Cross validation of experts versus registration methods for target localization in deep brain stimulation.

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatent of Parkinson's disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used.

Influence of the implanted pulse generator as reference electrode in finite element model of monopolar deep brain stimulation.

Electrical deep brain stimulation (DBS) is an efficient method to treat movement disorders. Many models of DBS, based mostly on finite elements, have recently been proposed to better understand the interaction between the electrical stimulation and the brain tissues. In monopolar DBS, clinically widely used, the implanted pulse generator (IPG) is used as reference electrode (RE). In this paper, the influence of the RE model of monopolar DBS is investigated. For that purpose, a finite element model of the full electric loop including the head, the neck and the superior chest is used. Head, neck and superior chest are made of simple structures such as parallelepipeds and cylinders. The tissues surrounding the electrode are accurately modelled from data provided by the diffusion tensor magnetic resonance imaging (DT-MRI). Three different configurations of RE are compared with a commonly used model of reduced size. The electrical impedance seen by the DBS system and the potential distribution are computed for each model. Moreover, axons are modelled to compute the area of tissue activated by stimulation. Results show that these indicators are influenced by the surface and position of the RE. The use of a RE model corresponding to the implanted device rather than the usually simplified model leads to an increase of the system impedance (+48%) and a reduction of the area of activated tissue (-15%).

Autosomal dominant dopa-responsive parkinsonism in a multigenerational Swiss family.

AIM: To describe a large family with autosomal dominant parkinsonism. BACKGROUND: Seven genes are directly implicated in autosomally inherited parkinsonism. However, there are several multigenerational large families known with no identifiable mutation. MATERIAL AND METHODS: Family members were evaluated clinically, by history and chart review. Genetic investigation included SCA2, SCA3, UCHL1, SNCA, LRRK2, PINK1, PRKN, PGRN, FMR1 premutation, and MAPT. The proband underwent brain fluorodopa PET (FD-PET) scan, and one autopsy was available. RESULTS: Eleven patients had a diagnosis of Parkinson's disease (PD), nine women. Mean age of onset was 52 with tremor-predominant dopa-responsive parkinsonism. Disease progression was slow but severe motor fluctuations occurred. One patient required subthalamic nucleus deep-brain stimulation with a good motor outcome. One patient had mental retardation, schizophrenia and became demented, and another patient was demented. Three patients and also two unaffected subjects had mild learning difficulties. All genetic tests yielded negative results. FD-PET showed marked asymmetric striatal tracer uptake deficiency, consistent with PD. Pathological examination demonstrated no Lewy bodies and immunostaining was negative for alpha-synuclein. CONCLUSION: Apart from a younger age of onset and a female predominance, the phenotype was indistinguishable from sporadic tremor-predominant PD, including FD-PET scan results. As known genetic causes of autosomal dominant PD were excluded, this family harbors a novel genetic defect.

Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Bilateral deep brain stimulation: the placement of the second electrode is not necessarily less accurate than that of the first one.

BACKGROUND: Deep brain stimulation (DBS) is recognized as an effective treatment for movement disorders. We recently changed our technique, limiting the number of brain penetrations to three per side. OBJECTIVES: The first aim was to evaluate the electrode precision on both sides of surgery since we implemented this surgical technique. The second aim was to analyse whether or not the electrode placement was improved with microrecording and macrostimulation. METHODS: We retrospectively reviewed operation protocols and MRIs of 30 patients who underwent bilateral DBS. For microrecording and macrostimulation, we used three parallel channels of the 'Ben Gun' centred on the MRI-planned target. Pre- and post-operative MRIs were merged. The distance between the planned target and the centre of the implanted electrode artefact was measured. RESULTS: There was no significant difference in targeting precision on both sides of surgery. There was more intra-operative adjustment of the second electrode positioning based on microrecording and macrostimulation, which allowed to significantly approach the MRI-planned target on the medial-lateral axis. CONCLUSION: There was more electrode adjustment needed on the second side, possibly in relation with brain shift. We thus suggest performing a single central track with electrophysiological and clinical assessment, with multidirectional exploration on demand for suboptimal clinical responses.

The human thalamus processes syntactic and semantic language violations

Numerous linguistic operations have been assigned to cortical brain areas, but the contributions of subcortical structures to human language processing are still being discussed. Using simultaneous EEG recordings directly from deep brain structures and the scalp, we show that the human thalamus systematically reacts to syntactic and semantic parameters of auditorily presented language in a temporally interleaved manner in coordination with cortical regions. In contrast, two key structures of the basal ganglia, the globus pallidus internus and the subthalamic nucleus, were not found to be engaged in these processes. We therefore propose that syntactic and semantic language analysis is primarily realized within cortico-thalamic networks, whereas a cohesive basal ganglia network is not involved in these essential operations of language analysis.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Parkinsonian tremor identification with multiple local field potential feature classification

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Antinociceptive effect of stimulating the zona incerta with glutamate in rats

The zona incerta (ZI) is a subthalamic nucleus connected to several structures, some of them known to be involved with antinociception. The 21 itself may be involved with both antinociception and nociception. The antinociceptive effects of stimulating the ZI with glutamate using the rat tail-flick test and a rat model of incision pain were examined. The effects of intraperitoneal antagonists of acetylcholine, noradrenaline, serotonin, dopamine, or opioids on glutamate-induced antinociception from the ZI in the tail-flick test were also evaluated. The injection of glutamate (7 mu g/0.25 mu l) into the ZI increased tail-flick latency and inhibited post-incision pain, but did not change the animal performance in a Rota-rod test. The injection of glutamate into sites near the ZI was non effective. The glutamate-induced antinociception from the ZI did not occur in animals with bilateral lesion of the dorsolateral funiculus, or in rats treated intraperitoneally with naloxone (1 and 2 m/kg), methysergide (1 and 2 m/kg) or phenoxybenzamine (2 m/kg), but remained unchanged in rats treated with atropine, mecamylamine, or haloperidol (all given at doses of 1 and 2 m/kg). We conclude that the antinociceptive effect evoked from the ZI is not due to a reduced motor performance, is likely to result from the activation of a pain-inhibitory mechanism that descends to the spinal cord via the dorsolateral funiculus, and involves at least opioid, serotonergic and a-adrenergic mechanisms. This profile resembles the reported effects of these antagonists on the antinociception caused by stimulating the periaqueductal gray or the pedunculopontine tegmental nucleus. (C) 2012 Elsevier Inc. All rights reserved.

Modulation of parvalbumin expression in the motor cortex of parkinsonian rats

Dopamine deficiency in Parkinson's disease leads to numerous molecular changes in basal ganglia. However, the consequences of these changes on the motor cortex remain unclear. Here we show that the immunoreactivity of parvalbumin, which is expressed in GABAergic interneurons, increases in the primary motor cortex of parkinsonian rats. This increase can be reversed by a subsequent lesion of the subthalamic nucleus. These results suggest that dopamine deficiency induces reversible changes in GABAergic cortical cells, which might be linked with parkinsonian symptoms.

A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease

OBJECT: The localization of any given target in the brain has become a challenging issue because of the increased use of deep brain stimulation to treat Parkinson disease, dystonia, and nonmotor diseases (for example, Tourette syndrome, obsessive compulsive disorders, and depression). The aim of this study was to develop an automated method of adapting an atlas of the human basal ganglia to the brains of individual patients. METHODS: Magnetic resonance images of the brain specimen were obtained before extraction from the skull and histological processing. Adaptation of the atlas to individual patient anatomy was performed by reshaping the atlas MR images to the images obtained in the individual patient using a hierarchical registration applied to a region of interest centered on the basal ganglia, and then applying the reshaping matrix to the atlas surfaces. RESULTS: Results were evaluated by direct visual inspection of the structures visible on MR images and atlas anatomy, by comparison with electrophysiological intraoperative data, and with previous atlas studies in patients with Parkinson disease. The method was both robust and accurate, never failing to provide an anatomically reliable atlas to patient registration. The registration obtained did not exceed a 1-mm mismatch with the electrophysiological signatures in the region of the subthalamic nucleus. CONCLUSIONS: This registration method applied to the basal ganglia atlas forms a powerful and reliable method for determining deep brain stimulation targets within the basal ganglia of individual patients.

Directional deep brain stimulation: an intraoperative double-blind pilot study.

Deep brain stimulation of different targets has been shown to drastically improve symptoms of a variety of neurological conditions. However, the occurrence of disabling side effects may limit the ability to deliver adequate amounts of current necessary to reach the maximal benefit. Computed models have suggested that reduction in electrode size and the ability to provide directional stimulation could increase the efficacy of such therapies. This has never been demonstrated in humans. In the present study, we assess the effect of directional stimulation compared to omnidirectional stimulation. Three different directions of stimulation as well as omnidirectional stimulation were tested intraoperatively in the subthalamic nucleus of 11 patients with Parkinson's disease and in the nucleus ventralis intermedius of two other subjects with essential tremor. At the trajectory chosen for implantation of the definitive electrode, we assessed the current threshold window between positive and side effects, defined as the therapeutic window. A computed finite element model was used to compare the volume of tissue activated when one directional electrode was stimulated, or in case of omnidirectional stimulation. All but one patient showed a benefit of directional stimulation compared to omnidirectional. A best direction of stimulation was observed in all the patients. The therapeutic window in the best direction was wider than the second best direction (P = 0.003) and wider than the third best direction (P = 0.002). Compared to omnidirectional direction, the therapeutic window in the best direction was 41.3% wider (P = 0.037). The current threshold producing meaningful therapeutic effect in the best direction was 0.67 mA (0.3-1.0 mA) and was 43% lower than in omnidirectional stimulation (P = 0.002). No complication as a result of insertion of the directional electrode or during testing was encountered. The computed model revealed a volume of tissue activated of 10.5 mm(3) in omnidirectional mode, compared with 4.2 mm(3) when only one electrode was used. Directional deep brain stimulation with a reduced electrode size applied intraoperatively in the subthalamic nucleus as well as in the nucleus ventralis intermedius of the thalamus significantly widened the therapeutic window and lowered the current needed for beneficial effects, compared to omnidirectional stimulation. The observed side effects related to direction of stimulation were consistent with the anatomical location of surrounding structures. This new approach opens the door to an improved deep brain stimulation therapy. Chronic implantation is further needed to confirm these findings.

Avaliação quantitativa dos efeitos da levodopa e da estimulação do núcleo subtalâmico sobre o equilíbrio em pacientes com doença de Parkinson

INTRODUÇÃO: Os efeitos da levodopa (LD) e da estimulação cerebral profunda (ECP) de núcleo subtalâmico (STN) sobre o equilíbrio e sintomas axiais são até o momento controversos. OBJETIVOS: Avaliar quantitativamente os efeitos da ECP de STN e da LD sobre o equilíbrio estático em pacientes com DP operados, em comparação com a LD em pacientes não operados. MÉTODOS: Trinta e um pacientes submetidos a ECP de STN entre 3 meses e 1 ano e meio antes da avaliação e 26 controles portadores de DP não operados, estágios Hoehn e Yahr 2 a 4 foram avaliados usando UPDRS para avaliação clínica e plataforma de força para avaliar oscilações posturais. O primeiro grupo foi avaliado com ECP e sem medicação, com ECP e com medicação e sem ECP e sem medicação. O segundo grupo foi avaliado com e sem medicação. Cada paciente foi avaliado com os olhos abertos e fechados. O deslocamento do centro de pressão anteroposterior, laterolateral, a área, velocidade e deslocamento total linear foram medidos pela plataforma de força. Os dados paramétricos foram comparados usando o teste t de Student e os dados não-paramétricos foram comparados pelo teste de Kruskal-Wallis. A avaliação clínica consistiu na parte 3 da escala UPDRS e na escala Hoehn e Yahr. Nível de significância estatística considerada foi p=0,05. RESULTADOS: Os pacientes não operados oscilaram mais quando sob efeito da levodopa do que sem medicação. No grupo operado, a maior oscilação é no grupo com ECP desligada e sem medicação. Tende a reduzir sob efeito da ECP apresenta redução significativa sob efeito simultâneo de ECP e levodopa. CONCLUSÃO: A associação da ECP de NST com medicação tem impacto positivo sobre o controle postural. O efeito da ECP de NST reverte o efeito negativo da levodopa sobre as oscilações observadas em pacientes não operados

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.