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Results of the impact of reading books and viewing television on neurodevelopment have been mixed, without definitive evaluation to date. Using data from 11,875 US adolescents in the Adolescent Brain and Cognitive Development (ABCD) study, we investigated the associations between reading and television viewing on brain morphology and neurocognitive performance. After quality control, 8,125 participants' MRI scans and cognitive tests were analyzed in relation to their reading and TV habits. Greater reading time was associated with higher cognitive performance and regionally-selective increases in cortical area, while greater TV viewing had a much smaller association with lower cognitive performance and decreased cortical area. Regionally, areas of spatial overlap in associations included the lateral temporal, inferior parietal, and inferior frontal lobes, while significant associations in the ventral and inferior temporal cortex and cingulate cortex were unique to reading habits. These relationships persisted after adjusting for demographics, socioeconomic factors, genetic ancestry, and imaging factors. The magnitude of reading associations exceeded those of TV viewing and was similar to established contributions of parental income and education on neurodevelopment. This study provides a comprehensive evaluation of how these behaviors correlate with early adolescent brain development across a large diverse population.

Background: Diagnostically adequate contrast and spatial resolution in brain MRI require prolonged scan times, leading to motion artifacts and image degradation in awake children. Rapid multi-parametric techniques can produce diagnostic images in awake children, which could help to avoid the need for sedation. Objective: To evaluate the utility of a rapid echo-planar imaging (EPI)–based multi-inversion spin and gradient echo (MI-SAGE) technique for generating multi-parametric quantitative brain maps and synthetic contrast images in awake pediatric participants. Materials and methods: In this prospective IRB-approved study, awake research participants 3–10 years old were scanned using MI-SAGE, MOLLI, GRASE, mGRE, and T1-, T2-, T2*-, and FLAIR-weighted sequences. The MI-SAGE T1, T2, and T2* maps and synthetic images were estimated offline. The MI-SAGE parametric values were compared to those from conventional mapping sequences including MOLLI, GRASE, and mGRE, with assessments of repeatability and reproducibility. Synthetic MI-SAGE images and conventional weighted images were reviewed by a neuroradiologist and scored using a 5-point Likert scale. Gray-to-white matter contrast ratios (GWRs) were compared between MI-SAGE synthetic and conventional weighted images. The results were analyzed using the Bland–Altman analysis and intra-class correlation coefficient (ICC). Results: A total of 24 healthy participants aged 3 years to 10 years (mean ± SD, 6.5 ± 1.9; 12 males) completed full imaging exams including the 54-s MI-SAGE acquisition and were included in the analysis. The MI-SAGE T1, T2, and T2* had biases of 32%, -4%, and 23% compared to conventional mapping methods using MOLLI, GRASE, and mGRE, respectively, with moderate to very strong correlations (ICC=0.49–0.99). All MI-SAGE maps exhibited strong to very strong repeatability and reproducibility (ICC=0.80 to 0.99). The synthetic MI-SAGE had average Likert scores of 2.1, 2.1, 2.9, and 2.0 for T1-, T2-, T2*-, and FLAIR-weighted images, respectively, while conventional acquisitions had Likert scores of 3.5, 3.6, 4.6, and 3.8 for T1-, T2-, T2*-, and FLAIR-weighted images, respectively. The MI-SAGE synthetic T1w, T2w, T2*w, and FLAIR GWRs had biases of 17%, 3%, 7%, and 1% compared to the GWR of images from conventional T1w, T2w, T2*w, and FLAIR acquisitions respectively. Conclusion: The derived T1, T2, and T2* maps were correlated with conventional mapping methods and showed strong repeatability and reproducibility. While synthetic MI-SAGE images had greater susceptibility artifacts and lower Likert scores than conventional images, the MI-SAGE technique produced synthetic weighted images with contrasts similar to conventional weighted images and achieved a ten-fold reduction in scan time. [ABSTRACT FROM AUTHOR]

Objectives: This paper studies the segmentation and detection of small metastatic brain tumors. This study aims to evaluate the feasibility of training a deep neural network for the segmentation and detection of metastatic brain tumors in MRI using a very small dataset of 33 cases, by leveraging large public datasets of primary tumors; Methods: This study explores various methods, including supervised learning, two transfer learning approaches, and self-supervised learning, utilizing U-net and Swin UNETR models; Results: The self-supervised learning approach utilizing the Swin UNETR model yielded the best performance. The Dice score for small brain tumors was approximately 0.19. Sensitivity reached 100%, while specificity was 54.5%. When excluding subjects with hyperintensities, the specificity improved to 80.0%; Conclusions: It is feasible to train a model using self-supervised learning and a small dataset for the segmentation and detection of small brain tumors. [ABSTRACT FROM AUTHOR]

Intensity inhomogeneity, a common artefact in brain magnetic resonance imaging, poses challenges in medical image analysis. Intensity inhomogeneity, also known as bias field, occurs in magnetic resonance images due to factors such as magnetic field non-uniformity, radiofrequency coil sensitivity, tissue properties, patient-related factors, scanner artefacts. It creates intensity non-uniformity inside the homogeneous tissue regions of the brain images. Thereby degrading the performance of diagnosis assessment. This systematic review proposes a first hand categorization of a range of methodologies for intensity inhomogeneity correction. In particular, an overview of retrospective techniques including the filtering methods, computational intelligence methods, fuzzy models, learning-based approaches, etc. is included. This paper also presents the emergence of learning-based techniques in developing the intensity inhomogeneity correction techniques. Additionally, major challenges, current trends, and future directions for research and development are discussed. Moreover, the characteristics of choosing a suitable method and the appropriate evaluation metric are elaborately presented. This paper may serve as a comprehensive resource for researchers, clinicians, and engineers interested in enhancing the quality and reliability of brain image analysis through effective intensity inhomogeneity correction techniques. [ABSTRACT FROM AUTHOR]

Purpose: This multicentric study aims to characterize and assess the occurrence of neuroradiological findings among patients with SARS-CoV-2 infection during the first Italian wave of the pandemic outbreak. Materials and Methods: Patients' data were collected between May 2020 and June 2020. Clinical and laboratory data, chest imaging, brain CT, and MRI imaging were included. Acquired data were centralized and analyzed in two hospitals: ASST Spedali Civili, Brescia, and IRRCS San Raffaele Research Hospital, Milan, Italy. COVID-19 patients were classified into two different subgroups, vascular and nonvascular. The vascular pattern was further divided into ischemic and hemorrhagic stroke groups. Results: Four hundred and fifteen patients from 20 different Italian Centers were enrolled in the study. The most frequent symptom was focal neurological deficit, found in 143 patients (34.5%). The most frequent neuroradiological finding was ischemic stroke in 122 (29.4%) patients. Forty-four (10.6%) patients presented a cerebral hemorrhage. Forty-seven patients had non-stroke neuroimaging lesions (11.3%). The most common was PRES-like syndrome (28%), SWI hypointensities (22%), and encephalitis (19%). The stroke group had higher CAD risk (37.5% vs 20%, p =.016) and higher D-dimer levels (1875 ng/mL vs 451 ng/mL, p <.001) compared to the negative group. Conclusion: Our study describes the biggest cohort study in Italy on brain imaging of COVID-19 patients and confirms that COVID-19 patients are at risk of strokes, possibly due to a pro-thrombotic microenvironment. Moreover, apart from stroke, the other neuroradiological patterns described align with the ones reported worldwide. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) has recently made unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of designing and developing computer aided diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adoption and integration into the healthcare system still seems far-fetched. Diagnostic radiology is no exception. Imagining techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans have been widely and very effectively employed by radiologists and neurologists for the differential diagnoses of neurological disorders for decades, yet no AI-powered systems to analyze such scans have been incorporated into the standard operating procedures of healthcare systems. Why? It is absolutely understandable that in diagnostic medicine, precious human lives are on the line, and hence there is no room even for the tiniest of mistakes. Nevertheless, with the advent of explainable artificial intelligence (XAI), the old-school black boxes of deep learning (DL) systems have been unraveled. Would XAI be the turning point for medical experts to finally embrace AI in diagnostic radiology? This review is a humble endeavor to find the answers to these questions. Methods: In this review, we present the journey and contributions of AI in developing systems to recognize, preprocess, and analyze brain MRI scans for differential diagnoses of various neurological disorders, with special emphasis on CAD systems embedded with explainability. A comprehensive review of the literature from 2017 to 2024 was conducted using host databases. We also present medical domain experts' opinions and summarize the challenges up ahead that need to be addressed in order to fully exploit the tremendous potential of XAI in its application to medical diagnostics and serve humanity. Results: Forty-seven studies were summarized and tabulated with information about the XAI technology and datasets employed, along with performance accuracies. The strengths and weaknesses of the studies have also been discussed. In addition, the opinions of seven medical experts from around the world have been presented to guide engineers and data scientists in developing such CAD tools. Conclusions: Current CAD research was observed to be focused on the enhancement of the performance accuracies of the DL regimens, with less attention being paid to the authenticity and usefulness of explanations. A shortage of ground truth data for explainability was also observed. Visual explanation methods were found to dominate; however, they might not be enough, and more thorough and human professor-like explanations would be required to build the trust of healthcare professionals. Special attention to these factors along with the legal, ethical, safety, and security issues can bridge the current gap between XAI and routine clinical practice. [ABSTRACT FROM AUTHOR]

PurposeMethodsResultsConclusionsTrilateral retinoblastoma (TRB), intracranial neoplasm in heritable retinoblastoma patients, is a very rare fatal disease. Many ocular oncology centers conduct routine screening of retinoblastoma patients by brain imaging. Nevertheless, there is a debate regarding its ability to prolong TRB patients’ survival and the number-needed-to-treat. We recommend baseline screening brain imaging in bilateral retinoblastoma patients, followed by imaging according to clinical need. We aim to see if this screening schedule has an impact on patients’ survival.In a retrospective observational study, we reviewed the medical records of patients diagnosed with bilateral retinoblastoma at a tertiary medical center ocular oncology unit between 1.7.1986 and 1.2.2020, who had at least 36 months follow-up or retinoblastoma-related death. We collected data on patients’ demographics, clinical features, systemic evaluation, treatment, follow-up, and outcome.The analysis included 109 patients, 60 males and 49 females, diagnosed with bilateral retinoblastoma at a median age of 7.0 months (range 0.43–70.5 months). Germline mutation was found in 43 patients (39.4%) and somatic mutation in 15 patients (13.8%). Genetic status was not recorded in 51 patients (46.8%). Fifty-eight patients (53.0%) underwent baseline brain imaging (MRI in 42 patients and CT scan in 16 patients), in all of whom it was within normal limits. During a median follow-up of 138 months (range 19–787 months), 35 children had follow-up brain imaging (MRI in 25 patients and CT in 10 patients). One patient developed symptomatic TRB during follow-up, and is alive and disease-free (0.9%, Cl:0.02%-5.6%). Looking at survival, six patients (5.5%) developed metastatic disease, and eight patients (7.3%) expired, in all of whom death was retinoblastoma-related.Due to TRB rarity, routine screening by baseline brain MRI may be sufficient, avoiding anesthesia, expenses, distress, and unnecessary interventions, without a significant impact on patients’ survival. Nevertheless, due to TRB fatality and treatment morbidity, periodic brain MRI until age 3 is a legitimate option. [ABSTRACT FROM AUTHOR]

Background and purpose: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources. Materials and methods: We implemented a convolution-based model (3D ResNet-50 U-Net with spatial and channel squeeze & excitation) and a Transformer-based model (3D Swin Transformer with a convolutional stem). The models were evaluated on two clinical datasets from Kaohsiung Chang Gung Memorial Hospital and National Center for High-Performance Computing. Four metrics were used for evaluation: Dice similarity coefficient, lesion segmentation, lesion F1-Score, and lesion sensitivity. Results: The Transformer-based model, with appropriate adjustments, outperformed the well-established convolution-based model in foreground Dice similarity coefficient, lesion F1-Score, and sensitivity, demonstrating robust segmentation accuracy. DRLoc enhanced the Transformer's performance, achieving comparable results on internal and benchmark datasets despite limited data availability. Conclusion: With comparable computational overhead, a Transformer-based model can surpass a well-established convolution-based model in white matter hyperintensities segmentation on small datasets by capturing global context effectively, making them suitable for clinical applications where computational resources are constrained. [ABSTRACT FROM AUTHOR]

DiGeorge syndrome (DGS), the most common microdeletion syndrome, affects multiple organs, including the heart, the nervous system, and the immune system. In this study, we aimed to evaluate the clinical, laboratory, brain magnetic resonance imaging (MRI), and neurocognitive findings of our patients with DGS. Clinical and laboratory data of 52 patients with DGS between June 2000 and March 2022 were evaluated retrospectively. Brain MRI and neuropsychologic tests were performed to assess the neurocognitive status of the patients. Fifty-two patients (28 males and 24 females) were included in our study. Fifteen of them died during the follow-up. All 37 patients who are alive had partial DGS. The median age of patients was 10 years and 7 months, and the median age at diagnosis was 5 years and 4 months. Bilateral conduction deceleration in the anterior visual pathways in six (20%) of 30 patients was determined by the visual evoked potentials. The auditory brainstem evoked potential test showed sensorineural hearing loss in 11 of 30 (36.6%) patients. Brain MRI disclosed brain parenchymal abnormalities in 18 of 25 (72%) patients. Impairments in executive functions, expressive language, and verbal memory were noted in 18 patients who were neuropsychologically assessed. It is important to keep in mind that patients with DGS may be accompanied by neurocognitive findings. Awareness of the potential for underlying psychiatric and neurodevelopment disorders is key to anticipatory guidance, optimization of therapies, and maximizing life quality. [ABSTRACT FROM AUTHOR]

Background The National Comprehensive Cancer Network considers "baseline staging" (whole body computed tomography or positron emission tomography scans with or without brain magnetic resonance imaging scans) for all patients with asymptomatic melanoma who had a positive sentinel lymph node biopsy result. The true yield of these workups is unknown. Methods We created cohorts of adult patients with malignant melanoma using the National Cancer Database (2012-2020) to mimic 3 common scenarios: 1) clinically node-negative disease, with positive sentinel lymph node biopsy results; 2) clinically node-negative disease, with negative sentinel lymph node biopsy results; and 3) clinically node-positive disease, with confirmed lymph node metastases. Multivariable regression, supervised decision trees, and nomograms were constructed to assess the risk of metastases based on key features. Results In total, 10 371 patients were in scenario 1, 55 172 were in scenario 2, and 4012 were in scenario 3. The proportion of patients with any metastatic disease (brain metastases) were as follows: 1.4% (0.3%) in scenario 1, 0.3% (<0.1%) in scenario 2, and 11.6% (1.6%) in scenario 3. On multivariable regression, Breslow depth greater than 4, ulceration, and lymphovascular invasion were associated with greater risk of metastatic disease. A supervised decision tree for patients in scenarios 1 and 2 found that the only groups with more than 2% risk of metastases were groups with T4 tumors or T2/T3 tumors with ulceration and lymphovascular invasion. Most groups had a negligible risk (<0.1%) of brain metastases. Conclusion This study is the first large analysis to guide the use of imaging for cutaneous melanoma. Among patients with clinically node negative disease, metastatic disease is uncommon, and brain metastases are exceedingly rare. Further investigation could promote a tailored approach to metastatic workups guided by individual risk factors. [ABSTRACT FROM AUTHOR]

Background: Limited licensed medications are available for multiple sclerosis (MS) in pediatric patients. Objective: To evaluate the efficacy, safety, and tolerability of alemtuzumab in pediatric patients with relapsing-remitting multiple sclerosis (RRMS) and disease activity on prior disease-modifying therapies (DMTs). Methods: LemKids was a multicenter, multinational, single-arm, open-label, switch (from ongoing DMT to alemtuzumab treatment) study in pediatric RRMS patients (aged 10–<18 years), with disease activity on DMT. The primary endpoint was a comparison of the number of new/enlarging T2 lesions on the magnetic resonance imaging of the brain between the prior-DMT period and alemtuzumab treatment. Results: This study was prematurely terminated due to low enrollment and an European Medicines Agency Article-20 pharmacovigilance review of alemtuzumab in adult RRMS. Of 46 screened patients, 16 were enrolled; 12 completed prior-DMT treatment period; 11 received alemtuzumab of whom 7 completed treatment. Patients on alemtuzumab developed fewer new/enlarging T2 lesions compared with prior-DMT (7 vs 178, relative risk (95% confidence interval): 0.04 (0.01-0.14)). No significant pharmacodynamic changes or safety concerns were noted in this limited dataset. Conclusion: Alemtuzumab treatment was associated with a low number of new/enlarging T2 lesions in pediatric patients with RRMS and was safe and well tolerated in seven patients during infusion and the initial 4 months. [ABSTRACT FROM AUTHOR]

Mitochondrial neurogastrointestinal encephalopathy (MNGIE) is a rare autosomal recessive disorder, manifesting with gastrointestinal dysmotility, cachexia, ptosis and peripheral neuropathy. Diffuse leukoencephalopathy in brain MRI is a hallmark of MNGIE. We report a case of a 21-year-old female with MNGIE, presenting with cachexia and chronic diarrhea. Brain MRI revealed lesions in the cerebral deep white matter and the pons, with sparing of the subcortical U-fibers and the cerebral cortex and no apparent involvement of the cerebellum, basal ganglia, and thalamus. A literature review led to the identification of 72 additional cases with MNGIE that underwent brain MRI. Leukoencephalopathy of the cerebral white matter was present in all but 2 patients. The objective of this study is to increase radiologists' awareness of this challenging-to-diagnose disease, as well as to demonstrate the value of advanced MRI techniques in understanding the underlying pathology. The presence of leukoencephalopathy on brain MRI in patients with cachexia and neurological manifestations, should raise the suspicion for MNGIE and trigger further biochemical and genetic testing.

Abstract To explore the potential efficacy of early initiation of intravenous cyclophosphamide (IVCPA), we reviewed consecutive four cases of super‐refractory cryptogenic‐new onset refractory status epilepticus (C‐NORSE) between 2015 and 2023. We compared functional outcomes at 3 months and 1 year after the onset between patients who received IVCPA within 20 days (early‐treated) and those who received it later (late‐treated). All patients (median age: 43 years) had a prodromal fever. Brain MRI revealed symmetrically increased FLAIR signals in the medial temporal lobes of all patients. Despite initiating antiseizure medications (ASMs) and first‐line immunotherapy (intravenous‐methylprednisolone and immunoglobulins) within a median of 3 days from onset, SE persisted >5 days. The diagnosis of C‐NORSE was suggested based on a high C‐NORSE score (6/6). Thus, all patients received IVCPA a median of 15.5 days after seizure onset (three within 20 days and one at 31 days). One of the three early‐treated patients also received tocilizumab. Early‐treated patients exhibited shorter sedation periods (median 29 vs. 75 days) and better 1 year functional status (mRS 1–2 vs. mRS 4) compared to the late‐treated patient. Early initiation of IVCPA and/or tocilizumab, along with ASMs, may contribute to a better one‐year functional status in super‐refractory C‐NORSE patients. Plain Language Summary This study demonstrates the potential efficacy of early administration of intravenous cyclophosphamide on one‐year functional status in patients with super‐refractory cryptogenic‐new onset refractory status epilepticus. “Early‐treated patients” who received it within 20 days of seizure onset achieved a good one‐year functional status. The “late‐treated patient” (Case 4) who received it later did not achieve a good functional status. Early initiation of cyclophosphamide, along with antiseizure medications, may contribute to a better one‐year functional status in this population.

Yong-Kun Ruan,1,2,&ast; Wang-Kai He,1,&ast; Qing-Qing Chen,1 Hua Hu3 1Department of Neurology, Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Zhuhai, 519020, People’s Republic of China; 2Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, People’s Republic of China; 3Department of Neurology, The First Hospital of Hunan University of Chinese Medicine, Changsha, 410007, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qing-Qing Chen, Department of Neurology, Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, No. 208 of Yuehua Street, Xiangzhou District, Zhuhai, 519020, People’s Republic of China, Tel +86-7568308361, Email chen_qq6699@163.com Hua Hu, Department of Neurology, The First Hospital of Hunan University of Chinese Medicine, No. 95 of Shaoshan Middle Road, Yuhua District, Changsha, 410007, People’s Republic of China, Tel +86-73185600120, Email hh201201201@163.comAbstract: Vertigo, including central and peripheral causes, is one of the common symptoms in patients who are admitted to neurological outpatient and emergency rooms. Despite the advancements in imaging techniques in recent years, central vertigo is difficult to identify and is often misdiagnosed in clinical practice. In this study, 4 patients were admitted to the hospital with complaints of dizziness or vertigo. Information about their symptoms, physical examinations and imaging were collected. Two patients were accurately diagnosed using diffusion-weighted imaging (DWI), a specific type of brain MRI. They received targeted treatments, which led to significant improvement, and were discharged nearly cured within a week. One patient with dorsolateral medullary infarction was misdiagnosed due to atypical symptoms, such as vertigo without the typical lateral medullary syndrome signs, and was discharged with a mild swallowing disorder after 2 weeks of treatment. One patient was diagnosed with both central and peripheral vertigo. It was observed that the symptoms of isolated vertigo caused by an acute lacunar infarction resolved more quickly than the accompanying physical symptoms. In summary, more attention should be paid to the diagnosis of isolated central vertigo, as early identification and intervention can improve a patient’s prognosis and reduce medical expenses.Keywords: cerebral infarction, isolated central vertigo, case series, central vestibular disorders, physical examination, brain MRI

Tuberculosis (TB) is an infection caused by Mycobacterium tuberculosis, an infectious disease endemic in developing countries. Indonesia is ranked second only to India in terms of TB incidence in the world. TB generally manifests in the respiratory system, which can then spread hematogeneously or lymphogeneously to extrapulmonary organs. Intracranial tuberculoma is a rare manifestation of TB when compared to the overall TB presentation. Central nervous system involvement ranges from 2-5% and increases to 15% in cases of AIDS-related TB, with the percentage of tuberculoma findings around 1% in other intracranial TB cases. The most common manifestation is tuberculous meningitis. Central nervous system (CNS) involvement is a severe manifestation of TB, with high mortality and neurological morbidity. In this case report, the author presented a 6-year-old girl with giant cerebral tuberculoma, which, at the time of surgery, resembled a neoplasm with a nonspecific history of TB. MRI can visualize abnormalities with specific characteristics; Clinically and radiologically, CNS TB can mimic other infections or noninfectious conditions such as neoplasms. Therefore, clinicians can take appropriate management actions in order to prevent mortality and disability due to sequelae in CNS TB cases.

Abstract Alzheimer’s disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments to slow its progression. Deep learning (DL) significantly enhances AD detection by analyzing brain imaging data to identify early biomarkers, improving diagnostic accuracy and predicting disease progression more precisely than traditional methods. In this article, we propose an ensemble methodology for DL models to detect AD from brain MRIs. We trained an enhanced Xception architecture once to produce multiple snapshots, providing diverse insights into MRI features. A decision-level fusion strategy was employed, combining decision scores with a RF meta-learner using a blending algorithm. The efficacy of our ensemble technique is confirmed by the experimental findings, which categorize Alzheimer’s into four groups with 99.14% accuracy. This methodology may help medical practitioners provide patients with Alzheimer’s with individualized care. Subsequent efforts will concentrate on enhancing the model’s efficacy via its generalization to a variety of datasets.

Abstract Objectives Gut-brain axis dysfunction has emerged as a key contributor to the pathogenesis of Crohn’s disease (CD). The elucidation of neural alterations may provide novel insights into its management. We aimed to develop a multiparameter brain MRI-based radiomics model (RM) for characterizing neural alterations in CD patients and to interpret these alterations using multiomics traits. Methods This prospective study enrolled 230 CD patients and 46 healthy controls (HCs). Participants voluntarily underwent brain MRI and psychological assessment (n = 155), blood metabolomics analysis (n = 260), and/or fecal 16S rRNA sequencing (n = 182). The RM was developed using 13 features selected from 13,870 first-order features extracted from multiparameter brain MRI in training cohort (CD, n = 75; HCs, n = 32) and validated in test cohort (CD, n = 34; HCs, n = 14). Multiomics data (including gut microbiomics, blood metabolomics, and brain radiomics) were compared between CD patients and HCs. Results In the training cohort, area under the receiver operating characteristic curve (AUC) of RM for distinguishing CD patients from HCs was 0.991 (95% confidence interval (CI), 0.975–1.000). In test cohort, RM showed an AUC of 0.956 (95% CI, 0.881–1.000). CD-enriched blood metabolites such as triacylglycerol (TAG) exhibited significant correlations with both brain features detected by RM and CD-enriched microbiota (e.g., Veillonella). One notable correlation was found between Veillonella and Ctx-Lh-Middle-Temporal-CBF-p90 (r = 0.41). Mediation analysis further revealed that dysbiosis, such as of Veillonella, may regulate the blood flow in the middle temporal cortex through TAG. Conclusion We developed a multiparameter MRI-based RM that characterized the neural alterations of CD patients, and multiomics data offer potential evidence to support the validity of our model. Our study may offer clues to help provide potential therapeutic targets. Critical relevance statement Our brain-gut axis study developed a novel model using multiparameter MRI and radiomics to characterize brain changes in patients with Crohn’s disease. We validated this model’s effectiveness using multiomics data, making it a potential biomarker for better patient management. Key Points Utilizing multiparametric MRI and radiomics techniques could unveil Crohn’s disease’s neurophenotype. The neurophenotype radiomics model is interpreted using multiomics data. This model may serve as a novel biomarker for Crohn’s disease management. Graphical Abstract

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition. A core mystery is the MRI explanation of individual differences in neurocognition and its manifestation in intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation must be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on artificial intelligence inferring human neurocognition. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Previous retrospective studies have reported vigabatrin‐associated brain abnormalities on magnetic resonance imaging (VABAM), although clinical impact is unknown. We evaluated the association between vigabatrin and predefined brain magnetic resonance imaging (MRI) changes in a large homogenous tuberous sclerosis complex (TSC) cohort and assessed to what extent VABAM‐related symptoms were reported in TSC infants.The Dutch TSC Registry and the EPISTOP cohort provided retrospective and prospective data from 80 TSC patients treated with vigabatrin (VGB) before the age of 2 years and 23 TSC patients without VGB. Twenty‐nine age‐matched non‐TSC epilepsy patients not receiving VGB were included as controls. VABAM, specified as T2/fluid‐attenuated inversion recovery hyperintensity or diffusion restriction in predefined brain areas, were examined on brain MRI before, during, and after VGB, and once in the controls (at approximately age 2 years). Additionally, the presence of VABAM accompanying symptoms was evaluated.Prevalence of VABAM in VGB‐treated TSC patients was 35.5%. VABAM‐like abnormalities were observed in 13.5% of all patients without VGB. VGB was significantly associated with VABAM (risk ratio [RR] = 3.57, 95% confidence interval [CI] = 1.43–6.39), whereas TSC and refractory epilepsy were not. In all 13 VGB‐treated patients with VABAM for whom posttreatment MRIs were available, VABAM entirely resolved after VGB discontinuation. The prevalence of symptoms was 11.7% in patients with VABAM or VABAM‐like MRI abnormalities and 4.3% in those without, implicating no significant association (RR = 2.76, 95% CI = .68–8.77).VABAM are common in VGB‐treated TSC infants; however, VABAM‐like abnormalities also occurred in children without either VGB or TSC. The cause of these MRI changes is unknown. Possible contributing factors are abnormal myelination, underlying etiology, recurrent seizures, and other antiseizure medication. Furthermore, the presence of VABAM (or VABAM‐like abnormalities) did not appear to be associated with clinical symptoms. This study confirms that the well‐known antiseizure effects of VGB outweigh the risk of VABAM and related symptoms. [ABSTRACT FROM AUTHOR]

Phenylketonuria, the most common inherited metabolic disease, results from a deficiency of phenylalanine hydroxylase enzyme activity that causes high blood phenylalanine levels. Most adults do not adhere to the gold standard therapy: lifelong treatment with a low‐phenylalanine diet. Elevated and fluctuating phenylalanine levels in untreated adults can cause white matter abnormalities, neurological symptoms, and cognitive dysfunction (executive function). Pegvaliase, a derivative of the phenylalanine ammonia‐lyase enzyme, metabolizes phenylalanine to trans‐cinnamic acid and ammonia, and is approved by the US Food and Drug Administration and European Medicines Agency for subcutaneous administration in adults with phenylketonuria and blood phenylalanine concentrations > 600 μmol/L. In clinical trials, it reduced blood phenylalanine, even in patients consuming an unrestricted diet. We report longitudinal results on the first three such adults, in whom phenylalanine levels were quantified monthly, starting 1 year before pegvaliase administration and continuing through achievement of a pegvaliase response (defined as six consecutive monthly blood phenylalanine concentrations < 360 μmol/L while consuming an unrestricted diet). Brain magnetic resonance imaging (MRI) and neuropsychological assessments were performed before starting therapy and after response was achieved. Our results show that all three patients had significantly reduced white matter hyperintensities on brain MRI and improved executive function on neuropsychological assessment, especially on the Paced Auditory Serial Addition Test, which is known to be very sensitive to white matter functioning. To the best of our knowledge, this is the first report of concomitant improvements in cognitive performance and white matter damage after a pharmacological intervention to normalize phenylalanine levels in adults with phenylketonuria consuming an unrestricted diet. [ABSTRACT FROM AUTHOR]

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Objective: The image quality of synthetized FLAIR (fluid attenuated inversion recovery) images is generally inferior to its conventional counterpart, especially regarding the lesion contrast mismatch. This work aimed to improve the lesion appearance through a hybrid methodology. Materials and methods: We combined a full brain 5-min MR-STAT acquisition followed by FLAIR synthetization step with an ultra-under sampled conventional FLAIR sequence and performed the retrospective and prospective analysis of the proposed method on the patient datasets and a healthy volunteer. Results: All performance metrics of the proposed hybrid FLAIR images on patient datasets were significantly higher than those of the physics-based FLAIR images (p < 0.005), and comparable to those of conventional FLAIR images. The small difference between prospective and retrospective analysis on a healthy volunteer demonstrated the validity of the retrospective analysis of the hybrid method as presented for the patient datasets. Discussion: The proposed hybrid FLAIR achieved an improved lesion appearance in the clinical cases with neurological diseases compared to the physics-based FLAIR images, Future prospective work on patient data will address the validation of the method from a diagnostic perspective by radiological inspection of the new images over a larger patient cohort. [ABSTRACT FROM AUTHOR]

Financial and health literacy is essential for older adults to navigate complex decision processes in late life. However, the neurobiological basis of age-related decline in financial and health literacy is poorly understood. This study aimed to characterize progression of neurodegenerative and vascular conditions over time, and to assess how these changes coincide with declining financial and health literacy in old age. Data came from 319 community-living older adults who were free of dementia at baseline, and underwent annual literacy assessments, as well as biennial 3-Tesla neuroimaging scans. Financial and health literacy was assessed using a battery of 32 items. Two in vivo neuroimaging markers of neurodegenerative and cerebrovascular conditions were used, i.e., hippocampal volume and the ARTS marker of arteriolosclerosis. A multivariate linear mixed effects model estimated the simultaneous changes in financial and health literacy, hippocampal volume, and the ARTS score. Over a mean of 7 years of follow-up, these older adults experienced a significant decline in financial and health literacy, a significant reduction in hippocampal volume, and a significant progression in ARTS score. Individuals with faster hippocampal atrophy had faster decline in literacy. Similarly, those with faster progression in ARTS also had faster decline in literacy. The correlation between the rates of hippocampal atrophy and declining literacy, however, was stronger than the correlation between the progression of ARTS with declining literacy. These findings suggest that neurodegeneration and, to a lesser extent, cerebrovascular conditions are correlated with declining financial and health literacy in old age. [ABSTRACT FROM AUTHOR]

Objectives: Gut-brain axis dysfunction has emerged as a key contributor to the pathogenesis of Crohn's disease (CD). The elucidation of neural alterations may provide novel insights into its management. We aimed to develop a multiparameter brain MRI-based radiomics model (RM) for characterizing neural alterations in CD patients and to interpret these alterations using multiomics traits. Methods: This prospective study enrolled 230 CD patients and 46 healthy controls (HCs). Participants voluntarily underwent brain MRI and psychological assessment (n = 155), blood metabolomics analysis (n = 260), and/or fecal 16S rRNA sequencing (n = 182). The RM was developed using 13 features selected from 13,870 first-order features extracted from multiparameter brain MRI in training cohort (CD, n = 75; HCs, n = 32) and validated in test cohort (CD, n = 34; HCs, n = 14). Multiomics data (including gut microbiomics, blood metabolomics, and brain radiomics) were compared between CD patients and HCs. Results: In the training cohort, area under the receiver operating characteristic curve (AUC) of RM for distinguishing CD patients from HCs was 0.991 (95% confidence interval (CI), 0.975–1.000). In test cohort, RM showed an AUC of 0.956 (95% CI, 0.881–1.000). CD-enriched blood metabolites such as triacylglycerol (TAG) exhibited significant correlations with both brain features detected by RM and CD-enriched microbiota (e.g., Veillonella). One notable correlation was found between Veillonella and Ctx-Lh-Middle-Temporal-CBF-p90 (r = 0.41). Mediation analysis further revealed that dysbiosis, such as of Veillonella, may regulate the blood flow in the middle temporal cortex through TAG. Conclusion: We developed a multiparameter MRI-based RM that characterized the neural alterations of CD patients, and multiomics data offer potential evidence to support the validity of our model. Our study may offer clues to help provide potential therapeutic targets. Critical relevance statement: Our brain-gut axis study developed a novel model using multiparameter MRI and radiomics to characterize brain changes in patients with Crohn's disease. We validated this model's effectiveness using multiomics data, making it a potential biomarker for better patient management. Key Points: Utilizing multiparametric MRI and radiomics techniques could unveil Crohn's disease's neurophenotype. The neurophenotype radiomics model is interpreted using multiomics data. This model may serve as a novel biomarker for Crohn's disease management. [ABSTRACT FROM AUTHOR]

This study leverages deep learning to analyze sex differences in brain MRI data, aiming to further advance fairness in medical imaging. We employed 3D T1-weighted Magnetic Resonance images from four diverse datasets: Calgary-Campinas-359, OASIS-3, Alzheimer's Disease Neuroimaging Initiative, and Cambridge Center for Aging and Neuroscience, ensuring a balanced representation of sexes and a broad demographic scope. Our methodology focused on minimal preprocessing to preserve the integrity of brain structures, utilizing a Convolutional Neural Network model for sex classification. The model achieved an accuracy of 87% on the test set without employing total intracranial volume (TIV) adjustment techniques. We observed that while the model exhibited biases at extreme brain sizes, it performed with less bias when the TIV distributions overlapped more. Saliency maps were used to identify brain regions significant in sex differentiation, revealing that certain supratentorial and infratentorial regions were important for predictions. Furthermore, our interdisciplinary team, comprising machine learning specialists and a radiologist, ensured diverse perspectives in validating the results. The detailed investigation of sex differences in brain MRI in this study, highlighted by the sex differences map, offers valuable insights into sex-specific aspects of medical imaging and could aid in developing sex-based bias mitigation strategies, contributing to the future development of fair AI algorithms. Awareness of the brain's differences between sexes enables more equitable AI predictions, promoting fairness in healthcare outcomes. Our code and saliency maps are available at https://github.com/mahsadibaji/sex-differences-brain-dl. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and communication. While there is no cure, early detection can facilitate treatments to slow its progression. Deep learning (DL) significantly enhances AD detection by analyzing brain imaging data to identify early biomarkers, improving diagnostic accuracy and predicting disease progression more precisely than traditional methods. In this article, we propose an ensemble methodology for DL models to detect AD from brain MRIs. We trained an enhanced Xception architecture once to produce multiple snapshots, providing diverse insights into MRI features. A decision-level fusion strategy was employed, combining decision scores with a RF meta-learner using a blending algorithm. The efficacy of our ensemble technique is confirmed by the experimental findings, which categorize Alzheimer's into four groups with 99.14% accuracy. This methodology may help medical practitioners provide patients with Alzheimer's with individualized care. Subsequent efforts will concentrate on enhancing the model's efficacy via its generalization to a variety of datasets. [ABSTRACT FROM AUTHOR]

Objectives Methods Results Significance Late‐onset epilepsy has the highest incidence among all age groups affected by epilepsy and often occurs in the absence of known clinical risk factors such as stroke and dementia. There is increasing evidence that brain changes contributing to epileptogenesis likely start years before disease onset, and we aim to relate cognitive and imaging correlates of subclinical brain injury to incident late‐onset epilepsy in a large, community‐based cohort.We studied Offspring Cohort of the Framingham Heart Study participants 45 years or older, who were free of prevalent stroke, dementia, or epilepsy, and had neuropsychological (NP) evaluation and brain magnetic resonance imaging (MRI). Cognitive measures included Visual Reproduction Delayed Recall, Logical Memory Delayed Recall, Similarities, Trail Making Test B minus A (TrTB–TrTA; attention and executive function), and a global measure of cognition derived from principal component analysis. MRI measures included total cerebral brain volume, cortical gray matter volume (CGMV), white matter hyperintensity volume (WMHV), and hippocampal volume. Incident epilepsy was identified through a review of administrative data and medical records. Cox proportional hazards regression models were used for the analyses. All analyses were adjusted for age, sex, and educational level (cognition only).Among participants who underwent NP testing (n = 2349, 45.81% male), 31 incident epilepsy cases were identified during follow‐up. Better performance on the TrTB–TrTA was associated with a lower risk of developing epilepsy (hazard ratio [HR] .25, 95% confidence interval [CI] .08–.73; p = .011). In the subgroup of participants with MRI (n = 2056, 46.01% male), 27 developed epilepsy. Higher WMHV was associated with higher epilepsy risk (HR 1.5, 95%CI 1.01–2.20; p = .042), but higher CGMV (HR .73, 95% CI .57–.93; p = .001) was associated with lower incidence of epilepsy.Better performance on the (TrTB–TrTA), a measure of executive function and attention, and higher cortical volumes are associated with lower risk of developing epilepsy. Conversely, higher WMHV, a measure of occult vascular injury, increases the risk. Our study shows that non‐invasive tests performed in mid‐life may help identify people at risk for developing epilepsy later in life. [ABSTRACT FROM AUTHOR]