Development of a modified weight-drop apparatus for closed-skull, repetitive mild traumatic brain injuries in a mouse model

Abstract

Repetitive mild traumatic brain injury (rmTBI) is a major contributor to long-term neurological dysfunction, yet many preclinical models lack precise control and quantification of biomechanical forces across impacts. We developed a reproducible, closed-skull mouse model of rmTBI using a custom-built weight-drop apparatus featuring a solenoid-based rebound arrest system, integrated high-speed videography, and accelerometry to track head kinematics during impact. Adult male and female mice received either a single impact or nine daily impacts. Linear and angular acceleration data were analyzed alongside behavioral and histological outcomes. Our apparatus delivered consistent impact and velocity forces with minimal inter-subject variability. Additionally, the animals experienced consistent linear and angular acceleration as measured using high-speed video capture. These impacts did not cause skull fracture or acute vascular hemorrhage, but impacted animals had increased return of righting reflex (RoRR) time, consistent with mild, concussion-like symptoms. Behavioral testing revealed reduced performance of rmTBI-affected mice in an olfaction-mediated foraging task (buried food task), particularly at later timepoints, consistent with progressive olfactory impairment. Immunohistochemical analysis of Iba1 and CD68 in the brain demonstrated sex-dependent microglial activation, with males showing higher expression levels in both single- and nine-impact models. Among the brain regions investigated, microglial activation was most pronounced in the corpus callosum, neocortex, and olfactory tubercle. These findings underscore the importance of including sex as a biological variable in rmTBI research and support the utility of this model for probing injury thresholds, regional vulnerability, and potential therapeutic interventions in repetitive head trauma.

Figure 1.. Construction of a Modified Weight-Drop Device:

A) Fully assembled modified weight-drop device. B) Close-up view of the custom control module, which includes a custom printed circuit board (PCB), an Arduino Uno v3, and an LCD screen. C) Close-up view of the weight sled, composed of a conical tube filled with steel airgun shot, a custom-printed conical holder, and an accelerometer. D) Close-up view of the high-speed camera placement alongside the solenoid, impactor bolt, and positioning lasers, presented with a 3D-printed mouse model on a foam pad. The inset shows a close-up view of the mouse’s position with the impactor tip above bregma.

Figure 2.. Impactor acceleration profiles following head impact in single- and repetitive-injury models.

(A, B) Impactor acceleration in the 1-hit model, shown as a representative trial (A) and group average ± SE (B), aligned with the moment of impact (dotted line at 0 ms). (C,D) Impactor acceleration in the 9-hit model, which used a solenoid-controlled restraint to prevent secondary impacts, shown as individual trial (C) and group average ± SE (D). Inserts illustrate key postural phases: Pre, During, and After impact. In (A, B), (i) marks the moment when the weight comes to rest on the animal’s head following impact. In (C, D), (i) marks the moment the weight sled contacts the solenoid, and (ii) marks when the weight comes to rest on the solenoid arm, effectively preventing secondary contact with the head. Inserts for (A) and (C) were created using BioRender. Confidence intervals for (B) and (D) are indicated by the shaded area.

Figure 3.. Kinematic profiles for the 9-hit rmTBI model with annotated video frame references.

(A) Representative linear (red) and angular (blue) acceleration traces from a single trial, aligned to impact (0 ms, vertical dotted line). Arrows labeled E-H correspond to times of video frames shown in Panes E-H. (B) Group average across all impacts (mean ± standard deviation). (C) Group averages (mean ± standard deviation) for each sex. (D) Group averages per impact. Darker shades reflect impacts later in the sequence. The shaded area reflects the standard deviation across all impacts (from Panel B). (E-H) Still frames with annotations made in Kinovea, showing angular displacement (radians) and linear distance. The specific frames shown reflect: (E) pre-impact rest, (F) peak downward angular acceleration, (G) peak upward angular acceleration (rebound), and (H) return to rest post-oscillation. Tracking points include the bottom frame border, ruler base, right ear midpoint, nose tip, and bottom of the impactor cap.

Figure 4.. Performance on the Buried Food Task following single injury.

(A, B) Retrieval time (in seconds) during the buried food task. (A) Comparison between control and injured animals averaged across all timepoints shows no significant difference in retrieval time. (B) Retrieval times separated by follow-up show no significant differences between control (Ctrl) and injured (Inj) groups at any timepoint. Horizontal bars indicate medians; boxes show interquartile ranges; individual points represent subjects. (C, D) Percentage of animals with successful food retrieval. (C) No significant difference in the overall proportion of successful retrievals between control and injured groups across all timepoints. (D) Percentage of successful retrievals by treatment group and timepoint. Control animals consistently showed higher success rates (80 – 85%) compared to injured animals (65%), no significant differences were found at any timepoint.

Figure 5.. Buried Food Task performance following rmTBI in the 9-hit model.

(A, B) Retrieval time (in seconds) during the buried food task. (A) Averaged across all timepoints, injured animals showed significantly longer retrieval times (p = 0.006). (B) Retrieval times separated by follow-up show no significant differences between control (Ctrl) and injured (Inj) groups at any timepoint. Horizontal bars indicate medians; boxes show interquartile ranges; individual points represent subjects. (C, D) Percentage of animals with successful food retrieval. (C) Control animals showed significantly higher rates of successful retrieval when compared to injured animals across all timepoints (p = 0.014). (D) Percentage of successful retrievals by treatment group and timepoint. Control animals consistently showed higher success rates after baseline (75 – 100%) compared to injured animals (40 – 80%), no significant differences were found at any timepoint.

Figure 6.. Representative IHC staining of cortical microglia across sex and injury conditions in a single mTBI model.

Fluorescent immunohistochemistry (IHC) images showing Iba1⁺ (red), CD68⁺ (green), and DAPI (blue) staining in sagittal brain sections from control and injured mice, separated by sex. Panels represent a control female (A), control male (B), injured female (C), and injured male (D). Each panel includes a whole-brain sagittal section, with two sequentially magnified insets highlighting microglial staining in a defined cortical region (green box). The first inset provides intermediate magnification, while the second (light blue box) shows high magnification of the boxed region within the first. Iba1⁺ staining marks total microglia, CD68⁺ indicates lysosomal activation, and co-localization reflects reactive or phagocytic microglia. DAPI labels all nuclei. Increased Iba1⁺ and CD68⁺ signal intensity is evident in male brains—particularly injured males (D)—compared to females, consistent with observed sex differences in cortical microglial activation. Arrows represent approximate site of impact.

Figure 7.. Quantitative analysis of microglial subpopulations by sex, region, and injury status.

Box plots showing microglial cell density and phenotype across the cerebellum (CB), cortex (CTX), olfactory tubercle (OT), and piriform cortex (PIR), separated by sex (M = male, F = female) and treatment (Control vs. Injured). (A) Resting Iba1+ microglial cell density. (B) Reactive microglial cell density. (C) CD68+ (phagocytic) microglial cell density. (D) Dual-labeled reactive/CD68⁺ microglia. (E) Density of dual-labeled reactive/CD68⁺ microglia by treatment and sex. Asterisks (*) denote significant differences (*p < 0.05; ‡p < 0.10); ns = not significant.

Figure 8.. Representative IHC staining of corpus callosum microglial activation across experimental groups in rmTBI model.

Sagittal brain slices were stained for Iba1 (red; microglia), CD68 (green; activated/phagocytic microglia), and DAPI (blue; nuclei). Each panel shows a full sagittal section with the corpus callosum highlighted in zoomed-in insets: the upper inset shows a regional overview, and the lower inset shows high-magnification views of individual microglial profiles. (A) Control female, (B) Control male, (C) Injured female, (D) Injured male. Increased CD68 signal intensity and altered microglial morphology are evident in the injured female and male brain (C, D), indicating enhanced microglial activation following rmTBI.

Figure 9.. Quantification of reactive and phagocytic microglia across brain regions and treatment groups.

(A) Percentage of positive pixels for reactive microglia across brain regions, separated by sex and treatment condition. (B) Percentage of CD68-positive pixels, representing phagocytic microglia, across the same groups and regions. Significant differences are indicated by asterisks (*p < 0.05); trend-level differences (†) and non-significant comparisons (ns) are also noted where appropriate.