Cross-Correlation Analysis of Thalamic Neurons and EMG Activity in Parkinsonian Tremor

1985 ◽  
Vol 48 (1-6) ◽  
pp. 305-308 ◽  
Author(s):  
F.A. Lenz ◽  
R.R. Tasker ◽  
H.C. Kwan ◽  
S. Schnider ◽  
R. Kwong ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Emg Activity ◽  
Thalamic Neurons ◽  
Parkinsonian Tremor ◽  
Cross Correlation Analysis

Cross-correlation analysis of cuneothalamic interactions in the rat somatosensory system: influence of receptive field topography and comparisons with thalamocortical interactions

1994 ◽  
Vol 72 (4) ◽  
pp. 1949-1972 ◽  
Author(s):  
K. D. Alloway ◽  
M. B. Wallace ◽  
M. J. Johnson
Keyword(s):  
Receptive Field ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Reverse Direction ◽  
Connection Strength ◽  
Thalamic Neuron ◽  
Thalamic Neurons ◽  
Functional Connections ◽  
Cross Correlation Analysis ◽  
Nucleus Cuneatus

1. We simultaneously recorded neuronal responses to cutaneous stimulation from matched somatotopic representations in the nucleus cuneatus and ventrobasal complex of intact, halothane-anesthetized rats. A total of 95 cuneate and 86 thalamic neurons representing hairy skin on the forelimb were activated by hair movements produced by air jets at multiple skin sites. Mean responsiveness was higher among neurons in nucleus cuneatus (34.4 spikes per stimulus) than in thalamus (23.7 spikes per stimulus), a result that was consistent with the greater proportion of “sustained” responses recorded in nucleus cuneatus (80%) than in the thalamus (62%). 2. Cross-correlation analysis of 166 pairs of cuneate and thalamic neurons showed that 56 neuron pairs displayed time-locked correlations in activity that were characterized primarily by excitatory interactions (44 pairs) or a combination of excitatory and inhibitory interactions (10 pairs). Unilateral interactions in the cuneothalamic direction (31 pairs) and reverse direction (11 pairs) were observed, as well as multiphasic interactions in both directions (14 pairs). Most excitatory interactions involved intervals of 1–7 ms between successive cuneate and thalamic discharges, whereas most inhibitory influences involved intervals > 7 ms. Connection strength, defined by the ratio of time-linked interactions to the number of cuneate discharges, varied widely among neuron pairs but was largest for interactions involving interspike intervals of < or = 15 ms. 3. The relationship between connection strength and receptive field topography was analyzed in 103 cuneate-thalamic neuron pairs. The region of skin shared by both neurons varied substantially among neuron pairs and the probability of detecting interactions increased proportionately with larger amounts of receptive field overlap. Neuron pairs with moderate (25–50%) amounts of receptive field overlap had connection strengths 3–4 times greater than neuron pairs with minimal (0–25%) overlap. Connection strength was essentially identical, however, for neuron pairs with moderate or large (> 50%) amounts of overlap. 4. Cuneate-thalamic neuron pairs displaying functional connections were usually tested at multiple peripheral sites, but only 37% (18 of 49) of these neuron pairs displayed interactions at more than one stimulation site. Stimulation at different sites altered the timing of interactions in seven neuron pairs, including three that showed timing shifts across time zero in the cross-correlation histogram. In neuron pairs displaying interactions at multiple sites, connection strengths for 67% of the cases were strongest when stimulation was delivered within the region of receptive field overlap.(ABSTRACT TRUNCATED AT 400 WORDS)

Cross-correlation analysis reveals laminar differences in thalamocortical interactions in the somatosensory system

1996 ◽  
Vol 75 (4) ◽  
pp. 1444-1457 ◽  
Author(s):  
M. J. Johnson ◽  
K. D. Alloway
Keyword(s):  
Correlation Analysis ◽  
Cortical Neurons ◽  
Cross Correlation ◽  
Deep Layer ◽  
Sensory Stimulation ◽  
Superficial Layer ◽  
Cortical Layers ◽  
Thalamic Neurons ◽  
Air Jet ◽  
Cross Correlation Analysis

1. Spontaneous and stimulus-induced activity were recorded from corresponding somatotopic representations in the ventroposterolateral nucleus (VPL) of the thalamus and primary somatosensory (SI) cortex of intact, halothane-anesthetized cats. Thalamic and cortical neurons with overlapping receptive fields on the hairy skin of the forelimb were excited by a series of interleaved air jets aimed at multiple skin sites. 2. The laminar locations of 68% (240 of 355) of the neurons recorded in SI cortex were histologically reconstructed and responses of these 240 SI neurons were analyzed with respect to responses recorded from 118 thalamic neurons. Maximum responsiveness during the initial onset (1st 100 ms) of air jet stimulation was similar for neurons distributed throughout all layers of SI cortex (2-4 spikes per stimulus) and did not differ significantly from VPL responses. During the subsequent plateau phase of the stimulus, VPL neurons discharged at a mean rate of 19.0 spikes/ s and neurons in cortical layers II, IIIa, IIIb, and IV discharged at similar rates. Mean responsiveness during the plateau phase of the stimulus was significantly reduced among neurons in cortical layers V and VI and only averaged 7.1 and 3.9 spikes/s, respectively. 3. Responses recorded simultaneously from pairs of thalamic and cortical neurons were analyzed with cross-correlation analysis to determine differences in the incidence and strength of neuronal interactions as a function of cortical layer. Among 421 thalamocortical neuron pairs displaying stimulus-induced responses, 68 neuron pairs exhibited significant interactions during air jet stimulation. A laminar analysis revealed that 28% (45 of 163) of the neurons in the middle cortical layers displayed significant interactions with thalamic neurons, whereas only 14% (13 of 92) of superficial layer neurons and 6% (10 of 166) of deep layer neurons were synchronized with thalamic activity during air jet stimulation. When thalamocortical efficacy for different layers of cortex was plotted as a cumulative frequency distribution, the strongest interactions in the middle cortical layers were twice as strong as interactions involving the superficial or deep cortical layers. 4. More than 70% of stimulus-induced interactions involved thalamic discharges followed by subsequent cortical discharges and the majority of these interactions involved interspike intervals of < or = 3 ms. Nearly 75% (27 of 37) of interactions in the thalamocortical direction that involved cortical neurons in layers IIIb and IV transpired within a 3-ms interspike interval. For interactions with superficial or deep cortical layers, the proportion of thalamocortical interactions transpiring within 3 ms was only 58% (7 of 12) and 33% (2 of 6), respectively. 5. Cross-correlation analysis of spontaneous activity indicated that 124 pairs of thalamic and cortical neurons displayed synchronous activity in the absence of sensory stimulation. A laminar analysis indicated that similar proportions of cortical neurons in each layer were synchronized with thalamic activity in the absence of cutaneous stimulation. Thus 27% (44 of 163) of middle layer neurons, 30% (28 of 92) of superficial layer neurons, and 31% (51 of 166) of deep layer neurons displayed spontaneous interactions with thalamic neurons. The temporal pattern of spontaneous activity was examined with autocorrelation analysis to determine whether neuronal oscillations were essential for coordinating thalamic and cortical activity in the absence of peripheral stimulation. Only 18.5% (23 of 124) of spontaneous interactions between thalamic and cortical neurons were associated with periodic activity, which suggests that thalamocortical synchronization occurs before the constituent neurons begin to oscillate. 6. The influence of sensory stimulation on spontaneous interactions was examined in 31 pairs of thalamic and cortical neurons that exhibited interactions during prestimulus and stimulus in

Một số ứng dụng của phương pháp phân tích tương quan chéo trong nghiên cứu khí tượng thủy văn - địa chất thủy văn

2003 ◽  
Vol 25 (3) ◽  
pp. 274-279
Author(s):  
Vũ Thanh Tâm
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Cross Correlation Analysis ◽  
Hydrogeological Study

Some applications of cross-correlation analysis in meteohydrological hydrogeological study

Cross-correlation analysis of on-orbit residual accelerations in Spacelab

1991 ◽  
Author(s):  
MELISSA ROGERS ◽  
J. ALEXANDER
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Cross Correlation Analysis

Application of Cross-correlation Analysis Method for Measurement of the Fluid Flow Rate Based on X-ray Radiation

2019 ◽  
Vol 11 (1) ◽  
pp. 01025-1-01025-5 ◽  
Author(s):  
N. A. Borodulya ◽  
◽  
R. O. Rezaev ◽  
S. G. Chistyakov ◽  
E. I. Smirnova ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Correlation Analysis ◽  
Flow Rate ◽  
Cross Correlation ◽  
Fluid Flow Rate ◽  
Analysis Method ◽  
X Ray ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

scholarly journals Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1571 ◽  
Author(s):  
Jhonatan Camacho Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Jabid Quiroga
Keyword(s):  
Correlation Analysis ◽  
Damage Assessment ◽  
Structural Damage ◽  
Cross Correlation ◽  
Data Driven ◽  
Cross Correlation Analysis ◽  
Data Driven Approach

POWER LAW AND STRETCHED EXPONENTIAL EFFECTS OF EXTREME EVENTS IN CHINESE STOCK MARKETS

2010 ◽  
Vol 09 (02) ◽  
pp. 203-217 ◽  
Author(s):  
XIAOJUN ZHAO ◽  
PENGJIAN SHANG ◽  
YULEI PANG
Keyword(s):  
Correlation Analysis ◽  
Power Law ◽  
Extreme Events ◽  
Stock Markets ◽  
Cross Correlation ◽  
Extreme Values ◽  
Cumulative Distribution ◽  
Stretched Exponential ◽  
Cross Correlation Analysis ◽  
Chinese Stock Markets

This paper reports the statistics of extreme values and positions of extreme events in Chinese stock markets. An extreme event is defined as the event exceeding a certain threshold of normalized logarithmic return. Extreme values follow a piecewise function or a power law distribution determined by the threshold due to a crossover. Extreme positions are studied by return intervals of extreme events, and it is found that return intervals yield a stretched exponential function. According to correlation analysis, extreme values and return intervals are weakly correlated and the correlation decreases with increasing threshold. No long-term cross-correlation exists by using the detrended cross-correlation analysis (DCCA) method. We successfully introduce a modification specific to the correlation and derive the joint cumulative distribution of extreme values and return intervals at 95% confidence level.

scholarly journals Technique and Computational Improvements in 4D STEM and Cross-Correlation Analysis

2021 ◽  
Vol 27 (S1) ◽  
pp. 1540-1541
Author(s):  
Tristan O'Neill ◽  
B. C. Regan ◽  
Matthew Mecklenburg
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Cross Correlation Analysis
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