Automatic extraction of the cingulum bundle in diffusion tensor tract-specific analysis: feasibility study in Parkinson's disease with and without dementia

A systematic review of diffusion tensor imaging and tractography in dementia with Lewy bodies and Parkinson's disease dementia

We reviewed studies using diffusion tensor imaging (DTI) and tractography to characterise white matter changes in Dementia with Lewy Bodies (DLB) and Parkinson's Disease Dementia (PDD). The search included MEDLINE and EMBASE, and we used a narrative strategy to synthesise the evidence. Data was extracted from 57 studies, of which the majority were considered 'good quality'. Subjects with DLB and PDD had widespread white matter changes compared to healthy controls and Parkinson's disease without cognitive impairment, with a relative sparing of the hippocampus. Compared to subjects with Alzheimer's disease (AD), DLB had greater changes in thalamic connectivity and in the nigroputaminal tract, while AD had greater changes in the parahippocampal white matter and fornix. Cognition was associated with widespread white matter changes, visual hallucinations with thalamic and cholinergic connectivity, and parkinsonism with changes in structures involved in motor control. DTI and tractography may therefore be well suited for discriminating DLB and PDD from other types of dementia, and for studying the aetiology of common symptoms.

A statistical approach in human brain connectome of Parkinson Disease in elderly people using Network Based Statistics

Parkinson's Disease (PD) is a progressive neurodegenerative disorder assumed to involve different areas of CNS and PNS. Thus, Diffusion Tensor Imaging (DTI) is used to examine the areas engaged in PD neurodegeneration. In the present study, we computed average tract length and fiber volume as a measure of white matter integrity and adopted Network Based Statistics (NBS) to conduct group analyses between age- and gender-matched PD patients and healthy control connectivity matrices. NBS is a powerful statistical tool that utilizes the presence of every link in connectivity matrices and controls family wise error rates (in weak sense). The major regions with significantly reduced interconnecting fiber volume or average tract length were cingulum, temporal lobe, frontal lobe, parahippocampus, hippocampus, olfactory lobe, and occipital lobe.

Automated tract extraction via atlas based Adaptive Clustering

Advancements in imaging protocols such as the high angular resolution diffusion-weighted imaging (HARDI) and in tractography techniques are expected to cause an increase in the tract-based analyses. Statistical analyses over white matter tracts can contribute greatly towards understanding structural mechanisms of the brain since tracts are representative of connectivity pathways. The main challenge with tract-based studies is the extraction of the tracts of interest in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest. In this work, we design a framework for automated extraction of white matter tracts. The framework introduces three main components, namely a connectivity based fiber representation, a fiber bundle atlas, and a clustering approach called Adaptive Clustering. The fiber representation relies on the connectivity signatures of fibers to establish an easy correspondence between different subjects. A group-wise clustering of these fibers that are represented by the connectivity signatures is then used to generate a fiber bundle atlas. Finally, Adaptive Clustering incorporates the previously generated clustering atlas as a prior, to cluster the fibers of a new subject automatically. Experiments on the HARDI scans of healthy individuals acquired repeatedly, demonstrate the applicability, reliability and the repeatability of our approach in extracting white matter tracts. By alleviating the seed region selection and the inclusion/exclusion ROI drawing requirements that are usually handled by trained radiologists, the proposed framework expands the range of possible clinical applications and establishes the ability to perform tract-based analyses with large samples.