Functional MRI Neurofeedback Outperforms Cognitive Behavioral Therapy for Reducing Tinnitus Distress: A Prospective Randomized Clinical Trial.

Background Cognitive behavioral therapy (CBT) is the current standard treatment for chronic severe tinnitus; however, preliminary evidence suggests that real-time functional MRI (fMRI) neurofeedback therapy may be more effective. Purpose To compare the efficacy of real-time fMRI neurofeedback against CBT for reducing chronic tinnitus distress. Materials and Methods In this prospective controlled trial, participants with chronic severe tinnitus were randomized from December 2017 to December 2021 to receive either CBT (CBT group) for 10 weekly group sessions or real-time fMRI neurofeedback (fMRI group) individually during 15 weekly sessions. Change in the Tinnitus Handicap Inventory (THI) score (range, 0-100) from baseline to 6 or 12 months was assessed. Secondary outcomes included four quality-of-life questionnaires (Beck Depression Inventory, Pittsburgh Sleep Quality Index, State-Trait Anxiety Inventory, and World Health Organization Disability Assessment Schedule). Questionnaire scores between treatment groups and between time points were assessed using repeated measures analysis of variance and the nonparametric Wilcoxon signed rank test. Results The fMRI group included 21 participants (mean age, 49 years ± 11.4 [SD]; 16 male participants) and the CBT group included 22 participants (mean age, 53.6 years ± 8.8; 16 male participants). The fMRI group showed a greater reduction in THI scores compared with the CBT group at both 6 months (mean score change, -28.21 points ± 18.66 vs -12.09 points ± 18.86; P = .005) and 12 months (mean score change, -30 points ± 25.44 vs -4 points ± 17.2; P = .01). Compared with baseline, the fMRI group showed improved sleep (mean score, 8.62 points ± 4.59 vs 7.25 points ± 3.61; P = .006) and trait anxiety (mean score, 44 points ± 11.5 vs 39.84 points ± 10.5; P = .02) at 1 month and improved depression (mean score, 13.71 points ± 9.27 vs 6.53 points ± 5.17; P = .01) and general functioning (mean score, 24.91 points ± 17.05 vs 13.06 points ± 10.1; P = .01) at 6 months. No difference in these metrics over time was observed for the CBT group (P value range, .14 to >.99). Conclusion Real-time fMRI neurofeedback therapy led to a greater reduction in tinnitus distress than the current standard treatment of CBT. ClinicalTrials.gov registration no.: NCT05737888; Swiss Ethics registration no.: BASEC2017-00813 © RSNA, 2024 Supplemental material is available for this article.

Author Correction: Closed-loop brain training: the science of neurofeedback.

In this article, the affiliation for Mohit Rana was incorrectly listed as the Institute for Biological and Medical Engineering, Department of Psychiatry, and Section of Neuroscience, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860 Hernán Briones, piso 2, Macul 782-0436, Santiago, Chile. The listed affiliation should have been the following: Departamento de Psiquiatría, Escuela de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile; and the Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile. An acknowledgement to Mohit Rana's funding source was also missing. The following sentence should have been included in the acknowledgments section: M.R. is supported by a Fondecyt postdoctoral fellowship (project no. 3100648).

A deep learning model for generating [18F]FDG PET Images from early-phase [18F]Florbetapir and [18F]Flutemetamol PET images.

INTRODUCTION: Amyloid-ß (Aß) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([18F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunction and disease progression and is complementary for AD diagnosis. Dual-scan acquisitions of amyloid PET allows the possibility to use early-phase amyloid-PET as a biomarker for neurodegeneration, proven to have a good correlation to [18F]FDG PET. The aim of this study was to evaluate the added value of synthesizing the later from the former through deep learning (DL), aiming at reducing the number of PET scans, radiation dose, and discomfort to patients. METHODS: A total of 166 subjects including cognitively unimpaired individuals (N = 72), subjects with mild cognitive impairment (N = 73) and dementia (N = 21) were included in this study. All underwent T1-weighted MRI, dual-phase amyloid PET scans using either Fluorine-18 Florbetapir ([18F]FBP) or Fluorine-18 Flutemetamol ([18F]FMM), and an [18F]FDG PET scan. Two transformer-based DL models called SwinUNETR were trained separately to synthesize the [18F]FDG from early phase [18F]FBP and [18F]FMM (eFBP/eFMM). A clinical similarity score (1: no similarity to 3: similar) was assessed to compare the imaging information obtained by synthesized [18F]FDG as well as eFBP/eFMM to actual [18F]FDG. Quantitative evaluations include region wise correlation and single-subject voxel-wise analyses in comparison with a reference [18F]FDG PET healthy control database. Dice coefficients were calculated to quantify the whole-brain spatial overlap between hypometabolic ([18F]FDG PET) and hypoperfused (eFBP/eFMM) binary maps at the single-subject level as well as between [18F]FDG PET and synthetic [18F]FDG PET hypometabolic binary maps. RESULTS: The clinical evaluation showed that, in comparison to eFBP/eFMM (average of clinical similarity score (CSS) = 1.53), the synthetic [18F]FDG images are quite similar to the actual [18F]FDG images (average of CSS = 2.7) in terms of preserving clinically relevant uptake patterns. The single-subject voxel-wise analyses showed that at the group level, the Dice scores improved by around 13% and 5% when using the DL approach for eFBP and eFMM, respectively. The correlation analysis results indicated a relatively strong correlation between eFBP/eFMM and [18F]FDG (eFBP: slope = 0.77, R2 = 0.61, P-value < 0.0001); eFMM: slope = 0.77, R2 = 0.61, P-value < 0.0001). This correlation improved for synthetic [18F]FDG (synthetic [18F]FDG generated from eFBP (slope = 1.00, R2 = 0.68, P-value < 0.0001), eFMM (slope = 0.93, R2 = 0.72, P-value < 0.0001)). CONCLUSION: We proposed a DL model for generating the [18F]FDG from eFBP/eFMM PET images. This method may be used as an alternative for multiple radiotracer scanning in research and clinical settings allowing to adopt the currently validated [18F]FDG PET normal reference databases for data analysis.

Can large language models pass official high-grade exams of the European Society of Neuroradiology courses? A direct comparison between OpenAI chatGPT 3.5, OpenAI GPT4 and Google Bard.

We compared different LLMs, notably chatGPT, GPT4, and Google Bard and we tested whether their performance differs in subspeciality domains, in executing examinations from four different courses of the European Society of Neuroradiology (ESNR) notably anatomy/embryology, neuro-oncology, head and neck and pediatrics. Written exams of ESNR were used as input data, related to anatomy/embryology (30 questions), neuro-oncology (50 questions), head and neck (50 questions), and pediatrics (50 questions). All exams together, and each exam separately were introduced to the three LLMs: chatGPT 3.5, GPT4, and Google Bard. Statistical analyses included a group-wise Friedman test followed by a pair-wise Wilcoxon test with multiple comparison corrections. Overall, there was a significant difference between the 3 LLMs (p < 0.0001), with GPT4 having the highest accuracy (70%), followed by chatGPT 3.5 (54%) and Google Bard (36%). The pair-wise comparison showed significant differences between chatGPT vs GPT 4 (p < 0.0001), chatGPT vs Bard (p < 0. 0023), and GPT4 vs Bard (p < 0.0001). Analyses per subspecialty showed the highest difference between the best LLM (GPT4, 70%) versus the worst LLM (Google Bard, 24%) in the head and neck exam, while the difference was least pronounced in neuro-oncology (GPT4, 62% vs Google Bard, 48%). We observed significant differences in the performance of the three different LLMs in the running of official exams organized by ESNR. Overall GPT 4 performed best, and Google Bard performed worst. This difference varied depending on subspeciality and was most pronounced in head and neck subspeciality.

Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor.

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) is a major source of health loss and disability worldwide. Accurate and timely diagnosis of TBI is critical for appropriate treatment and management of the condition. Neuroimaging plays a crucial role in the diagnosis and characterization of TBI. Computed tomography (CT) is the first-line diagnostic imaging modality typically utilized in patients with suspected acute mild, moderate and severe TBI. Radiology reports play a crucial role in the diagnostic process, providing critical information about the location and extent of brain injury, as well as factors that could prevent secondary injury. However, the complexity and variability of radiology reports can make it challenging for healthcare providers to extract the necessary information for diagnosis and treatment planning. METHODS/RESULTS/CONCLUSION: In this article, we report the efforts of an international group of TBI imaging experts to develop a clinical radiology report template for CT scans obtained in patients suspected of TBI and consisting of fourteen different subdivisions (CT technique, mechanism of injury or clinical history, presence of scalp injuries, fractures, potential vascular injuries, potential injuries involving the extra-axial spaces, brain parenchymal injuries, potential injuries involving the cerebrospinal fluid spaces and the ventricular system, mass effect, secondary injuries, prior or coexisting pathology).

Alzheimer's disease genetic pathways impact cerebrospinal fluid biomarkers and imaging endophenotypes in non-demented individuals.

INTRODUCTION: Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine. METHODS: We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity. RESULTS: CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication. DISCUSSION: This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies. HIGHLIGHTS: Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers. Inflammatory pathways are mostly related to cerebrovascular burden. White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.

Neuroimaging in Dementia: More than Typical Alzheimer Disease.

Alzheimer disease (AD) is the most common cause of dementia. The prevailing theory of the underlying pathology assumes amyloid accumulation followed by tau protein aggregation and neurodegeneration. However, the current antiamyloid and antitau treatments show only variable clinical efficacy. Three relevant points are important for the radiologic assessment of dementia. First, besides various dementing disorders (including AD, frontotemporal dementia, and dementia with Lewy bodies), clinical variants and imaging subtypes of AD include both typical and atypical AD. Second, atypical AD has overlapping radiologic and clinical findings with other disorders. Third, the diagnostic process should consider mixed pathologies in neurodegeneration, especially concurrent cerebrovascular disease, which is frequent in older age. Neuronal loss is often present at, or even before, the onset of cognitive decline. Thus, for effective emerging treatments, early diagnosis before the onset of clinical symptoms is essential to slow down or stop subsequent neuronal loss, requiring molecular imaging or plasma biomarkers. Neuroimaging, particularly MRI, provides multiple imaging parameters for neurodegenerative and cerebrovascular disease. With emerging treatments for AD, it is increasingly important to recognize AD variants and other disorders that mimic AD. Describing the individual composition of neurodegenerative and cerebrovascular disease markers while considering overlapping and mixed diseases is necessary to better understand AD and develop efficient individualized therapies.

Current state and guidance on arterial spin labeling perfusion MRI in clinical neuroimaging.

This article focuses on clinical applications of arterial spin labeling (ASL) and is part of a wider effort from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group to update and expand on the recommendations provided in the 2015 ASL consensus paper. Although the 2015 consensus paper provided general guidelines for clinical applications of ASL MRI, there was a lack of guidance on disease-specific parameters. Since that time, the clinical availability and clinical demand for ASL MRI has increased. This position paper provides guidance on using ASL in specific clinical scenarios, including acute ischemic stroke and steno-occlusive disease, arteriovenous malformations and fistulas, brain tumors, neurodegenerative disease, seizures/epilepsy, and pediatric neuroradiology applications, focusing on disease-specific considerations for sequence optimization and interpretation. We present several neuroradiological applications in which ASL provides unique information essential for making the diagnosis. This guidance is intended for anyone interested in using ASL in a routine clinical setting (i.e., on a single-subject basis rather than in cohort studies) building on the previous ASL consensus review.

Prediction of H3K27M Alteration Status in Brainstem Glioma Using Multi-Shell Diffusion MRI Metrics.

BACKGROUND: Multi-shell diffusion characteristics may help characterize brainstem gliomas (BSGs) and predict H3K27M status. PURPOSE: To identify the diffusion characteristics of BSG patients and investigate the predictive values of various diffusion metrics for H3K27M status in BSG. STUDY TYPE: Prospective. POPULATION: Eighty-four BSG patients (median age 10.5 years [IQR 6.8-30.0 years]) were included, of whom 56 were pediatric and 28 were adult patients. FIELD STRENGTH/SEQUENCE: 3 T, multi-shell diffusion imaging. ASSESSMENT: Diffusion kurtosis imaging and neurite orientation dispersion and density imaging analyses were performed. Age, gender, and diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis, intracellular volume fraction (ICVF), orientation dispersion index, and isotropic volume fraction (ISOVF), were compared between H3K27M-altered and wildtype BSG patients. STATISTICAL TESTS: Chi-square test, Mann-Whitney U test, multivariate analysis of variance (MANOVA), step-wise multivariable logistic regression. P-values <0.05 were considered significant. RESULTS: 82.4% pediatric and 57.1% adult patients carried H3K27M alteration. In the whole group, the H3K27M-altered BSGs demonstrated higher FA, AK and lower RD, ISOVF. The combination of age and median ISOVF showed fair performance for H3K27M prediction (AUC = 0.78). In the pediatric group, H3K27M-altered BSGs showed higher FA, AK, MK, ICVF and lower RD, MD, ISOVF. The combinations of median ISOVF, 5th percentile of FA, median MK and median MD showed excellent predictive power (AUC = 0.91). In the adult group, H3K27M-altered BSGs showed higher ICVF and lower RD, MD. The 75th percentile of RD demonstrated fair performance for H3K27M status prediction (AUC = 0.75). DATA CONCLUSION: Different alteration patterns of diffusion measures were identified between H3K27M-altered and wildtype BSGs, which collectively had fair to excellent predictive value for H3K27M alteration status, especially in pediatric patients. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Artificial intelligence tools in clinical neuroradiology: essential medico-legal aspects.

Commercial software based on artificial intelligence (AI) is entering clinical practice in neuroradiology. Consequently, medico-legal aspects of using Software as a Medical Device (SaMD) become increasingly important. These medico-legal issues warrant an interdisciplinary approach and may affect the way we work in daily practice. In this article, we seek to address three major topics: medical malpractice liability, regulation of AI-based medical devices, and privacy protection in shared medical imaging data, thereby focusing on the legal frameworks of the European Union and the USA. As many of the presented concepts are very complex and, in part, remain yet unsolved, this article is not meant to be comprehensive but rather thought-provoking. The goal is to engage clinical neuroradiologists in the debate and equip them to actively shape these topics in the future.

"Soap bubble" sign as an imaging marker for posterior fossa ependymoma Group B.

PURPOSE: To investigate the predictive value of the "soap bubble" sign on molecular subtypes (Group A [PFA] and Group B [PFB]) of posterior fossa ependymomas (PF-EPNs). METHODS: MRI scans of 227 PF-EPNs (internal retrospective discovery set) were evaluated by two independent neuroradiologists to assess the "soap bubble" sign, which was defined as clusters of cysts of various sizes that look like "soap bubbles" on T2-weighted images. Two independent cohorts (external validation set [n = 31] and prospective validation set [n = 27]) were collected to validate the "soap bubble" sign. RESULTS: Across three datasets, the "soap bubble" sign was observed in 21 PFB cases (7.4% [21/285] of PF-EPNs and 12.9% [21/163] of PFB); none in PFA. Analysis of the internal retrospective discovery set demonstrated substantial interrater agreement (1st Rating: κ = 0.71 [0.53-0.90], 2nd Rating: κ = 0.83 [0.68-0.98]) and intrarater agreement (Rater 1: κ = 0.73 [0.55-0.91], Rater 2: κ = 0.74 [0.55-0.92]) for the "soap bubble" sign; all 13 cases positive for the "soap bubble" sign were PFB (p = 0.002; positive predictive value [PPV] = 100%, negative predictive value [NPV] = 44%, sensitivity = 10%, specificity = 100%). The findings from the external validation set and the prospective validation set were similar, all cases positive for the "soap bubble" sign were PFB (p < 0.001; PPV = 100%). CONCLUSION: The "soap bubble" sign represents a highly specific imaging marker for the PFB molecular subtype of PF-EPNs.

Amide proton transfer-weighted imaging of pediatric brainstem glioma and its predicted value for H3 K27 alteration.

BACKGROUND: Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge. PURPOSE: To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging. MATERIAL AND METHODS: This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t-test. The ability of APTw for differential diagnosis was evaluated using logistic regression. RESULTS: Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2-48 years) and 12 patients with wild-type tumor (aged 3-53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80. CONCLUSION: APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs.

Closed-loop brain training: the science of neurofeedback.

Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain-machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.

Alzheimer resemblance atrophy index, BrainAGE, and normal pressure hydrocephalus score in the prediction of subtle cognitive decline: added value compared to existing MR imaging markers.

OBJECTIVES: Established visual brain MRI markers for dementia include hippocampal atrophy (mesio-temporal atrophy MTA), white matter lesions (Fazekas score), and number of cerebral microbleeds (CMBs). We assessed whether novel quantitative, artificial intelligence (AI)-based volumetric scores provide additional value in predicting subsequent cognitive decline in elderly controls. METHODS: A prospective study including 80 individuals (46 females, mean age 73.4 ± 3.5 years). 3T MR imaging was performed at baseline. Extensive neuropsychological assessment was performed at baseline and at 4.5-year follow-up. AI-based volumetric scores were derived from 3DT1: Alzheimer Disease Resemblance Atrophy Index (AD-RAI), Brain Age Gap Estimate (BrainAGE), and normal pressure hydrocephalus (NPH) index. Analyses included regression models between cognitive scores and imaging markers. RESULTS: AD-RAI score at baseline was associated with Corsi (visuospatial memory) decline (10.6% of cognitive variability in multiple regression models). After inclusion of MTA, CMB, and Fazekas scores simultaneously, the AD-RAI score remained as the sole valid predictor of the cognitive outcome explaining 16.7% of its variability. Its percentage reached 21.4% when amyloid positivity was considered an additional explanatory factor. BrainAGE score was associated with Trail Making B (executive functions) decrease (8.5% of cognitive variability). Among the conventional MRI markers, only the Fazekas score at baseline was positively related to the cognitive outcome (8.7% of cognitive variability). The addition of the BrainAGE score as an independent variable significantly increased the percentage of cognitive variability explained by the regression model (from 8.7 to 14%). The addition of amyloid positivity led to a further increase in this percentage reaching 21.8%. CONCLUSIONS: The AI-based AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs. KEY POINTS: • AD-RAI score at baseline was associated with Corsi score (visuospatial memory) decline. • BrainAGE score was associated with Trail Making B (executive functions) decrease. • AD-RAI index and BrainAGE scores have limited but significant added value in predicting the subsequent cognitive decline in elderly controls when compared to the established visual MRI markers of brain aging, notably MTA, Fazekas score, and number of CMBs.

The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology.

Artificial intelligence (AI)-based tools are gradually blending into the clinical neuroradiology practice. Due to increasing complexity and diversity of such AI tools, it is not always obvious for the clinical neuroradiologist to capture the technical specifications of these applications, notably as commercial tools very rarely provide full details. The clinical neuroradiologist is thus confronted with the increasing dilemma to base clinical decisions on the output of AI tools without knowing in detail what is happening inside the "black box" of those AI applications. This dilemma is aggravated by the fact that currently, no established and generally accepted rules exist concerning best clinical practice and scientific and clinical validation nor for the medico-legal consequences in cases of wrong diagnoses. The current review article provides a practical checklist of essential points, intended to aid the user to identify and double-check necessary aspects, although we are aware that not all this information may be readily available at this stage, even for certified and commercially available AI tools. Furthermore, we therefore suggest that the developers of AI applications provide this information.

A deep learning algorithm for white matter hyperintensity lesion detection and segmentation.

PURPOSE: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types. METHODS: We developed and evaluated "DeepWML", a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard). RESULTS: The overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool's performance increased with larger lesion volumes. CONCLUSION: DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation.

Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis.

PURPOSE: To summarize the predictive value of MRI for H3 K27M-mutant in midline gliomas using meta-analysis. METHODS: Systematic electronic searches of the PubMed, Embase, ISI Web of Science, and Cochrane Library up to Jun 31, 2021, were conducted by two experienced neuroradiologists with the keywords of "MRI," "Glioma," and "H3 K27M." The hierarchical summary receiver-operating characteristic (HSROC) model was used to calculate the pooled sensitivity, specificity, positive likelihood ratio (LR +), negative likelihood ratio (LR -), and diagnostic odds ratio (DOR). Coupled forest plots were used to evaluate the heterogeneity of the included studies. RESULTS: Of seven original studies with a total of 593 patients, 240 glioma patients were included, with 45.5-70.6% H3 K27M-mutant gliomas. Using MRI, a pooled sensitivity of 0.78 (95% CI, 0.66-0.87), specificity of 0.85 (95% CI, 0.76-0.91), LR + of 5.07 (95% CI, 3.19-8.08), LR - of 0.26 (95% CI, 0.16-0.42), and DOR of 19.80 (95% CI, 9.28-42.28) were achieved for H3 K27M-mutant prediction. Significant heterogeneity was observed among the studies in terms of sensitivity (Q = 16.83, df = 7, p = 0.02; I2 = 58.40 [95% CI, 25.83-90.97]), LR - (Q = 16.61, df = 7, p = 0.02; I2 = 57.87 [95% CI, 24.81-90.93]), and DOR (Q = 14.05, df = 7, p = 0.05; I2 = 50.18 [95% CI, 10.06-90.31]). CONCLUSIONS: This meta-analysis demonstrated a clinical value of MRI to predict H3 K27M-mutant in midline gliomas with a pooled sensitivity of 0.78 and specificity of 0.85.

Chronic cerebral blood flow alterations in traumatic brain injury and sports-related concussions.

PRIMARY OBJECTIVE: Traumatic brain injury (TBI) and sports-related concussion (SRC) may result in chronic functional and neuroanatomical changes. We tested the hypothesis that neuroimaging findings (cerebral blood flow (CBF), cortical thickness, and 1H-magnetic resonance (MR) spectroscopy (MRS)) were associated to cognitive function, TBI severity, and sex. RESEARCH DESIGN: Eleven controls, 12 athletes symptomatic following ≥3SRCs and 6 patients with moderate-severe TBI underwent MR scanning for evaluation of cortical thickness, brain metabolites (MRS), and CBF using pseudo-continuous arterial spin labeling (ASL). Cognitive screening was performed using the RBANS cognitive test battery. MAIN OUTCOMES AND RESULTS: RBANS-index was impaired in both injury groups and correlated with the injury severity, although not with any neuroimaging parameter. Cortical thickness correlated with injury severity (p = 0.02), while neuronal density, using the MRS marker ((NAA+NAAG)/Cr, did not. On multivariate analysis, injury severity (p = 0.0003) and sex (p = 0.002) were associated with CBF. Patients with TBI had decreased gray (p = 0.02) and white matter (p = 0.02) CBF compared to controls. CBF was significantly lower in total gray, white matter and in 16 of the 20 gray matter brain regions in female but not male athletes when compared to female and male controls, respectively. CONCLUSIONS: Injury severity correlated with CBF, cognitive function, and cortical thickness. CBF also correlated with sex and was reduced in female, not male, athletes. Chronic CBF changes may contribute to the persistent injury mechanisms in TBI and rSRC.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Susceptibility-weighted Imaging: Technical Essentials and Clinical Neurologic Applications.

Susceptibility-weighted imaging (SWI) evolved from simple two-dimensional T2*-weighted sequences to three-dimensional sequences with improved spatial resolution and enhanced susceptibility contrast. SWI is an MRI sequence sensitive to compounds that distort the local magnetic field (eg, calcium and iron), in which the phase information can differentiate. But the term SWI is colloquially used to denote high-spatial-resolution susceptibility-enhanced sequences across different MRI vendors and sequences even when phase information is not used. The imaging appearance of SWI and related sequences strongly depends on the acquisition technique. Initially, SWI and related sequences were mostly used to improve the depiction of findings already known from standard two-dimensional T2*-weighted neuroimaging: more microbleeds in patients who are aging or with dementia or mild brain trauma; increased conspicuity of superficial siderosis in Alzheimer disease and amyloid angiopathy; and iron deposition in neurodegenerative diseases or abnormal vascular structures, such as capillary telangiectasia. But SWI also helps to identify findings not visible on standard T2*-weighted images: the nigrosome 1 in Parkinson disease and dementia with Lewy bodies, the central vein and peripheral rim signs in multiple sclerosis, the peripheral rim sign in abscesses, arterial signal loss related to thrombus, asymmetrically prominent cortical veins in stroke, and intratumoral susceptibility signals in brain neoplasms.