fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment

Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images

The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a “proof of concept” about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis

Технология сборки и сварки труб диаметром 1420 мм.

Цель работы - разработка технологии и технико-экономического обоснования сборки и сварки магистрального трубопровода. В процессе работы был проведен сравнительный технико-экономический анализ сварки корневого слоя шва двумя способами сварки. Используемая технология, ручная дуговая сварка покрытыми электродами и предлагаемая – механизированная сварка в среде защитных газов.The work purpose - working out of technology and the feasibility report on assemblage and welding of two lashes of the main pipeline consisting of two one-tubes. In the course of work the comparative technical and economic analysis of procooking of a root layer of a seam has been carried out by two ways of welding. Used technology, manual arc welding by the covered electrodes and offered - the mechanised welding in the environment of protective gases

Sex-based differences in functional brain activity during working memory in survivors of pediatric acute lymphoblastic leukemia

BACKGROUND: Long-term survivors of pediatric acute lymphoblastic leukemia are at elevated risk for neurocognitive deficits and corresponding brain dysfunction. This study examined sex-based differences in functional neuroimaging outcomes in acute lymphoblastic leukemia survivors treated with chemotherapy alone. METHODS: Functional magnetic resonance imaging (fMRI) and neurocognitive testing were obtained in 123 survivors (46% male; median [min-max] age = 14.2 years [8.3-26.5 years]; time since diagnosis = 7.7 years [5.1-12.5 years]) treated on the St. Jude Total XV treatment protocol. Participants performed the n-back working memory task in a 3 T scanner. Functional neuroimaging data were processed (realigned, slice time corrected, normalized, smoothed) and analyzed using statistical parametric mapping with contrasts for 1-back and 2-back conditions, which reflect varying degrees of working memory and task load. Group-level fMRI contrasts were stratified by sex and adjusted for age and methotrexate exposure. Statistical tests were 2-sided (P \u3c .05 statistical significance threshold). RESULTS: Relative to males, female survivors exhibited less activation (ie, reduced blood oxygen dependent-level signals) in the right parietal operculum, supramarginal gyrus and inferior occipital gyrus, and bilateral superior frontal medial gyrus during increased working memory load (family-wise error-corrected P = .004 to .008, adjusting for age and methotrexate dose). Female survivors were slower to correctly respond to the 2-back condition than males (P \u3c .05), though there were no differences in overall accuracy. Performance accuracy was negatively correlated with fMRI activity in female survivors (Pearson\u27s r = -0.39 to -0.29, P = .001 to .02), but not in males. CONCLUSIONS: These results suggest the working memory network is more impaired in female survivors than male survivors, which may contribute to ongoing functional deficits

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task

IntroductionLearning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.MethodsWe study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.ResultsOur analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.DiscussionThe findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process

Quantifying the Link between Anatomical Connectivity, Gray Matter Volume and Regional Cerebral Blood Flow: An Integrative MRI Study

Background In the graph theoretical analysis of anatomical brain connectivity, the white matter connections between regions of the brain are identified and serve as basis for the assessment of regional connectivity profiles, for example, to locate the hubs of the brain. But regions of the brain can be characterised further with respect to their gray matter volume or resting state perfusion. Local anatomical connectivity, gray matter volume and perfusion are traits of each brain region that are likely to be interdependent, however, particular patterns of systematic covariation have not yet been identified. Methodology/Principal Findings We quantified the covariation of these traits by conducting an integrative MRI study on 23 subjects, utilising a combination of Diffusion Tensor Imaging, Arterial Spin Labeling and anatomical imaging. Based on our hypothesis that local connectivity, gray matter volume and perfusion are linked, we correlated these measures and particularly isolated the covariation of connectivity and perfusion by statistically controlling for gray matter volume. We found significant levels of covariation on the group- and regionwise level, particularly in regions of the Default Brain Mode Network. Conclusions/Significance Connectivity and perfusion are systematically linked throughout a number of brain regions, thus we discuss these results as a starting point for further research on the role of homology in the formation of functional connectivity networks and on how structure/function relationships can manifest in the form of such trait interdependency

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.</p

Metabolische Gehirn-Komputer Schnittstelle

Brain-Computer Interfaces (BCI) utilise neurophysiological signals originating in the brain to activate or deactivate external devices or computers (Donoghue 2002; Wolpaw, Birbaumer et al. 2002; Nicolelis 2003; Birbaumer and Cohen 2007). The neuronal signals can be recorded from inside the brain (invasive BCIs) or outside (non-invasive BCIs) of the brain. Most BCIs developed so far have used operant training of direct neuroelectric responses, Electroencephalography (EEG) waves, event-related potentials and brain oscillations (Birbaumer, Weber et al. 2006; Birbaumer and Cohen 2007). Compared to neuroelectric studies on regulation of brain activity, there have been fewer studies with metabolic signals from the brain (Sitaram, Caria et al. 2007; Weiskopf, Sitaram et al. 2007; Sitaram, Weiskopf et al. 2008). Near Infrared Spectroscopy (NIRS) and Functional magnetic resonance imaging (fMRI) present themselves as attractive methods of acquiring hemodynamic activity of the brain for a developing a BCI. In this study, we exploit NIRS and fMRI for the implementation of BCIs for the investigation of regulation of hemodynamic signals in the brain and their behavioural consequences. We propose that these methods could be used not only for communication and control in paralysis, but also as powerful tools for experiments in neuroscience and rehabilitation and treatment of neurological disorders.The neuronal signals can be recorded from inside the brain (invasive BCIs) or outside (non-invasive BCIs) of the brain. Most BCIs developed so far have used operant training of direct neuroelectric responses, Electroencephalography (EEG) waves, event-related potentials and brain oscillations (Birbaumer, Weber et al. 2006; Birbaumer and Cohen 2007). Compared to neuroelectric studies on regulation of brain activity, there have been fewer studies with metabolic signals from the brain (Sitaram, Caria et al. 2007; Weiskopf, Sitaram et al. 2007; Sitaram, Weiskopf et al. 2008). Near Infrared Spectroscopy (NIRS) and Functional magnetic resonance imaging (fMRI) present themselves as attractive methods of acquiring hemodynamic activity of the brain for a developing a BCI. In this study, we exploit NIRS and fMRI for the implementation of BCIs for the investigation of regulation of hemodynamic signals in the brain and their behavioural consequences. We propose that these methods could be used not only for communication and control in paralysis, but also as powerful tools for experiments in neuroscience and rehabilitation and treatment of neurological disorders