<p>Neuroimaging and neurophysiology techniques were used to study some aspects of cortical sensory and motor system reorganisation in patients in the chronic phase after stroke. Using Diffusion Tensor Imaging, we found that the degree of white matter integrity of the corticofugal tracts (CFT) was positively related to grip strength. Structural changes of the CFT were also associated with functional changes in the corticospinal pathways, measured using Transcranial Magnetic Stimulation. This suggests that structural and functional integrity of the CFT is essential for upper limb function after stroke.</p><p>Using functional magnetic resonance imaging (fMRI), to measure brain activity during slow and fast passive hand movements, we found that velocity-dependent brain activity correlated positively with neural contribution to passive movement resistance in the hand in ipsilateral primary sensory (S1) and motor (M1) cortex in both patients and controls. This suggests a cortical involvement in the hyperactive reflex response of flexor muscles upon fast passive stretch.</p><p>Effects of a four week passive-active movement training programme were evaluated in chronic stroke patients. The group improved in range of motion and upper limb function after the training. The patients also reported improvements in a variety of daily tasks requiring the use of the affected upper limb. </p><p>Finally, we used fMRI to explore if brain activity during passive hand movement is related to time after stroke, and if such activity can be affected with intense training. In patients, reduced activity over time was found in supplementary motor area (SMA), contralateral M1 and prefrontal and parietal association areas along with ipsilateral cerebellum. After training, brain activity increased in SMA, ipsilateral S1 and intraparietal sulcus, and contralateral cerebellum in parallel with functional improvements of the upper limb. The findings suggest a use-dependent modification of cortical activation patterns in the affected hand after stroke. </p>

Functional brain images are extraordinarily rich data sets that reveal distributed brain networks engaged in a wide variety of cognitive operations. It is a substantial challenge both to create models of cognition that mimic behavior and underlying cognitive processes and to choose a suitable analytic method to identify underlying brain networks. Most of the contemporary techniques used in analyses of functional neuroimaging data are based on univariate approaches in which single image elements (i.e. voxels) are considered to be computationally independent measures. Beyond univariate methods (e.g. statistical parametric mapping), multivariate approaches, which identify a network across all regions of the brain rather than a tessellation of regions, are potentially well suited for analyses of brain imaging data. A multivariate method (e.g. partial least squares) is a computational strategy that determines time-varying distributed patterns of the brain (as a function of a cognitive task). Compared to its univariate counterparts, a multivariate approach provides greater levels of sensitivity and reflects cooperative interactions among brain regions. Thus, by considering information across more than one measuring point, additional information on brain function can be revealed. Similarly, by considering information across more than one measuring technique, the nature of underlying cognitive processes become well-understood. Cognitive processes have been investigated in conjunction with multiple neuroimaging modalities (e.g. fMRI, sMRI, EEG, DTI), whereas the typical method has been to analyze each modality separately. Accordingly, little work has been carried out to examine the relation between different modalities. Indeed, due to the interconnected nature of brain processing, it is plausible that changes in one modality locally or distally modulate changes in another modality. This thesis focuses on multivariate and multimodal methods of image analysis applied to various cognitive questions. These methods are used in order to extract features that are inaccessible using univariate / unimodal analytic approaches. To this end, I implemented multivariate partial least squares analysis in study I and II in order to identify neural commonalities and differences between the available and accessible information in memory (study I), and also between episodic encoding and episodic retrieval (study II). Study I provided evidence of a qualitative differences between availability and accessibility signals in memory by linking memory access to modality-independent brain regions, and availability in memory to elevated activity in modality-specific brain regions. Study II provided evidence in support of general and specific memory operations during encoding and retrieval by linking general processes to the joint demands on attentional, executive, and strategic processing, and a process-specific network to core episodic memory function. In study II, III, and IV, I explored whether the age-related changes/differences in one modality were driven by age-related changes/differences in another modality. To this end, study II investigated whether age-related functional differences in hippocampus during an episodic memory task could be accounted for by age-related structural differences. I found that age-related local structural deterioration could partially but not entirely account for age-related diminished hippocampal activation. In study III, I sought to explore whether age-related changes in the prefrontal and occipital cortex during a semantic memory task were driven by local and/or distal gray matter loss. I found that age-related diminished prefrontal activation was driven, at least in part, by local gray matter atrophy, whereas the age-related decline in occipital cortex was accounted for by distal gray matter atrophy. Finally, in study IV, I investigated whether white matter (WM) microstructural differences mediated age-related decline in different cognitive domains. The findings implicated WM as one source of age-related decline on tasks measuring processing speed, but they did not support the view that age-related differences in episodic memory, visuospatial ability, or fluency were strongly driven by age-related differences in white-matter pathways. Taken together, the architecture of different aspects of episodic memory (e.g. encoding vs. retrieval; availability vs. accessibility) was characterized using a multivariate partial least squares. This finding highlights usefulness of multivariate techniques in guiding cognitive theories of episodic memory. Additionally, competing theories of cognitive aging were investigated by multimodal integration of age-related changes in brain structure, function, and behavior. The structure-function relationships were specific to brain regions and cognitive domains. Finally, we urged that contemporary theories on cognitive aging need to be extended to longitudinal measures to be further validated.

Neuroimaging and neurophysiology techniques were used to study some aspects of cortical sensory and motor system reorganisation in patients in the chronic phase after stroke. Using Diffusion Tensor Imaging, we found that the degree of white matter integrity of the corticofugal tracts (CFT) was positively related to grip strength. Structural changes of the CFT were also associated with functional changes in the corticospinal pathways, measured using Transcranial Magnetic Stimulation. This suggests that structural and functional integrity of the CFT is essential for upper limb function after stroke. Using functional magnetic resonance imaging (fMRI), to measure brain activity during slow and fast passive hand movements, we found that velocity-dependent brain activity correlated positively with neural contribution to passive movement resistance in the hand in ipsilateral primary sensory (S1) and motor (M1) cortex in both patients and controls. This suggests a cortical involvement in the hyperactive reflex response of flexor muscles upon fast passive stretch. Effects of a four week passive-active movement training programme were evaluated in chronic stroke patients. The group improved in range of motion and upper limb function after the training. The patients also reported improvements in a variety of daily tasks requiring the use of the affected upper limb. Finally, we used fMRI to explore if brain activity during passive hand movement is related to time after stroke, and if such activity can be affected with intense training. In patients, reduced activity over time was found in supplementary motor area (SMA), contralateral M1 and prefrontal and parietal association areas along with ipsilateral cerebellum. After training, brain activity increased in SMA, ipsilateral S1 and intraparietal sulcus, and contralateral cerebellum in parallel with functional improvements of the upper limb. The findings suggest a use-dependent modification of cortical activation patterns in the affected hand after stroke.

In two separate studies, we used diffusion tensor imaging (DTI)to examine white matter changes secondary to traumatic brain injury (TBI) and spinal cord injury (SCI). The first study examined the utility of DTI for a single case diagnosis of mild TBI (mTBI) and demonstrated that the anterior limb of the internal capsule and the genu of the corpus callosum were sensitive and specific to mTBI. The second study examined the sub-acute effects of SCI on white matter tissue in the brain and demonstrated that SCI patients have a significantly greater degree of FA asymmetry than control subjects in the superior and posterior corona radiata. The first study has provided preliminary proof of principal evidence that DTI can be used to diagnose mTBI in individual cases. The second study suggests that the degree of asymmetry may be a useful biomarker for detecting subtle white matter changes.

Diffusion Tensor Imaging (DTI) can quantify tissue integrity in normal-appearing white matter (NAWM). NAWM abnormalities present at the earliest time point implicate neurodegeneration operative from the outset of multiple sclerosis (MS). DTI scans were obtained at first attacks from 6 children later diagnosed with MS and 6 children with monophasic demyelination, and from 6 controls, matched for age. DTI scans were also obtained from 22 children with established MS with clinical onset before age 12 years and compared to age-matched controls. Atlas- and tractography-based image processing methods were utilized. DTI metrics distinguished MS patients from patients with monophasic demyelination and from controls at the first attack. Differences in NAWM between children with established early-onset MS and controls were only notable when DTI was obtained in adolescence. DTI provides valuable insights into NAWM in children with MS, although in the youngest patients such changes may require time to develop.

In two separate studies, we used diffusion tensor imaging (DTI)to examine white matter changes secondary to traumatic brain injury (TBI) and spinal cord injury (SCI). The first study examined the utility of DTI for a single case diagnosis of mild TBI (mTBI) and demonstrated that the anterior limb of the internal capsule and the genu of the corpus callosum were sensitive and specific to mTBI. The second study examined the sub-acute effects of SCI on white matter tissue in the brain and demonstrated that SCI patients have a significantly greater degree of FA asymmetry than control subjects in the superior and posterior corona radiata. The first study has provided preliminary proof of principal evidence that DTI can be used to diagnose mTBI in individual cases. The second study suggests that the degree of asymmetry may be a useful biomarker for detecting subtle white matter changes.

Diffusion Tensor Imaging (DTI) can quantify tissue integrity in normal-appearing white matter (NAWM). NAWM abnormalities present at the earliest time point implicate neurodegeneration operative from the outset of multiple sclerosis (MS). DTI scans were obtained at first attacks from 6 children later diagnosed with MS and 6 children with monophasic demyelination, and from 6 controls, matched for age. DTI scans were also obtained from 22 children with established MS with clinical onset before age 12 years and compared to age-matched controls. Atlas- and tractography-based image processing methods were utilized. DTI metrics distinguished MS patients from patients with monophasic demyelination and from controls at the first attack. Differences in NAWM between children with established early-onset MS and controls were only notable when DTI was obtained in adolescence. DTI provides valuable insights into NAWM in children with MS, although in the youngest patients such changes may require time to develop.

A Riemannian manifold is a mathematical concept that generalizes curved surfaces to higher dimensions, giving a precise meaning to concepts like angle, length, area, volume and curvature. A glimpse of the consequences of a non-flat geometry is given on the sphere, where the shortest path between two points – a geodesic – is along a great circle. Different from Euclidean space, the angle sum of geodesic triangles on the sphere is always larger than 180 degrees. Signals and data found in applied research are sometimes naturally described by such curved spaces. This dissertation presents basic research and tools for the analysis, processing and visualization of such manifold-valued data, with a particular emphasis on future applications in medical imaging and visualization. Two-dimensional manifolds, i.e. surfaces, enter naturally into the geometric modelling of anatomical entities, such as the human brain cortex and the colon. In advanced algorithms for processing of images obtained from computed tomography (CT) and ultrasound imaging (US), images themselves and derived local structure tensor fields may be interpreted as two- or three-dimensional manifolds. In diffusion tensor magnetic resonance imaging (DT-MRI), the natural description of diffusion in the human body is a second-order tensor field, which can be related to the metric of a manifold. A final example is the analysis of shape variations of anatomical entities, e.g. the lateral ventricles in the brain, within a population by describing the set of all possible shapes as a manifold. Work presented in this dissertation include: Probabilistic interpretation of intrinsic and extrinsic means in manifolds. A Bayesian approach to filtering of vector data, removing noise from sampled manifolds and signals. Principles for the storage of tensor field data and learning a natural metric for empirical data. The main contribution is a novel class of algorithms called LogMaps, for the numerical estimation of logp (x) from empirical data sampled from a low-dimensional manifold or geometric model embedded in Euclidean space. The logp (x) function has been used extensively in the literature for processing data in manifolds, including applications in medical imaging such as shape analysis. However, previous approaches have been limited to manifolds where closed form expressions of logp (x) have been known. The introduction of the LogMap framework allows for a generalization of the previous methods. The application of LogMaps to texture mapping, tensor field visualization, medial locus estimation and exploratory data analysis is also presented. / The electronic version is corrected for grammatical and spelling errors.

A complete scientific understanding of human nature requires delineation of the neurobiological characteristics underlying the unique features of the human mind. This effort can be facilitated by comparing the human brain with the brains of other living primate species. Humans are more susceptible to neurodegenerative diseases than other primate species, including our closest living primate relatives, the chimpanzees. Comparing age-related changes in brain structure between humans and non-human primates could, therefore, potentially shed light on the neurological basis of this human vulnerability. Further, human brains are lateralized with specialized cognitive and behavioral functions. Comparing the magnitude of hemispheric asymmetries in brain structure between humans and non-human primates can probe insights into this human specific capability and learn more about human evolution. Diffusion weighted MRI protocols were developed for different species, taking into account their neuroanatomical differences. For Chimpanzees, a multi-shot DWI sequence was developed and compared with a single-shot DWI sequence to determine which provided a better quality diffusion data free of acquisition related artifacts. Different simulation techniques were used to evaluate the effect of segmentation-related motion artifact (ghosting) on the multi-shot DTI data. Although both protocols generated high-resolution diffusion MRI data with correctable susceptibility-induced distortions, the single-shot protocol enables the acquisition of the high-resolution diffusion MRI data freed of ghosting and with twice the signal-to-noise ratio (SNR), for the same scan duration. The acquired chimpanzee and macaque diffusion data were used to compare the magnitude of microstructural asymmetries and age-related decline of brain white matter with those in humans. Hemispheric asymmetry results show a pattern of strong leftward asymmetry in human DTI indices that differs markedly from the chimpanzee (multi-shot data) and the rhesus macaque patterns involving both rightward and leftward asymmetries. The magnitude of leftward asymmetry increased for chimpanzees scanned with single-shot DTI sequence. Region of interest analyses within the corpus callosum revealed a significant age-related increase in fractional anisotropy (FA) in the genu for chimpanzees (multi-shot data) and no significant change in any region for macaques. Additionally, voxel-wise analysis using Tract Based Spatial Statistics (TBSS) revealed widespread age-related FA increases for chimpanzees (multi-shot data) and weak age-related decreases in FA for macaques across most white matter tracts. Overall, results from these multi-shot data analyses suggest that rhesus monkeys show age-related decreases in white matter integrity that parallel changes found in humans, whereas chimpanzees show age-related increases in white matter integrity. On the contrary, the single-shot data results for chimpanzees revealed no significant relationship between age and the different DTI indices. These noteworthy species differences may help to explain the unique features of the human mind and why humans are more susceptible to neurodegenerative diseases. Furthermore, these studies demonstrate the need for complementary histological studies of white matter microstructure in humans, chimpanzees and macaques to clarify the cellular and molecular basis of these findings.

Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Hu, Xiaoping; Committee Member: Brummer, Marijn; Committee Member: Duong, Tim; Committee Member: Keilholz, Shella; Committee Member: Schumacher, Eric. Part of the SMARTech Electronic Thesis and Dissertation Collection.