Autonomous Optimization of Targeted Stimulation of Neuronal Networks.

Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable 'state' to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit quantitative relationships underlying activity-response interaction in biological neuronal networks to choose optimal actions. Simple phenomenological models can be useful to validate the quality of the resulting controllers.

Evaluation of the relative accuracy of anthropometric indicators to assess body fatness as measured by air displacement plethysmography in Indian women.

OBJECTIVE: To assess the relative accuracy of anthropometric indicators of body fatness or adiposity as compared to the percent fat measured by air displacement plethysmography in Indian women with normal body mass index (BMI). METHODS: Percent body fat was assessed using the BodPod in 58 women, aged 30-56 years. The relative accuracy of anthropometric indicators of body fatness like BMI, waist-stature-ratio (WSR), waist-thigh-ratio (WTR), waist-hip-ratio (WHR), and circumferences at arm, neck, chest, waist, hip, and thigh were tested using an ROC curve analysis. RESULTS: Seventy-nine percent women had excess body fat (≥30%). Among the 10 indicators tested, BMI and arm circumference (AC) demonstrated high accuracy (AUC > 0.9) to assess body fatness with derived cutoffs of 21.2 kg/m(2) and 24.2 cm, respectively. WSR and circumferences at neck, chest, waist, hip, and thigh showed moderate accuracy to assess body fatness, whereas WHR and WTR demonstrated poor accuracy. CONCLUSIONS: BMI and AC were the best performing indicators of adiposity among the indicators studied. AC, being a single measurement, is practical and a good choice. Am. J. Hum. Biol. 28:743-745, 2016. © 2016 Wiley Periodicals, Inc.

Tracking axon initial segment plasticity using high-density microelectrode arrays: A computational study.

Despite being composed of highly plastic neurons with extensive positive feedback, the nervous system maintains stable overall function. To keep activity within bounds, it relies on a set of negative feedback mechanisms that can induce stabilizing adjustments and that are collectively termed "homeostatic plasticity." Recently, a highly excitable microdomain, located at the proximal end of the axon-the axon initial segment (AIS)-was found to exhibit structural modifications in response to activity perturbations. Though AIS plasticity appears to serve a homeostatic purpose, many aspects governing its expression and its functional role in regulating neuronal excitability remain elusive. A central challenge in studying the phenomenon is the rich heterogeneity of its expression (distal/proximal relocation, shortening, lengthening) and the variability of its functional role. A potential solution is to track AISs of a large number of neurons over time and attempt to induce structural plasticity in them. To this end, a promising approach is to use extracellular electrophysiological readouts to track a large number of neurons at high spatiotemporal resolution by means of high-density microelectrode arrays (HD-MEAs). However, an analysis framework that reliably identifies specific activity signatures that uniquely map on to underlying microstructural changes is missing. In this study, we assessed the feasibility of such a task and used the distal relocation of the AIS as an exemplary problem. We used sophisticated computational models to systematically explore the relationship between incremental changes in AIS positions and the specific consequences observed in simulated extracellular field potentials. An ensemble of feature changes in the extracellular fields that reliably characterize AIS plasticity was identified. We trained models that could detect these signatures with remarkable accuracy. Based on these findings, we propose a hybrid analysis framework that could potentially enable high-throughput experimental studies of activity-dependent AIS plasticity using HD-MEAs.

A modulated template-matching approach to improve spike sorting of bursting neurons.

In extracellular neural electrophysiology, individual spikes have to be assigned to their cell of origin in a procedure called "spike sorting". Spike sorting is an unsupervised problem, since no ground-truth information is generally available. Here, we focus on improving spike sorting performance, particularly during periods of high synchronous activity or so-called "bursting". Bursting entails systematic changes in spike shapes and amplitudes and remains a challenge for current spike sorting schemes. We use realistic simulated bursting recordings of high-density micro-electrode arrays (HD-MEAs) and we present a fully automated algorithm based on template matching with a focus on recovering missed spikes during bursts. To compare and benchmark spike-sorting performance after applying our method, we used ground-truth information of simulated recordings. We show that our approach can be effective in improving spike sorting performance during bursting. Further validation with experimental recordings is necessary.

First-trimester prenatal diagnosis of pyruvate kinase deficiency in an Indian family with the pyruvate kinase-Amish mutation.

Pyruvate kinase (PK) deficiency is a rare red cell glycolytic enzymopathy. The purpose of the present investigation was to offer prenatal diagnosis for PK deficiency to a couple who had a previous child with severe enzyme deficiency and congenital non-spherocytic hemolytic anemia. PK deficiency was identified in the family by assaying the enzyme activity in red cells. Chorionic villus sampling was performed in an 11-week gestation and the mutation was located in exon 10 of the PKLR gene characterized by polymerase chain reaction and using restriction endonuclease digestion with the MspI enzyme, which was confirmed by DNA sequencing on the ABI 310 DNA sequencer. Both the parents were heterozygous for the 1436G-->A [479 Arg-->His] mutation in exon 10 and the proband was homozygous for this mutation. The fetus was also heterozygous for this mutation and the pregnancy was continued. Prenatal diagnosis allowed the parents with a severely affected child with PK deficiency to have the reproductive choice of having the fetus tested in a subsequent pregnancy.

Abnormal amyloid ß42 expression and increased oxidative stress in plasma of CKD patients with cognitive dysfunction: A small scale case control study comparison with Alzheimer's disease.

BACKGROUND: Cognitive dysfunction has been increasingly recognized in chronic kidney disease (CKD) patients. Senile plaques are important pathophysiological characteristic of cognitive dysfunction. The major component of plaques is the amyloid ß (Aß) peptide released from proteolytic cleavage of amyloid precursor protein (APP). Plasma Aß has been a focus of the growing literature on blood based biomarkers for cognitive dysfunction. Oxidative stress is prevalent in CKD and it plays an important role in cognitive dysfunction. Increased oxidative stress leads to cause cleavage of APP and Aß production. The aim of this study is to assess the antioxidant status and Aß42 levels in plasma of CKD patients with cognitive dysfunction compared to CKD without cognitive dysfunction. METHODS: A total of 60 subjects divided into 30 CKD without cognitive dysfunction and 30 CKD with cognitive dysfunction based on neuropsychological assessment tests. To compare antioxidant status and Aß42 levels in plasma, the following groups such as healthy subjects (n = 30), normocytic normochromic anemia (n = 30) and Alzheimer's disease (AD, n = 10) patients were also maintained. Plasma Superoxide dismutase (SOD), Catalase (CAT), Glutathione peroxidase (GPx), Reduced glutathione (GSH) and lipid peroxidation (LPO) were determined by spectrophotometrically. Aß level was determined by immunoblotting method. The parameters were statistically compared with healthy, normocytic normochromic anemia and AD subjects. RESULTS: Like AD subjects, significantly increased Aß and LPO level while decreased SOD, CAT, GPx and GSH levels were observed in plasma of CKD patients with cognitive dysfunction when compared to healthy, CKD without cognitive dysfunction and normocytic normochromic anemic subjects. CONCLUSION: Results suggest that elevated plasma oxidative stress and Aß were seen in CKD patients with cognitive dysfunction may be attributed to pathological changes within the brain.

Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis.

We report the effectiveness of laser-induced breakdown spectroscopy (LIBS) in probing the content of pharmaceutical tablets and also investigate its feasibility for routine classification. This method is particularly beneficial in applications where its exquisite chemical specificity and suitability for remote and on site characterization significantly improves the speed and accuracy of quality control and assurance process. Our experiments reveal that in addition to the presence of carbon, hydrogen, nitrogen and oxygen, which can be primarily attributed to the active pharmaceutical ingredients, specific inorganic atoms were also present in all the tablets. Initial attempts at classification by a ratiometric approach using oxygen (∼777 nm) to nitrogen (742.36 nm, 744.23 nm and 746.83 nm) compositional values yielded an optimal value at 746.83 nm with the least relative standard deviation but nevertheless failed to provide an acceptable classification. To overcome this bottleneck in the detection process, two chemometric algorithms, i.e. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA), were implemented to exploit the multivariate nature of the LIBS data demonstrating that LIBS has the potential to differentiate and discriminate among pharmaceutical tablets. We report excellent prospective classification accuracy using supervised classification via the SIMCA algorithm, demonstrating its potential for future applications in process analytical technology, especially for fast on-line process control monitoring applications in the pharmaceutical industry.

First-trimester prenatal diagnosis of pyruvate kinase deficiency in an Indian family with the pyruvate kinase-Amish mutation

Pyruvate kinase (PK) deficiency is a rare red cell glycolytic enzymopathy. The purpose of the present investigation was to offer prenatal diagnosis for PK deficiency to a couple who had a previous child with severe enzyme deficiency and congenital non-spherocytic hemolytic anemia. PK deficiency was identified in the family by assaying the enzyme activity in red cells. Chorionic villus sampling was performed in an 11-week gestation and the mutation was located in exon 10 of the PKLR gene characterized by polymerase chain reaction and using restriction endonuclease digestion with the MspI enzyme, which was confirmed by DNA sequencing on the ABI 310 DNA sequencer. Both the parents were heterozygous for the 1436G-->A [479 Arg-->His] mutation in exon 10 and the proband was homozygous for this mutation. The fetus was also heterozygous for this mutation and the pregnancy was continued. Prenatal diagnosis allowed the parents with a severely affected child with PK deficiency to have the reproductive choice of having the fetus tested in a subsequent pregnancy.