Andrzej W. Przybyszewski

Striate cortex increases contrast gain of macaque LGN neurons

Recurrent projections comprise a universal feature of cerebral organization. Here, we show that the corticofugal projections from the striate cortex (VI) to the lateral geniculate nucleus (LGN) robustly and multiplicatively enhance the responses of parvocellular neurons, stimulated by gratings restricted to the classical receptive field and modulated in luminance, by over two-fold in a contrast-independent manner at all but the lowest contrasts. In the equiluminant plane, wherein stimuli are modulated in chromaticity with luminance held constant, such enhancement is strongly contrast dependent. These projections also robustly enhance the responses of magnocellular neurons but contrast independently only at high contrasts. Thus, these results have broad functional significance at both network and neuronal levels by providing the experimental basis and quantitative constraints for a wide range of models on recurrent projections and the control of contrast gain.

EMD APPROACH TO MULTICHANNEL EEG DATA — THE AMPLITUDE AND PHASE COMPONENTS CLUSTERING ANALYSIS

Human brains exhibit a possibility to control directly the intelligent computing applications in form of brain computer/machine interfacing (BCI/BMI) technologies. Neurophysiological signals and especially electroencephalogram (EEG) are the forms of brain electrical activity which can be easily captured and utilized for BCI/BMI applications. Those signals are unfortunately usually very highly contaminated by external noise caused by the presence of different devices in the environment creating electromagnetic interference. In this paper, we first decompose each of the recorded channels, in multichannel EEG recording environment, into intrinsic mode functions (IMF) which are a result of empirical mode decomposition (EMD) extended to multichannel analysis. We present novel and interesting results on human mental and cognitive states estimation based on analysis of the above-mentioned stimuli-related IMF components. The IMF components are further clustered for their spectral similarity in order to identify only those carrying responses to present stimuli to the subjects. The resulting targets only reconstruction allows us to identify when and to which stimuli intelligent application user is tuning at a time.

Localization of the subthalamic nucleus in Parkinson disease using multiunit activity

BACKGROUND Refinement of the subthalamic nucleus (STN) coordinates using intraoperative microelectrode recordings (MER) is routinely performed during deep brain stimulation (DBS) surgeries in Parkinson disease (PD). The commonly used criteria for electrophysiological localization of the STN are qualitative. The goal of this study was to validate quantitative STN detection algorithm (QD) derived from the multi-unit activity in a prospective setting. METHODS Ten PD patients underwent STN DBS surgery. The MUA was obtained by removing large spikes close to microelectrode using wavelet method and integrating the 500-2000Hz band in the power spectral density. The qualitative intraoperative mapping of the STN using MER (IOM) versus QD was compared using Bland-Altman and Pearsons correlation analysis. RESULTS The clinical efficacy was confirmed in all subjects. The mean difference between IOM and QD of the dorsal/ventral border was 0.31±0.84/0.44±0.47mm. Using Bland-Altman statistic, only 2/36 (5.6%) differences (one for the dorsal border and one for the ventral border) were out of ±2 sd line of measurement differences. Correlation between dorsal border/ventral border positions obtained by IOM and QD was 0.79, p<0.0001/0.91, p<0.0001. CONCLUSION Both methods are in reasonable agreement and are strongly correlated. The QD gives objective coordinates of the STN borders at high precision and may be more accurate than IOM. Prospective blinded comparative studies where the DBS leads will be placed using either QD or IOM are warranted.

Eye position influences contrast responses in V1 of alert monkey

Do our neurons in V1 respond differently when we look in different places? To answer this question, we have studied neuronal responses to moving bars in V1 of an alert monkey while it maintained different directions of gaze. The monkey was trained to fixate on an LED attached to the stimulus screen while the screen was placed in three positions: straight ahead or 0 deg (0 position), approximately 10 deg to the right (10R) or or to the left (10L) in the horizontal plane (h) in a constant vertical position (v). Recorded mean +/SE eye positions in minarc were: for 0 position (h,v) = (2.5+/5.7, 5.3+/-3.8), for 10R position (516+/-16, -32+/-3), for 10L position (-540+/-12, 31+/-5). We have recorded contrast responses in 21 cells. Changing eye position significantly influenced the maximum amplitude of the response in 13 cells. In 4 cells where maximum responses were unchanged, responses to lower contrasts changed significantly for different eye positions. In 7/17 cells in 0 position, in 5/17 cells in 10R position and in 5/17 in 10L position, responses were larger than in other two positions. We have fitted contrast responses r(c) with the Naka-Rushton equation: r(c) = Rmax*(c^n /(c^n + c50^n)), where Rmax is the maximum response, c contrast, c50 contrast at the half of Rmax, n nonlinearity. We have analyzed only those responses with a sufficiently good fit (estimated by the RMS). In most cases changing the eye position had small influence on n, but significant influence on Rmax and c50. We have analyzed 18 contrast responses to increment and decrement bars. Rmax changed, more than 20%, in 12 cases and c50 in 14 cases. In 10 measurements both Rmax and c50 changed as the eye position changed. Our preliminary data also suggest that the eye position could differently influence the size of the increment and decrement zones in the classical receptive field of V1 cells.

Basic Difference Between Brain and Computer: Integration of Asynchronous Processes Implemented as Hardware Model of the Retina

There exists a common view that the brain acts like a Turing machine: The machine reads information from an infinite tape (sensory data) and, on the basis of the machines state and information from the tape, an action (decision) is made. The main problem with this model lies in how to synchronize a large number of tapes in an adaptive way so that the machine is able to accomplish tasks such as object classification. We propose that such mechanisms exist already in the eye. A popular view is that the retina, typically associated with high gain and adaptation for light processing, is actually performing local preprocessing by means of its center-surround receptive field. We would like to show another property of the retina: The ability to integrate many independent processes. We believe that this integration is implemented by synchronization of neuronal oscillations. In this paper, we present a model of the retina consisting of a series of coupled oscillators which can synchronize on several scales. Synchronization is an analog process which is converted into a digital spike train in the output of the retina. We have developed a hardware implementation of this model, which enables us to carry out rapid simulation of multineuron oscillatory dynamics. We show that the properties of the spike trains in our model are similar to those found in vivo in the cat retina

Foundations of automatic system for intrasurgical localization of subthalamic nucleus in Parkinson patients

During deep brain stimulation DBS treatment of Parkinson disease, the target of the surgery is the subthalamic nucleus STN. This anatomical structure is small 9×7×4 mm and poorly visible using Computer Tomography CT or Magnetic Resonance Imaging MRI scans.Because of that, a multi-electrode micro recording system is used intra surgically for better localization of the target nucleus. This paper presents five different analytical methods, that can be used to construct an autonomic system which can aid neurosurgeons in precise localization of the

Foundations of recommender system for STN localization during DBS surgery in parkinson's patients

Subthalamic\;Nucleus

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients.

STN. Such system could be used during surgery in the environment of the operation theater. Described methods take as input signals recorded from the micro electrodes. Their result in turn allows one to tell which from the recorded signals comes from the STN. First method bases on the recorded action potentials, i.e. on electrical activity of neurons that are near electrodes recording tip. Second utilizes root mean square of recorded signals. Third takes into account amplitude of the background noise present in the recorded signal. The last two methods examine Low Frequency Background LFB and High Frequency Background HFB.

Data Mining and Machine Learning on the Basis from Reflexive Eye Movements Can Predict Symptom Development in Individual Parkinson’s Patients

During deep brain stimulation (DBS) treatment of Parkinson disease, the target of the surgery is the subthalamic nucleus (STN). As STN is small (9 x 7 x 4 mm) and poorly visible in CT or MRI, multi-electrode micro recording systems are used during DBS surgery for its better localization. This paper presents five different analytical methods, that can be used to construct an autonomic system assisting neurosurgeons in precise localization of the STN nucleus. Such system could be used during surgery in the environment of the operation theater. Signals recorded from the micro electrodes are taken as input in all five described methods. Their result in turn allows to tell which one from the recorded signals comes from the STN. First method utilizes root mean square of recorded signals. Second takes into account amplitude of the background noise present in the recorded signal. 3rd and 4th methods examine Low Frequency Background (LFB) and High Frequency Background (HFB). Finally, last one looks at correlation between recordings taken by different electrodes.

Discrimination of the Micro Electrode Recordings for STN Localization during DBS Surgery in Parkinson's Patients

We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease’s symptoms, with the help of various therapies. In the case of Parkinson’s disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist’s diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient ‘well-being’ scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naïve Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD.