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In recent years, it has been increasingly recognized that tumor growth relies not only on support from the surrounding microenvironment but also on the tumors capacity to adapt to – and actively manipulate – its niche. While targeting angiogenesis and modulating the local immune environment have been explored as therapeutic approaches, these strategies have yet to yield effective treatments for brain tumors and remain under refinement. More recently, the nervous system itself has been explored as a critical environmental support for cancer, with extensive neuro-tumoral interactions observed both intracranially and in extracranial sites containing neural components. In the brain, interactions between glioma cells as well as metastatic lesions with neural components have clinical implications for diagnostics, risk assessments, neurological sequelae, and the development of innovative therapeutics. Here, we review these neuro-tumoral dynamics, emphasizing aspects relevant to neurosurgical practice. [ABSTRACT FROM AUTHOR]

Traumatic brain injuries can result in short-lived and long-lasting neurological impairment. Identifying the correct recovery timeframe is challenging, as balance-based metrics may be negatively impacted if testing is performed soon after exercise. Thirty-two healthy controls and seventeen concussed individuals performed a series of balance challenges, including virtual reality optical flow perturbation. The control group completed a backpacking protocol to induce moderate fatigue. Concussed participants had lower spectral power in the motor cortex and central sulcus when compared to fatigued controls. Moreover, concussed participants experienced a decrease in overall theta band spectral power while fatigued controls showed an increase in theta band spectral power. This neural signature may be useful to distinguish between concussed and non-concussed fatigued participants in future assessments. [ABSTRACT FROM AUTHOR]

Objective: People diagnosed with cancer have to deal with the debilitating psychological implications of this disease. Although the clinical efficacy of psychological interventions is well documented, relatively little has been written on the neural correlates of these treatments in the context of oncology. The present work is the first to provide an overall perspective of the existing literature on this topic. It also considers the potential directions for future research. Methods: This scoping review was carried out across 5 databases (EMBASE, PsycINFO, OVID MEDLINE, CINAHL, COCHRANE CENTRAL), from conception dates until 3 December 2021. Results: From an initial set of 4172 records, 13 papers were selected for this review. They consisted of 9 randomized controlled studies (RCTs), 1 quasi-experiment, 2 single case studies, and 1 secondary quantitative analysis. The studies were also heterogeneous in terms of the patient and control populations, psychological interventions, and neuroimaging methodologies used. The findings from these few studies suggest that psychological interventions in oncology patients may modulate both cortical and subcortical brain activity, consistent with the brain areas involved in distress reactions in general and to cancer specifically. The implications of this scoping review in terms of future research are also discussed. Conclusions: The literature on the neural correlates of psychological interventions in cancer patients is very limited, and thus requires further exploration. The provision of psychological interventions offers cancer patients a more integrated approach to care, which may in turn help preserve both the physical and the psychological wellbeing of individuals with cancer. [ABSTRACT FROM AUTHOR]

Decoding human movement parameters from electroencephalograms (EEG) signals is of great value for human-machine collaboration. However, existing studies on hand movement direction decoding concentrate on the decoding of a single-hand movement direction from EEG signals given the opposite hand is maintained still. In practice, the cooperative movement of both hands is common. In this paper, we investigated the neural signatures and decoding of single-hand and both-hand movement directions from EEG signals. The potentials of EEG signals and power sums in the low frequency band of EEG signals from 24 channels were used as decoding features. The linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used for decoding. Experimental results showed a significant difference in the negative offset maximums of movement-related cortical potentials (MRCPs) at electrode Cz between single-hand and both-hand movements. The recognition accuracies for six-class classification, including two single-hand and four both-hand movement directions, reached 70.29%± 10.85% by using EEG potentials as features with the SVM classifier. These findings showed the feasibility of decoding single-hand and both-hand movement directions. This work can lay a foundation for the future development of an active human-machine collaboration system based on EEG signals and open a new research direction in the field of decoding hand movement parameters from EEG signals. [ABSTRACT FROM AUTHOR]

Establishing links between neural systems and health can be challenging since there is not a one-to-one mapping between brain regions and psychological states. Building sensitive and specific predictive models of health-relevant constructs using multivariate activation patterns of brain activation is a promising new direction. We illustrate the potential of this approach by building two 'neural signatures' of food craving regulation (CR) using multivariate machine learning and, for comparison, a univariate contrast. We applied the signatures to two large validation samples of overweight adults who completed tasks measuring CR ability and valuation during food choice. Across these samples, the machine learning signature was more reliable. This signature decoded CR from food viewing and higher signature expression was associated with less craving. During food choice, expression of the regulation signature was stronger for unhealthy foods and inversely related to subjective value, indicating that participants engaged in CR despite never being instructed to control their cravings. Neural signatures thus have the potential to measure spontaneous engagement of mental processes in the absence of explicit instruction, affording greater ecological validity. We close by discussing the opportunities and challenges of this approach, emphasizing what machine learning tools bring to the field of health neuroscience. [ABSTRACT FROM AUTHOR]

Acts of helping friends and strangers are part of everyday life. However, people vary significantly with respect to how often they help others and with respect to whom they actually help on a day-to-day basis. Despite everyday helping being so pervasive, these individual differences are poorly understood. Here, we used source-localized resting electroencephalography to measure objective and stable individual differences in neural baseline activation in combination with an ecologically valid method that allows assessment of helping behavior in the field. Results revealed that neural baseline activation in the right dorsolateral prefrontal cortex (DLPFC) - a brain region associated with self-control and strategic social behavior - predicts the daily frequency of helping friends, whereas the daily frequency of helping strangers was predicted by neural baseline activation in the dorsomedial prefrontal cortex (DMPFC) - a brain region associated with social cognition processes. These findings offer evidence that distinct neural signatures and associated psychological and cognitive processes may underlie the propensity to help friends and strangers in daily life. [ABSTRACT FROM AUTHOR]

Genetic and neurophysiological studies of electroencephalogram (EEG) have shown that an individual's brain activity during a given cognitive task is, to some extent, determined by their genes. In fact, the field of biometrics has successfully used this property to build systems capable of identifying users from their neural activity. These studies have always been carried out in isolated conditions, such as relaxing with eyes closed, identifying visual targets or solving mathematical operations. Here we show for the first time that the neural signature extracted from the spectral shape of the EEG is to a large extent independent of the recorded cognitive task and experimental condition. In addition, we propose to use this task-independent neural signature for more precise biometric identity verification. We present two systems: one based on real cepstrums and one based on linear predictive coefficients. We obtained verification accuracies above 89% on 4 of the 6 databases used. We anticipate this finding will create a new set of experimental possibilities within many brain research fields, such as the study of neuroplasticity, neurodegenerative diseases and brain machine interfaces, as well as the mentioned genetic, neurophysiological and biometric studies. Furthermore, the proposed biometric approach represents an important advance towards real world deployments of this new technology. [ABSTRACT FROM AUTHOR]

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