Primary Activity Pattern Research Articles

Dynamic Configuration of Coactive Micropatterns in the Default Mode Network During Wakefulness and Sleep.

Background: The default mode network (DMN) is a prominent intrinsic network that is observable in many mammalian brains. However, a few studies have investigated the temporal dynamics of this network based on direct physiological recordings. Methods: Herein, we addressed this issue by characterizing the dynamics of local field potentials from the rat DMN during wakefulness and sleep with an exploratory analysis. We constructed a novel coactive micropattern (CAMP) algorithm to evaluate the configurations of rat DMN dynamics, and further revealed the relationship between DMN dynamics with different wakefulness and alertness levels. Results: From the gamma activity (40-80 Hz) in the DMN across wakefulness and sleep, three spatially stable CAMPs were detected: a common low-activity level micropattern (cDMN), an anterior high-activity level micropattern (aDMN), and a posterior high-activity level micropattern (pDMN). A dynamic balance across CAMPs emerged during wakefulness and was disrupted in sleep stages. In the slow-wave sleep (SWS) stage, cDMN became the primary activity pattern, whereas aDMN and pDMN were the major activity patterns in the rapid eye movement sleep stage. In addition, further investigation revealed phasic relationships between CAMPs and the up-down states of the slow DMN activity in the SWS stage. Conclusion: Our study revealed that the dynamic configurations of CAMPs were highly associated with different stages of wakefulness, and provided a potential three-state model to describe the DMN dynamics for wakefulness and alertness. Impact statement In the current study, a novel coactive micropattern (CAMP) method was developed to elucidate fast default mode network (DMN) dynamics during wakefulness and sleep. Our findings demonstrated that the dynamic configurations of DMN activity are specific to different wakefulness stages and provided a three-state DMN CAMP model to depict wakefulness levels, thus revealing a potentially new neurophysiological representation of alertness levels. This work could elucidate the DMN dynamics underlying different stages of wakefulness and have important implications for the theoretical understanding of the neural mechanism of wakefulness and alertness.

DIURNAL ACTIVITY PATTERN OF A PAIR OF BREEDING NORTHERN PYGMY-OWLS

In 2012, we explored the activity pattern of a pair of breeding Northern Pygmy-Owls based on 15 d (and 15 nights) of video monitoring of the nest-cavity entrance during the nestling period. Video monitoring ended after all 6 owlets had left the nest. All instances of nest activity (n= 288) occurred during the daylight period from 1 h before sunrise to 1 h after sunset, so the pair was not nocturnal. Most of the activity (60%) occurred during the diurnal period (1 h after sunrise to 1 h before sunset), and the rest was in the crepuscular period (the first and last 2 h of daylight). Prey type factored heavily into the frequency of activity at certain times of day, and on certain days during the nestling period. Activity during the diurnal period was dominated by the combination of vertebrate prey deliveries (80% of diurnal activity) and enticements of young from the nest (14% of diurnal activity). In contrast, activity during the crepuscular period was dominated by insect prey deliveries (60% of crepuscular activity). Overnight nest attendance activity, arriving and departing the nest to tend young through the night, accounted for 11% of crepuscular activity. For the entire 15-d monitoring period, deliveries of vertebrate prey made up 60% of all activity. The frequency of daily vertebrate prey deliveries was relatively similar for the first 10 full days of monitoring (range 9 to 13 deliveries), then peaked 3 to 4 d prior to fledging (range 15 to 20 deliveries) before declining on the last 2 d of the period, when deliveries of moths and enticement activity increased. We concluded that the pair's activity during the nestling period was consistent with a diurnal activity pattern. We encourage other researchers to refine the meanings of the terms diurnal and crepuscular and clarify how those terms should be used to define the primary activity pattern of a species.

Calcium dynamics control K-ATP channel-mediated bursting in substantia nigra dopamine neurons: a combined experimental and modeling study.

Burst firing in medial substantia nigra (mSN) dopamine (DA) neurons has been selectively linked to novelty-induced exploration behavior in mice. Burst firing in mSN DA neurons, in contrast to lateral SN DA neurons, requires functional ATP-sensitive potassium (K-ATP) channels both in vitro and in vivo. However, the precise role of K-ATP channels in promoting burst firing is unknown. We show experimentally that L-type calcium channel activity in mSN DA neurons enhances open probability of K-ATP channels. We then generate a mathematical model to study the role of Ca2+ dynamics driving K-ATP channel function in mSN DA neurons during bursting. In our model, Ca2+ influx leads to local accumulation of ADP due to Ca-ATPase activity, which in turn activates K-ATP channels. If K-ATP channel activation reaches levels sufficient to terminate spiking, rhythmic bursting occurs. The model explains the experimental observation that, in vitro, coapplication of NMDA and a selective K-ATP channel opener, NN414, is required to elicit bursting as follows. Simulated NMDA receptor activation increases the firing rate and the rate of Ca2+ influx, which increases the activation of K-ATP. The model suggests that additional sources of hyperpolarization, such as GABAergic synaptic input, are recruited in vivo for burst termination or rebound burst discharge. The model predicts that NN414 increases the sensitivity of the K-ATP channel to ADP, promoting burst firing in vitro, and that that high levels of Ca2+ buffering, as might be expected in the calbindin-positive SN DA neuron subpopulation, promote rhythmic bursting pattern, consistent with experimental observations in vivo. NEW & NOTEWORTHY Recently identified distinct subpopulations of midbrain dopamine neurons exhibit differences in their two primary activity patterns in vivo: tonic (single spike) firing and phasic bursting. This study elucidates the biophysical basis of bursts specific to dopamine neurons in the medial substantia nigra, enabled by ATP-sensitive K+ channels and necessary for novelty-induced exploration. A better understanding of how dopaminergic signaling differs between subpopulations may lead to therapeutic strategies selectively targeted to specific subpopulations.

Inferring weekly primary activity patterns using public transport smart card data and a household travel survey

With the introduction of smart cards for electronic payment in public transport, massive spatio-temporal data have been recorded in many cities. This data represents how public transport users from a whole city or country travel during consecutive days or weeks. This work presents a method to identify temporal weekly patterns of primary activities performed by public transport users in Singapore. As continuous travel data from the same user during a week can be extracted, work-leisure cycles can be recognized. Using revealed trips from a 1% sample household travel survey, activities reached and left by public transport were extracted and used to develop two discrete choice models: one for workers and one for students. According to the start time and duration of an activity, these models estimate the likelihood of such activity to be HOME, WORK/STUDY or OTHER type. Then, consistent activities were extracted from public transport smart card transactions recorded during one week. Using the discrete choice models, these extracted activities were classified by type. For each user, a 14-dimension vector summarizes his/her primary activity pattern. Each vector is composed by start time and duration of his/her work or study activities during continuous 7days. To recognize the most common primary activity patterns of the city, the DBSCAN clustering algorithm was applied to these vectors. As the input data of this method is commonly available, the method can be applied to many cities in the world.