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PubMed2002

Possible role of adenosine in macula densa control of glomerular hemodynamics.

BACKGROUND: The macula densa (MD), a plaque of specialized tubular epithelial cells, senses changes in tubular NaCl concentration and sends a signal(s) that controls the resistance of the glomerular afferent arteriole (Af-Art). This mechanism, called tubuloglomerular feedback (TGF), is thought to be important in the homeostasis of body fluids and electrolytes. Our aim was to determine the range of NaCl concentrations in tubular fluid at the MD that would elicit the Af-Art response. In addition, we examined the possible involvement of adenosine in transmitting the signal from the MD to the Af-Art. METHODS: Rabbit Af-Arts and attached MD were simultaneously microperfused in vitro, keeping pressure in the Af-Art at 60 mm Hg. RESULTS: Increasing the Na+/Cl- concentration of the MD perfusate from 26/7 to 41/22 mEq/L decreased the luminal diameter of the terminal Af-Art segment by 10 +/- 4% (N=9; P < 0.01). The response was maximal at 55/36 mEq/L (18 +/- 6%), so that further elevation of NaCl concentration had no additional effect (20 +/- 6% at 84/65 mEq/L). When FK838 (10(-6) mol/L), a specific adenosine A1 receptor antagonist, was added to both Af-Art perfusate and bath, Af-Art constriction was completely abolished. The maximum response was 20 +/- 3% before FK838 and 0.6 +/- 1% afterward (N=12). Adding adenosine at 10(-8) mol/L to both bath and perfusate significantly augmented Af-Art constriction induced by increased NaCl at the MD (P < 0.01); however, adding 10-8 to 10-6 mol/L adenosine to the MD perfusate had no effect regardless of the NaCl concentration at the MD. CONCLUSIONS: These results demonstrate that MD control of Af-Art resistance is induced by relatively low NaCl concentrations at the MD, and that activation of the adenosine A1 receptor in the vascular and interstitial space (but not the tubular lumen) may be essential for signal transmission from the MD to the Af-Art.

Adenosine↗

PubMed2006

Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials.

We have developed and tested two electroencephalogram (EEG)-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KPLS classifier to map power spectra of 62-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject's average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: 1) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal electrooculograms (EOG) signals, 2) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from 12 electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle artifact is controlled via adaptive normalization of the SSVEP. Training of the classifier requires about 3 min. We have tested our system in real-time operation in three human subjects. Across subjects and sessions, control accuracy ranged from 80% to 100% correct with lags of 1-5 s for movement initiation and turning. We have also developed a realistic demonstration of our system for control of a moving map display (http://ti.arc.nasa.gov/).

Algorithms↗

PubMed1994

Purkinje cell complex spike activity during voluntary motor learning: relationship to kinematics.

1. We examined the relationship of cerebellar Purkinje cell discharge to the scaling of kinematics during a voluntary motor learning paradigm. The study focused on whether the occurrence of complex spike (CS) discharge was associated with kinematic changes. Two primates (Macaca mulatta) were trained to move a cursor using a two-joint manipulandum over a horizontal video screen from a start target to one of four target boxes. The relationship between the cursor and the hand (gain) was changed, requiring scaling of movement distance to complete the task. As previously described, when the novel gain was presented over 100-200 movement trials the animals adapted their movements by using a strategy of scaling the amplitude and velocity of the first phase of the movement while keeping time to peak velocity constant. 2. The paradigm consisted of four different phases. A control phase at a gain of 1.0 was initially performed. The learning phase over the next 180-210 movements used one of four gains (0.6, 0.75, 1.5, or 2.0). Last, a testing phase involved 80% of 100 trials at the learned gain and 20% of the trials at the control gain of 1.0. The distance control phase consisted of using a gain of 1.0 but having the animal move to targets placed at the distance and direction the hand moved in the adapted state. 3. Simple spikes (SSs) and CSs of 141 Purkinje cells recorded primarily in the intermediate and lateral regions of zones V and VI in three cerebellar hemispheres from the two primates were recorded during the distance control, control, learning, and testing phases. Some cells were recorded in lobule VII and Crus I. CS activity increased during the learning phase, as documented previously. The increase in CS discharge occurred before or during the first 200-300 ms of the movement. This is the same time period in which the kinematic changes necessary for adaptation to the novel gain occur. Of 141 Purkinje cells recorded during the learning paradigm, 104 (74%) demonstrated significant increases in CS firing rate during the learning-testing phase. Of these 104 cells, 82 had statistically significant SS modulation. 4. Movement trials with CSs were separated from the trials without CSs. Aligning the kinematic and spike train data on movement onset, the average velocity profiles were subtracted from each other and a strict statistical criterion applied to test for the significance of any differences. Movement trials randomly sorted into two groups served as a control.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗