Optogenetics and deep brain stimulation neurotechnologies

Brain neural network is composed of densely packed, intricately wired neurons whose activity patterns ultimately give rise to every behavior, thought, or emotion that we experience. Over the past decade, a novel neurotechnique, optogenetics that combines light and genetic methods to control or monitor neural activity patterns, has proven to be revolutionary in understanding the functional role of specific neural circuits. We here briefly describe recent advance in optogenetics and compare optogenetics with deep brain stimulation technology that holds the promise for treating many neurological and psychiatric disorders

Proximity of Substantia Nigra Microstimulation to Putative GABAergic Neurons Predicts Modulation of Human Reinforcement Learning

Neuronal firing in the substantia nigra (SN) immediately following reward is thought to play a crucial role in human reinforcement learning. As in Ramayya et al. (2014a) we applied microstimulation in the SN of patients undergoing deep brain stimulation (DBS) for the treatment of Parkinson's disease as they engaged in a two-alternative reinforcement learning task. We obtained microelectrode recordings to assess the proximity of the electrode tip to putative dopaminergic and GABAergic SN neurons and applied stimulation to assess the functional importance of these neuronal populations for learning. We found that the proximity of SN microstimulation to putative GABAergic neurons predicted the degree of stimulation-related changes in learning. These results extend previous work by supporting a specific role for SN GABA firing in reinforcement learning. Stimulation near these neurons appears to dampen the reinforcing effect of rewarding stimuli

2008 Progress Report on Brain Research

Highlights new research on various disorders, nervous system injuries, neuroethics, neuroimmunology, pain, sense and body function, stem cells and neurogenesis, and thought and memory. Includes essays on arts and cognition and on deep brain stimulation

Feasibility of diffusion and probabilistic white matter analysis in patients implanted with a deep brain stimulator.

Deep brain stimulation (DBS) for Parkinson\u27s disease (PD) is an established advanced therapy that produces therapeutic effects through high frequency stimulation. Although this therapeutic option leads to improved clinical outcomes, the mechanisms of the underlying efficacy of this treatment are not well understood. Therefore, investigation of DBS and its postoperative effects on brain architecture is of great interest. Diffusion weighted imaging (DWI) is an advanced imaging technique, which has the ability to estimate the structure of white matter fibers; however, clinical application of DWI after DBS implantation is challenging due to the strong susceptibility artifacts caused by implanted devices. This study aims to evaluate the feasibility of generating meaningful white matter reconstructions after DBS implantation; and to subsequently quantify the degree to which these tracts are affected by post-operative device-related artifacts. DWI was safely performed before and after implanting electrodes for DBS in 9 PD patients. Differences within each subject between pre- and post-implantation FA, MD, and RD values for 123 regions of interest (ROIs) were calculated. While differences were noted globally, they were larger in regions directly affected by the artifact. White matter tracts were generated from each ROI with probabilistic tractography, revealing significant differences in the reconstruction of several white matter structures after DBS. Tracts pertinent to PD, such as regions of the substantia nigra and nigrostriatal tracts, were largely unaffected. The aim of this study was to demonstrate the feasibility and clinical applicability of acquiring and processing DWI post-operatively in PD patients after DBS implantation. The presence of global differences provides an impetus for acquiring DWI shortly after implantation to establish a new baseline against which longitudinal changes in brain connectivity in DBS patients can be compared. Understanding that post-operative fiber tracking in patients is feasible on a clinically-relevant scale has significant implications for increasing our current understanding of the pathophysiology of movement disorders, and may provide insights into better defining the pathophysiology and therapeutic effects of DBS

ANN for Parkinson’s Disease Prediction

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%

Pain-motor integration in the primary motor cortex in Parkinson's disease

In Parkinson's disease (PD), the influence of chronic pain on motor features has never been investigated. We have recently designed a technique that combines nociceptive system activation by laser stimuli and primary motor cortex (M1) activation through transcranial magnetic stimulation (TMS), in a laser-paired associative stimulation design (Laser-PAS). In controls, Laser-PAS induces long-term changes in motor evoked potentials reflecting M1 long-term potentiation-like plasticity, arising from pain-motor integration

Quantitative analysis of language production in Parkinson's disease using a cued sentence generation task

The present study examined language production skills in Parkinson's disease (PD) patients. A unique cued sentence generation task was created in order to reduce demands on memory and attention. Differences in sentence production abilities according to disease severity and cognitive impairments were assessed. Language samples were obtained from 20 PD patients and 20 healthy control participants matched for age, sex and educational level. In addition, a cognitive test for verbal memory and resistance to cognitive interference was administered. Statistical comparisons revealed significant language changes in an advanced stage of the disease. Advanced PD patients showed a reduction in lexical diversity in notional verbs, which was absent in nouns. Cognitive dysfunctions such as impaired verbal memory are suggested to contribute to the typical noun/verb dissociation in PD patients. In addition, advanced PD patients produced more semantic perseverations, which may be related to set-switching problems. In conclusion, whether language disturbances in PD are the result of non-linguistic cognitive dysfunctions or reflect pure language deficits exacerbated by cognitive impairments, remains a matter of debate. However, the negative impact of cognitive dysfunctions may be important

Deep brain stimulation in schizophrenia

Deep brain stimulation (DBS) has successfully advanced treatment options of putative therapy-resistant neuropsychiatric diseases. Building on this strong foundation more and more mental disorders in the stadium of therapy-resistance are considered as possible indications for DBS. Especially schizophrenia with its associated severe and difficult to treat symptoms is gaining attention. This attention demands critical questions regarding the assumed mechanisms of DBS and its possible influence on the supposed pathophysiology of schizophrenia. Here we synoptically compare current approaches and theories of DBS and discuss the feasibility of DBS in schizophrenia as well as the transferability from other psychiatric disorders successfully treated with DBS. For this we consider recent advances in animal models of schizophrenic symptoms, results regarding the influence of DBS on dopaminergic transmission as well as data concerning neural oscillation and synchronization. In conclusion the use of DBS for some symptoms of schizophrenia seems to be a promising approach, but the lack of a comprehensive theory of the mechanisms of DBS as well as its impact on schizophrenia might void the use of DBS in schizophrenia at this point

Value encoding in the globus pallidus: fMRI reveals an interaction effect between reward and dopamine drive

The external part of the globus pallidus (GPe) is a core nucleus of the basal ganglia (BG) whose activity is disrupted under conditions of low dopamine release, as in Parkinson's disease. Current models assume decreased dopamine release in the dorsal striatum results in deactivation of dorsal GPe, which in turn affects motor expression via a regulatory effect on other nuclei of the BG. However, recent studies in healthy and pathological animal models have reported neural dynamics that do not match with this view of the GPe as a relay in the BG circuit. Thus, the computational role of the GPe in the BG is still to be determined. We previously proposed a neural model that revisits the functions of the nuclei of the BG, and this model predicts that GPe encodes values which are amplified under a condition of low striatal dopaminergic drive. To test this prediction, we used an fMRI paradigm involving a within-subject placebo-controlled design, using the dopamine antagonist risperidone, wherein healthy volunteers performed a motor selection and maintenance task under low and high reward conditions. ROI-based fMRI analysis revealed an interaction between reward and dopamine drive manipulations, with increased BOLD activity in GPe in a high compared to low reward condition, and under risperidone compared to placebo. These results confirm the core prediction of our computational model, and provide a new perspective on neural dynamics in the BG and their effects on motor selection and cognitive disorders