MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.
RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.
CONCLUSION: MRS has a role as a non-invasive tool for the detection of brain metabolite biomarkers that indicate brain metabolic health, which can be integral in the management of AD.
PATIENT CONCERNS: A 37-year-old construction worker was brought in by his wife and coworker due to a sudden loss of consciousness while resting after completing his work.
DIAGNOSES: Due to challenges faced during the coronavirus disease 2019 pandemic, as well as language barriers, a detailed history from the coworker who witnessed the patient's altered sensorium was not available. He was initially suspected of having encephalitis and brainstem stroke. However, subsequent investigations revealed multiorgan dysfunction with a normal brain computed tomography and cerebral computed tomography angiogram. In view of the multiple risk factors for heat stroke, pupillary constriction, and urine color suggestive of rhabdomyolysis, a diagnosis of heat stroke was made.
INTERVENTIONS: Despite delayed diagnosis, the patient's multiorgan dysfunction recovered within days with basic supportive care.
OUTCOMES: There were no noticeable complications on follow-up 14 months later.
LESSONS: Heat stroke can be easily confused with other neurological pathologies, particularly if no history can be obtained from the patient or informant. When approaching a comatose patient, we propose that serum creatinine kinase should be considered as an initial biochemical screening test.
MATERIALS AND METHODS: 18F-FDG PET/CT images of 14 healthy control (HC) subjects (MoCA score > 26 (mean+SD~ 26.93+0.92) with no clinical evidence of cognitive deficits or neurological disease) and 16 AD patients (MoCA ≤22 (mean+SD~18.6+9.28)) were pre-processed in SPM12 while using our developed Malaysian healthy control brain template. The AD patients were assessed for disease severity using ADAS-Cog neuropsychological test. KNE96 template was used for registration-induced deformation in comparison with the ICBM templates. All deformation fields were corrected using the Malaysian healthy control template. The images were then nonlinearly modified by DARTEL to segment grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) to produce group-specific templates. Age, intracranial volume, MoCA score, and ADASCog score were used as variables in two sample t test between groups. The inference of our brain analysis was based on a corrected threshold of p<0.001 using Z-score threshold of 2.0, with a positive value above it as hypometabolic. The relationship between regional atrophy in GM and WM atrophy were analysed by comparing the means of cortical thinning between normal control and three AD stages in 15 clusters of ROI based on Z-score less than 2.0 as atrophied.
RESULTS: One-way ANOVA indicated that the means were equal for TIV, F(2,11) = 1.310, p=0.309, GMV, F(2,11) = 0.923, p=0.426, WMV, F(2,11) = 0.158, p=0.856 and CSF, F(2,11) = 1.495 p=0.266. Pearson correlations of GM, WM and CSF volume between HC and AD groups indicated the presence of brain atrophy in GM (p=-0.610, p<0.0001), WM (p=-0.178, p=0.034) and TIV (p=-0.374, p=0.042) but showed increased CSF volume (p=0.602, p<0.0001). Voxels analysis of the 18FFDG PET template revealed that GM atrophy differs significantly between healthy control and AD (p<0.0001). Zscore comparisons in the region of GM & WM were shown to distinguish AD patients from healthy controls at the prefrontal cortex and parahippocampal gyrus. The atrophy rate within each ROI is significantly different between groups (c2=35.9021, df=3, p<0.0001), Wilcoxon method test showed statistically significant differences were observed between Moderate vs. Mild AD (p<0.0001), Moderate AD vs. healthy control (p=0.0005), Mild AD vs. HC (p=0.0372) and Severe AD vs. Moderate AD (p<0.0001). The highest atrophy rate within each ROI between the median values ranked as follows severe AD vs. HC (p<0.0001) > mild AD vs. HC (p=0.0091) > severe AD vs. moderate AD (p=0.0143).
CONCLUSION: We recommend a reliable method in measuring the brain atrophy and locating the patterns of hypometabolism using a group-specific template registered to a quantitatively validated KNE96 group-specific template. The studied regions together with neuropsychological test approach is an effective method for the determination of AD severity in a Malaysian population.
OBJECTIVE: This systematic review aimed to investigate the available literature on the shared molecular mechanisms of neuroinflammation in AD and epilepsy.
METHODS: The search included in this systematic review was obtained from 5 established databases. A total of 2,760 articles were screened according to inclusion criteria. Articles related to the modulation of the inflammatory biomarkers commonly associated with the progression of AD and epilepsy in all populations were included in this review.
RESULTS: Only 7 articles met these criteria and were chosen for further analysis. Selected studies include both in vitro and in vivo research conducted on rodents. Several neuroinflammatory biomarkers were reported to be involved in the cross-talk between AD and epilepsy.
CONCLUSION: Neuroinflammation was directly associated with the advancement of AD and epilepsy in populations compared to those with either AD or epilepsy. However, more studies focusing on common inflammatory biomarkers are required to develop standardized monitoring guidelines to prevent the manifestation of epilepsy and delay the progression of AD in patients.
METHODS: Blinded assessors coded baseline images for acute ischaemic signs (presence, extent, swelling and attenuation of acute lesions; and hyperattenuated arteries) and pre-existing changes (atrophy, leucoaraiosis and old ischaemic lesions). Logistic regression models assessed associations between imaging features and death at 7 and 90 days; good recovery (modified Rankin Scale scores 0-2 at 90 days) and sICH. Data are reported with adjusted ORs and 95% CIs.
RESULTS: 2916 patients (67±13 years, National Institutes of Health Stroke Scale 8 (5-14)) were included. Visible ischaemic lesions, severe hypoattenuation, large ischaemic lesion, swelling and hyperattenuated arteries were associated with 7-day death (OR (95% CI): 1.52 (1.06 to 2.18); 1.51 (1.01 to 2.18); 2.67 (1.52 to 4.71); 1.49 (1.03 to 2.14) and 2.17 (1.48 to 3.18)) and inversely with good outcome. Severe atrophy was inversely associated with 7-day death (0.52 (0.29 to 0.96)). Atrophy (1.52 (1.08 to 2.15)) and severe leucoaraiosis (1.74 (1.20 to 2.54)) were associated with 90-day death. Hyperattenuated arteries were associated with sICH (1.71 (1.01 to 2.89)). No imaging features modified the effect of alteplase dose.
CONCLUSIONS: Non-expert-defined brain imaging signs of brain frailty and acute ischaemia contribute to the prognosis of thrombolysis-treated AIS patients for sICH and mortality. However, these imaging features showed no interaction with alteplase dose.