Enhanced catecholamine transporter binding in the locus coeruleus of patients with early Parkinson disease

<p>Abstract</p> <p>Background</p> <p>Studies in animals suggest that the noradrenergic system arising from the locus coeruleus (LC) and dopaminergic pathways mutually influence each other. Little is known however, about the functional state of the LC in patients with Parkinson disease (PD).</p> <p>Methods</p> <p>We retrospectively reviewed clinical and imaging data of 94 subjects with PD at an early clinical stage (Hoehn and Yahr stage 1-2) who underwent single photon computed tomography imaging with FP-CIT ([¹²³I] N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl) tropane). FP-CIT binding values from the patients were compared with 15 healthy subjects: using both a voxel-based whole brain analysis and a volume of interest analysis of <it>a priori </it>defined brain regions.</p> <p>Results</p> <p>Average FP-CIT binding in the putamen and caudate nucleus was significantly reduced in PD subjects (43% and 57% on average, respectively; p < 0.001). In contrast, subjects with PD showed an increased binding in the LC (166% on average; p < 0.001) in both analyses. LC-binding correlated negatively with striatal FP-CIT binding values (caudate: contralateral, ρ = -0.28, p < 0.01 and ipsilateral ρ = -0.26, p < 0.01; putamen: contralateral, ρ = -0.29, p < 0.01 and ipsilateral ρ = -0.29, p < 0.01).</p> <p>Conclusions</p> <p>These findings are consistent with an up-regulation of noradrenaline reuptake in the LC area of patients with early stage PD, compatible with enhanced noradrenaline release, and a compensating activity for degeneration of dopaminergic nigrostriatal projections.</p

Parkinson’s disease mouse models in translational research

Animal models with high predictive power are a prerequisite for translational research. The closer the similarity of a model to Parkinson’s disease (PD), the higher is the predictive value for clinical trials. An ideal PD model should present behavioral signs and pathology that resemble the human disease. The increasing understanding of PD stratification and etiology, however, complicates the choice of adequate animal models for preclinical studies. An ultimate mouse model, relevant to address all PD-related questions, is yet to be developed. However, many of the existing models are useful in answering specific questions. An appropriate model should be chosen after considering both the context of the research and the model properties. This review addresses the validity, strengths, and limitations of current PD mouse models for translational research

The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces

Background Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. Objectives Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. Methods Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. Results The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. Conclusions Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for “closed-loop” algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration

The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces

Background Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. Objectives Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. Methods Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. Results The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. Conclusions Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for “closed-loop” algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration

The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces

Background Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. Objectives Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. Methods Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination. Results The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. Conclusions Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for “closed-loop” algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration