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Background and purposeNoncontrast head CTs are routinely acquired for patients with neurologic symptoms in the emergency department setting. Anecdotally, noncontrast head CTs performed in patients with prior negative findings with the same clinical indication are of low diagnostic yield. We hypothesized that the rate of acute findings in noncontrast head CTs performed in patients with a preceding study with negative findings would be lower compared with patients being imaged for the first time.Materials and methodsWe retrospectively evaluated patients in the emergency department setting who underwent noncontrast head CTs at our institution during a 4-year period, recording whether the patient had undergone a prior noncontrast head CT, the clinical indication for the examination, and the examination outcome. Positive findings on examinations were defined as those that showed any intracranial abnormality that would necessitate a change in acute management, such as acute hemorrhage, hydrocephalus, herniation, or interval worsening of a prior finding.ResultsDuring the study period, 8160 patients in the emergency department setting underwent a total of 9593 noncontrast head CTs; 88.2% (7198/8160) had a single examination, and 11.8% (962/8160) had at least 1 repeat examination. The baseline positive rate of the "nonrepeat" group was 4.3% (308/7198). The 911 patients in the "repeat" group with negative findings on a baseline/first CT had a total of 1359 repeat noncontrast head CTs during the study period. The rate of positive findings for these repeat examinations was 1.8% (25/1359), significantly lower than the 4.3% baseline rate (P

Background and Objectives: Treatment-resistant depression is a leading cause of disability. Our center's trial for neurosurgical intervention for treatment-resistant depression involves a staged workup for implantation of a personalized, closed-loop neuromodulation device for refractory depression. The first stage ("stage 1") of workup involves implantation of 10 stereoelectroencephalography (SEEG) electrodes bilaterally into 5 anatomically defined brain regions and involves a specialized preoperative imaging and planning workup and a frame-based operating protocol., Methods: We rely on diffusion tractography when planning stereotactic targets for 3 of 5 anatomic areas. We outline the rationale and fiber tracts that we focus on for targeting amygdala, ventral striatum and ventral capsule, and subgenual cingulate. We also outline frame-based stereotactic considerations for implantation of SEEG electrodes., Expected Outcomes: Our method has allowed us to safely target all 5 brain areas in 3 of 3 trial participants in this ongoing study, with adequate fiber bundle contact in each of the 3 areas targeted using tractography. Furthermore, we ultimately used tractography data from our stage 1 workup to guide targeting near relevant fiber bundles for stage 2 (implantation of a responsive neuromodulation device). On completion of our data set, we will determine the overlap between volume of tissue activated for all electrodes and areas of interest defined by anatomy and tractography., Discussion: Our protocol outlined for SEEG electrode implantation incorporates tractography and frame-based stereotaxy., (Copyright © Congress of Neurological Surgeons 2024. All rights reserved.)

Background: Deep brain stimulation (DBS) of the anterior limb of the internal capsule (ALIC) is an emerging treatment for severe, refractory obsessive-compulsive disorder (OCD). The therapeutic effects of DBS are hypothesized to be mediated by direct modulation of a distributed cortico-striato-thalmo-cortical network underlying OCD symptoms. However, the exact underlying mechanism by which DBS exerts its therapeutic effects still remains unclear., Method: In five participants receiving DBS for severe, refractory OCD (3 responders, 2 non-responders), we conducted a DBS On/Off cycling paradigm during the acquisition of functional MRI to determine the network effects of stimulation across a variety of bipolar configurations. We also performed tractography using diffusion-weighted imaging (DWI) to relate the functional impact of DBS to the underlying structural connectivity between active stimulation contacts and functional brain networks., Results: We found that therapeutic DBS had a distributed effect, suppressing BOLD activity within regions such as the orbitofrontal cortex, dorsomedial prefrontal cortex, and subthalamic nuclei compared to non-therapeutic configurations. Many of the regions suppressed by therapeutic DBS were components of the default mode network (DMN). Moreover, the estimated stimulation field from the therapeutic configurations exhibited significant structural connectivity to core nodes of the DMN., Conclusions: Therapeutic DBS for OCD suppresses BOLD activity within a distributed set of regions within the DMN relative to non-therapeutic configurations. We propose that these effects may be mediated by interruption of communication through structural white matter connections surrounding the DBS active contacts.

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Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background and Purpose: Deep learning algorithms for segmentation of multiple sclerosis (MS) plaques generally require training on large datasets. This manuscript evaluates the effect of transfer learning from segmentation of another pathology to facilitate use of smaller MS-specific training datasets. That is, a model trained for detection of one type of pathology was re-trained to identify MS lesions and active demyelination., Materials and Methods: In this retrospective study using MRI exams from 149 patients spanning 4/18/2014 to 7/8/2021, 3D convolutional neural networks were trained with a variable number of manually-segmented MS studies. Models were trained for FLAIR lesion segmentation at a single timepoint, new FLAIR lesion segmentation comparing two timepoints, and enhancing (actively demyelinating) lesion segmentation on T1 post-contrast imaging. Models were trained either de-novo or fine-tuned with transfer learning applied to a pre-existing model initially trained on non-MS data. Performance was evaluated with lesionwise sensitivity and positive predictive value (PPV)., Results: For single timepoint FLAIR lesion segmentation with 10 training studies, a fine-tuned model demonstrated improved performance [lesionwise sensitivity 0.55 ± 0.02 (mean ± standard error), PPV 0.66 ± 0.02] compared to a de-novo model (sensitivity 0.49 ± 0.02, p  = 0.001; PPV 0.32 ± 0.02, p  < 0.001). For new lesion segmentation with 30 training studies and their prior comparisons, a fine-tuned model demonstrated similar sensitivity (0.49 ± 0.05) and significantly improved PPV (0.60 ± 0.05) compared to a de-novo model (sensitivity 0.51 ± 0.04, p  = 0.437; PPV 0.43 ± 0.04, p  = 0.002). For enhancement segmentation with 20 training studies, a fine-tuned model demonstrated significantly improved overall performance (sensitivity 0.74 ± 0.06, PPV 0.69 ± 0.05) compared to a de-novo model (sensitivity 0.44 ± 0.09, p  = 0.001; PPV 0.37 ± 0.05, p  = 0.001)., Conclusion: By fine-tuning models trained for other disease pathologies with MS-specific data, competitive models identifying existing MS plaques, new MS plaques, and active demyelination can be built with substantially smaller datasets than would otherwise be required to train new models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor PC declared a shared affiliation with the authors at the time of review., (Copyright © 2023 Wahlig, Nedelec, Weiss, Rudie, Sugrue and Rauschecker.)

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., Competing Interests: Declaration of competing interest A.D.K. consults for Angelini, Eisai, Evecxia Therapeutics, Ferring Pharmaceuticals, Galderma, Harmony Biosciences, Idorsia, Jazz Pharmaceuticals, Janssen Pharmaceuticals, Lundbeck, Merck, Neurocrine Biosciences, Pernix Pharma, Sage Therapeutics, Takeda Pharmaceutical Company, Big Health, Millennium Pharmaceuticals, Otsuka Pharmaceutical and Neurawell Therapeutics. A.D.K. acknowledges support from Janssen Pharmaceuticals, Jazz Pharmaceuticals, Neurocrine, Axsome Therapeutics (no. AXS-05-301) and Reveal Biosensors. K.W.S. receives salary and equity options from Neumora Therapeutics. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: We explored the relationship between regional PRNP expression from healthy brain tissue and patterns of increased and decreased diffusion and regional brain atrophy in patients with sporadic Creutzfeldt-Jakob disease (sCJD)., Methods: We used PRNP microarray data from 6 healthy adult brains from Allen Brain Institute and T1-weighted and diffusion-weighted MRIs from 34 patients diagnosed with sCJD and 30 age- and sex-matched healthy controls to construct partial correlation matrices across brain regions for specific measures of interest: PRNP expression, mean diffusivity, volume, cortical thickness, and local gyrification index, a measure of cortical folding., Results: Regional patterns of PRNP expression in the healthy brain correlated with regional patterns of diffusion signal abnormalities and atrophy in sCJD. Among different measures of cortical morphology, regional patterns of local gyrification index in sCJD most strongly correlated with regional patterns of PRNP expression. At the vertex-wise level, different molecular subtypes of sCJD showed distinct regional correlations in local gyrification index across the cortex. Local gyrification index correlation patterns most closely matched patterns of PRNP expression in sCJD subtypes known to have greatest pathologic involvement of the cerebral cortex., Interpretation: These results suggest that the specific genetic and molecular environment in which the prion protein is expressed confer variable vulnerability to misfolding across different brain regions that is reflected in patterns of imaging findings in sCJD. Further work in larger samples will be needed to determine whether these regional imaging patterns can serve as reliable markers of distinct disease subtypes to improve diagnosis and treatment targeting., (© 2023 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Rationale and Objectives: Recent decades have seen a steady increase in noncontrast head CT utilization in the emergency department with a concurrent rise in the practice of physician assistants (PAs) and nurse practitioners (NPs). The goal of this study was to identify ordering and patient characteristics predictive of positive noncontrast head CTs in the ED. We hypothesized NP/PAs would have lower positivity rates compared to physicians, suggestive of relative overutilization., Materials and Methods: We retrospectively identified ED patients who underwent noncontrast head CTs at a single institution: a nonlevel 1 trauma center, during a 7-year period, recording examination positivity, ordering provider training/experience, and multiple additional ordering/patient attributes. Exam positivity was defined as any intracranial abnormality necessitating a change in acute management, such as acute hemorrhage, hydrocephalus, herniation, or worsening prior findings., Results: 6624 patients met inclusion criteria. 4.6% (280/6107) of physician exams were positive while 3.7% (19/517) of NP/PA exams were positive; however, differences were not significant. Increasing provider experience was not associated with positivity. Attributes with increased positivity were patient age (p < 0.001), daytime exam (p < 0.05), and indications regarding malignancy (p < 0.001) or focal neurologic deficit (p = 0.001). Attributes with decreased positivity were indications of trauma (p < 0.001) or vertigo/dizziness (p < 0.05)., Conclusion: We found no significant difference in rates of exam positivity between physicians and NP/PAs, even accounting for years of experience. This suggests increasing utilization of head CTs in the ED is not due to the increasing presence of NP/PAs, and may be reflective of general practice trends and clear diagnostic algorithms leading to head CT., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background and Purpose: Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gestational ages., Materials and Methods: This retrospective study included 246 patients with singleton pregnancies at 19-38 weeks gestation. A 3D U-Net was trained to segment the intracranial contents of 2D fetal brain MRIs in the axial, coronal, and sagittal planes. An additional 3D U-Net was trained to segment the brain from the output of the first model. Models were tested on MRIs of 10 patients (28 planes) via Dice coefficients and volume comparison with manual reference segmentations. Trained U-Nets were applied to 200 additional MRIs to develop normative reference intracranial and brain volumes across gestational ages and then to 9 pathologic fetal brains., Results: Fetal intracranial and brain compartments were automatically segmented in a mean of 6.8 (SD, 1.2) seconds with median Dices score of 0.95 and 0.90, respectively (interquartile ranges, 0.91-0.96/0.89-0.91) on the test set. Correlation with manual volume measurements was high (Pearson r = 0.996, P  < .001). Normative samples of intracranial and brain volumes across gestational ages were developed. Eight of 9 pathologic fetal intracranial volumes were automatically predicted to be >2 SDs from this age-specific reference mean. There were no effects of fetal sex, maternal diabetes, or maternal age on intracranial or brain volumes across gestational ages., Conclusions: Deep learning techniques can quickly and accurately quantify intracranial and brain volumes on clinical fetal brain MRIs and identify abnormal volumes on the basis of a normative reference standard., (© 2023 by American Journal of Neuroradiology.)

Background Assessment of appropriate brain myelination on T1- and T2-weighted MRI scans is based on gestationally corrected age (GCA) and requires subjective visual inspection of the brain with knowledge of normal myelination milestones. Purpose To develop a convolutional neural network (CNN) capable of estimating neonatal and infant GCA based on brain myelination on MRI scans. Materials and methods In this retrospective study from one academic medical center, brain MRI scans of patients aged 0-25 months with reported normal myelination were consecutively collected between January 1995 and June 2019. The GCA at MRI was manually calculated. After exclusion criteria were applied, T1- and T2-weighted MRI scans were preprocessed with skull stripping, linear registration, z scoring for normalization, and downsampling. A three-dimensional regression CNN was trained to predict GCA using mean absolute error (MAE) as its loss function. Attention maps were calculated using layer-wise relevance propagation. Models were validated on an external test set from the National Institutes of Health (NIH). Model MAEs were compared using Kruskal-Wallis and Mann-Whitney tests. Results A total of 518 neonates and infants (mean GCA, 67 weeks ± 33 [SD], 56% male) was included, comprising 469 T1-, 438 T2-, and 389 T1- and T2-weighted studies. Across 10 runs, MAEs of T1-, T2-, and T1- and T2-weighted networks were 9.8 ± 2.3, 9.1 ± 1.9, and 7.7 ± 1.7 weeks, respectively. Attention map analysis demonstrated increased network attention to the cerebellum, posterior white matter, and basal ganglia signal in neonates with GCA of less than 40 weeks and the anterior white matter signal in infants with GCA of more than 120 weeks, corresponding to the known progression of myelination. The T1- and T2-weighted network tested on the external NIH test set had an MAE of 9.1 weeks, which was reduced to 5.9 weeks with further training using half the external test set ( P < .001). Conclusion A three-dimensional convolutional neural network can predict the gestationally corrected age of neonates and infants aged 0-25 months based on brain myelination patterns on T1- and T2-weighted MRI scans. © RSNA, 2022 Online supplemental material is available for this article.

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., Competing Interests: Disclosures of conflicts of interest: J.D.R. American Society of Neuroradiology Foundation Grant in Artificial Intelligence funding for this work, including salary/time and computing resources; Radiology: Artificial Intelligence trainee editorial board alum. E.C. No relevant relationships. R.S. Consulting fees from Galileo CDS. D.W. Consulting fees from Galileo CDS from March 2019 to December 2020. J.B.C. No relevant relationships. S.C. No relevant relationships. C.P.H. Consulting fees from GE Healthcare; support for research travel from Siemens Healthineers; consulting fees from Data Safety Monitoring Board of uniQuire and Data Safety Monitoring Board of Focused Ultrasound Foundation; associate editor for Radiology; editorial board of Academic Radiology; Medical Advisory Board of PHACE Foundation. A.M.R. Radiology: Artificial Intelligence trainee editorial board alum. L.P.S. No relevant relationships. J.E.V.M. Grant from GE Healthcare, paid to UCSF; consulting fees from GE Healthcare, paid to author., (© 2022 by the Radiological Society of North America, Inc.)

Purpose: To 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 Methods: In 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., Results: The 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)., Conclusion: For 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., Competing Interests: Disclosures of conflicts of interest: A.M.R. Institution received American Society of Neuroradiology (ASNR) trainee grant and Carestream Health/Radiological Society of North America (RSNA) research scholar grant; former member of Radiology: Artificial Intelligence trainee editorial board. T.J.G. No relevant relationships. P.N. Formerly employed by Perceus and PrinterPress (urologic devices and orthopedic devices); grants/grants pending from Perceus for a urology device; patent from Perceus for a urology device; stock/stock options in PrinterPress (orthopedic devices). M.T.D. No relevant relationships. D.A.W. Consultancy fee from Galileo CDS for work not related to this publication; stock/stock options in Galileo CDS received for work not related to this publication. E.C. No relevant relationships. J.B.C. No relevant relationships. L.P.S. No relevant relationships. J.D.R. Institution received ASNR neuroradiology research grant in AI; former member of Radiology: Artificial Intelligence trainee editorial board. C.P.H. Medical imaging consultant for GE Healthcare; research travel expenses from Siemens Healthineers; member of data monitoring and safety boards for Insightec and UniQure; former member of Radiology editorial board., (2021 by the Radiological Society of North America, Inc.)

Background: Anti-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 Presentation: A 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., Conclusions: Our 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., (© 2021. The Author(s).)

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., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Purpose: fMRI 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., Methods: We 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., Results: First, 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., Conclusion: A 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., (© 2021. The Author(s).)

Importance: Incidental findings (IFs) are unexpected abnormalities discovered during imaging and can range from normal anatomic variants to findings requiring urgent medical intervention. In the case of brain magnetic resonance imaging (MRI), reliable data about the prevalence and significance of IFs in the general population are limited, making it difficult to anticipate, communicate, and manage these findings., Objectives: To determine the overall prevalence of IFs in brain MRI in the nonclinical pediatric population as well as the rates of specific findings and findings for which clinical referral is recommended., Design, Setting, and Participants: This cohort study was based on the April 2019 release of baseline data from 11 810 children aged 9 to 10 years who were enrolled and completed baseline neuroimaging in the Adolescent Brain Cognitive Development (ABCD) study, the largest US population-based longitudinal observational study of brain development and child health, between September 1, 2016, and November 15, 2018. Participants were enrolled at 21 sites across the US designed to mirror the demographic characteristics of the US population. Baseline structural MRIs were centrally reviewed for IFs by board-certified neuroradiologists and findings were described and categorized (category 1, no abnormal findings; 2, no referral recommended; 3; consider referral; and 4, consider immediate referral). Children were enrolled through a broad school-based recruitment process in which all children of eligible age at selected schools were invited to participate. Exclusion criteria were severe sensory, intellectual, medical, or neurologic disorders that would preclude or interfere with study participation. During the enrollment process, demographic data were monitored to ensure that the study met targets for sex, socioeconomic, ethnic, and racial diversity. Data were analyzed from March 15, 2018, to November 20, 2020., Main Outcomes and Measures: Percentage of children with IFs in each category and prevalence of specific IFs., Results: A total of 11 679 children (52.1% boys, mean [SD] age, 9.9 [0.62] years) had interpretable baseline structural MRI results. Of these, 2464 participants (21.1%) had IFs, including 2013 children (17.2%) assigned to category 2, 431 (3.7%) assigned to category 3, and 20 (0.2%) assigned to category 4. Overall rates of IFs did not differ significantly between singleton and twin gestations or between monozygotic and dizygotic twins, but heritability analysis showed heritability for the presence or absence of IFs (h2 = 0.260; 95% CI, 0.135-0.387)., Conclusions and Relevance: Incidental findings in brain MRI and findings with potential clinical significance are both common in the general pediatric population. By assessing IFs and concurrent developmental and health measures and following these findings over the longitudinal study course, the ABCD study has the potential to determine the significance of many common IFs.

Purpose: To 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 Methods: In 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 cm 3 ; interquartile range, 0.02-0.18 cm 3 ) 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., Results: The 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., Conclusion: A 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., Competing Interests: Disclosures of Conflicts of Interest: J.D.R. Activities related to the present article: current member of the Radiology: Artificial Intelligence trainee editorial board. Activities not related to the present article: institution received a grant from the American Society of Neuroradiology (ASNR Foundation grant in AI). Other relationships: disclosed no relevant relationships. D.A.W. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author was a consultant for Galileo CDS (work was done concurrently with position as consultant for Galileo CDS but Galileo GDS was not involved with the work under consideration); author has stock/stock options in Galileo GDS. Other relationships: disclosed no relevant relationships. J.B.C. disclosed no relevant relationships. A.M.R. Activities related to the present article: former member of the Radiology: Artificial Intelligence trainee editorial board. Activities not related to the present article: institution received an RSNA scholar grant; institution received an ASNR trainee grant; institution involved with NIH T-32 training program. Other relationships: disclosed no relevant relationships. B.L. disclosed no relevant relationships. S.B. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. L.P.S. disclosed no relevant relationships. C.P.H. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author is a research consultant for GE Healthcare; DSMB member of Focused Ultrasound Foundation; author received travel accommodations from Siemens Healthineers. Other relationships: disclosed no relevant relationships. J.E.V. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution received a grant from GE Healthcare. Other relationships: disclosed no relevant relationships., (2021 by the Radiological Society of North America, Inc.)

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.

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., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: An 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 Methods: A 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., Results: Sixty-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., Conclusion: Neuroradiological 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., (Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Meditation has shown to benefit a wide range of conditions and symptoms, but the neural mechanisms underlying the practice remain unclear. Magnetic resonance imaging (MRI) studies have investigated the structural brain changes due to the practice by examining volume, density, or cortical thickness changes. However, these studies have focused on adults; meditation's structural effects on the adolescent brain remain understudied. In this study, we investigated how meditation training affects the structure of the adolescent brain by scanning a group of 38 adolescents (16.48 ± 1.29 years) before and after participating in a 12-week meditation training. Subjects underwent Training for Awareness, Resilience, and Action (TARA), a program that mainly incorporates elements from mindfulness meditation and yoga-based practices. A subset of the adolescents also received an additional control scan 12 weeks before TARA. We conducted voxel-based morphometry (VBM) to assess gray matter volume changes pre- to post-training and during the control period. Subjects showed significant gray matter (GM) volume decreases in the left posterior insula and to a lesser extent in the left thalamus and left putamen after meditation training. There were no significant changes during the control period. Our results support previous findings that meditation affects regions associated with physical and emotional awareness. However, our results are different from previous morphometric studies in which meditation was associated with structural increases. We posit that this discrepancy may be due to the differences between the adolescent brain and the adult brain., (Copyright © 2020 Yuan, Connolly, Henje, Sugrue, Yang, Xu and Tymofiyeva.)

Radiogenomics, defined as the integrated analysis of radiologic imaging and genetic data, is a well-established tool shown to augment neuroimaging in the clinical diagnosis, prognostication, and scientific study of late-onset Alzheimer disease (LOAD). Early work using candidate single nucleotide polymorphisms (SNPs) identified genetic variation in APOE, BIN1, CLU, and CR1 as key modifiers of brain structure and function using magnetic resonance imaging (MRI). More recently, polygenic risk scores used in conjunction with MRI and positron emission tomography have shown great promise as a risk-stratification tool for clinical trials and care-management decisions. In addition, recent work using multimodal MRI and positron emission tomography as proxies of LOAD progression has identified novel risk variants that are enhancing our understanding of LOAD pathophysiology and progression. Herein, we highlight key studies and trends in the radiogenomics of LOAD over the past two decades and their implications for clinical practice and scientific research.

What is the future of neuroradiology in the era of precision medicine? As with any big change, this transformation in medicine presents both challenges and opportunities, and to flourish in this new environment we will have to adapt. It is difficult to predict exactly how neuroradiology will evolve in this shifting landscape, but there will be changes in both what we image and what we do. In terms of imaging, we will need to move beyond simply imaging brain anatomy and toward imaging function, both at the molecular and circuit level. In terms of what we do, we will need to move from the periphery of the clinical enterprise toward its center, with a new emphasis on integrating imaging with genetic and clinical data to form a comprehensive picture of the patient that can be used to direct further testing and care.The payoff is that these changes will align neuroradiology with the emerging field of precision psychiatry, which promises to replace symptom-based diagnosis and trial-and-error treatment of psychiatric disorders with diagnoses based on quantifiable genetic, imaging, physiologic, and behavioural criteria and therapies targeted to the particular pathophysiology of individual patients. Here we review some of the recent developments in behavioural genetics and neuroscience that are laying the foundation for precision psychiatry. By no means comprehensive, our goal is to introduce some of the perspectives and techniques that are likely to be relevant to the precision neuroradiologist of the future.

Importance: Mindfulness meditation training has been shown to be feasible in surgical trainees, but affective, cognitive, and performance benefits seen in other high-stress populations have yet to be evaluated., Objective: To explore potential benefits to stress, cognition, and performance in postgraduate year 1 (PGY-1) surgery residents receiving modified mindfulness-based stress reduction (modMBSR)., Design, Setting, and Participants: This follow-up study is an analysis of the Mindful Surgeon pilot randomized clinical trial of modMBSR (n = 12) vs an active control (n = 9), evaluated at baseline (T1), postintervention (T2), and 1 year (T3), took place at an academic medical center residency training program among PGY-1 surgery residents. Data were collected between June 2016 and June 2017 and analyzed from June 2017 to December 2017., Interventions: Weekly 2-hour modMBSR classes and 20 minutes of daily home practice during an 8-week period vs an active control (different content, same structure)., Main Outcomes and Measures: Preliminary evidence of efficacy was explored, primarily focusing on perceived stress and executive function and secondarily on burnout, depression, motor skill performance, and changes in blood oxygen level-dependent functional neuroimaging during an emotion regulation task. Group mean scores were calculated at T1, T2, and T3 and in linear mixed-effects multivariate analysis. Effect size for analysis of covariance is presented as partial η2 with the following cutoff points: small, less than 0.06; medium, 0.06 to 0.14; large, greater than 0.14., Results: Postgraduate year 1 surgery residents (N = 21; 8 [38%] women) were randomized to a modMBSR arm (n = 12) or an active control arm (n = 9). Linear mixed-effects modeling revealed differences at T2 and T3 in perceived stress (mean [SD] difference at T2: modMBSR, 1.42 [5.74]; control, 3.44 [6.71]; η2 = 0.07; mean [SD] difference at T3: modMBSR, 1.00 [4.18]; control, 1.33 [4.69]; η2 = 0.09) and in mindfulness (mean [SD] difference at T2: modMBSR, 3.08 [3.63]; control, 1.56 [4.28]; η2 = 0.13; mean [SD] difference at T3: modMBSR, 2.17 [3.66]; control, -0.11 [6.19]; η2 = 0.15). Burnout at T2 (mean [SD] difference: modMBSR, 4.50 [9.08]; control, 3.44 [6.71]; η2 = 0.01) and T3 (mean [SD] difference: modMBSR, 5.50 [9.96]; control, 5.56 [9.69]; η2 = 0.01) showed similar increase in both groups. Working memory increased more at T2 in the modMBSR arm (mean [SD] difference, 0.35 [0.60]) than in the control arm (mean [SD] difference, 0.21 [0.74]; η2 = 0.02) and at T3 (modMBSR, 0.68 [0.69]; control, 0.26 [0.58]; η2 = 0.20). Cognitive control decreased more in the control arm at T2 (mean [SD] difference at T2: modMBSR, 0.15 [0.40]; control, -0.07 [0.32]; η2 = 0.13) and at T3 (mean [SD] difference: modMBSR, 0.07 [0.59]; control, -0.26 [0.53]; η2 = 0.16). Mean (SD) circle-cutting time improved more at T2 in the modMBSR arm (-24.08 [63.00] seconds) than in the control arm (-4.22 [112.94] seconds; η2 = 0.23) and at T3 in the modMBSR arm (-4.83 [77.94] seconds) than in the control arm (11.67 [145.17] seconds; η2 = 0.13). Blood oxygen level-dependent functional neuroimaging during an emotional regulation task showed unique postintervention activity in the modMBSR arm in areas associated with executive function control (dorsolateral prefrontal cortex) and self-awareness (precuneus)., Conclusions and Relevance: In this pilot randomized clinical trial, modMBSR in PGY-1 surgery residents showed potential benefits to well-being and executive function, suggesting a powerful role for mindfulness-based cognitive training to support resident well-being and performance, as mandated by the Accreditation Council for Graduate Medical Education., Trial Registration: ClinicalTrials.gov identifier: NCT03141190.

Behavior deviating from our normative expectations often appears irrational. For example, even though behavior following the so-called matching law can maximize reward in a stationary foraging task, actual behavior commonly deviates from matching. Such behavioral deviations are interpreted as a failure of the subject; however, here we instead suggest that they reflect an adaptive strategy, suitable for uncertain, non-stationary environments. To prove it, we analyzed the behavior of primates that perform a dynamic foraging task. In such nonstationary environment, learning on both fast and slow timescales is beneficial: fast learning allows the animal to react to sudden changes, at the price of large fluctuations (variance) in the estimates of task relevant variables. Slow learning reduces the fluctuations but costs a bias that causes systematic behavioral deviations. Our behavioral analysis shows that the animals solved this bias-variance tradeoff by combining learning on both fast and slow timescales, suggesting that learning on multiple timescales can be a biologically plausible mechanism for optimizing decisions under uncertainty.

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 < 0.05 we identified 90 SNPs on 19 different chromosomes that were jointly associated with AD and CV-associated outcomes. In meta-analyses across three independent cohorts, we found four novel loci within MBLAC1 (chromosome 7, meta-p = 1.44 × 10 -9 ), MINK1 (chromosome 17, meta-p = 1.98 × 10 -7 ) and two chromosome 11 SNPs within the MTCH2/SPI1 region (closest gene = DDB2, meta-p = 7.01 × 10 -7 and closest gene = MYBPC3, meta-p = 5.62 × 10 -8 ). In a large 'AD-by-proxy' cohort from the UK Biobank, we replicated three of the four novel AD/CV pleiotropic SNPs, namely variants within MINK1, MBLAC1, and DDB2. Expression of MBLAC1, SPI1, MINK1 and DDB2 was differentially altered within postmortem AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid-associated RFs. We pinpoint a subset of cardiovascular-associated genes that strongly increase the risk for AD. Our collective findings support a disease model in which cardiovascular biology is integral to the development of clinical AD in a subset of individuals.

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.

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.

Hypothalamic hamartomas (HHs) are benign lesions that cause medically refractory seizures, behavioral disturbances, and endocrine dysfunction. Open resection of HHs does not guarantee seizure freedom and carries a relatively high risk of morbidity. Minimally invasive stereotactic laser ablation has recently been described as an effective and safe alternative for HH treatment. Prior studies have not, however, assessed HH lesion size and morphology, 2 factors that may influence treatment results and, ultimately, the generalizability of their findings. In this paper, the authors describe seizure outcomes for 5 pediatric patients who underwent laser ablation of sessile HHs. Lesions were treated using a frameless, interventional MRI-guided approach, which facilitated laser targeting to specific components of these complex lesions. The authors' experiences in these cases substantiate prior work demonstrating the effectiveness of laser therapy for HHs, while elucidating HH complexity as a potentially important factor in laser treatment planning, and in the interpretation of early studies describing this treatment method.

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.].

Background: Converging evidence suggests that immune-mediated dysfunction plays an important role in the pathogenesis of frontotemporal dementia (FTD). Although genetic studies have shown that immune-associated loci are associated with increased FTD risk, a systematic investigation of genetic overlap between immune-mediated diseases and the spectrum of FTD-related disorders has not been performed., Methods and Findings: Using large genome-wide association studies (GWASs) (total n = 192,886 cases and controls) and recently developed tools to quantify genetic overlap/pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with FTD-related disorders-namely, FTD, corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and amyotrophic lateral sclerosis (ALS)-and 1 or more immune-mediated diseases including Crohn disease, ulcerative colitis (UC), rheumatoid arthritis (RA), type 1 diabetes (T1D), celiac disease (CeD), and psoriasis. We found up to 270-fold genetic enrichment between FTD and RA, up to 160-fold genetic enrichment between FTD and UC, up to 180-fold genetic enrichment between FTD and T1D, and up to 175-fold genetic enrichment between FTD and CeD. In contrast, for CBD and PSP, only 1 of the 6 immune-mediated diseases produced genetic enrichment comparable to that seen for FTD, with up to 150-fold genetic enrichment between CBD and CeD and up to 180-fold enrichment between PSP and RA. Further, we found minimal enrichment between ALS and the immune-mediated diseases tested, with the highest levels of enrichment between ALS and RA (up to 20-fold). For FTD, at a conjunction false discovery rate < 0.05 and after excluding SNPs in linkage disequilibrium, we found that 8 of the 15 identified loci mapped to the human leukocyte antigen (HLA) region on Chromosome (Chr) 6. We also found novel candidate FTD susceptibility loci within LRRK2 (leucine rich repeat kinase 2), TBKBP1 (TBK1 binding protein 1), and PGBD5 (piggyBac transposable element derived 5). Functionally, we found that the expression of FTD-immune pleiotropic genes (particularly within the HLA region) is altered in postmortem brain tissue from patients with FTD and is enriched in microglia/macrophages compared to other central nervous system cell types. The main study limitation is that the results represent only clinically diagnosed individuals. Also, given the complex interconnectedness of the HLA region, we were not able to define the specific gene or genes on Chr 6 responsible for our pleiotropic signal., Conclusions: We show immune-mediated genetic enrichment specifically in FTD, particularly within the HLA region. Our genetic results suggest that for a subset of patients, immune dysfunction may contribute to FTD risk. These findings have potential implications for clinical trials targeting immune dysfunction in patients with FTD.

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 < 2.0 × 10 -16 ). Functionally, we found that these shared risk genes were associated with protein interaction and gene co-expression networks and showed enrichment for several neurodevelopmental pathways. Our findings suggest: (1) novel genetic overlap between CBD and PSP beyond the MAPT locus; (2) strong ties between CBD and FTD through the MAPT clade, and (3) unique combinations of overlapping genes that may, in part, influence selective regional or neuronal vulnerability observed in specific tauopathies.