Niki Pandria

Resting-state abnormalities in heroin-dependent individuals

Drug addiction is a major health problem worldwide. Recent neuroimaging studies have shed light into the underlying mechanisms of drug addiction as well as its consequences to the human brain. The most vulnerable, to heroin addiction, brain regions have been reported to be specific prefrontal, parietal, occipital, and temporal regions, as well as, some subcortical regions. The brain regions involved are usually linked with reward, motivation/drive, memory/learning, inhibition as well as emotional control and seem to form circuits that interact with each other. So, along with neuroimaging studies, recent advances in resting-state dynamics might allow further assessments upon the multilayer complexity of addiction. In the current manuscript, we comprehensively review and discuss existing resting-state neuroimaging findings classified into three overlapping and interconnected groups: functional connectivity alterations, structural deficits and abnormal topological properties. Moreover, behavioral traits of heroin-addicted individuals as well as the limitations of the currently available studies are also reviewed. Finally, in need of a contemporary therapy a multimodal therapeutic approach is suggested using classical treatment practices along with current neurotechonologies, such as neurofeedback and goal-oriented video-games.

Wireless Brain-Robot Interface: User Perception and Performance Assessment of Spinal Cord Injury Patients

Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly benefit from assistive robotics technology. The field of brain-computer interfaces (BCIs) has started to develop mature assistive applications for those patients. Nonetheless, noninvasive BCIs still lack accurate control of external devices along several degrees of freedom (DoFs). Unobtrusiveness, portability, and simplicity should not be sacrificed in favor of complex performance and user acceptance should be a key aim among future technological directions. In our study 10 subjects with SCI (one complete) and 10 healthy controls were recruited. In a single session they operated two anthropomorphic 8-DoF robotic arms via wireless commercial BCI, using kinesthetic motor imagery to perform 32 different upper extremity movements. Training skill and BCI control performance were analyzed with regard to demographics, neurological condition, independence, imagery capacity, psychometric evaluation, and user perception. Healthy controls, SCI subgroup with positive neurological outcome, and SCI subgroup with cervical injuries performed better in BCI control. User perception of the robot did not differ between SCI and healthy groups. SCI subgroup with negative outcome rated Anthropomorphism higher. Multi-DoF robotics control is possible by patients through commercial wireless BCI. Multiple sessions and tailored BCI algorithms are needed to improve performance.

A Systematic Review of Investigations into Functional Brain Connectivity Following Spinal Cord Injury

Background: Complete or incomplete spinal cord injury (SCI) results in varying degree of motor, sensory and autonomic impairment. Long-lasting, often irreversible disability results from disconnection of efferent and afferent pathways. How does this disconnection affect brain function is not so clear. Changes in brain organization and structure have been associated with SCI and have been extensively studied and reviewed. Yet, our knowledge regarding brain connectivity changes following SCI is overall lacking. Methods: In this study we conduct a systematic review of articles regarding investigations of functional brain networks following SCI, searching on PubMed, Scopus and ScienceDirect according to PRISMA-P 2015 statement standards. Results: Changes in brain connectivity have been shown even during the early stages of the chronic condition and correlate with the degree of neurological impairment. Connectivity changes appear as dynamic post-injury procedures. Sensorimotor networks of patients and healthy individuals share similar patterns but new functional interactions have been identified as unique to SCI networks. Conclusions: Large-scale, multi-modal, longitudinal studies on SCI patients are needed to understand how brain network reorganization is established and progresses through the course of the condition. The expected insight holds clinical relevance in preventing maladaptive plasticity after SCI through individualized neurorehabilitation, as well as the design of connectivity-based brain-computer interfaces and assistive technologies for SCI patients.

Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.

Evaluation of Neurofeedback on ADHD Using Mobile Health Technologies

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common disorders that affect children. The existing therapeutic modalities have been characterized by side effects and short-term outcomes. Therefore, neurofeedback as non-invasive, non-pharmacological and painless biofeedback technique could be a promising treatment option. In this study, we present the experimental design of a project that aims to evaluate the neurofeedback effect on ADHD using a mobile health application called WHAAM. The application enables the network creation of individuals involved in child care (parents, teachers, health professionals) to collect information on the behavior of the child but further to evaluate the effectiveness of an intervention through provided quantitative - statistical reports. Our experimental design consists of five stages: (1) Inform parents concerning the project and complete of a consent form, (2) Select the child’s behaviors to be observed and organization of child’s network, (3) Gather data based on the predefined behaviors before neurofeedback, (4) Completion 20 session of neurofeedback with simultaneous behavior monitoring through WHAAM application, (5) Final data gathering based on the predefined behaviors after neurofeedback. The implementation of this study will introduce an innovative and objective way to evaluate an intervention with parallel involvement of a child care network in intervention’s supervision.

The future of mobile health ADHD applications

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common disorders that affect children. The diagnosis and the Cognitive-Behavioral treatment approaches are based on childs behavioral assessment through pen and pen-and-paper procedures. A number of mobile applications have been designed not only to replace traditional methods but also to provide more accurate, objective, direct and reliable recordings, better management of ADHD symptoms, education and training about the ADHD or even tools for ADHD diagnosis. The WHAAM application through a virtual network provides features to monitor behaviors in a SMART way (Specific, Measurable, Attainable, Realistic and Timely). In other words, creating a network of people involved in childs care (parents, educators, health professionals, relatives), WHAAM app allows data collection when the behavior occurs accompanied by information about its content and environment. Subsequently, gathered data is visualized and evaluated making possible an intervention planning and programming by the involved health professional. Additionally, the WHAAM app provides tools for evaluation of intervention efficacy. However, as emerging technologies came to facilitate healthcare delivery, there is a need for a continuous challenging and progress. Therefore, additional health data collection through advanced sensors and storage in big data hubs might be the new challenge of the future m-health applications.

Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space

Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.

Exploring the Neuroplastic Effects of Biofeedback Training on Smokers

Smoking and stress cooccur in different stages of a nicotine addiction cycle, affecting brain function and showing additive impact on different physiological responses. Resting-state functional connectivity has shown potential in identifying these alterations. Nicotine addiction has been associated with detrimental effects on functional integrity of the central nervous system, including the organization of resting-state networks. Prolonged stress may result in enhanced activation of the default mode network (DMN). Considering that biofeedback has shown promise in alleviating physiological manifestations of stress, we aimed to explore the possible neuroplastic effects of biofeedback training on smokers. Clinical, behavioral, and neurophysiological (resting-state EEG) data were collected from twenty-seven subjects before and after five sessions of skin temperature training. DMN functional cortical connectivity was investigated. While clinical status remained unaltered, the degree of nicotine dependence and psychiatric symptoms were significantly improved. Significant changes in DMN organization and network properties were not observed, except for a significant increase of information flow from the right ventrolateral prefrontal cortex and right temporal pole cortex towards other DMN components. Biofeedback aiming at stress alleviation in smokers could play a protective role against maladaptive plasticity of connectivity. Multiple sessions, individualized interventions and more suitable methods to promote brain plasticity, such as neurofeedback training, should be considered.

Assessing Emotional Impact of Biofeedback and Neurofeedback Training in Smokers During a Smoking Cessation Project

This pilot study was conducted in the framework of SmokeFreeBrain project and it aimed at assessing the subjective emotional impact of skin temperature training and neurofeedback training on smokers by means of the AffectLecture application. The current paper constitutes a proof-of-concept, exploring the case of a single participant. The intervention consists of 5 sessions of biofeedback followed by 20 sessions of neurofeedback. Both pre- and post- biofeedback and neurofeedback training subjective scores of the participants mood were collected through the application. Based on our results, biofeedback training seems to promote alterations in mood, which are then maintained in the baseline mood scoring before neurofeedback training. Additionally, mood seems to be preserved after neurofeedback training. However, significant correlations between scoring and training performance have not been indicated.

Commercial BCI Control and Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept

Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms. Two BCI-naïve female subjects, a SCI patient and a healthy control subject participated in the proof-of-concept implementation. They were instructed to perform motor imagery (MI) while watching multiple movements of either arms or legs during walking, while under 128-channel EEG recording. They were, subsequently, asked to control two robotic arms (Mercury v2.0) using a commercial class EEG-BCI. They both achieved comparable performance levels of robotic control, 52.5% for the SCI patient and 56.9% for the healthy control. We performed a feasibility analysis of functional networks on the EEG-BCI recordings. Visual MI allows training on multiple imagined movements and shows promise in investigating differences in functional cortical networks associated with different motor tasks. This approach could allow the implementation of functional network-based BCIs in the future for complex movement control.