Analysis of striatal connectivity corresponding to striosomes and matrix in de novo Parkinson's disease and isolated REM behavior disorder.

Striosomes and matrix are two compartments that comprise the striatum, each having its own distinct immunohistochemical properties, function, and connectivity. It is currently not clear whether prodromal or early manifest Parkinson's disease (PD) is associated with any striatal matrix or striosomal abnormality. Recently, a method of striatal parcellation using probabilistic tractography has been described and validated, using the distinct connectivity of these two compartments to identify voxels with striosome- and matrix-like connectivity. The goal of this study was to use this approach in tandem with DAT-SPECT, a method used to quantify the level of nigrostriatal denervation, to analyze the striatum in populations of de novo diagnosed, treatment-naïve patients with PD, isolated REM behavioral disorder (iRBD) patients, and healthy controls. We discovered a shift in striatal connectivity, which showed correlation with nigrostriatal denervation. Patients with PD exhibited a significantly higher matrix-like volume and associated connectivity than healthy controls and higher matrix-associated connectivity than iRBD patients. In contrast, the side with less pronounced nigrostriatal denervation in PD and iRBD patients showed a decrease in striosome-like volume and associated connectivity indices. These findings could point to a compensatory neuroplastic mechanism in the context of nigrostriatal denervation and open a new avenue in the investigation of the pathophysiology of Parkinson's disease.

Application of spike sorting algorithm to neuronal signals originated from boron doped diamond micro-electrode arrays.

In this work we report on the implementation of methods for data processing signals from microelectrode arrays (MEA) and the application of these methods for signals originated from two types of MEAs to detect putative neurons and sort them into subpopulations. We recorded electrical signals from firing neurons using titanium nitride (TiN) and boron doped diamond (BDD) MEAs. In previous research, we have shown that these methods have the capacity to detect neurons using commercially-available TiN-MEAs. We have managed to cultivate and record hippocampal neurons for the first time using a newly developed custom-made multichannel BDD-MEA with 20 recording sites. We have analysed the signals with the algorithms developed and employed them to inspect firing bursts and enable spike sorting. We did not observe any significant difference between BDD- and TiN-MEAs over the parameters, which estimated spike shape variability per each detected neuron. This result supports the hypothesis that we have detected real neurons, rather than noise, in the BDD-MEA signal. BDD materials with suitable mechanical, electrical and biocompatibility properties have a large potential in novel therapies for treatments of neural pathologies, such as deep brain stimulation in Parkinson's disease.

Comparative study of the substantia nigra echogenicity and 123I-Ioflupane SPECT in patients with synucleinopathies with and without REM sleep behavior disorder.

OBJECTIVES: Hyperechogenicity of the substantia nigra (SN) and abnormal dopamine transporter-single-photon emission computed tomography (DAT-SPECT) are biomarkers commonly used in the assessment of prodromal synucleinopathy. Our goals were as follows: (1) to compare echogenicity of SN in idiopathic rapid eye movement (REM) behavior disorder (iRBD), Parkinson's disease (PD) without RBD (PD-noRBD), PD with RBD (PD + RBD), and control subjects; and (2) to examine association between SN degeneration assessed by DAT-SPECT and SN echogenicity. PATIENTS/METHODS: A total of 61 subjects with confirmed iRBD were examined using Movement Disorders Society-unified PD rating scale (MDS-UPDRS), TCS (transcranial sonography) and DAT-SPECT. The results were compared with 44 patients with PD (25% PD + RBD) and with 120 age-matched healthy subjects. RESULTS AND CONCLUSION: The abnormal SN area was found in 75.5% PD, 23% iRBD and 7.3% controls. Median SN echogenicity area in PD (0.27 ± 0.22 cm2) was higher compared to iRBD (0.07 ± 0.07 cm2; p < 0.0001) and controls (0.05 ± 0.03 cm2; p < 0.0001). SN echogenicity in PD + RBD was not significantly different from PD-noRBD (0.30 vs. 0.22, p = 0.15). Abnormal DAT-SPECT was found in 16 iRBD (25.4%) and 44 PD subjects (100%). No correlation between the larger SN area and corresponding putaminal binding index was found in iRBD (r = -0.13, p = 0.29), nor in PD (r = -0.19, p = 0.22). The results of our study showed that: (1) SN echogenicity area in iRBD was higher compared to controls, but the hyperechogenicity was present only in a minority of iRBD patients; (2) SN echogenicity and DAT-SPECT binding index did not correlate in either group; and (3) SN echogenicity does not differ between PD with/without RBD.

Automatic substantia nigra segmentation in neuromelanin-sensitive MRI by deep neural network in patients with prodromal and manifest synucleinopathy.

Neuromelanin (NM) is a black pigment located in the brain in substantia nigra pars compacta (SN) and locus coeruleus. Its loss is directly connected to the loss of nerve cells in this part of the brain, which plays a role in Parkinson's Disease. Magnetic resonance imaging (MRI) is an ideal tool to monitor the amount of NM in the brain in vivo. The aim of the study was the development of tools and methodology for the quantification of NM in a special neuromelanin-sensitive MRI images. The first approach was done by creating regions of interest, corresponding to the anatomical position of SN based on an anatomical atlas and determining signal intensity threshold. By linking the anatomical and signal intensity information, we were able to segment the SN. As a second approach, the neural network U-Net was used for the segmentation of SN. Subsequently, the volume characterizing the amount of NM in the SN region was calculated. To verify the method and the assumptions, data available from various patient groups were correlated. The main benefit of this approach is the observer-independency of quantification and facilitation of the image processing process and subsequent quantification compared to the manual approach. It is ideal for automatic processing many image sets in one batch.