to assist clinical diagnosis and optimize treatment. The current study aimed to establish a neurobiological signature of PTSD from the connectivity of large-scale brain networks and clarify the brain network mechanisms of PTSD. We examined fifty-seven unmedicated survivors with chronic PTSD and 59 matched trauma-exposed healthy controls (TEHCs) using resting-state functional magnetic resonance imaging (rs-fMRI). We extracted the node-to-network connectivity and obtained a feature vector with a dimensionality of 864 (108 nodes×8 networks) to represent each subject's functional connectivity (FC) profile. Multivariate pattern analysis with a relevance vector machine was then used to distinguish PTSD patients from TEHCs. We achieved a promising diagnostic accuracy of 89.2% in distinguishing PTSD patients from TEHCs. The most heavily weighted connections for PTSD classification were among the default mode network (DMN), visual network (VIS), somatomotor network, limbic network, and dorsal attention network (DAN). The strength of the anticorrelation of FC between the ventral medial prefrontal cortex (vMPFC) in DMN and the VIS and DAN was associated with the severity of PTSD. This study achieved relatively high accuracy in classifying PTSD patients vs. TEHCs at the individual level. This performance demonstrates that rs-fMRI-derived multivariate classification based on large-scale brain networks can provide potential signatures both to facilitate clinical diagnosis and to clarify the underlying brain network mechanisms of PTSD caused by natural disasters.

Unprecedented 4D spatiotemporal infant regional cerebral blood flow framework and region-specific physiology–function coupling across infancy were elucidated, highlighting strong physiology–function coupling specifically at the default-mode network to meet extraneuronal metabolic demand for network emergence.

A relationship between migraine without aura (MO) and patent foramen ovale (PFO) has been observed, but the neural basis underlying this relationship remains elusive. Utilizing independent component analysis via functional magnetic resonance imaging, we examined functional connectivity (FC) within and across networks in 146 patients with MO (75 patients with and 71 patients without PFO) and 70 healthy controls (35 patients each with and without PFO) to elucidate the individual effects of MO and PFO, as well as their interaction, on brain functional networks. The main effect of PFO manifested exclusively in the FC among the visual, auditory, default mode, dorsal attention and salience networks. Furthermore, the interaction effect between MO and PFO was discerned in brain clusters of the left frontoparietal network and lingual gyrus network, as well as the internetwork FC between the left frontoparietal network and the default mode network (DMN), the occipital pole and medial visual networks, and the dorsal attention and salience networks. Our findings suggest that the presence of a PFO shunt in patients with MO is accompanied by various FC changes within and across networks. These changes elucidate the intricate mechanisms linked to PFO-associated migraines and provide a basis for identifying novel noninvasive biomarkers.

目的 探讨精神分裂症患者脑静息态默认网络、中心执行网络及突显网络的网络内及网络间的功能连接特点.方法 对74例精神分裂症患者(患者组)和79名相匹配的健康者(对照组)进行静息态fMRI扫描.采取功能连接分析方法,以内侧前额叶皮质及右侧前岛叶为感兴趣区域分别提取默认网络、中心执行网络及突显网络,分析3个网络内及网络间静息态功能连接情况;并对有差异的功能连接与精神症状及病程等进行偏相关分析.结果 与对照组相比,患者组内侧前额叶皮质与双侧背外侧前额叶皮质(左侧-0.02±0.18与-0.12±0.12,t=4.25,P＜0.01;右侧-0.02±0.11与-0.10±0.10,t=5.40,P＜0.01)、双侧豆状壳核(左侧0.14±0.12与0.04±0.10,t=5.03,P＜0.01;右侧0.13±0.11与0.03±0.98,t=4.96,P＜0.01)的功能连接增强;右侧前岛叶与左侧额中回(-0.05±0.17与-0.07±0.17,t=4.60,P＜0.01)及楔前叶/后扣带回(0.01±0.13与-0.09±0.14,t=4.01,P＜0.01)的功能连接增强(均采用高斯随机场理论校正,体素水平P＜0.01,簇水平P＜0.05);默认网络与中心执行网络、突显网络间的负向功能连接减小甚至消失(Bonferroni校正P＜0.05).患者组PANSS阳性症状分与右侧前岛叶与楔前叶/后扣带回皮质间的功能连接呈正相关(r=0.232,P=0.025);患者组病程与右侧前岛叶与左侧额中回(r=0.288,P=0.007)、楔前叶/后扣带回(r=0.196,P=0.049)间的功能连接呈正相关.结论 精神分裂症患者存在静息态默认网络、中心执行网络及突显网络3个网络间的连接异常,可能与精神分裂症的病理机制有关.

Alzheimer disease (AD) is a neurological disorder with brain network dysfunction. Investigation of the brain network functional connectivity (FC) alterations using resting-state functional MRI (rs-fMRI) can provide valuable information about the brain network pattern in early AD diagnosis. To quantitatively assess FC patterns of resting-state brain networks and graph theory metrics (GTMs) to identify potential features for differentiation of amnestic mild cognitive impairment (aMCI) and late-onset AD from normal. Prospective. A total of 14 normal, 16 aMCI, and 13 late-onset AD. A 3.0 T; rs-fMRI: single-shot 2D-EPI and T1-weighted structure: MPRAGE. By applying bivariate correlation coefficient and Fisher transformation on the time series of predefined ROIs' pairs, correlation coefficient matrixes and ROI-to-ROI connectivity (RRC) were extracted. By thresholding the RRC matrix (with a threshold of 0.15), a graph adjacency matrix was created to compute GTMs. Region of interest (ROI)-based analysis: parametric multivariable statistical analysis (PMSA) with a false discovery rate using (FDR)-corrected P< 0.05 cluster-level threshold together with posthoc uncorrected P< 0.05 connection-level threshold. Graph-theory analysis (GTA): P-FDR-corrected < 0.05. One-way ANOVA and Chi-square tests were used to compare clinical characteristics. PMSA differentiated AD from normal, with a significant decrease in FC of default mode, salience, dorsal attention, frontoparietal, language, visual, and cerebellar networks. Furthermore, significant increase in overall FC of visual and language networks was observed in aMCI compared to normal. GTA revealed a significant decrease in global-efficiency (28.05 < 45), local-efficiency (22.98 < 24.05), and betweenness-centrality (14.60 < 17.39) for AD against normal. Moreover, a significant increase in local-efficiency (33.46 > 24.05) and clustering-coefficient (25 > 20.18) were found in aMCI compared to normal. This study demonstrated resting-state FC potential as an indicator to differentiate AD, aMCI, and normal. GTA revealed brain integration and breakdown by providing concise and comprehensible statistics. 1 TECHNICAL EFFICACY: Stage 2.

Disrupted functional connectivity of default mode and salience networks in chronic pancreatitis patients

Cognition in ESRD may be improved by kidney transplantation, but mechanisms are unclear. We explored patterns of resting-state networks with resting-state functional magnetic resonance imaging among patients with ESRD before and after kidney transplantation. Thirty-seven patients with ESRD scheduled for kidney transplantation and 22 age-, sex-, and education-matched healthy subjects underwent resting-state functional magnetic resonance imaging. Patients were imaged before and 1 and 6 months after kidney transplantation. Functional connectivity of seven resting-state subnetworks was evaluated: default mode network, dorsal attention network, central executive network, self-referential network, sensorimotor network, visual network, and auditory network. Mixed effects models tested associations of ESRD, kidney transplantation, and neuropsychological measurements with functional connectivity. Compared with controls, pretransplant patients showed abnormal functional connectivity in six subnetworks. Compared with pretransplant patients, increased functional connectivity was observed in the default mode network, the dorsal attention network, the central executive network, the sensorimotor network, the auditory network, and the visual network 1 and 6 months after kidney transplantation (P=0.01). Six months after kidney transplantation, no significant difference in functional connectivity was observed for the dorsal attention network, the central executive network, the auditory network, or the visual network between patients and controls. Default mode network and sensorimotor network remained significantly different from those in controls when assessed 6 months after kidney transplantation. A relationship between functional connectivity and neuropsychological measurements was found in specific brain regions of some brain networks. The recovery patterns of resting-state subnetworks vary after kidney transplantation. The dorsal attention network, the central executive network, the auditory network, and the visual network recovered to normal levels, whereas the default mode network and the sensorimotor network did not recover completely 6 months after kidney transplantation. Neural resting-state functional connectivity was lower among patients with ESRD compared with control subjects, but it significantly improved with kidney transplantation. Resting-state subnetworks exhibited variable recovery, in some cases to levels that were no longer significantly different from those of normal controls.

BackgroundShift work sleep disorder (SWSD) in nurses is highly prevalent and is increasingly recognized for its profound impact on human health. However, the brain functional network topology, which provides a comprehensive map of the brain’s information processing architecture, remains partially understood in nurses with SWSD.Methods45 nurses with SWSD and 45 healthy controls (HCs) underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Graph theoretical analysis was used to investigate alterations in brain functional network topology. Functional network connectivity was further examined in nurses with SWSD relative to HCs. Correlations between network metrics and clinical sleep scores were also examined.ResultsCompared to HCs, the SWSD group exhibited significantly lower global network metrics. Additionally, at the regional level, the SWSD group showed reduced nodal efficiency in specific regions, particularly within the visual processing areas and the caudate nucleus. Functional network connectivity analysis revealed a predominant pattern of weakened connectivity within the limbic network (LN), visual network (VN), default mode network (DMN), subcortical network (SN) and between the LN and SN in the SWSD group, although some inter-network connections were strengthened, predominantly the VN-ventral attention network (VAN), frontoparietal network (FPN)-VN, somatomotor network-VAN, and VN-DMN. Furthermore, poorer sleep quality correlated with reduced local efficiency in the visual cortex and insomnia severity was associated with weakened frontal connectivity.ConclusionsThis study reveals significant alterations in brain functional network topology and predominantly weakened functional connectivity across multiple brain networks, despite some strengthened inter-network links. These neuroimaging changes correlated with clinical measures of sleep disturbance. Our findings highlight compromised brain network organization in SWSD, offering insights into its neural mechanisms and potential biomarkers.

<bold>Objective</bold> To observe the differences in brain network activation and functional connectivity of patients with post-stroke cognitive impairment (PSCI) of different severity using functional near-infrared spectroscopy (fNIRS). <bold>Methods</bold> A total of 40 stroke patients in the outpatient and inpatient Rehabilitation Department in the Pudong District of Huashan Hospital, Fudan University from April to November 2020 were recruited and divided into normal group (<italic>n</italic>=14), mild to moderate group (<italic>n</italic>=11) and severe group (<italic>n</italic>=15) according to the mini-mental state examination (MMSE) score. Fugl-Meyer assessment scale (FMA) and functional comprehensive assessment (FCA) were used to evaluate the motor function of upper and lower limbs; 64-channel fNIRS functional imaging device was used to collect the resting-state brain network data of patients. The whole brain functional connectivity was analyzed based on oxyhemoglobin (HbO₂) and deoxyhemoglobin (HbR). Differences in functional connectivity strengths between homologous and heterologous brain networks in dorsal attention network (DAN), sensorimotor network (SEN), ventral attention network (VAN), frontoparietal network (FPN), default mode network (DMN) and visual network (VIS) regions were analyzed. <bold>Results</bold> (1) Motor function: there was no significant difference in FMA and FCA scores in three groups (<italic>P</italic>&gt;0.05). (2) Whole brain functional connectivity and strength of homologous brain network functional connectivity: there was no significant difference in whole brain functional connectivity based on HbO₂ and HbR in three groups (<italic>P</italic>&gt;0.05). Compared with the normal group, there were significant differences in the strength of functional connectivity of the in the SEN and FPN regions of the HbO₂-based homologous brain network in the severe group (<italic>P</italic>&lt;0.05), but there were no significant differences in the regions of interest of the HbR-based homologous brain network in the three groups (<italic>P</italic>&gt;0.05). (3) Functional connectivity strength of heterologous brain networks: compared with the normal group, the functional connectivity of SEN-VAN, SEN-DMN, SEN-FPN, VAN-FPN and DMN-FPN in the severe group decreased significantly, and the differences were statistically significant (<italic>P</italic>&lt;0.05). Compared with the mild to moderate group, the functional connectivity of SEN-DAN and SEN-FPN in the severe group decreased significantly, and the differences were statistically significant (<italic>P</italic>&lt;0.05). There was no significant difference of strength of functional connectivity based on HbR in the three groups (<italic>P</italic>&gt;0.05). (4) Correlation analysis between SEN-FPN heterologous functional connectivity and MMSE, FCA scores: MMSE score (<italic>r</italic>=0.32, <italic>P</italic>&lt;0.05) and FCA score (<italic>r</italic>=0.38, <italic>P</italic>&lt;0.05) were positively correlated with SEN-FPN functional connectivity. <bold>Conclusion</bold> The SEN-FPN functional connectivity may play an important role in the cognitive function of stroke patients. Altered functional connectivity patterns of SEN-FPN may be closely related to the occurrence and progression of PSCI, and changes in functional connectivity strength of SEN-FPN may be a potential imaging indicator of PSCI.

Limited understanding exists regarding the neurobiological mechanisms underlying non-suicidal self-injury (NSSI) and suicide attempts (SA) in depressed adolescents. The maturation of brain network is crucial during adolescence, yet the abnormal alternations in depressed adolescents with NSSI or NSSI+SA remain poorly understood. Resting-state functional magnetic resonance imaging data were collected from 114 depressed adolescents, classified into three groups: clinical control (non-self-harm), NSSI only, and NSSI+SA based on self-harm history. The alternations of resting-state functional connectivity (RSFC) were identified through support vector machine-based classification. Convergent alterations in NSSI and NSSI+SA predominantly centered on the inter-network RSFC between the Limbic network and the three core neurocognitive networks (SalVAttn, Control, and Default networks). Divergent alterations in the NSSI+SA group primarily focused on the Visual, Limbic, and Subcortical networks. Additionally, the severity of depressive symptoms only showed a significant correlation with altered RSFCs between Limbic and DorsAttn or Visual networks, strengthening the fact that increased depression severity alone does not fully explain observed FC alternations in the NSSI+SA group. Convergent alterations suggest a shared neurobiological mechanism along the self-destructiveness continuum. Divergent alterations may indicate biomarkers differentiating risk for SA, informing neurobiologically guided interventions.

Accumulating evidence suggests that hyper-connectivity is more characteristic of young children with autism spectrum disorder (ASD), while hypo-connectivity begins to emerge in adolescence and persists into adulthood. Despite increasing efforts being invested to explore the altered functional connectivity in ASD, the timing of the shift from intrinsic hyper-to hypo-connectivity of large-scale brain functional networks remains unclear. Here, we systematically depict the development of intrinsic functional connectivity in 800 participants from the Autism Brain Imaging Data Exchange. We first use independent component analyses to identify the large-scale brain functional networks. Then, we utilize the locally estimated scatterplot smoothing algorithm to fit the developmental trajectory of brain functional networks. Finally, we develop a "sliding threshold" method to detect the age stage at which the shift from hyper- to hypo-connectivity occurs in ASD. We identify six large-scale brain functional networks, including the default mode network (DMN), fronto-parietal network (FPN), salience network (SAN), auditory network (AN), somatomotor network (SMN), and visual network (VN). We find that primary networks (AN, SMN, and VN) undergo the shift from hyper- to hypo-connectivity earlier than high-order networks (DMN, FPN, and SAN) in ASD. At pre-adolescence, the SMN, AN, VN, DMN, SAN, and FPN undergo the shift from hyper- to hypo-connectivity in sequence in ASD. Our findings shed light on the age-related changes of intrinsic functional connectivity in ASD, highlighting the need for conceptualizing functional connectivity in ASD from a developmental perspective.

2601 A model for simple cortical cells with lagged/nonlaggd LGN inputs

BackgroundTauopathy is recognized not only as a pathological substrate but also exhibits a robust correlation with the clinical manifestations of dementia, leading to diverse neuropsychiatric manifestation. However, human brain functions as networks rather than modules. The conventional query of 'Where is the lesion (regionally)?’ may inadequately capture the entirety of dementia manifestations. Therefore, direction to focus brain networks, rather than regions, represents nature of human brain and may provide insights beyond blood and regional biomarkers.MethodIn the pilot analysis, amnestic mild cognitive impairment (MCI) or mild dementia (n = 30) were assessed, including demographic, clinical, serological, and neuroimaging data. Clinical metrics comprised Clinical Dementia Rating (CDR), Mini‐Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). Blood‐based biomarkers comprised plasma phosphorylated tau (p‐tau), amyloid‐beta centiloid (AßCL), neurofilament light (NfL), and astrocytic cytoskeleton intermediate filament protein (GFAP). Plasma p‐tau217 and p‐tau181 were measured using Mesoscale Discovery and Quanterix platforms. Position Emission Tomography with a tau tracer (tau‐PET) was used to calculate the standardized uptake value ratio (SUVR) per brain network, according to the Yeo atlas (2011) by Freesurfer. Correlation and statistical differences from network‐based tau‐PET to clinical and serological markers were performed.ResultAmid of network‐based approach, tau‐PET across all networks correlated significantly with p‐tau, notably with the limbic network displaying the highest correlation (r = 0.76, p&lt;0.01), followed by default mode network (r = 0.73, p&lt;0.01), and frontoparietal network (r = 0.71, p&lt;0.01). Despite this, cognitive scores behaved another way, which moderate‐to‐strong associations were found predominantly in the dorsal attention network, correlations ranged from 0.52 to 0.72 for MoCA, MMSE, and CDR sum of boxes (p&lt;0.01). While dorsal and ventral attention networks demonstrated moderate‐to‐strong correlations with cognitive scores, the limbic network exhibited only weak‐to‐moderate links. Visual and somatomotor networks showed variable correlations with cognition, whereas frontoparietal and default mode networks displayed moderate‐to‐strong links with both serological levels and cognitive scores. Lastly, the network‐based analyzes significantly outperformed the region‐based tau‐PET approach in demonstrating links with cognitive scores.ConclusionThis evidence highlights the potential of a network‐based approach to demonstrate relationships with patients' cognitive functions, serving as promising biomarkers beyond the constraints of regional‐based biomarkers, and reflecting the true nature of human brain.

Schizophrenia is characterized by the distributed dysconnectivity of resting-state multiple brain networks. However, the abnormalities of intra- and inter-network functional connectivity (FC) in schizophrenia and its relationship to symptoms remain unknown. The aim of the present study is to compare the intra- and inter-connectivity of the intrinsic networks between a large sample of patients with schizophrenia and healthy controls. Using the Region of interest (ROI) to ROI FC analyses, the intra- and inter-network FC of the eight resting state networks [default mode network (DMN); salience network (SN); frontoparietal network (FPN); dorsal attention network (DAN); language network (LN); visual network (VN); sensorimotor network (SMN); and cerebellar network (CN)] were investigated in 196 schizophrenia and 169-healthy controls. Compared to the healthy control group, the schizophrenia group exhibited increased intra-network FC in the DMN and decreased intra-network FC in the CN. Additionally, the schizophrenia group showed the decreased inter-network FC mainly involved the SN-DMN, SN-LN and SN-CN while increased inter-network FC in the SN-SMN and SN-DAN (p < 0.05, FDR-corrected). Our study suggests widespread intra- and inter-network dysconnectivity among large-scale RSNs in schizophrenia, mainly involving the DMN, SN and SMN, which may further contribute to the dysconnectivity hypothesis of schizophrenia.

The aim of this study was to investigate alterations in functional connectivity (FC) within and interactions between resting-state networks involved in salience, executive control, and interoception in participants with obesity (OB). Using resting-state functional magnetic resonance imaging with independent component analysis and FC, alterations within and interactions between resting-state networks in 35 OB and 35 normal-weightcontrols (NW) were investigated. Compared with NW, OB showed reduced FC strength in the ventromedial prefrontal cortex and posterior cingulate cortex/precuneus within the default-mode network, dorsal anterior cingulate cortex within the salience network (SN), bilateral dorsolateral prefrontal cortex-angular gyrus within the frontoparietal network (FPN), and increased FC strength in the insula (INS) (Pfamilywise error < 0.0125). The dorsal anterior cingulate cortex FC strength was negatively correlated with craving for food cues, left dorsolateral prefrontal cortex FC strength was negatively correlated with Yale Food Addiction Scale scores, and right INS FC strength was positively correlated with craving for high-calorie food cues. Compared with NW, OB also showed increased FC between theSN and FPN driven by altered FC of bilateral INS and anterior cingulate cortex-angular gyrus. Alterations in FC within and interactions between the SN, default-mode network, and FPN might contribute to the high incentive value of food (craving), lack of control of overeating (compulsive overeating), and increased awareness of hunger (impaired interoception) in OB.