MATERIAL AND METHODS: Forty-eight subjects (23 complicated mTBI [cmTBI] patients, 12 uncomplicated mTBI [umTBI] patients, and 13 controls) underwent magnetic resonance imaging scan with additional single voxel spectroscopy sequence. Magnetic resonance imaging scans for patients were done at an average of 10 hours (standard deviation 4.26) post injury. The single voxel spectroscopy adjacent to side of injury and noninjury regions were analysed to obtain absolute concentrations and ratio relative to creatine of the neurometabolites. One-way analysis of variance was performed to compare neurometabolite concentrations of the three groups, and a correlation study was done between the neurometabolite concentration and Glasgow Coma Scale.
RESULTS: Significant difference was found in ratio of N-acetylaspartate to creatine (NAA/Cr + PCr) (χ2(2) = 0.22, P
METHODS: We imputed the classical HLA alleles, amino acids, and haplotypes using the Immunochip genotyping data of 1260 RA cases (i.e., 530 Malays, 259 Chinese, 412 Indians, and 59 mixed ethnicities) and 1571 controls (i.e., 981 Malays, 205 Chinese, 297 Indians, and 87 mixed ethnicities) from the Malaysian Epidemiological Investigation of Rheumatoid Arthritis (MyEIRA) population-based case-control study. Stepwise logistic regression was performed to identify the independent genetic risk factors for RA within the HLA region.
RESULTS: We confirmed that the HLA-DRB1 amino acid at position 11 with valine residue conferred the strongest risk effect for ACPA-positive RA (OR = 4.26, 95% CI = 3.30-5.49, PGWAS = 7.22 × 10-29) in the Malays. Our study also revealed that HLA-DRB1 amino acid at position 96 with histidine residue was negatively associated with the risk of developing ACPA-positive RA in the Indians (OR = 0.48, 95% CI = 0.37-0.62, PGWAS = 2.58 × 10-08). Interestingly, we observed that HLA-DQB1*03:02 allele was inversely related to the risk of developing ACPA-positive RA in the Malays (OR = 0.17, 95% CI = 0.09-0.30, PGWAS = 1.60 × 10-09). No association was observed between the HLA variants and risk of developing ACPA-negative RA in any of the three major ethnic groups in Malaysia.
CONCLUSIONS: Our results demonstrate that the RA-associated genetic factors in the multi-ethnic Malaysian population are similar to those in the Caucasian population, despite significant differences in the genetic architecture of HLA region across populations. A novel and distinct independent association between the HLA-DQB1*03:02 allele and ACPA-positive RA was observed in the Malays. In common with the Caucasian population, there is little risk from HLA region for ACPA-negative RA.
SETTING: A single centre study, Malaysia.
PARTICIPANTS: Adults aged between 18 and 60 years with mTBI as a result of road traffic accident, with no previous history of head trauma, minimum of 9 years education and abnormal cognition at 3 months will be included. The exclusion criteria include pre-existing chronic illness or neurological/psychiatric condition, long-term medication that affects cognitive/psychological status, clinical evidence of substance intoxication at the time of injury and major polytrauma. Based on multiple estimated calculations, the minimum intended sample size is 50 participants (Cohen's d effect size=0.35; alpha level of 0.05; 85% power to detect statistical significance; 40% attrition rate).
INTERVENTIONS: Intervention group will receive individualised structured cognitive rehabilitation. Control group will receive the best patient-centred care for attention disorders. Therapy frequency for both groups will be 1 hour per week for 12 weeks.
OUTCOME MEASURES: Primary: Neuropsychological Assessment Battery-Screening Module (S-NAB) scores. Secondary: Diffusion Tensor Imaging (DTI) parameters and Goal Attainment Scaling score (GAS).
RESULTS: Results will include descriptive statistics of population demographics, CogniPlus cognitive program and metacognitive strategies. The effect of intervention will be the effect size of S-NAB scores and mean GAS T scores. DTI parameters will be compared between groups via repeated measure analysis. Correlation analysis of outcome measures will be calculated using Pearson's correlation coefficient.
CONCLUSION: This is a complex clinical intervention with multiple outcome measures to provide a comprehensive evidence-based treatment model.
ETHICS AND DISSEMINATION: The study protocol was approved by the Medical Research Ethics Committee UMMC (MREC ID NO: 2016928-4293). The findings of the trial will be disseminated through peer-reviewed journals and scientific conferences.
TRIAL REGISTRATION NUMBER: NCT03237676.
MATERIALS AND METHODS: Sixteen children with GDD underwent magnetic resonance imaging (MRI) and cross-sectional DTI. Formal developmental assessment of all GDD patients was performed using the Mullen Scales of Early Learning. An automated processing pipeline for the WMT assessment was implemented. The DTI-derived metrics of the children with GDD were compared to healthy children with normal development (ND).
RESULTS: Only two out of the 17 WMT demonstrated significant differences (p<0.05) in DTI parameters between the GDD and ND group. In the uncinate fasciculus (UF), the GDD group had lower mean values for fractional anisotropy (FA; 0.40 versus 0.44), higher values for mean diffusivity (0.96 versus 0.91×10-3 mm2/s) and radial diffusivity (0.75 versus 0.68×10-3 mm2/s) compared to the ND group. In the superior cerebellar peduncle (SCP), mean FA values were lower for the GDD group (0.38 versus 0.40). Normal myelination pattern of DTI parameters was deviated against age for GDD group for UF and SCP.
CONCLUSION: The UF and SCP WMT showed microstructural changes suggestive of compromised white matter maturation in children with GDD. The DTI metrics have potential as imaging markers for inadequate white matter maturation in GDD children.
METHODS: Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network.
RESULTS: A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%).
CONCLUSION: This paper demonstrated the feasibility and effectiveness of applying blind signal processing and deep learning techniques to PPG motion artifact detection, whereby manual feature thresholding was avoided and yet a high generalization ability was achieved.
METHODS: Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction.
RESULTS: DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued.
CONCLUSION: DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data.
KEY POINTS: Diffusion tensor imaging outperforms conventional MRI in depicting white matter maturation. • DTI will become an important clinical tool for diagnosing paediatric neurological diseases. • DTI appears especially helpful for developmental abnormalities, tumours and white matter disease. • An automated processing pipeline assists quantitative analysis of high throughput DTI data.
METHODS: 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3.
RESULTS: Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified.
CONCLUSION: Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD.
KEY POINTS: • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.
METHODS: A phantom study was performed to investigate the correlation of (1)H MRS-visible lipids with the signal loss ratio (SLR) obtained using IOP imaging. A cross-sectional study approved by the institutional review board was carried out in 22 patients with different glioma grades. The patients underwent scanning using IOP imaging and single-voxel spectroscopy (SVS) using 3T MRI. The brain spectra acquisitions from solid and cystic components were obtained and correlated with the SLR for different grades.
RESULTS: The phantom study showed a positive linear correlation between lipid quantification at 0.9 parts per million (ppm) and 1.3 ppm with SLR (r = 0.79-0.99, p