No abstract

In the present study, we investigated neural processes underlying programming experience. Individuals with high programming experience might develop a form of computational thinking, which they can apply on complex problem-solving tasks such as reasoning tests. Therefore, N = 20 healthy young participants with previous programming experience and N = 21 participants without any programming experience performed three reasoning tests: Figural Inductive Reasoning (FIR), Numerical Inductive Reasoning (NIR), Verbal Deductive Reasoning (VDR). Using multi-channel EEG measurements, task-related changes in alpha and theta power as well as brain connectivity were investigated. Group differences were only observed in the FIR task. Programmers showed an improved performance in the FIR task as compared to non-programmers. Additionally, programmers exhibited a more efficient neural processing when solving FIR tasks, as indicated by lower brain activation and brain connectivity especially in easy tasks. Hence, behavioral and neural measures differed between groups only in tasks that are similar to mental processes required during programming, such as pattern recognition and algorithmic thinking by applying complex rules (FIR), rather than in tasks that require more the application of mathematical operations (NIR) or verbal tasks (VDR). Our results provide new evidence for neural efficiency in individuals with higher programming experience in problem-solving tasks.

Background and purpose Serum neurofilament light chain (sNfL) is a promising biomarker of neuroaxonal damage in persons with multiple sclerosis (pwMS). In cross‐sectional studies, sNfL has been associated with disease activity and brain magnetic resonance imaging (MRI) changes; however, it is still unclear to what extent in particular high sNfL levels impact on subsequent disease evolution. Methods sNfL was quantified by an ultrasensitive single molecule array (Simoa) in 199 pwMS (median age = 34.2 years, 64.3% female) and 49 controls. All pwMS underwent 3‐T MRI to assess global and compartmental normalized brain volumes, T2‐lesion load, and cortical mean thickness. Follow‐up data and serum samples were available in 144 pwMS (median follow‐up time = 3.8 years). Linear and binary logistic models were used to estimate the independent contribution of sNfL for changes in MRI and Expanded Disability Status Scale (EDSS). Age‐corrected sNfL z‐scores from a normative database of healthy controls were used for sensitivity analyses. Results High sNfL levels at baseline were associated with atrophy measures of the whole brain (standardized beta coefficient βj = −0.352, p < 0.001), white matter (βj = −0.229, p = 0.007), thalamus (βj = −0.372, p = 0.004), and putamen (βj = −1.687, p = 0.012). pwMS with high levels of sNfL at baseline and follow‐up had a greater risk of EDSS worsening (p = 0.007). Conclusions Already single time point elevation of sNfL has a distinct effect on brain volume changes over a short‐term period, and repeated high levels of sNfL indicate accumulating physical disability. Serial assessment of sNfL may provide added value in the clinical management of pwMS.

Predictive coding theory is an influential view of perception and cognition. It proposes that subjective experience of the sensory information results from a comparison between the sensory input and the top-down prediction about this input, the latter being critical for shaping the final perceptual outcome. The theory is able to explain a wide range of phenomena extending from sensory experiences such as visual illusions to complex pathological states such as hallucinations and psychosis. In the current study we aimed at testing the proposed connection between different phenomena explained by the predictive coding theory by measuring the manifestation of top-down predictions at progressing levels of complexity, starting from bistable visual illusions (alternating subjective experience of the same sensory input) and pareidolias (alternative meaningful interpretation of the sensory input) to self-reports of hallucinations and delusional ideations in everyday life. Examining the correlation structure of these measures in 82 adult healthy subjects revealed a positive association between pareidolia proneness and a tendency for delusional ideations, yet without any relationship to bistable illusions. These results show that only a subset of the phenomena that are explained by the predictive coding theory can be attributed to one common underlying factor. Our findings thus support the hierarchical view of predictive processing with independent top-down effects at the sensory and cognitive levels.

IntroductionMotor imagery (MI) refers to the mental rehearsal of a physical action without muscular activity. Our previous studies showed that MI combined with rhythmic-auditory cues improved walking, fatigue and quality of life (QoL) in people with multiple sclerosis (pwMS). Largest improvements were seen after music and verbally cued MI. It is unclear whether actual cued gait training achieves similar effects on walking as cued MI in pwMS. Furthermore, in pwMS it is unknown whether any of these interventions leads to changes in brain activation. The purpose of this study is therefore to compare the effects of imagined and actual cued gait training and a combination thereof on walking, brain activation patterns, fatigue, cognitive and emotional functioning in pwMS.Methods and analysisA prospective double-blind randomised parallel multicentre trial will be conducted in 132 pwMS with mild to moderate disability. Randomised into three groups, participants will receive music, metronome and verbal cueing, plus MI of walking (1), MI combined with actual gait training (2) or actual gait training (3) for 30 min, 4× per week for 4 weeks. Supported by weekly phone calls, participants will practise at home, guided by recorded instructions. Primary endpoints will be walking speed (Timed 25-Foot Walk) and distance (2 min Walk Test). Secondary endpoints will be brain activation patterns, fatigue, QoL, MI ability, anxiety, depression, cognitive functioning, music-induced motivation-to-move, pleasure, arousal and self-efficacy. Data will be collected at baseline, postintervention and 3-month follow-up. MRI reference values will be generated using 15 matched healthy controls.Ethics and disseminationThis study follows the Standard Protocol Items: Recommendations for Interventional Trials-PRO Extension. Ethical approval was received from the Ethics Committees of the Medical Universities of Innsbruck (1347/2020) and Graz (33-056 ex 20/21), Austria. Results will be disseminated via national and international conferences and published in peer-reviewed journals.Trial registration numberDRKS00023978.

Predictive coding theory is an influential view of perception and cognition. It proposes that subjective experience of the sensory information results from a comparison between the sensory input and the top-down prediction about this input, the latter being critical for shaping the final perceptual outcome. The theory is able to explain a wide range of phenomena ranging from sensory experiences such as visual illusions to complex pathological states such as hallucinations and psychosis. In the current study we aimed at testing the proposed connection between different phenomena explained by the predictive coding theory by measuring the manifestation of top-down predictions at progressing levels of complexity, starting from bistable visual illusions (alternating subjective experience of the same sensory input) and pareidolias (alternative meaningful interpretation of the sensory input) to self-reports of hallucinations and delusional ideations in everyday life. Examining the correlation structure of these measures in 82 adult healthy subjects revealed a positive association between pareidolia proneness and a tendency for delusions, yet without any relationship to bistable illusions. These results show that only a subset of the phenomena that are explained by the predictive coding theory can be attributed to one common underlying factor. Our findings thus support the hierarchical view of predictive processing with independent top-down effects at the sensory and cognitive levels.