OBJECTIVE: To explore the genotype-phenotype correlation in a Chinese family with structural brain abnormalities due to variant of the TUBB gene. METHODS: A family undergoing prenatal diagnosis at Beijing Obstetrics and Gynecology Hospital in October 2024 was selected as the study subject. Clinical data were collected. Amniotic fluid sample was subjected to chromosomal copy number variation sequencing (CNV-seq). Trio whole-exome sequencing (Trio-WES) was carried out on the amniotic fluid and parental blood samples, and candidate variant was verified by Sanger sequencing. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: 2023-KY-076-01). RESULTS: Both prenatal ultrasound and fetal MRI showed deviation of brain midline, unilateral lateral ventriculomegaly, and bilateral gyral asymmetry. Trio-WES revealed that the fetus has harbored a maternally derived heterozygous missense variant of the TUBB gene [NM_178014.4: c.155A>G (p.N52S)]. Sanger sequencing confirmed that the woman and a previously terminated fetus both harbored the same variant. Both the proband and two fetuses exhibited similar neuroimaging abnormalities including midline deviation and asymmetrical gyri. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was classified as likely pathogenic (PM2_Supporting+PS2_Moderate+PS3). CONCLUSION The heterozygous c.155A>G (p.N52S) variant was the TUBB gene probably underlay the pathogenesis of the structural brain abnormalities in this family. Above findings have expanded the phenotypic spectrum associated with the variant and facilitated the prenatal diagnosis for this family.
Humans ; Female ; Pregnancy ; Prenatal Diagnosis ; Tubulin/genetics* ; Adult ; Brain/diagnostic imaging* ; Male ; Pedigree ; DNA Copy Number Variations/genetics* ; Exome Sequencing ; Genetic Association Studies ; Magnetic Resonance Imaging

Valacyclovir-associated neurotoxicity (VAN) is a recognized adverse effect in elderly patients with renal impairment but remains underdiagnosed due to its nonspecific presentation and overlap with acute neurologic emergencies. We report a 78-year-old Filipino female with end-stage renal disease on maintenance hemodialysis who developed acute disorientation, agitation, vivid visual hallucinations and generalized weakness shortly after initiation of valacyclovir for herpes zoster. Given the abrupt onset of neuropsychiatric symptoms, viral encephalitis was initially considered. Magnetic resonance imaging of the brain showed no evidence of acute infarction or encephalitis, while electroencephalography demonstrated diffuse generalized slowing consistent with an encephalopathic process. Review of the medication history revealed valacyclovir dosing that exceeded recommendations for patients with end-stage renal disease. Valacyclovir was discontinued and emergent hemodialysis was initiated resulting in marked improvement in sensorium after the second session and complete resolution of symptoms after the third. This case shows VAN as an important diagnostic mimic of acute encephalopathy in elderly patients with renal failure and emphasizes the critical role of early medication review in preventing unnecessary investigations and enabling prompt, reversible management.

BACKGROUND

Magnetic resonance imaging (MRI) has gained increased diagnostic utility for patients with transient global amnesia (TGA), particularly for unwitnessed events or those with diagnostic uncertainty based on clinical grounds.

The objectives are first, to determine the demographic and comorbid conditions of TGA patients; second, to determine the percentage of MRI diffusion weighted imaging (MRI DWI) hippocampal lesions, their time relationship from symptom onset, and their morphological characteristics; and lastly, to determine the dementia visual rating scale scores on neuroimaging for these patients.

A total of 20 TGA patients in a tertiary hospital from 2018 to 2022 were included in this retrospective study, and their medical records and neuroimaging were reviewed.

TGA patients had a mean age of 61.4 years and a female predominance. Prevalent comorbid conditions include hypertension, dyslipidemia, and diabetes, and the majority were discharged with antithrombotic medications. An emotionally triggering event was identified in 15% (n = 3). Mean symptom onset-to-scan time was 8.33 h, and one patient (detection rate of 5%) who underwent neuroimaging after 21.7 h demonstrated typical punctate hippocampal DWI hyperintensity. None exhibited significant cortical atrophy.

TGA patients showed female predominance, occurring mostly within the 5th–6th decade, with a moderate prevalence of vascular risk factors and absence of significant cerebral atrophy based on the Dementia Visual Rating Scales. A conventional MRI protocol yielded a 5% detection rate with a delay of 21 h from symptom onset. Hence, in a resource-limited setting such as the Philippines, it may be suggested, with limited evidence, that performing the procedure in TGA patients when the event is unwitnessed or uncertain could be reasonable, as correctly diagnosing TGA has therapeutic implications. Further studies may investigate prospectively the diagnostic utility of MRI, neuropsychological profile, and estimate cardiovascular and cognitive deterioration risk.

OBJECTIVE: To screen the population for acupuncture treatment of neck pain, using functional magnetic resonance imaging (fMRI) technology and based on machine learning algorithms. METHODS: Eighty patients with neck pain were recruited. Using FPX25 handheld pressure algometer, the tender points were detected in the areas with high-frequent onset of neck pain and high degree of acupoint sensitization. Acupuncture was delivered at 4 tender points with the lowest pain threshold, once every two days; and the treatment was given 3 times a week and for 2 consecutive weeks. The amplitude of low-frequency fluctuation (ALFF) of the brain before treatment was taken as a predictive feature to construct support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN) models to predict the responses of neck pain patients to acupuncture treatment. A longitudinal analysis of the ALFF features was performed before and after treatment to reveal the potential biological markers of the reactivity to the acupuncture therapy. RESULTS: The SVM model could successfully distinguish high responders (48 cases) and low responders (32 cases) to acupuncture treatment, and its accuracy rate reached 82.5%. Based on the SVM model, the ALFF values of 4 brain regions were identified as the consistent predictive features, including the right middle temporal gyrus, the right superior occipital gyrus, and the bilateral posterior cingulate gyrus. In the patients with high acupuncture response, the ALFF value in the left posterior cingulate gyrus decreased after treatment (P<0.05), whereas in the patients with low acupuncture response, the ALFF value in the right superior occipital gyrus increased after treatment (P<0.01). The longitudinal functional connectivity (FC) analysis found that compared with those before treatment, the high responders showed the enhanced FC after treatment between the left posterior cingulate gyrus and various regions, including the bilateral Crus1 of the cerebellum, the right insula, the bilateral angular gyrus, the left medial superior frontal gyrus, and the left middle cingulate gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05). In contrast, the low responders exhibited the enhanced FC between the left posterior cingulate gyrus and the left Crus2 of the cerebellum, the left middle temporal gyrus, the right posterior cingulate gyrus, and the left angular gyrus; besides, FC was reduced in low responders between the left posterior cingulate gyrus and the right supramarginal gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05). CONCLUSION This study validates the practicality of pre-treatment ALFF feature prediction for acupuncture efficacy on neck pain. The therapeutic effect of acupuncture on neck pain is potentially associated with its impact on the default mode network, and then, alter the pain perception and emotional regulation.
Humans ; Neck Pain/physiopathology* ; Acupuncture Therapy ; Female ; Male ; Adult ; Middle Aged ; Machine Learning ; Magnetic Resonance Imaging ; Young Adult ; Brain/physiopathology* ; Acupuncture Points ; Aged

Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain structure, function, metabolism, and hemodynamics. The advancement of artificial intelligence (AI) algorithms can compensate for issues such as the large and scattered nature of neuroimaging data, inconsistent quality, and high heterogeneity of image information. The integration of AI with neuroimaging can facilitate individualized, intelligent, and precise prediction of acupuncture and moxibustion effects, enable intelligent classification of differential acupuncture responses, and identify brain activation patterns. This paper focuses on EEG and MRI, analyzing how machine learning and deep learning optimize multimodal neuroimaging data and their applications in the study of acupuncture and moxibustion brain effects mechanisms. Furthermore, it highlights current research gaps and limitations to provide insights for future studies on acupuncture brain effects mechanisms.
Humans ; Acupuncture Therapy ; Brain/physiology* ; Moxibustion ; Neuroimaging/methods* ; Artificial Intelligence ; Magnetic Resonance Imaging ; Electroencephalography

INTRODUCTION: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc., Seoul, Republic of Korea) segmentation software in a Southeast Asian community-based cohort with normal cognition, mild cognitive impairment (MCI) and dementia. METHOD: Study participants belonged to the community-based Biomarker and Cognition Study in Singapore. Participants aged between 30 and 95 years, having cognitive concerns, with no diagnosis of major psychiatric, neurological or systemic disorders who were recruited consecutively between April 2022 and July 2023 were included. Participants underwent neuropsychological assessments and structural MRI, and were classified as cognitively normal, with MCI or with dementia. MRI pre-processing using automated pipelines, along with human-based visual ratings, were compared against AI-based automated AQUA output. Default mode network grey matter (GM) volumes were compared between cognitively normal, MCI and dementia groups. RESULTS: A total of 90 participants (mean age at visit was 63.32±10.96 years) were included in the study (30 cognitively normal, 40 MCI and 20 dementia). Non-parametric Spearman correlation analysis indicated that AQUA-based and human-based visual ratings were correlated with total (ρ=0.66; P<0.0001), periventricular (ρ=0.50; P<0.0001) and deep (ρ=0.57; P<0.0001) white matter hyperintensities (WMH). Additionally, volumetric WMH obtained from AQUA and automated pipelines was also strongly correlated (ρ=0.84; P<0.0001) and these correlations remained after controlling for age at visit, sex and diagnosis. Linear regression analyses illustrated significantly different AQUA-derived default mode network GM volumes between cognitively normal, MCI and dementia groups. Dementia participants had significant atrophy in the posterior cingulate cortex compared to cognitively normal participants (P=0.021; 95% confidence interval [CI] -1.25 to -0.08) and in the hippocampus compared to cognitively normal (P=0.0049; 95% CI -1.05 to -0.16) and MCI participants (P=0.0036; 95% CI -1.02 to -0.17). CONCLUSION Our findings demonstrate high concordance between human-based visual ratings and AQUA-based ratings of WMH. Additionally, the AQUA GM segmentation pipeline showed good differentiation in key regions between cognitively normal, MCI and dementia participants. Based on these findings, the automated AQUA software could aid clinicians in examining MRI scans of patients with cognitive impairment.
Humans ; Cognitive Dysfunction/pathology* ; Magnetic Resonance Imaging/methods* ; Male ; Middle Aged ; Female ; Aged ; Artificial Intelligence ; Software ; Dementia/diagnostic imaging* ; Aged, 80 and over ; Adult ; Singapore ; Neuropsychological Tests ; Brain/pathology* ; Cohort Studies ; Gray Matter/pathology* ; Southeast Asian People

BACKGROUND: Iron deposition plays a crucial role in the pathophysiology of Parkinson's disease (PD), yet the distribution pattern of iron deposition in the subcortical nuclei has been inconsistent across previous studies. We aimed to assess the difference patterns of iron deposition detected by quantitative iron-sensitive magnetic resonance imaging (MRI) between patients with PD and patients with atypical parkinsonian syndromes (APSs), and between patients with PD and healthy controls (HCs). METHODS: A systematic literature search was conducted on PubMed, Embase, and Web of Science databases to identify studies investigating the iron content in PD patients using the iron-sensitive MRI techniques (R2 * and quantitative susceptibility mapping [QSM]), up until May 1, 2023. The quality assessment of case-control and cohort studies was performed using the Newcastle-Ottawa Scale, whereas diagnostic studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Standardized mean differences and summary estimates of sensitivity, specificity, and area under the curve (AUC) were calculated for iron content, using a random effects model. We also conducted the subgroup-analysis based on the MRI sequence and meta-regression. RESULTS: Seventy-seven studies with 3192 PD, 209 multiple system atrophy (MSA), 174 progressive supranuclear palsy (PSP), and 2447 HCs were included. Elevated iron content in substantia nigra (SN) pars reticulata ( P <0.001) and compacta ( P <0.001), SN ( P <0.001), red nucleus (RN, P <0.001), globus pallidus ( P <0.001), putamen (PUT, P = 0.021), and thalamus ( P = 0.029) were found in PD patients compared with HCs. PD patients showed lower iron content in PUT ( P <0.001), RN ( P = 0.003), SN ( P = 0.017), and caudate nucleus ( P = 0.017) than MSA patients, and lower iron content in RN ( P = 0.001), PUT ( P <0.001), globus pallidus ( P = 0.004), SN ( P = 0.015), and caudate nucleus ( P = 0.001) than PSP patients. The highest diagnostic accuracy distinguishing PD from HCs was observed in SN (AUC: 0.85), and that distinguishing PD from MSA was found in PUT (AUC: 0.90). In addition, the best diagnostic performance was achieved in the RN for distinguishing PD from PSP (AUC: 0.86). CONCLUSIONS: Quantitative iron-sensitive MRI could quantitatively detect the iron content of subcortical nuclei in PD and APSs, while it may be insufficient to accurately diagnose PD. Future studies are needed to explore the role of multimodal MRI in the diagnosis of PD. REGISTRISION PROSPERO (CRD42022344413).
Humans ; Parkinson Disease/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Iron/metabolism*

BACKGROUND: At present, biopsy is essential for the diagnosis of prostate cancer (PCa) before radical prostatectomy (RP). However, with the development of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) and multiparametric magnetic resonance imaging (mpMRI), it might be feasible to avoid biopsy before RP. Herein, we aimed to explore the feasibility of avoiding biopsy before RP in patients highly suspected of having PCa after assessment of PSMA PET/CT and mpMRI. METHODS: Between December 2017 and April 2022, 56 patients with maximum standardized uptake value (SUVmax) of ≥4 and Prostate Imaging Reporting and Data System (PI-RADS) ≥4 lesions who received RP without preoperative biopsy were enrolled from two tertiary hospitals. The consistency between clinical and pathological diagnoses was evaluated. Preoperative characteristics were compared among patients with different pathological types, T stages, International Society of Urological Pathology (ISUP) grades, and European Association of Urology (EAU) risk groups. RESULTS: Fifty-five (98%) patients were confirmed with PCa by pathology, including 49 (89%) with clinically significant prostate cancer (csPCa, defined as ISUP grade ≥2 malignancy). One patient was diagnosed with high-grade prostatic intraepithelial neoplasia (HGPIN). CsPCa patients, compared with clinically insignificant prostate cancer (cisPCa) and HGPIN patients, were associated with a higher level of prostate-specific antigen (22.9 ng/mL vs . 10.0 ng/mL, P = 0.032), a lower median prostate volume (32.2 mL vs . 65.0 mL, P = 0.001), and a higher median SUVmax (13.3 vs . 5.6, P <0.001). CONCLUSIONS It might be feasible to avoid biopsy before RP for patients with a high probability of PCa based on PSMA PET/CT and mpMRI. However, the diagnostic efficacy of csPCa with PI-RADS ≥4 and SUVmax of ≥4 is inadequate for performing a procedure such as RP. Further prospective multicenter studies with larger sample sizes are necessary to confirm our perspectives and establish predictive models with PSMA PET/CT and mpMRI.
Humans ; Male ; Prostatectomy/methods* ; Prostatic Neoplasms/diagnosis* ; Middle Aged ; Aged ; Positron Emission Tomography Computed Tomography/methods* ; Biopsy ; Multiparametric Magnetic Resonance Imaging ; Prostate-Specific Antigen/metabolism*

BACKGROUND: The correlation between the morphological structure of the patellofemoral joint (PFJ) and the severity of knee joint osteoarthritis (KOA) remains uncertain. This study aims to investigate the correlation between the severity of knee joint osteoarthritis and the alignment of patellofemoral and patellar height on radiographs. METHODS: This multi-center, retrospective study analyzed the magnetic resonance imaging (MRI) scans and anteroposterior radiographs of 534 adult outpatients with KOA. To evaluate the radiographic severity of KOA, anteroposterior radiographs of the knee and the Kellgren-Lawrence (K-L) grade were used. Knee MRI scans were used to measure the patellar length ratio (PLR), sulcus angle (SA), lateral patellar tilt angle (LPTA), and the distance between tibial tuberosity and trochlear groove (TT-TG). We examined the association between the configuration of the PFJ, arrangement, and harshness of the KOA. Information on participants' demographics, such as age, sex, side, height, and weight, was collected. A chi-squared test was used for the correlation of radiographic severity of KOA with sex and the affected side. Spearman correlation was used for patellofemoral alignment or morphology and the radiographic severity of lateral KOA. Multiple linear regression models were used for the association between LPTA, SA, TT-TG, and severity of KOA after accounting for demographic variables. RESULTS: The study comprised of 534 patients; of these, 339 (63%) were female. A total of 586 knees were evaluated in this study. Age showed a strong positive correlation with KOA severity ( r  = 0.516, P <0.01), whereas LPTA showed a strong negative correlation ( r  = -0.662, P <0.01). Additionally, SA ( r  = 0.616, P <0.05), and TT-TG showed a strong positive correlation ( r  = 0.770, P <0.01) with tibiofemoral osteoarthritis (TFOA) severity. Multiple linear regression analysis indicated that knee osteoarthritis severity (β = -2.946, P <0.001) and side (β = -0.839, P  = 0.001) was associated with LPTA; knee osteoarthritis severity (β = 5.032, P <0.001) and age (β = -0.095, P <0.001) was associated with SA; knee osteoarthritis severity (β = 2.445, P <0.001), sex (β = -0.326, P  = 0.041), body mass index (β = -0.061, P  = 0.017) and age (β = -0.025, P <0.001) was associated with TT-TG. CONCLUSION Radiographic severity of KOA was positively associated with age, SA, and TT-TG but negatively associated with LPTA.
Humans ; Female ; Male ; Osteoarthritis, Knee/pathology* ; Middle Aged ; Retrospective Studies ; Aged ; Patellofemoral Joint/pathology* ; Magnetic Resonance Imaging ; Adult ; Patella/pathology* ; Radiography

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome