As imagens de ressonância magnética ponderadas por difusão retratam a difusividade de moléculas de água presentes em tecidos biológicos. Em estruturas biológicas altamente organizadas e compactas como fibras nervosas e musculares, a difusividade é maior na direção paralela às fibras do que perpendicularmente às mesmas. Essa propriedade permite a reconstrução digital das trajetórias das fibras, técnica denominada tractografia, representando uma das poucas formas não invasivas de investigação da conectividade anatômica e organização estrutural do cérebro e do coração. A metodologia de tractografia mais difundida faz uso da integração numérica da direção principal de difusividade para reconstruir essas trajetórias. Porém, esta técnica apresenta problemas como o erro intrínseco a métodos de integração numérica e o erro associado a regiões de incerteza nos dados de difusividade. Uma metodologia considerada mais robusta consiste da modelagem da tractografia como a simulação de um sistema de partículas. No entanto, tal metodologia possui diversos parâmetros que precisam ser otimizados para cada caso e apresenta alta complexidade computacional. Esta dissertação apresenta uma metodologia de tractografia global baseada em sistema de partículas, mas com custo computacional reduzido pois evita passos desnecessários da otimização para reconstrução das trajetórias. Avaliamos sua acurácia em conjuntos de dados com graus de complexidade crescentes utilizando imagens sintéticas de difusão construídas digitalmente e em imagens reais de difusão do miocárdio humano. Nesses testes foram observadas reduções no consumo de tempo e maior acurácia para metodologia global apresentada com relação às descritas na literatura. Essa metodologia possui o potencial de evidenciar a organização e arquitetura de diversos tecidos do corpo humano com maior fidelidade e menor tempo de reconstrução. / The diffusion magnetic resonance imaging portray the diffusivity of water molecules present on biological tissues. High organized and compact biological structures like neuronal fibres and muscles present higher diffusivity parallel to the fibres than perpendicular to those. This property allows the digital reconstruction of fibres trajectories, technique named tractography, being one of the few non invasive ways of investigation of the anatomical connectivity and structural organization of the brain and heart. The most common tractography methodology uses numerical integration following the main diffusion direction in order to reconstruct trajectories. Yet, this technique is prone to intrinsic error to numeric integration and the error associated to uncertainty regions on diffusion data. A methodology considered more robust to such problems consists on modelling tractography as a particle system simulation. However, such methodology has several parameters that require fine tuning for each case and has a high computational complexity. This dissertation presents a global tractography methodology based on particle system but at lower computational cost because of the avoidance of unnecessary optimization steps on trajectory reconstruction. We evaluate its accuracy on synthetic diffusion datasets of increasing complexity and on real human cardiac diffusion images. Theses tests evidence reduced time consumption and increased accuracy for the presented methodology compared to the ones described in the literature. This methodology has the potential to reveal the organization and architecture of several tissues from the human body with higher fidelity and lower reconstruction time.

This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.

This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.

No description available.

As imagens de ressonância magnética ponderadas por difusão retratam a difusividade de moléculas de água presentes em tecidos biológicos. Em estruturas biológicas altamente organizadas e compactas como fibras nervosas e musculares, a difusividade é maior na direção paralela às fibras do que perpendicularmente às mesmas. Essa propriedade permite a reconstrução digital das trajetórias das fibras, técnica denominada tractografia, representando uma das poucas formas não invasivas de investigação da conectividade anatômica e organização estrutural do cérebro e do coração. A metodologia de tractografia mais difundida faz uso da integração numérica da direção principal de difusividade para reconstruir essas trajetórias. Porém, esta técnica apresenta problemas como o erro intrínseco a métodos de integração numérica e o erro associado a regiões de incerteza nos dados de difusividade. Uma metodologia considerada mais robusta consiste da modelagem da tractografia como a simulação de um sistema de partículas. No entanto, tal metodologia possui diversos parâmetros que precisam ser otimizados para cada caso e apresenta alta complexidade computacional. Esta dissertação apresenta uma metodologia de tractografia global baseada em sistema de partículas, mas com custo computacional reduzido pois evita passos desnecessários da otimização para reconstrução das trajetórias. Avaliamos sua acurácia em conjuntos de dados com graus de complexidade crescentes utilizando imagens sintéticas de difusão construídas digitalmente e em imagens reais de difusão do miocárdio humano. Nesses testes foram observadas reduções no consumo de tempo e maior acurácia para metodologia global apresentada com relação às descritas na literatura. Essa metodologia possui o potencial de evidenciar a organização e arquitetura de diversos tecidos do corpo humano com maior fidelidade e menor tempo de reconstrução. / The diffusion magnetic resonance imaging portray the diffusivity of water molecules present on biological tissues. High organized and compact biological structures like neuronal fibres and muscles present higher diffusivity parallel to the fibres than perpendicular to those. This property allows the digital reconstruction of fibres trajectories, technique named tractography, being one of the few non invasive ways of investigation of the anatomical connectivity and structural organization of the brain and heart. The most common tractography methodology uses numerical integration following the main diffusion direction in order to reconstruct trajectories. Yet, this technique is prone to intrinsic error to numeric integration and the error associated to uncertainty regions on diffusion data. A methodology considered more robust to such problems consists on modelling tractography as a particle system simulation. However, such methodology has several parameters that require fine tuning for each case and has a high computational complexity. This dissertation presents a global tractography methodology based on particle system but at lower computational cost because of the avoidance of unnecessary optimization steps on trajectory reconstruction. We evaluate its accuracy on synthetic diffusion datasets of increasing complexity and on real human cardiac diffusion images. Theses tests evidence reduced time consumption and increased accuracy for the presented methodology compared to the ones described in the literature. This methodology has the potential to reveal the organization and architecture of several tissues from the human body with higher fidelity and lower reconstruction time.

INTRODUÇÃO: Imagens por tensores de difusão (diffusion tensor imaging - DTI) do cérebro são parte fundamental de exames de ressonância magnética (RM) na rotina clínica. Entretanto, não há informação científica suficiente para inferir a base histológica de parâmetros de imagens de DTI. Um dos achados frequentes nos exames de RM são as áreas de alteração de sinal na substância branca (AASSB). Neste estudo, analisamos imagens de RM post-mortem, em particular as AASSB, e seus correlatos histológicos e imuno-histoquímicos. OBJETIVOS: Descrever os parâmetros quantitativos de imagens de DTI imagens obtidas in cranio post-mortem, comparar com parâmetros de relaxometria e transferência de magnetização e avaliar seus correlatos histológicos nas AASSB e AHAN, utilizando técnicas de correspondência espacial ponto-a-ponto. MÉTODOS: Analisamos áreas distribuídas na substância branca de encéfalos de quatro sujeitos submetidos ao exame de RM post-mortem, aproximadamente 12,87 horas (±2,59). A RM foi realizada em sistema com campo de 3,0 T, sendo utilizadas imagens FLAIR; T2 multi-eco, DTI, Densidade de Prótons para cálculo da taxa de transferência de magnetização (magnetization transfer ratio - MTR). Foram delimitadas regiões de interesse (ROI) em 20 áreas contendo AASSB e 20 áreas homólogas aparentemente normais (AHAN). Para cada área, foram obtidos valores de anisotropia fracional (FA), coeficiente de difusão aparente (ADC), MTR e tempo T2. Os encéfalos foram seccionados em fatias de 0,4mm e fotografados em alta resolução. Estas imagens foram co-registradas tridimensionalmente com as imagens de RM, utilizando técnica semi-automatizada para correlação ponto-a-ponto. Os fragmentos analisados foram corados por hematoxilina-eosina (HE), Kluverx Barrera (KB), galocianina (GALL), anti-neurofilamento (anti-NF), antiproteína básica de mielina (anti-MBP) e anti-proteína ácida fibrilar glial (anti- GFAP). RESULTADOS: Os valores de FA foram 0,40 ±0,12 nas AASSB, e 0,52±0,15 nas AHAN. Os valores de ADC foram nas AASSB de 0,129±0,04 x 10-3mm2/s, e 0,108±0,03 x10-3mm2/s nas AHAN. Os valores de MTR foram 51,59±0,88 % nas AASSB, e 52,88±4,09 % nas AHAN. Os valores de RT2 foram 91,79±1,95 % nas AASSB, e 69,94±4,54 % nas AHAN. Os valores de HE foram 162,9±20,0 nas AASSB, e 163,3±20,4 nas AHAN. Os valores de GALL foram de 181,8±4,36 nas AASSB, e 167,11±20,65 nas AHAN. Os valores de KB foram de 168,9±19,08 nas AASSB, e 182.82±20,65 nas AHAN. Os valores de NF médios mensurados foram de 131,6±12,23 nas AASSB, e 128,03±4,74 nas AHAN. Os valores de MBP médios mensurados foram de 153,9 ±13,67 nas AASSB, e 153,18±12,69 nas AHAN. Os valores de GFAP médios mensurados foram de 94.61±7,85 nas AASSB, e 81,23±6,29 nas AHAN. Os testes não paramétricos de correlação (Spearman) entre variáveis de imagens de RM e histologia apresentaram correlação apenas entre FA e GFAP (r=-0,59; p < 0,005). DISCUSSÃO E CONCLUSÕES: De acordo com os resultados obtidos, podemos inferir que a presença de gliose é um processo patológico que pode ser detectado utilizando os parâmetros de FA nas imagens de DTI. Os resultados apresentados demonstram que imagens de RM adquiridas post-mortem podem servir para compreender melhor as alterações estruturais do tecido cerebral / BACKGROUND: Diffusion tensor imaging (DTI) is part of magnetic resonance imaging (MRI) acquisition methods available in clinical exams. However, there are not enough scientific basis to infer the histological substract of DTI parameters. White matter hyperintensities (WMH) are frequent findings in clinical MRI routine. In the last years, scientific evidences show that these injuries are not just age-related benign changes, but they could be associated to pathological processes. In this study we analyzed post-mortem MRI DTI data, specifically investigating WMH, their histological and immunohistochemical correlates. OBJECTIVES : At present study, we aimed to analyze quantifiable DTI parameters - Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) - relaxometry (RT2) and magnetization transfer ratio (MTR) in WMH compared and normal appearing white matter (NAWM) from images obtained postmortem and in situ, and assess their histological substracts at WMH and NAWM using a point-topoint correlation platform. METHODS : We analyzed 20 regions of interest (ROI) encompassing WMH and NAWM selected from four subjects using in situ post-mortem MRI data acquired in 3.0T MR system. The subjects were scanned with a post-mortem interval of approximately 12h54m (±2h36m). The MRI analysis included 3D T1, FLAIR; multi-echo T2, DTI, and proton density to calculate the rate of magnetization transfer (magnetization transfer ratio - MTR). In each ROI we obtained measures of FA, ADC, RT2 and MTR. Brain specimens were posteriorly fixed in celloidin, sectioned into 0.4mm slices and photographed in high resolution. These images were co-registered with three-dimensional MR images using semi-automated technique for correlation using a point-to-point method established in our group. WMH and NAWM ROIs from MRI were submitted to quantitative histological analysis based on optical density color-deconvolution technique. Histological section were stained in hematoxylin-eosin (HE) , Kluver-Barrera (KB) , galloccyanin (GALL), anti-neurofilament (anti-NF) , anti-basic myelin protein (anti-MBP) and anti-glial fibrillary acidic protein (anti-GFAP). RESULTS: The mean FA values were 0.40±0.12 in WMH, and 0.52±0.15 in NAWM. ADC in WMH was 0.1290±0.04, x10-3mm2/s and 0.1081± 0.03 x10-3mm2/s in NAWM. MTR was 51.59± 0.88% in WMH, and 52.88±4.09% in NAWM. RT2 was 91.79±1.95% in WMH, and 69.94±4.54% in NAWM. FA, ADC and RT2 values were different between WMH and NAWM in a non-parametric analysis. On the quantitative histological analysis, the HE was 162.9±20.0 in WMH , and 163.3±20.4% in NAWM. GALL values at WMH was 181.8±4.36 and 167.11±20.65 at NAWM. KB was 168.9±19.08 in WMH, and 182.82±20.65 at NAWM. NF mean were 131.6 ±12.23 in WMH, and 128.03 ±14.74 at NAWM. MBP was 153.9±13.67 at WMH, and 153.18±12.69 at NAWM. Only GFAP values were different between WMH and NAWM. Mean GFAP was 94.61±7.85 in WMH, and 81.23±6.29 at NAWM. Non-parametric test (Spearman) between MR images and histology, showed correlation only for FA vs GFAP (r=-0.594, p < 0.005). DISCUSSION AND CONCLUSIONS: DTI FA and ADC parameters (and RT2 values) showed different patterns in WMH compared to NAWM, indicating that MR images acquired post-mortem may serve to understand the structural changes of the brain tissue. The presence of gliosis is a pathological process seems to be related to FA value from DTI

INTRODUÇÃO: Imagens por tensores de difusão (diffusion tensor imaging - DTI) do cérebro são parte fundamental de exames de ressonância magnética (RM) na rotina clínica. Entretanto, não há informação científica suficiente para inferir a base histológica de parâmetros de imagens de DTI. Um dos achados frequentes nos exames de RM são as áreas de alteração de sinal na substância branca (AASSB). Neste estudo, analisamos imagens de RM post-mortem, em particular as AASSB, e seus correlatos histológicos e imuno-histoquímicos. OBJETIVOS: Descrever os parâmetros quantitativos de imagens de DTI imagens obtidas in cranio post-mortem, comparar com parâmetros de relaxometria e transferência de magnetização e avaliar seus correlatos histológicos nas AASSB e AHAN, utilizando técnicas de correspondência espacial ponto-a-ponto. MÉTODOS: Analisamos áreas distribuídas na substância branca de encéfalos de quatro sujeitos submetidos ao exame de RM post-mortem, aproximadamente 12,87 horas (±2,59). A RM foi realizada em sistema com campo de 3,0 T, sendo utilizadas imagens FLAIR; T2 multi-eco, DTI, Densidade de Prótons para cálculo da taxa de transferência de magnetização (magnetization transfer ratio - MTR). Foram delimitadas regiões de interesse (ROI) em 20 áreas contendo AASSB e 20 áreas homólogas aparentemente normais (AHAN). Para cada área, foram obtidos valores de anisotropia fracional (FA), coeficiente de difusão aparente (ADC), MTR e tempo T2. Os encéfalos foram seccionados em fatias de 0,4mm e fotografados em alta resolução. Estas imagens foram co-registradas tridimensionalmente com as imagens de RM, utilizando técnica semi-automatizada para correlação ponto-a-ponto. Os fragmentos analisados foram corados por hematoxilina-eosina (HE), Kluverx Barrera (KB), galocianina (GALL), anti-neurofilamento (anti-NF), antiproteína básica de mielina (anti-MBP) e anti-proteína ácida fibrilar glial (anti- GFAP). RESULTADOS: Os valores de FA foram 0,40 ±0,12 nas AASSB, e 0,52±0,15 nas AHAN. Os valores de ADC foram nas AASSB de 0,129±0,04 x 10-3mm2/s, e 0,108±0,03 x10-3mm2/s nas AHAN. Os valores de MTR foram 51,59±0,88 % nas AASSB, e 52,88±4,09 % nas AHAN. Os valores de RT2 foram 91,79±1,95 % nas AASSB, e 69,94±4,54 % nas AHAN. Os valores de HE foram 162,9±20,0 nas AASSB, e 163,3±20,4 nas AHAN. Os valores de GALL foram de 181,8±4,36 nas AASSB, e 167,11±20,65 nas AHAN. Os valores de KB foram de 168,9±19,08 nas AASSB, e 182.82±20,65 nas AHAN. Os valores de NF médios mensurados foram de 131,6±12,23 nas AASSB, e 128,03±4,74 nas AHAN. Os valores de MBP médios mensurados foram de 153,9 ±13,67 nas AASSB, e 153,18±12,69 nas AHAN. Os valores de GFAP médios mensurados foram de 94.61±7,85 nas AASSB, e 81,23±6,29 nas AHAN. Os testes não paramétricos de correlação (Spearman) entre variáveis de imagens de RM e histologia apresentaram correlação apenas entre FA e GFAP (r=-0,59; p < 0,005). DISCUSSÃO E CONCLUSÕES: De acordo com os resultados obtidos, podemos inferir que a presença de gliose é um processo patológico que pode ser detectado utilizando os parâmetros de FA nas imagens de DTI. Os resultados apresentados demonstram que imagens de RM adquiridas post-mortem podem servir para compreender melhor as alterações estruturais do tecido cerebral / BACKGROUND: Diffusion tensor imaging (DTI) is part of magnetic resonance imaging (MRI) acquisition methods available in clinical exams. However, there are not enough scientific basis to infer the histological substract of DTI parameters. White matter hyperintensities (WMH) are frequent findings in clinical MRI routine. In the last years, scientific evidences show that these injuries are not just age-related benign changes, but they could be associated to pathological processes. In this study we analyzed post-mortem MRI DTI data, specifically investigating WMH, their histological and immunohistochemical correlates. OBJECTIVES : At present study, we aimed to analyze quantifiable DTI parameters - Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) - relaxometry (RT2) and magnetization transfer ratio (MTR) in WMH compared and normal appearing white matter (NAWM) from images obtained postmortem and in situ, and assess their histological substracts at WMH and NAWM using a point-topoint correlation platform. METHODS : We analyzed 20 regions of interest (ROI) encompassing WMH and NAWM selected from four subjects using in situ post-mortem MRI data acquired in 3.0T MR system. The subjects were scanned with a post-mortem interval of approximately 12h54m (±2h36m). The MRI analysis included 3D T1, FLAIR; multi-echo T2, DTI, and proton density to calculate the rate of magnetization transfer (magnetization transfer ratio - MTR). In each ROI we obtained measures of FA, ADC, RT2 and MTR. Brain specimens were posteriorly fixed in celloidin, sectioned into 0.4mm slices and photographed in high resolution. These images were co-registered with three-dimensional MR images using semi-automated technique for correlation using a point-to-point method established in our group. WMH and NAWM ROIs from MRI were submitted to quantitative histological analysis based on optical density color-deconvolution technique. Histological section were stained in hematoxylin-eosin (HE) , Kluver-Barrera (KB) , galloccyanin (GALL), anti-neurofilament (anti-NF) , anti-basic myelin protein (anti-MBP) and anti-glial fibrillary acidic protein (anti-GFAP). RESULTS: The mean FA values were 0.40±0.12 in WMH, and 0.52±0.15 in NAWM. ADC in WMH was 0.1290±0.04, x10-3mm2/s and 0.1081± 0.03 x10-3mm2/s in NAWM. MTR was 51.59± 0.88% in WMH, and 52.88±4.09% in NAWM. RT2 was 91.79±1.95% in WMH, and 69.94±4.54% in NAWM. FA, ADC and RT2 values were different between WMH and NAWM in a non-parametric analysis. On the quantitative histological analysis, the HE was 162.9±20.0 in WMH , and 163.3±20.4% in NAWM. GALL values at WMH was 181.8±4.36 and 167.11±20.65 at NAWM. KB was 168.9±19.08 in WMH, and 182.82±20.65 at NAWM. NF mean were 131.6 ±12.23 in WMH, and 128.03 ±14.74 at NAWM. MBP was 153.9±13.67 at WMH, and 153.18±12.69 at NAWM. Only GFAP values were different between WMH and NAWM. Mean GFAP was 94.61±7.85 in WMH, and 81.23±6.29 at NAWM. Non-parametric test (Spearman) between MR images and histology, showed correlation only for FA vs GFAP (r=-0.594, p < 0.005). DISCUSSION AND CONCLUSIONS: DTI FA and ADC parameters (and RT2 values) showed different patterns in WMH compared to NAWM, indicating that MR images acquired post-mortem may serve to understand the structural changes of the brain tissue. The presence of gliosis is a pathological process seems to be related to FA value from DTI

Quantifying tissue volumes in pediatric brains from magnetic resonance (MR) images can provide insight into etiology and onset of neurological disease. Unbiased volumetric analysis can be applied to large population studies when automated image processing is possible. Standard segmentation strategies using adult atlases fail to account for varying tissue contrasts and types associated with the rapid growth and maturational changes seen in early neurodevelopment. The goal of this project was to develop an automated pipeline and two age-specific atlases capable of providing accurate tissue classification despite these challenges. The automated pipeline consisted of a stepwise initial atlas-to-subject registration, expectation maximization (EM) atlas based segmentation, and a post-processing level set segmentation for improved white/gray matter separation. This level set segmentation is a 3D and multiphase adaptation of a 2D method intended for use on images with the types of intensity Inhomogeneities found in MR images. The initial tissue maps required to determine spatial priors for the one-year-old atlas were created by manually cleaning the results of an adult atlas and the automated pipeline. Additional tissue maps were incrementally added until the spatial priors were sufficiently representative. The neonate atlas was similarly created, starting with the one-year-old atlas.

In general, highly hypnotizable individuals ("highs") have exhibited greater abilities to focus attention and inhibit pain than low hypnotizable individuals ("lows"). Furthermore, highs appear to have faster neural processing than lows. The present study investigated differences between lows and highs in morphological volume of some brain structures associated with inhibitory and excitatory neural processing, particularly the corpus callosum (CC). Participants were 18 healthy university students, aged 18 to 29, with no history of concussion or medical disorders. They were in a functional Magnetic Resonance Image (fMRI) study examining the neurophysiology of pain and hypnotic analgesia (Crawford, Horton, Harrington, et al., 1998; Downs et al., 1998). As assessed by the group version (Crawford & Allen, 1982) of the Stanford Hypnotic Susceptibility Scale, Form C (SHSS:C; Weitzenhoffer & Hilgard, 1962), there were eight highs (four women and four men; SHSS:C M = 11.0) and 10 lows (five men and five women; SHSS:C M = 2.1). Highs were able to successfully eliminate perception of pain and distress to experimental noxious stimuli. Their anatomical MRIs were measured to assess relationships between brain structure volume (CC, medial cortex, anterior brain regions) and hypnotizability. In comparison to lows, highs had a significantly larger CC volume in the rostrum and isthmus, inferred to reflect larger transcallosal axon diameter or greater axon myelination. For highs, but not lows, there were significant relationships between forebrain volume and the total CC, rostrum, and splenium. Findings provide support for the neuropsychophysiological model of Crawford and her associates (e.g. Crawford, 1994a, 1994b; Crawford & Gruzelier, 1992) proposing a more effective attentional system of inhibitory processes in highs than lows. Furthermore, the data suggest that the more effective systems of attentional and inhibitory processes enhanced neural processing speed, and interhemispheric transfer times seen in highs than lows, may be associated with morphological differences in certain anterior and posterior CC regions. These regions are known to be involved in the allocation of inhibitory and excitatory transfer of information between hemispheres. / Ph. D.

No description available.