Specific EEG resting state biomarkers in FXS and ASD.

BACKGROUND: Fragile X syndrome (FXS) and autism spectrum disorder (ASD) are neurodevelopmental conditions that often have a substantial impact on daily functioning and quality of life. FXS is the most common cause of inherited intellectual disability (ID) and the most common monogenetic cause of ASD. Previous literature has shown that electrophysiological activity measured by electroencephalogram (EEG) during resting state is perturbated in FXS and ASD. However, whether electrophysiological profiles of participants with FXS and ASD are similar remains unclear. The aim of this study was to compare EEG alterations found in these two clinical populations presenting varying degrees of cognitive and behavioral impairments. METHODS: Resting state EEG signal complexity, alpha peak frequency (APF) and power spectral density (PSD) were compared between 47 participants with FXS (aged between 5-20), 49 participants with ASD (aged between 6-17), and 52 neurotypical (NT) controls with a similar age distribution using MANCOVAs with age as covariate when appropriate. MANCOVAs controlling for age, when appropriate, and nonverbal intelligence quotient (NVIQ) score were subsequently performed to determine the impact of cognitive functioning on EEG alterations. RESULTS: Our results showed that FXS participants manifested decreased signal complexity and APF compared to ASD participants and NT controls, as well as altered power in the theta, alpha and low gamma frequency bands. ASD participants showed exaggerated beta power compared to FXS participants and NT controls, as well as enhanced low and high gamma power compared to NT controls. However, ASD participants did not manifest altered signal complexity or APF. Furthermore, when controlling for NVIQ, results of decreased complexity in higher scales and lower APF in FXS participants compared to NT controls and ASD participants were not replicated. CONCLUSIONS: These findings suggest that signal complexity and APF might reflect cognitive functioning, while altered power in the low gamma frequency band might be associated with neurodevelopmental conditions, particularly FXS and ASD.

Comparison between EEG and MEG of static and dynamic resting-state networks.

The characterisation of resting-state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.

Associations between differential connectivity patterns of executive control networks and APOE ɛ4 in the Alzheimer continuum.

The APOE ɛ4 allele and age are risk factors for Alzheimer's disease (AD) and contribute to decreased executive function. However, the influence of APOE ɛ4 on the executive control network (ECN) in the AD continuum is still unclear. This study included 269 participants aged between 50 and 95 years old, based on ADNI data, including 104 cognitively normal (CN) individuals, 72 individuals with early mild cognitive impairment (EMCI), 55 individuals with late mild cognitive impairment (LMCI), and 38 AD patients. Within each disease group, participants were subdivided into APOE ɛ4 carriers and non-carriers. We explored brain regions within the ECN affected by the interactions between genes and disease states by resting-state functional magnetic resonance imaging (fMRI) and voxel-based two-way analysis of variance (ANOVA). Subsequently, functional connectivity (FC) between seeds and peak clusters were extracted and correlated with the cognitive performance. We found that the damages of carrying APOE ɛ4 in ECNs mainly distributed in the fronto-parietal and parietal-temporal systems. Functional network intergroup differences indicated increased intrafrontal and fronto-parietal connectivity at the early stage of AD and increased connectivity between the parietal lobe and related regions at late disease in these APOE ɛ4 carriers. Our conclusion is that the functional connectivity in the ECN exhibits different distinguishably patterns of impairment in the AD continuum under the influence of the APOE ɛ4 allele. Patients with different genotypes showed heterogeneity in functional network changes in the early stages of disease, which may be a potential biomarker for early AD.

White matter microstructure and functional connectivity in the brains of infants with Turner syndrome.

Turner syndrome, caused by complete or partial loss of an X-chromosome, is often accompanied by specific cognitive challenges. Magnetic resonance imaging studies of adults and children with Turner syndrome suggest these deficits reflect differences in anatomical and functional connectivity. However, no imaging studies have explored connectivity in infants with Turner syndrome. Consequently, it is unclear when in development connectivity differences emerge. To address this gap, we compared functional connectivity and white matter microstructure of 1-year-old infants with Turner syndrome to typically developing 1-year-old boys and girls. We examined functional connectivity between the right precentral gyrus and five regions that show reduced volume in 1-year old infants with Turner syndrome compared to controls and found no differences. However, exploratory analyses suggested infants with Turner syndrome have altered connectivity between right supramarginal gyrus and left insula and right putamen. To assess anatomical connectivity, we examined diffusivity indices along the superior longitudinal fasciculus and found no differences. However, an exploratory analysis of 46 additional white matter tracts revealed significant group differences in nine tracts. Results suggest that the first year of life is a window in which interventions might prevent connectivity differences observed at later ages, and by extension, some of the cognitive challenges associated with Turner syndrome.

A novel method for sparse dynamic functional connectivity analysis from resting-state fMRI.

BACKGROUND: There is growing interest in understanding the dynamic functional connectivity (DFC) between distributed brain regions. However, it remains challenging to reliably estimate the temporal dynamics from resting-state functional magnetic resonance imaging (rs-fMRI) due to the limitations of current methods. NEW METHODS: We propose a new model called HDP-HSMM-BPCA for sparse DFC analysis of high-dimensional rs-fMRI data, which is a temporal extension of probabilistic principal component analysis using Bayesian nonparametric hidden semi-Markov model (HSMM). Specifically, we utilize a hierarchical Dirichlet process (HDP) prior to remove the parametric assumption of the HMM framework, overcoming the limitations of the standard HMM. An attractive superiority is its ability to automatically infer the state-specific latent space dimensionality within the Bayesian formulation. RESULTS: The experiment results of synthetic data show that our model outperforms the competitive models with relatively higher estimation accuracy. In addition, the proposed framework is applied to real rs-fMRI data to explore sparse DFC patterns. The findings indicate that there is a time-varying underlying structure and sparse DFC patterns in high-dimensional rs-fMRI data. COMPARISON WITH EXISTING METHODS: Compared with the existing DFC approaches based on HMM, our method overcomes the limitations of standard HMM. The observation model of HDP-HSMM-BPCA can discover the underlying temporal structure of rs-fMRI data. Furthermore, the relevant sparse DFC construction algorithm provides a scheme for estimating sparse DFC. CONCLUSION: We describe a new computational framework for sparse DFC analysis to discover the underlying temporal structure of rs-fMRI data, which will facilitate the study of brain functional connectivity.

Sex heterogeneity of dynamic brain activity and functional connectivity in autism spectrum disorder.

Sex heterogeneity has been frequently reported in autism spectrum disorders (ASD) and has been linked to static differences in brain function. However, given the complexity of ASD and diagnosis-by-sex interactions, dynamic characteristics of brain activity and functional connectivity may provide important information for distinguishing ASD phenotypes between females and males. The aim of this study was to explore sex heterogeneity of functional networks in the ASD brain from a dynamic perspective. Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were analyzed in 128 ASD subjects (64 males/64 females) and 128 typically developing control (TC) subjects (64 males/64 females). A sliding-window approach was adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dynamic functional connectivity (dFC) to characterize time-varying brain activity and functional connectivity respectively. We then examined the sex-related changes in ASD using two-way analysis of variance. Significant diagnosis-by-sex interaction effects were identified in the left anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC) and left precuneus in the dALFF analysis. Furthermore, there were significant diagnosis-by-sex interaction effects of dFC variance between the left ACC/mPFC and right ACC, left postcentral gyrus, left precuneus, right middle temporal gyrus and left inferior frontal gyrus, triangular part. These findings reveal the sex heterogeneity in brain activity and functional connectivity in ASD from a dynamic perspective, and provide new evidence for further exploring sex heterogeneity in ASD.

Electroencephalography-based endogenous phenotype of diagnostic transition from major depressive disorder to bipolar disorder.

The neuropathology of mood disorders, including the diagnostic transition from major depressive disorder (MDD) to bipolar disorder (BD), is poorly understood. This study investigated resting-state electroencephalography (EEG) activity in patients with MDD and those whose diagnosis changed from MDD to BD. Among sixty-eight enrolled patients with MDD, the diagnosis of 17 patients converted to BD during the study period. We applied machine learning techniques to differentiate the two groups using sensor- and source-level EEG features. At the sensor level, patients with BD showed higher theta band power at the AF3 channel and low-alpha band power at the FC5 channel compared to patients with MDD. At the source level, patients with BD showed higher theta band activity in the right anterior cingulate and low-alpha band activity in the left parahippocampal gyrus. These four EEG features were selected for discriminating between BD and MDD with the best classification performance showing an accuracy of 80.88%, a sensitivity of 76.47%, and a specificity of 82.35%. Our findings revealed distinct theta and low-alpha band activities in patients with BD and MDD. These differences could potentially serve as candidate neuromarkers for the diagnosis and diagnostic transition between the two distinct mood disorders.

The effects of prolonged sitting behavior on resting-state brain functional connectivity in college students post-COVID-19 rehabilitation: A study based on fNIRS technology.

Functional near-infrared spectroscopy (fNIRS) was used to explore the effects of sedentary behavior on the brain functional connectivity characteristics of college students in the resting state after recovering from Corona Virus Disease 2019 (COVID-19). Twenty-two college students with sedentary behavior and 22 college students with sedentary behavior and maintenance of exercise habits were included in the analysis; moreover, 8 â€‹min fNIRS resting-state data were collected. Based on the concentrations of oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) in the time series, the resting-state functional connection strength of the two groups of subjects, including the prefrontal cortex (PFC) and the lower limb supplementary motor area (LS), as well as the functional activity and functional connections of the primary motor cortex (M1) were calculated. The following findings were demonstrated. (1) Functional connection analysis based on HbO2 demonstrated that in the comparison of the mean functional connection strength of homologous regions of interest (ROIs) between the sedentary group and the exercise group, there was no significant difference in the mean functional strength of the ROIs between the two groups ( p > 0.05 ). In the comparison of the mean functional connection strengths of the two groups of heterologous ROIs, the functional connection strengths of the right PFC and the right LS ( p = 0.009 7 ), the left LS ( p = 0.012 7 ), and the right M1 ( p = 0.030 5 ) in the sedentary group were significantly greater. The functional connection strength between the left PFC and the right LS ( p = 0.031 2 ) and the left LS ( p = 0.037 0 ) was significantly greater. Additionally, the functional connection strength between the right LS and the right M1 ( p = 0.037 0 ) and the left LS ( p = 0.043 8 ) was significantly greater. (2) Functional connection analysis based on HbR demonstrated that there was no significant difference in functional connection strength between the sedentary group and the exercise group ( p > 0.05 ) or between the sedentary group and the exercise group ( p > 0.05 ). Similarly, there was no significant difference in the mean functional connection strength of the homologous and heterologous ROIs of the two groups. Additionally, there was no significant difference in the mean ROIs functional strength between the two groups ( p > 0.05 ). Experimental results and graphical analysis based on functional connectivity indicate that in this experiment, college student participants who exhibited sedentary behaviors showed an increase in fNIRS signals. Increase in fNIRS signals among college students exhibiting sedentary behaviors may be linked to their status post-SARS-CoV-2 infection and the sedentary context, potentially contributing to the strengthened functional connectivity in the resting-state cortical brain network. Conversely, the fNIRS signals decreased for the participants with exercise behaviors, who maintained reasonable exercise routines under the same conditions as their sedentary counterparts. The results may suggest that exercise behaviors have the potential to mitigate and reduce the impacts of sedentary behavior on the resting-state cortical brain network.

Periaqueductal gray connectivity in spinal cord injury-induced neuropathic pain.

BACKGROUND AND PURPOSE: Neuropathic pain (NP) is a debilitating condition following spinal cord injury (SCI). The role of periaqueductal gray (PAG) in NP development following SCI remains underexplored. Using resting-state functional MRI (rsfMRI), our study aimed to demonstrate the alterations in functional connectivity (FC) of PAG in NP following SCI. METHODS: Ten SCI patients (SCI + NP, n = 7, and SCI - NP, n = 3), alongside 10 healthy controls (HCs), were enrolled. rsfMRI was conducted followed by seed-to-voxel analysis using PAG as the seed region and then group-based analysis comprising three groups (SCI + NP, SCI - NP, and HC). Age and gender were considered as confounding variables. RESULTS: Compared to HCs, SCI + NP demonstrated decreased FC between PAG and right insula, right frontal orbital cortex, right pallidum, dorsal raphe nucleus (DRN), red nuclei (RN), substantia nigra (SN), and ventral posterolateral (VPL) thalamic nuclei. Compared to SCI - NP, SCI + NP demonstrated increased FC between PAG and posterior cingulate cortex (PCC), hippocampus, cerebellar vermis lobules IV and V, and thalamic structures (posterior and lateral pulvinar, the mediodorsal nuclei, and the ventral lateral nuclei). Additionally, decreased FC between the PAG and VPL, geniculate bodies, intralaminar nuclei of thalamus, DRN, RN, SN, and prefrontal cortex was observed in this comparison. CONCLUSIONS: Altered FC between PAG and right anterior insula, VPL, DRN, RN, SN, cerebellar vermis lobules IV and V, frontal cortex, and PCC was associated with NP sequelae of SCI. Additionally, SCI was independently associated with decreased FC between PAG and right posterior insula, cerebellar lobules IV and V, and cerebellar vermis lobules III, IV, and V.

Decreased functional connectivity is associated with increased levels of Cerebral Spinal Fluid soluble-PDGFRß, a marker of blood brain barrier breakdown, in older adults.

Resting-state functional connectivity (FC) is suggested to be cross-sectionally associated with both vascular burden and Alzheimer's disease (AD) pathology. For instance, studies in pre-clinical AD subjects have shown increases of cerebral spinal fluid soluble platelet-derived growth factor receptor-ß (CSF sPDGFRß, a marker of BBB breakdown) but have not demonstrated if this vascular impairment affects neuronal dysfunction. It's possible that increased levels of sPDGFRß in the CSF may correlate with impaired FC in metabolically demanding brain regions (i.e. Default Mode Network, DMN). Our study aimed to investigate the relationship between these two markers in older individuals that were cognitively normal and had cognitive impairment. Eighty-nine older adults without dementia from the University of Southern California were selected from a larger cohort. Region of interest (ROI) to ROI analyses were conducted using DMN seed regions. Linear regression models measured significant associations between BOLD FC strength among seed-target regions and sPDGFRß values, while covarying for age and sex. Comparison of a composite ROI created by averaging FC values between seed and all target regions among cognitively normal and impaired individuals was also examined. Using CSF sPDGFRß as a biomarker of BBB breakdown, we report that increased breakdown correlated with decreased functional connectivity in DMN areas, specifically the PCC, and while the hippocampus exhibited an interaction effect using CDR score, this was an exploratory analysis that we feel can lead to further research. Ultimately, we found that BBB breakdown, as measured by CSF sPDGFRß, is associated with neural networks, and decreased functional connections.

Task-specific topology of brain networks supporting working memory and inhibition.

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

Language networks of normal-hearing infants exhibit topological differences between resting and steady states: An fNIRS functional connectivity study.

Task-related studies have consistently reported that listening to speech sounds activate the temporal and prefrontal regions of the brain. However, it is not well understood how functional organization of auditory and language networks differ when processing speech sounds from its resting state form. The knowledge of language network organization in typically developing infants could serve as an important biomarker to understand network-level disruptions expected in infants with hearing impairment. We hypothesized that topological differences of language networks can be characterized using functional connectivity measures in two experimental conditions (1) complete silence (resting) and (2) in response to repetitive continuous speech sounds (steady). Thirty normal-hearing infants (14 males and 16 females, age: 7.8 ± 4.8 months) were recruited in this study. Brain activity was recorded from bilateral temporal and prefrontal regions associated with speech and language processing for two experimental conditions: resting and steady states. Topological differences of functional language networks were characterized using graph theoretical analysis. The normalized global efficiency and clustering coefficient were used as measures of functional integration and segregation, respectively. We found that overall, language networks of infants demonstrate the economic small-world organization in both resting and steady states. Moreover, language networks exhibited significantly higher functional integration and significantly lower functional segregation in resting state compared to steady state. A secondary analysis that investigated developmental effects of infants aged 6-months or below and above 6-months revealed that such topological differences in functional integration and segregation across resting and steady states can be reliably detected after the first 6-months of life. The higher functional integration observed in resting state suggests that language networks of infants facilitate more efficient parallel information processing across distributed language regions in the absence of speech stimuli. Moreover, higher functional segregation in steady state indicates that the speech information processing occurs within densely interconnected specialized regions in the language network.

Preclinical monitoring of radiation-induced brain injury via GluCEST MRI and resting-state fMRI at 7 T: an exploratory study on MRI-guided OAR avoidance.

PURPOSE: To assess the value of glutamate chemical exchange saturation transfer (GluCEST) after whole-brain radiotherapy (WBRT) as an imaging marker of radiation-induced brain injury (RBI) and to preliminarily show the feasibility of multiparametric MRI-guided organ at risk (OAR) avoidance. METHODS: Rats were divided into two groups: the control (CTRL) group (n = 9) and the RBI group (n = 9). The rats in the RBI group were irradiated with an X­ray radiator and then subjected to a water maze experiment 4 weeks later. In combination with high-performance liquid chromatography (HPLC), we evaluated the value of GluCEST applied to glutamate changes for RBI and investigated the effect of such changes on glutamatergic neuronal function. RESULTS: The average GluCEST values were markedly lower in the hippocampus and cerebral cortex. Positive correlations were observed between GluCEST values and regional homogeneity (ReHo) values in both the hippocampus and the cerebral cortex. HPLC showed a positive correlation with GluCEST values in the hippocampus. GluCEST values were positively correlated with spatial memory. CONCLUSION: GluCEST MRI provides a visual assessment of glutamate changes in RBI rats for monitoring OAR cognitive toxicity reactions and may be used as a biomarker of OAR avoidance as well as metabolism to facilitate monitoring and intervention in radiation damage that occurs after radiotherapy.

Specific static and dynamic functional network connectivity changes in thyroid-associated ophthalmopathy and it predictive values using machine learning.

Background: Thyroid-associated ophthalmopathy (TAO) is a prevalent autoimmune disease characterized by ocular symptoms like eyelid retraction and exophthalmos. Prior neuroimaging studies have revealed structural and functional brain abnormalities in TAO patients, along with central nervous system symptoms such as cognitive deficits. Nonetheless, the changes in the static and dynamic functional network connectivity of the brain in TAO patients are currently unknown. This study delved into the modifications in static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) among thyroid-associated ophthalmopathy patients using independent component analysis (ICA). Methods: Thirty-two patients diagnosed with thyroid-associated ophthalmopathy and 30 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. ICA method was utilized to extract the sFNC and dFNC changes of both groups. Results: In comparison to the HC group, the TAO group exhibited significantly increased intra-network functional connectivity (FC) in the right inferior temporal gyrus of the executive control network (ECN) and the visual network (VN), along with significantly decreased intra-network FC in the dorsal attentional network (DAN), the default mode network (DMN), and the left middle cingulum of the ECN. On the other hand, FNC analysis revealed substantially reduced connectivity intra- VN and inter- cerebellum network (CN) and high-level cognitive networks (DAN, DMN, and ECN) in the TAO group compared to the HC group. Regarding dFNC, TAO patients displayed abnormal connectivity across all five states, characterized by notably reduced intra-VN connectivity and CN connectivity with high-level cognitive networks (DAN, DMN, and ECN), alongside compensatory increased connectivity between DMN and low-level perceptual networks (VN and basal ganglia network). No significant differences were observed between the two groups for the three dynamic temporal metrics. Furthermore, excluding the classification outcomes of FC within VN (with an accuracy of 51.61% and area under the curve of 0.35208), the FC-based support vector machine (SVM) model demonstrated improved performance in distinguishing between TAO and HC, achieving accuracies ranging from 69.35 to 77.42% and areas under the curve from 0.68229 to 0.81667. The FNC-based SVM classification yielded an accuracy of 61.29% and an area under the curve of 0.57292. Conclusion: In summary, our study revealed that significant alterations in the visual network and high-level cognitive networks. These discoveries contribute to our understanding of the neural mechanisms in individuals with TAO, offering a valuable target for exploring future central nervous system changes in thyroid-associated eye diseases.

Meta-analysis of structural and functional brain abnormalities in early-onset schizophrenia.

Background: Previous studies based on resting-state functional magnetic resonance imaging(rs-fMRI) and voxel-based morphometry (VBM) have demonstrated significant abnormalities in brain structure and resting-state functional brain activity in patients with early-onset schizophrenia (EOS), compared with healthy controls (HCs), and these alterations were closely related to the pathogenesis of EOS. However, previous studies suffer from the limitations of small sample sizes and high heterogeneity of results. Therefore, the present study aimed to effectively integrate previous studies to identify common and specific brain functional and structural abnormalities in patients with EOS. Methods: The PubMed, Web of Science, Embase, Chinese National Knowledge Infrastructure (CNKI), and WanFang databases were systematically searched to identify publications on abnormalities in resting-state regional functional brain activity and gray matter volume (GMV) in patients with EOS. Then, we utilized the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software to conduct a whole-brain voxel meta-analysis of VBM and rs-fMRI studies, respectively, and followed by multimodal overlapping on this basis to comprehensively identify brain structural and functional abnormalities in patients with EOS. Results: A total of 27 original studies (28 datasets) were included in the present meta-analysis, including 12 studies (13 datasets) related to resting-state functional brain activity (496 EOS patients, 395 HCs) and 15 studies (15 datasets) related to GMV (458 EOS patients, 531 HCs). Overall, in the functional meta-analysis, patients with EOS showed significantly increased resting-state functional brain activity in the left middle frontal gyrus (extending to the triangular part of the left inferior frontal gyrus) and the right caudate nucleus. On the other hand, in the structural meta-analysis, patients with EOS showed significantly decreased GMV in the right superior temporal gyrus (extending to the right rolandic operculum), the right middle temporal gyrus, and the temporal pole (superior temporal gyrus). Conclusion: This meta-analysis revealed that some regions in the EOS exhibited significant structural or functional abnormalities, such as the temporal gyri, prefrontal cortex, and striatum. These findings may help deepen our understanding of the underlying pathophysiological mechanisms of EOS and provide potential biomarkers for the diagnosis or treatment of EOS.

Neurodevelopmental Subtypes of Functional Brain Organization in the ABCD Study Using a Rigorous Analytic Framework.

The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.

Correlation Between the Fractional Amplitude of Low-Frequency Fluctuation and Cognitive Defects in Alzheimer's Disease.

Background: The fractional amplitude of low-frequency fluctuations (fALFFs) can detect spontaneous brain activity. However, the association between abnormal brain activity and cognitive function, amyloid protein (Aß), and emotion in Alzheimer's disease (AD) patients remains unclear. Objective: This study aimed to survey alterations in fALFF in different frequency bands and the relationship between abnormal brain activity, depressive mood, and cognitive function to determine the potential mechanism of AD. Methods: We enrolled 34 AD patients and 32 healthy controls (HC). All the participants underwent resting-state magnetic resonance imaging, and slow-4 and slow-5 fALFF values were measured. Subsequently, the study determined the correlation of abnormal brain activity with mood and cognitive function scores. Results: AD patients revealed altered mfALFF values in the slow-5 and slow-4 bands. In the slow-4 band, the altered mfALFF regions were the right cerebellar crus I, right inferior frontal orbital gyrus (IFOG), right supramarginal gyrus, right precuneus, angular gyrus, and left middle cingulate gyrus. Elevated mfALFF values in the right IFOG were negatively associated with Montreal Cognitive Assessment scores, Boston Naming Test, and Aß1-42 levels. The mfALFF value of the AD group was lower than the HC group in the slow-5 band, primarily within the right inferior parietal lobule and right precuneus. Conclusions: Altered mfALFF values in AD patients are linked with cognitive dysfunction. Compared with HCs, Aß1-42 levels in AD patients are related to abnormal IFOG activity. Therefore, mfALFF could be a potential biomarker of AD.

Resting-state functional abnormalities in ischemic stroke: a meta-analysis of fMRI studies.

Ischemic stroke is a leading neurological cause of severe disabilities and death in the world and has a major negative impact on patients' quality of life. However, the neural mechanism of spontaneous fluctuating neuronal activity remains unclear. This meta-analysis explored brain activity during resting state in patients with ischemic stroke including 22 studies of regional homogeneity, amplitude of low-frequency fluctuation, and fractional amplitude of low-frequency fluctuation (692 patients with ischemic stroke, 620 healthy controls, age range 35-80 years, 41% female, 175 foci). Results showed decreased regional activity in the bilateral caudate and thalamus and increased regional activity in the left superior occipital gyrus and left default mode network (precuneus/posterior cingulate cortex). Meta-analysis of the amplitude of low-frequency fluctuation studies showed that increased activity in the left inferior frontal gyrus was reduced across the progression from acute to chronic phases. These findings may indicate that disruption of the subcortical areas and default mode network could be one of the core functional abnormalities in ischemic stroke. Altered brain activity in the inferior frontal gyrus could be the imaging indicator of brain recovery/plasticity after stroke damage, which offers potential insight into developing prediction models and therapeutic strategies for ischemic stroke rehabilitation and recovery.

Common and unique brain aging patterns between females and males quantified by large-scale deep learning.

There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently between males and females. Based on resting-state brain functional connectivity (FC) of 25,582 healthy participants (13,373 females) aged 49-76 years from the UK Biobank project, we employ deep learning with explainable AI to discover primary FCs related to progressive aging and reveal similarity and difference between females and males in brain aging. Using a nested cross-validation scheme, we conduct 4200 deep learning models to classify all paired age groups on the main data for females and males separately and then extract gender-common and gender-specific aging-related FCs. Next, we validate those FCs using additional 21,000 classifiers on the independent data. Our results support that aging results in reduced brain functional interactions for both females and males, primarily relating to the positive connectivity within the same functional domain and the negative connectivity between different functional domains. Regions linked to cognitive control show the most significant age-related changes in both genders. Unique aging effects in males and females mainly involve the interaction between cognitive control and the default mode, vision, auditory, and frontoparietal domains. Results also indicate females exhibit faster brain functional changes than males. Overall, our study provides new evidence about common and unique patterns of brain aging in females and males.

Resting-state electroencephalogram in drug-free subjects with at-risk mental states who later developed psychosis: a low-resolution electromagnetic tomography analysis.

Background and objectives: Several studies have reported on the resting-state electroencephalogram (EEG) power in patients with schizophrenia, with a decrease in α (especially α2) and an increase in δ and ß1 power compared with healthy control; however, reports on at-risk mental states (ARMS) are few. In this study, we measured the resting-state EEG power in ARMS, and investigated its features and the relationship between the power of the frequency bands and their diagnostic outcomes. Methods: Patients with ARMS who were not on any psychotropic medication and met the Comprehensive Assessment of At-Risk Mental State criteria were included. Patients who developed psychotic disorders were labeled as the ARMS-P group, while patients with ARMS who were followed up prospectively for more than 2 years and did not develop psychotic disorders were classified as the ARMS-NP group. EEGs were measured in the resting state, and frequencies were analyzed using standardized low-resolution brain electromagnetic tomography (sLORETA). Seven bands (δ, θ, α1, α2, ß1-3) underwent analysis. The sLORETA values (current source density [CSD]) were compared between the ARMS-P and ARMS-NP groups. Clinical symptoms were assessed at the time of EEG measurements using the Positive and Negative Syndrome Scale (PANSS). Results: Of the 39 patients included (25 males, 14 females, 18.8 ± 4.5 years old), eight developed psychotic disorders (ARMS-P). The ARMS-P group exhibited significantly higher CSD in the ß1 power within areas of the left middle frontal gyrus (MFG) compared with the ARMS-NP group (best match: X = -35, Y = 25, Z = 50 [MNI coordinates], Area 8, CSD = 2.33, p < 0.05). There was a significant positive correlation between the ß1/α ratio of the CSD at left MFG and the Somatic concern score measured by the PANSS. Discussion: Increased ß1 power was observed in the resting EEG before the onset of psychosis and correlated with a symptom. This suggests that resting EEG power may be a useful marker for predicting future conversion to psychosis and clinical symptoms in patients with ARMS.