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Nuclear Magnetic Resonance Spectroscopy

Reference to this article: ConductScience, Nuclear Magnetic Resonance Spectroscopy (2019). doi.org/10.55157/CS20191121

Two molecules may have the same number and type of atoms, but their properties would change depending on how they are arranged (i.e., the bonds linking them and their orientation). As an example, Ethanol and Dimethyl ether, both have one oxygen, two carbon, and six hydrogen atoms, but the structures and properties of both these compounds are different. Ethanol exists as a liquid and Dimethyl ether, on the other hand, is a poisonous gas [1]. Thus, it is crucial for scientists to identify the exact structure of the compounds in order to understand their properties. Nuclear Magnetic Resonance (NMR) spectroscopy is a technique used to precisely identify the molecular structure of the compound [2]. NMR results for unknown compounds can be scanned with respect to a library of known compounds to reveal the identity [3].

What do the individual letters “NMR” mean?
  1. Nuclear: NMR technique is concentrated around the properties of the nucleus of an atom. A closer examination of an atom reveals that it has a dense core that is made up of protons and neutrons. This core is called the nucleus of an atom. The number of protons in the nucleus is used to identify which element the atom is out of the 118 identified elements of the periodic table [4]
  2. Magnetic: A nucleus having an odd number of protons creates a magnetic field. 1H and 13C are commonly used in NMR since these have an odd number of protons. If we consider 1H, it has only one proton and no neutron thereby possessing this property of spin [5]. When a sample of these protons is placed in a magnetic field (say of strength B0), they will either align with (α(alpha) state) the magnetic field or against it (β(beta) state). The energy of the α spin state is lower than that of the β spin state[6, 7].
  3. Resonance: The proton in the α spin state can be converted to the higher energy β spin state by applying external radiofrequency energy. If these nuclei fall from the β spin state to the α spin state, it also emits radiofrequency energy[6]. The electrons (which surround a certain nucleus in the compound) for instance, would create a phenomenon known as diamagnetic shielding [7]. This simply means that the electrons would shield the nucleus from the effect of the magnetic field applied by the NMR machine. Due to this shielding, different amounts of radiofrequency energies would be required for different nuclei to change their spin state from α spin state to the β state. When all the nuclei are flipping between either state, they are said to be in resonance.

The energy difference ΔE between the two spin states is determined by the equation

ΔE= hV

h= Planks constant

V= frequency of the energy that we use on protons in α spin state.

We can further breakdown the equation as

ΔE= hV = h [γ/2π * B0 ]

where γ = Gyromagnetic ratio of the nucleus in the study.

Let’s look at the terms associated with NMR

Resonant Frequency:  This is the frequency at which the phenomenon of resonance occurs in the protons of the sample. Using the procedure described earlier, we would generate an NMR spectrum which will consist of various peaks, representative of energy necessary to bring each of the nuclei in the compound, in resonance. Since all the nuclei will need different energies from the radio frequency light to shift to the β spin state, this will result in different peaks on the graph.

Chemical shift: The interpretation of the NMR data is crucial for understanding the structure of the molecule in question. The NMR spectrum data, which is in the form of peaks on a graph, depicts the position of the signal from the spinning protons. Protons behave differently under the applied magnetic field depending on whether they are in an aliphatic, aromatic, or aldehydic electronic environment (Figure 1). Chemical shift in NMR represents the resonant frequency which is plotted on the NMR spectrum graph with respect to a reference compound [7]. Chemical shift is denoted by δ value and is represented by a scale from 0 to 10. The unit of chemical shift is PPM (parts per million). A commonly used reference compound in NMR is TMS (tetramethylsilane), which has a chemical shift 0.

J coupling: This is also called spin-spin coupling. It is the interaction that takes place between hydrogen atoms in a given molecule. This coupling causes the splitting of lines in NMR spectrum. The coupling constant is denoted by letter J. The distance between two adjacent H atoms would influence the value of the J constant. The coupling constant increases as this distance decrease. Furthermore, the orientation of the H atoms also has an effect on the spectral split. The J constant will be more if the H atoms are in Cis orientation than in trans orientation [8].

The chemical shift, J coupling, and resonant frequency parameters are different for each proton in the molecule being investigated. Therefore, in order to nail down the exact structure of the molecule, we need to generate NMR spectrum with high resolution and sharp peaks.

What can we do to get NMR peaks at high resolution?

We can do that in two ways:

  1. Shimming the magnet: The NMR instrument is capable of reaching a resolution capability within a few Hertz, and this is really an exceptional achievement. Let’s say NMR picks up a peak at 600,000,000 Hz and then picks up another peak at 600,000,009 Hz. This is a difference of just 9 Hz. This difference is in the 9th decimal place and is very subtle. This is where the process of shimming comes into play. The sample is spun at around 20 revolutions per second so that it experiences the same magnetic field inhomogeneity throughout. The challenge is then to also maintain a homogenous magnetic field at the long axis of the sample tube (also called the Z-axis).  This is done by shim coils. These coils compensate for the differences in the applied magnetic field to create a uniform magnetic field which is experienced by all the protons in the sample [9]
  2. Locking the magnetic field: Another problem with the NMR machine is that the magnetic field may slightly change over time and this phenomenon is called magnetic field drift. The instrument corrects this by locking on to the resonant frequency of deuterium in the solvent (e.g., deuterated chloroform). The instrument measures the deuterium frequency over and over again and sets it to a certain value (in Hertz). This phenomenon is called locking and it prevents loss of resolution in the NMR spectrum [10].
Deuterated solvents in NMR spectroscopy

The solvent has to be carefully chosen so that it does not contribute to the NMR spectrum. Deuterium (2H) is an isotope of hydrogen which has a neutron in the nucleus and does not have a spin, unlike 1H.  The most common solvent used in NMR spectroscopy is deuterated chloroform [11].

Sample handling in NMR spectroscopy

The quality of the sample determines the quality of the NMR spectrum generated.

  1. Usually, 5-25 mg of starting material is needed in the case of proton NMR.
  2. The sample should be homogeneously suspended to get a sharp NMR spectrum.
  3. NMR tube should be pre-washed with acetone to ensure cleanliness.
  4. The sample is degassed to get rid of oxygen which has paramagnetic properties.
  5. Test the solubility of the sample in the proper deuterated solvent.
Variations of NMR Spectroscopy
  1. 1-Dimensional NMR spectroscopy: This method is a first step in the characterization of the structure of a molecule plots intensity vs frequency (which is the chemical shifts in ppm). The signal is acquired after excitation with radiofrequency waves.
  2. 2-Dimensional NMR spectroscopy: This method is used to further map the coupling between H atoms. The intensity is plotted as a function of two frequencies rather than just one. The data is represented as contour plots (much like the topographical maps). In simple terms, a 2D NMR experiment involves a series of 1D NMR acquisitions [12].
  3. Biomolecular NMR spectroscopy: This method refers to using the NMR technique in studying biological material in conditions which are close to those in vivo.
  4. Solid-state NMR spectroscopy: It is now possible to observe drugs in action in membrane proteins with an advancement in the Biomolecular NMR spectroscopy technique. This is known as Solid-state NMR [13].
Instrumentation for NMR Spectroscopy

NMR spectrometer has the following components:

  1. a) A powerful magnet with a homogeneous magnetic field: Its job is to align nuclear spins in the sample.
  2. b) A sample holder.
  3. c) Shims: These are coils that maintain a uniform magnetic field.
  4. d) Locks: These are coils that transmit current and maintain a constant resonance frequency of deuterium in the solvent of the sample.
  5. e) A transmitter to emit radiofrequency waves: The radiofrequency causes a flip in the nuclear spins.
  6. f) A receiver and amplifier: The flip induced by radiofrequency is detected by the receiver and the signal is amplified for ease of visualization.
  7. g) A computer: To analyze signals from the detector and convert it into an NMR spectrum.
How to operate a 1H NMR machine
  1. The first step is sample preparation.
  2. The sample (in NMR tube) is placed in a sample holder. The height of the sample should be adjusted by the depth gauge according to the manufacturer’s guidelines.
  3. The sample tube is introduced in the magnet chamber.
  4. The rest of the process is monitored by a software.
  5. The solvent (which was used to prepare the sample) is selected in the software.
  6. NMR machine is then primed to other parameters such as temperature, gain, force tuning, angle of pulse, etc.
  7. If everything runs without any technical hiccups, we end up getting a NMR spectrum.
  8. TMS peak at 0 ppm acts as a marker for calibrating the NMR spectra.
  9. The number of unique proton environments could be identified by unique peaks in the spectrum.
  10. The area of the peak corresponds to the number of protons.
  11. The spin-spin coupling causes the peaks to split into sub-peaks.
  12. The information from chemical shifts and the area under the peaks are then used to determine the proton environment.
Commonly used NMR machines and models

Decades of technological advances in NMR machines has led to the advent of modern machines which are fast and give robust data. There are several models of NMR machines to choose from depending on what the purpose of your analysis is. In academia, for example, the most common ones used for regular purposes are 300 MHz Bruker Avance, 400 MHz Bruker Neo and 500 MHz Agilent ProPulse.

Application of NMR Spectroscopy

NMR is used:

  1. To determine the purity of a sample and its molecular structure [7].
  2. To quantify the percentages of different isoforms of the molecules in the sample [14].
  3. To measure reaction kinetics [15].
  4. To analyze biofuels [16].
  5. To determine edible oil composition, fat, and water content analysis in the food industry [17].
  6. To do metabolomics and identify products in body fluids [18, 19].
Advantages of NMR spectroscopy
  1. It is a high precision technique.
  2. It is reproducible.
  3. The sample can be recovered fully and can be used for other applications.
  4. Changes in metabolic reactions can be monitored over time [20].
  5. The data acquisition and the downstream data analysis is fast.
  6. No ionizing radiation is involved in the process, which limits the users and the samples to exposure to radioactivity.
Disadvantages and Limitations of NMR spectroscopy
  1. NMR spectroscopy requires expensive equipment.
  2. Determining the structures for higher molecular weight molecules is a problem.
  3. NMR is less sensitive than Mass spectroscopy (MS) since it requires a sample amount in milligrams, whereas it is a picogram from MS [21].
  4. Great care must be taken when using ferromagnetic materials in close proximity to NMR because of the strong magnet, as they could potentially become dangerous for the machine and user [22].
  5. Ionic states of elements cannot be deciphered using NMR.
  6. Hydrogen atoms in a molecule having similar resonant frequency could be hard to resolve.
  7. Only nuclei having magnetic moments could be analyzed.
Conclusion

NMR spectroscopy is miles ahead from its 8-decade old counterpart for which it bagged the Nobel prize in 1943. The modern NMR machines are spearheading research in various disciplines such as structural biology, food industry, polymer science, etc.

References
  1. J.C.M.a.J.B.S. Ho Teng, Thermochemical Characteristics of Dimethyl Ether — An Alternative Fuel for Compression-Ignition Engines, SAE Transactions, 110 (2001) 96-106.
  2. M. Lin, M. Li, A.V. Buevich, R. Osterman, A.M. Rustum, Rapid structure elucidation of drug degradation products using mechanism-based stress studies in conjunction with LC-MS(n) and NMR spectroscopy: identification of a photodegradation product of betamethasone dipropionate, J Pharm Biomed Anal, 50 (2009) 275-280.
  3. E. Jacoby, J. Davies, M.J. Blommers, Design of small molecule libraries for NMR screening and other applications in drug discovery, Curr Top Med Chem, 3 (2003) 11-23.
  4. U.S.N.L.o. Medicine, N.C.f.B. Information, National Center for Biotechnology Information. PubChem Database. Periodic Table of Elements, 2019.
  5. A.Abragam, The Principles of Nuclear Magnetism, Oxford University Press2006.
  6. M. Balci, Basic 1H- and 13C-NMR Spectroscopy, Elsevier Science2005.
  7. J. Fisher, Modern NMR Techniques for Synthetic Chemistry, 2014.
  8. E.L.H.a.D.E. Maxwell, Spin Echo Measurements of Nuclear Spin Coupling in Molecules, PHYSICAL REVIEW 88 (1952) 1070.
  9. W.A. Anderson, Electrical Current Shims for Correcting Magnetic Fields, Review of Scientific Instruments, 32 (1961) 241.
  10. P.C.M. Zijl, The use of deuterium as a nucleus for locking, shimming, and measuring NMR at high magnetic fields, Journal of Magnetic Resonance 75 (1987) 335-344.
  11. E.O.M. Nicholas R. Babij, Gregory T. Whiteker,* Belgin Canturk, Nakyen Choy, Lawrence C. Creemer, Carl V. De Amicis, Nicole M. Hewlett, Peter L. Johnson, James A. Knobelsdorf, Fangzheng Li, Beth A. Lorsbach, Benjamin M. Nugent, Sarah J. Ryan, Michelle R. Smith, and Qiang Yang, NMR Chemical Shifts of Trace Impurities: Industrially Preferred Solvents Used in Process and Green Chemistry, Org. Process Res. Dev, 20 (2016) 661-667.
  12. E.B. W. P. Aue, and R. R. Ernst, Two‐dimensional spectroscopy. Application to nuclear magnetic resonance, The Journal of Chemical Physics, 64 (1976).
  13. A. Watts, Solid-state NMR in drug design and discovery for membrane-embedded targets, Nat Rev Drug Discov, 4 (2005) 555-568.
  14. S.R. Choi, Y.J. Seo, M. Kim, Y. Eo, H.C. Ahn, A.R. Lee, C.J. Park, K.S. Ryu, H.K. Cheong, S.S. Lee, E. Jin, J.H. Lee, NMR study of the antifreeze activities of active and inactive isoforms of a type III antifreeze protein, FEBS Lett, 590 (2016) 4202-4212.
  15. K. VV, Molecular Thermodynamics Using Nuclear Magnetic Resonance (NMR) Spectroscopy., Inventions (Basel). (2019).
  16. B.L. Shimamoto GG, Tubino M., Alternative method to quantify biodiesel and vegetable oil in diesel-biodiesel blends through 1H NMR spectroscopy., Talanta. , 1 (2017) 121-125.
  17. D. Castejon, P. Fricke, M.I. Cambero, A. Herrera, Automatic (1)H-NMR Screening of Fatty Acid Composition in Edible Oils, Nutrients, 8 (2016) 93.
  18. J.L. Markley, R. Bruschweiler, A.S. Edison, H.R. Eghbalnia, R. Powers, D. Raftery, D.S. Wishart, The future of NMR-based metabolomics, Curr Opin Biotechnol, 43 (2017) 34-40.
  19. H. Boumaza, S. Markossian, B. Busi, G.J.P. Rautureau, K. Gauthier, B. Elena-Herrmann, F. Flamant, Metabolomic Profiling of Body Fluids in Mouse Models Demonstrates that Nuclear Magnetic Resonance Is a Putative Diagnostic Tool for the Presence of Thyroid Hormone Receptor alpha1 Mutations, Thyroid, 29 (2019) 1327-1335.
  20. M.C. Malet-Martino, R. Martino, Uses and limitations of nuclear magnetic resonance (NMR) spectroscopy in clinical pharmacokinetics, Clin Pharmacokinet, 20 (1991) 337-349.
  21. M.V.a.S. Elipe, Advantages and disadvantages of nuclear magnetic resonance spectroscopy as a hyphenated technique, Analytica Chimica Acta, 497 (2003) 1-25.
  22. S.J.B. John C. Chatham, Nuclear Magnetic Resonance Spectroscopy and Imaging in Animal Research, ILAR, 42 (2001) 189-208

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Introduction

In behavioral neuroscience, the Open Field Test (OFT) remains one of the most widely used assays to evaluate rodent models of affect, cognition, and motivation. It provides a non-invasive framework for examining how animals respond to novelty, stress, and pharmacological or environmental manipulations. Among the test’s core metrics, the percentage of time spent in the center zone offers a uniquely normalized and sensitive measure of an animal’s emotional reactivity and willingness to engage with a potentially risky environment.

This metric is calculated as the proportion of time spent in the central area of the arena—typically the inner 25%—relative to the entire session duration. By normalizing this value, researchers gain a behaviorally informative variable that is resilient to fluctuations in session length or overall movement levels. This makes it especially valuable in comparative analyses, longitudinal monitoring, and cross-model validation.

Unlike raw center duration, which can be affected by trial design inconsistencies, the percentage-based measure enables clearer comparisons across animals, treatments, and conditions. It plays a key role in identifying trait anxiety, avoidance behavior, risk-taking tendencies, and environmental adaptation, making it indispensable in both basic and translational research contexts.

Whereas simple center duration provides absolute time, the percentage-based metric introduces greater interpretability and reproducibility, especially when comparing different animal models, treatment conditions, or experimental setups. It is particularly effective for quantifying avoidance behaviors, risk assessment strategies, and trait anxiety profiles in both acute and longitudinal designs.

What Does Percentage of Time in the Centre Measure?

This metric reflects the relative amount of time an animal chooses to spend in the open, exposed portion of the arena—typically defined as the inner 25% of a square or circular enclosure. Because rodents innately prefer the periphery (thigmotaxis), time in the center is inversely associated with anxiety-like behavior. As such, this percentage is considered a sensitive, normalized index of:

  • Exploratory drive vs. risk aversion: High center time reflects an animal’s willingness to engage with uncertain or exposed environments, often indicative of lower anxiety and a stronger intrinsic drive to explore. These animals are more likely to exhibit flexible, information-gathering behaviors. On the other hand, animals that spend little time in the center display a strong bias toward the safety of the perimeter, indicative of a defensive behavioral state or trait-level risk aversion. This dichotomy helps distinguish adaptive exploration from fear-driven avoidance.

  • Emotional reactivity: Fluctuations in center time percentage serve as a sensitive behavioral proxy for changes in emotional state. In stress-prone or trauma-exposed animals, decreased center engagement may reflect hypervigilance or fear generalization, while a sudden increase might indicate emotional blunting or impaired threat appraisal. The metric is also responsive to acute stressors, environmental perturbations, or pharmacological interventions that impact affective regulation.

  • Behavioral confidence and adaptation: Repeated exposure to the same environment typically leads to reduced novelty-induced anxiety and increased behavioral flexibility. A rising trend in center time percentage across trials suggests successful habituation, reduced threat perception, and greater confidence in navigating open spaces. Conversely, a stable or declining trend may indicate behavioral rigidity or chronic stress effects.

  • Pharmacological or genetic modulation: The percentage of time in the center is widely used to evaluate the effects of pharmacological treatments and genetic modifications that influence anxiety-related circuits. Anxiolytic agents—including benzodiazepines, SSRIs, and cannabinoid agonists—reliably increase center occupancy, providing a robust behavioral endpoint in preclinical drug trials. Similarly, genetic models targeting serotonin receptors, GABAergic tone, or HPA axis function often show distinct patterns of center preference, offering translational insights into psychiatric vulnerability and resilience.

Critically, because this metric is normalized by session duration, it accommodates variability in activity levels or testing conditions. This makes it especially suitable for comparing across individuals, treatment groups, or timepoints in longitudinal studies.

A high percentage of center time indicates reduced anxiety, increased novelty-seeking, or pharmacological modulation (e.g., anxiolysis). Conversely, a low percentage suggests emotional inhibition, behavioral avoidance, or contextual hypervigilance. reduced anxiety, increased novelty-seeking, or pharmacological modulation (e.g., anxiolysis). Conversely, a low percentage suggests emotional inhibition, behavioral avoidance, or contextual hypervigilance.

Behavioral Significance and Neuroscientific Context

1. Emotional State and Trait Anxiety

The percentage of center time is one of the most direct, unconditioned readouts of anxiety-like behavior in rodents. It is frequently reduced in models of PTSD, chronic stress, or early-life adversity, where animals exhibit persistent avoidance of the center due to heightened emotional reactivity. This metric can also distinguish between acute anxiety responses and enduring trait anxiety, especially in longitudinal or developmental studies. Its normalized nature makes it ideal for comparing across cohorts with variable locomotor profiles, helping researchers detect true affective changes rather than activity-based confounds.

2. Exploration Strategies and Cognitive Engagement

Rodents that spend more time in the center zone typically exhibit broader and more flexible exploration strategies. This behavior reflects not only reduced anxiety but also cognitive engagement and environmental curiosity. High center percentage is associated with robust spatial learning, attentional scanning, and memory encoding functions, supported by coordinated activation in the prefrontal cortex, hippocampus, and basal forebrain. In contrast, reduced center engagement may signal spatial rigidity, attentional narrowing, or cognitive withdrawal, particularly in models of neurodegeneration or aging.

3. Pharmacological Responsiveness

The open field test remains one of the most widely accepted platforms for testing anxiolytic and psychotropic drugs. The percentage of center time reliably increases following administration of anxiolytic agents such as benzodiazepines, SSRIs, and GABA-A receptor agonists. This metric serves as a sensitive and reproducible endpoint in preclinical dose-finding studies, mechanistic pharmacology, and compound screening pipelines. It also aids in differentiating true anxiolytic effects from sedation or motor suppression by integrating with other behavioral parameters like distance traveled and entry count (Prut & Belzung, 2003).

4. Sex Differences and Hormonal Modulation

Sex-based differences in emotional regulation often manifest in open field behavior, with female rodents generally exhibiting higher variability in center zone metrics due to hormonal cycling. For example, estrogen has been shown to facilitate exploratory behavior and increase center occupancy, while progesterone and stress-induced corticosterone often reduce it. Studies involving gonadectomy, hormone replacement, or sex-specific genetic knockouts use this metric to quantify the impact of endocrine factors on anxiety and exploratory behavior. As such, it remains a vital tool for dissecting sex-dependent neurobehavioral dynamics.
 The percentage of center time is one of the most direct, unconditioned readouts of anxiety-like behavior in rodents. It is frequently reduced in models of PTSD, chronic stress, or early-life adversity. Because it is normalized, this metric is especially helpful for distinguishing between genuine avoidance and low general activity.

Methodological Considerations

  • Zone Definition: Accurately defining the center zone is critical for reliable and reproducible data. In most open field arenas, the center zone constitutes approximately 25% of the total area, centrally located and evenly distanced from the walls. Software-based segmentation tools enhance precision and ensure consistency across trials and experiments. Deviations in zone parameters—whether due to arena geometry or tracking inconsistencies—can result in skewed data, especially when calculating percentages.

     

  • Trial Duration: Trials typically last between 5 to 10 minutes. The percentage of time in the center must be normalized to total trial duration to maintain comparability across animals and experimental groups. Longer trials may lead to fatigue, boredom, or habituation effects that artificially reduce exploratory behavior, while overly short trials may not capture full behavioral repertoires or response to novel stimuli.

     

  • Handling and Habituation: Variability in pre-test handling can introduce confounds, particularly through stress-induced hypoactivity or hyperactivity. Standardized handling routines—including gentle, consistent human interaction in the days leading up to testing—reduce variability. Habituation to the testing room and apparatus prior to data collection helps animals engage in more representative exploratory behavior, minimizing novelty-induced freezing or erratic movement.

     

  • Tracking Accuracy: High-resolution tracking systems should be validated for accurate, real-time detection of full-body center entries and sustained occupancy. The system should distinguish between full zone occupancy and transient overlaps or partial body entries that do not reflect true exploratory behavior. Poor tracking fidelity or lag can produce significant measurement error in percentage calculations.

     

  • Environmental Control: Uniformity in environmental conditions is essential. Lighting should be evenly diffused to avoid shadow bias, and noise should be minimized to prevent stress-induced variability. The arena must be cleaned between trials using odor-neutral solutions to eliminate scent trails or pheromone cues that may affect zone preference. Any variation in these conditions can introduce systematic bias in center zone behavior. Use consistent definitions of the center zone (commonly 25% of total area) to allow valid comparisons. Software-based segmentation enhances spatial precision.

Interpretation with Complementary Metrics

Temporal Dynamics of Center Occupancy

Evaluating how center time evolves across the duration of a session—divided into early, middle, and late thirds—provides insight into behavioral transitions and adaptive responses. Animals may begin by avoiding the center, only to gradually increase center time as they habituate to the environment. Conversely, persistently low center time across the session can signal prolonged anxiety, fear generalization, or a trait-like avoidance phenotype.

Cross-Paradigm Correlation

To validate the significance of center time percentage, it should be examined alongside results from other anxiety-related tests such as the Elevated Plus Maze, Light-Dark Box, or Novelty Suppressed Feeding. Concordance across paradigms supports the reliability of center time as a trait marker, while discordance may indicate task-specific reactivity or behavioral dissociation.

Behavioral Microstructure Analysis

When paired with high-resolution scoring of behavioral events such as rearing, grooming, defecation, or immobility, center time offers a richer view of the animal’s internal state. For example, an animal that spends substantial time in the center while grooming may be coping with mild stress, while another that remains immobile in the periphery may be experiencing more severe anxiety. Microstructure analysis aids in decoding the complexity behind spatial behavior.

Inter-individual Variability and Subgroup Classification

Animals naturally vary in their exploratory style. By analyzing percentage of center time across subjects, researchers can identify behavioral subgroups—such as consistently bold individuals who frequently explore the center versus cautious animals that remain along the periphery. These classifications can be used to examine predictors of drug response, resilience to stress, or vulnerability to neuropsychiatric disorders.

Machine Learning-Based Behavioral Clustering

In studies with large cohorts or multiple behavioral variables, machine learning techniques such as hierarchical clustering or principal component analysis can incorporate center time percentage to discover novel phenotypic groupings. These data-driven approaches help uncover latent dimensions of behavior that may not be visible through univariate analyses alone.

Total Distance Traveled

Total locomotion helps contextualize center time. Low percentage values in animals with minimal movement may reflect sedation or fatigue, while similar values in high-mobility subjects suggest deliberate avoidance. This metric helps distinguish emotional versus motor causes of low center engagement.

Number of Center Entries

This measure indicates how often the animal initiates exploration of the center zone. When combined with percentage of time, it differentiates between frequent but brief visits (indicative of anxiety or impulsivity) versus fewer but sustained center engagements (suggesting comfort and behavioral confidence).

Latency to First Center Entry

The delay before the first center entry reflects initial threat appraisal. Longer latencies may be associated with heightened fear or low motivation, while shorter latencies are typically linked to exploratory drive or low anxiety.

Thigmotaxis Time

Time spent hugging the walls offers a spatial counterbalance to center metrics. High thigmotaxis and low center time jointly support an interpretation of strong avoidance behavior. This inverse relationship helps triangulate affective and motivational states.

Applications in Translational Research

  • Drug Discovery: The percentage of center time is a key behavioral endpoint in the development and screening of anxiolytic, antidepressant, and antipsychotic medications. Its sensitivity to pharmacological modulation makes it particularly valuable in dose-response assessments and in distinguishing therapeutic effects from sedative or locomotor confounds. Repeated trials can also help assess drug tolerance and chronic efficacy over time.
  • Genetic and Neurodevelopmental Modeling: In transgenic and knockout models, altered center percentage provides a behavioral signature of neurodevelopmental abnormalities. This is particularly relevant in the study of autism spectrum disorders, ADHD, fragile X syndrome, and schizophrenia, where subjects often exhibit heightened anxiety, reduced flexibility, or altered environmental engagement.
  • Hormonal and Sex-Based Research: The metric is highly responsive to hormonal fluctuations, including estrous cycle phases, gonadectomy, and hormone replacement therapies. It supports investigations into sex differences in stress reactivity and the behavioral consequences of endocrine disorders or interventions.
  • Environmental Enrichment and Deprivation: Housing conditions significantly influence anxiety-like behavior and exploratory motivation. Animals raised in enriched environments typically show increased center time, indicative of reduced stress and greater behavioral plasticity. Conversely, socially isolated or stimulus-deprived animals often show strong center avoidance.
  • Behavioral Biomarker Development: As a robust and reproducible readout, center time percentage can serve as a behavioral biomarker in longitudinal and interventional studies. It is increasingly used to identify early signs of affective dysregulation or to track the efficacy of neuromodulatory treatments such as optogenetics, chemogenetics, or deep brain stimulation.
  • Personalized Preclinical Models: This measure supports behavioral stratification, allowing researchers to identify high-anxiety or low-anxiety phenotypes before treatment. This enables within-group comparisons and enhances statistical power by accounting for pre-existing behavioral variation. Used to screen anxiolytic agents and distinguish between compounds with sedative vs. anxiolytic profiles.

Enhancing Research Outcomes with Percentage-Based Analysis

By expressing center zone activity as a proportion of total trial time, researchers gain a metric that is resistant to session variability and more readily comparable across time, treatment, and model conditions. This normalized measure enhances reproducibility and statistical power, particularly in multi-cohort or cross-laboratory designs.

For experimental designs aimed at assessing anxiety, exploratory strategy, or affective state, the percentage of time spent in the center offers one of the most robust and interpretable measures available in the Open Field Test.

Explore high-resolution tracking solutions and open field platforms at
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References

  • Prut, L., & Belzung, C. (2003). The open field as a paradigm to measure the effects of drugs on anxiety-like behaviors: a review. European Journal of Pharmacology, 463(1–3), 3–33.
  • Seibenhener, M. L., & Wooten, M. C. (2015). Use of the open field maze to measure locomotor and anxiety-like behavior in mice. Journal of Visualized Experiments, (96), e52434.
  • Crawley, J. N. (2007). What’s Wrong With My Mouse? Behavioral Phenotyping of Transgenic and Knockout Mice. Wiley-Liss.
  • Carola, V., D’Olimpio, F., Brunamonti, E., Mangia, F., & Renzi, P. (2002). Evaluation of the elevated plus-maze and open-field tests for the assessment of anxiety-related behavior in inbred mice. Behavioral Brain Research, 134(1–2), 49–57.

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