Simulating the Lyotropic Phase Behavior of a Partially Self-Complementary DNA Tetramer.

DNA oligomers in solution have been found to develop liquid crystal phases via a hierarchical process that involves Watson-Crick base pairing, supramolecular assembly into columns of duplexes, and long-range ordering. The multiscale nature of this phenomenon makes it difficult to quantitatively describe and assess the importance of the various contributions, particularly for very short strands. We performed molecular dynamics simulations based on the coarse-grained oxDNA model, aiming to depict all of the assembly processes involved and the phase behavior of solutions of the DNA GCCG tetramers. We find good quantitative matching to experimental data at both levels of molecular association (thermal melting) and collective ordering (phase diagram). We characterize the isotropic state and the low-density nematic and high-density columnar liquid crystal phases in terms of molecular order, size of aggregates, and structure, together with their effects on diffusivity processes. We observe a cooperative aggregation mechanism in which the formation of dimers is less thermodynamically favored than the formation of longer aggregates.

Immersive VR for upper-extremity rehabilitation in patients with neurological disorders: a scoping review.

BACKGROUND: Neurological disorders, such as stroke and chronic pain syndromes, profoundly impact independence and quality of life, especially when affecting upper extremity (UE) function. While conventional physical therapy has shown effectiveness in providing some neural recovery in affected individuals, there remains a need for improved interventions. Virtual reality (VR) has emerged as a promising technology-based approach for neurorehabilitation to make the patient's experience more enjoyable. Among VR-based rehabilitation paradigms, those based on fully immersive systems with headsets have gained significant attention due to their potential to enhance patient's engagement. METHODS: This scoping review aims to investigate the current state of research on the use of immersive VR for UE rehabilitation in individuals with neurological diseases, highlighting benefits and limitations. We identified thirteen relevant studies through comprehensive searches in Scopus, PubMed, and IEEE Xplore databases. Eligible studies incorporated immersive VR for UE rehabilitation in patients with neurological disorders and evaluated participants' neurological and motor functions before and after the intervention using clinical assessments. RESULTS: Most of the included studies reported improvements in the participants rehabilitation outcomes, suggesting that immersive VR represents a valuable tool for UE rehabilitation in individuals with neurological disorders. In addition, immersive VR-based interventions hold the potential for personalized and intensive training within a telerehabilitation framework. However, further studies with better design are needed for true comparison with traditional therapy. Also, the potential side effects associated with VR head-mounted displays, such as dizziness and nausea, warrant careful consideration in the development and implementation of VR-based rehabilitation programs. CONCLUSION: This review provides valuable insights into the application of immersive VR in UE rehabilitation, offering the foundation for future research and clinical practice. By leveraging immersive VR's potential, researchers and rehabilitation specialists can design more tailored and patient-centric rehabilitation strategies, ultimately improving the functional outcome and enhancing the quality of life of individuals with neurological diseases.

Electroencephalographic interbrain synchronization in children with disabilities, their parents, and neurologic music therapists.

As with typically developing children, children with cerebral palsy and autism spectrum disorder develop important socio-emotional rapport with their parents and healthcare providers. However, the neural mechanisms underlying these relationships have been less studied. By simultaneously measuring the brain activity of multiple individuals, interbrain synchronization could serve as a neurophysiological marker of social-emotional responses. Music evokes emotional and physiological responses and enhances social cohesion. These characteristics of music have fostered its deployment as a therapeutic medium in clinical settings. Therefore, this study investigated two aspects of interbrain synchronization, namely, its phase and directionality, in child-parent (CP) and child-therapist (CT) dyads during music and storytelling sessions (as a comparison). A total of 17 participants (seven cerebral palsy or autism spectrum disorder children [aged 12-18 years], their parents, and three neurologic music therapists) completed this study, comprising seven CP and seven CT dyads. Each music therapist worked with two or three children. We found that session type, dyadic relationship, frequency band, and brain region were significantly related to the degree of interbrain synchronization and its directionality. Particularly, music sessions and CP dyads were associated with higher interbrain synchronization and stronger directionality. Delta (.5-4 Hz) range showed the highest phase locking value in both CP and CT dyads in frontal brain regions. It appears that synchronization is directed predominantly from parent to child, that is, parents and music therapists' brain activity tended to influence a child's. Our findings encourage further research into neural synchrony in children with disabilities, especially in musical contexts, and its implications for social and emotional development.

Evaluating Dual Task Neurological Costs with Functional Near-Infrared Spectroscopy: A Preliminary Report in Healthy Athletes.

BACKGROUND: Dual task assessments, which simultaneously challenge and assess cognitive and motor performance, have been used to improve the assessment of athletes with sports-related concussions (SRC). Our lab created a Dual Task Screen (DTS) to evaluate athletes with SRCs, and we have established that it is a valid behavioral measure, as it consistently elicits poorer behavioral performance under dual, compared to single, task conditions. Here, we used a Neuroimaging-Compatible (NC) version of the DTS, named the NC-DTS, which uses portable functional near-infrared spectroscopy (fNIRS) to assess behavioral performance and neural recruitment during single and dual tasks. Our study objective was to evaluate healthy athletes and establish whether the NC-DTS is a valid dual task neurological assessment that can elicit different patterns of neural recruitment during dual versus single task conditions. METHODS: Twenty-five healthy collegiate athletes completed the NC-DTS in a single laboratory visit. The NC-DTS includes a lower and upper extremity subtask; both include single motor, single cognitive, and dual task conditions. The NC-DTS was administered in a block design, where conditions (i.e., single motor, single cognitive, and dual task) were repeated five times to generate average behavioral performance and task-dependent neural recruitment in superficial cortical regions including: prefrontal cortex, bilateral primary motor and sensory cortices, and posterior parietal cortex. Neural recruitment was measured with fNIRS and quantified using oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) metrics. A single-tailed, within subject t-test was used to compare average dual task behavioral performance to average single task behavioral performance. Pairwise comparisons, that were family-wise-error (FWE) corrected, were used to compare localized neural recruitment during dual versus single task conditions. RESULTS: As observed in previous studies, the NC-DTS elicited significantly poorer behavioral performance under dual, compared to single, task conditions. Additionally, dual task conditions of the NC-DTS elicited significantly greater neural recruitment in regions of the brain associated with attention allocation and task-specific demands in three of four comparisons. CONCLUSIONS: These preliminary results suggest that the NC-DTS is a valid dual task neurological assessment which warrants future work using the NC-DTS to evaluate athletes with SRCs.

Characterizing Counterion-Dependent Aggregation of Rhodamine B by Classical Molecular Dynamics Simulations.

The aggregation in a solution of charged dyes such as Rhodamine B (RB) is significantly affected by the type of counterion, which can determine the self-assembled structure that in turn modulates the optical properties. RB aggregation can be boosted by hydrophobic and bulky fluorinated tetraphenylborate counterions, such as F5TPB, with the formation of nanoparticles whose fluorescence quantum yield (FQY) is affected by the degree of fluorination. Here, we developed a classical force field (FF) based on the standard generalized Amber parameters that allows modeling the self-assembling process of RB/F5TPB systems in water, consistent with experimental evidence. Namely, the classical MD simulations employing the re-parametrized FF reproduce the formation of nanoparticles in the RB/F5TPB system, while in the presence of iodide counterions, only RB dimeric species can be formed. Within the large, self-assembled RB/F5TPB aggregates, the occurrence of an H-type RB-RB dimer can be observed, a species that is expected to quench RB fluorescence, in agreement with the experimental data of FQY. The outcome provides atomistic details on the role of the bulky F5TPB counterion as a spacer, with the developed classical FF representing a step towards reliable modeling of dye aggregation in RB-based materials.

Automated movement recognition to predict motor impairment in high-risk infants: a systematic review of diagnostic test accuracy and meta-analysis.

AIM: To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high-risk infants. METHOD: We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high-risk infants to predict motor impairment, including cerebral palsy (CP) and non-CP motor impairments. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies-2. Meta-analyses were performed using hierarchical summary receiver operating characteristic models. RESULTS: Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5 months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval [CI] 0.68-0.77) and 0.70 (95% CI 0.65-0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74-100) and 91% (95% CI 83-93) respectively. Sensor-based technologies had higher specificity (0.88, 95% CI 0.80-0.93). INTERPRETATION: Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta-analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.

Exploring the capabilities of optical pump X-ray probe NEXAFS spectroscopy to track photo-induced dynamics mediated by conical intersections.

X-ray spectroscopy is gaining a growing interest in the scientific community, as it represents a versatile and powerful experimental toolbox for probing the dynamics of both core and valence electronic excitations, nuclear motions and material structure, with element and site specificity. Among the various X-ray based techniques, near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, which investigates the energy and probability of resonant core-to-valence transitions, has started to be applied to organic molecules: a recent UV-pump X-ray probe time-resolved NEXAFS experiment [Wolf et al., Nat. Commun., 2017, 8, 1] has shown the capability of the technique to provide information about the ultrafast internal conversion between the bright ππ* and the dark nπ* electronic states of the nucleobase thymine. In the present contribution we introduce an accurate theoretical approach for the simulation of NEXAFS spectra of organic molecules, employing azobenzene as a test case. The electronic structure calculations, which provide both energy levels and transition probabilities of core-to-valence excitations, were here performed with a high level multiconfigurational method, the restricted active space self consistent field (RASSCF/RASPT2). GS- and nπ*-NEXAFS spectra were obtained on the top of key molecular geometries (as the optimized cis, trans and conical intersection(s) structures) as well as along the fundamental isomerization coordinates (namely, symmetric and asymmetric bendings of the phenyl rings, and torsion around the central dihedral). We eventually characterize and explain the origin of the simulated signals, highlighting the specific signatures that make it possible to follow the excited state evolution from the nπ* Franck-Condon point, towards the conical intersection(s).

Molecular organizations of conical mesogenic fullerenes.

We have studied liquid crystal phases formed by fullerenes functionalized with mesogenic groups yielding a cone-shaped molecular structure. We have modelled these shuttlecock-like molecules with a set of Gay-Berne particles grafted with flexible springs to a spherical core and we have studied, using Monte Carlo simulations, their phase organization, also with a view to examining their possible use as candidate organic photovoltaic materials. We have found that, upon cooling from the isotropic phase, the system forms a columnar phase, like in the experimental work of Kato and coworkers [T. Kato et al., Nature, 2002, 419, 702]. However the phase is made of polar stacks extending not more than about ten molecules, which could limit their usefulness in enhancing and directing charge transport for possible photovoltaic applications.

Doping liquid crystals with nanoparticles. A computer simulation of the effects of nanoparticle shape.

We have studied, using Monte Carlo computer simulations, the effects that nanoparticles of similar size and three different shapes (spherical, elongated and discotic) dispersed at different concentrations in a liquid crystal (LC), have on the transition temperature, order parameter and mobility of the suspension. We have modelled the nanoparticles as berry-like clusters of spherical Lennard-Jones sites and the NP with a Gay-Berne model. We find that the overall phase behaviour is not affected by the addition of small amounts (xN = 0.1-0.5%) of nanoparticles, with the lowest perturbation obtained with disc-like nanoparticles at the lowest concentration. We observe a general decrease of the clearing temperature and a reduction in the orientational order with a change in its temperature variation, particularly in the case of the xN = 0.5% dispersions and with a more pronounced effect when the nanoparticles are spherical.

Supramolecular organization of functional organic materials in the bulk and at organic/organic interfaces: a modeling and computer simulation approach.

The molecular organization of functional organic materials is one of the research areas where the combination of theoretical modeling and experimental determinations is most fruitful. Here we present a brief summary of the simulation approaches used to investigate the inner structure of organic materials with semiconducting behavior, paying special attention to applications in organic photovoltaics and clarifying the often obscure jargon hindering the access of newcomers to the literature of the field. Special attention is paid to the choice of the computational "engine" (Monte Carlo or Molecular Dynamics) used to generate equilibrium configurations of the molecular system under investigation and, more importantly, to the choice of the chemical details in describing the molecular interactions. Recent literature dealing with the simulation of organic semiconductors is critically reviewed in order of increasing complexity of the system studied, from low molecular weight molecules to semiflexible polymers, including the challenging problem of determining the morphology of heterojunctions between two different materials.

Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol.

INTRODUCTION: Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients' health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors. METHODS AND ANALYSIS: We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods. ETHICS AND DISSEMINATION: The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation. TRIAL REGISTRATION NUMBER: NCT05747040.

How can cry acoustics associate newborns' distress levels with neurophysiological and behavioral signals?

Introduction: Even though infant crying is a common phenomenon in humans' early life, it is still a challenge for researchers to properly understand it as a reflection of complex neurophysiological functions. Our study aims to determine the association between neonatal cry acoustics with neurophysiological signals and behavioral features according to different cry distress levels of newborns. Methods: Multimodal data from 25 healthy term newborns were collected simultaneously recording infant cry vocalizations, electroencephalography (EEG), near-infrared spectroscopy (NIRS) and videos of facial expressions and body movements. Statistical analysis was conducted on this dataset to identify correlations among variables during three different infant conditions (i.e., resting, cry, and distress). A Deep Learning (DL) algorithm was used to objectively and automatically evaluate the level of cry distress in infants. Results: We found correlations between most of the features extracted from the signals depending on the infant's arousal state, among them: fundamental frequency (F0), brain activity (delta, theta, and alpha frequency bands), cerebral and body oxygenation, heart rate, facial tension, and body rigidity. Additionally, these associations reinforce that what is occurring at an acoustic level can be characterized by behavioral and neurophysiological patterns. Finally, the DL audio model developed was able to classify the different levels of distress achieving 93% accuracy. Conclusion: Our findings strengthen the potential of crying as a biomarker evidencing the physical, emotional and health status of the infant becoming a crucial tool for caregivers and clinicians.

Multi-modal analysis of infant cry types characterization: Acoustics, body language and brain signals.

BACKGROUND: Infant crying is the first attempt babies use to communicate during their initial months of life. A misunderstanding of the cry message can compromise infant care and future neurodevelopmental process. METHODS: An exploratory study collecting multimodal data (i.e., crying, electroencephalography (EEG), near-infrared spectroscopy (NIRS), facial expressions, and body movements) from 38 healthy full-term newborns was conducted. Cry types were defined based on different conditions (i.e., hunger, sleepiness, fussiness, need to burp, and distress). Statistical analysis, Machine Learning (ML), and Deep Learning (DL) techniques were used to identify relevant features for cry type classification and to evaluate a robust DL algorithm named Acoustic MultiStage Interpreter (AMSI). RESULTS: Significant differences were found across cry types based on acoustics, EEG, NIRS, facial expressions, and body movements. Acoustics and body language were identified as the most relevant ML features to support the cause of crying. The DL AMSI algorithm achieved an accuracy rate of 92%. CONCLUSIONS: This study set a precedent for cry analysis research by highlighting the complexity of newborn cry expression and strengthening the potential use of infant cry analysis as an objective, reliable, accessible, and non-invasive tool for cry interpretation, improving the infant-parent relationship and ensuring family well-being.

Temperature dependence of charge mobility in model discotic liquid crystals.

We address the calculation of charge carrier mobility of liquid-crystalline columnar semiconductors, a very promising class of materials in the field of organic electronics. We employ a simple coarse-grained theoretical approach and study in particular the temperature dependence of the mobility of the well-known triphenylene family of compounds, combining a molecular-level simulation for reproducing the structural changes and the Miller-Abrahams model for the evaluation of the transfer rates within the hopping regime. The effects of electric field, positional and energetic disorder are also considered. Simulations predict a low energetic disorder (~0.05 eV), slightly decreasing with temperature within the crystal, columnar and isotropic phases, and fluctuations of the square transfer integral of the order of 0.003 eV(2). The shape of the temperature-dependent mobility curve is however dominated by the variation of the transfer integral and barely affected by the disorder. Overall, this model reproduces semi-quantitatively all the features of experimentally measured mobilities, on one hand reinforcing the correctness of the hopping transport picture and of its interplay with system morphology, and on the other suggesting future applications for off-lattice modeling of organic electronics devices.

A comparison and classification of oscillatory characteristics in speech perception and covert speech.

Covert speech, the mental imagery of speaking, has been studied increasingly to understand and decode thoughts in the context of brain-computer interfaces. In studies of speech comprehension, neural oscillations are thought to play a key role in the temporal encoding of speech. However, little is known about the role of oscillations in covert speech. In this study, we investigated the oscillatory involvements in covert speech and speech perception. Data were collected from 10 participants with 64 channel EEG. Participants heard the words, 'blue' and 'orange', and subsequently mentally rehearsed them. First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes (tasks) were conducted to determine statistical differences in frequency and time (t-CWT). In the current experiment, a task comprised speech perception or covert rehearsal of a word while a condition was the discrimination between tasks. Features were extracted using t-CWT and subsequently classified using a support vector machine. θ and γ phase amplitude coupling (PAC) was also assessed within tasks and across conditions between perception and covert activities (i.e. cross-task). All binary classifications accuracies (80-90%) significantly exceeded chance level, supporting the use of t-CWT in determining relative oscillatory involvements. While the perception condition dynamically invoked all frequencies with more prominent θ and α activity, the covert condition favoured higher frequencies with significantly higher γ activity than perception. Moreover, the perception condition produced significant θ-γ PAC, possibly corroborating a reported linkage between syllabic and phonemic sampling. Although this coupling was found to be suppressed in the covert condition, we found significant cross-task coupling between perception θ and covert speech γ. Covert speech processing appears to be largely associated with higher frequencies of EEG. Importantly, the significant cross-task coupling between speech perception and covert speech, in the absence of within-task covert speech PAC, seems to support the notion that the γ- and θ-bands reflect, respectively, shared and unique encoding processes across tasks.

A pediatric near-infrared spectroscopy brain-computer interface based on the detection of emotional valence.

Brain-computer interfaces (BCIs) are being investigated as an access pathway to communication for individuals with physical disabilities, as the technology obviates the need for voluntary motor control. However, to date, minimal research has investigated the use of BCIs for children. Traditional BCI communication paradigms may be suboptimal given that children with physical disabilities may face delays in cognitive development and acquisition of literacy skills. Instead, in this study we explored emotional state as an alternative access pathway to communication. We developed a pediatric BCI to identify positive and negative emotional states from changes in hemodynamic activity of the prefrontal cortex (PFC). To train and test the BCI, 10 neurotypical children aged 8-14 underwent a series of emotion-induction trials over four experimental sessions (one offline, three online) while their brain activity was measured with functional near-infrared spectroscopy (fNIRS). Visual neurofeedback was used to assist participants in regulating their emotional states and modulating their hemodynamic activity in response to the affective stimuli. Child-specific linear discriminant classifiers were trained on cumulatively available data from previous sessions and adaptively updated throughout each session. Average online valence classification exceeded chance across participants by the last two online sessions (with 7 and 8 of the 10 participants performing better than chance, respectively, in Sessions 3 and 4). There was a small significant positive correlation with online BCI performance and age, suggesting older participants were more successful at regulating their emotional state and/or brain activity. Variability was seen across participants in regards to BCI performance, hemodynamic response, and discriminatory features and channels. Retrospective offline analyses yielded accuracies comparable to those reported in adult affective BCI studies using fNIRS. Affective fNIRS-BCIs appear to be feasible for school-aged children, but to further gauge the practical potential of this type of BCI, replication with more training sessions, larger sample sizes, and end-users with disabilities is necessary.

Automatic pain assessment on cancer patients using physiological signals recorded in real-world contexts.

Pain assessment represents the first fundamental stage for proper pain management, but currently, methods applied in clinical practice often lack in providing a satisfying characterization of the pain experience. Automatic methods based on the analysis of physiological signals (e.g., photoplethysmography, electrodermal activity) promise to overcome these limitations, also providing the possibility to record these signals through wearable devices, thus capturing the physiological response in everyday life. After applying preprocessing, feature extraction and feature selection methods, we tested several machine learning algorithms to develop an automatic classifier fed with physiological signals recorded in real-world contexts and pain ratings from 21 cancer patients. The best algorithm achieved up to 72% accuracy. Although performance can be improved by enlarging the dataset, preliminary results proved the feasibility of assessing pain by using physiological signals recorded in real-world contexts.

Does music induce interbrain synchronization between a non-speaking youth with cerebral palsy (CP), a parent, and a neurologic music therapist? A brief report.

Shared emotional experiences during musical activities among musicians can be coupled with brainwave synchronization. For non-speaking individuals with CP, verbal communication may be limited in expressing mutual empathy. Therefore, this case study explored interbrain synchronization among a non-speaking CP (female, 18 yrs), her parent, and a music therapist by measuring their brainwaves simultaneously during four music and four storytelling sessions. In only the youth-parent dyad, we observed a significantly higher level of interbrain synchronization during music rather than story-telling condition. However, in both the youth-parent and youth-therapist dyad, regardless of condition type, significant interbrain synchronization emerged in frontal and temporal lobes in the low-frequency bands, which are associated with socio-emotional responses. Although interbrain synchronization may have been induced by multiple factors (e.g., external stimuli, shared empathetic experiences, and internal physiological rhythms), the music activity setting deserves further study as a potential facilitator of neurophysiological synchrony between youth with CP and caregivers/healthcare providers.

Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study.

The General Movements Assessment requires extensive training. As an alternative, a novel automated movement analysis was developed and validated in preterm infants. Infants < 31 weeks' gestational age or birthweight ≤ 1500 g evaluated at 3−5 months using the general movements assessment were included in this ambispective cohort study. The C-statistic, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for a predictive model. A total of 252 participants were included. The median gestational age and birthweight were 274/7 weeks (range 256/7−292/7 weeks) and 960 g (range 769−1215 g), respectively. There were 29 cases of cerebral palsy (11.5%) at 18−24 months, the majority of which (n = 22) were from the retrospective cohort. Mean velocity in the vertical direction, median, standard deviation, and minimum quantity of motion constituted the multivariable model used to predict cerebral palsy. Sensitivity, specificity, positive, and negative predictive values were 55%, 80%, 26%, and 93%, respectively. C-statistic indicated good fit (C = 0.74). A cluster of four variables describing quantity of motion and variability of motion was able to predict cerebral palsy with high specificity and negative predictive value. This technology may be useful for screening purposes in very preterm infants; although, the technology likely requires further validation in preterm and high-risk term populations.

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach.

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients' etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.