Brain Magnetic Resonance Imaging Features Research Articles

Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment.

Accurately predicting when patients with mild cognitive impairment (MCI) will progress to dementia is a formidable challenge. This work aims to develop a predictive deep learning model to accurately predict future cognitive decline and magnetic resonance imaging (MRI) marker changes over time at the individual level for patients with MCI. We recruited 657 amnestic patients with MCI from the Samsung Medical Center who underwent cognitive tests, brain MRI scans, and amyloid-β (Aβ) positron emission tomography (PET) scans. We devised a novel deep learning architecture by leveraging an attention mechanism in a recurrent neural network. We trained a predictive model by inputting age, gender, education, apolipoprotein E genotype, neuropsychological test scores, and brain MRI and amyloid PET features. Cognitive outcomes and MRI features of an MCI subject were predicted using the proposed network. The proposed predictive model demonstrated good prediction performance (AUC = 0.814 ± 0.035) in five-fold cross-validation, along with reliable prediction in cognitive decline and MRI markers over time. Faster cognitive decline and brain atrophy in larger regions were forecasted in patients with Aβ (+) than with Aβ (-). The proposed method provides effective and accurate means for predicting the progression of individuals within a specific period. This model could assist clinicians in identifying subjects at a higher risk of rapid cognitive decline by predicting future cognitive decline and MRI marker changes over time for patients with MCI. Future studies should validate and refine the proposed predictive model further to improve clinical decision-making.

Association of Incident Stroke Risk With an IL-18-Centered Inflammatory Network Biomarker Composite.

A coordinated network of circulating inflammatory molecules centered on the pleotropic pro-atherogenic cytokine interleukin-18 (IL-18) is linked to cerebral small vessel disease. We sought to validate the association of this inflammatory biomarker network with incident stroke risk, cognitive impairment, and imaging metrics in a sample of the Framingham Offspring Cohort. Using available baseline measurements of serum levels of IL-18, GDF (growth and differentiation factor)-15, soluble form of receptor for advanced glycation end products, myeloperoxidase, and MCP-1 (monocyte chemoattractant protein-1) from Exam 7 of the Framingham Offspring Cohort (1998-2001), we constructed a population-normalized, equally weighted log-transformed mean Z-score value representing the average level of each serum analyte to create an inflammatory composite score (ICS5). Multivariable regression models were used to determine the association of ICS5 with incident stroke, brain magnetic resonance imaging features, and cognitive testing performance. We found a significant association between ICS5 score and increased risk for incident all-cause stroke (hazard ratio, 1.48 [95% CI, 1.05-2.08]; P=0.024) and ischemic stroke (hazard ratio, 1.51 [95% CI, 1.03-2.21]; P=0.033) in the Exam 7 cohort of 2201 subjects (mean age 62±9 years; 54% female) aged 45+ years with an all-cause incident stroke rate of 6.1% (135/2201) and ischemic stroke rate of 4.9% (108/2201). ICS5 and its component serum markers are all associated with the Framingham Stroke Risk Profile score (β±SE, 0.19±0.02; P<0.0001). In addition, we found a significant inverse association of ICS5 with a global cognitive score, derived from a principal components analysis of the neuropsychological battery used in the Framingham cohort (-0.08±0.03; P=0.019). No association of ICS5 with magnetic resonance imaging metrics of cerebral small vessel disease was observed. Circulating serum levels of inflammatory biomarkers centered on IL-18 are associated with an increased risk of stroke and cognitive impairment in the Framingham Offspring Cohort. Linking specific inflammatory pathways to cerebral small vessel disease may enhance individualized quantitative risk assessment for future stroke and vascular cognitive impairment.

The Brainbox-a tool to facilitate correlation of brain magnetic resonance imaging features to histopathology.

Magnetic resonance imaging (MRI) has limitations in identifying underlying tissue pathology, which is relevant for neurological diseases such as multiple sclerosis, stroke or brain tumours. However, there are no standardized methods for correlating MRI features with histopathology. Thus, here we aimed to develop and validate a tool that can facilitate the correlation of brain MRI features to corresponding histopathology. For this, we designed the Brainbox, a waterproof and MRI-compatible 3D printed container with an integrated 3D coordinate system. We used the Brainbox to acquire post-mortem ex vivo MRI of eight human brains, fresh and formalin-fixed, and correlated focal imaging features to histopathology using the built-in 3D coordinate system. With its built-in 3D coordinate system, the Brainbox allowed correlation of MRI features to corresponding tissue substrates. The Brainbox was used to correlate different MR image features of interest to the respective tissue substrate, including normal anatomical structures such as the hippocampus or perivascular spaces, as well as a lacunar stroke. Brain volume decreased upon fixation by 7% (P = 0.01). The Brainbox enabled degassing of specimens before scanning, reducing susceptibility artefacts and minimizing bulk motion during scanning. In conclusion, our proof-of-principle experiments demonstrate the usability of the Brainbox, which can contribute to improving the specificity of MRI and the standardization of the correlation between post-mortem ex vivo human brain MRI and histopathology. Brainboxes are available upon request from our institution.

Evolution of human brain left-right asymmetry - Old genes with new functions.

The human brain is generally anatomically symmetrical, boasting mirror-like brain regions in the left and right hemispheres. Despite this symmetry, fine-scale structural asymmetries are prevalent and are believed to be responsible for distinct functional divisions within the brain. Prior studies propose that these asymmetric structures are predominantly primate-specific or even unique to humans, suggesting that the genes contributing to the structural asymmetry of the human brain might have evolved recently. In our study, we identified approximately 1,500 traits associated with human brain asymmetry by collecting paired brain MRI features from the UK Biobank. Each trait is measured in a specific region of one hemisphere and mirrored in the corresponding region of the other hemisphere. Conducting genome-wide association studies on these traits, we identified over 1,000 quantitative trait loci (QTLs). Around these index single nucleotide polymorphisms (SNPs), we found approximately 200 genes that are enriched in brain-related Gene Ontology terms and are predominantly up-regulated in brain tissues. Interestingly, most of these genes are evolutionarily old, originating just prior to the emergence of Bilateria (bilaterally symmetrical animals) and Euteleostomi (bony vertebrates with a brain), at a significantly higher ratio than expected. Further analyses of these genes reveal a brain-specific up-regulation in humans relative to other mammalian species. This suggests that the structural asymmetry of the human brain has been shaped by evolutionarily ancient genes that have assumed new functions over time.

Clinical characteristics of autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy in children: A case series of 16 patients

PurposeTo investigate the clinical characteristics of autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy in children. MethodsWe reviewed the medical records of Children's Hospital of Chongqing Medical University from January 2020 to September 2021 and retrospectively analysed the clinical features, magnetic resonance imaging (MRI) findings, laboratory findings, treatment and outcome of children with autoimmune GFAP astrocytopathy. ResultsSixteen patients were included: 6 and 10 tested positive for GFAP-IgG in cerebrospinal fluid (CSF) and both CSF and serum, respectively. The median patient age was 115 months (range: 36–180 months), and 7 patients (43.8%) were male. All patients had the clinical syndrome of encephalitis/meningoencephalitis with or without myelitis: encephalitis (8), meningoencephalitis (3), encephalomyelitis (1) and meningoencephalomyelitis (4). The most common clinical symptoms were fever (11), altered consciousness (11), headache (10) and seizure (9). Four patients developed central respiratory failure for which mechanical ventilation was needed. All patients showed hyperintense T2-weighted lesions on brain MRI in the cerebral white matter (13), brainstem (11), basal ganglia (11), thalamus (9), and cerebellum (3). Nine patients (56%) had abnormal hyperintense lesions in the bilateral basal ganglia and thalamus. Six of 12 patients who underwent gadolinium-enhanced brain MRI showed abnormal enhancement images, and five of them showed linear perivascular radial enhancement. The modified Rankin scale (mRS) score decreased significantly in most patients after immunotherapy. Two patients with coexisting neural autoantibodies relapsed; however, 15 patients who were followed up successfully had favorable outcomes at the last follow-up. ConclusionChildren with autoimmune GFAP astrocytopathy usually have a clinical syndrome of encephalitis/meningoencephalitis with or without myelitis. Except for the linear perivascular radial gadolinium enhancement pattern, hyperintense lesions in the bilateral basal ganglia and thalamus might be another characteristic brain MRI finding of autoimmune GFAP astrocytopathy in children. Although a few patients with coexisting neural autoantibodies might relapse, children with autoimmune GFAP astrocytopathy usually have favorable outcomes after immunotherapy.

Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification

Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis.

A radiomics model developed from pre-metastatic brain magnetic resonance imaging to predict brain metastasis in non–small-cell lung cancer: Evidence to the seed-and-soil theory.

2030 Background: Steven Paget’s “seed and soil” theory suggests that both the primary tumor and the target host organ play important roles in successful metastasis development. In this proof-of-concept study, we proposed that inter-individual differences exist in the brain’s congeniality for developing brain metastasis (BM), and validated the theory via developing a non-invasive radiomics BM prediction model. Methods: 256 non-small cell lung cancer (NSCLC) patients with no BM at baseline brain magnetic resonance imaging (MRI) were selected, 128 patients developed BM within three years after diagnosis and 128 remained BM-free. For radiomics analysis, both the BM and non-BM groups were randomly distributed into training and testing datasets at an 70%:30% ratio. Baseline brain MRI (representing the soil) and chest computed tomography (CT, representing the seed) radiomics features were extracted to develop the BM prediction models. We first developed radiomics models using the training dataset and subsequently validated the models in the testing dataset. A radiomics BM score (RadBM score) was generated and BM-free survival were compared between RadBM score-high and RadBM score-low groups. Results: The radiomics model developed from baseline brain MRI features alone can predict BM development in NSCLC patients. Furthermore, a fusion model integrating brain MRI features with primary tumor CT features (seed-and-soil model) provided synergetic effect and was more efficient in predicting BM (areas under the receiver operating characteristic curve 0.84[95% confidence interval: 0.80–0.89] and 0.80 [95% confidence interval: 0.71–0.88] in the training and testing datasets, respectively). BM-free survival was significantly shorter in the RadBM score-high group versus the RadBM score-low group (Log-rank P &lt; 0.001). Cumulative BM rate at 3-year were 75.8% and 24.2% for RadBM score-high and RadBM score-low group, respectively. Conclusions: The results demonstrated that intrinsic features of a non-metastatic organ exert a significant impact on metastasis development, which is first-in-class in metastasis prediction studies and provided novel evidence for the "seed-and-soil" theory of tumor metastasis. Radiomics BM prediction model utilizing both pre-metastatic brain and primary tumor features might provide a useful tool for identifying NSCLC patients more prone to develop BM and providing individualized management for these patients.

Cerebral Malaria and Cytotoxic Lesions of the Corpus Callosum.

To the Editors: Malaria is a common cause of morbidity and mortality worldwide. According to the WHO, 229 million cases and 409,000 deaths were reported worldwide in 2020.1 Children account for a high burden of malaria-related mortality and are at a higher risk of developing cerebral malaria (CM).1 A 12-year-old, previously healthy female from Guinea-Bissau was admitted 1 day after arriving in Portugal with a 2-day history of headache, generalized myalgia, and high-grade fever. Physical examination was unremarkable, except for pallor, icteric sclerae, and nontender hepatomegaly (4 cm). The patient was hemodynamically stable (BP, 101/52 mmHg; HR, 111 bpm), but shortly after admission, vomiting, lethargy, and impaired consciousness were observed (Glasgow coma score 11). She presented with anemia (hemoglobin, 10.0 g/dL; normal reference [NR]: 12.0–16.0), thrombocytopenia (78 × 109/L; NR: 150–450) and significant elevation of inflammatory markers (C-reactive protein, 301.0 mg/L; NR, <5; and procalcitonin, >100 ng/mL; NR, <0.1). No electrolyte disturbances, hypoglycemia, or acidosis. Plasmodium falciparum blood smear was positive, and severe malaria was diagnosed due to high parasitemia (28.5%/erythrocytes–11.872.000 parasites/µL) and central nervous system (CNS) involvement other infections were excluded. The cerebrospinal fluid revealed pleocytosis (15 cells), generalized slowing on the electroencephalogram, and a lesion in the splenium of the corpus callosum with T2 hypersignal and restricted diffusion (Fig. 1), compatible with a cytotoxic lesion of the corpus callosum (CLOCCs) on brain magnetic resonance imaging (MRI). CM was diagnosed based on severely impaired consciousness, CLOCCs, and no other identifiable causes of coma.FIGURE 1.: MRI axial images T2 weighted (A), DWI (B) and ADC map (C): oval lesion in the splenium of the corpus callosum (arrow) with T2 hypersignal and diffusion restriction with DWI hypersignal and ADC map hyposignal, corresponding to cytotoxic edema.The patient was treated with parenteral quinine and clindamycin, due to the immediate unavailability of artesunate, plus ceftriaxone. She became afebrile with progressive improvement in neurological status after 48 h and complete parasite clearance on day 3. Artemether-lumefantrine was initiated on day 5, and the patient was discharged on day 8. CM is defined as a disturbance of consciousness in a patient with Plasmodium parasitemia and no other identifiable cause.2 Despite the reported low mortality rate of pediatric imported malaria in Europe (<1%), CM entails a risk of life-threatening acute CNS effects with diffuse encephalopathy and often rapidly progressive coma, as observed in our patient.3 Distinctive brain MRI features involving both white and gray matter structures have been reported in a large cohort of Malawian children with CM. Increased volume, T2 hyperintensity, and Diffusion-weighted imaging (DWI) abnormalities affecting the cortical area (61.7%), subcortical and periventricular white matter (71.7%), basal ganglia (84.2%) and the corpus callosum (49.2%) were the most frequent features.4 CLOCCS was a common MRI finding in CM, as suggested by DWI/Apparent diffusion coefficient mismatch with DWI increased signal and Apparent diffusion coefficient low signal,4 as observed in our patient (Fig. 1). The corpus callosum, mostly the splenium, is a region vulnerable to inflammatory cytokinopathy, driven by malaria, seizures or other causes.5 The higher density of cytokines and glutamate receptors in this area leads to high extracellular glutamate levels, inducing cytotoxic edema upon infection, which is frequently reversible.5 CM in children is the most severe complication of P. falciparum infection and needs a high suspicion index in nonendemic countries. Altered MRI and CLOCCs, although unspecific, are associated with CM.

Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust.

Brain cancers caused by malignant brain tumors are one of the most fatal cancer types with a low survival rate mostly due to the difficulties in early detection. Medical professionals therefore use various invasive and non-invasive methods for detecting and treating brain tumors at the earlier stages thus enabling early treatment. The main non-invasive methods for brain tumor diagnosis and assessment are brain imaging like computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. In this paper, the focus is on detection and segmentation of brain tumors from 2D and 3D brain MRIs. For this purpose, a complete automated system with a web application user interface is described which detects and segments brain tumors with more than 90% accuracy and Dice scores. The user can upload brain MRIs or can access brain images from hospital databases to check presence or absence of brain tumor, to check the existence of brain tumor from brain MRI features and to extract the tumor region precisely from the brain MRI using deep neural networks like CNN, U-Net and U-Net++. The web application also provides an option for entering feedbacks on the results of the detection and segmentation to allow healthcare professionals to add more precise information on the results that can be used to train the model for better future predictions and segmentations.

Ischemic Heart Disease and Vascular Risk Factors Are Associated With Accelerated Brain Aging

BackgroundIschemic heart disease (IHD) has been linked with poor brain outcomes. The brain magnetic resonance imaging–derived difference between predicted brain age and actual chronological age (brain-age delta in years, positive for accelerated brain aging) may serve as an effective means of communicating brain health to patients to promote healthier lifestyles. ObjectivesThe authors investigated the impact of prevalent IHD on brain aging, potential underlying mechanisms, and its relationship with dementia risk, vascular risk factors, cardiovascular structure, and function. MethodsBrain age was estimated in subjects with prevalent IHD (n = 1,341) using a Bayesian ridge regression model with 25 structural (volumetric) brain magnetic resonance imaging features and built using UK Biobank participants with no prevalent IHD (n = 35,237). ResultsPrevalent IHD was linked to significantly accelerated brain aging (P < 0.001) that was not fully mediated by microvascular injury. Brain aging (positive brain-age delta) was associated with increased risk of dementia (OR: 1.13 [95% CI: 1.04-1.22]; P = 0.002), vascular risk factors (such as diabetes), and high adiposity. In the absence of IHD, brain aging was also associated with cardiovascular structural and functional changes typically observed in aging hearts. However, such alterations were not linked with risk of dementia. ConclusionsPrevalent IHD and coexisting vascular risk factors are associated with accelerated brain aging and risk of dementia. Positive brain-age delta representing accelerated brain aging may serve as an effective communication tool to show the impact of modifiable risk factors and disease supporting preventative strategies.

Magnetic resonance imaging of human variegated squirrel bornavirus 1 (VSBV-1) encephalitis reveals diagnostic pattern indistinguishable from Borna disease virus 1 (BoDV-1) encephalitis but typical for bornaviruses

ABSTRACT Human bornavirus encephalitis is an emerging disease caused by the variegated squirrel bornavirus 1 (VSBV-1) and the Borna disease virus 1 (BoDV-1). While characteristic brain magnetic resonance imaging (MRI) changes have been described for BoDV-1 encephalitis, only scarce diagnostic data in VSBV-1 encephalitis exist. We systematically analysed brain MRI scans from all known VSBV-1 encephalitis patients. Initial and follow-up scans demonstrated characteristic T2 hyperintense lesions in the limbic system and the basal ganglia, followed by the brainstem. No involvement of the cerebellar cortex was seen. Deep white matter affection occurred in a later stage of the disease. Strict symmetry of pathologic changes was seen in 62%. T2 hyperintense areas were often associated with low T1 signal intensity and with mass effect. Sinusitis in three patients on the first MRI and an early involvement of the limbic system suggest an olfactory route of VSBV-1 entry. The viral spread could occur per continuitatem to adjacent anatomical brain regions or along specific neural tracts to more distant brain regions. The number and extent of lesions did not correlate with the length of patients’ survivals. The overall pattern closely resembles that described for BoDV-1 encephalitis. The exact bornavirus species can thus not be deduced from imaging results alone, and molecular testing and serology should be performed to confirm the causative bornavirus. As VSBV-1 is likely of tropical origin, and MRI investigations are increasingly available globally, imaging techniques might be helpful to facilitate an early presumptive diagnosis of VSBV-1 encephalitis when molecular and/or serological testing is not available.

A "Seed-and-Soil" Radiomics Model Predicts Brain Metastasis Development in Lung Cancer: Implications for Risk-Stratified Prophylactic Cranial Irradiation.

Introduction: Brain is a major site of metastasis for lung cancer, and effective therapy for developed brain metastasis (BM) is limited. Prophylactic cranial irradiation (PCI) has been shown to reduce BM rate and improve survival in small cell lung cancer, but this result was not replicated in unselected non-small cell lung cancer (NSCLC) and had the risk of inducing neurocognitive dysfunctions. We aimed to develop a radiomics BM prediction model for BM risk stratification in NSCLC patients. Methods: 256 NSCLC patients with no BM at baseline brain magnetic resonance imaging (MRI) were selected; 128 patients developed BM within three years after diagnosis and 128 remained BM-free. For radiomics analysis, both the BM and non-BM groups were randomly distributed into training and testing datasets at an 70%:30% ratio. Both brain MRI (representing the soil) and chest computed tomography (CT, representing the seed) radiomic features were extracted to develop the BM prediction models. We first developed the radiomic models using the training dataset (89 non-BM and 90 BM cases) and subsequently validated the models in the testing dataset (39 non-BM and 38 BM cases). A radiomics BM score (RadBM score) was generated, and BM-free survival were compared between RadBM score-high and RadBM score-low groups. Results: The radiomics model developed from baseline brain MRI features alone can predict BM development in NSCLC patients. A fusion model integrating brain MRI features with primary tumor CT features (seed-and-soil model) provided synergetic effect and was more efficient in predicting BM (areas under the receiver operating characteristic curve 0.84 (95% confidence interval: 0.80−0.89) and 0.80 (95% confidence interval: 0.71−0.88) in the training and testing datasets, respectively). BM-free survival was significantly shorter in the RadBM score-high group versus the RadBM score-low group (Log-rank, p < 0.001). Hazard ratios for BM were 1.056 (95% confidence interval: 1.044−1.068) per 0.01 increment in RadBM score. Cumulative BM rates at three years were 75.8% and 24.2% for the RadBM score-high and RadBM score-low groups, respectively. Only 1.2% (7/565) of the BM lesions were located within the hippocampal avoidance region. Conclusion: The results demonstrated that intrinsic features of a non-metastatic brain exert a significant impact on BM development, which is first-in-class in metastasis prediction studies. A radiomics BM prediction model utilizing both primary tumor and pre-metastatic brain features might provide a useful tool for individualized PCI administration in NSCLC patients more prone to develop BM.

Brain magnetic resonance imaging predictors in anti-N-methyl-D-aspartate receptor encephalitis.

Brain magnetic resonance imaging (MRI) findings in anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis are nonspecific and rarely have obvious associations with clinical characteristics and outcomes. This study aimed to comprehensively describe the MRI features of patients with NMDAR encephalitis, examine their associations with clinical characteristics, and evaluate their predictive power for disease recurrence and prognosis. We retrospectively extracted the clinical data and brain MRI findings of 144 patients with NMDAR encephalitis. Patients underwent a 2-year follow-up to assess disease outcomes. We evaluated the associations of brain MRI findings at the onset with clinical characteristics, recurrence, and prognosis. Initial MRI showed typical abnormalities in 65 patients (45.1%); of these, 34 (29.3%) developed recurrence and 10 (9.4%) had poor prognosis (mRS ≥3). Binary logistic regression analyses revealed that insula abnormalities were associated with acute seizure (odds ratio [OR]=3.048, 95% confidence interval [CI]: 1.026-9.060) and white matter lesions were associated with cognitive impairment (OR=2.730, 95% CI: 1.096-6.799). Risk factors for a poor 2-year prognosis included a higher number of brain MRI abnormalities (OR=1.573, 95% CI: 1.129-2.192) and intensive care unit (ICU) admissions (OR=15.312, 95% CI: 1.684-139.198). The risk factors for 2-year recurrence included abnormalities of the thalamus (HR=3.780, 95% CI: 1.642-8.699). Brain MRI features of patients with NMDAR encephalitis were associated with clinical manifestations, prognosis, and recurrence. Higher numbers of MRI abnormalities and ICU admissions were predictive of poor prognosis. Abnormalities of the thalamus constituted a recurrence-related risk factor.

Brain Magnetic Resonance Imaging Features of Nicotine-Dependent Individuals and Its Correlation with Polymorphisms of Dopamine D Receptor Gene.

The aim of this research was developed to provide a scientific basis for individualized prevention, clinical diagnosis, and corrective treatment of nicotine addiction. The objects were 214 cases in the smoke group and 43 cases in the control group. According to the Fagerstrom Nicotine Dependence Test (FTND), the smokers were divided into mild nicotine dependence group (FTND < 6 points, 138 cases) and nicotine severe dependence group (≥6 points, 76 cases). The brain structure in long-term smokers was evaluated by using magnetic resonance imaging (MRI). The nicotine dependence was further analyzed by grouping the included individuals, and some candidate genes related to nicotine addiction were screened by combining with bioinformatics analysis. The family research strategy was adopted to detect nicotine addiction susceptibility genes and their polymorphisms. The MRI imaging results showed that the bilateral thalamus, right parietal, and left lens gram-molecule volume (GMV) were negatively correlated with smoking index and smoking years in the smoking group. The GMV of the posterior cingulate cortex in the severe nicotine dependence group was lower than that of the control group, and the GMVs of bilateral thalamus and bilateral superior limbic gyrus in the mild nicotine dependence group were lower than those of the control group. The gene polymorphism detection showed that rs6275 was highly polymorphic in the target population and the frequency of rs6275-C allele was 53.26%. Therefore, the MRI imaging characteristics suggested that the affected brain regions of smokers and people with varying degrees of nicotine dependence were mainly concentrated in response-related pathways and the limbic system and had cumulative effects on the central nervous system. In addition, the M6275 polymorphism of DRD2 gene was associated with susceptibility to nicotine addiction in Chinese population, and the M6275-C allele had a protective effect on susceptibility to nicotine addiction and smoking initiation.

A Case of Rheumatoid Meningitis

Rheumatoid meningitis, a very rare complication, is not well-recognised, and there are few reports describing its treatment. We report the case of a 74-year-old Japanese woman who was diagnosed with rheumatoid meningitis by characteristic brain magnetic resonance imaging (MRI) and was successfully treated with glucocorticoids. We observed fluid-attenuated inversion recovery and diffusion-weighted imaging hyperintensity, which had a meningeal gadolinium-enhancing characteristic of rheumatoid meningitis. We suggest that it is possible to diagnose this disease based on characteristic MRI findings and treat patients early using glucocorticoids.

Brain MRI features of anti-N-methyl-D-aspartate (anti-NMDA) receptor encephalitis secondary to central nervous system infection in adult patients.

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis secondary to central nervous system (CNS) infection is a unique subtype of the autoimmune-mediated disease, of which the imaging features are unclear. To compare the brain magnetic resonance imaging (MRI) features between the anti-NMDAR encephalitis secondary to CNS infection and that without initial infection. A total of 70 adult patients with anti-NMDAR encephalitis were retrospectively enrolled (24 in the post-infection group, 46 in the non-infection-related group). Their clinical and imaging features (lesion distribution, lesion shape, enhancement pattern, brain atrophy) were reviewed and summarized. Lesion distributions were compared between the two groups on lesion probability maps. The patients with normal brain MRI scans in the post-infection group were less than those in the non-infection related group (29% vs. 63%; P = 0.0113). Among the 24 patients in the post-infection group, visible lesions were shown at the anti-NMDAR encephalitis onset in 17 patients; lesion distribution was more diffuse than the non-infection-related group, showing higher lesion peak probabilities in the bilateral hippocampus, frontal lobe, temporal lobe, insula, and cingulate. The lesions with contrast enhancement were also more common in the post-infection group than the non-infection-related group (7/13 vs. 2/10). Brain atrophy was observed in eight patients in the post-infection group and three in the non-infection-related group. Anti-NMDAR encephalitis secondary to CNS infection has its imaging features-extensive lesion distribution, leptomeningeal enhancement, early atrophy, and necrosis-that could deepen the understanding of the pathophysiology and manifestation of the autoimmune encephalitis besides the classic type.

Neuroradiological Mimics of Periventricular Leukomalacia

Periventricular leukomalacia (PVL) is a term reserved to describe white matter injury in the premature brain. In this review article, the authors highlight the common and rare pathologies mimicking the chronic stage of PVL and propose practical clinico-radiological criteria that would aid in diagnosis and management. The authors first describe the typical brain MRI (magnetic resonance imaging) features of PVL. Based on their clinical presentation, pathologic entities and their neuroimaging findings were clustered into distinct categories. Three clinical subgroups were identified: healthy children, children with stable/nonprogressive neurological disorder, and those with progressive neurological disorder. The neuroradiological discriminators are described in each subgroup with relevant differential diagnoses. The mimics were broadly classified into normal variants, acquired, and inherited disorders. The term "PVL" should be used appropriately as it reflects its pathomechanism. The phrase "white matter injury of prematurity" or "brain injury of prematurity" is more specific. Discrepancies in imaging and clinical presentation must be tread with caution and warrant further investigations to exclude other possibilities.

Improved Alzheimer's Disease Detection by MRI Using Multimodal Machine Learning Algorithms.

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.

Diagnostic analysis of baseline brain MRI features in infants with congenital cytomegalovirus infection: a simplified scoring system

To characterise the magnetic resonance imaging (MRI) features of infants with congenital cytomegalovirus (CMV) and categorise those into a simplified MRI scoring system. Three neuroradiologists reviewed the examinations of 71 infants retrospectively and scored for the presence of a white matter signal abnormality and structural lesion and each MRI was given a score of 0, 1, 2, or 3 for normal, structural abnormality alone, white matter abnormality alone, white matter abnormality plus structural lesion, respectively. Imaging features were outlines according to symptomatology. Chi-square and Spearman's rho were used to test relationships between MRI features and viral loads and MRI score/symptomatic disease respectively. Cohen's Kappa coefficient was used to assess interobserver agreement. Of the 49 abnormal studies, 40% (n=20) were seen in asymptomatic infants. The commonest finding was white matter signal abnormality, followed by cyst formation and polymicrogyria (86%, n=42; 71%, n=35; and 33%, n=16, respectively). Cysts were significantly positively correlated with white matter abnormalities and polymicrogyria. On the MRI score, 31%, 10%, 15%, and 44% obtained a score of 0, 1, 2, and 3, respectively; the MRI score was positively correlated with log-transformed viral loads. Interobserver agreement for the presence of white matter signal abnormality, cyst formation, malformations of cortical development (MCD), and global MRI score was excellent (k=0.82, 0.94, 0.96, and 0.86, respectively). Baseline MRI provides information valuable for treatment decisions, especially in "asymptomatic" infants. The simplified scoring system is easier to use, incorporating solely the imaging findings that are anticipated to have an effect on clinical outcome.

Clinical, laboratory and brain Magnetic Resonance Imaging (MRI) characteristics of asymptomatic and symptomatic HIV-negative neurosyphilis patients

IntroductionThere are few studies concerning the differences between asymptomatic neurosyphilis (ANS) and symptomatic neurosyphilis (SNS). This study aimed to summarize clinical, laboratory and brain Magnetic Resonance Imaging (MRI) characteristics of HIV-negative patients with ANS and SNS. MethodsData from 43 HIV-negative patients with ANS and 59 HIV-negative patients with SNS were retrospectively collected from our hospital between December 2012 and December 2018. ResultsCompared with the ANS group, SNS group had more patients that were male, age≥45 years, had brain MRI abnormalities, and exhibited higher serum/cerebrospinal fluid (CSF) TRUST titer, CSF WBC count, CSF protein concentration (P < 0.05). Multivariate regression analysis revealed that male sex, age ≥45 years and CSF TRUST titer were risk factors for SNS [odds ratio (OR) = 7.946,P = 0.001;OR = 3.757, P = 0.041; OR = 2.713, P = 0.002; respectively]. The brain MRI findings of 78 patients without comorbidities showed that ischemic infarct lesions presented in 17/37 (45.95%) of patients with ANS; infarct ischemic stroke (73.17%) especially multiple cerebral infractions (46.34%), cerebral atrophy (48.78%) were also common presentations in the SNS group. ConclusionsPatients with HIV-negative ANS and SNS presented different clinical, laboratory and brain MRI features. Male sex, age ≥45 years and elevated CSF TRUST titer may have an increased risk of developing neurological symptoms. Brain MRI abnormalities may present prior to clinical symptoms. Multiple cerebral infarctions without explained reasons or cerebral atrophy should alert clinicians the possibility of SNS.