Evaluating the Severity and Prognosis of Acute Traumatic Cervical Spinal Cord Injury: A Novel Classification Using Diffusion Tensor Imaging and Diffusion Tensor Tractography.

STUDY DESIGN: Retrospective observational cohort study. OBJECTIVE: We explored the relationship between diffusion tensor imaging (DTI) parameters and prognosis in patients with acute traumatic cervical spinal cord injury (ATCSCI). SUMMARY OF BACKGROUND DATA: DTI has been used to diagnose spinal cord injury; nevertheless, its role remains controversial. METHODS: We analyzed retrospectively 24 patients with ATCSCI who were examined using conventional T2-weighted imaging and DTI. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were recorded at the injured site. Diffusion tensor tractography (DTT) was used to measure the spinal cord white matter fiber volume (MWFV). American Spinal Injury Association (ASIA) grades were recorded. Correlations between DTI parameters and ASIA scores were evaluated using Spearman correlation coefficients. RESULTS: FA values at injured sites were significantly lower than those of the control group, whereas ADC values in injured and control groups were not significantly different. DTT revealed that ATCSCI could be divided into four types: Type A1-complete rupture of spinal cord white matter fiber (MWF); Type A2-partial rupture of MWF; Type B-most MWF retained with severe compression or abnormal fiber conduction direction; and Type C-MWF basically complete with slight compression. Preoperative physical examinations revealed complete injury (ASIA A) in patients with A1 (n = 4) and A2 (n = 4). The ASIA grades or scores of A2 were improved to varying degrees, whereas there was no significant improvement in A1. FA values and MWFV of ASIA B, C, and D were significantly higher than those of ASIA A. FA and MWFV were correlated with ASIA motor score preoperatively and at final follow-up. CONCLUSION: We propose a classification for the severity of ATCSCI based on DTI and DTT that may explain why some patients with ASIA A recover, whereas others do not.Level of Evidence: 4.

Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI.

Background: Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. Methods: Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). Results: The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.570, p = 0.11). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.646 (p = 0.021). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.680, p = 0.005). Conclusions: FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

Tractografía en fenómeno de Kernohan: presentación de un caso de hematoma subdural agudo / Tractography in Kernohan’s phenomenon: report of a case of acute subdural haematoma

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Classification of diffusion tensor images for the early detection of Alzheimer's disease.

Early detection of Alzheimer's disease (AD) is important since treatments are more efficacious when used at the beginning of the disease. Despite significant advances in diagnostic methods for AD, there is no single diagnostic method for AD with high accuracy. We developed a support vector machine (SVM) model that classifies mild cognitive impairment (MCI) and normal control subjects using probabilistic tractography and tract-based spatial statistics of diffusion tensor imaging (DTI) data. MCI is an intermediate state between normal aging and AD, so finding MCI is important for an early diagnosis of AD. The key features of DTI data we identified through extensive analysis include the fractional anisotropy (FA) values of selected voxels, their average FA value, and the volume of fiber pathways from a pre-defined seed region. In particular, the volume of the fiber pathways to thalamus is the most powerful single feature in classifying MCI and normal subjects regardless of the age of the subjects. The best performance achieved by the SVM model in a 10-fold cross validation and in independent testing was sensitivity of 100%, specificity of 100% and accuracy of 100%.