Two experiments were designed to selectively prime each cerebral hemisphere to evaluate Kinsbourne's selective activation model. The left hemisphere priming manipulation for both experiments was subvocal rehearsal of neutral words, whereas right hemisphere primes consisted of imagery (Experiment 1) and subvocal rehearsal of affective words (Experiment 2). Reaction time performance in the visual field contralateral to the activated hemisphere was hypothesized to improve. No evidence supporting this hypothesis was found in these experiments, though experimental tasks had significant interference effects. In Experiment 1, non-specific interference effects were found across visual fields for both experimental tasks, suggesting the hemispheres were not preferentially recruited. Right visual field interference was observed in Experiment 2 for subvocal rehearsal, particularly of affective words, implicating selective left hemisphere activation. This finding indicates an overloading of the left hemisphere's affective perceptual capabilities. Significantly, no equivalent right hemisphere effects were observed, suggesting different functional space characteristics for affect perception across the hemispheres. These findings appear to substantiate a structural rather than activational model of functional cerebral asymmetries, but interference effects do unequivocally support either model. Furthermore, in Experiment 2, neutral faces were perceived as angry equally often in both visual fields, though neutral faces in the control and affective rehearsal conditions were more frequently perceived as angry relative to the neutral rehearsal condition. Methodological issues are presented to account for these findings. / Master of Science

Large scale functional brain networks, defined by synchronized spontaneous oscillations between spatially distinct anatomical regions, are essential to brain function and have been implicated in disease states, cognitive capacity, and many sensing and motor processes. In this work, we sever the corpus callosum in the rodent model to determine if structural connectivity (specifically the primary interhemispheric pathway) organizes and influences bilateral functional connectivity and brain-wide spatiotemporal dynamic activity patterns. Prior to the callosotomy work, resting state brain networks were evaluated using blood oxygen level dependent (BOLD) and cerebral blood volume (CBV) magnetic resonance imaging contrast mechanisms, and revealed that BOLD and CBV provide highly similar spatial maps of functional connectivity; however, the amplitude of BOLD connectivity was generally stronger. The effects of extended anesthetic durations on functional connectivity were also evaluated revealing extended isoflurane anesthetic periods prior to the switch to dexmedetomidine attenuates functional activity for a longer duration as compared to a shorter isoflurane paradigm. We also observed a secondary significant evolution of functional metrics occurring during long durations of dexmedetomidine use under the currently accepted and refined dexmedetomidine sedation paradigm. Taking these previous findings into account, we moved forward with the callosotomy study. Functional network integrity was evaluated in sham and full callosotomy groups using BOLD and electrophysiology. Functional connectivity analysis indicated a similar significant reduction in bilateral connectivity in the full callosotomy group as compared to the sham group across both recording modalities. Spatiotemporal dynamic analysis revealed bilaterally symmetric propagating waves of activity in the sham data, but none were present in the full callosotomy data; however, the emergence of unilateral spatiotemporal patterns became prominent following the callosotomy. This finding suggests that the corpus callosum could be largely responsible for maintaining bilateral network integrity, but non-bilaterally symmetric propagating waves occur in the absence of the corpus callosum, suggesting a possible subcortical driver of the dynamic cascading event. This work represents a robust finding indicating the corpus callosum's influence on maintaining integrity in bilateral functional networks.

Functional magnetic resonance imaging (fMRI) based on blood oxygenation level dependent (BOLD) contrast has become a widespread technique in brain research. The central challenge in fMRI is the detection of relatively small activity-induced signal changes in the presence of various other signal fluctuations. Physiological fluctuations due to respiration and cardiac pulsation are dominant sources of confounding variability in BOLD fMRI. This dissertation seeks to characterize and compensate for non-neural physiological fluctuations in fMRI. First, the dissertation presents an improved and generalized technique for correcting T1 effect in cardiac-gated fMRI data incorporating flip angle estimated from fMRI dataset itself. Using an unscented Kalman filter, spatial maps of flip angle and T1 relaxation are estimated simultaneously from the cardiac-gated time series. Accounting for spatial variation in flip angle, the new method is able to remove the T1 effects robustly, in the presence of significant B1 inhomogeneity. The technique is demonstrated with simulations and experimental data. Secondly, this dissertation describes a generalized retrospective technique to precisely model and remove physiological fluctuations from fMRI signal: Physiological Impulse Response Function Estimation and Correction (PIRFECT). It is found that the modeled long-term physiological fluctuations explained significant variance in grey matter, even after removing short-term physiological effects. Finally, application of the proposed technique is observed to substantially increase the intra-session reproducibility of resting-state networks.

Eight-letter upper case arrays containing vertically symmetrical (VS), e.g., A-T-U-W, horizontally symmetrical (HS), e.g., B-D-C-E, doubly symmetrical (DS), e.g., H-I-O-X, and non-symmetrical (NS), e.g., F-G-L-R, were tachistoscopically exposed bilaterally for 50 ms. to fifteen male and fifteen female undergraduates. The number of letters correctly recognized for each classification condition was used as the criterion measure. A fixed, two-factor design with the second factor being repeated was analyzed using a repeated measures analysis of variance. Consequent to testing Null Hypothesis 1 (that there is no difference between the classification conditions), a check was made for the presence of a significant interaction between gender and classification condition (Null Hypothesis 2). Because Null Hypothesis 1 was rejected and there was no interaction present, the classification group means were tested using a post hoc multiple comparison procedure identified as Tukey's Honestly Significant Difference (HSD) test. Test statistics for the Tukey HSD contrasts found that significantly more VS letters were reported than DS, HS, and NS letters. Significantly more DS letters were reported than HS and NS letters. No difference in report accuracy was found between HS and NS letters. This is in sharp contrast to studies that count only responses reported in the same left-to-right order as the tachistoscopic presentation, i.e., order of report. Previous studies using an order of report method found vertically asymmetrical letters to be reported more accurately than vertically symmetrical ones. The present study disregarded order of from an order of report. It was emphasized that the subject maintain focus on the fixation dot and not attempt to scan the letter-array pattern in a left-to-right direction, as the lettersdid not have to be reported in their respective positions. A different explanation for the Harcum (1964) directionality and Bryden (1968) masking interpretations follows from an order of report method activating additional processing mechanisms such as working memory that are ordinarily not needed to process letter features.Results obtained by the present study are discussed in terms of a reversal of spatial information for touch, kinesthesis, and sound to match the brain’s reversed retino-cortical projection. / Department of Educational Psychology

No description available.

Doctorat en sciences psychologiques / info:eu-repo/semantics/nonPublished

No description available.

The purpose of the study was to determine if there is an effect on retention of vocabulary when music and imagery are used, to accelerate learning. As background for the study four brain functioning theories as developed from recent neuroscientific research were explored. These were the theory of right/left cerebral dominance (Sperry & others), triune brain theory (MacLean), taxon and locale long term memory (O'Keefe & Nadel), and holographic memory (Pribram). These four theories all suggest multiple channels of input will increase information retention. The research utilized three conditions: an experimental condition of music plus imagery, an experimental condition of music only, and a no treatment condition.

Sutherland, Sparks and Lehmann (2010) proposed a new theory of memory consolidation, termed Distributed Reinstatement Theory (DRT), where the hippocampus (HPC) is needed for initial encoding but some types of memories are established in non-HPC systems through post-learning HPC activity. An evaluation of the current methodology of temporary inactivation was conducted experimentally. By permanently implanting two bilateral guide cannulae in the HPC and infusing ropivacaine cellular activity could be reduced by 97%. Rats were trained in a context-fear paradigm. Six learning episodes distributed across three days made the memory resistant to HPC inactivation while three episodes did not. Blocking post-learning HPC activity following three of six training sessions failed to reduce the rat’s memory of the fearful context. These results fail to support DRT and indicate that one or more memory systems outside the HPC can acquire context memory without HPC post-event activity. / x, 85 leaves : ill. ; 29 cm

Functional magnetic resonance imaging (fMRI) has emerged as the method of choice to non-invasively investigate brain function in humans. Though brain is known to act as a nonlinear system, here has not been much effort to explore the applicability of nonlinear analysis techniques to fMRI data. Also, recent trends have suggested that functional localization as a model of brain function is incomplete and efforts are being made to develop models based on networks of regions to understand brain function. Therefore this thesis attempts to introduce the twin concepts of nonlinear dynamics and network analysis into a broad spectrum of fMRI data analysis techniques. First, we characterized the nonlinear univariate dynamics of fMRI noise using the concept of embedding to explain the origin of tissue-specific differences of baseline activity in the brain. The embedding concept was extended to the multivariate case to study nonlinear functional connectivity in the distributed motor network during resting state and continuous motor task. The results showed that the nonlinear method may be more sensitive to the desired gray matter signal. Subsequently, the scope of connectivity was extended to include directional interactions using Granger causality. An integrated approach was developed to alleviate the confounding effect of the spatial variability of the hemodynamic response and graph theory was employed to characterize the network topology. This methodology proved effective in characterizing the dynamics of cortical networks during motor fatigue. The nonlinear extension of Granger causality showed that it was more robust in the presence of confounds such as baseline drifts. Finally, we utilized the integration of the spatial correlation function to study connectivity in local brain networks. We showed that our method is robust and can reveal interesting information including the default mode network during resting state. Application of this technique to anesthesia data showed dose dependent suppression of local connectivity in the default mode network, particularly in the frontal areas. Given the body of evidence emerging from our studies, nonlinear and network characterization of fMRI data seems to provide novel insights into brain function.