Numerical processing in the human parietal cortex during experimental and natural conditions

Human cognition is traditionally studied in experimental conditions wherein confounding complexities of the natural environment are intentionally eliminated. Thus, it remains unknown how a brain region involved in a particular experimental condition is engaged in natural conditions. Here we use electrocorticography to address this uncertainty in three participants implanted with intracranial electrodes and identify activations of neuronal populations within the intraparietal sulcus region during an experimental arithmetic condition. In a subsequent analysis, we report that the same intraparietal sulcus neural populations are activated when participants, engaged in social conversations, refer to objects with numerical content. Our prototype approach provides a means for both exploring human brain dynamics as they unfold in complex social settings and reconstructing natural experiences from recorded brain signals

Neural Basis of Functional Connectivity MRI

The brain is hierarchically organized across a range of scales. While studies based on electrophysiology and anatomy have been fruitful on the micron to millimeter scale, findings based on functional connectivity MRI (fcMRI) suggest that a higher level of brain organization has been largely overlooked. These findings show that the brain is organized into networks, and each network extends across multiple brain areas. This large-scale, across-area brain organization is functionally relevant and stable across subjects, primate species, and levels of consciousness. This dissertation addresses the neural origin of MRI functional connectivity. fcMRI relies on temporal correlation in at-rest blood oxygen level dependent (BOLD) fluctuations. Thus, understanding the neural origin of at-rest BOLD correlation is of critical significance. By shedding light on the origin of the large-scale brain organization captured by fcMRI, it will guide the design and interpretation of fcMRI studies. Prior investigations of the neural basis of BOLD have not addressed the at-rest BOLD correlation, and they have been focusing on task-related BOLD. At-rest BOLD correlation captured by fcMRI likely reflects a distinct physiological process that is different from that of task-related BOLD, since these two kinds of BOLD dynamics are different in their temporal scale, spatial spread, energy consumption, and their dependence on consciousness. To address this issue, we develop a system to simultaneously record oxygen and electrophysiology in at-rest, awake monkeys. We demonstrate that our oxygen measurement, oxygen polarography, captures the same physiological phenomenon as BOLD by showing that task-related polarographic oxygen responses and at-rest polarographic oxygen correlation are similar to those of BOLD. These results validate the use of oxygen polarography as a surrogate for BOLD to address the neural origin of MRI functional connectivity. Next, we show that at-rest oxygen correlation reflects at-rest correlation in electrophysiological signals, especially spiking activity of neurons. Using causality analysis, we show that oxygen is driven by slow changes in raw local field potential levels (slow LFP), and slow LFP itself is driven by spiking activity. These results provide critical support to the idea that oxygen correlation reflects neural activity, and pose significant challenges to the traditional view of neurohemodynamic coupling. In addition, we find that at-rest correlation does not originate from criticality, which has been the dominant hypothesis in the field. Instead, we show that at-rest correlation likely reflects a specific and potentially localized oscillatory process. We suggest that this oscillatory process could be a result of the delayed negative feedback loop between slow LFP and spiking activity. Thus, we conclude that at-rest BOLD correlation captured by fcMRI is driven by at-rest slow LFP correlation, which is itself driven by spiking activity correlation. The at-rest spiking activity correlation, itself, is likely driven by an oscillatory process. Future studies combining recording with interventional approaches, like pharmacological manipulation and microstimulation, will help to elucidate the circuitry underlying the oscillatory process and its potential functional role

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

EEG and fMRI are important tools in cognitive and clinical neuroscience. Combined EEGfMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological-haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals, and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (“EEG-fMRI mapping”), or exploring a range of EEGderived quantities to determine which best explain co-localised BOLD fluctuations (“local EEG-fMRI coupling”). While reviewing studies of different forms of brain activity (epileptic and non-epileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG-fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations

Brain connectivity studied by fMRI: homologous network organization in the rat, monkey, and human

The mammalian brain is composed of functional networks operating at different spatial and temporal scales — characterized by patterns of interconnections linking sensory, motor, and cognitive systems. Assessment of brain connectivity has revealed that the structure and dynamics of large-scale network organization are altered in multiple disease states suggesting their use as diagnostic or prognostic indicators. Further investigation into the underlying mechanisms, organization, and alteration of large-scale brain networks requires homologous animal models that would allow neurophysiological recordings and experimental manipulations. My current dissertation presents a comprehensive assessment and comparison of rat, macaque, and human brain networks based on evaluation of intrinsic low-frequency fluctuations of the blood oxygen-level-dependent (BOLD) fMRI signal. The signal fluctuations, recorded in the absence of any task paradigm, have been shown to reflect anatomical connectivity and are presumed to be a hemodynamic manifestation of slow fluctuations in neuronal activity. Importantly, the technique circumvents many practical limitations of other methodologies and can be compared directly between multiple species. Networks of all species were found underlying multiple levels of sensory, motor, and cognitive processing. Remarkable homologous functional connectivity was found across all species, however network complexity was dramatically increased in primate compared to rodent species. Spontaneous temporal dynamics of the resting-state networks were also preserved across species. The results demonstrate that rats and macaques share remarkable homologous network organization with humans, thereby providing strong support for their use as an animal model in the study of normal and abnormal brain connectivity as well as aiding the interpretation of electrophysiological recordings within the context of large-scale brain networks

Monkey in the middle: why non-human primates are needed to bridge the gap in resting-state investigations

Resting-state investigations based on the evaluation of intrinsic low-frequency fluctuations of the BOLD fMRI signal have been extensively utilized to map the structure and dynamics of large-scale functional network organization in humans. In addition to increasing our knowledge of normal brain connectivity, disruptions of the spontaneous hemodynamic fluctuations have been suggested as possible diagnostic indicators of neurological and psychiatric disease states. Though the non-invasive technique has been received with much acclamation, open questions remain regarding the origin, organization, phylogenesis, as well as the basis of disease-related alterations underlying the signal patterns. Experimental work utilizing animal models, including the use of neurophysiological recordings and pharmacological manipulations, therefore, represents a critical component in the understanding and successful application of resting-state analysis, as it affords a range of experimental manipulations not possible in human subjects. In this article, we review recent rodent and non-human primate studies and based on the examination of the homologous brain architecture propose the latter to be the best-suited model for exploring these unresolved resting-state concerns. Ongoing work examining the correspondence of functional and structural connectivity, state-dependency and the neuronal correlates of the hemodynamic oscillations are discussed. We then consider the potential experiments that will allow insight into different brain states and disease-related network disruptions that can extend the clinical applications of resting-state fMRI (RS-fMRI)

A Neurophysiologic Study Of Visual Fatigue In Stereoscopic Related Displays

Two tasks were investigated in this study. The first study investigated the effects of alignment display errors on visual fatigue. The experiment revealed the following conclusive results: First, EEG data suggested the possibility of cognitively-induced time compensation changes due to a corresponding effect in real-time brain activity by the eyes trying to compensate for the alignment. The magnification difference error showed more significant effects on all EEG band waves, which were indications of likely visual fatigue as shown by the prevalence of simulator sickness questionnaire (SSQ) increases across all task levels. Vertical shift errors were observed to be prevalent in theta and beta bands of EEG which probably induced alertness (in theta band) as a result of possible stress. Rotation errors were significant in the gamma band, implying the likelihood of cognitive decline because of theta band influence. Second, the hemodynamic responses revealed that significant differences exist between the left and right dorsolateral prefrontal due to alignment errors. There was also a significant difference between the main effect for power band hemisphere and the ATC task sessions. The analyses revealed that there were significant differences between the dorsal frontal lobes in task processing and interaction effects between the processing lobes and tasks processing. The second study investigated the effects of cognitive response variables on visual fatigue. Third, the physiologic indicator of pupil dilation was 0.95mm that occurred at a mean time of 38.1min, after which the pupil dilation begins to decrease. After the average saccade rest time of 33.71min, saccade speeds leaned toward a decrease as a possible result of fatigue on-set. Fourth, the neural network classifier showed visual response data from eye movement were identified as the best predictor of visual fatigue with a classification accuracy of 90.42%. Experimental data confirmed that 11.43% of the participants actually experienced visual fatigue symptoms after the prolonged task

Biomarkers of neurological tissue injury and inflammation in paediatric tuberculous meningitis

Includes bibliographical references.[Background] Tuberculous meningitis (TBM) in children has high mortality and neurological morbidity rates. The assessment of disease severity and prognostication are difficult because several factors influence initial presentation, and advanced tools for these are lacking. Biomarkers of neurological injury could help to assess severity and to prognosticate, but have not been assessed in paediatric TBM. This study examined serum and cerebrospinal fluid (CSF) biomarkers of neurological injury in paediatric TBM in association with clinical and physiological data, radiology, inflammatory markers, and outcome. [ Methods ] Serum and CSF (ventricular and lumbar) samples were taken on admission and over 3 weeks in children with probable TBM and hydrocephalus. These were analysed with ELISA for neuromarkers S100B, neuron-specific enolase (NSE) and glial fibrillary acidic protein (GFAP), and with Luminex multianalyte array assay for a panel of inflammatory markers. Results were compared with 2 controls groups. Computerized tomography was done on admission and magnetic resonance imaging (brain, spine and magnetic resonance angiography) at 3 weeks. Brain oxygenation was monitored invasively and non-invasively in selected patients. Clinical and neurodevelopmental outcomes were assessed at 6 months. Data were analysed with various statistical tools, including principal component analysis. [ Results ] Data were collected from 44 children. Of these, 16% died and 36% had disability (25% mildmoderate, 11% severe). S100B, NSE, GFAP and inflammatory markers were elevated in CSF on admission and for up to 3 weeks, but not in serum. Elevated neuromarkers were significantly associated with poor outcome and increased over time in patients who died, although combined inflammatory biomarkers decreased. Cerebral infarcts occurred in 66% of patients and were associated with neuromarker elevation. Novel findings on spinal MRI were the high frequency of asymptomatic disease. Cerebral vascular pathology was documented frequently on imaging but did not predict infarcts. Low brain oxygenation was common and in keeping with physiological events and outcome. [ Conclusion ] CSF neuro- and inflammatory markers are elevated in TBM. Neuromarkers were prognostic of clinical and radiological outcome and an increasing trend suggested ongoing injury. This does not appear to be related to ongoing inflammation as measured by cytokines but may reflect the ongoing secondary injury processes initiated by inflammation

Near-Infrared Spectroscopy for Brain Computer Interfacing

A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user and may even lead to abandonment of the device. A more suitable and usable interface is needed to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. This work implements a novel approach to BCI design, using noninvasive near-infrared spectroscopic (NIRS) techniques to develop a userfriendly optical BCI. NIRS is a practical non-invasive optical technique that can detect characteristic haemodynamic responses relating to neural activity. This thesis describes the use of NIRS to develop an accessible BCI system requiring very little user training. In harnessing the optical signal for BCI control an assessment of NIRS signal characteristics is carried out and detectable physiological effects are identified for BCI development. The investigations into various mental tasks for controlling the BCI show that motor imagery functions can be detected using NIRS. The optical BCI (OBCI) system operates in realtime characterising the occurrence of motor imagery functions, allowing users to control a switch - a “Mindswitch”. This work demonstrates the great potential of optical imaging methods for BCI development and brings to light an innovative approach to this field of research