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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.

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.

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.

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.

Background and purpose Cognitive impairment (CI) in multiple sclerosis (MS) is associated with bidirectional changes in resting‐state centrality measures. However, practicable functional magnetic resonance imaging (fMRI) biomarkers of CI are still lacking. The aim of this study was to assess the graph‐theory‐based degree rank order disruption index (kD) and its association with cognitive processing speed as a marker of CI in patients with MS (PwMS) in a secondary cross‐sectional fMRI analysis. Methods Differentiation between PwMS and healthy controls (HCs) using kD and its correlation with CI (Symbol Digit Modalities Test) was compared to established imaging biomarkers (regional degree, volumetry, diffusion‐weighted imaging, lesion mapping). Additional associations were assessed for fatigue (Fatigue Scale for Motor and Cognitive Functions), gait and global disability. Results Analysis in 56 PwMS and 58 HCs (35/27 women, median age 45.1/40.5 years) showed lower kD in PwMS than in HCs (median −0.30/−0.06, interquartile range 0.55/0.54; p = 0.009, Mann–Whitney U test), yielding acceptable yet non‐superior differentiation (area under curve 0.64). kD and degree in medial prefrontal cortex (MPFC) correlated with CI (kD/MPFC Spearman's ρ = 0.32/−0.45, p = 0.019/0.001, n = 55). kD also explained fatigue (ρ = −0.34, p = 0.010, n = 56) but neither gait nor disability. Conclusions kD is a potential biomarker of CI and fatigue warranting further validation. Multiple sclerosis was associated with global disruption of degree rank ordering (kD), a graph‐theoretical measure of resting‐state functional connectivity. Degree rank order disruption explained deficits in cognitive processing speed and fatigue score, but was not correlated with gait or global disability assessments. Whilst slowing of cognitive processing was associated with increased degree centrality in the medial prefrontal cortex, fatigue was accompanied by a higher degree in the cerebellum and caudate nuclei and a lower degree in the left fusiform gyrus.

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.

INTRODUCTION The role of information processing speed (IPS) on relationships between episodic memory (EM) and central remodeling features in amnestic mild cognitive impairment (aMCI) was investigated. METHODS Neuropsychological evaluations and multimodal magnetic resonance imaging were performed on 48 patients diagnosed with aMCI and 50 healthy controls (HC). Moderation models explored the moderating effect of IPS on associations between EM and imaging features at single‐region, connectivity, and network levels. RESULTS IPS significantly enhanced the positive correlations between recall and cortical thickness of left inferior temporal gyrus. IPS also notably amplified negative correlations between recognition and functional connectivity (FC) of left inferior parietal lobe and right occipital, as well as between recall/recognition and nodal clustering coefficient of left anterior cingulate cortex. DISCUSSION IPS functioned as a moderator of associations between recall and neuroimaging metrics at the “single region‐connectivity‐network” level, providing new insights for cognitive rehabilitation in aMCI patients. Highlights aMCI patients exhibited brain functional and structural remodeling alterations. IPS moderated relations between episodic memory and brain remodeling metrics. Therapy targeted at IPS can be considered for improving episodic memory in aMCI.

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.

The underlying mechanisms for neurodegeneration in multiple sclerosis are complex and incompletely understood. Multivariate and multimodal investigations integrating demographic, clinical, multi-omics, and neuroimaging data provide opportunities for nuanced analyses, aimed to define disease progression markers. We used data from a 12-year longitudinal multicenter cohort of 88 people with multiple sclerosis, to test the predictive value of multi-omics, T 1 -weighted MRI (lesion count and volume, lesion-filled brain-predicted age), clinical examinations, self-reports on quality of life, demographics, and general health-related variables for future functional and cognitive disability. Systematic increases in Expanded Disability Status Scale (EDSS) scores were used to stratify a progressive disability group (PDG) from relatively stabile disability. A processing speed decline group (PSDG) was defined by a ≥ 20% decrease of Paced Auditory Serial Addition Test score from previous timepoints. We used a multiverse approach to identify which baseline variables were most predictive for PDG and PSDG memberships, considering multiple analysis paths. Future disability (median area under the curve: mAUC = 0.83 ± 0.04, median Brier score: mBS = 0.16 ± 0.02) and the loss of processing speed (mAUC = 0.89 ± 0.05, mBS = 0.10 ± 0.03) could be successfully classified across models. Varibles significantly (median p-values < 0.05) predicting stable disability included receiving disease modifying treatment at 12-year follow-up (median Odds Ratio: mOR PDG = 7.44 ± 4.07, p median = 0.013, proportion of the OR’s directionality: PORSD = 100%), lower baseline EDSS for each 1-unit (mOR PDG = 0.25 ± 0.11, p median = 0.013, PORSD = 100%), and counter-intuitively every year increase in baseline age (mOR PDG = 1.12 ± 0.04, p median = 0.020, PORSD = 100%), and lower vitamin A per 1 umol/L (mOR PDG = 0.10 ± 0.05, p median = 0.016, PORSD = 99.7%) and D levels per 1 nmol/L (mOR PDG = 0.95 ± 0.02, p median = 0.025, PORSD = 100%). Variables significantly predicting stable processing speed were receiving disease modifying treatment at 12-year follow-up (mOR PSDG = 0.10 ± 0.08, p median = 0.013, PORSD = 100%) and baseline PASAT score (mOR PSDG = 0.86 ± 0.03, p median = 0.005, PORSD = 99.73%). These findings were supported by an additional simulation study. Concordant with the literature, disease modifying treatments influence disability progression, as well as a higher EDSS and PASAT scores at measurement start. Experimental and counterintuitive findings on vitamin A and D levels require further validation. The large variability across models suggests a strong influence of analytic flexibility, such as the selection of covariates.

INTRODUCTION Amnestic mild cognitive impairment (aMCI) exhibits biased functional connectivity (FC) abnormalities impairing neural plasticity modulation. This study aimed to identify unbiased FC deficits using a brain‐wide association study (BWAS) and investigate repetitive transcranial magnetic stimulation (rTMS)‐driven plasticity restoration. METHODS BWAS identified unbiased FC‐altered voxels (robustness‐validated). Region‐of‐interest (ROI)‐wise FC analysis localized disrupted circuits, which were modulated through precuneus‐targeted rTMS. Effective connectivity (EC) tested whether the precuneus exerted a causal influence on these disrupted circuits. Correlation analyses linked FC plasticity to cognitive and clinical outcomes. RESULTS Eleven brain regions with 31 altered unbiased FC circuits were robustly identified, centered on the bilateral anterior cingulate cortex (ACC). rTMS causally restored FC in the right supracallosal ACC and postcentral gyrus, correlating with improved information processing speed (IPS). Remarkably, 80.77% (21/26) of aMCI responded clinically to rTMS. DISCUSSION This study first maps unbiased FC lesions in aMCI, confirming rTMS‐mediated ACC plasticity causally enhances IPS. These findings inform network‐targeted therapies to delay Alzheimer's disease (AD) progression. Highlights This is the first study to robustly map unbiased FC lesions in aMCI patients using a BWAS. rTMS causally restored FC in right supracallosal ACC in aMCI patients. FC and EC recovery demonstrated causal links to improvements in IPS and MoCA scores. Remarkably, 80.77% (21/26) of aMCI patients responded clinically to rTMS modulation.