A model for estimating the brainstem volume in normal healthy individuals and its application to diffuse axonal injury patients.

Diffuse axonal injury (DAI) is a subtype of traumatic brain injury that causes acute-phase consciousness disorders and widespread chronic-phase brain atrophy. Considering the importance of brainstem damage in DAI, a valid method for evaluating brainstem volume is required. We obtained volume measurements from 182 healthy adults by analyzing T1-weighted magnetic resonance images, and created an age-/sex-/intracranial volume-based quantitative model to estimate the normal healthy volume of the brainstem and cerebrum. We then applied this model to the volume measurements of 22 DAI patients, most of whom were in the long-term chronic phase and had no gross focal injury, to estimate the percentage difference in volume from the expected normal healthy volume in different brain regions, and investigated its association with the duration of posttraumatic amnesia (which is an early marker of injury severity). The average loss of the whole brainstem was 13.9%. Moreover, the percentage loss of the whole brainstem, and particularly of the pons and midbrain, was significantly negatively correlated with the duration of posttraumatic amnesia. Our findings suggest that injury severity, as denoted by the duration of posttraumatic amnesia, is among the factors affecting the chronic-phase brainstem volume in patients with DAI.

Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects.

Aim: Diffuse axonal injury (DAI) is one of the most common pathological features of traumatic brain injury (TBI). Diffusion tensor imaging (DTI) indices can be used to identify and quantify white matter microstructural changes following DAI. Recently, many studies have used DTI with various machine learning approaches to predict white matter microstructural changes following TBI. The current study sought to examine whether our classification approach using multiple DTI indices in conjunction with machine learning is a useful tool for diagnosing/classifying TBI patients and healthy controls. Methods: Participants were adult patients with chronic TBI (n = 26) with DAI pathology, and age- and sex-matched healthy controls (n = 26). DTI images were obtained from all participants. Tract-based spatial statistics analyses were applied to DTI images. Classification models were built using principal component analysis and support vector machines. Receiver operator characteristic curve analysis and area under the curve were used to assess the classification performance of the different classifiers. Results: Tract-based spatial statistics revealed significantly decreased fractional anisotropy, as well as increased mean diffusivity, axial diffusivity, and radial diffusivity in patients with TBI compared with healthy controls (all p-values < 0.01). The principal component analysis and support vector machine-based machine learning classification using combined DTI indices classified patients with TBI and healthy controls with an accuracy of 90.5% with an area under the curve of 93 ± 0.09. Conclusion: These results highlight the potential of our approach combining multiple DTI measures to identify patients with TBI.

Extracting Apathy From Depression Syndrome in Traumatic Brain Injury by Using a Clustering Method.

OBJECTIVE: Depression and apathy are common after traumatic brain injury (TBI), and different intervention strategies are recommended for each. However, a differential diagnosis can be difficult in clinical settings, especially given that apathy is considered to be a symptom of depression. In this study, the investigators aimed to isolate apathy from depression among patients with TBI and to examine whether apathy is exclusively associated with the amount of daily activity, as previously reported in the literature. METHODS: Eighty-eight patients with chronic TBI completed the Japanese versions of the 21-item Beck Depression Inventory-II (BDI-II) and the Starkstein Apathy Scale (AS). Daily activity was measured with a 24-hour life log. A hierarchical cluster analysis was applied to divide the BDI-II data into separable components, and components' correlations with results of the AS and 24-hour life log scale were evaluated. RESULTS: The BDI-II and AS revealed that 37 patients (42.0%) had both depression and apathy. BDI-II data were classified into four separate clusters (somatic symptoms, loss of self-worth, affective symptoms, and apathy symptoms). Loss of self-worth and apathy symptoms subscores were significantly positively correlated with total AS score (r=0.32, p=0.002, and r=0.52, p<0.001, respectively). The apathy symptoms subscore was significantly correlated with the amount of daily activity (r=-0.29, p=0.009). CONCLUSIONS: The findings suggest that the BDI-II can differentiate between apathy and depression among patients with TBI, which is essential when selecting intervention options. Moreover, apathy symptoms predicted patients' real-life daily activity.

A venous mechanism of ventriculomegaly shared between traumatic brain injury and normal ageing.

Recently, age-related timing dissociation between the superficial and deep venous systems has been observed; this was particularly pronounced in patients with normal pressure hydrocephalus, suggesting a common mechanism of ventriculomegaly. Establishing the relationship between venous drainage and ventricular enlargement would be clinically relevant and could provide insight into the mechanisms underlying brain ageing. To investigate a possible link between venous drainage and ventriculomegaly in both normal ageing and pathological conditions, we compared 225 healthy subjects (137 males and 88 females) and 71 traumatic brain injury patients of varying ages (53 males and 18 females) using MRI-based volumetry and a novel perfusion-timing analysis. Volumetry, focusing on the CSF space, revealed that the sulcal space and ventricular size presented different lifespan profiles with age; the latter presented a quadratic, rather than linear, pattern of increase. The venous timing shift slightly preceded this change, supporting a role for venous drainage in ventriculomegaly. In traumatic brain injury, a small but significant disease effect, similar to idiopathic normal pressure hydrocephalus, was found in venous timing, but it tended to decrease with age at injury, suggesting an overlapping mechanism with normal ageing. Structural bias due to, or a direct causative role of ventriculomegaly was unlikely to play a dominant role, because of the low correlation between venous timing and ventricular size after adjustment for age in both patients and controls. Since post-traumatic hydrocephalus can be asymptomatic and occasionally overlooked, the observation suggested a link between venous drainage and CSF accumulation. Thus, hydrocephalus, involving venous insufficiency, may be a part of normal ageing, can be detected non-invasively, and is potentially treatable. Further investigation into the clinical application of this new marker of venous function is therefore warranted.

[Brodmann Areas 11, 46, and 47: Emotion, Memory, and Empathy].

Brodmann area 11 is one of the main constituent of the orbitofrontal cortex, and area 46 is that of the dorsolateral prefrontal cortex. The main function of Brodmann area 11 is the processing of emotion and value, whereas the main function of Brodmann area 46 is the processing of cognitive information, including working memory. In comparison, the function of area 47 is more complex. This area is related to the feeling of empathy towards the story contents of others, which is thought to be the emotional aspect of this area, while this area is also activated during automated action. This is in contrast with the function of area 46, which is involved in willed action. In addition, area 47 in the left hemisphere plays an important role in syntax processing.