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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]

The processing of reinforcers and punishers is crucial to adapt to an ever changing environment and its dysregulation is prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks.

This study aimed to explore the relationships between potential neurophysiological biomarkers and upper limb motor function recovery in stroke patients, specifically focusing on combining two neurophysiological markers: electroencephalography (EEG) and transcranial magnetic stimulation (TMS). This cross-sectional study analyzed neurophysiological, clinical, and demographical data from 102 stroke patients from the DEFINE cohort. We searched for correlations of EEG and TMS measurements combined to build a prediction model for upper limb motor functionality, assessed by five outcomes, across five assessments: Fugl-Meyer Assessment (FMA), Handgrip Strength Test (HST), Finger Tapping Test (FTT), Nine-Hole Peg Test (9HPT), and Pinch Strength Test (PST). Our multivariate models agreed on a specific neural signature: higher EEG Theta/Alpha ratio in the frontal region of the lesioned hemisphere is associated with poorer motor outcomes, while increased MEP amplitude in the non-lesioned hemisphere correlates with improved motor function. These relationships are held across all five motor assessments, suggesting the potential of these neurophysiological measures as recovery biomarkers. Our findings indicate a potential neural signature of brain compensation in which lower frequencies of EEG power are increased in the lesioned hemisphere, and lower corticospinal excitability is also increased in the non-lesioned hemisphere. We discuss the meaning of these findings in the context of motor recovery in stroke. [ABSTRACT FROM AUTHOR]

Background: Impaired emotion regulation (ER) contributes to major depression and suicidal ideation and behavior. ER is typically studied by explicitly directing participants to regulate, but this may not capture spontaneous tendencies of individuals with depression to engage ER in daily life., Methods: In 82 participants with major depressive disorder, we examined the relationship of spontaneous engagement of ER to real-world responses to stress. We used a machine learning-derived neural signature reflecting neural systems that underlie cognitive reappraisal (an ER strategy) to identify reappraisal-related activity while participants recalled negative autobiographical memories under the following conditions: 1) unstructured recall; 2) distanced recall, a form of reappraisal; and 3) immersed recall (comparison condition). Participants also completed a week of ecological momentary assessment measuring daily stressors, suicidal ideation, and negative affect., Results: Higher reappraisal signature output for the unstructured period, a proxy for the spontaneous tendency to engage ER, was associated with greater increases in suicidal ideation following stressors (b = 0.083, p = .041). Higher signature output for distanced recall, a proxy for the capacity to engage ER when directed, was associated with lower negative affect following stressors (b = -0.085, p = .029). Output for the immerse period was not associated with ecological momentary assessment outcomes., Conclusions: Findings suggest that in major depressive disorder, the spontaneous tendency to react to negative memories with attempts to reappraise may indicate greater reactivity to negative cues, while intact capacity to use reappraisal when directed may be associated with more adaptive responses to stress. These data have implications for understanding stress-related increases in suicide risk in depression., (Copyright © 2024. Published by Elsevier Inc.)

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]

Pathophysiological models are urgently needed for personalized treatments of mental disorders. However, most potential neural markers for psychopathology are limited by low interpretability, prohibiting reverse inference from brain measures to clinical symptoms and traits. Neural signatures—i.e. multivariate brain-patterns trained to be both sensitive and specific to a construct of interest—might alleviate this problem, but are rarely applied to mental disorders. We tested whether previously developed neural signatures for negative affect and discrete emotions distinguish between healthy individuals and those with mental disorders characterized by emotion dysregulation, i.e. Borderline Personality Disorder (BPD) and complex Post-traumatic Stress Disorder (cPTSD). In three different fMRI studies, a total sample of 192 women (49 BPD, 62 cPTSD, 81 healthy controls) were shown pictures of scenes with negative or neutral content. Based on pathophysiological models, we hypothesized higher negative and lower positive reactivity of neural emotion signatures in participants with emotion dysregulation. The expression of neural signatures differed strongly between neutral and negative pictures (average Cohen's d = 1.17). Nevertheless, a mega-analysis on individual participant data showed no differences in the reactivity of neural signatures between participants with and without emotion dysregulation. Confidence intervals ruled out even small effect sizes in the hypothesized direction and were further supported by Bayes factors. Overall, these results support the validity of neural signatures for emotional states during fMRI tasks, but raise important questions concerning their link to individual differences in emotion dysregulation.

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]

Linguistics is a relatively young field. The birth of a new, vibrant field of research often brings with it certain challenges such as the initial absence of an uncontroversial canon and a certain lack of terminological clarity. Following the example of closely allied disciplines, this work aims to register ambiguities in the use of ten terms in linguistics, with the overarching aim to aid field-internal coherence and field-external visibility. Among other issues, we discuss the influential ‘three factors’ model, labeling, reference, and E-/I-language. Addressing the challenge of looking back while moving forward, we compile a collection of definitions and/or presentations extracted from knowledge-rich contexts for each term, grounded in current usages. We first reflect on previous usages in order to present the first definitions of these terms and track terminological ambiguities that arose throughout their subsequent use. We then attempt to transition towards terminological clarity, providing specific recommendations for a more transparent use of these terms.

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]

• The BRS significantly predicts the magnitude of rewards and losses using distributed neural patterns with high accuracy. • The predictions of the BRS generalizes across samples and tasks involving rewards and losses and do not accurately distinguish between different levels of disgust when outcomes are contingent on participants' choices. • Outside of the action-outcome framework the BRS may capture processes related to information seeking. • Combined with neural signatures already developed (e.g. for guilt, pain) the BRS can be used to differentiate the contribution of various affective and motivational neurocognitive processes to social decisions. The processing of reinforcers and punishers is crucial to adapt to an ever changing environment and its dysregulation is prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks. [ABSTRACT FROM AUTHOR]

The overall comfort of train passenger is influenced by many environmental factors such as vibration, noise and pressure. However, the couple effect of these influencing factors causes the difficulty in evaluating the overall comfort. This study revealed the potential comfort degradation mechanisms in high-speed railway environments and proposed a machine learning evaluation model to assess passenger comfort. Here, the subjective overall comfort ratings and the electroencephalography (EEG) of twenty passengers were collected in the field tests. Compared with passengers who were in a state of comfort, the brain areas (BA6/13/20/24/31/40/47) of passengers who felt uncomfortable were all significantly activated in the beta band. Based on the neural signature above, three related human reactions when passengers feel uncomfortable were recognized, including perceiving the environment, inducing negative emotions, and finally producing body movement intention. To assess the overall comfort of train passengers, six kinds of features extracted from EEG signals were used to train an evaluation model based on the LightGBM algorithm. This work offers a neurological explanation for the mechanisms of degradation of overall comfort and provides a novel and effective method to assess it. • Carried out a field test in high-speed railway operating environments. • Collect the EEG signals and questionnaires to assess passenger overall comfort. • Establish a machine learning model for overall comfort evaluation. • First-of-its-kind that reveal the discomfort mechanism based on neural signatures. • Prove that the emotional regulation has a great influence on passenger comfort. [ABSTRACT FROM AUTHOR]

Recently, the presence of very precise intra-burst firing patterns, which have been named neural signatures, has been reported in bursting neurons of the pyloric network of the lobster stomatogastric nervous system [A. Szücs, R.D. Pinto, M.I. Rabinovich, H.D.I. Abarbanel, A.I. Selverston, Synaptic modulation of the interspike interval signatures of bursting pyloric neurons, J. Neurophysiol. 89, (2003) 1363–1377]. This finding suggests that intra-burst activity may be essential in neural transmission. Typical analysis of intra-burst activity uses the first spike in the burst as the time reference, and computes ISI distributions and return maps to characterize the signatures. In this paper we show that the first spike may not be the best time reference, and propose a new method to align the bursts that minimizes the overlap among the firing time distributions of the different spikes in the burst. The method can be applied to real recordings in order to obtain a better characterization of intra-burst activity. [Copyright &y& Elsevier]