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      Gait variability is linked to the atrophy of the Nucleus Basalis of Meynert and is resistant to STN DBS in Parkinson’s disease

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          Abstract

          Parkinson’s disease (PD) is a systemic brain disorder where the cortical cholinergic network begins to degenerate early in the disease process. Readily accessible, quantitative, and specific behavioral markers of the cortical cholinergic network are lacking. Although degeneration of the dopaminergic network may be responsible for deficits in cardinal motor signs, the control of gait is a complex process and control of higher-order aspects of gait, such as gait variability, may be influenced by cognitive processes attributed to cholinergic networks. We investigated whether swing time variability, a metric of gait variability that is independent from gait speed, was a quantitative behavioral marker of cortical cholinergic network integrity in PD.

          Twenty-two individuals with PD and subthalamic nucleus (STN) deep brain stimulation (PD-DBS cohort) and twenty-nine age-matched controls performed a validated stepping-in-place (SIP) task to assess swing time variability off all therapy. The PD-DBS cohort underwent structural MRI scans to measure gray matter volume of the Nucleus Basalis of Meynert (NBM), the key node in the cortical cholinergic network. In order to determine the role of the dopaminergic system on swing time variability, it was measured ON and OFF STN DBS in the PD-DBS cohort, and on and off dopaminergic medication in a second PD cohort of thirty-two individuals (PD-med). A subset of eleven individuals in the PD-DBS cohort completed the SIP task again off all therapy after three years of continuous DBS to assess progression of gait impairment.

          Swing time variability was significantly greater (i.e., worse) in PD compared to controls and greater swing time variability was related to greater atrophy of the NBM, as was gait speed. STN DBS significantly improved cardinal motor signs and gait speed but did not improve swing time variability, which was replicated in the second cohort using dopaminergic medication. Swing time variability continued to worsen in PD, off therapy, after three years of continuous STN DBS, and NBM atrophy showed a trend for predicting the degree of increase. In contrast, cardinal motor signs did not progress. These results demonstrate that swing time variability is a reliable marker of cortical cholinergic health, and support a framework in which higher-order aspects of gait control in PD are reliant on the cortical cholinergic system, in contrast to other motor aspects of PD that rely on the dopaminergic network.

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          Staging of brain pathology related to sporadic Parkinson’s disease

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            Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.

            Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z < 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise values represent the amount of cluster-like local spatial support. The method is thus referred to as "threshold-free cluster enhancement" (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding.
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              Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain.

              One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.
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                Author and article information

                Journal
                9500169
                20475
                Neurobiol Dis
                Neurobiol Dis
                Neurobiology of disease
                0969-9961
                1095-953X
                23 October 2020
                10 October 2020
                December 2020
                03 December 2020
                : 146
                : 105134
                Affiliations
                [a ]Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
                [b ]Stanford University School of Medicine, Department of Neurosurgery, Stanford, CA, USA
                Author notes
                [* ]Corresponding author at: Stanford Movement Disorders Center (SMDC), Department of Neurology and Neurological Sciences, 300 Pasteur Drive Stanford University School of Medicine, Stanford, CA 94305, USA. hbs@ 123456stanford.edu (H.M. Bronte-Stewart).
                Article
                NIHMS1638376
                10.1016/j.nbd.2020.105134
                7711311
                33045357
                75592374-42bc-49df-abe4-5f47a1c9920c

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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                Neurosciences
                gait,nucleus basalis of meynert,parkinson’s disease,cholinergic,deep brain stimulation,basal forebrain,acetylcholine,dopaminergic,gait variability

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