Activity Patterns Research Articles

Understanding the neural code of stress to control anhedonia.

Anhedonia, the diminished drive to seek, value, and learn about rewards, is a core feature of major depressive disorder1-3. The neural underpinnings of anhedonia and how this emotional state drives behaviour remain unclear. Here we investigated the neural code of anhedonia by taking advantage of the fact that when mice are exposed to traumatic social stress, susceptible animals become socially withdrawn and anhedonic, whereas others remain resilient. By performing high-density electrophysiology to record neural activity patterns in the basolateral amygdala (BLA) and ventral CA1 (vCA1), we identified neural signatures of susceptibility and resilience. When mice actively sought rewards, BLA activity in resilient mice showed robust discrimination between reward choices. By contrast, susceptible mice exhibited a rumination-like signature, in which BLA neurons encoded the intention to switch or stay on a previously chosen reward. Manipulation of vCA1 inputs to the BLA in susceptible mice rescued dysfunctional neural dynamics, amplified dynamics associated with resilience, and reversed anhedonic behaviour. Finally, when animals were at rest, the spontaneous BLA activity of susceptible mice showed a greater number of distinct neural population states. This spontaneous activity allowed us to decode group identity and to infer whether a mouse had a history of stress better than behavioural outcomes alone. This work reveals population-level neural dynamics that explain individual differences in responses to traumatic stress, and suggests that modulating vCA1-BLA inputs can enhance resilience by regulating these dynamics.

Analyzing usage patterns from video data through deep learning: The case of an urban park

Rapid urbanization, urban density, and COVID-19 effects have highlighted the need for high-quality urban parks within walking distance. A high-quality urban park maximizes a neighborhood's spatial, safety, and social potential, which are key factors to the well-being of its residents. Most studies evaluating urban parks rely on questionnaires, observations, interviews, and post-occupancy methods. These traditional methods are limited regarding the spatial and temporal dimensions as well as the size of the sample under investigation. In this paper, we demonstrate a new approach to evaluating urban parks by focusing on individuals' activity patterns, using big data extracted from city cameras by utilizing deep learning and computer vision. Our case study is a small urban park, Katznelson Garden, located in Or Yehuda, Israel. The imagery data is analyzed in relation to the gender of the parks' users, along with spatial and temporal analysis. Thus, activities during different hours of the day, days of the week, and in various parts of the urban park are identified. The results of our study revealed that females' and males' activity patterns are different and depend on the hour of the day and the type of park characteristics. Moreover, we found that activity levels and patterns varied according to the day of the week. As many cities seek to design better urban parks tailored to their residents' needs, these study findings can contribute to planning decisions by paving the way to customizing the design of urban parks in accordance with the revealed behavior.

MODELING OF HUMAN BEHAVIOR FOR SMARTPHONE WITH USING MACHINE LEARNING ALGORITHM

The article focuses on exploring human behavior recognition as an alternative means of identifying and authenticating smartphone users. The process involves obtaining raw data, extracting features, and making classifications. In this study, a single accelerometer-equipped smartphone is utilized to sense users' walking patterns for experimental data. Unlike traditional machine learning algorithms, a deep learning approach is employed. The paper introduces a novel Convolutional Neural Network (CNN) model for user identification based on activity patterns. The experiment uses a publicly available walking activity dataset for user identification. The CNN model achieves an impressive 99.88% accuracy in recognizing users from their walking patterns. Additionally, the article conducts a comparative analysis with classical machine learning algorithms such as Ada-Boost, Decision Tree, GaussianNB, Linear Discriminant, Logistic Regression, Quadratic Discriminant, and Random Forest. While Random Forest reaches a commendable accuracy of 95.78%, the CNN model surpasses it in terms of both recognition time and accuracy.

Sculpting new visual categories into the human brain

Learning requires changing the brain. This typically occurs through experience, study, or instruction. We report an alternate route for humans to acquire visual knowledge, through the direct sculpting of activity patterns in the human brain that mirror those expected to arise through learning. We used neurofeedback from closed-loop real-time functional MRI to create new categories of visual objects in the brain, without the participants' explicit awareness. After neural sculpting, participants exhibited behavioral and neural biases for the learned, but not for the control categories. The ability to sculpt new perceptual distinctions into the human brain offers a noninvasive research paradigm for causal testing of the link between neural representations and behavior. As such, beyond its current application to perception, our work potentially has broad relevance for advancing understanding in other domains of cognition such as decision-making, memory, and motor control.

Changes in daily activity patterns throughout the year in a free-living South American subterranean rodent (Ctenomys coludo)

Changes in daily activity patterns throughout the year in a free-living South American subterranean rodent (Ctenomys coludo)

Fractal motor activity during wakefulness and sleep: a window into depression recency and symptom recurrence

Abstract Background Motor activity fluctuations in healthy adults exhibit fractal patterns characterized by consistent temporal correlations across wide-ranging time scales. However, these patterns are disrupted by aging and psychiatric conditions. This study aims to investigate how fractal patterns vary across the sleep–wake cycle, differ based on individuals' recency of depression diagnosis, and change before and after a depressive episode. Methods Using actigraphy from two cohorts (n = 378), we examined fractal motor activity patterns both between individuals without depression and with varying recencies of depression and within individuals before and after depressive symptom recurrence. To evaluate fractal patterns, we quantified temporal correlations in motor activity fluctuations across different time scales using a scaling exponent, α. Linear mixed models were utilized to assess the influence of the sleep–wake cycle, (recency of) depression, and their interaction on α. Results Fractal activity patterns in all individuals varied across the sleep–wake cycle, showing stronger temporal correlations during wakefulness (larger α = 1.035 ± 0.003) and more random activity fluctuations during sleep (smaller α = 0.784 ± 0.004, p < 0.001). This sleep–wake difference was reduced in recently depressed individuals (1–6 months), leading to larger α during sleep (0.836 ± 0.017), compared to currently depressed (0.781 ± 0.018, p = 0.006), remitted (0.776 ± 0.014, p < 0.001), and never-depressed individuals (0.773 ± 0.016, p < 0.001). Moreover, remitted individuals who experienced depressive symptom recurrence during antidepressant tapering exhibited a larger α during sleep after the symptom onset as compared to before (after: α = 0.703 ± 0.022; before: α = 0.680 ± 0.022; p < 0.001). Conclusions These findings suggest a link between fractal motor activity patterns during sleep and depressive symptom recurrence in remitted individuals and those with recent depression.

Rhythmic astrocytic GABA production synchronizes neuronal circadian timekeeping in the suprachiasmatic nucleus

AbstractAstrocytes of the suprachiasmatic nucleus (SCN) can regulate sleep-wake cycles in mammals. However, the nature of the information provided by astrocytes to control circadian patterns of behavior is unclear. Neuronal circadian activity across the SCN is organized into spatiotemporal waves that govern seasonal adaptations and timely engagement of behavioral outputs. Here, we show that astrocytes across the mouse SCN exhibit instead a highly uniform, pulse-like nighttime activity. We find that rhythmic astrocytic GABA production via polyamine degradation provides an inhibitory nighttime tone required for SCN circuit synchrony, thereby acting as an internal astrocyte zeitgeber (or “astrozeit”). We further identify synaptic GABA and astrocytic GABA as two key players underpinning coherent spatiotemporal circadian patterns of SCN neuronal activity. In describing a new mechanism by which astrocytes contribute to circadian timekeeping, our work provides a general blueprint for understanding how astrocytes encode temporal information underlying complex behaviors in mammals.

Electroencephalographic power ratio and peak frequency difference associate with central sensitization in chronic pain.

Central sensitization, or increased responsiveness of the central nervous system to sensory input, is present in many chronic pain patients. Clinically, it is detected through subjective, patient-reported measures. There is a need for reliable, direct measurements of neural response to controlled stimuli to quantify neuronal dysfunction in pain. The goal of this work is to investigate cortical activity, recorded via electroencephalogram (EEG), during objective and calibrated painful stimulation in chronic pain patients.&#xD;Approach. Chronic pain patients (N=8) and healthy controls (N=8) participated in this study. We recorded electroencephalography (EEG) at rest (baseline) and during evoked pain tasks, including thermal and mechanical stimuli. The evoked pain was applied following the quantitative sensory testing (QST) protocol, which is a research technique that applies objective, calibrated painful stimuli. &#xD;Main results. Peak alpha frequency at rest was significantly lower in chronic pain patients compared to healthy controls (p<0.0002), while EEG alpha/theta and alpha/beta power ratios at rest were higher in patients (p<0.0002). During thermal QST, these power ratios decreased in patients and increased in controls (p<0.0002 for both). During mechanical QST, power ratios decreased or did not change. Furthermore, the peak theta-beta frequency difference at baseline was significantly lower in patients compared to controls (p<0.0002). During thermal QST, this difference increased in patients and decreased in controls; during mechanical QST, this difference increased in both patients and controls (p<0.0002). Functional connectivity analysis showed that controls had greater baseline theta connectivity strength that increased during mechanical QST (p<0.0002).&#xD;Significance. This work demonstrates differential patterns of EEG activity at rest and during acute painful stimulation in chronic pain patients compared to healthy controls. These measures may quantify an individual's tendency to experience chronic pain and central sensitization and serve as diagnostic biomarkers.

The thalamus encodes and updates context representations during hierarchical cognitive control.

Cognitive flexibility relies on hierarchically structured task representations that organize task contexts, relevant environmental features, and subordinate decisions. Despite ongoing interest in the human thalamus, its role in cognitive control has been understudied. This study explored thalamic representation and thalamocortical interactions that contribute to hierarchical cognitive control in humans. We found that several thalamic nuclei, including the anterior, mediodorsal, ventrolateral, and pulvinar nuclei, exhibited stronger evoked responses when subjects switch between task contexts. Decoding analysis revealed that thalamic activity encodes task contexts within the hierarchical task representations. To determine how thalamocortical interactions contribute to task representations, we developed a thalamocortical functional interaction model to predict task-related cortical representation. This data-driven model outperformed comparison models, particularly in predicting activity patterns in cortical regions that encode context representations. Collectively, our findings highlight the significant contribution of thalamic activity and thalamocortical interactions for contextually guided hierarchical cognitive control.

Assessing the Co-Occurrence of European Pine Marten (Martes martes) With Humans and Domestic Cats on a Mediterranean Island.

Anthropogenic activities often lead to changes in the distribution and behavior of wild species. The mere presence of humans and free-roaming domestic cats (Felis catus) can affect wildlife communities; however, responses to these disturbances might not be ubiquitous and may vary with local conditions. We investigated European pine marten's (Martes martes) distribution on Elba Island, Italy, where the species is the only wild carnivore. In this system, pine martens act as the top predator, and human presence is mostly driven by seasonal tourism. We evaluated (1) pine marten's occurrence in relation to vegetation type and elevation and the potential effects of proximity to settlements, (2) whether pine marten's distribution was associated with the co-occurrence of humans and domestic cats, and, if so, (3) whether these co-occurrence patterns were associated with proximity to anthropogenic infrastructures. Additionally, we explored similarities in activity patterns between pine marten and the other two species. We collected camera-trap data at 77 locations throughout Elba Island in February-July 2020. Using single-season multistate occupancy models, we found evidence that pine martens' occupancy was generally high across all vegetation types and elevation, and proximity to settlements was only weakly associated with the species occurrence. Contrary to expectations, we found no evidence of an association between pine martens' distribution and the presence of either humans or free-roaming domestic cats on Elba Island. Opposing activity patterns might have facilitated pine martens' co-existence with humans, with pine martens being active at ground level almost exclusively during nighttime. On the contrary, cats and pine martens showed similar activity patterns, and further studies are needed to define the co-existence mechanisms. These findings have important management implications and suggest that response to direct and indirect anthropogenic pressures can be highly context-dependent and mediated by the availability of resources and competition mechanisms.

Insights into the spatial ecology of the world’s most ancient dog: high-altitude movements of New Guinea dingoes

Knowledge of an animal’s activity patterns, home range size and space use is fundamental to understanding their basic ecology, and obtaining spatial data is an important research priority for newly discovered species, data-deficient species of conservation concern, or species of great scientific or public interest. Here we report the first spatial data obtained from wild-living dingoes near Puncak Jaya, New Guinea. Based on information from four dingoes monitored with GPS tracking collars between 2018 and 2022, we report that dingo home range sizes can be up to 128km2, dingoes travel up to 56.8km per day, and they utilise rainforest and alpine habitats up to 4,630m above sea level. Dingoes at the site regularly traversed steep, rocky and barren alpine mountain crevasses to access more fertile areas at lower altitudes on the other side. These results imply that New Guinea dingoes may have physiological and genetic adaptations that enable them to live in high-altitude low-oxygen environments similar to Himalayan wolves, Ethiopian wolves, and other canids found in high-altitude areas. As the first domesticated animal and the world’s most ancient dog, and given their historic and current cultural significance, their illusiveness in the wild, and their trophic position as the largest terrestrial predators on the second-largest island in the world, we believe that further research on the ecology of New Guinea dingoes will continue to reveal important insights valuable for our understanding of human and animal ecology in this global biodiversity hotspot.

Novel age constraints on offshore islets around Taiwan with implications for the Northern Taiwan Volcanic Zone

Determining the exact age of volcanic activity in the Northern Taiwan Volcanic Zone (NTVZ) is essential for comprehending Taiwan's tectonic evolution. The timing and duration of the volcanic processes that formed the NTVZ remain uncertain despite the availability of geochronological, geochemical, and geophysical data. While extensive efforts have been directed towards investigating the age of the Tatun Volcanic Group, the primary feature of the NTVZ, many other aspects require further examination. This study concentrates on two offshore islets in the northern region of the NTVZ. We present new 40Ar/39Ar ages of 0.252 ± 0.018 Ma for a lava flow, representing an early stage of volcanic activity, and 0.118 ± 0.012 Ma for a dike, representing the latest stage of volcanic activity around Pengjia Islet. A sample collected from Mianhua Islet did not yield a reasonable age due to high uncertainty, likely attributable to young and low radiogenic argon. Field observations support the interpretation that volcanic activity on both islets is considerably younger than previously estimated. This study corroborates the hypothesis from geophysical studies that submarine NTVZ features to record a potentially active magmatic reservoir offshore of northern Taiwan. By refining our understanding of spatio-temporal patterns in volcanic activity, this study contributes to volcanic hazard assessment in Taiwan and surrounding areas.

Ensemble responses of auditory midbrain neurons in the cat to speech stimuli at different signal-to-noise ratios

Originally reserved for those who are profoundly deaf, cochlear implantation is now common for people with partial hearing loss, particularly when combined with a hearing aid. This combined intervention enhances speech comprehension and sound quality when compared to electrical stimulation alone, particularly in noisy environments, but the physiological basis for the benefits is not well understood. Our long-term aim is to elucidate the underlying physiological mechanisms of this improvement, and as a first step in this process, we have investigated in normal hearing cats, the degree to which the patterns of neural activity evoked in the inferior colliculus (IC) by speech sounds in various levels of noise allows discrimination between those sounds. Neuronal responses were recorded simultaneously from 32 sites across the tonotopic axis of the IC in anaesthetised normal hearing cats (n=7). Speech sounds were presented at 20, 40 and 60dB SPL in quiet and with increasing levels of additive noise (signal-to-noise ratios (SNRs) –20, –15, –10, –5, 0, +5, +10, +15, +20dB). Neural discrimination was assessed using a Euclidean measure of distance between neural responses, resulting in a function reflecting speech sound differentiation across various SNRs. Responses of IC neurons reliably encoded the speech stimuli when presented in quiet, with optimal performance when an analysis bin-width of 5-10ms was used. Discrimination thresholds did not depend on stimulus level and were best for shorter analysis binwidths. This study sheds light on how the auditory midbrain represents speech sounds and provides baseline data with which responses to electro-acoustic speech sounds in partially deafened animals can be compared.

E-cycling intention versus behavioral change: Investigating longitudinal changes in e-cycling intention and actual behavior change in daily commuting

By offering the opportunity to make longer trips at a lower level of physical activity, the e-bike provides a promising alternative to car use. Despite all advantages (e-)cycling brings to urban accessibility, the environment, physical and mental health, not all car commuters regard the e-bike as a suitable alternative yet in their daily activity patterns. This study reports on changes in behavioral intention and actual e-cycling brought about by an e-cycling incentive program in the province of Noord-Brabant, the Netherlands. The impact of the program on behavioral intention and the actual change to e-cycling were analyzed based on a longitudinal three-wave survey design on past, intended, and actual commuting behavior. To explore the changes in behavioral intentional, the differences between intention and actual behavior and the factors influencing them, descriptive and ordinal logistic regression analyses were conducted. To explore the dynamics between e-cycling intentions and behavior a longitudinal structural equation model was developed. In general, this study shows that the incentive program has a positive impact on participants’ behavioral change to e-cycling during the incentive program. Results show that two-third of the participants actually use the e-bike as much as they intended at the start of the program. People who were used to taking the conventional bicycle to work before the stimulation program, are more consistent between their intention and behavior. Results also show that personal beliefs, habits, and goal-related variables do not influence the intention–behavior consistency.

Influence of season, sex, and interspecific interactions on the diel activity patterns of two sympatric African small carnivores

Animal activity patterns vary seasonally and between species, facilitating species coexistence. In Africa, however, factors affecting the activity of many small carnivores remain poorly understood, especially for congeneric and sympatric species whose similar sizes may lead to interspecific competition. Here, we investigated differences and variations in the activity patterns of two sympatric Viverridae species in a seasonal African landscape. We continuously radio-tracked 15 small-spotted genets (Genetta genetta) and five Cape genets (G. tigrina) over 24-h cycles throughout the year. We analysed the effects of season, sex, and interspecific interactions on circadian rhythms using multi-cosinor regression models. Both species maintained a nocturnal activity pattern year-round, decreasing activity significantly during the cold-dry season. This pattern aligns with the thermoregulatory hypothesis—especially for species with an elongated body like genets—suggesting decreased activity under extreme cold weather conditions to conserve energy. Females in both species were less active than males, possibly due to their smaller home ranges, especially during the cold-dry season. These effects were particularly pronounced in Cape genets, which primarily inhabit riverine forests. Female Cape genets adjusted their activity onset, possibly to minimize encounters with males, mostly during the hot-wet season when caring for their offspring. Small-spotted genets shifted their activity onset and peak in riverine forests—areas of potential contact with Cape genets—compared to areas without Cape genets. Overall, our study underscores the critical role of seasonal environmental changes and interspecific interactions in shaping the activity patterns of two carnivore species within a semi-arid Albany Thicket landscape.

The application of integrating electroencephalograph-based emotion recognition technology into brain–computer interface systems for the treatment of depression: a narrative review

Dysregulation of the prefrontal cortex, amygdala, and hippocampus, along with alterations in P300 amplitude and abnormalities in the theta and beta bands, has been closely linked to the onset and pathophysiology of depression. Consequently, integrating electroencephalograph-based emotion recognition technology into brain‒computer interface systems offers the potential for real-time identification and modulation of emotional states through continuous interaction between the brain‒computer interface system and brain activity. This closed-loop system could precisely control neural stimulation in brain regions associated with emotional disorders, potentially alleviating the distressing memories of traumatic events. Although the efficacy of the brain‒computer interface in treating depression still requires validation through extensive clinical trials, its inherent real-time feedback and adaptive capabilities present a promising avenue for depression therapy. This review aims to explore the neuroanatomical mechanisms and neural activity patterns associated with depression and evaluate the potential of brain‒computer interface technology as a treatment modality. The objectives include summarizing key brain regions and neural networks involved in depression, analyzing their activity patterns, and assessing the impact of brain‒computer interface technology on these regions to provide theoretical support for future clinical trials. Significant functional abnormalities have been identified in the prefrontal cortex, amygdala, and hippocampus of patients with depression. The gray matter density, functional connectivity, and neural activity in these regions are closely associated with the severity of depressive symptoms. Common features in patients with depression include a reduced P300 amplitude and increased θ and α current density. Brain‒computer interface technology has demonstrated potential in modulating these abnormal neural activities, particularly in emotion recognition and regulation. When combined with techniques such as repetitive transcranial magnetic stimulation and deep brain stimulation, brain‒computer interface may provide effective interventions for managing emotional states in patients with depression. This review confirms the association between depression and functional abnormalities in specific brain regions and suggests that brain‒computer interface technology offers promising therapeutic potential by modulating abnormal neural activity. Brain‒computer interface could represent a novel treatment approach for depression. Future research should focus on validating the practical applications, efficacy, and safety of brain‒computer interface in treating depression.

Circadian modulation of behavioural stress indicators varies between diurnal and nocturnal fish species

In the wild, most animals experience daily fluctuations in threats and resources that are synchronised with environmental time cues such as the light-dark cycle. Consequently, animals have evolved daily behavioural patterns (i.e., diurnal or nocturnal) that enhance their fitness by, for example, reducing the temporal overlapping with predators. In fish, previous studies revealed stronger physiological stress responses during the resting period of the species. However, little is known about the circadian modulation of stress indicators and how they are influenced by daily behavioural patterns. In this research, we investigated the behavioural stress responses of two farmed fish species with different activity patterns: the diurnal Nile tilapia (Oreochromis niloticus) and the nocturnal tench (Tinca tinca). To this end, we examined the behavioural response of individuals exposed to the diving test every 4 h over a 24 h cycle (n = 12 fish/species/time point). Results indicated that most behavioural indicators varied according to the time of day, aligning with the daily rhythmic pattern of the two species. Tilapia exhibited stronger stress responses to novelty during the dark phase, while tench displayed higher stress during the light phase. This was supported by stress-related behaviours such as freezing and erratic movements (in both tilapia and tench) and bottom-dwelling (in tench only). These results indicated that stress responses peaked during the resting phase of each species, although behavioural indicators exhibiting this daily variation did not completely coincide between the studied species. Overall, these findings suggest interspecific differences in the daily modulation of behavioural stress indicators in farmed fish, an effect with potential relevance for welfare. Understanding the activity rhythmic patterns, resting periods, and associated daily variation in stress for each fish species of interest can precisely help tailor farming procedures to minimise suffering.

Longitudinal evolution of physical activity type and eating and weight concerns among adolescents

PurposeTo provide a prospective 2 year follow-up of a previously enrolled adolescent high school sample, regarding body image and eating concerns, and patterns of sports type and physical activity.MethodsSports type, weekly time devoted to it, and psychopathological self-reports (Eating Disorders Examination Questionnaire 6.0, Body Uneasiness Test, and Muscle Dysmorphia Disorder Inventory) were evaluated longitudinally in a general population sample enrolled in a previous study, testing prospective variations in an observational setting.ResultsAt follow-up, girls expressed increased dietary restraint and body uneasiness as compared to baseline, whereas boys expressed increased body uneasiness—and specifically in the avoidance, depersonalisation and compulsive self-monitoring dimensions. Among both sexes, a significant shift towards individual activities or lack of activity was observed after 2 years, with a reduction in team sports involvement. Among girls, time devoted to exercise significantly decreased at follow-up.ConclusionsThe present findings indirectly confirm an increased vulnerability to dietary, bodily and appearance-based concerns among adolescents. The role of different patterns of physical activity and exercise time may interact bidirectionally with these problematic areas, considering that previous studies point out increased concerns among inactive subjects, and among those who choose individual activities.Level of evidence: Level IV-longitudinal observational study.

Integrating Wearable Health Technology into Patient Care: Implications for Chronic Disease Management and Preventive Health

The integration of wearable health technology into patient care represents a transformative approach to managing chronic diseases and promoting preventive health. This article examines the potential of wearable devices, such as fitness trackers, smartwatches, and continuous monitoring sensors, to enhance patient outcomes by providing real-time data on key health indicators, including heart rate, blood pressure, physical activity, and sleep patterns. Through an analysis of current applications and advancements in wearable technology, this study assesses how these devices contribute to early disease detection, improved patient engagement, and proactive management of chronic conditions like diabetes, hypertension, and cardiovascular diseases. Findings indicate that wearable health technology supports more personalized and responsive care models, allowing healthcare providers to track patient progress remotely, adjust treatments as needed, and foster greater adherence to health interventions. Additionally, the study highlights challenges related to data privacy, interoperability, and patient adherence, underscoring the importance of addressing these issues to fully realize the potential of wearable technology in healthcare. By providing insights into the effective integration of wearable devices in clinical settings, this research offers valuable guidance for healthcare providers, policymakers, and technology developers aiming to improve chronic disease management and advance preventive healthcare strategies.

Activity pattern of antioxidant enzymes in relation to the time of exposure of hexavalent chromium to Nile tilapia Oreochromis niloticus.

Hexavalent chromium (Cr (VI)), a toxicant of environmental concern, frequently enters into water bodies and produces oxidative stress in fish. The antioxidant enzymes, Superoxide dismutase (SOD), Catalase (CAT), and Glutathion S-transferase (GST) are activated to counteract the oxidative stress in fish. This study explores the pattern of activation of these enzymes in gill, muscle, liver, and kidney tissues of Nile tilapia Oreochromis niloticus exposed to 9.35 mg/L and 18.70 mg/L of Cr (VI) for 96 h. The optimal hour of activity of these enzymes was revealed through extensive regression analysis. The results indicate a bell-shaped time response curve in the activity of the enzymes in both the treatments, except CAT in the gill of fish exposed to 18.70 mg/L Cr (VI) and GST in the gill, liver, and kidney of fish exposed to 18.70 mg/L Cr (VI). The results indicate that the optimal hour of activity of SOD changes in tandem with CAT, SOD responding first followed by CAT, both diminishing within 96 h. However, deviating from the bell-shaped pattern, the activity of CAT in gill and GST in gill, liver, and kidney in fish exposed to 18.70 mg/L Cr (VI) continued to rise even at 96 h, indicating that these antioxidant enzymes could not diminish the oxidative stress produced by the higher dose of Cr (VI). It was concluded that the activity of SOD, CAT, and GST between 30 and 70 h in the gill, liver, and kidney of Nile tilapia could serve as excellent biomarkers of oxidative stress under low doses of Cr (VI).