Development curves of communication and social interaction in individuals with cerebral palsy

Aim: To determine development curves of communication and social interaction from childhood into adulthood for individuals with cerebral palsy (CP). Method: This Pediatric Rehabilitation Research in the Netherlands (PERRIN)-DECADE study longitudinally assessed 421 individuals with CP, aged from 1 to 20 years at baseline, after 13 years (n=121 at follow-up). Communication and social interactions were assessed using the Vineland Adaptive Behavior Scales. We estimated the average maximum performance limit (level) and age at which 90% of the limit was reached (age90) using nonlinear mixed-effects modeling. Results: One-hundred individuals without intellectual disability were aged 21 to 34 years at follow-up (39 females, 61 males) (mean age [SD] 28y 5mo [3y 11mo]). Limits of individuals without intellectual disability, regardless of Gross Motor Function Classification System (GMFCS) level, approached the maximum score and were significantly higher than those of individuals with intellectual disability. Ages90 ranged between 3 and 4 years for receptive communication, 6 and 7 years for expressive communication and interrelationships, 12 and 16 years for written communication, 13 and 16 years for play and leisure, and 14 and 16 years for coping. Twenty-one individuals with intellectual disability were between 21 and 27 years at follow-up (8 females, 13 males) (mean age [SD] 24y 7mo [1y 8mo]). Individuals with intellectual disability in GMFCS level V showed the least favourable development, but variation between individuals with intellectual disability was large. Interpretation: Individuals with CP and without intellectual disability show developmental curves of communication and social interactions similar to typically developing individuals, regardless of their level of motor function. Those with intellectual disability reach lower performance levels and vary largely in individual development. What this paper adds: Communication and social interactions in individuals with cerebral palsy without intellectual disability develop similarly to typically developing individuals. Communication and social interactions of individuals with intellectual disability develop less favourably and show large variation

Childhood factors predict participation of young adults with cerebral palsy in domestic life and interpersonal relationships: a prospective cohort study

Purpose: To determine childhood predictors of participation in domestic life and interpersonal relationships of young adults with cerebral palsy (CP). Materials and methods: This 13-year follow-up of an existing cohort (baseline age 9–13 years) included 67 young adults with CP (age 21–27 years). The Vineland adaptive behavior scales (VABS) and Life Habits questionnaire were used to assess attendance and difficulty in participation in domestic life and interpersonal relationships. Baseline factors were categorised according to the international classification of functioning, disability, and health. Stepwise multiple linear regression analyses determined significant predictors (p < 0.05). Results: Lower manual ability, intellectual disability (ID), epilepsy and lower motor capacity predicted decreased future participation in domestic life, and/or interpersonal relationships (explained variance R2 ¼ 67–87%), whereas no association was found with environmental and personal factors. Extending models with baseline fine motor skills, communication, and interpersonal relationships increased R2 to 79–90%. Conclusions: Childhood factors account for 79–90% of the variation in young adult participation in domestic life and interpersonal relationships of individuals with CP. Children with limited motor capacity, low manual ability, ID, or epilepsy are at risk for restrictions in participation in young adulthood. Addressing fine motor, communication, and social skills in paediatric rehabilitation might promote young adult participation

The Network Architecture of Cortical Processing in Visuo-spatial Reasoning

Reasoning processes have been closely associated with prefrontal cortex (PFC), but specifically emerge from interactions among networks of brain regions. Yet it remains a challenge to integrate these brain-wide interactions in identifying the flow of processing emerging from sensory brain regions to abstract processing regions, particularly within PFC. Functional magnetic resonance imaging data were collected while participants performed a visuo-spatial reasoning task. We found increasing involvement of occipital and parietal regions together with caudal-rostral recruitment of PFC as stimulus dimensions increased. Brain-wide connectivity analysis revealed that interactions between primary visual and parietal regions predominantly influenced activity in frontal lobes. Caudal-to-rostral influences were found within left-PFC. Right-PFC showed evidence of rostral-to-caudal connectivity in addition to relatively independent influences from occipito-parietal cortices. In the context of hierarchical views of PFC organization, our results suggest that a caudal-to-rostral flow of processing may emerge within PFC in reasoning tasks with minimal top-down deductive requirements

Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention

Adherence to physiotherapy clinical guideline acute ankle injury and determinants of adherence: a cohort study

Background. Clinical guidelines are considered important instruments to improve quality in health care. In physiotherapy, insight in adherence to guidelines is limited. Knowledge of adherence is important to identify barriers and to enhance implementation. Purpose of this study is to investigate the ability to adherence to recommendations of the guideline Acute ankle injury, and to identify patient characteristics that determine adherence to the guideline. Methods. Twenty-two physiotherapists collected data of 174 patients in a prospective cohort study, in which the course of treatment was systematically registered. Indicators were used to investigate adherence to recommendations. Patient characteristics were used to identify prognostic factors that may determine adherence to the guideline. Correlation between patient characteristics and adherence to outcome-indicators (treatment sessions, functioning of patient, accomplished goals) was calculated using univariate logistic regression. To calculate explained variance of combined patient characteristics, multivariate analysis was performed. Results. Adherence to individual recommendations varied from 71% to 100%. In 99 patients (57%) the physiotherapists showed adherence to all indicators. Adherence to preset maximum of six treatment sessions for patients with severe ankle injury was 81% (132 patients). The odds to receive more than six sessions were statistically significant for three patient characteristics: females (OR:3.89; 95%CI: 1.41-10.72), recurrent sprain (OR: 6.90; 95%CI: 2.34 - 20.37), co-morbidity (OR: 25.92; 95% CI: 6.79 - 98.93). All factors together explained 40% of the variance. Inclusion of physiotherapist characteristics in the regression model showed that work-experience reduced the odds to receive more than six sessions (OR: 0.2; 95%CI: 0.06 - 0.77), and increased explained variance to 45%. Conclusion. Adherence to the clinical guideline Acute ankle sprain showed that the guideline is applicable in daily practice. Adherence to the guideline, even in a group of physiotherapists familiar with the guideline, showed possibilities for improvement. The necessity to exceed the expected number of treatment sessions may be explained by co-morbidity and recurrent sprains. It is not clear why female patients were treated with more sessions. Experience of the physiotherapist reduced the number of treatment sessions. Quality indicators may be used for audit and feedback as part of the implementation strategy

Driving and Driven Architectures of Directed Small-World Human Brain Functional Networks

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome

Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis

Characterizing how different cortical rhythms interact and how their interaction changes with sensory stimulation is important to gather insights into how these rhythms are generated and what sensory function they may play. Concepts from information theory, such as Transfer Entropy (TE), offer principled ways to quantify the amount of causation between different frequency bands of the signal recorded from extracellular electrodes; yet these techniques are hard to apply to real data. To address the above issues, in this study we develop a method to compute fast and reliably the amount of TE from experimental time series of extracellular potentials. The method consisted in adapting efficiently the calculation of TE to analog signals and in providing appropriate sampling bias corrections. We then used this method to quantify the strength and significance of causal interaction between frequency bands of field potentials and spikes recorded from primary visual cortex of anaesthetized macaques, both during spontaneous activity and during binocular presentation of naturalistic color movies. Causal interactions between different frequency bands were prominent when considering the signals at a fine (ms) temporal resolution, and happened with a very short (ms-scale) delay. The interactions were much less prominent and significant at coarser temporal resolutions. At high temporal resolution, we found strong bidirectional causal interactions between gamma-band (40–100 Hz) and slower field potentials when considering signals recorded within a distance of 2 mm. The interactions involving gamma bands signals were stronger during movie presentation than in absence of stimuli, suggesting a strong role of the gamma cycle in processing naturalistic stimuli. Moreover, the phase of gamma oscillations was playing a stronger role than their amplitude in increasing causations with slower field potentials and spikes during stimulation. The dominant direction of causality was mainly found in the direction from MUA or gamma frequency band signals to lower frequency signals, suggesting that hierarchical correlations between lower and higher frequency cortical rhythms are originated by the faster rhythms

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups