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The aftereffects of completed prospective memory (PM) refer to the phenomenon that, after PM task completion, it interferes with the subsequent task or results in the repetition of the previous intended behavior. Inhibition processing and monitoring processing are two important theoretical perspectives to explain the emergence of the aftereffects of PM. The present study aimed at exploring the processing mechanisms of PM aftereffects. In experiment 1, the response delay time was manipulated during the intention response to assess the role of response inhibition in the aftereffects of PM. In experiment 2, the convenience of response was manipulated by changing different response keys during task response phase, to further examine the effect of response inhibition. The results of Experiment 1 showed that the response speed of the ongoing tasks in the experimental group was slower than that in the control group under the non-delay condition. The results of Experiment 2 also showed that both convenient response group and inconvenient response group had slower response speed than control group. The results of Experiment 1 showed that more commission errors were generated under the delay condition. The results of the ongoing tasks indicate that PM aftereffects involve a controlled processing in both experiments. The results of commission errors in Experiment 1 indicate that the controlled processing involved in PM aftereffects is inhibition rather than monitoring, which supports the inhibition view.

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T‐MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS. We developed an advanced machine learning pipeline to identify brain structural magnetic resonance imaging (MRI) volumes that, along with demographic and clinical data, predict information processing speed (IPS) performance, assessed with the Symbol Digit Modalities Test (SDMT), of patients with multiple sclerosis (MS). In this study, we used a multicenter 3T‐MRI data set of 540 MS patients. We confirmed that damage of cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS.

Multiple object tracking (MOT) is a critical and active research topic in computer vision, serving as a fundamental technique across various application domains such as human–robot interaction, autonomous driving, and surveillance. MOT typically consists of two key components: detection, which produces bounding boxes around objects, and association, which links current detections to existing tracks. Two main approaches have been proposed: one-shot and two-shot methods. While previous works have improved MOT systems in terms of both speed and accuracy, most works have focused primarily on enhancing association performance, often overlooking the impact of accelerating detection. Thus, we propose a high-speed MOT system that balances real-time performance, tracking accuracy, and robustness across diverse environments. Our system comprises two main components: (1) a hybrid tracking framework that integrates low-frequency deep learning-based detection with classical high-speed tracking, and (2) a detection label-based tracker management strategy. We evaluated our system in six scenarios using a high-speed camera and compared its performance against seven state-of-the-art (SOTA) two-shot MOT methods. Our system achieved up to 470 fps when tracking two objects, 243 fps with three objects, and 178 fps with four objects. In terms of tracking accuracy, our system achieved the highest MOTA, IDF1, and HOTA scores with high-accuracy detection. Even with low detection accuracy, it demonstrated the potential of long-term association for high-speed tracking, achieving comparable or better IDF1 scores. We hope that our multi-processing architecture contributes to the advancement of MOT research and serves as a practical and efficient baseline for systems involving multiple asynchronous modules.

Applications of the diffusion decision model (DDM) to the study of cognitive individual differences consistently find that the model’s drift rate ( v ) parameter forms a cohesive factor across many tasks and relates to measures of higher-order cognitive functioning, including general cognitive ability and working memory. This parameter is often interpreted as a measure of “processing speed,” a traditional psychometric construct thought to reflect an individual’s basic speed of information processing across tasks. However, conceptual differences between v and traditional notions of processing speed make this mapping far from straightforward. Racing accumulator models, which provide a more flexible and comprehensive account of behavioral data than the DDM, allow for the speed with which individuals accumulate evidence to be dissociated from the efficiency with which they accumulate task-relevant evidence (versus task-irrelevant evidence). We applied the DDM and a racing accumulator model to three tasks across three independent datasets to gauge the extent to which v parameter findings from the cognitive individual differences literature reflect speed of evidence accumulation (SEA) versus efficiency of evidence accumulation (EEA). Across all tasks, v was more strongly related to EEA than SEA. EEA was consistently related to measures of general cognitive ability, working memory, and executive function whereas SEA explained <1% of the variance in each. These findings suggest individual differences in the DDM’s v parameter, and its relations with higher-order cognitive abilities, primarily reflect EEA rather than SEA and challenge the widespread practice of equating v with the traditional “processing speed” construct.

General intelligence is a robust predictor of important life outcomes, including educational and occupational attainment, successfully managing everyday life situations, good health and longevity. Some neuronal correlates of intelligence have been discovered, mainly indicating that larger cortices in widespread parieto-frontal brain networks and efficient neuronal information processing support higher intelligence. However, there is a lack of established associations between general intelligence and any basic structural brain parameters that have a clear functional meaning. Here, we provide evidence that lower brain-wide white matter tract integrity exerts a substantial negative effect on general intelligence through reduced information-processing speed. Structural brain magnetic resonance imaging scans were acquired from 420 older adults in their early 70s. Using quantitative tractography, we measured fractional anisotropy and two white matter integrity biomarkers that are novel to the study of intelligence: longitudinal relaxation time (T1) and magnetisation transfer ratio. Substantial correlations among 12 major white matter tracts studied allowed the extraction of three general factors of biomarker-specific brain-wide white matter tract integrity. Each was independently associated with general intelligence, together explaining 10% of the variance, and their effect was completely mediated by information-processing speed. Unlike most previously established neurostructural correlates of intelligence, these findings suggest a functionally plausible model of intelligence, where structurally intact axonal fibres across the brain provide the neuroanatomical infrastructure for fast information processing within widespread brain networks, supporting general intelligence.

Change blindness involves a failure to detect changes in a visual scene, despite the differences being available to visual perception. Although change detection is crucial for many every day or professional behaviours (e.g., driving, occupations such as radiology), little is known about the individual differences that contribute to change detection ability or the cognitive domains involved. Previous research has implicated attentional and visual memory processes in successful change detection. The present study used a naturalistic change detection task, the Alternate Forms Flicker Task (AFFT), paired with a battery of nine cognitive tests, to ascertain which cognitive domains are most strongly associated with change detection accuracy in a sample of 260 participants. Strongest correlates with AFFT accuracy were tasks assessing top-down attentional search (Visual Attentional Capture and Control Task), visuospatial ability and memory (Austin Maze and Object 2back Task), and visual inspection time (Subtle Cognitive Impairment Test). Principal Axis Factoring extraction with Promax rotation showed that cognitive flexibility, visuospatial working memory, and attention and processing speed collectively accounted for 41% of the variability in test battery scores. Using these three factors, a multiple linear regression model significantly accounted for 10.0% of the variability in AFFT scores. Visuospatial working memory was the principal factor, indicating that individual differences in visuospatial working memory likely contribute to differences in change detection accuracy.

BackgroundCognitive impairment (CI) is a disabling symptom of multiple sclerosis (MS). Axonal damage disrupts neural circuits and may play a role in determining CI, but its detection and monitoring are not routinely performed. Cerebrospinal fluid (CSF) neurofilament light chain (NfL) is a promising marker of axonal damage in MS.ObjectiveTo retrospectively examine the relationship between CSF NfL and CI in MS patients.MethodsCSF NfL concentration was measured in 28 consecutive newly diagnosed MS patients who underwent a neuropsychological evaluation with the Brief Repeatable Battery of Neuropsychological tests (BRBN).ResultsCSF NfL was higher in patients with overall CI (947.8 ± 400.7 vs 518.4 ± 424.7 pg/mL, p < 0.01), and with impairment in information processing speed (IPS) (820.8 ± 413.6 vs 513.6 ± 461.4 pg/mL, p < 0.05) and verbal fluency (1292 ± 511 vs 582.8 ± 395.4 pg/mL, p < 0.05), and it positively correlated with the number of impaired BRBN tests (r = 0.48, p = 0.01) and cognitive domains (r = 0.47, p = 0.01). Multivariate analyses taking into account potential confounders confirmed these findings.ConclusionCSF NfL is higher in MS patients with CI and impaired IPS and verbal fluency. Large myelinated axons injury, causing neural disconnection, may be an important determinant of CI in MS and can be reliably measured through CSF NfL.

Information processing speed (IPS) is a core cognitive deficit in people with multiple sclerosis (PwMS). Previous efforts have associated IPS performance to frontal regions, but were constrained by limited temporal resolution. In this work, we employed a data‐driven method, the time delay embedded‐hidden Markov model (TDE‐HMM), to identify task‐specific states that are spectrally defined with distinct temporal and spatial profiles. We used magnetoencephalographic (MEG) data recorded while healthy controls and PwMS performed a cognitive task designed to capture IPS, the Symbol Digit Modalities Test (SDMT). The TDE‐HMM identified five task‐relevant states, supporting a tri‐factor contribution to IPS: sensory speed (occipital visual detection and processing), cognitive speed (prefrontal executive and frontoparietal attention shift), and motor speed (sensorimotor). We observed reduced prefrontal activation in PwMS, while peak features across prefrontal, frontoparietal, and occipital networks were associated with task reaction time and clinical SDMT performance. This work can drive future research for MS treatments targeting IPS improvements.

Slowed information processing speed (IPS) is the core contributor to cognitive impairment in patients with late-life depression (LLD). The hippocampus is an important link between depression and dementia, and it may be involved in IPS slowing in LLD. However, the relationship between a slowed IPS and the dynamic activity and connectivity of hippocampal subregions in patients with LLD remains unclear. One hundred thirty-four patients with LLD and 89 healthy controls were recruited. Sliding-window analysis was used to assess whole-brain dynamic functional connectivity (dFC), dynamic fractional amplitude of low-frequency fluctuations (dfALFF) and dynamic regional homogeneity (dReHo) for each hippocampal subregion seed. Cognitive impairment (global cognition, verbal memory, language, visual-spatial skill, executive function and working memory) in patients with LLD was mediated by their slowed IPS. Compared with the controls, patients with LLD exhibited decreased dFC between various hippocampal subregions and the frontal cortex and decreased dReho in the left rostral hippocampus. Additionally, most of the dFCs were negatively associated with the severity of depressive symptoms and were positively associated with various domains of cognitive function. Moreover, the dFC between the left rostral hippocampus and middle frontal gyrus exhibited a partial mediation effect on the relationships between the scores of depressive symptoms and IPS. Patients with LLD exhibited decreased dFC between the hippocampus and frontal cortex, and the decreased dFC between the left rostral hippocampus and right middle frontal gyrus was involved in the underlying neural substrate of the slowed IPS.

The purpose of this study is to compare online neuropsychological test performance of older adults across self-reported diagnoses of being cognitively normal, mild cognitive impairment, and dementia due to Alzheimer's disease and to determine the association of memory concerns and family history of dementia on cognitive performance. Participants completed the Cogstate Brief Battery unsupervised at home. Data from 6463 participants over the age of 55 years were analyzed. Adults with the diagnosis of mild cognitive impairment and Alzheimer's disease were associated with poorer performance on all cognitive tests than cognitively normal adults (P < .05 for all), and online cognitive test performance significantly improved diagnostic classification (P < .001). Poorer performance on all cognitive measures was associated with memory concern (P < .001 for all) but not family history of dementia. Our results provide preliminary support for the use of cognitive tests taken online without supervision as a means to improve the efficiency of participant screening and recruitment for clinical trials.