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.

Diffusion tensor-based analysis of white matter in dogs with idiopathic epilepsy

Introduction

The understanding of epileptic seizure pathogenesis has evolved over time, and it is now generally accepted that not only are cortical and subcortical areas involved but also the connection of these regions in the white matter (WM). Recent human neuroimaging studies confirmed the involvement of the WM in several epilepsy syndromes. Neuroimaging studies investigating WM integrity with diffusion tensor imaging (DTI) in canine idiopathic epilepsy are lacking. This study aimed to test the hypothesis that WM diffusion changes can be found in dogs affected by idiopathic epilepsy.

Method

Twenty-six dogs with idiopathic epilepsy (15 Border Collies and 11 Greater Swiss Mountain dogs) and 24 healthy controls (11 Beagle dogs, 5 Border Collies, and 8 Greater Swiss Mountain dogs) were prospectively enrolled. Most dogs with idiopathic epilepsy (17/26) were enrolled within 3 months after seizure onset. Diffusion tensor imaging of the brain with 32 diffusion directions (low b value = 0 s/mm2; maximal b value = 800 s/mm2) was performed in a 3 Tesla scanner. Tract-based spatial statistics (TBSS), a voxel-based approach, was used to investigate changes in fractional anisotropy (FA) and mean diffusivity (MD) in the idiopathic epilepsy group compared to the healthy control group. Additionally, FA and MD were investigated in the region of corpus callosum and cingulate white matter in both groups.

Results

We observed subtle changes in WM DTI between the idiopathic epilepsy group and the healthy control group limited to cingulate WM, with a significantly lower FA in the idiopathic epilepsy group compared to the healthy control group in the region of interest (ROI) approach (p = 0.027). No significant changes were found between the idiopathic epilepsy group and the healthy control group in the TBSS analysis and in the corpus callosum in the ROI approach.

Conclusion

This study supports the cingulate area as a target structure in canine epilepsy. The subtle changes only might be explained by the short duration of epilepsy, small sample sizes, and the higher variability in canine brain anatomy. Furthermore, all included dogs showed generalized tonic-clonic seizures, possibly affected by generalized epilepsy syndrome, which are also associated with less pronounced DTI changes in humans than focal epilepsy syndromes.

"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.

Automatic MRI volumetry in asymptomatic cases at risk for normal pressure hydrocephalus

The occurrence of significant Alzheimer's disease (AD) pathology was described in approximately 30% of normal pressure hydrocephalus (NPH) cases, leading to the distinction between neurodegenerative and idiopathic forms of this disorder. Whether or not there is a specific MRI signature of NPH remains a matter of debate. The present study focuses on asymptomatic cases at risk for NPH as defined with automatic machine learning tools and combines automatic MRI assessment of cortical and white matter volumetry, risk of AD (AD-RAI), and brain age gap estimation (BrainAge). Our hypothesis was that brain aging and AD process-independent volumetric changes occur in asymptomatic NPH-positive cases. We explored the volumetric changes in normal aging-sensitive (entorhinal cortex and parahippocampal gyrus/PHG) and AD-signature areas (hippocampus), four control cortical areas (frontal, parietal, occipital, and temporal), and cerebral and cerebellar white matter in 30 asymptomatic cases at risk for NPH (NPH probability >30) compared to 30 NPH-negative cases (NPH probability <5) with preserved cognition. In univariate regression models, NPH positivity was associated with decreased volumes in the hippocampus, parahippocampal gyrus (PHG), and entorhinal cortex bilaterally. The strongest negative association was found in the left hippocampus that persisted when adjusting for AD-RAI and Brain Age values. A combined model including the three parameters explained 36.5% of the variance, left hippocampal volumes, and BrainAge values, which remained independent predictors of the NPH status. Bilateral PHG and entorhinal cortex volumes were negatively associated with NPH-positive status in univariate models but this relationship did not persist when adjusting for BrainAge, the latter remaining the only predictor of the NPH status. We also found a negative association between bilateral cerebral and cerebellar white matter volumes and NPH status that persisted after controlling for AD-RAI or Brain Age values, explaining between 50 and 65% of its variance. These observations support the idea that in cases at risk for NPH, as defined by support vector machine assessment of NPH-related MRI markers, brain aging-related and brain aging and AD-independent volumetric changes coexist. The latter concerns volume loss in restricted hippocampal and white matter areas that could be considered as the MRI signature of idiopathic forms of NPH.

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.

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.

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.

Deep learning based on preoperative magnetic resonance (MR) images improves the predictive power of survival models in primary spinal cord astrocytomas

BACKGROUND

Prognostic models for spinal cord astrocytoma patients are lacking due to the low incidence of the disease. Here, we aim to develop a fully automated deep learning (DL) pipeline for stratified overall survival (OS) prediction based on preoperative MR images.

METHODS

A total of 587 patients diagnosed with intramedullary tumors were retrospectively enrolled in our hospital to develop an automated pipeline for tumor segmentation and OS prediction. The automated pipeline included a T2WI-based tumor segmentation model and 3 cascaded binary OS prediction models (1-year, 3-year, and 5-year models). For the tumor segmentation model, 439 cases of intramedullary tumors were used to model training and testing using a transfer learning strategy. A total of 138 patients diagnosed with astrocytomas were included to train and test the OS prediction models via 10 × 10-fold cross-validation using CNNs.

RESULTS

The dice of the tumor segmentation model with the test set was 0.852. The results indicated that the best input of OS prediction models was a combination of T2W and T1C images and the tumor mask. The 1-year, 3-year, and 5-year automated OS prediction models achieved accuracies of 86.0%, 84.0%, and 88.0% and AUCs of 0.881 (95% CI 0.839-0.918), 0.862 (95% CI 0.827-0.901), and 0.905 (95% CI 0.867-0.942), respectively. The automated DL pipeline achieved 4-class OS prediction (<1 year, 1-3 years, 3-5 years, and >5 years) with 75.3% accuracy.

CONCLUSIONS

We proposed an automated DL pipeline for segmenting spinal cord astrocytomas and stratifying OS based on preoperative MR images.

Seeing in my way or your way: impact of intelligence, attention, and empathy on brain reactivity

Previous studies showed that neurotypical adults are able to engage in unconscious analyses of others' mental states in the context of automatic perspective taking and experience systematic difficulties when judging the conflicts between their own (Self) and another's (Other) perspective. Several functional MRI (fMRI) studies reported widespread activation of mentalizing, salience, and executive networks when adopting the Other compared to Self perspective. This study aims to explore whether cognitive and emotional parameters impact on brain reactivity in dot perspective task (dPT). We provide here an fMRI analysis based on individual z-scores in eighty-two healthy adults who underwent the Samson's dPT after detailed assessment of fluid intelligence, attention, levels of alexithymia and social cognition abilities. Univariate regression models were used to explore the association between brain activation patterns and psychological variables. There was a strong positive association between Wechsler Adult Intelligence Scale (WAIS) and fMRI z-scores in Self perspective. When the Other perspective is taken, Continuous Performance Test (CPT)-II parameters were negatively associated with fMRI z-scores. Individuals with higher Toronto Alexithymia scale (TAS) score and lower scores in mini-Social cognition and Emotional Assessment (SEA) displayed significantly higher egocentric interference-related fMRI z-scores. Our data demonstrate that brain activation when focusing on our own perspective depends on the levels of fluid intelligence. Decreased attentional recruitment and decreased inhibitory control affects the brain efforts to adopt the Other perspective. Egocentric interference-associated brain fMRI activation was less marked in cases with better empathy abilities but the opposite was true for persons who experience increased difficulties in the recognition of emotions.

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.

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.

Patterns of multiple brain network activation in dot perspective task

In this functional MRI (fMRI) study on 82 healthy adults using the dot perspective task, inconsistency of perspectives was associated with a significant increase of the mean reaction time and number of errors both in Self and Other conditions. Unlike the Arrow (non-mentalizing), the Avatar (mentalizing) paradigm was characterized by the recruitment of parts of the mentalizing and salience networks. These data provide experimental evidence supporting the fMRI distinction between mentalizing and non-mentalizing stimuli. A widespread activation of classical theory of mind (ToM) areas but also of salience network and decision making areas was observed in the Other compared to Self-conditions. Compared to Self-Consistent, Self-Inconsistent trials were related to increased activation in the lateral occipital cortex, right supramarginal and angular gyrus as well as inferior, superior and middle frontal gyri. Compared to the Other-Consistent, Other-Inconsistent trials yielded strong activation in the lateral occipital cortex, precuneus and superior parietal lobule, middle and superior precentral gyri and left frontal pole. These findings reveal that altercentric interference relies on areas involved in self-other distinction, self-updating and central executive functions. In contrast, egocentric interference needs the activation of the mirror neuron system and deductive reasoning, much less related to pure ToM abilities.

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury.

Eigenvector centrality dynamics are related to Alzheimer’s disease pathological changes in non-demented individuals

Amyloid-β accumulation starts in highly connected brain regions and is associated with functional connectivity alterations in the early stages of Alzheimer’s disease. This regional vulnerability is related to the high neuronal activity and strong fluctuations typical of these regions. Recently, dynamic functional connectivity was introduced to investigate changes in functional network organization over time. High dynamic functional connectivity variations indicate increased regional flexibility to participate in multiple subnetworks, promoting functional integration. Currently only a limited number of studies have explored the temporal dynamics of functional connectivity in the pre-dementia stages of Alzheimer’s disease. We study the associations between abnormal cerebrospinal fluid amyloid and both static and dynamic properties of functional hubs, using eigenvector centrality, and their relationship with cognitive performance, in 701 non-demented participants from the European Prevention of Alzheimer’s Dementia (EPAD) cohort.

Voxel-wise eigenvector centrality was computed for the whole functional magnetic resonance imaging time series (static), and within a sliding window (dynamic). Differences in static eigenvector centrality between amyloid positive (A+) and negative (A-) participants and amyloid-tau (AT) groups were found in a general linear model. Dynamic eigenvector centrality standard deviation and range were compared between groups within clusters of significant static eigenvector centrality differences, and within 10 canonical resting-state networks. The effect of the interaction between amyloid status and cognitive performance on dynamic eigenvector centrality variability was also evaluated with linear models. Models were corrected for age, sex and education level.

Lower static centrality was found in A + participants in posterior brain areas including a parietal and an occipital cluster; higher static centrality was found in a medio-frontal cluster. Lower eigenvector centrality variability (standard deviation) occurred in A + participants in the frontal cluster. The default mode network and the dorsal visual networks of A + participants had lower dynamic eigenvector centrality variability. Centrality variability in the Default Mode Network and dorsal visual networks were associated with cognitive performance in the A- and A + groups, with lower variability being observed in A + participants with good cognitive scores.

Our results support the role and timing of eigenvector centrality alterations in very early stages of Alzheimer’s disease and show that centrality variability over time adds relevant information on the dynamic patterns that cause static eigenvector centrality alterations. We propose that dynamic eigenvector centrality is an early biomarker of the interplay between early Alzheimer’s disease pathology and cognitive decline.

Epidemiology of healthcare-associated SARS-CoV-2 outbreaks in Germany between March 2020 and May 2022

BACKGROUND

Outbreaks in healthcare facilities played a pivotal role in the course of the coronavirus (COVID-19) pandemic.

AIM

To investigate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks in hospitals, outpatient care, and rehabilitation facilities in Germany from March 2020 to May 2022.

METHODS

Data from the German mandatory notification system were used to describe outbreaks by number of cases and case fatality ratio (CFR), and outbreak cases by age and gender. Using Pearson correlation, the dynamics of cases in the general population were compared with cases in healthcare-associated infection (HAI) SARS-CoV-2 outbreaks before and after the start of the vaccination campaign. Additionally, a counterfactual scenario was used to estimate numbers of prevented HAI cases, using the phase before vaccination as baseline.

FINDINGS

By the end of May 2022, 8941 healthcare-associated outbreaks were observed with 73,626 cases: 51,504 in hospitals, 15,524 in outpatient care, and 6598 in rehabilitation facilities. Median number of cases per outbreak was 4 (range: 2-342) and cases were more frequently reported in women with 46,818 (63.6%). Overall CFR was 8.1%, higher in men (12.4%) than in women (5.7%). After the vaccination campaign was fully introduced, the association between increasing incidence in the general population and consecutive outbreak cases was decreased by a factor of 10. Furthermore, our counterfactual analysis suggests that more than 55,000 outbreak cases could have been prevented until the end of 2021.

CONCLUSION

The vaccination campaign in combination with non-pharmaceutical measures was key to reduce number, size and CFR of healthcare-associated outbreaks.

Effects of gastric bypass surgery on brain connectivity responses to hypoglycemia

INTRODUCTION

Roux-en-Y gastric bypass (RYGB) leads to beneficial effects on glucose homeostasis, and attenuated hormonal counterregulatory responses to hypoglycemia are likely to contribute. RYGB also induces alterations in neural activity of cortical and subcortical brain regions. We aimed to characterize RYGB-induced changes in resting-state connectivity of specific brain regions of interest for energy homeostasis and behavioral control during hypoglycemia.

METHOD

Ten patients with BMI > 35 kg/m² were investigated with brain PET/MR imaging during a hyperinsulinemic normo- and hypoglycemic clamp, before and 4 months after RYGB. Hormonal levels were assessed throughout the clamp. Resting-state (RS) fMRI scans were acquired in the glucose-lowering phase of the clamp, and they were analyzed with a seed-to-voxel approach.

RESULTS

RS connectivity during initiation of hypoglycemia was significantly altered after RYGB between nucleus accumbens, thalamus, caudate, hypothalamus and their crosstalk with cortical and subcortical regions. Connectivity between the nucleus accumbens and the frontal pole was increased after RYGB, and this was associated with a reduction of ACTH (r = -0.639, p = 0.047) and cortisol (r = -0.635, p = 0.048) responses. Instead, connectivity between the caudate and the frontal pole after RYGB was reduced and this was associated with less attenuation of glucagon response during the hypoglycemic clamp (r = -0.728, p = 0.017), smaller reduction in fasting glucose (r = -0.798, p = 0.007) and less excess weight loss (r = 0.753, p = 0.012). No other significant associations were found between post-RYGB changes in ROI-to-voxel regional connectivity hormonal responses and metabolic or anthropometric outcomes.

CONCLUSION

RYGB alters brain connectivity during hypoglycemia of several neural pathways involved in reward, inhibitory control, and energy homeostasis. These changes are associated with altered hormonal responses to hypoglycemia and may be involved in the glucometabolic outcome of RYGB.

Automated Classification of Intramedullary Spinal Cord Tumors and Inflammatory Demyelinating Lesions Using Deep Learning

Accurate differentiation of intramedullary spinal cord tumors and inflammatory demyelinating lesions and their subtypes are warranted because of their overlapping characteristics at MRI but with different treatments and prognosis. The authors aimed to develop a pipeline for spinal cord lesion segmentation and classification using two-dimensional MultiResUNet and DenseNet121 networks based on T2-weighted images. A retrospective cohort of 490 patients (118 patients with astrocytoma, 130 with ependymoma, 101 with multiple sclerosis [MS], and 141 with neuromyelitis optica spectrum disorders [NMOSD]) was used for model development, and a prospective cohort of 157 patients (34 patients with astrocytoma, 45 with ependymoma, 33 with MS, and 45 with NMOSD) was used for model testing. In the test cohort, the model achieved Dice scores of 0.77, 0.80, 0.50, and 0.58 for segmentation of astrocytoma, ependymoma, MS, and NMOSD, respectively, against manual labeling. Accuracies of 96% (area under the receiver operating characteristic curve [AUC], 0.99), 82% (AUC, 0.90), and 79% (AUC, 0.85) were achieved for the classifications of tumor versus demyelinating lesion, astrocytoma versus ependymoma, and MS versus NMOSD, respectively. In a subset of radiologically difficult cases, the classifier showed an accuracy of 79%-95% (AUC, 0.78-0.97). The established deep learning pipeline for segmentation and classification of spinal cord lesions can support an accurate radiologic diagnosis. Supplemental material is available for this article. © RSNA, 2022 Keywords: Spinal Cord MRI, Astrocytoma, Ependymoma, Multiple Sclerosis, Neuromyelitis Optica Spectrum Disorder, Deep Learning.

Socioeconomic differences in contact reduction in the first COVID-19-wave in Germany (CoMoLo-study)

Background

The COVID-19 pandemic has led to physical distancing measures across a range of countries to control the spread of the virus. Evidence referring to contact dynamics in different socioeconomic populations is still sparse and may contribute to the explaining of socioeconomic inequality of infections.

Methods

Data came from two early COVID-19 hotspots in Germany using the CORONA-MONITORING-lokal study (CoMoLo). The sample (n = 3,637) was restricted to working age (18-67 years). We calculated the association of individual education and occupation status (low, middle, high) and self-reported private and professional contact reductions. Using weighting factors (adaptation to local age, gender and education distribution), we performed multivariate Poisson regressions (prevalence ratios; PR) with different sets of covariates: hotspot, age, sex, country of birth, household size, contact level before physical distancing measures and home office.

Results

The descriptive analyses show a clear socioeconomic gradient in private (low education: 70,0%; middle: 79,1%; high: 86,2%) and professional contact reductions (low education: 54,6%; middle: 61,3; high: 77,2%). The multivariate analyses confirm these associations, with a stronger gradient for professional contacts (private contact reduction: PR low vs. high education = 0,83 [KI:0.74-0.93]; professional contact reduction: PR low vs. high education = 0,75 [KI:0.64-0.89]) as well as for professional contact reduction when occupational status is considered instead of education.

Conclusions

Our results show disadvantages in groups with lower educational or occupational status in private and professional contact reductions in the first pandemic wave. This might result in a higher risk of infection. Preventive measures that a) adequately explain the importance of contact restrictions and b) facilitate the implementation of these reductions seem necessary to better protect structurally disadvantaged people during epidemics.

Key messages

• Groups with lower educational or occupational status were less likely of being able to reduce their private and professional contacts in the first wave of the pandemic.

• Socioeconomic differences were more pronounced in professional contact reduction compared to private contact reduction.

Waning of SARS-CoV-2 IgG antibodies after vaccination: first results from the CoMoLo follow-up 2021

Background

In 2020, the study “Corona-Monitoring Lokal” (CoMoLo) assessed seroprevalences of SARS-CoV-2 IgG antibodies in four study locations that were particularly affected by outbreaks in the early stages of the pandemic in Germany. One of the objectives of the 2021 follow-up was to examine the development of immunological parameters over time, including the extent of IgG antibody waning after vaccination.

Methods

Venous blood specimens were collected from a sample of initial study participants over a 2-week period between May and October 2021, with an oversampling of seropositive or previously infected individuals. Levels of IgG antibodies to the SARS-CoV-2 spike protein were measured from serum using Anti-SARS-CoV-2-QuantiVac-ELISA (IgG) tests by Euroimmun. Information on SARS-CoV-2 vaccinations or known infections was collected via online questionnaires or telephone interviews.

Results

A total of 3328 participants (74% response) gave blood specimens for this follow-up study, with questionnaire information available for 2843 (85%) of these. Preliminary analyses suggest that in participants who had received two doses of a vaccine more than 3 weeks before giving blood (n = 1583), IgG levels decreased exponentially by about 9.8% (95%CI 9.1% - 10.4%) with each additional week since the last dose, when controlling for age, sex, and type of vaccine. There was evidence of this waning effect differing by vaccine type. Antibody levels also appear to decline with increasing age, according to preliminary results. Final results of the linear model used to assess the dynamics and predictive factors of antibody levels will be reported.

Conclusions

This follow-up study will add evidence to an improved understanding of antibody waning after SARS-CoV-2 vaccination. Preliminary results are in line with international studies and may be helpful for discussions on potential benefits of further vaccinations in Germany.

Key messages

• Antibodies induced by COVID-19 vaccination wane over time. The magnitude of this effect differs by vaccine type. Antibodies also decreased with increasing age.

• Our results may be helpful for discussions on potential benefits of further COVID-19 vaccinations in Germany.

Wearing a KN95/FFP2 facemask induces subtle yet significant brain functional connectivity modifications restricted to the salience network

Background

The use of facemasks is one of the consequences of the coronavirus disease 2019 (COVID-19) pandemic. We used resting-state functional magnetic resonance imaging (fMRI) to search for subtle changes in brain functional connectivity, expected notably related to the high-level salience network (SN) and default mode network (DMN).

Methods

Prospective crossover design resting 3-T fMRI study with/without wearing a tight FFP2/KN95 facemask, including 23 community-dwelling male healthy controls aged 29.9 ± 6.9 years (mean ± standard deviation). Physiological parameters, respiration frequency, and heart rate were monitored. The data analysis was performed using the CONN toolbox.

Results

Wearing an FFP2/KN95 facemask did not impact respiration or heart rate but resulted in a significant reduction in functional connectivity between the SN as the seed region and the left middle frontal and precentral gyrus. No difference was found when the DMN, sensorimotor, visual, dorsal attention, or language networks were used as seed regions. In the absence of significant changes of physiological parameter respiration and heart rate, and in the absence of changes in lower-level functional networks, we assume that those subtle modifications are cognitive consequence of wearing facemasks.

Conclusions

The effect of wearing a tight FFP2/KN95 facemask in men is limited to high-level functional networks. Using the SN as seed network, we observed subtle yet significant decreases between the SN and the left middle frontal and precentral gyrus. Our observations suggest that wearing a facemask may change the patterns of functional connectivity with the SN known to be involved in communication, social behavior, and self-awareness.

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.

Early-phase 18F-Florbetapir and 18F-Flutemetamol images as proxies of brain metabolism in a memory clinic setting

Background: Alzheimer's disease (AD) neuropathologic changes are β-amyloid (Aβ) deposition, pathologic tau, and neurodegeneration. Dual-phase amyloid-PET might be able to evaluate Aβ deposition and neurodegeneration with a single tracer injection. Early-phase amyloid-PET scans provide a proxy for cerebral perfusion, which has shown good correlations with neural dysfunction measured through metabolic consumption, while the late frames depict amyloid distribution. Our study aims to assess the comparability between early-phase amyloid-PET scans and 18F-fluorodeoxyglucose (18F-FDG)-PET brain topography at the individual level, and their ability to discriminate patients. Methods: 166 subjects evaluated at the Geneva Memory Center, ranging from cognitively unimpaired to Mild Cognitive Impairment (MCI) and dementia, underwent early-phase amyloid-PET - using either 18F-florbetapir (eFBP) (n = 94) or 18F-flutemetamol (eFMM) (n = 72) - and 18F-FDG-PET. Aβ status was assessed. Standardized uptake value ratios (SUVR) were extracted to evaluate the correlation of eFBP/eFMM and their respective 18F-FDG-PET scans. The single-subject procedure was applied to investigate hypometabolism and hypoperfusion maps and their spatial overlap by Dice coefficient. Receiver operating characteristic analyses were performed to compare the discriminative power of eFBP/eFMM, and 18F-FDG-PET SUVR in AD-related metaROI between Aβ-negative healthy controls and cases in the AD continuum. Results: Positive correlations were found between eFBP/eFMM and 18F-FDG-PET SUVR independently of Aβ status and Aβ radiotracer (R>0.72, p<0.001). eFBP/eFMM single-subject analysis revealed clusters of significant hypoperfusion with good correspondence to hypometabolism topographies, independently of the underlying neurodegenerative patterns. Both eFBP/eFMM and 18F-FDG-PET SUVR significantly discriminated AD patients from controls in the AD-related metaROIs (AUCFBP = 0.888; AUCFMM=0.801), with 18F-FDG-PET performing slightly better, however not significantly (all p-value higher than 0.05), than others (AUCFDG=0.915 and 0.832 for subjects evaluated with 18F-FBP and 18F-FMM, respectively). Conclusion: The distribution of perfusion was comparable to that of metabolism at the single-subject level by parametric analysis, particularly in the presence of a high neurodegeneration burden. Our findings indicate that eFBP/eFMM imaging can replace 18F-FDG-PET imaging, as they reveal typical neurodegenerative patterns, or allow to exclude the presence of neurodegeneration. The finding shows cost-saving capacities of amyloid-PET and supports the routine use of the modality for individual classification in clinical practice.

Spatial-Temporal Patterns of β-Amyloid Accumulation: A Subtype and Stage Inference Model Analysis

BACKGROUND AND OBJECTIVES

β-amyloid (Aβ) staging models assume a single spatial-temporal progression of amyloid accumulation. We assessed evidence for Aβ accumulation subtypes by applying the data-driven Subtype and Stage Inference (SuStaIn) model to amyloid-PET data.

METHODS

Amyloid-PET data of 3,010 participants were pooled from 6 cohorts (ALFA+, EMIF-AD, ABIDE, OASIS, and ADNI). Standardized uptake value ratios were calculated for 17 regions. We applied the SuStaIn algorithm to identify consistent subtypes in the pooled dataset based on the cross-validation information criterion and the most probable subtype/stage classification per scan. The effects of demographics and risk factors on subtype assignment were assessed using multinomial logistic regression.

RESULTS

Participants were mostly cognitively unimpaired (n = 1890 [62.8%]), had a mean age of 68.72 (SD 9.1) years, 42.1% were APOE ε4 carriers, and 51.8% were female. A 1-subtype model recovered the traditional amyloid accumulation trajectory, but SuStaIn identified 3 optimal subtypes, referred to as frontal, parietal, and occipital based on the first regions to show abnormality. Of the 788 (26.2%) with strong subtype assignment (>50% probability), the majority was assigned to frontal (n = 415 [52.5%]), followed by parietal (n = 199 [25.3%]) and occipital subtypes (n = 175 [22.2%]). Significant differences across subtypes included distinct proportions of APOE ε4 carriers (frontal 61.8%, parietal 57.1%, occipital 49.4%), participants with dementia (frontal 19.7%, parietal 19.1%, occipital 31.0%), and lower age for the parietal subtype (frontal/occipital 72.1 years, parietal 69.3 years). Higher amyloid (Centiloid) and CSF p-tau burden was observed for the frontal subtype; parietal and occipital subtypes did not differ. At follow-up, most participants (81.1%) maintained baseline subtype assignment and 25.6% progressed to a later stage.

DISCUSSION

Whereas a 1-trajectory model recovers the established pattern of amyloid accumulation, SuStaIn determined that 3 subtypes were optimal, showing distinct associations with Alzheimer disease risk factors. Further analyses to determine clinical utility are warranted.

The Open-Access European Prevention of Alzheimer's Dementia (EPAD) MRI dataset and processing workflow

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features - i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features - Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) - were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

Personality Impact on Alzheimer's Disease-Signature and Vascular Imaging Markers: A PET-MRI Study

BACKGROUND

Several studies postulated that personality is an independent determinant of cognitive trajectories in old age.

OBJECTIVE

This study explores the impact of personality on widely used Alzheimer's disease (AD) and vascular imaging markers.

METHODS

We examined the association between personality and three classical AD imaging markers (centiloid-based-amyloid load, MRI volumetry in hippocampus, and media temporal lobe atrophy), and two vascular MRI parameters (Fazekas score and number of cortical microbleeds) assessed at baseline and upon a 54-month-follow-up. Personality was assessed with the Neuroticism Extraversion Openness Personality Inventory-Revised. Regression models were used to identify predictors of imaging markers including sex, personality factors, presence of APOE ɛ4 allele and cognitive evolution over time.

RESULTS

Cortical GM volumes were negatively associated with higher levels of Conscientiousness both at baseline and follow-up. In contrast, higher scores of Openness were related to better preservation of left hippocampal volumes in these two time points and negatively associated with medial temporal atrophy at baseline. Amyloid load was not affected by personality factors. Cases with higher Extraversion scores displayed higher numbers of cortical microbleeds at baseline.

CONCLUSION

Personality impact on brain morphometry is detected only in some among the routinely used imaging markers. The most robust associations concern the positive role of high levels of Conscientiousness and Openness on AD-signature MRI markers. Higher extraversion levels are associated with increased vulnerability to cortical microbleeds pointing to the fact that the socially favorable traits may have a detrimental effect on brain integrity in old age.

Increased resting state connectivity in the anterior default mode network of idiopathic epileptic dogs

Epilepsy is one of the most common chronic, neurological diseases in humans and dogs and considered to be a network disease. In human epilepsy altered functional connectivity in different large-scale networks have been identified with functional resting state magnetic resonance imaging. Since large-scale resting state networks have been consistently identified in anesthetised dogs’ application of this technique became promising in canine epilepsy research. The aim of the present study was to investigate differences in large-scale resting state networks in epileptic dogs compared to healthy controls. Our hypothesis was, that large-scale networks differ between epileptic dogs and healthy control dogs. A group of 17 dogs (Border Collies and Greater Swiss Mountain Dogs) with idiopathic epilepsy was compared to 20 healthy control dogs under a standardized sevoflurane anaesthesia protocol. Group level independent component analysis with dimensionality of 20 components, dual regression and two-sample t test were performed and revealed significantly increased functional connectivity in the anterior default mode network of idiopathic epileptic dogs compared to healthy control dogs (p = 0.00060). This group level differences between epileptic dogs and healthy control dogs identified using a rather simple data driven approach could serve as a starting point for more advanced resting state network analysis in epileptic dogs.

MRI of nigrosome-1: A potential triage tool for patients with suspected parkinsonism

BACKGROUND AND PURPOSE

Susceptibility-weighted imaging (SWI) of nigrosome-1 is an emerging and clinically applicable imaging marker for parkinsonism, which can be derived from routinely performed brain MRI. The purpose of the study was to assess whether SWI can be used as a triage tool for more efficient selection of subsequent Dopamine Transporter Scan (DaTSCAN) single-photon emission computed tomography (SPECT).

METHODS

We examined 72 consecutive patients with suspected parkinsonism with both DaTSCAN SPECT and SWI (48 in Philips Ingenia, 24 in GE Signa). Additionally, we examined 24 healthy controls with SWI (14 in Philips Ingenia, 10 in GE Signa). Diagnostic performance of SWI and DaTSCAN SPECT was assessed on the basis of clinical diagnosis, in terms of sensitivity, specificity, and diagnostic accuracy.

RESULTS

A total of 54 parkinsonism patients (69 years ± 9, 32 men), 18 nonparkinsonism patients (69.4 years ± 9, 10 men), and 24 healthy controls (62 years ± 8, 10 men) were recruited. SWI had a specificity of 92% and a sensitivity of 74%, whereas DaTSCAN SPECT had 83% and 94%, respectively. By preselecting patients with abnormal or inconclusive SWI, the diagnostic performance of DaTSCAN SPECT improved (specificity 100%, sensitivity 95%). Scans from Philips were associated with significantly lower image quality compared to GE (p < .001). The experienced rater outperformed the less experienced one in diagnostic accuracy (82% vs. 68%).

CONCLUSIONS

SWI can be used as triage tool because normal SWI can in most cases rule out parkinsonism. However, the performance of SWI depends on acquisition parameters and rater's experience.

Determinants of Cognitive Trajectories in Normal Aging: A Longitudinal PET-MRI Study in a Community-based Cohort

BACKGROUND

The determinants of the progressive decrement of cognition in normal aging are still a matter of debate. Alzheimer disease (AD)-signature markers and vascular lesions, but also psychological variables such as personality factors, are thought to have an impact on the longitudinal trajectories of neuropsychological performances in healthy elderly individuals.

OBJECTIVE

The current research aimed to identify the main determinants associated with cognitive trajectories in normal aging.

METHODS

We performed a 4.5-year longitudinal study in 90 older community-dwellers coupling two neuropsychological assessments, medial temporal atrophy (MTA), number of cerebral microbleeds (CMB), and white matter hyperintensities (WMH) at inclusion, visual rating of amyloid and FDG PET at follow-up, and APOE genotyping. Personality factors were assessed at baseline using the NEO-PIR. Univariate and backward stepwise regression models were built to explore the association between the continuous cognitive score (CCS) and both imaging and personality variables.

RESULTS

The number of strictly lobar CMB at baseline (4 or more) was related to a significant increase in the risk of cognitive decrement. In multivariable models, amyloid positivity was associated with a 1.73 unit decrease of the CCS at follow-up. MTA, WMH and abnormal FDG PET were not related to the cognitive outcome. Among personality factors, only higher agreeableness was related to better preservation of neuropsychological performances.

CONCLUSION

CMB and amyloid positivity are the only imaging determinants of cognitive trajectories in this highly selected series of healthy controls. Among personality factors, higher agreeableness confers a modest but significant protection against the decline of cognitive performances.

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.

Microbleeds and Medial Temporal Atrophy Determine Cognitive Trajectories in Normal Aging: A Longitudinal PET-MRI Study

BACKGROUND

The cognitive trajectories in normal aging may be affected by medial temporal atrophy (MTA) and amyloid burden, as well as vascular pathologies such as cortical microbleeds (CMB) and white matter hyperintensities (WMH).

OBJECTIVE

We addressed here the role of imaging markers in their prediction in a real-world situation.

METHODS

We performed a 4.5-year longitudinal study in 90 older community-dwellers coupling two neuropsychological assessments, MTA estimated with the Schelten's scale, number of CMB, and WMH evaluated with the Fazekas score at inclusion and follow-up, visual rating of amyloid PET and glucose hypometabolism at follow-up, and APOE genotyping. Regression models were built to explore the association between the continuous cognitive score (CCS) and imaging parameters.

RESULTS

The number of strictly lobar CMB at baseline (4 or more) was related to a 5.5-fold increase of the risk of cognitive decrement. This association persisted in multivariable models explaining 10.6% of the CCS decrease variance. MTA, and Fazekas score at baseline and amyloid positivity or abnormal FDG PET, were not related to the cognitive outcome. The increase of right MTA at follow-up was the only correlate of CCS decrease both in univariate and multivariable models explaining 9.2% of its variance.

CONCLUSION

The present data show that the accumulation of more than four CMB is associated with significant cognitive decrement over time in highly educated elderly persons. They also reveal that the progressive deterioration of cognitive performance within the age-adjusted norms is also related to the increase of visually assessed MTA.

Prediction of Subtle Cognitive Decline in Normal Aging: Added Value of Quantitative MRI and PET Imaging

Quantitative imaging processing tools have been proposed to improve clinic-radiological correlations but their added value at the initial stages of cognitive decline is still a matter of debate. We performed a longitudinal study in 90 community-dwelling elders with three neuropsychological assessments during a 4.5 year follow-up period, and visual assessment of medial temporal atrophy (MTA), white matter hyperintensities, cortical microbleeds (CMB) as well as amyloid positivity, and presence of abnormal FDG-PET patterns. Quantitative imaging data concerned ROI analysis of MRI volume, amyloid burden, and FDG-PET metabolism in several AD-signature areas. Multiple regression models, likelihood-ratio tests, and areas under the receiver operating characteristic curve (AUC) were used to compare quantitative imaging markers to visual inspection. The presence of more or equal to four CMB at inclusion and slight atrophy of the right MTL at follow-up were the only parameters to be independently related to the worst cognitive score explaining 6% of its variance. This percentage increased to 24.5% when the ROI-defined volume loss in the posterior cingulate cortex, baseline hippocampus volume, and MTL metabolism were also considered. When binary classification of cognition was made, the area under the ROC curve increased from 0.69 for the qualitative to 0.79 for the mixed imaging model. Our data reveal that the inclusion of quantitative imaging data significantly increases the prediction of cognitive changes in elderly controls compared to the single consideration of visual inspection.

Application of the ATN classification scheme in a population without dementia: Findings from the EPAD cohort

BACKGROUND

We classified non-demented European Prevention of Alzheimer's Dementia (EPAD) participants through the amyloid/tau/neurodegeneration (ATN) scheme and assessed their neuropsychological and imaging profiles.

MATERIALS AND METHODS

From 1500 EPAD participants, 312 were excluded. Cerebrospinal fluid cut-offs of 1000 pg/mL for amyloid beta (Aß)1-42 and 27 pg/mL for p-tau181 were validated using Gaussian mixture models. Given strong correlation of p-tau and t-tau (R2  = 0.98, P < 0.001), neurodegeneration was defined by age-adjusted hippocampal volume. Multinomial regressions were used to test whether neuropsychological tests and regional brain volumes could distinguish ATN stages.

RESULTS

Age was 65 ± 7 years, with 58% females and 38% apolipoprotein E (APOE) ε4 carriers; 57.1% were A-T-N-, 32.5% were in the Alzheimer's disease (AD) continuum, and 10.4% suspected non-Alzheimer's pathology. Age and cerebrovascular burden progressed with biomarker positivity (P < 0.001). Cognitive dysfunction appeared with T+. Paradoxically higher regional gray matter volumes were observed in A+T-N- compared to A-T-N- (P < 0.001).

DISCUSSION

In non-demented individuals along the AD continuum, p-tau drives cognitive dysfunction. Memory and language domains are affected in the earliest stages.

Prediction of H3K27M-mutant brainstem glioma by amide proton transfer-weighted imaging and its derived radiomics

PURPOSE

H3K27M-mutant associated brainstem glioma (BSG) carries a very poor prognosis. We aimed to predict H3K27M mutation status by amide proton transfer-weighted (APTw) imaging and radiomic features.

METHODS

Eighty-one BSG patients with APTw imaging at 3T MR and known H3K27M status were retrospectively studied. APTw values (mean, median, and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student's T/Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine learning algorithm (support vector machine) in randomly selected train (n = 64) and test (n = 17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks, and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm.

RESULTS

BSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, sensitivity of 0.92, and specificity of 0.80 in the test set. Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71-0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, sensitivity of 0.88, and specificity of 0.85 for predicting H3K27M-mutation status.

CONCLUSION

BSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTw-derived radiomics could accurately predict a H3K27M-mutant status in BSG patients.

Effects of Gastric Bypass Surgery on the Brain: Simultaneous Assessment of Glucose Uptake, Blood Flow, Neural Activity, and Cognitive Function During Normo- and Hypoglycemia

While Roux-en-Y gastric bypass (RYGB) surgery in obese individuals typically improves glycemic control and prevents diabetes, it also frequently causes asymptomatic hypoglycemia. Previous work showed attenuated counterregulatory responses following RYGB. The underlying mechanisms as well as the clinical consequences are unclear. In this study, 11 subjects without diabetes with severe obesity were investigated pre- and post-RYGB during hyperinsulinemic normo-hypoglycemic clamps. Assessments were made of hormones, cognitive function, cerebral blood flow by arterial spin labeling, brain glucose metabolism by ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography, and activation of brain networks by functional MRI. Post- versus presurgery, we found a general increase of cerebral blood flow but a decrease of total brain FDG uptake during normoglycemia. During hypoglycemia, there was a marked increase in total brain FDG uptake, and this was similar for post- and presurgery, whereas hypothalamic FDG uptake was reduced during hypoglycemia. During hypoglycemia, attenuated responses of counterregulatory hormones and improvements in cognitive function were seen postsurgery. In early hypoglycemia, there was increased activation post- versus presurgery of neural networks in brain regions implicated in glucose regulation, such as the thalamus and hypothalamus. The results suggest adaptive responses of the brain that contribute to lowering of glycemia following RYGB, and the underlying mechanisms should be further elucidated.

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.

Brain connectivity and metacognition in persons with subjective cognitive decline (COSCODE): rationale and study design

Background

Subjective cognitive decline (SCD) is the subjective perception of a decline in memory and/or other cognitive functions in the absence of objective evidence. Some SCD individuals however may suffer from very early stages of neurodegenerative diseases (such as Alzheimer’s disease, AD), minor psychiatric conditions, neurological, and/or somatic comorbidities. Even if a theoretical framework has been established, the etiology of SCD remains far from elucidated. Clinical observations recently lead to the hypothesis that individuals with incipient AD may have overestimated metacognitive judgements of their own cognitive performance, while those with psychiatric disorders typically present underestimated metacognitive judgements. Moreover, brain connectivity changes are known correlates of AD and psychiatric conditions and might be used as biomarkers to discriminate SCD individuals of different etiologies. The aim of the COSCODE study is to identify metacognition, connectivity, behavioral, and biomarker profiles associated with different etiologies of SCD. Here we present its rationale and study design.

Methods

COSCODE is an observational, longitudinal (4 years), prospective clinical cohort study involving 120 SCD, and 80 control study participants (40 individuals with no cognitive impairment, and 40 living with mild cognitive impairment - MCI, or dementia due to AD), all of which will undergo diffusion magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) as well as behavioral and biomarker assessments at baseline and after 1 and 2 years. Both hypothesis-driven and data-driven cluster analysis approaches will be used to identify SCD sub-types based on metacognition, connectivity, behavioral, and biomarker features.

Conclusion

COSCODE will allow defining and interpreting the constellation of signs and symptoms associated with different etiologies of SCD, paving the way to the development of cost-effective risk assessment and prevention protocols.