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K. H. Yang and H. Mao
5.2.1 Lack of Sufficient Biomechanical and Injury Data
for Model Validations
One major advantage of using an FE head model lies in its capability of predicting
the site-specific head and brain responses. Experimental studies using human
cadavers have provided valuable insights into fractures of the cranial [81, 106, 107]
and facial bones [2, 74], regional intracranial pressures [30, 71, 95], and how the
brain moved within the skull upon closed-head impacts at some target locations
[30, 31]. Results from these studies have been widely cited for validating the head
models described in the previous section. However, these experiments were not
explicitly designed to secure data for model validations. For example, none of these
studies acquired detailed head geometries, both external (such as width and breath of
the head) and internal (such as the size of ventricles) dimensions, to create subjectspecific models.
Also, only a limited number of subjects were tested and results were scattered
widely. Even though extraordinary efforts have been invested to perfuse the cadaver
head and to resolve issues like intracranial air bubbles, which could greatly affect
brain motion, there were still significant inconsistent results seen in tests subjected
to the same impact severities. As such, several datasets were discarded in the
Hardy experiments. Also, it is difficult to determine whether the variations seen in
the remaining datasets were due to experimental errors, subject-specific geometric
effects, age differences, or other potential reasons. With all the deficiencies that
could potentially occurred in experiments, Giordano and Kleiven [23] developed
a weighted averaging method to screen out datasets that might have significant
biases. On one hand, this proposed rigorous approach may help in improving the
repeatability of experimental data, thus increasing the model correlation scores.
On the other hand, removals of any datasets in order to better correlate the
computational model predictions with experimental data have hardly ever been the
standard procedures. More research is needed on this topic.
For TBI (especially the mild form of TBI or mTBI) cases in which no structural
damages were presented soon after the initial impact, cadaveric studies are of limited
benefits. To resolve this issue, kinematic data and corresponding injury outcomes
collected from live subjects, such as animals and volunteers, are commonly accepted
for studying TBI. In animal studies using simple experimental protocol, kinematic
data can be easily deduced. In American football games and motor sports in which
‘volunteers’ are involved, kinematic data can be derived indirectly from high-speed
videos or directly from athletes wearing various types of acceleration and velocity
sensors. For injury outcomes, behavioural studies are conducted to determine the
overall state of brain injury, while histologic methods are used to identify the location and extent of histopathological changes in animals at various post-impact time
points. Due to the lengthy processes of conducting histologic evaluations, only a
handful regions of interest (ROIs) are investigated in most animal studies. In human,
evaluations that are based on combined neuropsychological assessment, structural
and functional magnetic resonance imaging (MRI), electroencephalography (EEG),
K. H. Yang and H. Mao
5.2.1 Lack of Sufficient Biomechanical and Injury Data
for Model Validations
One major advantage of using an FE head model lies in its capability of predicting
the site-specific head and brain responses. Experimental studies using human
cadavers have provided valuable insights into fractures of the cranial [81, 106, 107]
and facial bones [2, 74], regional intracranial pressures [30, 71, 95], and how the
brain moved within the skull upon closed-head impacts at some target locations
[30, 31]. Results from these studies have been widely cited for validating the head
models described in the previous section. However, these experiments were not
explicitly designed to secure data for model validations. For example, none of these
studies acquired detailed head geometries, both external (such as width and breath of
the head) and internal (such as the size of ventricles) dimensions, to create subjectspecific models.
Also, only a limited number of subjects were tested and results were scattered
widely. Even though extraordinary efforts have been invested to perfuse the cadaver
head and to resolve issues like intracranial air bubbles, which could greatly affect
brain motion, there were still significant inconsistent results seen in tests subjected
to the same impact severities. As such, several datasets were discarded in the
Hardy experiments. Also, it is difficult to determine whether the variations seen in
the remaining datasets were due to experimental errors, subject-specific geometric
effects, age differences, or other potential reasons. With all the deficiencies that
could potentially occurred in experiments, Giordano and Kleiven [23] developed
a weighted averaging method to screen out datasets that might have significant
biases. On one hand, this proposed rigorous approach may help in improving the
repeatability of experimental data, thus increasing the model correlation scores.
On the other hand, removals of any datasets in order to better correlate the
computational model predictions with experimental data have hardly ever been the
standard procedures. More research is needed on this topic.
For TBI (especially the mild form of TBI or mTBI) cases in which no structural
damages were presented soon after the initial impact, cadaveric studies are of limited
benefits. To resolve this issue, kinematic data and corresponding injury outcomes
collected from live subjects, such as animals and volunteers, are commonly accepted
for studying TBI. In animal studies using simple experimental protocol, kinematic
data can be easily deduced. In American football games and motor sports in which
‘volunteers’ are involved, kinematic data can be derived indirectly from high-speed
videos or directly from athletes wearing various types of acceleration and velocity
sensors. For injury outcomes, behavioural studies are conducted to determine the
overall state of brain injury, while histologic methods are used to identify the location and extent of histopathological changes in animals at various post-impact time
points. Due to the lengthy processes of conducting histologic evaluations, only a
handful regions of interest (ROIs) are investigated in most animal studies. In human,
evaluations that are based on combined neuropsychological assessment, structural
and functional magnetic resonance imaging (MRI), electroencephalography (EEG),