Dynamic Adaptation of Brain Networks from Rest to Task and Application to Stroke Research

Examining how motor task modulates brain activity plays a critical role of understanding how cerebral motor system works and could further be applied in the research of motor disability due to neurological diseases. Recent advances in neuroimaging have resulted in numerous studies focusing on motor-induced modulation of brain activity and most widely used strategy of these studies was identifying activated brain regions during motor task through event-related/block-design experiment paradigms. Despite progress obtained, motor-related activation analysis mainly focused on modulation of brain activities for individual brain regions. However, human brain is known to be an integrated network, and the adaptation of brain in response to motor task could be reflected by the modulation of brain networks. Thus, investigating the spatiotemporal modulation of task-state brain networks during motor task would provide system-level information regarding the underlying adaptation of cerebral motor system in response to the motor task and could be further applied in the research of motor disability due to neurological diseases. Although the task-state functional connectivity (FC) and networks have been examined by previous studies, there are still several aspects needed to be explored: (1) Previous task-state FC studies were mainly based on functional magnetic resonance imaging (fMRI) and potential of applying functional near-infrared spectroscopy (fNIRS), which is a promising complementary modality to fMRI because of its low cost and relatively high temporal resolution (10 Hz in sampling rate compared with less than 1Hz in sampling rate for fMRI), in task-state FC studies should be explored. (2) In the fMRI studies, the task-state brain network was mainly investigated from the perspective of static FC, which focuses on the spatial pattern of the task-state brain networks. However, brain activities and cognitive processes are known to be dynamic and adaptive and the newly emerging dynamic FC analysis could further provide temporal patterns of the task-state brain networks. Thus, the spatiotemporal pattern of task-state brain network during motor task is still needed to be investigated by dynamic FC analysis based on fMRI; (3) Task-state brain network analysis has not been applied in the research of stroke, and the relationship between task-state brain network and stroke recovery has not been investigated; Therefore, in this thesis, we aim to investigate the task-state brain networks during motor task using both static and dynamic FC analysis based on fNIRS and fMRI to reveal the motor task-specific spatiotemporal changes of brain networks compared with resting conditions, and further applied the task-state brain network analysis in the research of stroke recovery In this thesis, fMRI and fNIRS were employed to record the brain hemodynamic signals, and static FC and dynamic FC were used to investigate spatiotemporal pattern of task-state brain network during motor task. In addition, the fMRI data of stroke patients were recorded at four time points post stroke, and the reorganization of task-state brain network as well as its relationship to stroke recovery were examined. Specific results are described as follow: (1) Through static FC analysis of fNIRS during rest and motor preparation, increased FC were identified during motor preparation, especially the FC connecting right dorsolateral prefrontal cortex (DLPFC) with contralateral primary somatosensory cortex (S1) and primary motor cortex (M1) as well as the FC connecting contralateral S1 with ipsilateral S1 and M1. Channels located in sensorimotor networks and right DLPFC were also found activated during motor preparation. Our findings demonstrated that the sensorimotor network was interacting with high-level cognitive brain network to maintain the motor preparation state. (2) Through dynamic FC analysis of fNIRS during rest and motor execution, increased variability of FC connecting contralateral premotor and supplementary motor cortex (PMSMC) and M1 was identified, and the nodal strength variability of these two brain regions were also increased during motor execution. Our findings demonstrated that contralateral M1 and PMSMC were interacting with each other actively and dynamically to facilitate the fist opening and closing. (3) Through dynamic FC analysis on fMRI data, two principal FC states during rest and one principal FC state during motor task were identified. The 1st principal FC state in rest was similar to that in task, which likely represented intrinsic network architecture and validated the broadly similar spatial patterns between rest and task. However, the presence of a 2nd principal FC state with increased FC between default-mode network (DMN) and motor network (MN) in rest with shorter "dwell time" could imply the transient functional relationship between DMN and MN to establish the "default mode" for motor system. In addition, the more frequent shifting between two principal FC states in rest indicated that the brain networks dynamically maintained the "default mode" for the motor system. In contrast, during task, the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity, validating the distinct temporal patterns between rest and task. Our findings suggested that the principal states could show a link between the rest and task states, and verified our hypothesis on overall spatial similarity but distinct temporal patterns of dynamic brain networks between rest and task states. (4) Task-state motor network was applied in the research of motor disability due to stroke and topological reorganization of task-state motor network was identified during sub-acute phase post stroke. In addition, for the first time, our study found the topological configuration of task-state motor network at the early recovery stage were capable of predicting the motor function restoration during sub-acute phase. In general, the findings demonstrated the reorganization and potential prognostic value of task-state brain network after stroke, which provided new insights into understanding the brain reorganization and stroke rehabilitation. In summary, this thesis used two neuroimaging modalities (fMRI and fNIRS) to investigate how brain networks, especially the motor network and high-level cognitive network, would reorganize spatiotemporally from resting-state to motor tasks through both static and dynamic FC analysis, and further applied the task-state brain network analysis in the research of motor disability due to stroke. Our findings revealed the underlying spatiotemporal adaptation of brain networks in response to motor task and demonstrated the potential clinical prognostic value of task-state motor network during stroke recovery. The novelties of this thesis are as follow: (1) dynamic FC was innovatively applied in revealing the motor task-specific spatiotemporal changes of brain networks compared with resting conditions; (2) task-state motor network was applied in the research of stroke recovery; (3) static and dynamic FC analysis were innovatively applied in fNIRS data.Ph.D., Biomedical Engineering -- Drexel University, 201

Influence of Early Bilingual Exposure in the Developing Human Brain.

190 p.La adquisición del lenguaje es un proceso que ese encuentra determinado tanto por mecanismos de desarrollo cognitivo, como por la experiencia lingüística durante los primeros años de vida. Aunque se trata de un proceso relativamente complejo, los bebés muestran una gran habilidad para el aprendizaje del lenguaje. Un entorno de aprendizaje lingüístico bilingüe podría considerarse aun más complejo, ya que los bebés están expuestos a las características lingüísticas de dos lenguas simultáneamente. En primer lugar, los bebés que crecen en un entorno bilingüe tienen que ser capaces de darse cuenta de que están expuestos a dos lenguas diferentes, y posteriormente deben separar y aprender las características especificas de cada una de ellas; por ejemplo, los distintos fonemas, palabras o estructuras gramaticales. Aunque la exposición lingüística total de los bebés bilingües debería ser comparable a la de los bebés monolingües, es probable que la exposición a cada una de las lenguas de su entorno sea menor, ya que tienen que dividir su tiempo de exposición entre ambas. Si bien los bebés bilingües parecen no tener problemas para enfrentarse a un contexto de aprendizaje potencialmente más complejo, ya que alcanzan las distintas etapas de adquisición del lenguaje a un ritmo similar a los bebés monolingües, sí se han observado adaptaciones a nivel conductual y a nivel de funcionamiento cerebral que podrían producirse como consecuencia de este contexto.Basque Center on cognition, brain and languag

Influence of Early Bilingual Exposure in the Developing Human Brain.

190 p.La adquisición del lenguaje es un proceso que ese encuentra determinado tanto por mecanismos de desarrollo cognitivo, como por la experiencia lingüística durante los primeros años de vida. Aunque se trata de un proceso relativamente complejo, los bebés muestran una gran habilidad para el aprendizaje del lenguaje. Un entorno de aprendizaje lingüístico bilingüe podría considerarse aun más complejo, ya que los bebés están expuestos a las características lingüísticas de dos lenguas simultáneamente. En primer lugar, los bebés que crecen en un entorno bilingüe tienen que ser capaces de darse cuenta de que están expuestos a dos lenguas diferentes, y posteriormente deben separar y aprender las características especificas de cada una de ellas; por ejemplo, los distintos fonemas, palabras o estructuras gramaticales. Aunque la exposición lingüística total de los bebés bilingües debería ser comparable a la de los bebés monolingües, es probable que la exposición a cada una de las lenguas de su entorno sea menor, ya que tienen que dividir su tiempo de exposición entre ambas. Si bien los bebés bilingües parecen no tener problemas para enfrentarse a un contexto de aprendizaje potencialmente más complejo, ya que alcanzan las distintas etapas de adquisición del lenguaje a un ritmo similar a los bebés monolingües, sí se han observado adaptaciones a nivel conductual y a nivel de funcionamiento cerebral que podrían producirse como consecuencia de este contexto.Basque Center on cognition, brain and languag

Towards a comprehensive understanding of brain machinery by correlative microscopy.

Unraveling the complexity of brain structure and function is the biggest challenge of contemporary science. Due to their flexibility, optical techniques are the key to exploring this intricate network. However, a single imaging technique can reveal only a small part of this machinery due to its inherent multilevel organization. To obtain a more comprehensive view of brain functionality, complementary approaches have been combined. For instance, brain activity was monitored simultaneously on different spatiotemporal scales with functional magnetic resonance imaging and calcium imaging. On the other hand, dynamic information on the structural plasticity of neuronal networks has been contextualized in a wider framework combining two-photon and light-sheet microscopy. Finally, synaptic features have been revealed on previously in vivo imaged samples by correlative light-electron microscopy. Although these approaches have revealed important features of brain machinery, they provided small bridges between specific spatiotemporal scales, lacking an omni-comprehensive view. In this perspective, we briefly review the state of the art of correlative techniques and propose a wider methodological framework fusing multiple levels of brain investigation

Neural dynamics of visual awareness investigated by means of Fast Optical Imaging and EEG

The search for the Neural Correlate of Consciousness (NCC, Koch, 2004) is one of the unresolved problems of cognitive neuroscience. Although great efforts have been made to seek to answer this fundamental question, theories about the neural basis of consciousness provide different and competing answers. The heterogeneity of the NCCs interpretations could be due to a methodological gap since so far studies trying to unveil the neural correlates of visual awareness have employed techniques that can reach a high level of resolution only in one dimension (i.e., space or time) resulting to be inadequate to investigate the spatio-temporal dynamics related to conscious vision. The following studies aim to elucidate the controversial search for the neural correlates of visual awareness, by proposing innovative and cutting-edge approaches that allow to move beyond these issues. In the first study, availing of EEG and EROS (Event-Related Optical Signal) techniques we seek to unravel the spatio-temporal dynamics occurring when a visual stimulus enters consciousness. To do so, participants’ brain activity is recorded during the performance of a discrimination task by means of EEG and EROS in separate sessions. EEG allows to investigate the electrophysiological correlates of visual awareness and to identify their exact timing, while EROS permits to disentangle which brain regions and in what order of activation are involved when the stimulus is reported as consciously perceived. Results revealed that when the stimulus entered the consciousness, it elicited a sustained activation in LOC, suggesting that this brain region could represent a reliable neural correlate of consciousness. Interestingly, this sustained activation occurred within the temporal window of VAN (Visual Awareness Negativity), corroborating the idea that LOC could serve as the cortical generator of VAN, which is typically considered a reliable marker of conscious vision. In the second study, EEG signal was decomposed into maximally independent components by means of ICA (Independent Component Analysis) in order to unveil the cortical generators and the time-courses of independent neural sources that significantly contribute to the ERP correlates of visual awareness (i.e., Visual Awareness Negativity and Late Positivity). It emerged that the neural sources of VAN seem to be localized in posterior brain regions, including occipital and temporal cortex, while LP seems to reflect a combination of multiple sources spread over frontal, parietal and occipito-temporal cortex. Overall, the present results provide innovative insights into the search for the neural correlates of visual awareness

Input and output order of recall as early markers of cognitive decline

This thesis explored the effects of age on free recall patterns in episodic memory. Neuropsychological and neuroimaging instruments were used to investigate the input (i.e., serial position effects) and output (i.e., temporal vs. spatial contiguity) of free recall in younger vs. healthy older individuals and in older adults with cognitive decline. In study 1 (Chapter 4), primacy (intended as the tendency to better remember items presented at the beginning of a list compared to the middle) at delayed recall was the most accurate serial position effect in predicting conversion to early stage Mild Cognitive Impairment (MCI), from a baseline of cognitively functioning older adults. In study 2 (Chapter 5), age differences in the use of spatial vs. temporal contiguity (intended as the tendency to retrieve items following the temporal, or spatial, context in which they have been learned) were explored in younger vs. healthy older adults. It was found that temporal contiguity was the most utilised associative process in both groups, although older adults showed lower temporal contiguity compared to younger adults. In study 3 (Chapter 6), the universality of temporal contiguity and the relationship between attentional processes and the output order of free recall were examined. Temporal vs. spatial contiguity were investigated during tasks meant to interfere with encoding processes, that is Divided Attention (DA) tasks and tasks involving presentation of verbal vs. pictorial material. Results showed consistent use of temporal contiguity in all experimental conditions, therefore suggesting the ubiquity of temporal contiguity and its involvement in retrieval processes. In study 4 (Chapter 7), the output order in free recall was investigated in younger and older adults in relation to prefrontal blood oxygenation, by means of functional Near-Infrared Spectroscopy (fNIRS). It was found that areas involved during temporal contiguity change with age, as younger adults showed greater activity of the right prefrontal cortex, whilst older adults engaged alternative or opposite regions. In study 5 (Chapter 8) the use of unrelated memory lists was investigated as a sensitive measure to detect age-related differences on the use of temporal vs. spatial contiguity. Moreover, age-associated differences in the use of temporal contiguity were explored at immediate vs. delayed recall. It was found that unrelated lists are able to detect age-related changes in the use of contiguity effects, and that temporal contiguity is negatively affected in both younger and older adults at delayed recall. In study 6 (Chapter 9) temporal clustering was investigated as potential predictor of conversion to Cognitive Unimpaired Declining (CUD) status, from a baseline of cognitively functioning older individuals. Results supported the hypothesis that temporal contiguity is a marker of cognitive decline, also when controlling for genetic information and for variables typically used in clinical practice. In summary, the findings of this thesis show that the input and output order of free recall, although quite stable, decline with age and that they may be added as a potential tool for early detection in clinical settings, and in the research field

Intelligent Hardware-Software Processing of High-Frequency Scanning Data

The constant emission of polluting gases is causing an urgent need for timely detection of harmful gas mixtures in the atmosphere. A method and algorithm of the determining spectral composition of gas with a gas analyzer using an artificial neural network (ANN) were suggested in the article. A small closed gas dynamic system was designed and used as an experimental bench for collecting and quantifying gas concentrations for testing the proposed method. This device was based on AS7265x and BMP180 sensors connected in parallel to a 3.3 V compatible Arduino Uno board via QWIIC. Experimental tests were conducted with air from the laboratory room, carbon dioxide (CO2), and a mixture of pure oxygen (O2) with nitrogen (N2) in a 9:1 ratio. Three ANNs with one input, one hidden and one output layer were built. The ANN had 5, 10, and 20 hidden neurons, respectively. The dataset was divided into three parts: 70% for training, 15% for validation, and 15% for testing. The mean square error (MSE) error and regression were analyzed during training. Training, testing, and validation error analysis were performed to find the optimal iteration, and the MSE versus training iteration was plotted. The best indicators of training and construction were shown by the ANN with 5 (five) hidden layers, and 16 iterations are enough to train, test and verify this neural network. To test the obtained neural network, the program code was written in the MATLAB. The proposed scheme of the gas analyzer is operable and has a high accuracy of gas detection with a given error of 3%. The results of the study can be used in the development of an industrial gas analyzer for the detection of harmful gas mixtures