Clinical Brain Magnetic Resonance Imaging Research Articles

DNA palette code for time-series archival data storage

Abstract The long-term preservation of large volumes of infrequently accessed cold data poses challenges to the storage community. Deoxyribonucleic Acid (DNA) is considered a promising solution due to its inherent physical stability and significant storage density. The information density and decoding sequence coverage are two important metrics that influence the efficiency of DNA data storage. In this study, we propose a novel coding scheme called DNA Palette code, which is suitable for cold data, especially time-series archival datasets. These datasets are not frequently accessed but necessitate reliable long-term storage for retrospective research. The DNA Palette code employs unordered combinations of index-free oligonucleotides (oligos) to represent binary information. It can achieve high net information density encoding and lossless decoding with low sequencing coverage. When sequencing reads are corrupted, it can still effectively recover partial information, preventing the complete failure of file retrieval. The in vitro testing of clinical brain magnetic resonance imaging (MRI) data storage, as well as simulation validations using large-scale public MRI datasets (10 GB), planetary science datasets, and meteorological datasets, demonstrate the advantages of our coding scheme, including high information density, low decoding sequence coverage, and wide applicability.

Brain and Ventricle Volume Alterations in Idiopathic Normal Pressure Hydrocephalus Determined by Artificial Intelligence-Based MRI Volumetry.

The aim of this study was to employ artificial intelligence (AI)-based magnetic resonance imaging (MRI) brain volumetry to potentially distinguish between idiopathic normal pressure hydrocephalus (iNPH), Alzheimer's disease (AD), and age- and sex-matched healthy controls (CG) by evaluating cortical, subcortical, and ventricular volumes. Additionally, correlations between the measured brain and ventricle volumes and two established semi-quantitative radiologic markers for iNPH were examined. An IRB-approved retrospective analysis was conducted on 123 age- and sex-matched subjects (41 iNPH, 41 AD, and 41 controls), with all of the iNPH patients undergoing routine clinical brain MRI prior to ventriculoperitoneal shunt implantation. Automated AI-based determination of different cortical and subcortical brain and ventricular volumes in mL, as well as calculation of population-based normalized percentiles according to an embedded database, was performed; the CE-certified software mdbrain v4.4.1 or above was used with a standardized T1-weighted 3D magnetization-prepared rapid gradient echo (MPRAGE) sequence. Measured brain volumes and percentiles were analyzed for between-group differences and correlated with semi-quantitative measurements of the Evans' index and corpus callosal angle: iNPH patients exhibited ventricular enlargement and changes in gray and white matter compared to AD patients and controls, with the most significant differences observed in total ventricular volume (+67%) and the lateral (+68%), third (+38%), and fourth (+31%) ventricles compared to controls. Global ventriculomegaly and marked white matter reduction with concomitant preservation of gray matter compared to AD and CG were characteristic of iNPH, whereas global and frontoparietally accentuated gray matter reductions were characteristic of AD. Evans' index and corpus callosal angle differed significantly between the three groups and moderately correlated with the lateral ventricular volumes in iNPH patients [Evans' index (r > 0.83, p ≤ 0.001), corpus callosal angle (r < -0.74, p ≤ 0.001)]. AI-based MRI volumetry in iNPH patients revealed global ventricular enlargement and focal brain atrophy, which, in contrast to healthy controls and AD patients, primarily involved the supratentorial white matter and was marked temporomesially and in the midbrain, while largely preserving gray matter. Integrating AI volumetry in conjunction with traditional radiologic measures could enhance iNPH identification and differentiation, potentially improving patient management and therapy response assessment.

Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging

ObjectiveSegmentation of medical images is a crucial process in various image analysis applications. Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis. One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging (MRI) data. It allows for precise quantitative examination of the brain, which aids in diagnosis, identification, and classification of disorders. Consequently, the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning. MethodsThis study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans using a fractional Henry horse herd gas optimization-based Shepard convolutional neural network (FrHHGO-based ShCNN). To segment the clinical brain MRI images into white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) tissues, the proposed framework was evaluated on the Lifespan Human Connectome Projects (HCP) database. The hybrid optimization algorithm, FrHHGO, integrates the fractional Henry gas optimization (FHGO) and horse herd optimization (HHO) algorithms. Training required 30 min, whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s. ResultsCompared to the results obtained with no refinements, the Skull stripping refinement showed significant improvement. As the method included a preprocessing stage, it was flexible enough to enhance image quality, allowing for better results even with low-resolution input. Maximum precision of 93.2%, recall of 91.5%, Dice score of 91.1%, and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN, which was superior to all other approaches. ConclusionThe proposed method may outperform existing state-of-the-art methodologies in qualitative and quantitative measurements across a wide range of medical modalities. It might demonstrate its potential for real-life clinical application.

Neuroanatomical correlates of gross manual dexterity in children with unilateral spastic cerebral palsy.

Unilateral spastic Cerebral Palsy (UCP) results from congenital brain injury, and Magnetic Resonance Imaging (MRI) has a role in understanding the etiology and severity of brain insult. In UCP, functional impairment predominantly occurs in the upper limb (UL) of the more affected side, where manual ability and dexterity are typically reduced. Also, mirror movements (MMs), are often present in UCP, with a further possible negative functional impact. This study aims to investigate the relationships among neuroanatomical characteristics of brain injury at MRI, manual functional impairment and MMs, in children with UCP. Thirty-five children with UCP participated in the study (20, M = 15, F, mean age 9.2 ± 3.5 years). Brain lesions at MRI were categorized according to the Magnetic Resonance Classification System (MRICS) and by using a semi-quantitative MRI (sqMRI) scale. Gross manual performance was assessed through Manual Ability Classification System (MACS) and the Box and Block Test (BBT), and MMs by Woods and Teuber scale, for both hands. Non-parametric correlation analyses were run to determine the relationship between neuroanatomical and functional features. Regression models were run to explore the contribution of neuroanatomical features and MMs to UL function. Correlation analyses revealed moderate to strong associations between sqMRI scores contralateral to the more affected side and UL functional impairment on MACS and BBT, with more severe brain injuries significantly correlating with poorer function in the more affected hand. No association emerged between brain lesion severity scores and MMs. MRICS showed no association with MACS or BBT, while a significant correlation emerged between MRICS category and MMs in the more affected hand, with brain lesion category that are suggestive of presumed earlier injury being associated with more severe MMs. Finally, exploratory regression analyses showed that neuroanatomical characteristics of brain injury and MMs contributed to the variability of UL functional impairment. This study contributes to the understanding of the neuroanatomical and neurological correlates of some aspects of manual functional impairment in UCP by using a simple clinical brain MRI assessment.

Autism-associated brain differences can be observed in utero using MRI.

Developmental changes that occur before birth are thought to be associated with the development of autism spectrum disorders. Identifying anatomical predictors of early brain development may contribute to our understanding of the neurobiology of autism spectrum disorders and allow for earlier and more effective identification and treatment of autism spectrum disorders. In this study, we used retrospective clinical brain magnetic resonance imaging data from fetuses who were diagnosed with autism spectrum disorders later in life (prospective autism spectrum disorders) in order to identify the earliest magnetic resonance imaging-based regional volumetric biomarkers. Our results showed that magnetic resonance imaging-based autism spectrum disorder biomarkers can be found as early as in the fetal period and suggested that the increased volume of the insular cortex may be the most promising magnetic resonance imaging-based fetal biomarker for the future emergence of autism spectrum disorders, along with some additional, potentially useful changes in regional volumes and hemispheric asymmetries.

Linking brain structure, cognition, and sleep: insights from clinical data.

To use relatively noisy routinely collected clinical data (brain magnetic resonance imaging (MRI) data, clinical polysomnography (PSG) recordings, and neuropsychological testing), to investigate hypothesis-driven and data-driven relationships between brain physiology, structure, and cognition. We analyzed data from patients with clinical PSG, brain MRI, and neuropsychological evaluations. SynthSeg, a neural network-based tool, provided high-quality segmentations despite noise. A priori hypotheses explored associations between brain function (measured by PSG) and brain structure (measured by MRI). Associations with cognitive scores and dementia status were studied. An exploratory data-driven approach investigated age-structure-physiology-cognition links. Six hundred and twenty-three patients with sleep PSG and brain MRI data were included in this study; 160 with cognitive evaluations. Three hundred and forty-two participants (55%) were female, and age interquartile range was 52 to 69 years. Thirty-six individuals were diagnosed with dementia, 71 with mild cognitive impairment, and 326 with major depression. One hundred and fifteen individuals were evaluated for insomnia and 138 participants had an apnea-hypopnea index equal to or greater than 15. Total PSG delta power correlated positively with frontal lobe/thalamic volumes, and sleep spindle density with thalamic volume. rapid eye movement (REM) duration and amygdala volume were positively associated with cognition. Patients with dementia showed significant differences in five brain structure volumes. REM duration, spindle, and slow-oscillation features had strong associations with cognition and brain structure volumes. PSG and MRI features in combination predicted chronological age (R2 = 0.67) and cognition (R2 = 0.40). Routine clinical data holds extended value in understanding and even clinically using brain-sleep-cognition relationships.

SOFNet: Optical-flow based large-scale slice augmentation of brain MRI

Brain magnetic resonance imaging (MRI) data from multiple centers often exhibit variations in imaging conditions, such as the types of nuclear magnetic resonance instruments used and the presence of random noise. Additionally, discrepancies in the gap between MRI slices further complicate the usability of the data for advanced artificial intelligence (AI) analysis. Deep learning-based methods have emerged as practical solutions to address the challenge. However, existing research has largely overlooked the augmentation of brain MRI data, particularly when confronted with significant slice gaps, such as around 6 mm observed in our clinical brain MRI slices. In response to this research gap, we aim to develop novel approaches for augmenting brain MRI data, focusing on more significant slice gaps. To achieve this, we propose SOFNet, which utilizes the optical flow-based and encoder–decoder backbone. The primary objective of our model is to interpolate MRI slices while preserving feature consistency. Leveraging the optical flow method, which has exhibited exceptional performance compared to other super-resolution algorithms, our proposed approach has been evaluated on three distinct brain MRI datasets, explicitly addressing the gap between 4.2 mm and 6.0 mm. The experimental results highlight the significant enhancement in super-resolution quality achieved by SOFNet in generating adapted brain MRI data, surpassing other single-image super-resolution (SISR) methods in feature completion. To ensure the credibility of the interpolated brain MRI slices, we conducted experiments on three axes of MRI based on metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These experiments demonstrate the effectiveness of our approach in transforming low-resolution MRI data into clear and reliable brain MRIs, thereby enabling improved analysis using AI techniques.

Magnetic resonance imaging and neuropsychological findings for predicting of cognitive deterioration in memory clinic patients.

The severity of cerebral small vessel disease (SVD) on magnetic resonance imaging (MRI) has been assessed using hypertensive arteriopathy SVD and cerebral amyloid angiopathy (CAA)-SVD scores. In addition, we reported the modified CAA-SVD score including cortical microinfarcts and posterior dominant white matter hyperintensity. Each SVD score has been associated with cognitive function, but the longitudinal changes remain unclear. Therefore, this study prospectively examined the prognostic value of each SVD score, imaging findings of cerebral SVD, and neuropsychological assessment. This study included 29 patients diagnosed with mild cognitive impairment or mild dementia at memory clinic in our hospital, who underwent clinical dementia rating (CDR) and brain MRI (3D-fluid attenuated inversion recovery, 3D-double inversion recovery, and susceptibility-weighted imaging) at baseline and 1 year later. Each SVD score and neuropsychological tests including the Mini-Mental State Examination, Japanese Raven's Colored Progressive Matrices, Trail Making Test -A/-B, and the Rivermead Behavioral Memory Test were evaluated at baseline and 1 year later. Twenty patients had unchanged CDR (group A), while nine patients had worsened CDR (group B) after 1 year. At baseline, there was no significant difference in each SVD score; after 1 year, group B had significantly increased CAA-SVD and modified CAA-SVD scores. Group B also showed a significantly higher number of lobar microbleeds than group A at baseline. Furthermore, group B had significantly longer Japanese Raven's Colored Progressive Matrices and Trail Making test-A times at baseline. After 1 year, group B had significantly lower Mini-Mental State Examination, Japanese Raven's Colored Progressive Matrices, and Rivermead Behavioral Memory Test scores and significantly fewer word fluency (letters). Patients with worsened CDR 1 year after had a higher number of lobar microbleeds and prolonged psychomotor speed at baseline. These findings may become predictors of cognitive deterioration in patients who visit memory clinics.

Assessment of Brain Magnetic Resonance and Spectroscopy Imaging Findings and Outcomes After Pediatric Cardiac Arrest

Morbidity and mortality after pediatric cardiac arrest are chiefly due to hypoxic-ischemic brain injury. Brain features seen on magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) after arrest may identify injury and aid in outcome assessments. To analyze the association of brain lesions seen on T2-weighted MRI and diffusion-weighted imaging and N-acetylaspartate (NAA) and lactate concentrations seen on MRS with 1-year outcomes after pediatric cardiac arrest. This multicenter cohort study took place in pediatric intensive care units at 14 US hospitals between May 16, 2017, and August 19, 2020. Children aged 48 hours to 17 years who were resuscitated from in-hospital or out-of-hospital cardiac arrest and who had a clinical brain MRI or MRS performed within 14 days postarrest were included in the study. Data were analyzed from January 2022 to February 2023. Brain MRI or MRS. The primary outcome was an unfavorable outcome (either death or survival with a Vineland Adaptive Behavior Scales, Third Edition, score of <70) at 1 year after cardiac arrest. MRI brain lesions were scored according to region and severity (0 = none, 1 = mild, 2 = moderate, 3 = severe) by 2 blinded pediatric neuroradiologists. MRI Injury Score was a sum of T2-weighted and diffusion-weighted imaging lesions in gray and white matter (maximum score, 34). MRS lactate and NAA concentrations in the basal ganglia, thalamus, and occipital-parietal white and gray matter were quantified. Logistic regression was performed to determine the association of MRI and MRS features with patient outcomes. A total of 98 children, including 66 children who underwent brain MRI (median [IQR] age, 1.0 [0.0-3.0] years; 28 girls [42.4%]; 46 White children [69.7%]) and 32 children who underwent brain MRS (median [IQR] age, 1.0 [0.0-9.5] years; 13 girls [40.6%]; 21 White children [65.6%]) were included in the study. In the MRI group, 23 children (34.8%) had an unfavorable outcome, and in the MRS group, 12 children (37.5%) had an unfavorable outcome. MRI Injury Scores were higher among children with an unfavorable outcome (median [IQR] score, 22 [7-32]) than children with a favorable outcome (median [IQR] score, 1 [0-8]). Increased lactate and decreased NAA in all 4 regions of interest were associated with an unfavorable outcome. In a multivariable logistic regression adjusted for clinical characteristics, increased MRI Injury Score (odds ratio, 1.12; 95% CI, 1.04-1.20) was associated with an unfavorable outcome. In this cohort study of children with cardiac arrest, brain features seen on MRI and MRS performed within 2 weeks after arrest were associated with 1-year outcomes, suggesting the utility of these imaging modalities to identify injury and assess outcomes.

A Review of Brain and Pituitary Gland MRI Findings in Patients with Ataxia and Hypogonadism.

The association of cerebellar ataxia and hypogonadism occurs in a heterogeneous group of disorders, caused by different genetic mutations often associated with a recessive inheritance. In these patients, magnetic resonance imaging (MRI) plays a pivotal role in the diagnostic workflow, with a variable involvement of the cerebellar cortex, alone or in combination with other brain structures. Neuroimaging involvement of the pituitary gland is also variable. Here, we provide an overview of the main clinical and conventional brain and pituitary gland MRI imaging findings of the most common genetic mutations associated with the clinical phenotype of ataxia and hypogonadism, with the aim of helping neuroradiologists in the identification of these disorders.

SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry.

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain.

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.

Deep learning with multiresolution handcrafted features for brain MRI segmentation

The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve positron emission tomography (PET) attenuation correction. One of the main challenges of creating pseudo CT images is the difficulty to obtain an accurate segmentation of the bone tissue in brain MR images. Deep convolutional neural networks (CNNs) have been widely and efficiently applied to perform MR image segmentation. The aim of this work is to propose a segmentation approach that combines multiresolution handcrafted features with CNN-based features to add directional properties and enrich the set of features to perform segmentation. The main objective is to efficiently segment the brain into three tissue classes: bone, soft tissue, and air. The proposed method combines non subsampled Contourlet (NSCT) and non subsampled Shearlet (NSST) coefficients with CNN’s features using different mechanisms. The entropy value is calculated to select the most useful coefficients and reduce the input’s dimensionality. The segmentation results are evaluated using fifty clinical brain MR and CT images by calculating the precision, recall, dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC). The results are also compared to other methods reported in the literature. The DSC of the bone class is improved from 0.6179 ± 0.0006 to 0.6416 ± 0.0006. The addition of multiresolution features of NSCT and NSST with CNN’s features demonstrates promising results. Moreover, NSST coefficients provide more useful information than NSCT coefficients.

The Characteristics of White Matter Hyperintensities in Patients With Migraine.

BackgroundThe presence of white matter hyperintensities (WMHs) in migraine is well-documented, but the location of WMH in patients with migraine is insufficiently researched. This study assessed WMH in patients with migraine using a modified version of the Scheltens visual rating scale, a semiquantitative scale for categorizing WMH in periventricular, lobar, basal ganglia, and infratentorial regions.MethodsIn total, 263 patients with migraine (31 men and232 women) enrolled in the American Registry for Migraine Research (ARMR) from Mayo Clinic Arizona and who had clinical brain magnetic resonance imaging (MRI) were included in this study. Those with imaging evidence for gross anatomical abnormalities other than WMHs were excluded. A board-certified neuroradiologist identified WMHs on axial T2 and fluid-attenuated inversion recovery (FLAIR) sequences. WMHs were characterized via manual inspection and categorized according to the scale's criteria.ResultsResults showed that 95 patients (36.1%, mean age: 41.8 years) had no WMHs on axial T2 and FLAIR imaging and 168 patients (63.9%, mean age: 51.4 year) had WMHs. Of those with WMHs, 94.1% (n = 158) had lobar hyperintensities (frontal: 148/158, 93.7%; parietal: 57/158, 36.1%; temporal: 35/158, 22.1%; and occipital: 9/158, 5.7%), 13/168, 7.7% had basal ganglia WMHs, 49/168, 29.1% had periventricular WMHs, and 17/168, 10.1% had infratentorial WMHs. In addition, 101/168 patients (60.1%) had bilateral WMHs and 67/168 (39.9%) had unilateral WMHs (34 right hemisphere/33 left hemisphere).DiscussionAmong ARMR participants who were enrolled by Mayo Clinic Arizona and who had clinical brain MRIs, nearly two-thirds had WMHs. The WMHs were the most common in the frontal lobes. Describing the features of WMHs in those with migraine, and comparing them with WMHs attributable to other etiologies, might be useful for developing classifiers that differentiate between migraine-specific WMH and other causes of WMH.

Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer's disease

Deep learning has the potential to standardize and automate diagnostics for complex medical imaging data, but real-world clinical images are plagued by a high degree of heterogeneity and confounding factors that may introduce imbalances and biases to such processes. To address this, we developed and applied a data matching algorithm to 467,464 clinical brain magnetic resonance imaging (MRI) data from the Mass General Brigham (MGB) healthcare system for Alzheimer's disease (AD) classification. We identified 18 technical and demographic confounding factors that can be readily distinguished by MRI or have significant correlations with AD status and isolated a training set free from these confounds. We then applied an ensemble of 3D ResNet-50 deep learning models to classify brain MRIs between groups of AD, mild cognitive impairment (MCI), and healthy controls. From a confounder-free matched dataset of 287,367 MRI files, we achieved an area under the receiver operating characteristic (AUROC) of 0.82 in distinguishing healthy controls from patients with AD or MCI. We also showed that confounding factors in heterogeneous clinical data could lead to artificial gains in model performance for disease classification, which our data matching approach could correct. This approach could accelerate using deep learning models for clinical diagnosis and find broad applications in medical image analysis.

Brain Magnetic Resonance Imaging Findings in Infantile Spasms.

Background: Infantile spasms are an age-specific epileptic disorder. They occur in infancy and early childhood. They can be caused by multiple etiologies. Structural abnormalities represent an important cause of infantile spasms. Brain magnetic resonance imaging (MRI) is one of the integral modalities in the evaluation of this condition. Purpose: The aim of this study is to review and analyze the clinical characteristics and brain MRI findings in a cohort of children diagnosed with infantile spasms. Material and Methods: A cohort of fifty-six children diagnosed with infantile spasms in infancy and early childhood was included. All of them underwent brain MRI for evaluation. The study was conducted in the period from January 2016 to January 2020. Results: Females comprised 57% of the cohort. The mean age for seizure onset was 5.9 months (SD 2.7). Forty-one patients (73%) had active epilepsy, and 51% were diagnosed with global developmental delay. Consanguinity was present in 59% of the cohort. Most of the follow-up MRIs showed structural abnormalities (84%). Hypoxia was reported in 17% of MRIs. Malformations of cortical development were seen in five patients. Brain MRI findings were normal in 16% of patients, and delayed myelination was seen in nineteen patients. Most of the children with active epilepsy (64%) and developmental delay (82%) had an abnormal brain MRI. It was noticed that abnormal second brain MRIs were more likely to be associated with active epilepsy and developmental delay (p = 0.05). Conclusions: Brain MRI is an integral part of infantile spasms’ clinical evaluation. Infantile spasms and abnormal brain MRI can be associated with active epilepsy and global developmental delay.

Applying Deep Learning to Accelerated Clinical Brain Magnetic Resonance Imaging for Multiple Sclerosis.

Background: Magnetic resonance (MR) scans are routine clinical procedures for monitoring people with multiple sclerosis (PwMS). Patient discomfort, timely scheduling, and financial burden motivate the need to accelerate MR scan time. We examined the clinical application of a deep learning (DL) model in restoring the image quality of accelerated routine clinical brain MR scans for PwMS.Methods: We acquired fast 3D T1w BRAVO and fast 3D T2w FLAIR MRI sequences (half the phase encodes and half the number of slices) in parallel to conventional parameters. Using a subset of the scans, we trained a DL model to generate images from fast scans with quality similar to the conventional scans and then applied the model to the remaining scans. We calculated clinically relevant T1w volumetrics (normalized whole brain, thalamic, gray matter, and white matter volume) for all scans and T2 lesion volume in a sub-analysis. We performed paired t-tests comparing conventional, fast, and fast with DL for these volumetrics, and fit repeated measures mixed-effects models to test for differences in correlations between volumetrics and clinically relevant patient-reported outcomes (PRO).Results: We found statistically significant but small differences between conventional and fast scans with DL for all T1w volumetrics. There was no difference in the extent to which the key T1w volumetrics correlated with clinically relevant PROs of MS symptom burden and neurological disability.Conclusion: A deep learning model that improves the image quality of the accelerated routine clinical brain MR scans has the potential to inform clinically relevant outcomes in MS.

A Novel Digital Care Management Platform to Monitor Clinical and Subclinical Disease Activity in Multiple Sclerosis.

In multiple sclerosis (MS), the early detection of disease activity or progression is key to inform treatment changes and could be supported by digital tools. We present a novel CE-marked and FDA-cleared digital care management platform consisting of (1) a patient phone/web application and healthcare professional portal (icompanion) including validated symptom, disability, cognition, and fatigue patient-reported outcomes; and (2) clinical brain magnetic resonance imaging (MRI) quantifications (icobrain ms). We validate both tools using their ability to detect (sub)clinical disease activity (known-groups validity) and real-world data insights. Surveys showed that 95.6% of people with MS (PwMS) were interested in using an MS app, and 98.2% were interested in knowing about MRI changes. The icompanion measures of disability (p < 0.001) and symptoms (p = 0.005) and icobrain ms MRI parameters were sensitive to (sub)clinical differences between MS subtypes. icobrain ms also decreased intra- and inter-rater lesion count variability and increased sensitivity for detecting disease activity/progression from 24% to 76% compared to standard radiological reading. This evidence shows PwMS’ interest, the digital care platform’s potential to improve the detection of (sub)clinical disease activity and care management, and the feasibility of linking different digital tools into one overarching MS care pathway.

Fitness-to-drive for glioblastoma patients: Guidance from the Swiss Neuro-Oncology Society (SwissNOS) and the Swiss Society for Legal Medicine (SGRM).

The management of brain tumour patients who would like to resume driving is complex, and needs multidisciplinary input and a consensus among treating physicians. The Swiss Neuro-Oncology Society (SwissNOS) and the Swiss Society for Legal Medicine (SGRM) aim to provide guidance on how to assess “fitness-to-drive” of glioblastoma patients and to harmonise the relevant procedures in Switzerland. At several meetings, Swiss neuro-oncologists discussed common practices on how to advise patients with a stable, i.e., non-progressive, glioblastoma, who wish to resume driving after the initial standard tumour treatment. All participants of the SwissNOS meetings were invited twice to return a questionnaire (modified Delphi process) on specific tools/procedures they commonly use to assess “fitness-to-drive” of their patients. Answers were analysed to formulate a tentative consensus for a structured and reasonable approach. Consensus on minimum requirements for a “fitness-to-drive” programme for glioblastoma patients could be reached among Swiss neuro-oncologists. The recommendations were based on existing guidelines and expert opinions regarding patients with seizures, visual disturbances, cognitive impairment or focal deficits for safe driving. At this point in time, the Swiss neuro-oncologists agreed on the following requirements for glioblastoma patients after the initial standard therapy and without a seizure for at least 12 months: (1) stable cranial magnetic resonance imaging (MRI) according to Response Assessment in Neuro-Oncology (RANO) criteria, to be repeated every 3 months; (2) thorough medical history, including current or new medication, a comprehensive neurological examination at baseline (T0) and every 3 months thereafter, optionally an electrocencephalogram (EEG) at baseline; (3) ophthalmological examination including visual acuity and intact visual fields; and (4) optional neuropsychological assessment with a focus on safe driving. Test results have to be compatible with safe driving at any time-point. Patients should be informed about test results and optionally sign a document. We propose regular thorough clinical neurological examination and brain MRI, optional EEG, neuropsychological and visual assessments to confirm “fitness-to-drive” for glioblastoma patients after initial tumour-directed therapy. The proposed “fitness-to-drive” assessments for glioblastoma patients serves as the basis for a prospective Swiss Pilot Project GLIODRIVE (BASEC ProjectID 2020-00365) to test feasibility, adherence and safety in a structured manner for patients who wish to resume driving. Research will focus on confirming the usefulness of the proposed tools in predicting “fitness-to-drive” and match results with events obtained from the road traffic registry (Strassenverkehrsamt).

Clinical seizures and unfavorable brain MRI patterns in neonates with hypoxic ischemic encephalopathy.

The aim was to examine whether clinical seizures and amplitude-integrated electroencephalogram (aEEG) patterns in infants with hypoxic ischemic encephalopathy (HIE) can predict the extent of brain injury on magnetic resonance images (MRI) and the long-term neurodevelopmental outcomes at 18∼24 months of age.HIE infants who underwent therapeutic hypothermia (TH) between June 2014 and March 2017 were included in this study. Infants with clinical seizure were analyzed for aEEG patterns and the extent of brain injury on MRI findings. Clinical seizure, aEEG, and brain MRI were assessed and compared with neurodevelopmental outcomes at 18∼24 months of age.Among the 97 HIE infants enrolled in this study with brain MRI scans, 78 (73.1%) TH-treated HIE infants exhibited clinical seizures. More abnormalities on a EEGs and more significant use of first and second antiepileptic drugs (AEDs) were significantly higher in the clinical-seizure group with longer hospitalized days. At a corrected 18 to 24 months of age, HIE infants in the clinical-seizure group with more extension of injury lesions on diffusion-weighted MRI scans exhibited significantly more delayed neurodevelopment. A risk factor analysis indicated that male infants who stayed in the hospital for more than 11 days were at a higher risk of having clinical seizures. The lesion size in MRI greater than 37 pixels was a risk factor with an 81.8% accuracy.Seizures in HIE infants may predict abnormal brain MRI scans and abnormal neurodevelopment at 18 to 24 months of age.