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Diffusion Tensor Imaging (DTI) is an advanced magnetic resonance imaging (MRI) technique. DTI provides quantitative information at microstuructural level via its parameter indices e.g. mean diffusivity (MD) and fractional anisotropy (FA). It also allows for visualization of neuron fibres through a specific technique called fibre tractography. Leukoaraiosis is an asymptomatic pathological condition of the brain white matter which appears hyperintense on T2-weighted MRI images. Association of leukoaraiosis with age and ischemic heart disease have been previously reported. The objective of this study is to compare MD and FA values measured in various areas of the brain white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) in humans using DTI. 30 subjects with leukoaraiosis and 12 subjects without leukoaraiosis underwent brain scan using GE 1.5 Tesla MRI system. Region of interests were located in the CSF and various WM and GM areas. Comparison of MD and FA values was made between leukoaraiosis tissue (LA) and normal appearing brain tissue (NABT) measured within the same leukoaraiosis subjects, and with normal brain tissue (CONTROL) of healthy control subjects. LA demonstrated a significantly higher MD and lower FA compared to NABT and CONTROL in frontal and occipital WM areas. No differences were observed in MD in any brain region between NABT and CONTROL. Whereas no differences were observed in FA between NABT and CONTROL except in the occipital WM. Fibre tractography showed 31.7% to 56.1% lesser fibre tracts in LA subjects compared to CONTROL subjects. Significant differences were found between pathological tissue compared to normal appearing brain tissue and normal brain tissue. Fibre tractography exposed reduced number of neural fibres in leukoaraiosis subjects as compared to normal subjects.
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Imagem de Tensor de DifusãoRESUMO
Introduction: This multiple-subject fMRI study continue to further investigate brain activation within and effective connectivity between the significantly (p<0.001) activated primary motor area (M1), supplementary motor area (SMA) with the inclusion of BA44 during unimanual (UNIright and UNIleft) and bimanual (BIM) self-paced tapping of hand fingers. Methods: The activation extent (spatial and height) and effective connectivity were analysed using statistical parametric mapping (SPM), dynamic causal modeling (DCM) and the novel method of Bayesian model selection (BMS) for group studies. Results: Group results for UNIright and UNIleft showed contra-lateral and ipsi-lateral involvement of M1 and SMA. The results for BIM showed bilateral activation in M1, SMA and BA44. A larger activation area but with lower percentage of signal change (PSC) are observed in the left M1 due to the control on UNIright as compared to the right M1 due to the control on UNIleft. This is discussed as due to the influence of the tapping rate effects that is greater than what would be produced by the average effects of the dominant and sub-dominant hand. However, the higher PSC observed in the right M1 is due to a higher control demand used by the brain in coordinating the tapping of the sub-dominant hand fingers. Connectivity analysis indicated M1 as the intrinsic input for UNIright and UNIleft while for BIM, the inputs were both M1s. During unilateral finger tapping, the contra-lateral M1 acts as the input center which in turn triggers the propagation of signal unidirectionally to other regions of interest. The results obtained for BIM (BIMleft and BIMright) however yield a model with less number of significant connection. M1-M1 connection is unidirectional for UNIleft and UNIright originating from contra-lateral M1, and is inhibited during BIM. Conclusion: By taking into consideration the presence of outliers that could have arisen in any subject under study, BMS for group study has successfully chosen a model that has the best balance between accuracy (fit) and complexity.
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Objective: This study investigates functional specialisation in, and effective connectivity between the precentral gyrus (PCG) and supplementary motor area (SMA) in seven right handed female subjects. Methods: Unimanual (UNIright and UNIleft) and bimanual (BIM) self-paced tapping of hand fingers were performed by the subjects to activate PCG and SMA. Brain activations and effective connectivity were analysed using statistical parametric mapping (SPM), dynamic causal modeling (DCM) and Bayesian model selection (BMS) and were reported based on group fixed (FFX) and random (RFX) effects analyses. Results: Group results showed that the observed brain activation for UNIright and UNIleft fulfill contralateral behavior of motor coordination with a larger activation area for UNIright. The activation for BIM occurs in both hemispheres with BIMright showing higher extent of activation as compared to BIMleft. Region of interest (ROI) analyses reveal that the number of activated voxel (NOV) and percentage of signal change (PSC) on average is higher in PCG than SMA for all tapping conditions. However, comparing between hemispheres for both UNI and BIM, higher PSC is observed in the right PCG and the left SMA. DCM and BMS results indicate that most subjects prefer PCG as the intrinsic input for UNIright and UNIleft. The input was later found to be bi-directionally connected to SMA for UNIright.The bi-directional model was then used for BIM in the left and right hemispheres. The model was in favour of six out of seven subjects. DCM results for BIM indicate the existance of interhemispheric connectivity between the right and left hemisphere PCG. Conclusion: The findings strongly support the existence of functional specialisation and integration i.e. effective connectivity in human brain during finger tapping and can be used as baselines in determining the probable motor coordination pathways and their connection strength in a population of subjects
