Changes In Network Connectivity Research Articles

White Matter Function and Network Abnormalities in Patients with Diabetic Retinopathy.

This study aims to explore changes in white matter function and network connectivity in individuals with DR. This study included 46 patients with DR and 43 age- and gender-matched healthy control (HC) participants were enrolled in the study. The aim was to investigate inter-group differences in white matter (WM) function and to analyze changes in the WM network among DR patients. Increased degree centrality (DC) values were observed in the middle cerebellar peduncle and genu of the corpus callosum, while higher fractional amplitude of low-frequency fluctuations (fALFF) values were found in the left superior corona radiata, right anterior corona radiata, and right superior longitudinal fasciculus. Conversely, reduced regional homogeneity (ReHo) values were noted in the left posterior thalamic radiation among patients with DR compared to HC, with statistical correction applied The SVM classification accuracy for distinguishing between DR and HC patients based on WM measures indicated values of 81.52%, 80.43%, and 89.13% for DC, fALFF, and ReHo, respectively, with respective area under the curve (AUC) values of 0.87, 0.85, and 0.93. Furthermore, alterations were detected within specific brain regions including the body of corpus callosum (BCC), splenium of corpus callosum (SCC), genu of corpus callosum (GCC), left posterior thalamic radiation (PTR), right anterior corona radiata (ACR), and right posterior corona radiata (PCR) in the DR group compared to HCs, with an intra-network decrease in connectivity. Interestingly, the left superior longitudinal fasciculus (SLF) within the DR group exhibited an intra-network increase compared to the HC group. DR exhibited abnormal white matter functional alterations, particularly affecting the fiber pathways linking the visual network to the sensory-motor network.

MACHINE-LEARNING TECHNIQUES IN MULTIPLE SCLEROSIS PREDICTION USING EEG

The diagnosis and quantification of Multiple Sclerosis (MS) have typically depended on skilled doctors recognizing visual patterns, such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), which resulted in a costly, time-consuming and non-reproducible process. The application of Machine Learning (ML) in MS diagnosis has been getting a lot of attention in the last few years due to the volume of scientific data, the heterogeneity of disease courses, and the variety of diagnostic methods. The EEG has the capability to detect important changes in the brain’s inherent electrical activity, which are influenced by changes in the neural network connections associated with inflammatory demyelinating and neural damage characteristic of the MS. Utilizing multimodal machine learning over the clinically available data may be a contemporary strategy with amazing potential to facilitate early diagnosis. Considering recent EEG investigations as well as the accessibility to their datasets would be the initial steps in utilizing the ML in MS diagnosis. This paper provides a systematic review of the latest techniques for MS diagnostic based on PRISMA guidelines with the prospect of ML application in their investigations. The goal is to find if EEG could be considered a robust and accurate technique for MS diagnosis. In accordance with PRISMA guidelines, we consider 111 papers. In our review, 404 people are considered including 209 with MS and 195 healthy controls. As a result, we generated an updated investigation looking at the ML approaches as well as utilizing EEG as an accurate, but less often used method to help with early diagnosis of MS. We summarize, analyze, discuss, and synthesize the recently published works, current trends, and open research issues. The review also points out knowledge gaps about the necessity of validating results and addressing constraints such as limited sample number. Our investigation proves that the precision of the supervised strategies such as [Formula: see text]NN and SVM is typically higher than that of the unsupervised strategies. On the other hand, by utilizing various techniques such as splitting EEG signal sub-bands, signal windowing, and identifying effective features from the data analysis approaches, we have been able to achieve significant classification accuracy higher than 99%. According to the high degree of accuracy of the results, this approach is becoming the focal point of the research works. The high diagnostic accuracy of the proposed machine learning techniques on the EEG signal analysis shows its potential capacity to become a more widespread procedure as the MS diagnostic techniques. To develop the reliability of these methods, gathering, and analyzing more EEG signals from MS patients and creating more suitable EEG protocols are essential in future studies. In the same way, for the dynamic and online analyses of the EEG signals, in-depth studies are needed to determine more effective EEG protocols and machine-learning techniques.

TCP Congestion Management Using Deep Reinforcement Trained Agent for RED

ABSTRACTIncreasing data transmission volumes are causing more frequent and more severe network congestion. In order to handle spikes in network traffic, a substantially bigger buffer has been included into the system. Bufferbloat, which happens when a bigger buffer is implemented, exacerbates network congestion. Using the transfer control protocol (TCP) congestion management strategy with active queue management (AQM) can fix this issue. As congestion increases, it becomes increasingly difficult to forecast and fine‐tune dynamic AQM/TCP systems in order to achieve acceptable performance. To shed new light on the AQM system, we plan to use deep reinforcement learning (DRL) techniques. It is possible that AQM can learn about the appropriate drop policy the same way people do when using a model‐free technique like DRL‐AQM. After training in a simple network scenario, DRL‐AQM is able to recognize complex patterns in the data traffic model and apply them to improve performance in a wide variety of scenarios. Offline training precedes deployment in our approach. In many cases, the model does not require any further parameter tweaks after training. Even in the most complicated networks, AQM algorithms have proven to be effective, regardless of the network's complexity. Minimizing buffer capacity use is an important goal of DRL‐AQM. It automatically and continually adjusts to changes in network connectivity.

8607 Gender Affirming Hormone Therapy: Influence of Brain Network Connectivity Changes on Affect

Abstract Disclosure: J. Andreano: None. A. Lopez: None. M. Misra: None. Background: Previous research indicates that acute gonadal treatments and fluctuations in ovarian hormone levels over the course of the menstrual cycle can alter patterns of communication between brain regions, as well as producing significant changes in self-reported affect. Recent research from our laboratory indicates changes in both brain connectivity and affect with gender affirming hormone therapy (GAHT). However, the relationship between these changes with GAHT are not well understood. Additionally, it is difficult to separate influences of GAHT and transgender identity in understanding differences in connectvity and affect from cisgender controls. A better understanding of the influence of GAHT on intrinsic network connectivity could point the way towards improved GAHT treatment regimens that minimize affective side effects. Objective: To assess relationships between changes in brain network connectivity and affective changes following GAHT, identifying regions in which changes of connectivity are associated with changes in self-reported affect, and to examine differences in brain network connectivity between transgender participants and cisgender controls. Methods: 14 transgender individuals receiving GAHT (estradiol n =7, testosterone n = 7)) were scanned using functional magnetic imaging (fMRI) at rest at two timepoints: baseline, prior to treatment, and 6 months into GAHT. Additionally, 20 cisgender controls (10 male, 10 female) were scanned once each. The time course of neural activity was computed for each voxel in the brain, then tested for correlation with each other voxel to develop a whole-brain connectome for each participant. Maps of average connectivity to two seeds known to associate with two major networks in the brain (posterior cingulate cortex for Default Mode Network, and anterior insula for Salience Network) were then generated for each participant. Within transgender participants, these maps were then compared between the baseline and 6 month timepoints. Additionally, baseline scans were also compared to scans from cisgender controls corresponding to biological sex. Results: Within-subjects analyses of transgender participants showed diverse associations between changes in seed-based connectivity and changes in self-reported anxiety and depression following GAHT in both the default mode and salience networks. Between subjects comparisons showed significantly higher connectivity within the salience network in trans men relative to cis women, as well as greater inter-network connectivity in this group. In contrast, cis men showed greater connectivity within both default mode and salience networks relative to trans women. Conclusions: Changes in affect following GAHT appear related to GAHT-induced changes in network connectivity; these influences appear independent of differences between cis and transgender individuals. Presentation: 6/2/2024

Insights from triggers and prodromal symptoms on how migraine attacks start: The threshold hypothesis.

The prodrome or premonitory phase is the initial phase of a migraine attack, and it is considered as a symptomatic phase in which prodromal symptoms may occur. There is evidence that attacks start 24-48 hours before the headache phase. Individuals with migraine also report several potential triggers for their attacks, which may be mistaken for premonitory symptoms and hinder migraine research. This review aims to summarize published studies that describe contributions to understanding the fine difference between prodromal/premonitory symptoms and triggers, give insights for research, and propose a way forward to study these phenomena. We finally aim to formulate a theory to unify migraine triggers and prodromal symptoms. For this purpose, a comprehensive narrative review of the published literature on clinical, neurophysiological and imaging evidence on migraine prodromal symptoms and triggers was conducted using the PubMed database. Brain activity and network connectivity changes occur during the prodromal phase. These changes give rise to prodromal/premonitory symptoms in some individuals, which may be falsely interpreted as triggers at the same time as representing the early manifestation of the beginning of the attack. By contrast, certain migraine triggers, such as stress, hormone changes or sleep deprivation, acting as a catalyst in reducing the migraine threshold, might facilitate these changes and increase the chances of a migraine attack. Migraine triggers and prodromal/premonitory symptoms can be confused and have an intertwined relationship with the hypothalamus as the central hub for integrating external and internal body signals. Differentiating migraine triggers and prodromal symptoms is crucial for shedding light on migraine pathophysiology and improve migraine management.

Sex-Specific Effects of Anxiety on Cognition and Activity-Dependent Neural Networks: Insights from (Female) Mice and (Wo)Men

Neuropsychiatric symptoms (NPS), such as depression and anxiety, are observed in 90% of Alzheimer's disease (AD) patients, two-thirds of whom are women. NPS usually manifest long before AD onset creating a therapeutic opportunity. Here, we examined the impact of anxiety on AD progression and the underlying brain-wide neuronal mechanisms. To gain mechanistic insight into how anxiety impacts AD progression, we performed a cross-sectional analysis on mood, cognition, and neural activity utilizing the ArcCreERT2 x enhanced yellow fluorescent protein (eYFP) x APP/PS1 (AD) mice. The ADNI dataset was used to determine the impact of anxiety on AD progression in human subjects. Female APP/PS1 mice exhibited anxiety-like behavior and cognitive decline at an earlier age than control (Ctrl) mice and male mice. Brain-wide analysis of c-Fos+ revealed changes in regional correlations and overall network connectivity in APP/PS1 mice. Sex-specific eYFP+/c-Fos+ changes were observed; female APP/PS1 mice exhibited less eYFP+/c-Fos+ cells in dorsal CA3 (dCA3), while male APP/PS1 mice exhibited less eYFP+/c-Fos+ cells in the dorsal dentate gyrus (dDG). In the ADNI dataset, anxiety predicted transition to dementia. Female subjects positive for anxiety and amyloid transitioned more quickly to dementia than male subjects. While future studies are needed to understand whether anxiety is a predictor, a neuropsychiatric biomarker, or a comorbid symptom that occurs during disease onset, these results suggest that there are sex differences in AD network dysfunction, and that personalized medicine may benefit male and female AD patients rather than a one size fits all approach.

Impacts of River Network Connectivity on Flood Signatures and Severity Regulated by Flood Control Projects

The operation of hydraulic projects within plain river networks to mitigate floods can alter river network connectivity patterns, subsequently affecting flood processes. This study employed the MIKE 11 model to simulate flood processes under three different river network connectivity scenarios. Based on the simulations, we propose a method to evaluate flood intensity severity by integrating three flood characteristic indices: Slope of the Flow Duration Curve (SFDC), Rising Climb Index (RCI), and Flashiness Index (FI). These indices assess the overall magnitude of change, the rate of rise, and process fluctuations, respectively. Results indicate that changes in river network connectivity significantly impact RCI and SFDC, more than FI. Compared to the natural river network connectivity mode, changes in urban or watershed river network connectivity resulted in a significant decrease in RCI values by 3–37% or 18–38% across various return periods, with the rate of change in RCI values increasing as the return period lengthened. The impact of urban river network connectivity changes on SFDC within the Changzhou urban area was more pronounced under high-magnitude storm conditions, causing a 61% reduction. Furthermore, changes in watershed river network connectivity had a larger effect on SFDC under low-magnitude storm conditions than under high-intensity storms. Over 80% of the rivers under natural connectivity conditions exhibited flood intensity severity of Level III or higher, particularly in the Chenshu–Qingyang area. The alterations in connectivity significantly decreased flood intensity severity, with 85% to 91% of rivers showing the lowest flood intensity severity of Level I. Under a 100-year rainstorm scenario, flood risk shifted from within the flood protection envelope to outside it in the Changzhou urban area. The results will provide an important scientific basis for regional flood management in plains with dense rivers.

Adolescent Thalamoprefrontal Inhibition Leads to Changes in Intrinsic Prefrontal Network Connectivity.

Adolescent inhibition of thalamocortical projections from postnatal days P20 to 50 leads to long-lasting deficits in prefrontal cortex function and cognition in the adult mouse. While this suggests a role of thalamic activity in prefrontal cortex maturation, it is unclear how inhibition of these projections affects prefrontal circuitry during adolescence. Here, we used chemogenetic tools to inhibit thalamoprefrontal projections in male/female mice from P20 to P35 and measured synaptic inputs to prefrontal pyramidal neurons by layer (either II/III or V/VI) and projection target (mediodorsal thalamus (MD), nucleus accumbens (NAc), or callosal prefrontal projections) 24 h later using slice physiology. We found a decrease in the frequency of excitatory and inhibitory currents in layer II/III NAc and layer V/VI MD-projecting neurons while layer V/VI NAc-projecting neurons showed an increase in the amplitude of excitatory and inhibitory currents. Regarding cortical projections, the frequency of inhibitory but not excitatory currents was enhanced in contralateral mPFC-projecting neurons. Notably, despite these complex changes in individual levels of excitation and inhibition, the overall balance between excitation and inhibition in each cell was only altered in the contralateral mPFC projections. This finding suggests homeostatic regulation occurs within subcortically but not intracortical callosal-projecting neurons. Increased inhibition of intraprefrontal connectivity may therefore be particularly important for prefrontal cortex circuit maturation. Finally, we observed cognitive deficits in the adult mouse using this narrowed window of thalamocortical inhibition.

Cognitive and Salience Network Connectivity Changes following a Single Season of Repetitive Head Impact Exposure in High School Football.

During a season of high school football, adolescents with actively developing brains experience a considerable number of head impacts. Our aim was to determine whether repetitive head impacts in the absence of a clinically diagnosed concussion during a season of high school football produce changes in cognitive performance or functional connectivity of the salience network and its central hub, the dorsal anterior cingulate cortex. Football players were instrumented with the Head Impact Telemetry System during all practices and games, and the helmet sensor data were used to compute a risk-weighted exposure metric (RWEcp), accounting for the cumulative risk during the season. Participants underwent MRI and a cognitive battery (ImPACT) before and shortly after the football season. A control group of noncontact/limited-contact-sport athletes was formed from 2 cohorts: one from the same school and protocol and another from a separate, nearly identical study. Sixty-three football players and 34 control athletes were included in the cognitive performance analysis. Preseason, the control group scored significantly higher on the ImPACT Visual Motor (P = .04) and Reaction Time composites (P = .006). These differences increased postseason (P = .003, P < .001, respectively). Additionally, the control group had significantly higher postseason scores on the Visual Memory composite (P = .001). Compared with controls, football players showed significantly less improvement in the Verbal (P = .04) and Visual Memory composites (P = .01). A significantly greater percentage of contact athletes had lower-than-expected scores on the Verbal Memory (27% versus 6%), Visual Motor (21% versus 3%), and Reaction Time composites (24% versus 6%). Among football players, a higher RWEcp was significantly associated with greater increments in ImPACT Reaction Time (P = .03) and Total Symptom Scores postseason (P = .006). Fifty-seven football players and 13 control athletes were included in the imaging analyses. Postseason, football players showed significant decreases in interhemispheric connectivity of the dorsal anterior cingulate cortex (P = .026) and within-network connectivity of the salience network (P = .018). These decreases in dorsal anterior cingulate cortex interhemispheric connectivity and within-network connectivity of the salience network were significantly correlated with deteriorating ImPACT Total Symptom (P = .03) and Verbal Memory scores (P = .04). Head impact exposure during a single season of high school football is negatively associated with cognitive performance and brain network connectivity. Future studies should further characterize these short-term effects and examine their relationship with long-term sequelae.

Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study.

Representing data using time-resolved networks is valuable for analyzing functional data of the human brain. One commonly used method for constructing time-resolved networks from data is sliding window Pearson correlation (SWPC). One major limitation of SWPC is that it applies a high-pass filter to the activity time series. Therefore, if we select a short window (desirable to estimate rapid changes in connectivity), we will remove important low-frequency information. Here, we propose an approach based on single sideband modulation (SSB) in communication theory. This allows us to select shorter windows to capture rapid changes in the time-resolved functional network connectivity (trFNC). We use simulation and real resting-state functional magnetic resonance imaging (fMRI) data to demonstrate the superior performance of SSB+SWPC compared to SWPC. We also compare the recurring trFNC patterns between individuals with the first episode of psychosis (FEP) and typical controls (TC) and show that FEPs stay more in states that show weaker connectivity across the whole brain. A result exclusive to SSB+SWPC is that TCs stay more in a state with negative connectivity between subcortical and cortical regions. Based on all the results, we argue that SSB+SWPC is more sensitive for capturing temporal variation in trFNC.

Analyzing road network robustness in major Chinese cities through hierarchical stroke-based representations

ABSTRACT In this study, we explore the robustness of urban road networks, a crucial aspect of urban infrastructure whose failure can significantly disrupt urban functionality. Our approach offers a fresh perspective on network robustness by focusing on intrinsic street hierarchies within stroke-based representations across 20 major Chinese cities. These networks exhibit common complex properties, such as being scale-free and small-world. We determine street hierarchies based on the power law distribution of two key graph centrality measures: degree and betweenness. Our methodology involves a targeted attack strategy, sequentially removing roads from the highest to the lowest hierarchy in each urban street network. This process yields robustness profiles characterized by monotonic decreasing curves, which reflect changes in network connectivity and efficiency. The profiles not only provide valuable insights into urban structure and function, but also offer significant implications for practical road planning and urban resilience strategies.

Changes of brain functional network in Alzheimer’s disease and frontotemporal dementia: a graph-theoretic analysis

BackgroundAlzheimer’s disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment.MethodsWe analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters.ResultsBoth AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD.ConclusionsOur findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.

Mapping Motor Cortical Network Excitability and Connectivity Changes in De Novo Parkinson's Disease.

Transcranial magnetic stimulation-electroencephalography (TMS-EEG) has demonstrated decreased excitability in the primary motor cortex (M1) and increased excitability in the pre-supplementary motor area (pre-SMA) in moderate-advanced Parkinson's disease (PD). The aim was to investigate whether these abnormalities are evident from the early stages of the disease, their behavioral correlates, and relationship to cortico-subcortical connections. Twenty-eight early, drug-naive (de novo) PD patients and 28 healthy controls (HCs) underwent TMS-EEG to record TMS-evoked potentials (TEPs) from the primary motor cortex (M1) and the pre-SMA, kinematic recording of finger-tapping movements, and a 3T-MRI (magnetic resonance imaging) scan to obtain diffusion tensor imaging (DTI) reconstruction of white matter (WM) tracts connecting M1 to the ventral lateral anterior thalamic nucleus and pre-SMA to the anterior putamen. We found reduced M1 TEP P30 amplitude in de novo PD patients compared to HCs and similar pre-SMA TEP N40 amplitude between groups. PD patients exhibited smaller amplitude and slower velocity in finger-tapping movements and altered structural integrity in WM tracts of interest, although these changes did not correlate with TEPs. M1 hypoexcitability is a characteristic of PD from early phases and may be a marker of the parkinsonian state. Pre-SMA hyperexcitability is not evident in early PD and possibly emerges at later stages of the disease. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Mobile application-based phonetic training facilitates Chinese-English learners’ learning of L2

BackgroundNumerous studies have claimed that high variability phonetic training (HVPT) has significant efficacy in improving learners’ phonological decoding ability. Although mobile learning (ML) based HVPT program integrated with sound instructional design also demonstrated the effectiveness, its reliability and validity need to be further verified. AimsThis study evaluated the training's efficacy in improving Chinese learners' perception and production of English words by comparing learners' brain activation changes and network connectivity changes because of the training. SamplesParticipants were 40 undergraduate students. MethodsThis study developed a Wechat application to implement a self-regulated high-variability-phonetic-training (HVPT) for 20 university students; employed a modified phonics screening test (MPST) as a benchmark test to assess learners’ phonetic decoding ability before and after the training; designed a quasi-experiment (control/experimental design) for 40 participants; most importantly, this study used a functional near infrared spectroscopy (fNIRS) device to monitor brain activation and networks synchronisation for participants in both groups before and after training. ResultsThe results showed that learners' performance on 5-phoneme pseudowords was significantly improved. The training inhibited learners’ right fusiform gyrus. A significant correlation was observed between enhanced RMTG and increased testing scores. ConclusionThe transfer effect of Mobile MVPT on long pseudowords is significant, suggesting its efficacy in helping learners apply the phonetic patterns perceived from the training to segmentally analyse new difficult words.

Neuronal functional connectivity is impaired in a layer dependent manner near chronically implanted intracortical microelectrodes in C57BL6 wildtype mice

Objective. This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near chronically implanted microelectrodes. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how the FBR affects the functional stability of neural circuits near implanted brain–computer interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders. Approach. This study utilized single-shank, 16-channel,100 µm site-spacing Michigan-style microelectrodes (3 mm length, 703 µm2 site area) that span all cortical layers and the hippocampal CA1 region. Sex balanced C57BL6 wildtype mice (11–13 weeks old) received perpendicularly implanted microelectrode in left primary visual cortex. Electrophysiological recordings were performed during both spontaneous activity and visual sensory stimulation. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of local field potential (LFP) and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation. Main results. The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations. Significance. This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study’s understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

Analysis of functional connectivity changes in attention networks and default mode networks in patients with depression and insomnia.

Major Depressive Disorder (MDD) and Insomnia Disorder (ID) are prevalent psychiatric conditions often occurring concurrently, leading to substantial impairment in daily functioning. Understanding the neurobiological underpinnings of these disorders and their comorbidity is crucial for developing effective interventions. This study aims to analyze changes in functional connectivity within attention networks and default mode networks in patients with depression and insomnia. The functional connectivity alterations in individuals with MDD, ID, comorbid MDD and insomnia (iMDD), and healthy controls (HC) were assessed from a cohort of 174 participants. They underwent rs-fMRI scans, demographic assessments, and scale evaluations for depression and sleep quality. Functional connectivity analysis was conducted using region-of-interest (ROI) and whole-brain methods. The MDD and iMDD groups exhibited higher Hamilton Depression Scale (HAMD) scores compared to HC and ID groups (P < 0.001). Both ID and MDD groups displayed enhanced connectivity between the left and right orbital frontal cortex compared to HC (P < 0.05), while the iMDD group showed reduced connectivity compared to HC and ID groups (P < 0.05). In the left insula, reduced connectivity with the right medial superior frontal gyrus was observed across patient groups compared to HC (P < 0.05), with the iMDD group showing increased connectivity compared to MDD (P < 0.05). Moreover, alterations in functional connectivity between the left thalamus and left temporal pole were found in iMDD compared to HC and MDD (P < 0.05). Correlation analyses revealed associations between abnormal connectivity and symptom severity in MDD and ID groups. Our findings demonstrate distinct patterns of altered functional connectivity in individuals with MDD, ID, and iMDD compared to healthy controls. These findings contribute to a better understanding of the pathophysiology of depression and insomnia, which could be used as a reference for the diagnosis and treatments of these patients.

Graph Analysis of the Visual Cortical Network during Naturalistic Movie Viewing Reveals Increased Integration and Decreased Segregation Following Mild TBI.

Traditional neuroimaging methods have identified alterations in brain activity patterns following mild traumatic brain injury (mTBI), particularly during rest, complex tasks, and normal vision. However, studies using graph theory to examine brain network changes in mTBI have produced varied results, influenced by the specific networks and task demands analyzed. In our study, we employed functional MRI to observe 17 mTBI patients and 54 healthy individuals as they viewed a simple, non-narrative underwater film, simulating everyday visual tasks. This approach revealed significant mTBI-related changes in network connectivity, efficiency, and organization. Specifically, the mTBI group exhibited higher overall connectivity and local network specialization, suggesting enhanced information integration without overwhelming the brain's processing capabilities. Conversely, these patients showed reduced network segregation, indicating a less compartmentalized brain function compared to healthy controls. These patterns were consistent across various visual cortex subnetworks, except in primary visual areas. Our findings highlight the potential of using naturalistic stimuli in graph-based neuroimaging to understand brain network alterations in mTBI and possibly other conditions affecting brain integration.

Abnormal dynamic functional connectivity in young nondisabling intracerebral hemorrhage patients.

Previous resting-state functional magnetic resonance imaging studies on intracerebral hemorrhage patients have focused more on the static characteristics of brain activity, while the time-varying effects during scanning have received less attention. Therefore, the current study aimed to explore the dynamic functional network connectivity changes of intracerebral hemorrhage patients. Using independent component analysis, the sliding window approach, and the k-means clustering analysis method, different dynamic functional network connectivity states were detected from resting-state functional magnetic resonance imaging data of 37 intracerebral hemorrhage patients and 44 healthy controls. The inter-group differences in dynamic functional network connectivity patterns and temporal properties were investigated, followed by correlation analyses between clinical scales and abnormal functional indexes. Ten resting-state networks were identified, and the dynamic functional network connectivity matrices were clustered into four different states. The transition numbers were decreased in the intracerebral hemorrhage patients compared with healthy controls, which was associated with trail making test scores in patients. The cerebellar network and executive control network connectivity in State 1 was reduced in patients, and this abnormal dynamic functional connectivity was positively correlated with the animal fluency test scores of patients. The current study demonstrated the characteristics of dynamic functional network connectivity in intracerebral hemorrhage patients and revealed that abnormal temporal properties and functional connectivity may be related to the performance of different cognitive domains after ictus. These results may provide new insights into exploring the neurocognitive mechanisms of intracerebral hemorrhage.

Non-trivial relationship between behavioral avalanches and internal neuronal dynamics in a recurrent neural network.

Neuronal activity gives rise to behavior, and behavior influences neuronal dynamics, in a closed-loop control system. Is it possible then, to find a relationship between the statistical properties of behavior and neuronal dynamics? Measurements of neuronal activity and behavior have suggested a direct relationship between scale-free neuronal and behavioral dynamics. Yet, these studies captured only local dynamics in brain sub-networks. Here, we investigate the relationship between internal dynamics and output statistics in a mathematical model system where we have access to the dynamics of all network units. We train a recurrent neural network (RNN), initialized in a high-dimensional chaotic state, to sustain behavioral states for durations following a power-law distribution as observed experimentally. Changes in network connectivity due to training affect the internal dynamics of neuronal firings, leading to neuronal avalanche size distributions approximating power-laws over some ranges. Yet, randomizing the changes in network connectivity can leave these power-law features largely unaltered. Specifically, whereas neuronal avalanche duration distributions show some variations between RNNs with trained and randomized decoders, neuronal avalanche size distributions are invariant, in the total population and in output-correlated sub-populations. This is true independent of whether the randomized decoders preserve power-law distributed behavioral dynamics. This demonstrates that a one-to-one correspondence between the considered statistical features of behavior and neuronal dynamics cannot be established and their relationship is non-trivial. Our findings also indicate that statistical properties of the intrinsic dynamics may be preserved, even as the internal state responsible for generating the desired output dynamics is perturbed.

Gamma oscillations induced by 40-Hz visual-auditory stimulation for the treatment of acute-phase limb motor rehabilitation after stroke: study protocol for a prospective randomized controlled trial

BackgroundThe incidence of hemiparetic limb dysfunction reaches 85% in stroke patients, emerging as a critical factor influencing their daily lives. However, the effectiveness of current rehabilitation treatments is considerably limited, particularly in patients with upper extremity impairment. This study aims to conduct a prospective clinical trial to validate the safety and effectiveness of gamma oscillations induced by 40-Hz visual-auditory stimulation in treating post-stroke upper limb dysfunction and to explore the relevant mechanisms.MethodsThis trial is a prospective, randomized controlled, double-blind study. All enrolled patients were randomly assigned to two groups. The experimental group received intervention through 40-Hz visual-auditory stimulation, while the control group underwent intervention with randomly matched visual-auditory stimulation frequencies. The primary efficacy endpoint is the change in motor function. Secondary efficacy endpoints include motor-evoked potentials, cerebral hemodynamic changes, neural network connectivity, and alterations in synaptic-related genes. Safety evaluation included major adverse events, all-cause mortality, and photosensitive epilepsy. Assessments will be conducted at baseline, after a 14-day treatment period, and during subsequent follow-up visits (at 3 and 6 months) post-treatment. The differences between the two groups will be compared.DiscussionThis study will evaluate the safety and efficacy of gamma oscillations induced by 40-Hz visual-auditory stimulation in treating patients with upper extremity dysfunction after an acute cerebral stroke. Concurrently, we will explore potential mechanisms, including changes in synaptic-related genes and neural network connectivity. This trial is expected to provide evidence for the effectiveness of this new technique in treating upper extremity dysfunction after a stroke and improving patients’ quality of life.Trial registrationThe study protocol has been registered with the Chinese Clinical Trial Registry (ChiCTR) under registration number ChiCTR2300076579 on October 12, 2023.