Notes

Continual reaction to erlotinib along with rapamycin in a affected person together with child anaplastic oligodendroglioma.
This paper presents comparison of brain connectivity estimators of distracted drivers and non-distracted drivers based on statistical analysis. Twelve healthy volunteers with more than one year of driving experience participated in this experiment. Lane-keeping tasks and the Math problem-solving task were introduced in the experiment and EEGs (electroencephalogram) were used to record the brain waves. Granger-Geweke causality (GGC), directed transfer function (DTF) and partial directed coherence (PDC) brain connectivity estimation methods were used in brain connectivity analysis. Correlation test and a student's t-test were conducted on the connectivity matrixes. Results show a significant difference between the mean of distracted drivers and non-distracted driver's brain connectivity matrixes. GGC and DTF methods student's t-tests shows a p-value below 0.05 with the correlation coefficients varying from 0.62 to 0.38. PDC connectivity estimation method does not show a significant difference between the connectivity matrixes means unless it is compared with lane keeping task and the normal driving task. Furthermore, it shows a strong positive correlation between the connectivity matrixes.Alzheimer's disease (AD) affects approximately 30 million people worldwide, and this number is predicted to triple by 2050 unless further discoveries facilitate the early detection and prevention of the disease. Computerized walkways for simultaneous assessment of motor-cognitive performance, known as a dual-task assessment, has been used to associate changes in gait characteristics to mild cognitive impairment (MCI) with early-stage disease. However, to our best knowledge, there is no validated method to detect MCI using the collective analysis of these gait characteristics. In this paper, we develop a machine learning approach to analyze the gait data from the dual-task assessment in order to detect subjects with cognitive impairment from healthy individuals. We collected dual-task gait data from a computerized walkway of a total of 92 subjects with 31 healthy control (HC) and 61 MCI. Using support vector machine (SVM) and gradient tree boosting, we developed a classifier to differentiate MCI from HC subjects and compared the results with a paper-based questionnaire assessment that has been commonly used in clinical practice. SVM provided the highest accuracy of 77.17% with 81.97% sensitivity and 67.74% specificity. Our results indicate the potential of machine learning + dual-task assessment to enable early diagnosis of cognitive decline before it advances to dementia and AD, so that early intervention or prevention strategies can be initiated.Music is a medium for people to convey and express information, and different music styles show distinct effects on the emotion and cognitive activities of human brain. The construal level denotes the abstract level of the representation of objects, and as a useful indicator has been widely used to measure people's cognitive activities. Meanwhile, the neurophysiological responses in the prefrontal cortex (PFC) are associated with human cognitive activities. This work investigated the influence of music style on cognitive activities by measuring the construal level and PFC activation level. Two music styles (i.e., soothing and uplifting) were presented to listeners in the experiments. A behavioral experiment measured listeners' subjective construal levels, while the PFC activation levels were measured by a functional near-infrared spectroscopy. Results showed that compared with uplifting music, soothing music increased the subjective construal level; and the PFC activation level was higher in uplifting music than that in soothing music. These findings suggested that different music styles had distinct effects on the construal level and PFC activation level, providing evidences that music styles could impact people's cognitive activities.Attention lapses (ALs) are common phenomenon, which can affect our performance and productivity by slowing or suspending responsiveness. Occurrence of ALs during continuous monitoring tasks, such as driving or operating machinery, can lead to injuries and fatalities. However, we have limited understanding of what happens in the brain when ALs intrude during such continuous tasks. Here, we analyzed fMRI data from a study, in which participants performed a continuous visuomotor tracking task during fMRI scanning. A total of 68 ALs were identified from 20 individuals, using visual rating of tracking performance and video-based eye-closure. ALs were found to be associated with increased BOLD fMRI activity partially in the executive control network, and sensorimotor network. Surprisingly, we found no evidence of deactivations.Spatial attention is an important feature for filtering everyday inputs. The direction of the attention can be guided by the use of visual, auditory or tactile stimuli. The literature regarding the effect of cueing spatial attention in visual search tasks consistently shows an improvement in accuracy and reaction time. So far, most studies have used two-dimensional setups, for which ecological validity may be questioned. In this study with healthy participants, we investigated the feasibility of a virtual reality-based setup. We examined the feasibility and compared the performance in a visual search task as auditory, tactile or combined cues were given. The results revealed high usability and a significantly higher detection rate for combined audio-tactile cues compared to auditory cues alone.The users' mental state such as attention variations can have an effect on the brain-computer interface (BCI) performance. In this project, we implemented an adaptive online BCI system with alterations in the users' attention. Twelve electroencephalography (EEG) signals were obtained from six patients with Amyotrophic Lateral Sclerosis (ALS). Participants were asked to execute 40 trials of ankle dorsiflexion concurrently with an auditory oddball task. EEG channels, classifiers and features with superior offline performance in the training phase of the classification of attention level were selected to use in the online mode for prediction the attention status. A feedback was provided to the users to reduce the amount of attention diversion created by the oddball task. The findings revealed that the users' attention can control an online BCI system and real-time neurofeedback can be applied to focus the attention of the user back onto the main task.Coaching aims to unlock the human's potential, self-awareness and responsibility, improving the professional performances and the personal satisfaction. Its effectiveness is known to depend on the degree of bonding and mutual engagement of the coaching relationship. In this exploratory study we recorded synchronised EEG and SC data from both coach and coachee during 36 individual sessions, performed following 2 different coaching methods. Our principal aim was to investigate the temporal evolution of the bonding and the mutual engagement along the different steps of a session, by means of a "similarity" metric based on the DTW distance between signals (namely, S-TVM). We found significant differences between session phases for the EEG-related S-TVMs (BAR, BATR and AWI), with maximum values (defined as "tuning") all in the same phase, but differentiated between the two experiments. The results suggest a temporal concurrency of the engagement and emotional tunings, whose specific location seems to be a function of the coaching approach.With the aging population and rising rates of mobility disability, the demand for advanced smart rollators is increasing. To design control systems which improve safety and reliability, accurate prediction of human intent is required. In this paper, we present a classification method to predict intent of the rollator user using indirect inputs. The proposed classification algorithm uses data collected from an inertial measurement unit and an encoder implemented into a rollator. The developed intent estimation method is experimentally verified on our modified robotic platform. For our experiment with 7 healthy young adults, KNN classification algorithm was able to predict 3 intents (turn left, turn right and walk straight) with 92.9 % accuracy.The wrist is an essential component in performing the activities of daily living (ADLs) associated with a high quality of life. After a neurological disorder, motor function of the hand and wrist can be affected, reducing quality of life. Many experiments have illustrated that more wrist flexion/extension is required than radial/ulnar deviation when performing ADLs; however, how this result translates to efficiency in performing ADLs has not been investigated. Motivated by clinical assessment during neurorehabilitation, in this paper we investigate with able-bodied participants how performing tasks representative of the Jebsen-Taylor Hand Function Test are impacted when a splint constrains the user to a single rotational degree of freedom of the wrist. Twenty participants enrolled in the study, performing five tasks under five conditions, including constraint to pure flexion/extension and radial/ulnar deviation. The importance of wrist movement direction in performing ADLs efficiently found in this study could shape clinical wrist rehabilitation paradigms and wrist rehabilitation robot designs.Olfactory perception is intrinsically tied to emotional processing, in both behavior and neurophysiology. Despite advances in olfactory-affective neuroscience, it is unclear how separate attributes of odor stimuli contribute to olfactoryinduced emotions, especially within the positive segment of the hedonic dimension to avoid potential cross-valence confounds. In this study, we examined how pleasantness and intensity of fragrances relate to different grades of positive affect. Our results show that greater odor pleasantness and intensity are independently associated with stronger positive affect. Pleasantness has a greater influence than intensity in evoking a positive vs. neutral affect, whereas intensity is more impactful than pleasantness in evoking an extreme positive vs. positive response. Autonomic response, as assessed by the galvanic skin response (GSR) was found to decrease with increasing pleasantness but not intensity. This clarifies how olfactory and affective processing induce significant downstream effects in peripheral physiology and self-reported affective experience, pertinent to the thriving field of olfactory neuromarkerting.Haptic virtual environments have been used to assess cognitive and fine motor function. For tasks performed in physical environments, upper extremity movement is usually separated into reaching and object manipulation phases using fixed velocity thresholds. However, these thresholds can result in premature segmentation due to additional trajectory adjustments common in virtual environments. In this work, we address the issues of premature segmentation and the lack of a measure to characterize the spatial distribution of a trajectory while targeting an object. We propose a combined relative distance and velocity segmentation procedure and use principal component analysis (PCA) to capture the spatial distribution of the participant's targeting phase. Synthetic data and 3D motion data from twenty healthy adults were used to evaluate the methods with positive results. We found that these methods quantify motor skill improvement of healthy participants performing repeated trials of a haptic virtual environment task.
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