Search

Our understanding of brain iron regulation and its disruption in disease is limited. Excess iron affects motor circuitry, contributing to Parkinson's disease (PD) risk. The molecular mechanisms regulating central iron levels, beyond a few well-known genes controlling peripheral iron, remain unclear. We generated scores based on the archetypal brain iron accumulation observed in magnetic resonance imaging scans of individuals with excessive dietary iron absorption and hemochromatosis risk. Genome-wide analysis revealed that this score is highly heritable, identifying loci associated with iron homeostasis, and driven by peripheral iron levels. Our score predicted gait abnormalities and showed a U-shaped relationship with PD risk, identifying individuals with threefold increased risk. These results establish a hormetic relationship between brain iron and PD risk, where central iron levels are strongly determined by genetics via peripheral iron. This framework combining forward and reverse genetics is a powerful study design to understand genomic drivers underlying high dimensional phenotypes.

Supplemental material is available for this article.

Supplemental material is available for this article.

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

IntroductionA hexanucleotide repeat expansion (HRE) intronic to chromosome 9 open reading frame 72 (C9orf72) is recognized as the most common genetic cause of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and ALS-FTD. Identifying genes that show similar regional co-expression patterns to C9orf72 may help identify novel gene targets and biological mechanisms that mediate selective vulnerability to ALS and FTD pathogenesis.MethodsWe leveraged mRNA expression data in healthy brain from the Allen Human Brain Atlas to evaluate C9orf72 co-expression patterns. To do this, we correlated average C9orf72 expression values in 51 regions across different anatomical divisions (cortex, subcortex, and cerebellum) with average gene expression values for 15,633 protein-coding genes, including 54 genes known to be associated with ALS, FTD, or ALS-FTD. We then performed imaging transcriptomic analyses to evaluate whether the identified C9orf72 co-expressed genes correlated with patterns of cortical thickness in symptomatic C9orf72 pathogenic HRE carriers (n = 19) compared to controls (n = 23). Lastly, we explored whether genes with significant C9orf72 imaging transcriptomic correlations (i.e., "C9orf72 imaging transcriptomic network") were enriched in specific cell populations in the brain and enriched for specific biological and molecular pathways.ResultsA total of 2,120 genes showed an anatomical distribution of gene expression in the brain similar to C9orf72 and significantly correlated with patterns of cortical thickness in C9orf72 HRE carriers. This C9orf72 imaging transcriptomic network was differentially expressed in cell populations previously implicated in ALS and FTD, including layer 5b cells, cholinergic neurons in the spinal cord and brainstem and medium spiny neurons of the striatum, and was enriched for biological and molecular pathways associated with protein ubiquitination, autophagy, cellular response to DNA damage, endoplasmic reticulum to Golgi vesicle-mediated transport, among others.ConclusionConsidered together, we identified a network of C9orf72 associated genes that may influence selective regional and cell-type-specific vulnerabilities in ALS/FTD.

Hexanucleotide repeat expansion (HRE) within C9orf72 is the most common genetic cause of frontotemporal dementia (FTD). Thalamic atrophy occurs in both sporadic and familial FTD but is thought to distinctly affect HRE carriers. Separately, emerging evidence suggests widespread derepression of transposable elements (TEs) in the brain in several neurodegenerative diseases, including C9orf72 HRE-mediated FTD (C9-FTD). Whether TE activation can be measured in peripheral blood and how the reduction in peripheral C9orf72 expression observed in HRE carriers relates to atrophy and clinical impairment remain unknown. We used FreeSurfer software to assess the effects of C9orf72 HRE and clinical diagnosis (n = 78 individuals, male and female) on atrophy of thalamic nuclei. We also generated a novel, human, whole-blood RNA-sequencing dataset to determine the relationships among peripheral C9orf72 expression, TE activation, thalamic atrophy, and clinical severity (n = 114 individuals, male and female). We confirmed global thalamic atrophy and reduced C9orf72 expression in HRE carriers. Moreover, we identified disproportionate atrophy of the right mediodorsal lateral nucleus in HRE carriers and showed that C9orf72 expression associated with clinical severity, independent of thalamic atrophy. Strikingly, we found global peripheral activation of TEs, including the human endogenous LINE-1 element L1HS L1HS levels were associated with atrophy of multiple pulvinar nuclei, a thalamic region implicated in C9-FTD. Integration of peripheral transcriptomic and neuroimaging data from human HRE carriers revealed atrophy of specific thalamic nuclei, demonstrated that C9orf72 levels relate to clinical severity, and identified marked derepression of TEs, including L1HS, which predicted atrophy of FTD-relevant thalamic nuclei.SIGNIFICANCE STATEMENT Pathogenic repeat expansion in C9orf72 is the most frequent genetic cause of FTD and amyotrophic lateral sclerosis (ALS; C9-FTD/ALS). The clinical, neuroimaging, and pathologic features of C9-FTD/ALS are well characterized, whereas the intersections of transcriptomic dysregulation and brain structure remain largely unexplored. Herein, we used a novel radiogenomic approach to examine the relationship between peripheral blood transcriptomics and thalamic atrophy, a neuroimaging feature disproportionately impacted in C9-FTD/ALS. We confirmed reduction of C9orf72 in blood and found broad dysregulation of transposable elements-genetic elements typically repressed in the human genome-in symptomatic C9orf72 expansion carriers, which associated with atrophy of thalamic nuclei relevant to FTD. C9orf72 expression was also associated with clinical severity, suggesting that peripheral C9orf72 levels capture disease-relevant information.

BACKGROUND: Humans routinely shift their sleepiness and wakefulness levels in response to emotional factors. The diversity of emotional factors that modulates sleep-wake levels suggests that the ascending arousal network may be intimately linked with networks that mediate mood. Indeed, while animal studies have identified select limbic structures that play a role in sleep-wake regulation, the breadth of corticolimbic structures that directly modulates arousal in humans remains unknown. OBJECTIVE: We investigated whether select regional activation of the corticolimbic network through direct electrical stimulation can modulate sleep-wake levels in humans, as measured by subjective experience and behavior. METHODS: We performed intensive inpatient stimulation mapping in two human participants with treatment resistant depression, who underwent intracranial implantation with multi-site, bilateral depth electrodes. Stimulation responses of sleep-wake levels were measured by subjective surveys (i.e. Stanford Sleepiness Scale and visual-analog scale of energy) and a behavioral arousal score. Biomarker analyses of sleep-wake levels were performed by assessing spectral power features of resting-state electrophysiology. RESULTS: Our findings demonstrated three regions whereby direct stimulation modulated arousal, including the orbitofrontal cortex (OFC), subgenual cingulate (SGC), and, most robustly, ventral capsule (VC). Modulation of sleep-wake levels was frequency-specific: 100Hz OFC, SGC, and VC stimulation promoted wakefulness, whereas 1Hz OFC stimulation increased sleepiness. Sleep-wake levels were correlated with gamma activity across broad brain regions. CONCLUSIONS: Our findings provide evidence for the overlapping circuitry between arousal and mood regulation in humans. Furthermore, our findings open the door to new treatment targets and the consideration of therapeutic neurostimulation for sleep-wake disorders.

Neural networks were trained for segmentation and longitudinal assessment of posttreatment diffuse glioma. A retrospective cohort (from January 2018 to December 2019) of 298 patients with diffuse glioma (mean age, 52 years ± 14 [SD]; 177 men; 152 patients with glioblastoma, 72 patients with astrocytoma, and 74 patients with oligodendroglioma) who underwent two consecutive multimodal MRI examinations were randomly selected into training (n = 198) and testing (n = 100) samples. A posttreatment tumor segmentation three-dimensional nnU-Net convolutional neural network with multichannel inputs (T1, T2, and T1 postcontrast and fluid-attenuated inversion recovery [FLAIR]) was trained to segment three multiclass tissue types (peritumoral edematous, infiltrated, or treatment-changed tissue [ED]; active tumor or enhancing tissue [AT]; and necrotic core). Separate longitudinal change nnU-Nets were trained on registered and subtracted FLAIR and T1 postlongitudinal images to localize and better quantify and classify changes in ED and AT. Segmentation Dice scores, volume similarities, and 95th percentile Hausdorff distances ranged from 0.72 to 0.89, 0.90 to 0.96, and 2.5 to 3.6 mm, respectively. Accuracy rates of the posttreatment tumor segmentation and longitudinal change networks being able to classify longitudinal changes in ED and AT as increased, decreased, or unchanged were 76%-79% and 90%-91%, respectively. The accuracy levels of the longitudinal change networks were not significantly different from those of three neuroradiologists (accuracy, 90%-92%; κ, 0.58-0.63; P > .05). The results of this study support the potential clinical value of artificial intelligence-based automated longitudinal assessment of posttreatment diffuse glioma. Keywords: MR Imaging, Neuro-Oncology, Neural Networks, CNS, Brain/Brain Stem, Segmentation, Quantification, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

ImportanceHereditary hemochromatosis (HH) is an autosomal recessive genetic disorder that leads to iron overload. Conflicting results from previous research has led some to believe the brain is spared the toxic effects of iron in HH.ObjectiveTo test the association of the strongest genetic risk variant for HH on brainwide measures sensitive to iron deposition and the rates of movement disorders in a substantially larger sample than previous studies of its kind.Design, setting, and participantsThis cross-sectional retrospective study included participants from the UK Biobank, a population-based sample. Genotype, health record, and neuroimaging data were collected from January 2006 to May 2021. Data analysis was conducted from January 2021 to April 2022. Disorders tested included movement disorders (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10], codes G20-G26), abnormalities of gait and mobility (ICD-10 codes R26), and other disorders of the nervous system (ICD-10 codes G90-G99).ExposuresHomozygosity for p.C282Y, the largest known genetic risk factor for HH.Main outcomes and measuresT2-weighted and T2* signal intensity from brain magnetic resonance imaging scans, measures sensitive to iron deposition, and clinical diagnosis of neurological disorders.ResultsThe total cohort consisted of 488 288 individuals (264 719 female; ages 49-87 years, largely northern European ancestry), 2889 of whom were p.C282Y homozygotes. The neuroimaging analysis consisted of 836 individuals: 165 p.C282Y homozygotes (99 female) and 671 matched controls (399 female). A total of 206 individuals were excluded from analysis due to withdrawal of consent. Neuroimaging analysis showed that p.C282Y homozygosity was associated with decreased T2-weighted and T2* signal intensity in subcortical motor structures (basal ganglia, thalamus, red nucleus, and cerebellum; Cohen d >1) consistent with substantial iron deposition. Across the whole UK Biobank (2889 p.C282Y homozygotes, 485 399 controls), we found a significantly increased prevalence for movement disorders in male homozygotes (OR, 1.80; 95% CI, 1.28-2.55; P = .001) but not female individuals (OR, 1.09; 95% CI, 0.70-1.73; P = .69). Among the 31 p.C282Y male homozygotes with a movement disorder, only 10 had a concurrent HH diagnosis.Conclusions and relevanceThese findings indicate increased iron deposition in subcortical motor circuits in p.C282Y homozygotes and confirm an increased association with movement disorders in male homozygotes. Early treatment in HH effectively prevents the negative consequences of iron overload in the liver and heart. Our work suggests that screening for p.C282Y homozygosity in high-risk individuals also has the potential to reduce brain iron accumulation and to reduce the risk of movement disorders among male individuals who are homozygous for this mutation.

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.

PurposeTo assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss.Materials and methodsIn this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients.ResultsThe U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; P = .10). Addition of IN2 training data with heterogeneous pathologic features, representing only 10% (34 of 329) of total training data, increased performance to baseline (Dice score, 0.77; P < .001). This final model produced total lesion volumes with a high correlation to the reference standard (Spearman r = 0.98).ConclusionFor brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.Keywords: Neural Networks, Brain/Brain Stem, Segmentation Supplemental material is available for this article. © RSNA, 2021.

BackgroundAnti-NMDA receptor encephalitis is an immune-mediated disorder characterized by antibodies against the GluN1 subunit of the NMDA receptor that is increasingly recognized as a treatable cause of childhood epileptic encephalopathy. In adults, the disorder has been associated with reversible changes in brain volume over the course of treatment and recovery, but in children, little is known about its time course and associated imaging manifestations.Case presentationA previously healthy 20-month-old boy presented with first-time unprovoked seizures, dysautonomia, and dyskinesia. Paraneoplastic workup was negative, but CSF was positive for anti-NMDAR antibodies. The patient's clinical condition waxed and waned over a 14-month course of treatment with first- and second-line immunotherapies (including steroids, IVIG, rituximab, and cyclophosphamide). Serial brain MRIs scans obtained at 5 time points spanning this same period showed no abnormal signal or enhancement but were remarkable for cycles of reversible regional cortical volume loss. All scans included identical 1-mm resolution 3D T1-weighted sequences obtained on the same 3 T scanner. Using a novel longitudinal processing stream in FreeSurfer6 (Reuter M, et. al, Neuroimage 61:1402-18, 2012) we quantified the rate of change in cortical volume at each vertex (% volume change per month) between consecutive scans and correlated these changes with the time course of the patient's treatment and clinical response. We found regionally specific changes in cortical volume (up to 7% per month) that preferentially affected the frontal and occipital lobes and paralleled the patient's clinical course, with clinical decline associated with volume loss and clinical improvement associated with volume gain.ConclusionsOur results suggest that reversible cortical volume loss in anti-NMDA encephalitis has a regional specificity that mirrors many of the clinical symptoms associated with the disorder and tracks the dynamics of disease severity over time. This case illustrates how quantitative morphometric techniques can be applied to clinical imaging data to reveal patterns of brain change that may provide insight into disease pathophysiology. More widespread application of this approach might reveal regional and temporal patterns specific to different types of autoimmune encephalitis, providing a tool for diagnosis and a surrogate marker for monitoring treatment response.

Deep brain stimulation is a promising treatment for neuropsychiatric conditions such as major depression. It could be optimized by identifying neural biomarkers that trigger therapy selectively when symptom severity is elevated. We developed an approach that first used multi-day intracranial electrophysiology and focal electrical stimulation to identify a personalized symptom-specific biomarker and a treatment location where stimulation improved symptoms. We then implanted a chronic deep brain sensing and stimulation device and implemented a biomarker-driven closed-loop therapy in an individual with depression. Closed-loop therapy resulted in a rapid and sustained improvement in depression. Future work is required to determine if the results and approach of this n-of-1 study generalize to a broader population.

PurposefMRI is increasingly used for presurgical language mapping, but lack of standard methodology has made it difficult to combine/compare data across institutions or determine the relative efficacy of different approaches. Here, we describe a quantitative analytic framework for determining language laterality in clinical fMRI that addresses these concerns.MethodsWe retrospectively analyzed fMRI data from 59 patients who underwent presurgical language mapping at our institution with identical imaging and behavioral protocols. First, we compared the efficacy of different regional masks in capturing language activations. Then, we systematically explored how laterality indices (LIs) computed from these masks vary as a function of task and activation threshold. Finally, we determined the percentile threshold that maximized the correlation between the results of our LI approach and the laterality assessments from the original clinical radiology reports.ResultsFirst, we found that a regional mask derived from a meta-analysis of the fMRI literature better captured language task activations than masks based on anatomically defined language areas. Then, we showed that an LI approach based on this functional mask and percentile thresholding of subject activation can quantify the relative ability of different language tasks to lateralize language function at the population level. Finally, we determined that the 92nd percentile of subject-level activation provides the optimal LI threshold with which to reproduce the original clinical reports.ConclusionA quantitative framework for determining language laterality that uses a functionally-derived language mask and percentile thresholding of subject activation can combine/compare results across tasks and patients and reproduce clinical assessments of language laterality.

PurposeTo develop and validate a neural network for automated detection and segmentation of intracranial metastases on brain MRI studies obtained for stereotactic radiosurgery treatment planning.Materials and methodsIn this retrospective study, 413 patients (average age, 61 years ± 12 [standard deviation]; 238 women) with a total of 5202 intracranial metastases (median volume, 0.05 cm3; interquartile range, 0.02-0.18 cm3) undergoing stereotactic radiosurgery at one institution were included (January 2017 to February 2020). A total of 563 MRI examinations were performed among the patients, and studies were split into training (n = 413), validation (n = 50), and test (n = 100) datasets. A three-dimensional (3D) U-Net convolutional network was trained and validated on 413 T1 postcontrast or subtraction scans, and several loss functions were evaluated. After model validation, 100 discrete test patients, who underwent imaging after the training and validation patients, were used for final model evaluation. Performance for detection and segmentation of metastases was evaluated using Dice scores, false discovery rates, and false-negative rates, and a comparison with neuroradiologist interrater reliability was performed.ResultsThe median Dice score for segmenting enhancing metastases in the test set was 0.75 (interquartile range, 0.63-0.84). There were strong correlations between manually segmented and predicted metastasis volumes (r = 0.98, P < .001) and between the number of manually segmented and predicted metastases (R = 0.95, P < .001). Higher Dice scores were strongly correlated with larger metastasis volumes on a logarithmically transformed scale (r = 0.71). Sensitivity across the whole test sample was 70.0% overall and 96.4% for metastases larger than 6 mm. There was an average of 0.46 false-positive results per scan, with the positive predictive value being 91.5%. In comparison, the median Dice score between two neuroradiologists was 0.85 (interquartile range, 0.80-0.89), with sensitivity across the test sample being 87.9% overall and 98.4% for metastases larger than 6 mm.ConclusionA 3D U-Net-based convolutional neural network was able to segment brain metastases with high accuracy and perform detection at the level of human interrater reliability for metastases larger than 6 mm.Keywords: Adults, Brain/Brain Stem, CNS, Feature detection, MR-Imaging, Neural Networks, Neuro-Oncology, Quantification, Segmentation© RSNA, 2021.

Deep brain stimulation is a promising treatment for severe depression, but lack of efficacy in randomized trials raises questions regarding anatomical targeting. We implanted multi-site intracranial electrodes in a severely depressed patient and systematically assessed the acute response to focal electrical neuromodulation. We found an elaborate repertoire of distinctive emotional responses that were rapid in onset, reproducible, and context and state dependent. Results provide proof of concept for personalized, circuit-specific medicine in psychiatry.

BackgroundParkinson's disease (PD) and schizophrenia (SCZ) are heritable brain disorders that involve dysregulation of the dopaminergic system. Epidemiological studies have reported potential comorbidity between the disorders, and movement disturbances are common in patients with SCZ before treatment with antipsychotic drugs. Despite this, little is known about shared genetic etiology between the disorders.MethodsWe analyzed recent large genome-wide association studies on patients with SCZ (N = 77,096) and PD (N = 417,508) using a conditional/conjunctional false discovery rate (FDR) approach to evaluate overlap in common genetic variants and improve statistical power for genetic discovery. Using a variety of biological resources, we functionally characterized the identified genomic loci.ResultsWe observed genetic enrichment in PD conditional on associations with SCZ and vice versa, indicating polygenic overlap. We then leveraged this cross-trait enrichment using conditional FDR analysis and identified 9 novel PD risk loci and 1 novel SCZ locus at conditional FDR < .01. Furthermore, we identified 9 genomic loci jointly associated with PD and SCZ at conjunctional FDR < .05. There was an even distribution of antagonistic and agonistic effect directions among the shared loci, in line with the insignificant genetic correlation between the disorders. Of 67 genes mapped to the shared loci, 65 are expressed in the human brain and show cell type-specific expression profiles.ConclusionsAltogether, the study increases understanding of the genetic architectures underlying SCZ and PD, indicating that common molecular genetic mechanisms may contribute to overlapping pathophysiological and clinical features between the disorders.

Delineation and quantification of normal and abnormal brain tissues on Magnetic Resonance Images is fundamental to the diagnosis and longitudinal assessment of neurological diseases. Here we sought to develop a convolutional neural network for automated multiclass tissue segmentation of brain MRIs that was robust at typical clinical resolutions and in the presence of a variety of lesions. We trained a 3D U-Net for full brain multiclass tissue segmentation from a prior atlas-based segmentation method on an internal dataset that consisted of 558 clinical T1-weighted brain MRIs (453/52/53; training/validation/test) of patients with one of 50 different diagnostic entities (n = 362) or with a normal brain MRI (n = 196). We then used transfer learning to refine our model on an external dataset that consisted of 7 patients with hand-labeled tissue types. We evaluated the tissue-wise and intra-lesion performance with different loss functions and spatial prior information in the validation set and applied the best performing model to the internal and external test sets. The network achieved an average overall Dice score of 0.87 and volume similarity of 0.97 in the internal test set. Further, the network achieved a median intra-lesion tissue segmentation accuracy of 0.85 inside lesions within white matter and 0.61 inside lesions within gray matter. After transfer learning, the network achieved an average overall Dice score of 0.77 and volume similarity of 0.96 in the external dataset compared to human raters. The network had equivalent or better performance than the original atlas-based method on which it was trained across all metrics and produced segmentations in a hundredth of the time. We anticipate that this pipeline will be a useful tool for clinical decision support and quantitative analysis of clinical brain MRIs in the presence of lesions.

Rationale and objectivesAn increasing number of neurological complications and corresponding radiological findings have been reported in patients with COVID-19 infection. The purpose of this study is to systematically review the current literature on COVID-19-associated neuroradiological findings and examine the prevalence of different findings in patients with both severe and mild COVID-19 infection.Materials and methodsA comprehensive literature search of the PubMed and Embase databases was performed. Any studies reporting CT or MRI neuroimaging findings in patients with confirmed COVID-19 infection were included. Patient demographics, main radiological findings, neurological symptoms, and severity of COVID-19 infection were tabulated and quantified according to infection severity.ResultsSixty-one studies published between 2019 and 2020 comprising 711 patients were analyzed according to severity of respiratory symptoms. The main neuroradiological findings for patients with mild classification were cranial nerve abnormalities, ischemic infarction, and white matter abnormalities, while the main findings in patients with severe classification were white matter abnormalities, ischemic infarction, and hemorrhagic events.ConclusionNeuroradiological manifestations in COVID-19 infection are highly heterogeneous and differ based on the severity of COVID-19 infection. Cranial nerve abnormalities appear exclusive to mild infection, with a high degree of olfactory tract involvement, while hemorrhagic events are more common in severe infection. Notably, ischemic infarction was equally prevalent in both mild and severe COVID-19 infection. Healthcare providers treating COVID-19 patients should be aware of these potential complications and consider neurological assessment and neuroimaging studies when indicated.

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Mounting evidence indicates that the polygenic basis of late-onset Alzheimer's disease can be harnessed to identify individuals at greatest risk for cognitive decline. We have previously developed and validated a polygenic hazard score comprising of 31 single nucleotide polymorphisms for predicting Alzheimer's disease dementia age of onset. In this study, we examined whether polygenic hazard scores are associated with: (i) regional tracer uptake using amyloid PET; (ii) regional volume loss using longitudinal MRI; (iii) post-mortem regional amyloid-β protein and tau associated neurofibrillary tangles; and (iv) four common non-Alzheimer's pathologies. Even after accounting for APOE, we found a strong association between polygenic hazard scores and amyloid PET standard uptake volume ratio with the largest effects within frontal cortical regions in 980 older individuals across the disease spectrum, and longitudinal MRI volume loss within the entorhinal cortex in 607 older individuals across the disease spectrum. We also found that higher polygenic hazard scores were associated with greater rates of cognitive and clinical decline in 632 non-demented older individuals, even after controlling for APOE status, frontal amyloid PET and entorhinal cortex volume. In addition, the combined model that included polygenic hazard scores, frontal amyloid PET and entorhinal cortex volume resulted in a better fit compared to a model with only imaging markers. Neuropathologically, we found that polygenic hazard scores were associated with regional post-mortem amyloid load and neuronal neurofibrillary tangles, even after accounting for APOE, validating our imaging findings. Lastly, polygenic hazard scores were associated with Lewy body and cerebrovascular pathology. Beyond APOE, we show that in living subjects, polygenic hazard scores were associated with amyloid deposition and neurodegeneration in susceptible brain regions. Polygenic hazard scores may also be useful for the identification of individuals at the highest risk for developing multi-aetiological dementia.

Cardiovascular (CV)- and lifestyle-associated risk factors (RFs) are increasingly recognized as important for Alzheimer's disease (AD) pathogenesis. Beyond the ε4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV-associated genes also increase risk for AD. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV-associated RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), triglycerides (TG), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TG, TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR

Purpose To evaluate whether patients with neurofibromatosis type 1 (NF1)-a multisystem neurodevelopmental disorder with myriad imaging manifestations, including focal transient myelin vacuolization within the deep gray nuclei, brainstem, and cerebellum-exhibit differences in cortical and subcortical structures, particularly in subcortical regions where these abnormalities manifest. Materials and Methods In this retrospective study, by using clinically obtained three-dimensional T1-weighted MR images and established image analysis methods, 10 intracranial volume-corrected subcortical and 34 cortical regions of interest (ROIs) were quantitatively assessed in 32 patients with NF1 and 245 age- and sex-matched healthy control subjects. By using linear models, ROI cortical thicknesses and volumes were compared between patients with NF1 and control subjects, as a function of age. With hierarchic cluster analysis and partial correlations, differences in the pattern of association between cortical and subcortical ROI volumes in patients with NF1 and control subjects were also evaluated. Results Patients with NF1 exhibited larger subcortical volumes and thicker cortices of select regions, particularly the hippocampi, amygdalae, cerebellar white matter, ventral diencephalon, thalami, and occipital cortices. For the thalami and pallida and 22 cortical ROIs in patients with NF1, a significant inverse association between volume and age was found, suggesting that volumes decrease with increasing age. Moreover, compared with those in control subjects, ROIs in patients with NF1 exhibited a distinct pattern of clustering and partial correlations. Discussion Neurofibromatosis type 1 is characterized by larger subcortical volumes and thicker cortices of select structures. Most apparent within the hippocampi, amygdalae, cerebellar white matter, ventral diencephalon, thalami and occipital cortices, these neurofibromatosis type 1-associated volumetric changes may, in part, be age dependent. © RSNA, 2018 Online supplemental material is available for this article.

Tuberous sclerosis complex (TSC), a heritable neurodevelopmental disorder, is caused by mutations in the TSC1 or TSC2 genes. To date, there has been little work to elucidate regional TSC1 and TSC2 gene expression within the human brain, how it changes with age, and how it may influence disease. Using a publicly available microarray dataset, we found that TSC1 and TSC2 gene expression was highest within the adult neo-cerebellum and that this pattern of increased cerebellar expression was maintained throughout postnatal development. During mid-gestational fetal development, however, TSC1 and TSC2 expression was highest in the cortical plate. Using a bioinformatics approach to explore protein and genetic interactions, we confirmed extensive connections between TSC1/TSC2 and the other genes that comprise the mammalian target of rapamycin (mTOR) pathway, and show that the mTOR pathway genes with the highest connectivity are also selectively expressed within the cerebellum. Finally, compared to age-matched controls, we found increased cerebellar volumes in pediatric TSC patients without current exposure to antiepileptic drugs. Considered together, these findings suggest that the cerebellum may play a central role in TSC pathogenesis and may contribute to the cognitive impairment, including the high incidence of autism spectrum disorder, observed in the TSC population.

Neurodegenerative diseases likely share common underlying pathobiology. Although prior work has identified susceptibility loci associated with various dementias, few, if any, studies have systematically evaluated shared genetic risk across several neurodegenerative diseases. Using genome-wide association data from large studies (total n = 82,337 cases and controls), we utilized a previously validated approach to identify genetic overlap and reveal common pathways between progressive supranuclear palsy (PSP), frontotemporal dementia (FTD), Parkinson's disease (PD) and Alzheimer's disease (AD). In addition to the MAPT H1 haplotype, we identified a variant near the chemokine receptor CXCR4 that was jointly associated with increased risk for PSP and PD. Using bioinformatics tools, we found strong physical interactions between CXCR4 and four microglia related genes, namely CXCL12, TLR2, RALB, and CCR5. Evaluating gene expression from post-mortem brain tissue, we found that expression of CXCR4 and microglial genes functionally related to CXCR4 was dysregulated across a number of neurodegenerative diseases. Furthermore, in a mouse model of tauopathy, expression of CXCR4 and functionally associated genes was significantly altered in regions of the mouse brain that accumulate neurofibrillary tangles most robustly. Beyond MAPT, we show dysregulation of CXCR4 expression in PSP, PD, and FTD brains, and mouse models of tau pathology. Our multi-modal findings suggest that abnormal signaling across a 'network' of microglial genes may contribute to neurodegeneration and may have potential implications for clinical trials targeting immune dysfunction in patients with neurodegenerative diseases.

[This corrects the article DOI: 10.1371/journal.pmed.1002487.].

There is an urgent need for identifying nondemented individuals at the highest risk of progressing to Alzheimer's disease (AD) dementia. Here, we evaluated whether a recently validated polygenic hazard score (PHS) can be integrated with known in vivo cerebrospinal fluid (CSF) or positron emission tomography (PET) biomarkers of amyloid, and CSF tau pathology to prospectively predict cognitive and clinical decline in 347 cognitive normal (CN; baseline age range = 59.7-90.1, 98.85% white) and 599 mild cognitively impaired (MCI; baseline age range = 54.4-91.4, 98.83% white) individuals from the Alzheimer's Disease Neuroimaging Initiative 1, GO, and 2. We further investigated the association of PHS with post-mortem amyloid load and neurofibrillary tangles in the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort (N = 485, age at death range = 71.3-108.3). In CN and MCI individuals, we found that amyloid and total tau positivity systematically varies as a function of PHS. For individuals in greater than the 50th percentile PHS, the positive predictive value for amyloid approached 100%; for individuals in less than the 25th percentile PHS, the negative predictive value for total tau approached 85%. High PHS individuals with amyloid and tau pathology showed the steepest longitudinal cognitive and clinical decline, even among APOE ε4 noncarriers. Among the CN subgroup, we similarly found that PHS was strongly associated with amyloid positivity and the combination of PHS and biomarker status significantly predicted longitudinal clinical progression. In the ROSMAP cohort, higher PHS was associated with higher post-mortem amyloid load and neurofibrillary tangles, even in APOE ε4 noncarriers. Together, our results show that even after accounting for APOE ε4 effects, PHS may be useful in MCI and preclinical AD therapeutic trials to enrich for biomarker-positive individuals at highest risk for short-term clinical progression.

Corticobasal degeneration (CBD), progressive supranuclear palsy (PSP) and a subset of frontotemporal dementia (FTD) are neurodegenerative disorders characterized by tau inclusions in neurons and glia (tauopathies). Although clinical, pathological and genetic evidence suggests overlapping pathobiology between CBD, PSP, and FTD, the relationship between these disorders is still not well understood. Using summary statistics (odds ratios and p values) from large genome-wide association studies (total n = 14,286 cases and controls) and recently established genetic methods, we investigated the genetic overlap between CBD and PSP and CBD and FTD. We found up to 800-fold enrichment of genetic risk in CBD across different levels of significance for PSP or FTD. In addition to NSF (tagging the MAPT H1 haplotype), we observed that SNPs in or near MOBP, CXCR4, EGFR, and GLDC showed significant genetic overlap between CBD and PSP, whereas only SNPs tagging the MAPT haplotype overlapped between CBD and FTD. The risk alleles of the shared SNPs were associated with expression changes in cis-genes. Evaluating transcriptome levels across adult human brains, we found a unique neuroanatomic gene expression signature for each of the five overlapping gene loci (omnibus ANOVA p

BackgroundClinical, pathological and genetic overlap between sporadic frontotemporal dementia (FTD), Alzheimer's disease (AD) and Parkinson's disease (PD) has been suggested; however, the relationship between these disorders is still not well understood. Here we evaluated genetic overlap between FTD, AD and PD to assess shared pathobiology and identify novel genetic variants associated with increased risk for FTD.MethodsSummary statistics were obtained from the International FTD Genomics Consortium, International PD Genetics Consortium and International Genomics of AD Project (n>75 000 cases and controls). We used conjunction false discovery rate (FDR) to evaluate genetic pleiotropy and conditional FDR to identify novel FTD-associated SNPs. Relevant variants were further evaluated for expression quantitative loci.ResultsWe observed SNPs within the HLA, MAPT and APOE regions jointly contributing to increased risk for FTD and AD or PD. By conditioning on polymorphisms associated with PD and AD, we found 11 loci associated with increased risk for FTD. Meta-analysis across two independent FTD cohorts revealed a genome-wide signal within the APOE region (rs6857, 3'-UTR=PVRL2, p=2.21×10-12), and a suggestive signal for rs1358071 within the MAPT region (intronic=CRHR1, p=4.91×10-7) with the effect allele tagging the H1 haplotype. Pleiotropic SNPs at the HLA and MAPT loci associated with expression changes in cis-genes supporting involvement of intracellular vesicular trafficking, immune response and endo/lysosomal processes.ConclusionsOur findings demonstrate genetic pleiotropy in these neurodegenerative diseases and indicate that sporadic FTD is a polygenic disorder where multiple pleiotropic loci with small effects contribute to increased disease risk.