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The NIH Toolbox (NIHTB) Pattern Comparison Processing Speed Test was developed to assess processing speed within the NIHTB for the Assessment of Neurological Behavior and Function Cognition Battery (NIHTB-CB). This study highlights validation data collected in adults ages 18–85 on this measure and reports descriptive data, test–retest reliability, construct validity, and preliminary work creating a composite index of processing speed. Results indicated good test–retest reliability. There was also evidence for both convergent and discriminant validity; the Pattern Comparison Processing Speed Test demonstrated moderate significant correlations with other processing speed tests (i.e., WAIS-IV Coding, Symbol Search and Processing Speed Index), small significant correlations with measures of working memory (i.e., WAIS-IV Letter-Number Sequencing and PASAT), and non-significant correlations with a test of vocabulary comprehension (i.e., PPVT-IV). Finally, analyses comparing and combining scores on the NIHTB Pattern Comparison Processing Speed Test with other measures of simple reaction time from the NIHTB-CB indicated that a Processing Speed Composite score performed better than any test examined in isolation. The NIHTB Pattern Comparison Processing Speed Test exhibits several strengths: it is appropriate for use across the lifespan (ages, 3–85 years), it is short and easy to administer, and it has high construct validity. (JINS, 2014, 20, 1–12)

microRNAs (miRNAs) are promising biomarkers for many diseases, including multiple sclerosis (MS). The neurofilament light chain (NfL) is a biomarker that can detect axonal damage in different neurological diseases. The objective of this study was to evaluate the association of the expression profile of pre-selected miRNAs and NfL levels with clinical and radiological variables in MS patients. We conducted a 1-year longitudinal prospective study in MS patients with different clinical forms. We measured clinical disability using the expanded disability status scale (EDSS), the magnetic resonance imaging (MRI) volumetry baseline, and cognitive functioning using the processing speed test (PST) at baseline and 1 year later. Selected serum miRNAs and serum NfL (sNfL) levels were quantified. Seventy-three patients were recruited. MiR-126.3p correlated with EDSS and cognitive status at baseline and miR-126.3p and miR-9p correlated with cognitive deterioration at 1 year. Correlations with regional brain volumes were observed between miR-126.3p and the cortical gray matter, cerebellum, putamen, and pallidum; miR-146a.5p with the cerebellum and pallidum; miR-29b.3p with white matter and the pallidum; miR-138.5p with the pallidum; and miR-9.5p with the thalamus. sNfL was correlated with miR-9.5p. miR-146a.5p was also associated with the MS phenotype. These data justify future studies to further explore the utility of miRNAs (mirR-126.3p, miR-146.5p, and miR.9-5p) and sNfL levels as biomarkers of MS.

Background Cognitive impairment is one of the most common and debilitating symptoms of relapsing–remitting multiple sclerosis (RRMS). Digital cognitive biomarkers require less time and resources and are rapidly gaining popularity in clinical settings. We examined the longitudinal trajectory of the iPad‐based Processing Speed Test (PST) and predictors of PST scores. Methods We prospectively enrolled RRMS patients between 2017 and 2021 across six Australian MS centres. Longitudinal data was analysed with mixed effect modelling and latent class mixed models. We then examined whether latent class group membership predicted confirmed decrease in correct PST responses. Results We recruited a total of 1093 participants, of which 724 had complete baseline data with a median follow up duration of 2 years. At a population level, PST trajectory was stable. A small practice effect was present up to the 4th visit. Age, baseline disability, T2 lesion volume, male sex and depression were associated with lower correct PST responses, whilst years of education and full/part‐time employment were associated with more correct PST responses. We identified four latent class trajectories of PST. The worst latent class was typified by low baseline PST and lack of a practice effect. Being in the worst latent class was associated with a greater hazard of time to sustained 5% decrease in PST (HR 2.84, 95% CI 1.16–6.94, p = 0.02). Conclusion Worse baseline cognitive performance and lack of a practice effect predicted future cognitive decline in RRMS.

Background and Aims: Preoperative cognitive function screening can help identify high-risk patients, but resource-intensive testing limits its widespread use. A novel self-administered tablet computer-based Processing Speed Test (PST) was used to assess cognitive \"executive\" function in non-cardiac surgery patients, but the relationship between preoperative test scores and postoperative outcomes is unclear. The primary outcome was a composite of 30-day readmission/death. The secondary outcome was a collapsed composite of discharge to a long-term care facility/death. Exploratory outcomes were 1) time to discharge alive, 2) 1-year mortality and 3) a collapsed composite of postoperative complications. Methods: This retrospective study, after approval, was conducted in elective non-cardiac surgery patients ≥65 years old. We assessed the relationship between processing speed test scores and primary/secondary outcomes using multivariable logistic regression, adjusting for potential confounding variables. Results: Overall 1568 patients completed the PST, and the mean ± standard deviation test score was 33 ± 10. The higher PST score is associated with better executive function. A 10-unit increase in the test score was associated with an estimated 19% lower 30-day readmission/death odds, with an odds ratio (OR) and 95% confidence interval (CI) of 0.81 (0.68, 0.96) (P = 0.015). Similarly, 10-unit increase in test score was associated with an estimated 26% lower odds of long-term care need/death, with OR (95% CI) of 0.74 (0.61, 0.91) (P = 0.004). We also found statistically significant associations between the test scores and time to discharge alive and to 1-year mortality, however, not with a composite of postoperative complications. Conclusion: Elderly non-cardiac surgery patients with better PST scores were less likely to be readmitted, need long-term care after discharge or die within 30 days. Preoperative assessment of cognitive function using a simple self-administered test is feasible and may guide perioperative care.

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.

Personalized exoskeleton assistance provides users with the largest improvements in walking speed 1 and energy economy 2 – 4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskeleton optimization can be performed rapidly and under real-world conditions. We designed a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed. We developed a data-driven method for optimizing exoskeleton assistance outdoors using wearable sensors and found that it was equally effective as laboratory methods, but identified optimal parameters four times faster. We performed real-world optimization using data collected during many short bouts of walking at varying speeds. Assistance optimized during one hour of naturalistic walking in a public setting increased self-selected speed by 9 ± 4% and reduced the energy used to travel a given distance by 17 ± 5% compared with normal shoes. This assistance reduced metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s −1 . Human movements encode information that can be used to personalize assistive devices and enhance performance. A portable ankle exoskeleton uses a data-driven method and wearable sensors to adapt to the user as they walk in a natural setting.

Autism spectrum disorder (ASD) is characterized by a variety of social and non-social behavioral deficits. One potential mechanism that could unify this diverse profile of behaviors is slower processing speed. Seventy-six high-functioning adults with ASD were compared to 64 matched controls on standardized measures of processing speed. Participants with ASD were significantly slower on all measures, and on the composite score from the three tests (d’s > .65). ASD participants with slower processing speeds scored higher on the ADOS Communication and Reciprocal Social Interaction scale (r = .34). These findings provide evidence of slower processing speeds in adults with ASD, and that this may be contributing to impairments in social communication skills. Interventions that improve processing speed might improve social communication abilities in ASD.

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.

Introduction Insomnia causes substantial health-care cost burden and is a risk factor for neurocognitive impairment. Increased DMN activity has been implicated in the hyperarousal theory of insomnia. In this pilot clinical trial, DMN was targeted via the left inferior parietal lobe using a brief 40 second cTBS Transcranial Magnetic Stimulation (rTMS). We hypothesized that neurocognitive improvements due to cTBS would be mediated by polysomnographic (PSG) parameters of slow wave sleep. Methods Twenty participants (12 female; age=26.9, SD=6.6 years) meeting criteria for insomnia completed a counterbalanced sham-controlled crossover design in which they served as their own controls on two separate nights of in-laboratory assessments. Sessions for both conditions included neurocognitive assessments at baseline, after stimulation (cTBS/sham) before bed, and the following morning. After stimulation, participants were allowed an 8-hour sleep opportunity from 2300 to 0700 monitored with PSG. The symbol coding and digit span subtests of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) exam are the main assessments in the current investigation. Results Improvements in attention and processing speed were observed after a night of sleep following both active TMS (cTBS; B = 11.05, p < .001) and sham TMS conditions (B = 8.10, p = .002) the prior day. In the cTBS condition only, these improvements in attention were mediated by latency to slow wave sleep (N3), such that shorter latencies were related to greater improvements on a simple attention task (RBANS digit span; B = -0.75, p < .001). This effect of night to morning improvement in attention and processing speed was also found in the sham condition for the coding task (B = 60.81, p < .001), but not for digit span. No effects of slow wave sleep duration on RBANS coding or digit span were observed in either condition. Conclusion Improvements in simple attention performance in response to cTBS may be, in part, due to slow wave sleep parameters. Moreover, results suggest that simple attention may have been more sensitive to the effects of cTBS, whereas processing speed may have been a factor of lower slow wave sleep onset latency. Support (if any) DoD PRMRPD W81XWH2010173

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.