Lower Processing Speed Research Articles

Sleep disturbances and psychomotor retardation in the prediction of cognitive impairments in patients with major depressive disorder.

Symptoms of depression and comorbid anxiety are known risk factors for cognitive impairment in major depressive disorder (MDD). Understanding their relationships is crucial for developing targeted interventions to mitigate cognitive impairments in MDD patients. We expect that the severity of sleep disturbances and other depressive symptoms will be positively correlated with the degree of cognitive impairments. We also hypothesize that anxiety symptoms, especially psychic anxiety, is a key factor in predicting cognitive performance in MDD patients and may indirectly contribute to cognitive impairment by affecting sleep disturbances and other potential factors. To determine which dimension of the depressive and anxiety symptoms predicts cognitive impairment during a depressive episode. A comprehensive neurocognitive test battery assessed executive function, attention, processing speed, and memory in 162 medication-free MDD patients and 142 matched healthy controls. The 24-item Hamilton Depression Rating Scale was used to assess depressive symptoms, and the 14-item Hamilton Anxiety Scale was used to assess anxiety symptoms. Linear regression analyses and mediation analyses were conducted to evaluate the impact of depressive and anxiety symptoms, as well as their interactions, on cognitive impairments. Among the depressive symptoms, sleep disturbances were associated with poorer executive function (P = 0.004), lower processing speed (P = 0.047), and memory impairments (P < 0.001), and psychomotor retardation (PR) was associated with lower processing speed in patients with MDD (P = 0.019). Notably, PR was found to mediate the impact of sleep disturbances on the processing speed. Regarding anxiety symptoms, psychic anxiety, rather than somatic anxiety, was associated with cognitive impairments in all aspects. Sleep disturbances mediated the effect of psychic anxiety on executive function [β = -0.013, BC CI (-0.027, -0.001)] and memory [β = -0.149, BC CI (-0.237, -0.063)], while PR mediated its effect on processing speed (β = -0.023, BC CI (-0.045, -0.004)]. Sleep disturbances may be a key predictor of poorer executive function, lower processing speed, and memory loss, while PR is crucial for lower processing speed during a depressive episode. Psychic anxiety contributes to all aspects of cognitive impairments, mediated by sleep disturbances and PR.

Metasurface Enabled Multi‐Target and Multi‐Wavelength Diffraction Neural Networks

AbstractBenefiting from low power consumption and high processing speed, there is a growing interest in diffraction neural networks (DNNs), which are typically showcased with 3D printing devices, leading to large volumes, high costs, and low levels of integration. Metasurfaces can desirably manipulate wavefronts of electromagnetic waves, providing a compact platform for mimicking DNNs with novel functions. Although multi‐wavelength and multi‐target recognition provides a richer and more detailed understanding of complex environments, existing architectures are primarily trained to classify a single target at a specific wavelength. A metasurface approach is proposed to design multiplexed DNNs that can classify multiple targets and spatial sequences across various wavelengths in multiple channels. To realize multi‐task processing, the dielectric metasurface is designed based on phase and wavelength multiplexing, which can integrate multi‐target DNNs with different tasks such as operating at distinct wavelengths and classifying diverse targets. The efficacy of this method is exemplified through the numerical simulation and experimental demonstration of recognizing a single target with two wavelengths, two targets at a single wavelength, and two targets at dual wavelengths. This compact metasurface approach enables the design of multi‐target and multi‐wavelength DNNs, opening a new window to develop massively parallel processing and versatile artificial intelligence systems.

Brain activations elicited during task-switching generalize beyond the task: A partial least squares correlation approach to combine fMRI signals and cognition.

An underlying hypothesis for broad transfer from cognitive training is that the regional brain signals engaged during the training task are related to the transfer tasks. However, it is unclear whether the brain activations elicited from a specific cognitive task can generalize to performance of other tasks, esp. in normal aging where cognitive training holds much promise. In this large dual-site functional magnetic resonance imaging (fMRI) study, we aimed to characterize the neurobehavioral correlates of task-switching in normal aging and examine whether the task-switching-related fMRI-blood-oxygen-level-dependent (BOLD) signals, engaged during varieties of cognitive control, generalize to other tasks of executive control and general cognition. We therefore used a hybrid blocked and event-related fMRI task-switching paradigm to investigate brain regions associated with multiple types of cognitive control on 129 non-demented older adults (65-85 years). This large dataset provided a unique opportunity for a data-driven partial least squares-correlation approach to investigate the generalizability of multiple fMRI-BOLD signals associated with task-switching costs to other tasks of executive control, general cognition, and demographic characteristics. While some fMRI signals generalized beyond the scanned task, others did not. Results indicate right middle frontal brain activation as detrimental to task-switching performance, whereas inferior frontal and caudate activations were related to faster processing speed during the fMRI task-switching, but activations of these regions did not predict performance on other tasks of executive control or general cognition. However, BOLD signals from the right lateral occipital cortex engaged during the fMRI task positively predicted performance on a working memory updating task, and BOLD signals from the left post-central gyrus that were disengaged during the fMRI task were related to slower processing speed in the task as well as to lower general cognition. Together, these results suggest generalizability of these BOLD signals beyond the scanned task. The findings also provided evidence for the general slowing hypothesis of aging as most variance in the data were explained by low processing speed and global low BOLD signal in older age. As processing speed shared variance with task-switching and other executive control tasks, it might be a possible basis of generalizability between these tasks. Additional results support the dedifferentiation hypothesis of brain aging, as right middle frontal activations predicted poorer task-switching performance. Overall, we observed that the BOLD signals related to the fMRI task not only generalize to the performance of other executive control tasks, but unique brain predictors of out-of-scanner performance can be identified.

Neurocognitive Profile in Pediatric Kidney Transplant Candidates: Effects of Medical and Sociodemographic Factors.

We evaluated the effects of kidney failure etiology, dialysis, and sociodemographic factors on the subdomains of intellectual functioning in pediatric kidney transplant candidates. This retrospective study included 78 pediatric kidney transplant candidates who completed a Wechsler Intelligence Scale assessment during pre-transplant neuropsychological evaluation between 1/1/2010 and 10/31/2022. Linear regression models were employed to examine the effects of kidney failure etiology, dialysis status, neighborhood area deprivation, and race on subdomains of intellectual functioning. The mean scores of various intellectual functioning domains in pediatric kidney transplant candidates were significantly lower than in the general population (ps <0.001). After adjusting for covariates, patients with congenital anomalies of the kidney and urinary tract had significantly lower processing speed (M=85; 95% CI: 79-91) compared to patients with nephrotic syndrome (M=99; 95% CI: 90-107) and other etiologies (M=84; 95% CI: 78-90) (p=0.003). Patients living in high-level deprivation neighborhoods showed lower working memory performance (M=84, 95% CI: 77-91) than patients living in median-level (M=91, 95% CI: 87-95) and low-level (M=98, 95% CI: 92-104) neighborhood area deprivation (p=0.03). Patients from marginalized racial groups demonstrated lower verbal skills (M=80, 95% CI: 74-87) than White patients (M=92, 95% CI: 88-97) (p=0.02). Additionally, patients receiving dialysis showed higher reasoning skills (M=98, 95% CI: 90-104) than patients without dialysis (M= 90, 95% CI: 86-95) (p=0.04). Neurocognitive development in pediatric kidney transplant candidates is associated with medical and sociodemographic factors. Strategies to monitor, treat, and accommodate neurocognitive concerns need to be considered to optimize long-term medical and social outcomes.

Femtosecond laser darkened copper surfaces: Characterization for possible application as laser beam blocker

This study focuses on the optical and mechanical properties of copper surfaces darkened by femtosecond laser irradiation, with regard to their applicability as laser beam blockers. Copper surfaces were processed with a Ti-Sapphire laser (1 kHz repetition rate, ∼100–600 mJ/cm2 fluence), while scanning parameters were optimized for low reflectivity and processing speed. A total reflectivity below 6 % was obtained for the 250–2500 nm wavelength range. SEM imaging and ion beam etching showed submicron period structures covered with high density nanoparticles, with a total depth of ∼3–4 μm. Numerical reflectivity simulations suggest that besides the structures, the formation of oxides also contributes to the observed darkening. Thermal and mechanical stability, chemical resistance and laser damage threshold were studied. Temperatures above 200 °C caused surface degradation. Scratch tests showed that the nanostructured layers could withstand ∼60 MPa contact pressure. The surfaces could resist 48 h treatment with various acids at 10−3 M concentration. The laser damage threshold for the applied femtosecond laser showed that the surface could withstand the unfocused beam of most high-frequency femtosecond lasers. The results indicate that laser darkened copper can be a promising candidate for use in laser beam blockers.

Examining cognition and brain networks using magnetoencephalography in paediatric autoimmune encephalitis and acute disseminated encephalomyelitis: a preliminary study.

Paediatric autoimmune encephalitis, including acute disseminated encephalomyelitis, are inflammatory brain diseases presenting with cognitive deficits, psychiatric symptoms, seizures, MRI and EEG abnormalities. Despite improvements in disease recognition and early immunotherapy, long-term outcomes in paediatric autoimmune encephalitis remain poor. Our aim was to understand functional connectivity changes that could be associated with negative developmental outcomes across different types of paediatric autoimmune encephalitis using magnetoencephalography. Participants were children diagnosed with paediatric autoimmune encephalitis at least 18 months before testing and typically developing children. All completed magnetoencephalography recording at rest, T1 MRI scans and neuropsychology testing. Brain connectivity (specifically in delta and theta) was estimated with amplitude envelope correlation, and network efficiency was measured using graph measures (global efficiency, local efficiency and modularity). Twelve children with paediatric autoimmune encephalitis (11.2 ± 3.5 years, interquartile range 9 years; 5M:7F) and 12 typically developing controls (10.6 ± 3.2 years, interquartile range 7 years; 8M:4F) participated. Children with paediatric autoimmune encephalitis did not differ from controls in working memory (t(21) = 1.449; P = 0.162; d = 0.605) but had significantly lower processing speed (t(21) = 2.463; P = 0.023; Cohen's d = 1.028). Groups did not differ in theta network topology measures. The paediatric autoimmune encephalitis group had a significantly lower delta local efficiency across all thresholds tested (d = -1.60 at network threshold 14%). Theta modularity was associated with lower working memory (β = -0.781; t(8) = -2.588, P = 0.032); this effect did not survive correction for multiple comparisons (P(corr) = 0.224). Magnetoencephalography was able to capture specific network alterations in paediatric autoimmune encephalitis patients. This preliminary study demonstrates that magnetoencephalography is an appropriate tool for assessing children with paediatric autoimmune encephalitis and could be associated with cognitive outcomes.

Combining a passive spatial photonic reservoir computer with a semiconductor laser increases its nonlinear computational capacity.

Photonic reservoir computing has been used to efficiently solve difficult and time-consuming problems. The physical implementations of such reservoirs offer low power consumption and fast processing speed due to their photonic nature. In this paper, we investigate the computational capacity of a passive spatially distributed reservoir computing system. It consists of a network of waveguides connected via optical splitters and combiners. A limitation of its reservoir is that it is fully linear and that the nonlinearity - which is often required for solving computing tasks - is only introduced in the output layer. To address this issue, we investigate the incorporation of an additional active nonlinear component into the system. Our approach involves the integration of a single semiconductor laser in an external optical delay line within the architecture. Based on numerical simulations, we show that the architecture with this semiconductor laser has a nonlinear computational capacity that is significantly increased as compared to the original passive architecture, which can be beneficial to solving difficult computational tasks.

Deep SBP+ 2.0: a physics-driven generation capability enhanced framework to reconstruct a space-bandwidth product expanded image from two image shots

The space-bandwidth product (SBP) limitation makes it difficult to obtain an image with both a high spatial resolution and a large field of view (FoV) through commonly used optical imaging systems. Although FoV and spectrum stitch provide solutions for SBP expansion, they rely on spatial and spectral scanning, which lead to massive image captures and a low processing speed. To solve the problem, we previously reported a physics-driven deep SBP-expanded framework (Deep SBP+) [J. Opt. Soc. Am. A 40, 833 (2023)JOAOD60740-323210.1364/JOSAA.480920]. Deep SBP+ can reconstruct an image with both high spatial resolution and a large FoV from a low-spatial-resolution image in a large FoV and several high-spatial-resolution images in sub-FoVs. In physics, Deep SBP+ reconstructs the convolution kernel between the low- and high-spatial-resolution images and improves the spatial resolution through deconvolution. But Deep SBP+ needs multiple high-spatial-resolution images in different sub-FoVs, inevitably complicating the operations. To further reduce the image captures, we report an updated version of Deep SBP+ 2.0, which can reconstruct an SBP expanded image from a low-spatial-resolution image in a large FoV and another high-spatial-resolution image in a sub-FoV. Different from Deep SBP+, the assumption that the convolution kernel is a Gaussian distribution is added to Deep SBP+ 2.0 to make the kernel calculation simple and in line with physics. Moreover, improved deep neural networks have been developed to enhance the generation capability. Proven by simulations and experiments, the receptive field is analyzed to prove that a high-spatial-resolution image in the sub-FoV can also guide the generation of the entire FoV. Furthermore, we also discuss the requirement of the sub-FoV image to obtain an SBP-expanded image of high quality. Considering its SBP expansion capability and convenient operation, the updated Deep SBP+ 2.0 can be a useful tool to pursue images with both high spatial resolution and a large FoV.

Study of Chaotic-based Audio Encryption Algorithms: A Review

Nowadays, multimedia communication has become very widespread and this requires it to be protected from attackers and transmitted securely for reliability. Encryption and decryption techniques are useful in providing effective security for speech signals to ensure that these signals are transmitted with secure data and prevent third parties or the public from reading private messages. Due to the rapid improvement in digital communications over the recent period up to the present, the security of voice data transmitted over various networks has been classified as a favored field of study in earlier years. The contributions to audio encryption are discussed in this review. This Comprehensive review mainly focuses on presenting several kinds of methods for audio encryption and decryption the analysis of these methods with their advantages and disadvantages have been investigated thoroughly. It will be classified into encryption based on traditional methods and encryption based on advanced chaotic systems. They are divided into two types, continuous-time system, and discrete-time system, and also classified based on the synchronization method and the implementation method. In the fields of information and communications security, system designers face many challenges in both cost, performance, and architecture design, Field Programmable gate arrays (FPGAs) provide an excellent balance between computational power and processing flexibility. In addition, encryption methods will be classified based on Chaos-based Pseudo Random Bit Generator, Fractional-order systems, and hybrid chaotic generator systems, which is an advantageous point for this review compared with previous ones. Audio algorithms are presented, discussed, and compared, highlighting important advantages and disadvantages. Audio signals have a large volume and a strong correlation between data samples. Therefore, if traditional cryptography systems are used to encrypt such huge data, they gain significant overhead. Standard symmetric encryption systems also have a small key-space, which makes them vulnerable to attacks. On the other hand, encryption by asymmetric algorithms is not ideal due to low processing speed and complexity. Therefore, great importance has been given to using chaotic theory to encode audio files. Therefore, when proposing an appropriate encryption method to ensure a high degree of security, the key space, which is the critical part of every encryption system, and the key sensitivity must be taken into account. The key sensitivity is related to the initial values and control variables of the chaotic system chosen as the audio encryption algorithm. In addition, the proposed algorithm should eliminate the problems of periodic windows, such as limited chaotic range and non-uniform distribution, and the quality of the recovered audio signal remains good, which confirms the convenience, reliability, and high security.

Risk factors for domain-specific neurocognitive outcome in pediatric survivors of a brain tumor in the posterior fossa-Results of the HIT 2000 trial.

Neurocognition can be severely affected in pediatric brain tumor survivors. We analyzed the association of cognitive functioning with radiotherapy dose, postoperative cerebellar mutism syndrome (pCMS), hydrocephalus, intraventricular methotrexate (MTX) application, tumor localization, and biology in pediatric survivors of a posterior fossa tumor. Subdomain-specific neurocognitive outcome data from 279 relapse-free survivors of the HIT-2000 trial (241 medulloblastoma and 38 infratentorial ependymoma) using the Neuropsychological Basic Diagnostic tool based on Cattell-Horn-Carroll's model for intelligence were analyzed. Cognitive performance 5.14 years (mean; range = 1.52-13.02) after diagnosis was significantly below normal for all subtests. Processing speed and psychomotor abilities were most affected. Influencing factors were domain-specific: CSI-dose had a strong impact on most subtests. pCMS was associated with psychomotor abilities (β = -0.25 to -0.16) and processing speed (β = -0.32). Postoperative hydrocephalus correlated with crystallized intelligence (β = -0.20) and short-term memory (β = -0.15), age with crystallized intelligence (β = 0.15) and psychomotor abilities (β = -0.16 and β = -0.17). Scores for fluid intelligence (β = -0.23), short-term memory (β = -0.17) and visual processing (β = -0.25) declined, and scores for selective attention improved (β = 0.29) with time after diagnosis. The dose of CSI was strongly associated with neurocognitive outcomes. Low psychomotor abilities and processing speed both in patients treated with and without CSI suggest a strong contribution of the tumor and its surgery on these functions. Future research therefore should analyze strategies to both reduce CSI dose and toxicity caused by other treatment modalities.

Online identification and feed-forward compensation of nonlinear friction in servo system based on RBF neural network model

In this paper, an online identification and compensation method of nonlinear friction based on radial basis function (RBF) neural network model is proposed for the influence of nonlinear friction on machining accuracy in the low speed process of servo feed system of CNC machine tools. First, a three-layer single-input-output RBF neural network model is established for describing the nonlinear friction of servo feeding system. Second, the neural network online learning algorithm is improved based on adaptive gain, which improves the stability and accuracy of the algorithm. Finally, experiments were carried out on a three-axis milling machine to compensate the friction in the servo feed system in real time based on the online identification results. The results show that the method can effectively improve the online identification accuracy and convergence rate, and effectively improved the low-speed performance of the servo feed system.

Associations between accelerometer-measured circadian rest-activity rhythm, brain structural and genetic mechanisms, and dementia.

Knowledge of how circadian rhythm influences brain health remains limited. We aimed to investigate the associations of accelerometer-measured circadian rest-activity rhythm (CRAR) with incident dementia, cognitive dysfunction, and structural brain abnormalities in the general population and underlying biological mechanisms. Fifty-seven thousand five hundred and two participants aged over 60 years with accelerometer data were included to investigate the association of CRAR with incidental dementia. Non-parametric CRAR parameters were utilized, including activity level during active periods of the day (M10), activity level during rest periods of the day (L5), and the relative difference between the M10 and L5 (relative amplitude, RA). Associations of CRAR with cognitive dysfunction and brain structure were studied in a subset of participants. Neuroimaging-transcriptomics analysis was utilized to identify the underlying molecular mechanisms. Over 6.86 (4.94-8.78) years of follow-up, 494 participants developed dementia. The risk of incident dementia was associated with decreasing M10 (hazard ratio [HR] 1.45; 95% conference interval [CI], 1.28-1.64) and RA (HR 1.37; 95% CI, 1.28-1.64), increasing L5 (HR 1.14, 95% CI 1.07-1.21) and advanced L5 onset time (HR 1.12; 95% CI, 1.02-1.23). The detrimental associations were exacerbated by APOE ε4 status and age (>65 years). Decreased RA was associated with lower processing speed (Beta -0.04; SE 0.011), predominantly mediated by abnormalities in subcortical regions and white matter microstructure. The genes underlying CRAR-related brain regional structure variation were enriched for synaptic function. Our study underscores the potential of intervention targeting at maintaining a healthy CRAR pattern to prevent dementia risk.

Neurocognition and brain functional connectivity in a non-clinical population-based sample with psychotic experiences

We characterized the neurocognitive profile of communed-based individuals and unaffected siblings of patients with psychosis from Brazil reporting psychotic experiences (PEs). We also analyzed associations between PEs and the intra and inter-functional connectivity (FC) in the Default Mode Network (DMN), the Fronto-Parietal Network (FPN) and the Salience Network (SN) measured by functional magnetic resonance imaging. The combined sample of communed-based individuals and unaffected siblings of patients with psychosis comprised 417 (neurocognition) and 85 (FC) volunteers who were divided as having low (<75th percentile) and high (≥75th percentile) PEs (positive, negative, and depressive dimensions) assessed by the Community Assessment of Psychic Experiences. The neurocognitive profile and the estimated current brief intellectual quotient (IQ) were assessed using the digit symbol (processing speed), arithmetic (working memory), block design (visual learning) and information (verbal learning) subtests of Wechsler Adult Intelligence Scale-third edition. Logistic regression models were performed for neurocognitive analysis. For neuroimaging, we used the CONN toolbox to assess FC between the specified regions, and ROI-to-ROI analysis. In the combined sample, high PEs (all dimensions) were related to lower processing speed performance. High negative PEs were related to poor visual learning performance and lower IQ, while high depressive PEs were associated with poor working memory performance. Those with high negative PEs presented FPN hypoconnectivity between the right and left lateral prefrontal cortex. There were no associations between PEs and the DMN and SN FC. Brazilian individuals with high PEs showed neurocognitive impairments like those living in wealthier countries. Hypoconnectivity in the FPN in a community sample with high PEs is coherent with the hypothesis of functional dysconnectivity in schizophrenia.

A deep neural network approach with attention mechanism to improve the quality of target observation for UAVs

Owing to the limitations of onboard equipment, the direct acquisition of high-resolution images in unmanned aerial vehicle (UAV) target observation tasks is a challenge. However, with the development of deep learning, super-resolution reconstruction has become a popular technique for enhancing image resolution. Though, current super-resolution reconstruction methods can enhance UAV target observation images to some extent, most of them are unable to strike a balance between the number of parameters, computational cost, and reconstruction effect. Furthermore, limited research has been conducted for overcoming the cascade coupling problem in UAV target observation images. To solve these problems and provide a lightweight, fast, and high-quality super-resolution method for UAV target observation images, an enhanced attention dense cross network (EADCN) is proposed. This method includes excellent attention mechanisms, such as the double large kernel attention module (D-LKA) and multi-aware channel attention distillation block (MAADB). In addition to benchmark datasets, a UAV target observation dataset (UAV-TOD) is created to evaluate the performance of EADCN. The experimental results demonstrate that the EADCN has fewer parameters, low computational complexity, high accuracy, and fast processing speed, making it suitable for deployment in UAVs.

ЕФЕКТИВНІСТЬ КРИПТОВАЛЮТ ЯК ЗАСОБУ МІЖНАРОДНИХ ПЛАТЕЖІВ: АНАЛІЗ ВАРТОСТІ, ШВИДКОСТІ ТА БЕЗПЕКИ ТРАНЗАКЦІЙ

This article focuses on the effectiveness of using cryptocurrencies as a means for international payments, with an emphasis on the analysis of cost, speed, and security of transactions. The aim of the study is to explore the potential of blockchain technologies to optimize international financial operations. This research considers the performance of cryptocurrencies in international payments, focusing on an analysis of cost, speed, and security of transactions, utilizing data from various sources and case studies. Blockchain technologiessuch as Ripple and Solana demonstrate high throughput, capable of processing up to 65,000 transactions per second (TPS).In comparison, traditional banking systems offer significantly lower processing speeds, withBitcoin handling around 7 TPS and Ethereum up to 15 TPS. The anticipated Ethereum 2.0 network upgrade projects an increase to 100,000 TPS, significantly enhancing their potential in international payments. Traditional banking transactions can incur fees of up to $50 USD, whereas blockchain networks can reduce costs to less than $0.01 per transaction. The implementation of distributed ledger technology could save up to $27 billion by 2030 on international transactions. According to research, the average cost of an international remittance is 6.4% of a $200 transaction, indicating the potential for significantsavingsthrough the adoption of cryptocurrencies. Cryptocurrency payments are protected using cryptographic methods and decentralization, reducing the risks of fraud and attacks. By adhering to "privacy by default" principles, transaction confidentiality can be further enhanced, including generating new addresses for each transaction. Regular security audits and multi-factor authentication provide an additional layer of protection. It can be asserted that blockchain technology offers significant advantages in speed, cost, and security of transactions, which could radically transform the landscape of international trade and financial transfers. The practical significance of the research lies in identifying the potential of cryptocurrencies as a reliable, cost-effective, and secure tool for international payments, which may alter the global money transfer system.

The Influence of Prior Head Injury on Late‐Life Cognition and White Matter Microstructure in Older Adults At‐Risk of Alzheimer’s disease

AbstractBackgroundThe long‐term combined effects of mild traumatic brain injury (mTBI) and APOE ε4‐allele carrier status (APOE+/‐), a genetic risk factor of Alzheimer’s disease (AD), on cognition and white matter structural integrity in older adults at‐risk of AD is not fully understood.MethodTo investigate mTBI effects on late‐life neurobehavioural outcomes, a subsample of the PREVENT‐AD cohort was analyzed. Participants were cognitively normal adults over age 55 with a parental or multiple‐sibling history of AD. The mTBI group included individuals who self‐reported at least one prior mTBI with loss of consciousness and/or memory gap (n = 48, 33.3% APOE4+). Rey Auditory Verbal Learning Test measured immediate and delayed memory, Color‐Word Interference Test measured cognitive flexibility and inhibitory control, and Trail Making Test measured processing speed and attention.The control group included demographically‐matched (age, sex, years of education) participants from the same cohort without self‐reported mTBI (n = 62, 29.0% APOE4+). Data were analyzed using 2‐way ANOVAs with weighted means on years of education, corrected for multiple comparisons. To evaluate underlying structural differences in white matter tracts between mTBI and control groups, diffusion tensor imaging (DTI) metrics, including fractional anisotropy, axial and radial diffusivity, using the JHU‐atlas parcellation.ResultsmTBI/APOE4+ individuals performed significantly worse on cognitive outcomes of delayed recall, cognitive flexibility, and inhibitory control compared to controls; however, they performed significantly better on tests of processing speed and visual attention compared to mTBI/APOE4‐ individuals. DTI results showed that the mTBI group, compared to the control group, had significantly decreased axial diffusivity in the left internal capsule, right superior longitudinal fasciculus, and superior frontooccipital fasciculus (SFOF) (p&lt;0.05 uncorrected). Within the mTBI group, significant positive correlation between lower processing speed and decreased axial diffusivity of SFOF, accounting for age and sex.ConclusionOur findings suggest that prior mTBI impacts cognitive aging in older adults at‐risk of AD differentially depend on APOE4 status, where APOE4+ individuals with prior mTBI had worse working memory, cognitive flexibility, and inhibitory control but better processing speed outcomes. Lower cognitive outcomes in mTBI individuals may be supported by decreased diffusivity in specific white matter tracts. These results can inform dementia prevention in these individuals.

Persistent financial adversity across adulthood and cognitive ageing: A lifecourse investigation.

AbstractBackgroundCognitive decline in older age has significant health, economic and social consequences and there is evidence that financial adversity might play a role. Financial adversity, for many, is not an acute but a chronic stressor. However, little is known about the effect of persistence exposure to financial adversity across the lifecourse on cognitive ageing.MethodUsing data from the 1946 British birth cohort (N = 2,759) with an embedded neuroimaging study (Insight46; N = 356‐468), we examined the prospective association between financial adversity (low household income, financial hardships; 26‐53 years) and cognitive performance (69 years) and decline (53‐69 years), as well as volumetric measures of brain health (69‐71 years) in older adulthood. Cognitive decline was modelled using latent growth curves, with derived intercepts and slopes used in subsequent regression analyses, adjusted for sex, childhood socioeconomic circumstances (SEC), baseline cognition and internalising symptoms, and educational attainment. Sex, childhood SEC and APOE‐ɛ4 genetic risk were further tested as moderators in regression models using interaction terms. This study has been pre‐registered (https://doi.org/10.17605/OSF.IO/6Y9NV).ResultIncreased exposure to financial adversity showed dose‐response associations with slower verbal memory decline and lower processing speed and verbal memory at 69 years (Table 1). Each increased period of exposure to low household income or financial hardships was associated with a 0.11 and 0.08 standard deviation (SD) decrease in processing speed, and a 0.32 and 0.22 SD decrease in verbal memory at 69 years, respectively. Males persistently exposed to financial hardships showed slower processing speed decline but increased brain atrophy compared to females. Those whose fathers were in unskilled profession or unemployed in childhood, and who also persistently experienced low income themselves showed greater brain atrophy (Fig. 1). APOE‐ɛ4 carriers who persistently experienced financial adversity were also at greater risk for brain atrophy (Fig. 2).ConclusionPersistent financial adversity is directly associated with cognitive performance and may be differentially associated with brain atrophy depending on demographic and genetic factors. Reducing the frequency of financial adversity over the lifecourse may improve cognitive performance and protect against genetic susceptibility to brain atrophy and subsequent dementia risk in the ageing population.

The Influence of Prior Head Injury on Late‐Life Cognition and White Matter Microstructure in Older Adults At‐Risk of Alzheimer’s disease

AbstractBackgroundThe long‐term combined effects of mild traumatic brain injury (mTBI) and APOE e4‐allele carrier status (APOE+/‐), a genetic risk factor of Alzheimer’s disease (AD), on cognition and white matter structural integrity in older adults at‐risk of AD is not fully understood.MethodTo investigate mTBI effects on late‐life neurobehavioural outcomes, a subsample of the PREVENT‐AD cohort was analyzed. Participants were cognitively normal adults over age 55 with a parental or multiple‐sibling history of AD. The mTBI group included individuals who self‐reported at least one prior mTBI with loss of consciousness and/or memory gap (n = 48, 33.3% APOE4+). Rey Auditory Verbal Learning Test measured immediate and delayed memory, Color‐Word Interference Test measured cognitive flexibility and inhibitory control, and Trail Making Test measured processing speed and attention. The control group included demographically‐matched (age, sex, years of education) participants from the same cohort without self‐reported mTBI (n = 62, 29.0% APOE4+). Data were analyzed using 2‐way ANOVAs with weighted means on years of education, corrected for multiple comparisons. To evaluate underlying structural differences in white matter tracts between mTBI and control groups, diffusion tensor imaging (DTI) metrics, including fractional anisotropy, axial and radial diffusivity, using the JHU‐atlas parcellation.ResultmTBI/APOE4+ individuals performed significantly worse on cognitive outcomes of delayed recall, cognitive flexibility, and inhibitory control compared to controls; however, they performed significantly better on tests of processing speed and visual attention compared to mTBI/APOE4‐ individuals. DTI results showed that the mTBI group, compared to the control group, had significantly decreased axial diffusivity in the left internal capsule, right superior longitudinal fasciculus, and superior frontooccipital fasciculus (SFOF) (p&lt;0.05 uncorrected). Within the mTBI group, significant positive correlation between lower processing speed and decreased axial diffusivity of SFOF, accounting for age and sex.ConclusionOur findings suggest that prior mTBI impacts cognitive aging in older adults at‐risk of AD differentially depend on APOE4 status, where APOE4+ individuals with prior mTBI had worse working memory, cognitive flexibility, and inhibitory control but better processing speed outcomes. Lower cognitive outcomes in mTBI individuals may be supported by decreased diffusivity in specific white matter tracts. These results can inform dementia prevention in these individuals.

Plasma Biomarkers of Alzheimer Disease in Women With and Without HIV

Blood-based biomarkers associated with increased risk of Alzheimer disease (AD) are understudied in people living with and without HIV, particularly women. To determine whether baseline or 1-year changes in plasma amyloid-β40 (Aβ40), Aβ42, ratio of Aβ42 to Aβ40, total tau (t-tau), phosphorylated tau 231 (p-tau231), glial fibrillary acidic protein (GFAP), and/or neurofilament light chain (NFL) are associated with neuropsychological performance (NP) among women living with HIV (WLWH) and women living without HIV (WLWOH). This longitudinal, prospective, cohort study with 1-year repeated clinical measures (NP only measured once) and biospecimen collection occurred between 2017 and 2019. Participants were women aged 40 years or older from 10 clinical research sites in cities across the US that were part of the Women's Interagency HIV Study. Data analysis was conducted from April to December 2022. Laboratory-confirmed HIV status and AD biomarkers. Sociodemographically adjusted NP T-scores (attention and working memory, executive function, processing speed, memory, learning, verbal fluency, motor function, and global performance) were the primary outcomes. Baseline and 1-year fasting plasma Aβ40, Aβ42, t-tau, p-tau231, GFAP, and NFL levels were measured and analyzed using multivariable linear regression. The study consisted of 307 participants (294 aged ≥50 years [96%]; 164 African American or Black women [53%]; 214 women with a high school education or higher [70%]; 238 women who were current or former smokers [78%]; and 236 women [77%] who were overweight or obese [body mass index >25]) including 209 WLWH and 98 WLWOH. Compared with WLWOH at baseline, WLWH performed worse on learning (mean [SD] T-score 47.8 [11.3] vs 51.4 [10.5]), memory (mean [SD] T-score 48.3 [11.6] vs 52.4 [10.2]), verbal fluency (mean [SD] T-score 48.3 [9.8] vs 50.7 [8.5]), and global (mean [SD] T-score 49.2 [6.8] vs 51.1 [5.9]) NP assessments. Baseline median Aβ40, GFAP, and NFL levels were higher among WLWH vs WLWOH. There were no differences in 1-year biomarker change by HIV serostatus. Lower learning, memory, and motor NP were associated with 1-year Aβ40 increase; lower learning and motor with Aβ42 increase; lower motor with p-tau231 increase; and lower processing speed, verbal fluency and motor with NFL increase in the entire sample. Among WLWH, a 1-year increase in Aβ40 from baseline to follow-up was associated with worse learning, memory, and global NP; a 1-year increase in t-tau with worse executive function; and a 1-year increase in NFL with worse processing speed. Among WLWOH, a 1-year increase in Aβ40 and Aβ42 were associated with poorer memory performance and NFL was associated with poorer motor performance. These findings suggest that increases in certain plasma AD biomarkers are associated with NP in WLWH and WLWOH and may be associated with later onset of AD, and measuring these biomarkers could be a pivotal advancement in monitoring aging brain health and development of AD among women with and without HIV.

A Pipeline Defect Instance Segmentation System Based on SparseInst.

Deep learning algorithms have achieved encouraging results for pipeline defect segmentation. However, existing defect segmentation methods may encounter challenges in accurately segmenting the complex features of pipeline defects and suffer from low processing speeds. Therefore, in this study, we propose Pipe-Sparse-Net, a pipeline defect segmentation system that combines StyleGAN3 to segment the complex forms of underground drainage pipe defects. First, we introduce a data augmentation algorithm based on StyleGAN3 to enlarge the dataset. Next, we propose Pipe-Sparse-Net, a pipeline segmentation model based on SparseInst, to accurately predict the defect regions in drainage pipes. Experimental results demonstrate that the segmentation accuracy of this model can reach 91.4% with a processing speed of 56.7 frames per second (FPS). To validate the superiority of this method, comparative experiments were conducted against Yolact, Condinst, and Mask R-CNN, and the model achieved a speed improvement of 45% while increasing the accuracy by more than 4%.