Disruption Of Connectivity Research Articles

Unraveling the Pathogenesis of Post-Stroke Depression in a Hemorrhagic Mouse Model through Frontal Lobe Circuitry and JAK-STAT Signaling.

Post-stroke depression is a common complication that imposes significant burdens and challenges on patients. The occurrence of depression is often associated with frontal lobe hemorrhage, however, current understanding of the underlying mechanisms remains limited. Here, the pathogenic mechanisms associated with the circuitry connectivity, electrophysiological alterations, and molecular characteristics are investigated related to the frontal lobe in adult male mice following unilateral injection of blood in the medial prefrontal cortex (mPFC). It is demonstrated that depression is a specific neurological complication in the unilateral hematoma model of the mPFC, and the ventral tegmental area (VTA) shows a higher percentage of connectivity disruption compared to the lateral habenula (LHb) and striatum (STR). Additionally, long-range projections originating from the frontal lobe demonstrate higher damage percentages within the connections between each region and the mPFC. mPFC neurons reveal reduced neuronal excitability and altered synaptic communication. Furthermore, transcriptomic analysis identifies the involvement of the Janus Kinase-Signal Transducer and Activator of Transcription (JAK-STAT)signaling pathway, and targeting the JAK-STAT pathway significantly alleviates the severity of depressive symptoms. These findings improve the understanding of post-hemorrhagic depression and may guide the development of efficient treatments.

Exploring functional and structural connectivity disruptions in spinocerebellar ataxia type 3: Insights from gradient analysis.

Spinocerebellar Ataxia Type 3 (SCA3) is a rare genetic ataxia that impacts the entire brain and is characterized as a neurodegenerative disorder affecting the neural network. This study explores how alterations in the functional hierarchy, connectivity, and structural changes within specific brain regions significantly contribute to the heterogeneity of symptom manifestations in patients with SCA3. We prospectively recruited 51 patients with SCA3 and 59 age-and sex-matched healthy controls. All participants underwent comprehensive multimodal neuroimaging and clinical assessments. In SCA3 patients, an innovative approach utilizing gradients in resting-state functional connectivity (FC) was employed to examine atypical patterns of hierarchical processing topology from sensorimotor to supramodal regions in the cerebellum and cerebrum. Coupling analyses of abnormal FC and structural connectivity among regions of interest (ROIs) in the brain were also performed to characterize connectivity alterations. Additionally, relationships between quantitative ROI values and clinical variables were explored. Patients with SCA3 exhibited either compression or expansion within the primary sensorimotor-to-supramodal gradient through four distinct calculation methods, along with disruptions in FC and structural connectivity coupling. A comprehensive correlation was identified between the altered gradients and the clinical manifestations observed in patients. Notably, altered fractional anisotropy values were not significantly correlated with clinical variables. Abnormal gradients and connectivity in the cerebellar and cerebral cortices in SCA3 patients may contribute to disrupted motor-to-supramodal functions. Moreover, these findings support the potential utility of FCG analysis as a biomarker for diagnosing SCA3 and assessing treatment efficacy.

Enhancing the Quality of Judicial Service Delivery through E-Filing System: The Case of Rukwa District Courts in Tanzania.

E-filing system is advocated for improving the quality of judicial service delivery worldwide; andthis has proved successful in developed countries. In 2016, the Tanzania judicial organ piloted e- filing system and later in 2018, a countrywide rollout proceeded. Despite the adoption of the e- fil ing system, anomalies in the procedures for case filing are common especially in Rukwa Region. Previous studies have not given enough consideration to the aspects and dimensions of quality judicial service delivery through e-filing system such as duration, accessibility and convenience in district courts. This knowledge gap made it difficult for scholars and policy implementers to comprehend the current situation in district courts where reforms in judicial service delivery are ongoing. This study aimed to examine the contribution of e-filing system in the quality of judicial service delivery in Rukwa District courts. This work applied a mixed approach with a case study design using both primary and secondary data. Primary data were collected from 78 respondents through survey method, while seven (7) key informants were involved in interviews and supplemented by non-participant observation method. On the other hand, secondary data were collected through a documentary review. The statistical package for social sciences (SPSS) was used to generate frequencies, percentages followed by correlation analysis. Findings revealed that e-filing system had a potential to improve the quality of judicial service delivery through time saving, increased accessibility and enhanced convenience of specific judicial services such as the case filing. However, the potential of the e-filing system was challenged by disruptions in internet connectivity, limited interoperability and inadequate ICT facilities mainly desktops. The study recommends fostering reliable internet connectivity, enabling the system to operate off-line, to integrate the system with more technological innovations, and optimizing ICT facilities such as laptops or desktops to meet the needs.

Vulnerability Assessment and Topology Reconstruction of Task Chains in UAV Networks

With the increasing complexity of environments and the diversity of task chains, individual unmanned aerial vehicles (UAVs) often struggle to satisfy the demands of task chains, including load capacity improvement, information perception, and information procession. In complex task chains involving various UAVs, such as area reconnaissance and fire rescue, any attack on critical UAVs can greatly disrupt the execution of the entire task chain by causing equipment damage or connectivity disruption. To ensure network resilience post attack, identifying vulnerable nodes in the UAV network becomes crucial. In this paper, a Vulnerability-based Topology Reconstruction Mechanism (VUTRM) is proposed to rank the importance of nodes in task chains and formulate a topology reconstruction. It consists of two parts: the first part is a Multi-metric Node Vulnerability Assessment Algorithm (MENVAL) used to rank the importance of nodes in task chains, and the second part is a Node Importance-based Topology Reconstruction Algorithm (NITRA) used to reconstruct the UAV network with the obtained node ranking. Finally, simulations carried out with simulation software demonstrate that our proposed method accurately identifies network vulnerabilities and promptly implements effective reconstruction measures to minimize network damage.

Reversibility of Cognitive Deficits and Functional Connectivity With Transfusion in Children With Sickle Cell Disease.

People with sickle cell disease (SCD) are at risk of cognitive dysfunction independent of stroke. Diminished functional connectivity in select large-scale networks and white matter integrity reflect the neurologic consequences of SCD. Because chronic transfusion therapy is neuroprotective in preventing stroke and strengthening executive function abilities in people with SCD, we hypothesized that red blood cell (RBC) transfusion facilitates the acute reversal of disruptions in functional connectivity while white matter integrity remains unaffected. Children with SCD receiving chronic transfusion therapy underwent a brain MRI measuring white matter integrity with diffusion tensor imaging and resting-state functional connectivity within 3 days before and after transfusion of RBCs. Cognitive assessments with the NIH Toolbox were acquired after transfusion and then immediately before the following transfusion cycle. Sixteen children with a median age of 12.5 years were included. Global assessments of functional connectivity using homotopy (p = 0.234) or modularity (p = 0.796) did not differ with transfusion. Functional connectivity within the frontoparietal network significantly strengthened after transfusion (median intranetwork Z-score 0.21 [0.17-0.30] before transfusion, 0.29 [0.20-0.36] after transfusion, p < 0.001), while there was not a significant change seen within the sensory motor, visual, auditory, default mode, dorsal attention, or cingulo-opercular networks. Corresponding to the change within the frontoparietal network, there was a significant improvement in executive function abilities after transfusion (median executive function composite score 87.7 [81.3-90.7] before transfusion, 90.3 [84.3-93.7] after transfusion, p = 0.021). Participants with stronger connectivity in the frontoparietal network before transfusion had a significantly greater improvement in the executive function composite score with transfusion (r = 0.565, 95% CI 0.020-0.851, p = 0.044). While functional connectivity and executive abilities strengthened with transfusion, there was not a significant change in white matter integrity as assessed by fractional anisotropy and mean diffusivity within 16 white matter tracts or globally with tract-based spatial statistics. Strengthening of functional connectivity with concomitant improvement in executive function abilities with transfusion suggests that functional connectivity MRI could be used as a biomarker for acutely reversible neurocognitive injury as novel therapeutics are developed for people with SCD.

Age Trajectories of the Structural Connectome in Child and Adolescent Offspring of Individuals With Bipolar Disorder or Schizophrenia

BackgroundOffspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated. MethodsA total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity. ResultsOffspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder. ConclusionsThe findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.

Cognitive impairment in multiple sclerosis: from phenomenology to neurobiological mechanisms.

Multiple sclerosis (MS) is an autoimmune-mediated disease of the central nervous system characterized by inflammation, demyelination and chronic progressive neurodegeneration. Among its broad and unpredictable range of clinical symptoms, cognitive impairment (CI) is a common and disabling feature greatly affecting the patients' quality of life. Its prevalence is 20% up to 88% with a wide variety depending on the phenotype of MS, with highest frequency and severity in primary progressive MS. Involving different cognitive domains, CI is often associated with depression and other neuropsychiatric symptoms, but usually not correlated with motor and other deficits, suggesting different pathophysiological mechanisms. While no specific neuropathological data for CI in MS are available, modern research has provided evidence that it arises from the disease-specific brain alterations. Multimodal neuroimaging, besides structural changes of cortical and deep subcortical gray and white matter, exhibited dysfunction of fronto-parietal, thalamo-hippocampal, default mode and cognition-related networks, disruption of inter-network connections and involvement of the γ-aminobutyric acid (GABA) system. This provided a conceptual framework to explain how aberrant pathophysiological processes, including oxidative stress, mitochondrial dysfunction, autoimmune reactions and disruption of essential signaling pathways predict/cause specific disorders of cognition. CI in MS is related to multi-regional patterns of cerebral disturbances, although its complex pathogenic mechanisms await further elucidation. This article, based on systematic analysis of PubMed, Google Scholar and Cochrane Library, reviews current epidemiological, clinical, neuroimaging and pathogenetic evidence that could aid early identification of CI in MS and inform about new therapeutic targets and strategies.

DEVELOPMENT OF NETWORK SIMULATION MODEL FOR EVALUATING THE EFFICIENCY OF DISTRIBUTED CONSENSUS TAKING INTO ACCOUNT THE INSTABILITY OF NETWORK CONNECTIONS

The dynamic and unpredictable nature of network environments poses a significant challenge for distributed systems, particularly those relying on consensus algorithms for state management and fault tolerance. To address this challenge, this article introduces a novel simulation model designed to study the impact of unstable network connections on clusters running consensus algorithms. The model is engineered to mimic varying degrees of network instability, including latency fluctuations and connection disruptions, which are characteristic of real-world distributed systems. Our proposed model represents a significant advancement in the simulation of distributed networks. It employs a sophisticated network emulation layer capable of generating a wide spectrum of unstable network conditions. The core of the model is a highly configurable consensus mechanism simulator that allows for the adjustment of key parameters such as heartbeat intervals, election timeouts, and message loss rates. This level of configurability enables a comprehensive analysis of consensus behaviors under different network scenarios. The article focuses on the methodology behind the development of the model, detailing the theoretical underpinnings and the implementation strategies used to ensure a realistic representation of network instability. We also discuss the potential applications of the model, which extend beyond academic research into practical domains where distributed ledger technologies and distributed databases are prevalent. Through the deployment of this model, researchers and system architects can gain deeper insights into the resilience and adaptability of consensus algorithms. The model serves as a tool for preemptively identifying and addressing potential issues in distributed systems, facilitating the development of more robust and reliable technologies. In summary, the article showcases the design and capabilities of a new model that enables an in-depth understanding of the delicate interplay between network instability and consensus efficiency. By focusing on the model itself, the article aims to lay a foundation for future studies and improvements in the field of distributed systems.

Aberrant individual structure covariance network in patients with mesial temporal lobe epilepsy.

Mesial temporal lobe epilepsy (mTLE) is a complex neurological disorder that has been recognized as a widespread global network disorder. The group-level structural covariance network (SCN) could reveal the structural connectivity disruption of the mTLE but could not reflect the heterogeneity at the individual level. This study adopted a recently proposed individual structural covariance network (IDSCN) method to clarify the alternated structural covariance connection mode in mTLE and to associate IDSCN features with the clinical manifestations and regional brain atrophy. We found significant IDSCN abnormalities in the ipsilesional hippocampus, ipsilesional precentral gyrus, bilateral caudate, and putamen in mTLE patients than in healthy controls. Moreover, the IDSCNs of these areas were positively correlated with the gray matter atrophy rate. Finally, we identified several connectivities with weak associations with disease duration, frequency, and surgery outcome. Our research highlights the role of hippo-thalamic-basal-cortical circuits in the pathophysiologic process of disrupted whole-brain morphological covariance networks in mTLE, and builds a bridge between brain-wide covariance network changes and regional brain atrophy.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

Potential correlations between asymmetric disruption of functional connectivity and metabolism in major depressive disorder

ObjectivePrevious research has suggested a connection between major depressive disorder (MDD) and certain comorbidities, including gastrointestinal issues, thyroid dysfunctions, and glycolipid metabolism abnormalities. However, the relationships between these factors and asymmetrical alterations in functional connectivity (FC) in adults with MDD remain unclear. MethodWe conducted a study on a cohort of 42 MDD patients and 42 healthy controls (HCs). Participants underwent comprehensive clinical assessments, including evaluations of blood lipids and thyroid hormone levels, as well as resting-state functional magnetic resonance imaging (Rs-fMRI) scans. Data analysis involved correlation analysis to compute the parameter of asymmetry (PAS) for the entire brain's functional connectome. We then examined the interrelationships between abnormal PAS regions in the brain, thyroid hormone levels, and blood lipid levels. ResultsThe third-generation ultra-sensitive thyroid stimulating hormone (TSH3UL) level was found to be significantly lower in MDD patients compared to HCs. The PAS score of the left inferior frontal gyrus (IFG) decreased, while the bilateral posterior cingulate cortex (Bi-PCC) PAS increased in MDD patients relative to HCs. Notably, the PAS score of the left IFG negatively correlated with both TSH and total cholesterol (CHOL) levels. However, these correlations lose significance after the Bonferroni correction. ConclusionMDD patients demonstrated abnormal asymmetry in resting-state FC (Rs-FC) within the fronto-limbic system, which may be associated with CHOL and thyroid hormone levels.

Structural and functional connectivity in hydrocephalus: a scoping review.

Optimizing the treatment of hydrocephalus remains a major challenge in adult and pediatric neurosurgery. Currently, clinical treatment relies heavily on anatomic imaging of ventricular size and clinical presentation. The emergence of functional and structural brain connectivity imaging has provided the basis for a new paradigm in the management of hydrocephalus. Here we review the pertinent advances in this field. Following PRISMA-ScR guidelines for scoping reviews, we searched PubMed for relevant literature from 1994 to April 2023 using hydrocephalus and MRI-related terms. Included articles reported original MRI data on human subjects with hydrocephalus, while excluding non-English or pre-1994 publications that didn't match the study framework. The review identified 44 studies that investigated functional and/or structural connectivity using various MRI techniques across different hydrocephalus populations. While there is significant heterogeneity in imaging technology and connectivity analysis, there is broad consensus in the literature that 1) hydrocephalus is associated with disruption of functional and structural connectivity, 2) this disruption in cerebral connectivity can be further associated with neurologic compromise 3) timely treatment of hydrocephalus restores both cerebral connectivity and neurologic compromise. The robustness and consistency of these findings vary as a function of patient age, hydrocephalus etiology, and the connectivity region of interest studied. Functional and structural brain connectivity imaging shows potential as an imaging biomarker that may facilitate optimization of hydrocephalus treatment. Future research should focus on standardizing regions of interest as well as identifying connectivity analysis most pertinent to clinical outcome.

Incident Dementia After Spontaneous Intracerebral Hemorrhage.

Post-stroke cognitive impairment and dementia (PSCID) is a complication that affects long-term functional outcomes after stroke. Studies on dementia after long-term follow-up in stroke have focused predominantly on ischemic stroke, which may be different from the development of dementia after spontaneous intracerebral hemorrhage (ICH). In this review, we summarize the existing data and hypotheses on the development of dementia after spontaneous ICH, review the management of post-ICH dementia, and suggest areas for future research. Dementia after spontaneous ICH has a cumulative incidence of up to 32.0-37.4% at 5 years post-ICH. Although the pathophysiology of post-ICH dementia has not been fully understood, two main theoretical frameworks can be considered: 1) the triggering role of ICH (both primary and secondary brain injury) in precipitating cognitive decline and dementia; and 2) the contributory role of pre-existing brain pathology (including small vessel disease and neurodegenerative pathology), reduced cognitive reserve, and genetic factors predisposing to cognitive dysfunction. These pathophysiological pathways may have synergistic effects that converge on dysfunction of the neurovascular unit and disruptions in functional connectivity leading to dementia post-ICH. Management of post-ICH dementia may include screening and monitoring, cognitive therapy, and pharmacotherapy. Non-invasive brain stimulation is an emerging therapeutic modality under investigation for safety and efficacy. Our review highlights that there remains a paucity of data and standardized reporting on incident dementia after spontaneous ICH. Further research is imperative for determining the incidence, risk factors, and pathophysiology of post-ICH dementia, in order to identify new therapies for the treatment of this debilitating condition.

Latency optimized C-RAN in optical backhaul and RF fronthaul architecture for beyond 5G network: A comprehensive survey

The need for high-speed data connection has significantly strained Mobile Network Operators (MNOs) due to an exponential increase in traffic. Numerous innovative technologies have been used to fulfill consumer service needs in capacity, high-speed data, network coverage, and Quality of Service (QoS), but they still require more thought. Because of several advantageous features in terms of resource utilization, service deployment, Capital Expenditure (CAPEX), Operating Expense (OPEX), and network management, the Cloud-based Radio Access Network (C-RAN) has played a significant role in mitigating the issues faced by mobile network operators. However, the C-RAN technology places high demands on the mobile fronthaul network due to the risk of connection interruptions. This article thoroughly analyzes the essential technologies, problems, design, needs, and solutions for effective fronthaul and backhaul communication a fifth-generation (5G) and beyond systems. There have been reviews of optical-based fronthaul and backhaul technologies. This article makes suggestions for ways to make fronthaul and backhaul communication systems more effective by lowering the costs, bandwidth, latency, and power requirements.

Median Absolute Deviation for BGP Anomaly Detection

The stability and reliability of the global Internet infrastructure heavily rely on the Border Gateway Protocol (BGP), a crucial protocol that facilitates the exchange of routing information among various Autonomous Systems, ensuring seamless connectivity worldwide. However, BGP inherently possesses a susceptibility to abnormal routing behaviors, potentially leading to significant connectivity disruptions. Despite extensive efforts, accurately detecting and effectively mitigating such abnormalities persist as tough challenges. To tackle these, this article proposes a novel statistical approach employing the median absolute deviation under certain constraints to proactively detect anomalies in BGP. By applying advanced analysis techniques, this research offers a robust method for the early detection of anomalies, such as Internet worms, configuration errors, and link failures. This innovative approach has been empirically validated, achieving an accuracy rate of 90% and a precision of 95% in identifying these disruptions. This high level of precision and accuracy not only confirms the effectiveness of the statistical method employed but also marks a significant step forward for enhancing the stability and reliability of the global Internet infrastructure.

Brain functional connectivity alterations in patients with anterior cruciate ligament injury

Recent advancements in neuroimaging have illustrated that anterior cruciate ligament (ACL) injuries could impact the central nervous system (CNS), causing neuroplastic changes in the brain beyond the traditionally understood biomechanical consequences. While most of previous functional magnetic resonance imaging (fMRI) studies have focused on localized cortical activity changes post-injury, emerging research has suggested disruptions in functional connectivity across the brain. However, these prior investigations, albeit pioneering, have been constrained by two limitations: a reliance on small-sample participant cohorts, often limited to two to three patients, potentially limiting the generalizability of findings, and an adherence to region of interest based analysis, which may overlook broader network interactions. To address these limitations, our study employed resting-state fMRI to assess whole-brain functional connectivity in 15 ACL-injured patients, comparing them to matched controls using two distinct network analysis methods. Using Network-Based Statistics, we identified widespread reductions in connectivity that spanned across multiple brain regions. Further modular connectivity analysis showed significant decreases in inter-modular connectivity between the sensorimotor and cerebellar modules, and intra-modular connectivity within the default-mode network in ACL-injured patients. Our results thus highlight a shift from localized disruptions to network-wide dysfunctions, suggesting that ACL injuries induce widespread CNS changes. This enhanced understanding has the potential to stimulate the development of strategies aiming to restore functional connectivity and improve recovery outcomes.

Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach

Background The prediction of post-stroke language function is essential for the development of individualized treatment plans based on the personal recovery potential of aphasic stroke patients. Objective To establish a framework for integrating information on connectivity disruption of the language network based on routinely collected clinical magnetic resonance (MR) images into Random Forest modeling to predict post-stroke language function. Methods Language function was assessed in 76 stroke patients from the Non-Invasive Repeated Therapeutic Stimulation for Aphasia Recovery trial, using the Token Test (TT), Boston Naming Test (BNT), and Semantic Verbal Fluency (sVF) Test as primary outcome measures. Individual infarct masks were superimposed onto a diffusion tensor imaging tractogram reference set to calculate Change in Connectivity scores of language-relevant gray matter regions as estimates of structural connectivity disruption. Multivariable Random Forest models were derived to predict language function. Results Random Forest models explained moderate to high amount of variance at baseline and follow-up for the TT (62.7% and 76.2%), BNT (47.0% and 84.3%), and sVF (52.2% and 61.1%). Initial language function and non-verbal cognitive ability were the most important variables to predict language function. Connectivity disruption explained additional variance, resulting in a prediction error increase of up to 12.8% with variable omission. Left middle temporal gyrus (12.8%) and supramarginal gyrus (9.8%) were identified as among the most important network nodes. Conclusion Connectivity disruption of the language network adds predictive value beyond lesion volume, initial language function, and non-verbal cognitive ability. Obtaining information on connectivity disruption based on routine clinical MR images constitutes a significant advancement toward practical clinical application.

Multi-modal Neuroimaging Phenotyping of Mnemonic Anosognosia in the Aging Brain

BackgroundUnawareness is a behavioral condition characterized by a lack of self-awareness of objective memory decline. In the context of Alzheimer’s Disease (AD), unawareness may develop in predementia stages and contributes to disease severity and progression. Here, we use in-vivo multi-modal neuroimaging to profile the brain phenotype of individuals presenting altered self-awareness of memory during aging.MethodsAmyloid- and tau-PET (N = 335) and resting-state functional MRI (N = 713) imaging data of individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4)/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study were used in this research. We applied whole-brain voxel-wise and region-of-interest analyses to characterize the cortical intersections of tau, amyloid, and functional connectivity networks underlying unawareness in the aging brain compared to aware, complainer and control groups.ResultsIndividuals with unawareness present elevated amyloid and tau burden in midline core regions of the default mode network compared to aware, complainer or control individuals. Unawareness is characterized by an altered network connectivity pattern featuring hyperconnectivity in the medial anterior prefrontal cortex and posterior occipito-parietal regions co-locating with amyloid and tau deposition.ConclusionsUnawareness is an early behavioral biomarker of AD pathology. Failure of the self-referential system in unawareness of memory decline can be linked to amyloid and tau burden, along with functional network connectivity disruptions, in several medial frontal and parieto-occipital areas of the human brain.

Predicting network congestion by extending betweenness centrality to interacting agents.

We present a simple model to predict network activity at the edge level by extending a known approximation method to compute betweenness centrality with a repulsive mechanism to prevent unphysical densities. By taking into account the strong interaction effects often observed in real phenomena, we aim to obtain an improved measure of edge usage during rush hours as traffic congestion patterns emerge in urban networks. In this approach, the network is iteratively populated by agents following dynamically evolving fastest paths who are progressively attracted towards uncongested parts of the network as the global traffic volume increases. Following the transition of the network state from empty to saturated, we study the emergence of congestion and the progressive disruption of global connectivity due to a relatively small fraction of crowded edges. We assess the predictive power of our model by comparing the speed distribution against a large experimental data set for the London area with remarkable results, which also translate into a qualitative similarity of the congestion maps. Also, percolation analysis confirms the quantitative agreement of the model with the real data for London. We perform simulations for seven other topologically different cities to obtain the Fisher critical exponent τ that shows no common functional dependence on the traffic level. The critical exponent γ, studied to assess the power-law decay of spatial correlation, is found to be inversely proportional to the number of vehicles for both real and simulated traffic. This simulation approach seems particularly fit to describe qualitative and quantitative properties of the network loading process, culminating in peak-hour congestion, by using only topological and geographical network features.

Alterations in brain functional connectivity in patients with mild cognitive impairment: A systematic review and meta-analysis of functional near-infrared spectroscopy studies.

Emerging evidences suggest that cognitive deficits in individuals with mild cognitive impairment (MCI) are associated with disruptions in brain functional connectivity (FC). This systematic review and meta-analysis aimed to comprehensively evaluate alterations in FC between MCI individuals and healthy control (HC) using functional near-infrared spectroscopy (fNIRS). Thirteen studies were included in qualitative analysis, with two studies synthesized for quantitative meta-analysis. Overall, MCI patients exhibited reduced resting-state FC, predominantly in the prefrontal, parietal, and occipital cortex. Meta-analysis of two studies revealed a significant reduction in resting-state FC from the right prefrontal to right occipital cortex (standardized mean difference [SMD]=-.56; p<.001), left prefrontal to left occipital cortex (SMD=-.68; p<.001), and right prefrontal to left occipital cortex (SMD=-.53; p<.001) in MCI patients compared to HC. During naming animal-walking task, MCI patients exhibited enhanced FC in the prefrontal, motor, and occipital cortex, whereas a decrease in FC was observed in the right prefrontal to left prefrontal cortex during calculating-walking task. In working memory tasks, MCI predominantly showed increased FC in the medial and left prefrontal cortex. However, a decreased in prefrontal FC and a shifted in distribution from the left to the right prefrontal cortex were noted in MCI patients during a verbal frequency task. In conclusion, fNIRS effectively identified abnormalities in FC between MCI and HC, indicating disrupted FC as potential markers for the early detection of MCI. Future studies should investigate the use of task- and region-specific FC alterations as a sensitive biomarker for MCI.