Introduction

The hippocampus, located in the brain’s temporal lobe and responsible for memory and spatial orientation, is among the most vulnerable regions to external mechanical loads1,2,3. Hippocampal injury can result in significant consequences, including short-term memory loss and disorientation, as well as long-term chronic cognitive decline and an elevated risk of neurodegenerative diseases1,3,4,5. Such injuries impose substantial psychological stress on patients and lead to considerable financial burdens due to increased medical expenses and reduced productivity6. Therefore, investigating hippocampal injury is crucial for elucidating its underlying mechanisms and advancing the development of effective head protection strategies.

Animal models, particularly rat models, are invaluable tools for investigating hippocampal injury7. This is attributed to their cost-effectiveness, ease of feeding and management, and the close resemblance of their physiological and pathological responses to those of humans, making them well-suited for mimicking human head injury mechanisms8,9. The closed head injury (CHI) rat model is widely utilized in traumatic brain injury research as it effectively simulates real-world head injury scenarios10. This model applies external mechanical loads to an intact skull, inducing rapid head acceleration and deceleration, which generates shear stresses and subsequently results in brain tissue damage11,12. In the CHI model, higher impact strengths generally led to more severe hippocampal injuries, whereas animals with greater body weight (greater weeks of age) tended to exhibit less injury under the same load13,14,15. However, this relationship is complex and cannot be fully captured by single-variable analysis. Consequently, developing a model to quantify the combined effects of mechanical loading strength and body weight on hippocampal injury is essential for providing a robust scientific foundation to predict the extent of injury under varying conditions.

Currently, studies quantifying the effects of impact strength and animal body weight on hippocampal injury have primarily focused on behavioral and pathological changes. Striking the heads of mice using a impactor resulted in prolonged times to locate the hidden platform in the water maze, with higher impact strengths correlating to longer search times16. These behavioral alterations indicate damage to key brain regions, such as the hippocampus, as evidenced by pathological changes including astrocyte proliferation and cell apoptosis17,18. At equivalent impact strengths, smaller body weights were associated with greater memory impairments in rats, as demonstrated by deficits in novel object recognition15. These behavioral impairments were closely linked to increased apoptosis and microglial activation within the hippocampus19. This information is crucial for advancing our understanding of how impact strength and body weight influence behavioral deficits following hippocampal injury and the associated pathological changes in key brain regions.

At present, quantitative studies on hippocampal injury are primarily focused on examining the effects of either impact strength or animal body weight on behavioral changes and pathological characteristics15,16,17,18,19. However, in practical experimental scenarios, hippocampal injury is often influenced by the combined effects of both impact strength and rat body weight. Therefore, this study aims to establish a quantitative relationship between impact strength, body weight, and hippocampal injury in rats, with the goal of predicting hippocampal injury and providing a framework for assessing hippocampal damage under different impact conditions.

Methods

The design of animal experiments, experimental procedures and methods of animal execution were approved by the Experimental Animal Welfare Ethics Review Committee of the Third Military Medical University (No. AMUWEC20210836). All procedures complied with the requirements of animal ethics and welfare and were conducted in accordance with ethical standards and the ARRIVE guidelines.

Experimental protocol

To investigate the quantitative relationship between hippocampal injury, impact strength, and body weight, a quadratic orthogonal polynomial regression model was employed. This approach utilizes the orthogonal properties of polynomial functions to design the experimental protocol, enabling the derivation of nonlinear regression equations that describe the relationship between experimental factors and the response variables20. The quadratic orthogonal polynomial regression model is widely applied in engineering fields to elucidate associations between experimental factors and outcomes21. Figure 1 illustrates the workflow of the quadratic orthogonal polynomial regression analysis.

Fig. 1
figure 1

Flow chart of quadratic orthogonal regression analysis.

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As depicted in the flowchart in Fig. 1, impact strength and rat body weight were identified as the experimental factors for this study. Based on the literature, impact strengths of 0.3 MPa, 0.5 MPa, and 0.7 MPa were selected to generate impact velocities corresponding to mild, moderate, and severe TBI (Traumatic Brain Injury)12,22. These pressures were applied to the cranio-parietal region of light-weight (L), moderate-weight (M), and heavy-weight (H) rats to simulate injuries ranging from mild to severe.

Based on the experimental factors and levels, the L9 (34) orthogonal table was selected for the experimental design (as shown in Table 1). This orthogonal table is suitable for experiments involving fewer than four factors, with three levels for each factor20. In Table 1, the first column represents the experimental group, with naming based on the combination of levels for each test factor. For instance, “0.3 MPa-L” denotes a combination experiment involving 0.3 MPa impact strength and light rat body weight. The second column A specifies the arrangement of different levels of impact strength, while the third column B organizes the levels of rat body weight. The fourth and fifth columns (C and D) are not assigned to specific experimental factors. The last column \(y_{i}\), represents the experimental results and is used to record the outcomes obtained from the tests.

Table 1 L9 (34) Table of experimental factor levels.
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Animal preparation

A total of 72 male rats were prepared for the experiment. These were categorized based on body weight into three groups: 26 light-weight rats (203.1 g ± 8.2 g, 8 weeks old), 24 medium-weight rats (301.2 g ± 8.8 g, 10 weeks old), and 22 heavy-weight rats (405.5 g ± 12.3 g, 12 weeks old). Rats were purchased from the Animal Experiment Center of Daping Hospital. The experimental rats were kept in an environment with a room temperature of 24 °C and a photoperiod of 12 h. The rats were fed and watered freely before the start of the experiment for 12 h. During the 12 h of the experiment, the rats were given water only.

Rats TBI experiment

According to the experimental protocol outlined in Table 1, rats with corresponding body weights were selected for the experiments. In cases where rats died during the experiments, replacements were provided to ensure that each group ultimately had five surviving rats at the conclusion of the study. Additionally, three sham groups were established for each weight class. Finaly, the experimental design included nine injury groups and three sham groups. The selection of five rats per group was based on previous studies investigating traumatic brain injury in rodents, where similar sample sizes have been used to detect significant differences in behavioral and histological outcomes23,24.

Using the BIM-IV head impact machine22 (Fig. 2A), impact experiments were conducted on rat heads following the protocol outlined in Table 1. Prior to the impact test, rats were anesthetized by mask inhalation of isoflurane (RWD Life Sciences, China) using a small animal anesthesia machine (RWD Life Sciences, China). The initial concentration of isoflurane was set at 2%, and gradually increased to 4.0–5.0% in 100% oxygen over the course of 3–5 min. Anesthesia was confirmed by gently squeezing the rats’ toes. After the rats were anesthetized, a steel helmet (2 mm thick and 10 mm in diameter) was affixed to the top of the rat’s head using cyanoacrylate adhesive at a pre-scraped area (approximately the midline between Bregma and Lambda) to prevent skull fractures. A mold with acceleration and angular velocity sensors was secured approximately 5 mm from the front edge of the helmet using cyanoacrylate adhesive and tape, as shown in Fig. 2B. Details of the mold and sensors are shown in Figs. 2E and 2F. The rats were continuously anesthetized via 2% isoflurane throughout the installation of the helmet and sensor. Tape was prearranged underneath the elastic mesh to prevent secondary injuries that might be caused by the head bouncing during impact, as shown in Fig. 2D. The data acquisition system was run at a sampling frequency of 10 kHz and filtered in SAE 1000. A high-speed camera was used to record the entire process during the experiment. The high-speed camera sampled at 1000 fps and had a resolution of 1440 × 1080 pixels.

Fig. 2
figure 2

Diagram of the TBI experiment. A BIM-IV rat pneumatic impact machine, ① regulator, ② impact cylinder, ③ impact rod, ④ acrylic box, ⑤ sponge, ⑥ elastic mesh, ⑦ high-speed video camera, B position of the helmet, and C strong adhesive tape for preventing secondary rebound.

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The anesthetized rats were placed in an acrylic square box containing a sponge (hardness 40D) with the head sticking out of the sponge and placed on a 3-cm wide elastic support mesh. The impact rod was pulled down so that the impact head was fully aligned with the helmet, with a distance of 50 mm between them. The impact was initiated by pressing the trigger button, which simultaneously activated the high-speed camera and sensor data acquisition system to record the resulting impact velocity and the mechanical response of the head. Isoflurane delivery was stopped immediately after the impact, and the animals were continuously monitored until they fully regained mobility. The total duration of isoflurane exposure was approximately 6 min. Sham group animals underwent all procedures except for the impact.

Behavioral and pathological tests in rats

To evaluate the memory capacity of the rats, the Morris Water Maze (MWM) test was conducted 24 h post-injury25. The specific procedure was as follows: rats underwent 5 days of behavioral training, followed by a memory test 24 h post-injury. Briefly, a black platform (10 cm in diameter, 39 cm in height) was placed in the southeast quadrant of a circular pool (160 cm in diameter, 50 cm in depth). The pool was filled with water to a depth of 40 cm, and black ink was added to render the submerged platform invisible. The water temperature was maintained at 24 °C using a thermostat. The pool was symbolically divided into four quadrants, east, west, south, and north, and the starting point of each quadrant was marked with alternating square and triangular markings in red, yellow, white, and green on the pool wall (Fig. 3). Five days prior to the impact test, rats underwent training in the MWM four times daily. During each session, rats were released from the midpoint of one of the four quadrant edges (without repetition in the same quadrant) and trained to locate a hidden platform submerged underwater by observing visual markers on the pool wall. A 20-min interval was maintained between each training session. If a rat failed to locate the platform within 60 s, the experimenter gently guided it onto the platform, where it remained for 15–20 s before being dried and returned to its cage. Following the injury, a formal MWM test was conducted. Injured rats were released four times at random positions along the midpoint of the quadrant edges (without repetition in the same quadrant), and the time taken to locate the hidden platform was recorded. Post-injury, an increase in the time required to find the platform was observed, indicating memory impairment. Shorter times and more direct paths to the platform reflected better memory performance, whereas longer times and more erratic paths indicated impaired memory.

Fig. 3
figure 3

Morris water maze.

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Pathological examination was employed to identify cellular alterations indicative of hippocampal injury in rats and to quantify the extent of damage across different hippocampal regions. At 24 h post-injury, rats were deeply anesthetized with an overdose of 2% pentobarbital sodium solution (Merck, Germany; 120 mg/kg, intraperitoneal injection). The anesthetic solution was freshly prepared using sterile saline (Kelun Group, China). The depth of anesthesia was confirmed by the absence of withdrawal reflexes (e.g., toe pinch response) and spontaneous movement. After anesthesia, rats were euthanized by cervical dislocation. The chest cavity was then opened to expose the heart, and the right auricle was incised while the left ventricle was perfused with normal saline (Kelun Group, China). Perfusion was continued with 4% paraformaldehyde (P0099, Beyotime, China) after the efflux of clear fluid. Once the limbs and tail became rigid, the brains were carefully extracted and fixed in 4% paraformaldehyde (P0099, Beyotime, China) for 48 h. The fixed tissues were then dehydrated, embedded in paraffin, sectioned into slices 4 μm thick, and stained with hematoxylin–eosin (C0105S, Beyotime, China). Histological sections corresponding to Bregma − 3.6 mm were scanned panoramically at 40×magnification. Subsequently, the number of cells in the CA1, CA3, and dentate gyrus (DG) regions of the hippocampus was precisely counted using a 100 μm × 100 μm grid.

Quadratic orthogonal regression equation fitting

The percentage of neuronal loss in the hippocampus was selected as an indicator for deriving the quadratic orthogonal regression equation. This choice was based on the fact that pathological indicators directly reflect specific cellular, tissue, and molecular damage in the hippocampus, making them more closely associated with the strength of impact and the body weight of the rats. In contrast, the results of the water maze test are influenced by multiple factors, including the overall condition of the animal (e.g., locomotor activity, stress levels, and visual acuity) and damage to non-targeted brain regions.

The expression for the quadratic orthogonal equation chosen in this paper as,

$$y = f + ax_{1} + bx_{2} + cx_{1}^{2} + dx_{2}^{2} + ex_{1} x_{2}$$
(1)

where a,b are primary terms, c,d,e secondary coefficients, and f is a constant.

Experimental factor coding and tabulation

Coding changes in factor levels was performed on Table 1 and the coding formula is shown in Eq. (2),

$$\left\{ {\begin{array}{*{20}c} {x_{1} = \frac{{2\left( {A - 0.5MPa} \right)}}{0.7MPa - 0.3MPa}} \\ {x_{2} = \frac{{2\left( {B - 300g} \right)}}{400g - 200g}} \\ \end{array} } \right.$$
(2)

where \(x_{1}\) is the factor corresponding to the impact strength in the coding space and \(x_{2}\) is the factor corresponding to the weight in the coding space. From Eq. (2), \(x_{1}\) = − 1, 0, 1 when A = 0.3 MPa, 0.5 MPa, 0.7 MPa, and \(x_{1}\) = − 1, 0, 1 when B = 200 g, 300 g, 400 g.

According to Eq. (2), the impact strength and body weight factors in Table 1 were replaced with the corresponding coded space factors of \(x_{1}\) and \(x_{2}\). The coded space experimental factor list is shown in Table 2.

Table 2 Factor table for coding space experiments.
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Tests of orthogonality of experimental protocols

Since the total number of trials is 9, the coefficient matrix of this quadratic equation as,

$$X = \left[ {\begin{array}{*{20}c} 1 & { - 1} & { - 1} & 1 & 1 & 1 \\ 1 & { - 1} & 0 & 1 & 0 & 0 \\ 1 & { - 1} & 1 & 1 & 1 & { - 1} \\ 1 & 0 & { - 1} & 0 & 1 & 0 \\ 1 & 0 & 0 & 0 & 0 & 0 \\ 1 & 0 & 1 & 0 & 1 & 0 \\ 1 & 1 & { - 1} & 1 & 1 & { - 1} \\ 1 & 1 & 0 & 1 & 0 & 0 \\ 1 & 1 & 1 & 1 & 1 & 1 \\ \end{array} } \right]$$
(3)

At this point, column 1 and columns 4 and 5 of the X matrix are not orthogonal and need to be orthogonally changed to make them orthogonal. Using the centering method, each element of columns 4 and 5 can be subtracted from the average value of the column to obtain the coded value with orthogonality (as shown in Table 3).

Table 3 Scheme of experimental factors in the coding space after orthogonalization.
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Calculation of regression coefficients and significance testing of equations

The coefficients \(b_{j}\) of the regression equation of impact strength, rat body weight and hippocampal injury were calculated as follows,

$$b_{j} = \mathop \sum \limits_{i = 1}^{N} x_{ij} y_{j} /\mathop \sum \limits_{i = 1}^{N} x_{ij}^{2} \left( {i = 3, \ldots ,8;j = 1,2, \ldots ,9} \right)$$
(4)

where \(x_{ij}\) experimental factor coded values and \(y_{i}\) is the experimental indicator value.

The total deviation sum of squares \(S\), is calculated as,

$$S = \mathop \sum \limits_{j = 1}^{N} \left( {y_{j} - \overline{y}} \right)^{2} j = 1,2, \ldots ,9$$
(5)

where \(y_{j}\) is the mean value of the experimental indicator.

The sum of the squares of the deviations of the columns is calculated as follows,

$$S_{i} = \frac{{\mathop \sum \nolimits_{i = 1}^{N} x_{ij}^{2} y_{i}^{2} }}{{\mathop \sum \nolimits_{i = 1}^{N} x_{ij}^{2} }}\;i = 3, \ldots ,8;\;j = 1,2, \ldots ,9$$
(6)

The regression sum of squares, \(S_{c}\), is equal to the sum of the squares of the deviations of the columns, and is calculated as follows,

$$S_{c} = \mathop \sum \limits_{i = 1}^{N} S_{i} \;i = 1, \ldots ,6$$
(7)

The residual deviation sum of squares, \(S_{r}\), is calculated as follows

$$S_{r} = S - S_{c}$$
(8)

The formula for the regression coefficient test as,

$${\text{F}} = \frac{{S_{c} /f_{c} }}{{S_{r} /f_{r} }}$$
(9)

Results

Rat mortality and skull fracture

The statistical data regarding mortality and skull fractures among the rats are presented in Table 4. The overall mortality rate was 16.7%, with deaths occurring non-randomly. Instead, these events were predominantly associated with specific experimental conditions, namely, impact strengths of 0.5 MPa and 0.7 MPa combined with lower body weights. In these combinations of impacts, the main causes of rat death were apnea and skull fracture, with apnea causing the majority of rat deaths, and skull fracture was also found in the surviving rats, the numbers of which are shown in Table 4.

Table 4 Number of rats died and skull fractures under different impact conditions.
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MWM results

Figure 4 illustrates the results of the MWM test for rats exposed to different combinations of impact conditions. Figure 4A depicts the swimming trajectories of the rats during the final test session on day 6 as they searched for the hidden platform. Rats in the sham group quickly located the hidden platforms using short and direct paths. In contrast, as the impact strength increased, the swimming trajectories became progressively longer and more disorganized, indicating that impact strength significantly influenced the rats’ memory performance. Under the same impact strength, rats weighing 200 g exhibited more disorganized trajectories, whereas those weighing 400 g demonstrated relatively clearer paths. This observation suggests that body weight also plays a role in the memory performance of rats.

Fig. 4
figure 4

MWM results of rats under different impact combinations. (A) Swimming trajectories of sham and injured rats on day 6 trial during the search for the hidden platform, (B–D) Time folds of pre-injury and post-injury to find the hidden platform for rats of different body weights under 0.3 MPa, 0.5 MPa, 0.7 MPa impact strength, (E–G) Swimming speeds during the MWM test in the corresponding rats.

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The time data for rats locating the hidden platform (Fig. 4B–D) further corroborated the observed trends. As the number of training days increased, the time required to find the hidden platform decreased across all groups, reflecting an improvement in learning and memory abilities with repeated training. Under the same body weight condition, the time to find the hidden platform was significantly longer in the higher impact strength group (e.g., 0.7 MPa) compared to the lower impact strength group (e.g., 0.3 MPa), indicating that greater impact strength resulted in more severe memory impairments. Similarly, at the same impact strength, rats weighing 400 g required less time to find the hidden platform compared to rats weighing 200 g, suggesting that heavier rats were less susceptible to memory deficits. In summary, higher impact strength was associated with greater memory impairment, while increased body weight appeared to mitigate the extent of memory impairment. These findings demonstrate that impact strength is positively correlated with the severity of memory impairment, whereas body weight is negatively correlated with the degree of impairment.

To evaluate whether motor impairments influenced cognitive performance, we analyzed the swimming speed of rats across different groups before and after injury during MWM testing (Fig. 4E–G). The results showed that swimming speed remained stable over the course of the experiment, with no noticeable differences observed between injured and sham groups. This suggests that the injury did not lead to observable locomotor impairments, and differences in spatial learning performance were more likely due to cognitive deficits rather than motor dysfunction.

Pathologic findings of hippocampus

Closed head impact resulted in a symmetrical distribution of brain damage across the left and right hemispheres in rats. Therefore, only the CA1, CA3, and DG regions on the right hemisphere were selected for pathological analysis. Figure 5 displays the cellular morphological changes observed in the CA1, CA3, and DG regions. In the sham group, neurons in these regions were tightly arranged, exhibited regular morphology, and showed no significant pathological changes. However, as the impact strength increased from 0.3 to 0.7 MPa, varying degrees of pathological damage were observed in all regions.

Fig. 5
figure 5

Pathologic results of CA1, CA3, and DG regions of the hippocampus in rats under different combinations of impact conditions.

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In the CA1 region, increasing impact strength progressively led to pathological changes, including neuronal disarrangement, nuclear consolidation, and vacuole formation (highlighted by red arrows). At 0.7 MPa, rats weighing 200 g exhibited pronounced vacuole formation and significant neuronal loss, whereas rats weighing 300 g and 400 g under the same impact conditions retained some neurons with regular arrangements and displayed fewer vacuoles. The CA3 region exhibited the most severe pathological changes among the hippocampal regions. As the impact strength increased, this region showed extensive neuronal loss, nuclear consolidation, and large vacuole formation (marked by red arrows). At 0.7 MPa, neurons in the CA3 region of rats weighing 200 g and 300 g displayed widespread nuclear consolidation, while rats weighing 400 g maintained partial neuronal arrangement, although vacuoles and cell loss were still evident. The DG region followed a similar trend of damage. At 0.3 MPa, the granule cell layer in the DG region showed mild disorganization. However, at 0.7 MPa, rats weighing 200 g exhibited significant vacuole formation, nuclear consolidation (red arrows), and substantial disruption of the cellular hierarchy. In contrast, at the same impact strength, the DG region of 300 g and 400 g rats partially preserved their granular cell layer hierarchy, with fewer vacuoles observed. These findings indicate that impact strength is a critical determinant of hippocampal damage, with higher impact strength resulting in more severe pathological alterations. Additionally, body weight plays a mitigating role; heavier rats experienced less hippocampal damage under identical impact conditions, suggesting that body weight provides some degree of protection against impact-induced neuronal injury.

The results of the pathological tests were counted for subsequent regression analysis, and the results are shown in Table 5.

Table 5 Percentage of cell loss in CA1, CA3 and DG regions of the hippocampus under different impact combinations (mean ± SD).
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Quadratic regression equation between impact strength, rat body weight and hippocampal injury

Based on the results in Table 5, the regression coefficients of each item were calculated separately using the formulae in the statistical analysis as a way to establish the quadratic regression model of impact strength, body weight of the rats and the CA1, CA3 and DG regions of the hippocampus.

$${\text{CA1}}:y = 21.0 + 10.2x_{1} - 6.2x_{2} + 1.5x_{1}^{2} - 1.5x_{2}^{2} - 1.25x_{1} x_{2} \left( {\alpha_{r} = 0.01,\alpha_{lf} = 0.1} \right)$$
(10)
$${\text{CA3}}:y = 26.9 + 10.2x_{1} - 6.2x_{2} + 3x_{1}^{2} - 2.4x_{2}^{2} - x_{1} x_{2} \left( {\alpha_{r} = 0.01,\alpha_{lf} = 0.25} \right)$$
(11)
$${\text{DG}}:y = 26.5 + 12.8x_{1} - 5.3x_{2} + 3.9x_{1}^{2} - 4.8x_{2}^{2} - 0.8x_{1} x_{2} \left( {\alpha_{r} = 0.01,\alpha_{lf} = 0.1} \right)$$
(12)

The regression model was tested for the significance of the regression coefficients, the significance of the regression equation, and the misfit test, and all three of these tests were performed using the F-test. The level of significance \(\alpha_{r}\) of the regression equation for CA1, CA3 and DG are 0.01, indicating that the regression equation is significant. The test for misfit of the equation reveals that the significant level \(\alpha_{lf}\) of the regression equation for CA1, CA3 and DG are 0.1, 0.25 and 0.1 respectively, indicating that the sum of squares of misfit is basically caused by experimental error and the regression equation is not misfit.

Discussion

In this study, the combined effects of impact strength and rat body weight on hippocampal injury were systematically analyzed using a quadratic orthogonal regression model, enabling the development of a quantitative relationship between these factors and hippocampal injury. Compared to previous studies that primarily focused on single-variable effects, a significant advantage of this study lies in the introduction of a multifactorial analytical framework, which provides a more comprehensive understanding of the complex interplay between mechanical loading and individual body weight in the context of injury mechanisms. The findings of this study offer novel insights into the mechanisms underlying impact-related brain injury and establish a robust scientific foundation for the development of more precise brain protection strategies.

Impact strength significantly influenced hippocampus-associated cognitive impairments. In the MWM test, the time required for rats to locate the hidden platform increased significantly with higher impact intensities, consistent with findings reported in the literature. For instance, Maruichi et al.26 demonstrated that rats subjected to an impact intensity of 70 psi took longer to find the platform compared to those exposed to 60 psi. Similarly, Zohar et al.16 observed that rats experiencing a 30 g weight drop exhibited longer platform latency than those with a 20 g weight drop. These results can be attributed to the fact that higher impact strengths deliver greater energy to brain tissue, resulting in more severe mechanical damage, including extensive apoptosis and axonal injury17. Furthermore, higher impact forces are likely to trigger more pronounced secondary injury mechanisms, such as heightened inflammatory responses and oxidative stress, thereby exacerbating neuronal loss and neurological damage27,28.

At the same impact strength, rats with higher body weight exhibited milder memory deficits and hippocampal damage. The influence of body weight on TBI outcomes has been highlighted in several studies. For example, Zvejniece et al.14 reported that higher body weights significantly improved tolerance to brain injury in rats. Similarly, Guilhaume-Corrêa et al.15 observed that heavier animals had less post-injury cognitive deficits, possibly because body weight improves the distribution and absorption of impact energy, thereby reducing brain tissue damage. These findings may be attributed to the more intricate neuroinflammatory regulatory mechanisms in heavier individuals, which promote effective tissue repair and stress response tolerance following injury. This not only mitigates acute injury but also facilitates subsequent recovery processes. The results of the present study align with these findings, providing additional evidence for the protective role of body weight in TBI and underscoring the importance of considering individual body weight as a critical factor in animal models of TBI.

Hippocampal injury was assessed at 24 h post-injury, as this time point represents the peak of neuronal degeneration29,30, allowing for a clear evaluation of early cognitive deficits before longer-term compensatory mechanisms take effect. Behavioral testing at this 24-h time point revealed significant spatial memory impairments in injured rats compared to the sham group, consistent with previous findings31. The underlying mechanisms of these deficits can be attributed to two primary forms of hippocampal injury. First, direct neuronal damage occurs due to mechanical impact, leading to immediate hippocampal cell loss. Second, secondary injury arises from trauma-induced disruption of neural and vascular networks, triggering progressive functional decline32. This delayed pathological process further exacerbates cognitive impairment, highlighting the complex interplay between acute and secondary injury mechanisms.

The results of memory in the MWM of rats are consistent with previous reports in the literature. For example, in Chen et al. study33, rats showed decreased spatial memory capacity by significantly prolonging the latency to find a hidden platform in the MWM after head injury, indicating a decrease in spatial memory capacity. These findings provide further evidence of the effects of neurological injury on cognition. While MWM is widely used to assess spatial learning and memory, it primarily relies on hippocampus-dependent navigation. However, additional behavioral paradigms could provide complementary insights into other cognitive and emotional domains. For example, Trace Fear Conditioning could be used to assess associative learning and memory, while the Social Avoidance Test may provide insights into anxiety-related behaviors. Incorporating these tests in future studies may help further elucidate the broader neurocognitive effects of injury beyond spatial memory impairments34.

Regarding the selection of the number of rats for the behavioral test, we chose 5 rats per group for the experiment, and this sample size is considered effective in detecting differences in pathophysiological indices (e.g., tissue damage or biochemical markers). However, compared to some studies that have used larger sample sizes in behavioral tests, such as Yang et al.35, who used twelve animals in similar experiments, the smaller sample size may have affected our sensitivity to detecting more subtle behavioral differences. Therefore, the potential impact of sample size needs to be considered when interpreting behavioral results. Nonetheless, some significant behavioral trends were observed in this study with the available sample size, suggesting that the experimental design and methods used have some degree of validity. To further improve the robustness of the results, future studies may consider increasing the sample size to enhance the sensitivity of the detection of behavioral changes.

The regression models developed in this study provide a quantitative framework for predicting hippocampal injury based on impact strength and body weight. These models allow researchers to estimate the extent of neuronal loss in different hippocampal regions without the need for extensive histological analysis, thereby optimizing experimental design and reducing the number of animals required for preliminary testing. Future TBI studies can utilize these models to refine impact parameters, ensuring controlled and reproducible injury severity across different experimental conditions.

Limitation

First, this study only tested the MWM change results in rats 24 h after injury, and it was found that the memory cognitive behavior changes were not obvious in 400 g rats, which further suggests that the cognitive deficits in rats may be a long-term process, and long-term water maze behavioral testing is considered in the future. Second, differences in body weight among the experimental groups are inherently linked to age, as younger rats tend to weigh less. Given that younger brains remain in active neurodevelopment and are more vulnerable to traumatic brain injury, some of the observed hippocampal injury differences may have been influenced by both body weight and brain maturation. Future studies could further explore this aspect by using age-matched groups with controlled body weight variations to better distinguish the effects of brain maturity from those of body weight. Third, for damage evaluation, the changes in the cellular level of the rat hippocampus were evaluated by HE staining, but this pathology examination method is difficult to realize in the actual human hippocampal damage evaluation, and a noninvasive method (medical imaging) to detect hippocampal damage is considered in the future. Fourth, pathologic testing was limited to a specific coronal cross-section of Bregma − 3.6 mm. Although this choice provides detailed quantitative data on the damage at this site, a broader range, such as the Bregma − 3.6 mm ± 0.6 mm region, will be considered in the future in order to obtain more accurate results. Fifth, in terms of the selection of test factors, the possible effects of damage to the brain caused by the separate action of head translation and rotation were not considered. This is mainly due to the fact that the separation of translational and rotational loads cannot be accomplished with the existing equipment. In the future, consideration will be given to developing a new experimental setup or finding new avenues of research to accomplish this study. The material properties of the brain exhibit significant viscoelastic properties with a volume model that is much larger than the shear modulus, and the rotational motion of the rat’s head may have a large effect on the extent of the injury, which will be given attention in future studies. Sixth, given the interspecies variability, there are limitations in directly mapping the results of TBI experiments in rats to humans. In the future, it is expected that the experimental results of this study can be scientifically mapped to the human head by combining the finite element model analysis of rat and human head and based on the principle of brain tissue stiffness similarity, thus further promoting the development of the related fields.

Conclusion

In this study, we established a quantitative relationship between impact strength, rat body weight, and hippocampal injury, revealing the complex interplay between mechanical load and body weight in hippocampal damage. Our findings demonstrated that hippocampal neuronal loss was significantly exacerbated with increasing impact strength, while greater body weight exerted a protective effect against injury. This quantitative model not only confirms the critical roles of these factors in the injury mechanism but also provides a valuable tool for predicting hippocampal injury under various conditions. Looking ahead, this model has the potential to be applied to the individualized assessment of traumatic brain injury and to offer theoretical support for the design and optimization of brain protection devices.