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Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Obesity and excess adiposity at midlife are risk factors for Alzheimer disease (AD). Visceral fat is known to be associated with insulin resistance and a pro-inflammatory state, the two mechanisms involved in AD pathology. We assessed the association of obesity, MRI-determined abdominal adipose tissue volumes, and insulin resistance with PET-determined amyloid and tau uptake in default mode network areas, and MRI-determined brain volume and cortical thickness in AD cortical signature in the cognitively normal midlife population. Thirty-two middle-aged (age: 51.27±6.12 years, 15 males, body mass index (BMI): 32.28±6.39 kg/m2) cognitively normal participants, underwent bloodwork, brain and abdominal MRI, and amyloid and tau PET scan. Visceral and subcutaneous adipose tissue (VAT, SAT) were semi-automatically segmented using VOXel Analysis Suite (Voxa). FreeSurfer was used to automatically segment brain regions using a probabilistic atlas. PET scans were acquired using [11C]PiB and AV-1451 tracers and were analyzed using PET unified pipeline. The association of brain volumes, cortical thicknesses, and PiB and AV-1451 standardized uptake value ratios (SUVRs) with BMI, VAT/SAT ratio, and insulin resistance were assessed using Spearman's partial correlation. VAT/SAT ratio was associated significantly with PiB SUVRs in the right precuneus cortex (p=0.034) overall, controlling for sex. This association was significant only in males (p=0.044), not females (p=0.166). Higher VAT/SAT ratio and PiB SUVRs in the right precuneus cortex were associated with lower cortical thickness in AD-signature areas predominantly including bilateral temporal cortices, parahippocampal, medial orbitofrontal, and cingulate cortices, with age and sex as covariates. Also, higher BMI and insulin resistance were associated with lower cortical thickness in bilateral temporal poles. In midlife cognitively normal adults, we demonstrated higher amyloid pathology in the right precuneus cortex in individuals with a higher VAT/SAT ratio, a marker of visceral obesity, along with a lower cortical thickness in AD-signature areas associated with higher visceral obesity, insulin resistance, and amyloid pathology.

Background: Neuroimaging studies often quantify tau burden in standardized brain regions to assess Alzheimer disease (AD) progression. However, this method ignores another key biological process in which tau spreads to additional brain regions. We have developed a metric for calculating the extent tau pathology has spread throughout the brain and evaluate the relationship between this metric and tau burden across early stages of AD., Methods: 445 cross-sectional participants (aged ≥ 50) who had MRI, amyloid PET, tau PET, and clinical testing were separated into disease-stage groups based on amyloid positivity and cognitive status (older cognitively normal control, preclinical AD, and symptomatic AD). Tau burden and tau spatial spread were calculated for all participants., Findings: We found both tau metrics significantly elevated across increasing disease stages (p < 0.0001) and as a function of increasing amyloid burden for participants with preclinical (p < 0.0001, p = 0.0056) and symptomatic (p = 0.010, p = 0.0021) AD. An interaction was found between tau burden and tau spatial spread when predicting amyloid burden (p = 0.00013). Analyses of slope between tau metrics demonstrated more spread than burden in preclinical AD (β = 0.59), but then tau burden elevated relative to spread (β = 0.42) once participants had symptomatic AD, when the tau metrics became highly correlated (R = 0.83)., Interpretation: Tau burden and tau spatial spread are both strong biomarkers for early AD but provide unique information, particularly at the preclinical stage. Tau spatial spread may demonstrate earlier changes than tau burden which could have broad impact in clinical trial design., Funding: This research was supported by the Knight Alzheimer Disease Research Center (Knight ADRC, NIH grants P30AG066444, P01AG026276, P01AG003991), Dominantly Inherited Alzheimer Network (DIAN, NIH grants U01AG042791, U19AG03243808, R01AG052550-01A1, R01AG05255003), and the Barnes-Jewish Hospital Foundation Willman Scholar Fund., Competing Interests: Declaration of interests Data collection and sharing for this project was supported by the Dominantly Inherited Alzheimer Network (DIAN, U19-AG032438) funded by the National Institute on Aging (NIA), the Alzheimer's Association (SG-20-690363-DIAN), the German Center for Neurodegenerative Diseases (DZNE), the Queen Square Dementia Biomedical Research Centre and the Medical Research Council Dementias Platform UK (MR/L023784/1 and MR/009076/1). Partial support has also been provided by research and development grants for dementia from the Japan Agency for Medical Research and Development (JP22dk0207049), AMED, the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), Korea Dementia Research Center (KDRC) funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (HI21C0066), the Spanish Institute of Health Carlos III (ISCIII), the Canadian Institutes of Health Research (TAD-125697), the Canadian Consortium of Neurodegeneration and Aging, the Brain Canada Foundation, Fonds de Recherche du Québec, and the Raul Carrea Institute for Neurological Research (FLENI). In addition to the acknowledged funding sources for this research, the authors of this manuscript have received financial support from the National Institutes of Health (BMA, TLSB, JH, JCM, CX), National Institute on Aging (AJA, RJB, CC), National Institute of Neurological Disorders and Stroke (RJB), Alzheimer's Association (RJB, CC), DIAN-TU-001 Tau NextGen and OLE (RJB), Biogen (RJB), AbbVie (RJB), Bristol Myers Squibb (RJB), Novartis (RJB), Centene Corporation (RJB), Rainwater Foundation (RJB), Association for Frontotemporal Degeneration FTD Biomarkers Initiative (RJB), BrightFocus Foundation (RJB), Cure Alzheimer's Fund (RJB), Coins for Alzheimer's Research Trust Fund (RJB), Eisai (RJB), The Foundation for Barnes-Jewish Hospital (RJB), TargetALS (RJB), Good Ventures Foundation (RJB), DIAN-TU Pharma Consortium (RJB), Eli Lilly (RJB, TLSB), Avid Radiopharmaceuticals (TLSB), Hoffman-La Roche (RJB), CogState (RJB), Signant (RJB), Siemens (TLSB), and Michael J. Fox Foundation (CC). Travel support was received from Hoffman-La Roche (RJB), Alzheimer's Association Roundtable (RJB), Duke Margolis Alzheimer's Roundtable (RJB), BrightFocus Foundation (RJB), Tau Consortium Investigator's Meeting (RJB), Fondazione Prada (RJB), NAPA Advisory Council on Alzheimer's Research (RJB), and Somalogics (CC). Consultations have been declared for Biogen (TLSB), Eli Lilly (TLSB), Eisai (TLSB), Siemens (TLSB), Bristol Myers Squibb (TLSB), Alector (CC), Circular Genomics (CC), Parabon Nanolabs (JH), Hoffman-La Roche (JH), AlzPath (JH), Prothena (JH), Barcelona Brain Research Center (JCM), Native Alzheimer Disease-Related Resource Center in Minority Aging Research (JCM), and Diadem (CX). Honoraria was received from Korean Dementia Association (RJB), American Neurological Association (RJB), Fondazione Prada (RJB), Weill Cornell Medical College (RJB), Harvard University (RJB), Biogen (TLSB), Eisai (TLSB), Montefiore Grand Rounds NY (JCM), and Tetra-Inst ADRC seminar series Grand Rounds NY (JCM). Patents have been declared for NfL as a marker for ICANs due to CAR-T (BMA), methods for measuring the metabolism of CNS derived biomolecules in vivo (RJB), methods for measuring the metabolism of neutrally derived biomolecules in vivo (RJB), plasma based methods for detecting CNS amyloid disposition (RJB), plasma based methods for determining A-Beta amyloidosis (RJB), methods of treating based on site-specific tau phosphorylation (RJB), and tau kinetic measurements (RJB). Authors participated on advisory boards for VID (BMA), NNTC (BMA), Hoffman-La Roche/Genentech (RJB), Biogen (RJB), UK Dementia Research Institute at University College London (RJB), Stanford University (RJB), Next Generation Translational Proteomics for Alzheimer's and Related Dementias (RJB), C2N Diagnostics (RJB), Eisai (TLSB), Siemens (TLSB), Eli Lilly (TLSB), Bristol Myers Squibb (TLSB), NIH-sponsored external advisor grants (TLSB), NIA-sponsored Caring Bridge and Wall-E (JH), Cure Alzheimer's Fund Research Strategy Council (JCM), Indiana University LEADS Advisory Board (JCM), and FDA Advisory Committee on Imaging Medical Products (CX). Other declared interests include royalties with equity ownership from C2N Diagnostics (RJB), partnering and receiving stock from Circular Genomics (CC), and being the executive director of DIAN (AD). Participation in the ASNR Alzheimer's and ARIA Study Group, QIBA Amyloid PET Working Group, Alzheimer's Association Clinical Tau PET Work Group, and American College of Radiology/AlzNet Work Group (TLSB). Precursors for radiopharmaceuticals and technology transfer were received from Avid Radiopharmaceuticals/Eli Lilly, LMI, and Cerveau/Lantheus (TLSB). Drugs and services were received from Eisai, Janssen, and Hoffman- La Roche for the DIAN-TU Next Generation Trial and DIAN-TU Gantenerumab Open Label Extension (RJB). All other authors have nothing to disclose., (Copyright © 2024. Published by Elsevier B.V.)

Introduction: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease., Methods: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status., Results: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R 2  = 0.95), fluorodeoxyglucose (R 2  = 0.93), and atrophy (R 2  = 0.95) in mutation carriers compared to non-carriers., Discussion: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions., (© 2021 the Alzheimer's Association.)

Objective: To enable use of clinical magnetic resonance images (MRIs) to quantify abnormalities in normal appearing (NA) white matter (WM) and gray matter (GM) in multiple sclerosis (MS) and to determine associations with MS-related disability. Identification of these abnormalities heretofore has required specialized scans not routinely available in clinical practice., Methods: We developed an analytic technique which normalizes image intensities based on an intensity atlas for quantification of WM and GM abnormalities in standardized MRIs obtained with clinical sequences. Gaussian mixture modeling is applied to summarize image intensity distributions from T1-weighted and 3D-FLAIR (T2-weighted) images from 5010 participants enrolled in a multinational database of MS patients which collected imaging, neuroperformance and disability measures., Results: Intensity distribution metrics distinguished MS patients from control participants based on normalized non-lesional signal differences. This analysis revealed non-lesional differences between relapsing MS versus progressive MS subtypes. Further, the correlation between our non-lesional measures and disability was approximately three times greater than that between total lesion volume and disability, measured using the patient derived disease steps. Multivariate modeling revealed that measures of extra-lesional tissue integrity and atrophy contribute uniquely, and approximately equally, to the prediction of MS-related disability., Interpretation: These results support the notion that non-lesional abnormalities correlate more strongly with MS-related disability than lesion burden and provide new insight into the basis of abnormalities in NA WM. Non-lesional abnormalities distinguish relapsing from progressive MS but do not distinguish between progressive subtypes suggesting a common progressive pathophysiology. Image intensity parameters and existing biomarkers each independently correlate with MS-related disability., (© 2021 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Recent functional magnetic resonance imaging studies have demonstrated that moment-to-moment variability in the blood oxygen level-dependent (BOLD) signal is related to age differences, cognition, and symptomatic Alzheimer's disease (AD). However, no studies have examined BOLD variability in the context of preclinical AD. We tested relationships between resting-state BOLD variability and biomarkers of amyloidosis, tauopathy, and neurodegeneration in a large (N = 321), well-characterized sample of cognitively normal adults (age = 39-93), using multivariate machine learning techniques. Furthermore, we controlled for cardiovascular health factors, which may contaminate resting-state BOLD variability estimates. BOLD variability, particularly in the default mode network, was related to cerebrospinal fluid (CSF) amyloid-β42 but was not related to CSF phosphorylated tau-181. Furthermore, BOLD variability estimates were also related to markers of neurodegeneration, including CSF neurofilament light protein, hippocampal volume, and a cortical thickness composite. Notably, relationships with hippocampal volume and cortical thickness survived correction for cardiovascular health and also contributed to age-related differences in BOLD variability. Thus, BOLD variability may be sensitive to preclinical pathology, including amyloidosis and neurodegeneration in AD-sensitive areas., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Fusion of preoperative and intraoperative magnetic resonance imaging (iMRI) studies during stereotactic navigation may be very useful for procedures such as tumor resections but can be subject to error because of image distortion., Objective: To assess the impact of 3-dimensional (3D) vs 2-dimensional (2D) image distortion correction on the accuracy of auto-merge image fusion for stereotactic neurosurgical images acquired with iMRI using a head phantom in different surgical positions., Methods: T1-weighted intraoperative images of the head phantom were obtained using 1.5T iMRI. Images were postprocessed with 2D and 3D image distortion correction. These studies were fused to T1-weighted preoperative MRI studies performed on a 1.5T diagnostic MRI. The reliability of the auto-merge fusion of these images for 2D and 3D correction techniques was assessed both manually using the stereotactic navigation system and via image analysis software., Results: Eight surgical positions of the head phantom were imaged with iMRI. Greater image distortion occurred with increased distance from isocenter in all 3 axes, reducing accuracy of image fusion to preoperative images. Visually reliable image fusions were accomplished in 2/8 surgical positions using 2D distortion correction and 5/8 using 3D correction. Three-dimensional correction yielded superior image registration quality as defined by higher maximum mutual information values, with improvements ranging between 2.3% and 14.3% over 2D correction., Conclusion: Using 3D distortion correction enhanced the reliability of surgical navigation auto-merge fusion of phantom images acquired with iMRI across a wider range of head positions and may improve the accuracy of stereotactic navigation using iMRI images., (Copyright © 2020 by the Congress of Neurological Surgeons.)

Background: Use of functional MRI (fMRI) in pre-surgical planning is a non-invasive method for pre-operative functional mapping for patients with brain tumors, especially tumors located near eloquent cortex. Currently, this practice predominantly involves task-based fMRI (T-fMRI). Resting state fMRI (RS-fMRI) offers an alternative with several methodological advantages. Here, we compare group-level analyses of RS-fMRI vs. T-fMRI as methods for language localization., Purpose: To contrast RS-fMRI vs. T-fMRI as techniques for localization of language function., Methods: We analyzed data obtained in 35 patients who had both T-fMRI and RS-fMRI scans during the course of pre-surgical evaluation. The RS-fMRI data were analyzed using a previously trained resting-state network classifier. The T-fMRI data were analyzed using conventional techniques. Group-level results obtained by both methods were evaluated in terms of two outcome measures: (1) inter-subject variability of response magnitude and (2) sensitivity/specificity analysis of response topography, taking as ground truth previously reported maps of the language system based on intraoperative cortical mapping as well as meta-analytic maps of language task fMRI responses., Results: Both fMRI methods localized major components of the language system (areas of Broca and Wernicke) although not with equal inter-subject consistency. Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system., Conclusion: We demonstrate several advantages of classifier-based mapping of language representation in the brain. Language T-fMRI activated task-general (i.e., not language-specific) functional systems in addition to areas of Broca and Wernicke. In contrast, classifier-based analysis of RS-fMRI data generated maps confined to language-specific regions of the brain., Competing Interests: The authors have declared that no competing interests exist.

This review article provides a general overview on the various methodologies for quantifying brain structure on magnetic resonance images of the human brain. This overview is followed by examples of applications in Alzheimer dementia and mild cognitive impairment. Other examples will include traumatic brain injury and other neurodegenerative dementias. Finally, an overview of general principles for protocol acquisition of magnetic resonance imaging for volumetric quantification will be discussed along with the current choices of FDA cleared algorithms for use in clinical practice.

Functional magnetic resonance imaging (fMRI) is an important tool for pre-surgical evaluation of eloquent cortex. Classic task-based paradigms require patient participation and individual imaging sequence acquisitions for each functional domain that is being assessed. Resting state fMRI (rs-fMRI), however, enables functional localization without patient participation and can evaluate numerous functional domains with a single imaging session. To date, post-processing of this resting state data has been resource intensive, which limits its widespread application for routine clinical use. Through a novel automated algorithm and advanced imaging IT structure, we report the clinical application and the large-scale integration of rs-fMRI into routine neurosurgical practice. One hundred and ninety one consecutive patients underwent a 3T rs-fMRI, 83 of whom also underwent both motor and language task-based fMRI. Data were processed using a novel, automated, multi-layer perceptron algorithm and integrated into stereotactic navigation using a streamlined IT imaging pipeline. One hundred eighty-five studies were performed for intracranial neoplasm, 14 for refractory epilepsy and 33 for vascular malformations or other neurological disorders. Failure rate of rs-fMRI of 13% was significantly better than that for task-based fMRI (38.5%,) (p <0.001). In conclusion, at Washington University in St. Louis, rs-fMRI has become an integral part of standard imaging for neurosurgical planning. Resting state fMRI can be used in all patients, and due to its lower failure rate than task-based fMRI, it is useful for patients who are unable to cooperate with task-based studies., Competing Interests: Eric C. Leuthardt has ownership of stock in Neurolutions and Inner Cosmos. This does not alter our adherence to PLOS ONE policies on sharing data and materials (as detailed online in the guide for authors).

This article compares resting-state functional magnetic resonance (fMR) imaging with task fMR imaging for presurgical functional mapping of the sensorimotor (SM) region. Before tumor resection, 38 patients were scanned using both methods. The SM area was anatomically defined using 2 different software tools. Overlap of anatomic regions of interest with task activation maps and resting-state networks was measured in the SM region. A paired t-test showed higher overlap between resting-state maps and anatomic references compared with task activation when using a maximal overlap criterion. Resting state-derived maps are more comprehensive than those derived from task fMR imaging., (Copyright © 2017 Elsevier Inc. All rights reserved.)

PET/MR imaging benefits neurologic clinical care and research by providing spatially and temporally matched anatomic MR imaging, advanced MR physiologic imaging, and metabolic PET imaging. MR imaging sequences and PET tracers can be modified to target physiology specific to a neurologic disease process, with applications in neurooncology, epilepsy, dementia, cerebrovascular disease, and psychiatric and neurologic research. Simultaneous PET/MR imaging provides efficient acquisition of multiple temporally matched datasets, and opportunities for motion correction and improved anatomic assignment of PET data. Current challenges include optimizing MR imaging-based attenuation correction and necessity for dual expertise in PET and MR imaging., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Positron emission tomography (PET) with 15 O-tracers is commonly used to measure brain hemodynamic parameters such as cerebral blood flow, cerebral blood volume, and cerebral metabolic rate of oxygen. Conventionally, the absolute quantification of these parameters requires an arterial input function that is obtained invasively by sampling blood from an artery. In this work, we developed and validated an image-derived arterial input function technique that avoids the unreliable and burdensome arterial sampling procedure for full quantitative 15 O-PET imaging. We then compared hemodynamic PET imaging performed on a PET/MR hybrid scanner against a conventional PET only scanner. We demonstrated the proposed imaging-based technique was able to generate brain hemodynamic parameter measurements in strong agreement with the traditional arterial sampling based approach. We also demonstrated that quantitative 15 O-PET imaging can be successfully implemented on a PET/MR hybrid scanner.

Aim: To accurately quantify the radioactivity concentration measured by PET, emission data need to be corrected for photon attenuation; however, the MRI signal cannot easily be converted into attenuation values, making attenuation correction (AC) in PET/MRI challenging. In order to further improve the current vendor-implemented MR-AC methods for absolute quantification, a number of prototype methods have been proposed in the literature. These can be categorized into three types: template/atlas-based, segmentation-based, and reconstruction-based. These proposed methods in general demonstrated improvements compared to vendor-implemented AC, and many studies report deviations in PET uptake after AC of only a few percent from a gold standard CT-AC. Using a unified quantitative evaluation with identical metrics, subject cohort, and common CT-based reference, the aims of this study were to evaluate a selection of novel methods proposed in the literature, and identify the ones suitable for clinical use., Methods: In total, 11 AC methods were evaluated: two vendor-implemented (MR-AC DIXON and MR-AC UTE ), five based on template/atlas information (MR-AC SEGBONE (Koesters et al., 2016), MR-AC ONTARIO (Anazodo et al., 2014), MR-AC BOSTON (Izquierdo-Garcia et al., 2014), MR-AC UCL (Burgos et al., 2014), and MR-AC MAXPROB (Merida et al., 2015)), one based on simultaneous reconstruction of attenuation and emission (MR-AC MLAA (Benoit et al., 2015)), and three based on image-segmentation (MR-AC MUNICH (Cabello et al., 2015), MR-AC CAR-RiDR (Juttukonda et al., 2015), and MR-AC RESOLUTE (Ladefoged et al., 2015)). We selected 359 subjects who were scanned using one of the following radiotracers: [ 18 F]FDG (210), [ 11 C]PiB (51), and [ 18 F]florbetapir (98). The comparison to AC with a gold standard CT was performed both globally and regionally, with a special focus on robustness and outlier analysis., Results: The average performance in PET tracer uptake was within ±5% of CT for all of the proposed methods, with the average±SD global percentage bias in PET FDG uptake for each method being: MR-AC DIXON (-11.3±3.5)%, MR-AC UTE (-5.7±2.0)%, MR-AC ONTARIO (-4.3±3.6)%, MR-AC MUNICH (3.7±2.1)%, MR-AC MLAA (-1.9±2.6)%, MR-AC SEGBONE (-1.7±3.6)%, MR-AC UCL (0.8±1.2)%, MR-AC CAR-RiDR (-0.4±1.9)%, MR-AC MAXPROB (-0.4±1.6)%, MR-AC BOSTON (-0.3±1.8)%, and MR-AC RESOLUTE (0.3±1.7)%, ordered by average bias. The overall best performing methods (MR-AC BOSTON , MR-AC MAXPROB , MR-AC RESOLUTE and MR-AC UCL , ordered alphabetically) showed regional average errors within ±3% of PET with CT-AC in all regions of the brain with FDG, and the same four methods, as well as MR-AC CAR-RiDR , showed that for 95% of the patients, 95% of brain voxels had an uptake that deviated by less than 15% from the reference. Comparable performance was obtained with PiB and florbetapir., Conclusions: All of the proposed novel methods have an average global performance within likely acceptable limits (±5% of CT-based reference), and the main difference among the methods was found in the robustness, outlier analysis, and clinical feasibility. Overall, the best performing methods were MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically. These methods all minimized the number of outliers, standard deviation, and average global and local error. The methods MR-ACMUNICH and MR-ACCAR-RiDR were both within acceptable quantitative limits, so these methods should be considered if processing time is a factor. The method MR-ACSEGBONE also demonstrates promising results, and performs well within the likely acceptable quantitative limits. For clinical routine scans where processing time can be a key factor, this vendor-provided solution currently outperforms most methods. With the performance of the methods presented here, it may be concluded that the challenge of improving the accuracy of MR-AC in adult brains with normal anatomy has been solved to a quantitatively acceptable degree, which is smaller than the quantification reproducibility in PET imaging., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.

Unlabelled: Hybrid PET and MR scanners have become a reality in recent years, with the benefits of reduced radiation exposure, reduction of imaging time, and potential advantages in quantification. Appropriate attenuation correction remains a challenge. Biases in PET activity measurements were demonstrated using the current MR-based attenuation-correction technique. We aimed to investigate the impact of using a standard MR-based attenuation correction technique on the clinical and research utility of a PET/MR hybrid scanner for amyloid imaging., Methods: Florbetapir scans were obtained for 40 participants on a hybrid scanner with simultaneous MR acquisition. PET images were reconstructed using both MR- and CT-derived attenuation maps. Quantitative analysis was performed for both datasets to assess the impact of MR-based attenuation correction to absolute PET activity measurements as well as target-to-reference ratio (SUVR). Clinical assessment was also performed by a nuclear medicine physician to determine amyloid status based on the criteria in the Food and Drug Administration prescribing information for florbetapir., Results: MR-based attenuation correction led to underestimation of PET activity for most parts of the brain, with a small overestimation for deep brain regions. There was also an overestimation of SUVRs with cerebellar reference. SUVR measurements obtained from the 2 attenuation-correction methods were strongly correlated. Clinical assessment of amyloid status resulted in identical classification as positive or negative regardless of the attenuation-correction methods., Conclusion: MR-based attenuation correction causes biases in quantitative measurements. The biases may be accounted for by a linear model, although the spatial variation cannot be easily modeled. The quantitative differences, however, did not affect clinical assessment as positive or negative., (© 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)

Background and Purpose: Stroke mimics (SM) challenge the initial assessment of patients presenting with possible acute ischemic stroke (AIS). When SM is considered likely, intravenous tissue-type plasminogen activator (tPA) may be withheld, risking an opportunity to treat AIS. Although computed tomography is routinely used for tPA decision making, magnetic resonance imaging (MRI) may diagnose AIS when SM is favored but not certain. We hypothesized that a hyperacute MRI (hMRI) protocol would identify tPA-eligible AIS patients among those initially favored to have SM., Methods: A streamlined hMRI protocol was designed based on barriers to rapid patient transport, MRI acquisition, and post-MRI tPA delivery. Neurologists were trained to order hMRI when SM was favored and tPA was being withheld. The use of hMRI for tPA decision making, door-to-needle times, and outcomes were compared before hMRI implementation (pre-hMRI: August 1, 2011 to July 31, 2013) and after (post-hMRI, August 1, 2013, to January 15, 2015)., Results: Post hMRI, 57 patients with suspected SM underwent hMRI (median MRI-order-to-start time, 29 minutes), of whom, 11 (19%) were diagnosed with AIS and 7 (12%) received tPA. Pre-hMRI, no tPA-treated patients were screened with hMRI. Post hMRI, 7 of 106 (6.6%) tPA-treated patients underwent hMRI to aid in decision making because of suspected SM (0% versus 6.6%; P=0.001). To ensure standard care was maintained after implementing the hMRI protocol, pre- versus post-hMRI tPA-treated cohorts were compared and did not differ: door-to-needle time (39 versus 37 minutes; P=0.63), symptomatic hemorrhage rate (4.5% versus 1.9%; P=0.32), and favorable discharge location (85% versus 89%; P=0.37)., Conclusions: A streamlined hMRI protocol permitted tPA administration to a small, but significant, subset of AIS patients initially considered to have SM., (© 2016 American Heart Association, Inc.)

The purpose of this manuscript is to provide an introduction to resting-state functional magnetic resonance imaging (RS-fMRI) and to review the current application of this new and powerful technique in the preoperative setting using our institute's extensive experience. RS-fMRI has provided important insights into brain physiology and is an increasingly important tool in the clinical setting. As opposed to task-based functional MRI wherein the subject performs a task while being scanned, RS-fMRI evaluates low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal while the subject is at rest. Multiple resting state networks (RSNs) have been identified, including the somatosensory, language, and visual networks, which are of primary importance for presurgical planning. Over the past 4 years, we have performed over 300 RS-fMRI examinations in the clinical setting and these have been used to localize eloquent somatosensory and language cortices before brain tumor resection. RS-fMRI is particularly useful in this setting for patients who are unable to cooperate with the task-based paradigm, such as young children or those who are sedated, paretic, or aphasic.Although RS-fMRI is still investigational, our experience indicates that this method is ready for clinical application in the presurgical setting.

Aim: MR-based correction for photon attenuation in PET/MRI remains challenging, particularly for neurological applications requiring quantitation of data. Existing methods are either not sufficiently accurate or are limited by the computation time required. The goal of this study was to develop an MR-based attenuation correction method that accurately separates bone tissue from air and provides continuous-valued attenuation coefficients for bone., Materials and Methods: PET/MRI and CT datasets were obtained from 98 subjects (mean age [±SD]: 66yrs [±9.8], 57 females) using an IRB-approved protocol and with informed consent. Subjects were injected with 352±29MBq of (18)F-Florbetapir tracer, and PET acquisitions were begun either immediately or 50min after injection. CT images of the head were acquired separately using a PET/CT system. Dual echo ultrashort echo-time (UTE) images and two-point Dixon images were acquired. Regions of air were segmented via a threshold of the voxel-wise multiplicative inverse of the UTE echo 1 image. Regions of bone were segmented via a threshold of the R2* image computed from the UTE echo 1 and UTE echo 2 images. Regions of fat and soft tissue were segmented using fat and water images decomposed from the Dixon images. Air, fat, and soft tissue were assigned linear attenuation coefficients (LACs) of 0, 0.092, and 0.1cm(-1), respectively. LACs for bone were derived from a regression analysis between corresponding R2* and CT values. PET images were reconstructed using the gold standard CT method and the proposed CAR-RiDR method., Results: The RiDR segmentation method produces mean Dice coefficient±SD across subjects of 0.75±0.05 for bone and 0.60±0.08 for air. The CAR model for bone LACs greatly improves accuracy in estimating CT values (28.2%±3.0 mean error) compared to the use of a constant CT value (46.9%±5.8, p<10(-6)). Finally, the CAR-RiDR method provides a low whole-brain mean absolute percent-error (MAPE±SD) in PET reconstructions across subjects of 2.55%±0.86. Regional PET errors were also low and ranged from 0.88% to 3.79% in 24 brain ROIs., Conclusion: We propose an MR-based attenuation correction method (CAR-RiDR) for quantitative PET neurological imaging. The proposed method employs UTE and Dixon images and consists of two novel components: 1) accurate segmentation of air and bone using the inverse of the UTE1 image and the R2* image, respectively and 2) estimation of continuous LAC values for bone using a regression between R2* and CT-Hounsfield units. From our analysis, we conclude that the proposed method closely approaches (<3% error) the gold standard CT-scaled method in PET reconstruction accuracy., (Copyright © 2015 Elsevier Inc. All rights reserved.)

There is a growing emphasis on examining preclinical levels of Alzheimer's disease (AD)-related pathology in the absence of cognitive impairment. Previous work examining biomarkers has focused almost exclusively on memory, although there is mounting evidence that attention also declines early in disease progression. In the current experiment, 2 attentional control tasks were used to examine alterations in task-evoked functional magnetic resonance imaging data related to biomarkers of AD pathology. Seventy-one cognitively normal individuals (females = 44, mean age = 63.5 years) performed 2 attention-demanding cognitive tasks in a design that modeled both trial- and task-level functional magnetic resonance imaging changes. Biomarkers included amyloid β42, tau, and phosphorylated tau measured from cerebrospinal fluid and positron emission tomography measures of amyloid deposition. Both tasks elicited widespread patterns of activation and deactivation associated with large task-level manipulations of attention. Importantly, results from both tasks indicated that higher levels of tau and phosphorylated tau pathologies were associated with block-level overactivations of attentional control areas. This suggests early alteration in attentional control with rising levels of AD pathology., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: To develop a positron emission tomography (PET) attenuation correction method for brain PET/magnetic resonance (MR) imaging by estimating pseudo computed tomographic (CT) images from T1-weighted MR and atlas CT images., Materials and Methods: In this institutional review board-approved and HIPAA-compliant study, PET/MR/CT images were acquired in 20 subjects after obtaining written consent. A probabilistic air segmentation and sparse regression (PASSR) method was developed for pseudo CT estimation. Air segmentation was performed with assistance from a probabilistic air map. For nonair regions, the pseudo CT numbers were estimated via sparse regression by using atlas MR patches. The mean absolute percentage error (MAPE) on PET images was computed as the normalized mean absolute difference in PET signal intensity between a method and the reference standard continuous CT attenuation correction method. Friedman analysis of variance and Wilcoxon matched-pairs tests were performed for statistical comparison of MAPE between the PASSR method and Dixon segmentation, CT segmentation, and population averaged CT atlas (mean atlas) methods., Results: The PASSR method yielded a mean MAPE ± standard deviation of 2.42% ± 1.0, 3.28% ± 0.93, and 2.16% ± 1.75, respectively, in the whole brain, gray matter, and white matter, which were significantly lower than the Dixon, CT segmentation, and mean atlas values (P < .01). Moreover, 68.0% ± 16.5, 85.8% ± 12.9, and 96.0% ± 2.5 of whole-brain volume had within ±2%, ±5%, and ±10% percentage error by using PASSR, respectively, which was significantly higher than other methods (P < .01)., Conclusion: PASSR outperformed the Dixon, CT segmentation, and mean atlas methods by reducing PET error owing to attenuation correction.

Background and Purpose: Elevated levels of amyloid deposition as well as white matter damage are thought to be risk factors for Alzheimer Disease (AD). Here we examined whether qualitative ratings of white matter damage predicted cognitive impairment beyond measures of amyloid., Materials and Methods: The study examined 397 cognitively normal, 51 very mildly demented, and 11 mildly demented individuals aged 42-90 (mean 68.5). Participants obtained a T2-weighted scan as well as a positron emission tomography scan using (11)[C] Pittsburgh Compound B. Periventricular white matter hyperintensities (PVWMHs) and deep white matter hyperintensities (DWMHs) were measured on each T2 scan using the Fazekas rating scale. The effects of amyloid deposition and white matter damage were assessed using logistic regressions., Results: Levels of amyloid deposition (ps < 0.01), as well as ratings of PVWMH (p < 0.01) and DWMH (p < 0.05) discriminated between cognitively normal and demented individuals., Conclusions: The amount of amyloid deposition and white matter damage independently predicts cognitive impairment. This suggests a diagnostic utility of qualitative white matter scales in addition to measuring amyloid levels.

Background/aims: We investigated the histopathological correlates of white matter hyperintensities (WMHs) in participants with Alzheimer's disease (AD) or cerebrovascular disease, and in aged controls., Methods: We reviewed 57 participants who had neuropathology and in whom neuroimaging was done. In addition to AD pathology, cortical microinfarcts, lacunes, and cerebral hemorrhages were assessed. Small-vessel disease included arteriolosclerosis and cerebral amyloid angiopathy. Postmortem brain tissue corresponding to regions of WMHs was investigated in 14 participants. The variables included: demyelination of the deep and periventricular white matter (WM), atrophy of the ventricular ependyma, and thickness of blood vessels. Partial Spearman's rank test and linear regression analysis, adjusted for age at the clinical evaluation and the duration to death, were performed., Results: The severity of arteriosclerosis was correlated with the volume of periventricular hyperintensity (PVH) estimated by magnetic resonance imaging. Deep white matter hyperintensity (DWMH) volume was correlated with the presence of cortical microinfarcts and cerebral hemorrhages. The severity of the breakdown of the ventricular lining was correlated with PVHs, and DWMHs correlated with the severity of deep WM demyelination. The diameter of small blood vessels was not associated with WMHs., Conclusion: WMHs are consistent with small-vessel disease and increase the tissue water content. We found no association between WMHs and the thickness of small blood vessels., (© 2014 S. Karger AG, Basel.)

Background: Susceptibility-weighted imaging is a relatively new magnetic resonance imaging sequence that can identify lesions of multiple sclerosis in adults. This study was designed to determine if susceptibility-weighted imaging is a useful discriminator between children who develop multiple sclerosis and children with monophasic acute disseminated encephalomyelitis., Methods: Eighteen children who presented with acute central nervous system demyelination and had a brain magnetic resonance imaging study including susceptibility-weighted imaging within 6 months of the first clinical attack were studied. Final diagnosis was based on international consensus definitions. Brain lesions detected on the fluid-attenuated inversion recovery sequence were assessed for abnormal signal on susceptibility-weighted imaging. The burden of susceptibility abnormalities was then analyzed for differences between the multiple sclerosis and acute disseminated encephalomyelitis groups., Results: Eight patients had a final diagnosis of acute disseminated encephalomyelitis and ten had multiple sclerosis. Twenty-two percent of fluid-attenuated inversion recovery lesions were identified on susceptibility-weighted imaging. The percentage of fluid-attenuated inversion recovery lesions identified on susceptibility-weighted imaging differed between the multiple sclerosis and acute disseminated encephalomyelitis groups (P = 0.04). The median percentage (minimum-maximum) of lesions identified on susceptibility-weighted imaging in the multiple sclerosis group was 0.22 (0-0.68) and in the acute disseminated encephalomyelitis group was 0.0 (0-0.17)., Conclusion: Susceptibility-weighted imaging may be a useful technique in differentiating acute disseminated encephalomyelitis from multiple sclerosis at initial presentation., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Objective: Most migraineurs develop cutaneous allodynia during migraines, and many have cutaneous sensitization between attacks. Atypical pain modulation via the descending pain system may contribute to this sensitization and allodynia. The objective of this study was to test the hypothesis that compared with non-allodynic migraineurs, allodynic migraineurs have atypical periaqueductal gray (PAG) and nucleus cuneiformis (NCF) resting-state functional connectivity (rs-fc) with other pain processing regions., Design: Ten minutes resting-state blood-oxygen-level-dependent data were collected from 38 adult migraineurs and 20 controls. Seed-based analyses compared whole-brain rs-fc with PAG and with NCF in migraineurs with severe ictal allodynia (N = 8) to migraineurs with no ictal allodynia (N = 8). Correlations between the strength of functional connections that differed between severely allodynic and non-allodynic migraineurs with allodynia severity were determined for all migraineurs (N = 38). PAG and NCF rs-fc in all migraineurs was compared with rs-fc in controls., Results: Migraineurs with severe allodynia had stronger PAG and NCF rs-fc to other brainstem, thalamic, insula and cerebellar regions that participate in discriminative pain processing, as well as to frontal and temporal regions implicated in higher order pain modulation. Evidence that these rs-fc differences were specific for allodynia included: 1) strong correlations between some rs-fc strengths and allodynia severity among all migraineurs; and 2) absence of overlap when comparing rs-fc differences in severely allodynic vs non-allodynic migraineurs with those in all migraineurs vs controls., Conclusion: Atypical rs-fc of brainstem descending modulatory pain regions with other brainstem and higher order pain-modulating regions is associated with migraine-related allodynia., (Wiley Periodicals, Inc.)

Objective: To assess regional patterns of gray and white matter atrophy in familial Alzheimer disease (FAD) mutation carriers., Methods: A total of 192 participants with volumetric T1-weighted MRI, genotyping, and clinical diagnosis were available from the Dominantly Inherited Alzheimer Network. Of these, 69 were presymptomatic mutation carriers, 50 were symptomatic carriers (31 with Clinical Dementia Rating [CDR] = 0.5, 19 with CDR > 0.5), and 73 were noncarriers from the same families. Voxel-based morphometry was used to identify cross-sectional group differences in gray matter and white matter volume., Results: Significant differences in gray matter (p < 0.05, family-wise error-corrected) were observed between noncarriers and mildly symptomatic (CDR = 0.5) carriers in the thalamus and putamen, as well as in the temporal lobe, precuneus, and cingulate gyrus; the same pattern, but with more extensive changes, was seen in those with CDR > 0.5. Significant white matter differences between noncarriers and symptomatic carriers were observed in the cingulum and fornix; these form input and output connections to the medial temporal lobe, cingulate, and precuneus. No differences between noncarriers and presymptomatic carriers survived correction for multiple comparisons, but there was a trend for decreased gray matter in the thalamus for carriers closer to their estimated age at onset. There were no significant increases of gray or white matter in asymptomatic or symptomatic carriers compared to noncarriers., Conclusions: Atrophy in FAD is observed early, both in areas commonly associated with sporadic Alzheimer disease and also in the putamen and thalamus, 2 regions associated with early amyloid deposition in FAD mutation carriers.

Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly progressive dementia (RPD) that can be difficult to identify antemortem, with definitive diagnosis requiring tissue confirmation. We describe the clinical, magnetic resonance imaging (MRI), cerebrospinal fluid (CSF), and electroencephalogram (EEG) measures of a small cohort of 30 patients evaluated for RPD. Clinical and diagnostic measures were cross-sectionally obtained from 17 sCJD patients (15 definite, two probable), 13 non-prion rapidly progressive dementia patients (npRPD), and 18 unimpaired controls. In a subset of patients (nine sCJD and nine npRPD) diffusion tensor imaging (DTI) measures [fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD)] were also obtained for the caudate, corpus callosum, posterior limb of the internal capsule, pulvinar, precuneus, and frontal lobe. Differences among groups were assessed by an analysis of variance. Compared to npRPD individuals, sCJD patients had cerebellar dysfunction, significantly higher CSF tau, "positive" CSF 14-3-3, and hyperintensities on diffusion-weighted imaging (DWI) that met previously established imaging criteria for sCJD. EEG changes were similar for the two groups. In addition, sCJD patients had significant decreases in DTI measures (MD, AD, RD but not FA) within the caudate and pulvinar compared to either npRPD patients or unimpaired controls. Our results confirm that CSF abnormalities and MRI (especially DWI) can assist in distinguishing sCJD patients from npRPD patients. Future longitudinal studies using multiple measures (including CSF and MRI) are needed for evaluating pathological changes seen in sCJD patients.

Glioblastoma Mulitforme is highly infiltrative, making precise delineation of tumor margin difficult. Multimodality or multi-parametric MR imaging sequences promise an advantage over anatomic sequences such as post contrast enhancement as methods for determining the spatial extent of tumor involvement. In considering multi-parametric imaging sequences however, manual image segmentation and classification is time-consuming and prone to error. As a preliminary step toward integration of multi-parametric imaging into clinical assessments of primary brain tumors, we propose a machine-learning based multi-parametric approach that uses radiologist generated labels to train a classifier that is able to classify tissue on a voxel-wise basis and automatically generate a tumor segmentation. A random forests classifier was trained using a leave-one-out experimental paradigm. A simple linear classifier was also trained for comparison. The random forests classifier accurately predicted radiologist generated segmentations and tumor extent.

Objective: Correlation of diffusion tensor imaging (DTI) with histochemical staining for demyelination and axonal damage in multiple sclerosis (MS) ex vivo human cervical spinal cords., Background: In MS, demyelination, axonal degeneration, and inflammation contribute to disease pathogenesis to variable degrees. Based upon in vivo animal studies with acute injury and histopathologic correlation, we hypothesized that DTI can differentiate between axonal and myelin pathologies within humans., Methods: DTI was performed at 4.7 T on 9 MS and 5 normal control fixed cervical spinal cord blocks following autopsy. Sections were then stained for Luxol fast blue (LFB), Bielschowsky silver, and hematoxylin and eosin (H&E). Regions of interest (ROIs) were graded semi-quantitatively as normal myelination, mild (<50%) demyelination, or moderate-severe (>50%) demyelination. Corresponding axonal counts were manually determined on Bielschowsky silver. ROIs were mapped to co-registered DTI parameter slices. DTI parameters evaluated included standard quantitative assessments of apparent diffusion coefficient (ADC), relative anisotropy (RA), axial diffusivity and radial diffusivity. Statistical correlations were made between histochemical gradings and DTI parameters using linear mixed models., Results: Within ROIs in MS subjects, increased radial diffusivity distinguished worsening severities of demyelination. Relative anisotropy was decreased in the setting of moderate-severe demyelination compared to normal areas and areas of mild demyelination. Radial diffusivity, ADC, and RA became increasingly altered within quartiles of worsening axonal counts. Axial diffusivity did not correlate with axonal density (p=0.091)., Conclusions: Increased radial diffusivity can serve as a surrogate for demyelination. However, radial diffusivity was also altered with axon injury, suggesting that this measure is not pathologically specific within chronic human MS tissue. We propose that radial diffusivity can serve as a marker of overall tissue integrity within chronic MS lesions. This study provides pathologic foundation for on-going in vivo DTI studies in MS., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of 'sporadic' late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease. We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer's Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset. Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants. These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.

Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer's disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18-94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.

Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals. We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.

Patients with Alzheimer's disease vary in their ability to sustain cognitive abilities in the presence of brain pathology. A major open question is which brain mechanisms may support higher reserve capacity, i.e. relatively high cognitive performance at a given level of Alzheimer's pathology. Higher functional MRI-assessed functional connectivity of a hub in the left frontal cortex is a core candidate brain mechanism underlying reserve as it is associated with education (i.e. a protective factor often associated with higher reserve) and attenuated cognitive impairment in prodromal Alzheimer's disease. However, no study has yet assessed whether such hub connectivity of the left frontal cortex supports reserve throughout the evolution of pathological brain changes in Alzheimer's disease, including the presymptomatic stage when cognitive decline is subtle. To address this research gap, we obtained cross-sectional resting state functional MRI in 74 participants with autosomal dominant Alzheimer's disease, 55 controls from the Dominantly Inherited Alzheimer's Network and 75 amyloid-positive elderly participants, as well as 41 amyloid-negative cognitively normal elderly subjects from the German Center of Neurodegenerative Diseases multicentre study on biomarkers in sporadic Alzheimer's disease. For each participant, global left frontal cortex connectivity was computed as the average resting state functional connectivity between the left frontal cortex (seed) and each voxel in the grey matter. As a marker of disease stage, we applied estimated years from symptom onset in autosomal dominantly inherited Alzheimer's disease and cerebrospinal fluid tau levels in sporadic Alzheimer's disease cases. In both autosomal dominant and sporadic Alzheimer's disease patients, higher levels of left frontal cortex connectivity were correlated with greater education. For autosomal dominant Alzheimer's disease, a significant left frontal cortex connectivity × estimated years of onset interaction was found, indicating slower decline of memory and global cognition at higher levels of connectivity. Similarly, in sporadic amyloid-positive elderly subjects, the effect of tau on cognition was attenuated at higher levels of left frontal cortex connectivity. Polynomial regression analysis showed that the trajectory of cognitive decline was shifted towards a later stage of Alzheimer's disease in patients with higher levels of left frontal cortex connectivity. Together, our findings suggest that higher resilience against the development of cognitive impairment throughout the early stages of Alzheimer's disease is at least partially attributable to higher left frontal cortex-hub connectivity.

BackgroundModels of Alzheimer's disease propose a sequence of amyloid β (Aβ) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal.MethodsBetween Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at ClinicalTrials.gov (number NCT00869817) FINDINGS: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aβ deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aβ accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning).InterpretationMutation carriers had elevations in Aβ deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologies varied throughout the brain, suggesting differential regional and temporal vulnerabilities to Aβ, metabolic decline, and structural atrophy, which should be taken into account when using biomarkers in a clinical setting as well as designing and evaluating clinical trials.FundingUS National Institutes of Health, the German Center for Neurodegenerative Diseases, and the Medical Research Council Dementias Platform UK.

IntroductionIdentifying at what point atrophy rates first change in Alzheimer's disease is important for informing design of presymptomatic trials.MethodsSerial T1-weighted magnetic resonance imaging scans of 94 participants (28 noncarriers, 66 carriers) from the Dominantly Inherited Alzheimer Network were used to measure brain, ventricular, and hippocampal atrophy rates. For each structure, nonlinear mixed-effects models estimated the change-points when atrophy rates deviate from normal and the rates of change before and after this point.ResultsAtrophy increased after the change-point, which occurred 1-1.5 years (assuming a single step change in atrophy rate) or 3-8 years (assuming gradual acceleration of atrophy) before expected symptom onset. At expected symptom onset, estimated atrophy rates were at least 3.6 times than those before the change-point.DiscussionAtrophy rates are pathologically increased up to seven years before "expected onset". During this period, atrophy rates may be useful for inclusion and tracking of disease progression.

IntroductionWhite matter hyperintensity (WMH) volume on MRI is increased among presymptomatic individuals with autosomal dominant mutations for Alzheimer's disease (AD). One potential explanation is that WMH, conventionally considered a marker of cerebrovascular disease, are a reflection of cerebral amyloid angiopathy (CAA) and that increased WMH in this population is a manifestation of this vascular form of primary AD pathology. We examined whether the presence of cerebral microbleeds, a marker of CAA, mediates the relationship between WMH and estimated symptom onset in individuals with and without autosomal dominant mutations for AD.Participants and methodsParticipants (n = 175, mean age = 41.1 years) included 112 with an AD mutation and 63 first-degree non-carrier controls. We calculated the estimated years from expected symptom onset (EYO) and analyzed baseline MRI data for WMH volume and presence of cerebral microbleeds. Mixed effects regression and tests of mediation were used to examine microbleed and WMH differences between carriers and non-carriers and to test the whether the association between WMH and mutation status is dependent on the presence of microbleeds.ResultsMutation carriers were more likely to have microbleeds than non-carriers (p

IntroductionThe objective of this study was to evaluate the relationship between self-reported exercise levels and Alzheimer's disease (AD) biomarkers, in a cohort of autosomal dominant AD mutation carriers.MethodsIn 139 presymptomatic mutation carriers from the Dominantly Inherited Alzheimer Network, the relationship between self-reported exercise levels and brain amyloid load, cerebrospinal fluid (CSF) Aβ42, and CSF tau levels was evaluated using linear regression.ResultsNo differences in brain amyloid load, CSF Aβ42, or CSF tau were observed between low and high exercise groups. Nevertheless, when examining only those already accumulating AD pathology (i.e., amyloid positive), low exercisers had higher mean levels of brain amyloid than high exercisers. Furthermore, the interaction between exercise and estimated years from expected symptom onset was a significant predictor of brain amyloid levels.DiscussionOur findings indicate a relationship exists between self-reported exercise levels and brain amyloid in autosomal dominant AD mutation carriers.

ImportanceAutosomal dominant Alzheimer disease (ADAD) is caused by rare genetic mutations in 3 specific genes in contrast to late-onset Alzheimer disease (LOAD), which has a more polygenetic risk profile.ObjectiveTo assess the similarities and differences in functional connectivity changes owing to ADAD and LOAD.Design, setting, and participantsWe analyzed functional connectivity in multiple brain resting state networks (RSNs) in a cross-sectional cohort of participants with ADAD (n = 79) and LOAD (n = 444), using resting-state functional connectivity magnetic resonance imaging at multiple international academic sites.Main outcomes and measuresFor both types of AD, we quantified and compared functional connectivity changes in RSNs as a function of dementia severity measured by the Clinical Dementia Rating Scale. In ADAD, we qualitatively investigated functional connectivity changes with respect to estimated years from onset of symptoms within 5 RSNs.ResultsA decrease in functional connectivity with increasing Clinical Dementia Rating scores were similar for both LOAD and ADAD in multiple RSNs. Ordinal logistic regression models constructed in one type of Alzheimer disease accurately predicted clinical dementia rating scores in the other, further demonstrating the similarity of functional connectivity loss in each disease type. Among participants with ADAD, functional connectivity in multiple RSNs appeared qualitatively lower in asymptomatic mutation carriers near their anticipated age of symptom onset compared with asymptomatic mutation noncarriers.Conclusions and relevanceResting-state functional connectivity magnetic resonance imaging changes with progressing AD severity are similar between ADAD and LOAD. Resting-state functional connectivity magnetic resonance imaging may be a useful end point for LOAD and ADAD therapy trials. Moreover, the disease process of ADAD may be an effective model for the LOAD disease process.

Objective: Biomarkers of Alzheimer disease vary between groups of self‐identified Black and White individuals in some studies. This study examined whether the relationships between biomarkers or between biomarkers and cognitive measures varied by racialized groups. Methods: Cerebrospinal fluid (CSF), amyloid positron emission tomography (PET), and magnetic resonance imaging measures were harmonized across four studies of memory and aging. Spearman correlations between biomarkers and between biomarkers and cognitive measures were calculated within each racialized group, then compared between groups by standard normal tests after Fisher's Z‐transformations. Results: The harmonized dataset included at least one biomarker measurement from 495 Black and 2,600 White participants. The mean age was similar between racialized groups. However, Black participants were less likely to have cognitive impairment (28% vs 36%) and had less abnormality of some CSF biomarkers including CSF Aβ42/40, total tau, p‐tau181, and neurofilament light. CSF Aβ42/40 was negatively correlated with total tau and p‐tau181 in both groups, but at a smaller magnitude in Black individuals. CSF Aβ42/40, total tau, and p‐tau181 had weaker correlations with cognitive measures, especially episodic memory, in Black than White participants. Correlations of amyloid measures between CSF (Aβ42/40, Aβ42) and PET imaging were also weaker in Black than White participants. Importantly, no differences based on race were found in correlations between different imaging biomarkers, or in correlations between imaging biomarkers and cognitive measures. Interpretation: Relationships between CSF biomarkers but not imaging biomarkers varied by racialized groups. Imaging biomarkers performed more consistently across racialized groups in associations with cognitive measures. ANN NEUROL 2024;95:495–506 [ABSTRACT FROM AUTHOR]

Major imaging biomarkers of Alzheimer's disease include amyloid deposition [imaged with [(11)C]Pittsburgh compound B (PiB) PET], altered glucose metabolism (imaged with [(18)F]fluro-deoxyglucose PET), and structural atrophy (imaged by MRI). Recently we published the initial subset of imaging findings for specific regions in a cohort of individuals with autosomal dominant Alzheimer's disease. We now extend this work to include a larger cohort, whole-brain analyses integrating all three imaging modalities, and longitudinal data to examine regional differences in imaging biomarker dynamics. The anatomical distribution of imaging biomarkers is described in relation to estimated years from symptom onset. Autosomal dominant Alzheimer's disease mutation carrier individuals have elevated PiB levels in nearly every cortical region 15 y before the estimated age of onset. Reduced cortical glucose metabolism and cortical thinning in the medial and lateral parietal lobe appeared 10 and 5 y, respectively, before estimated age of onset. Importantly, however, a divergent pattern was observed subcortically. All subcortical gray-matter regions exhibited elevated PiB uptake, but despite this, only the hippocampus showed reduced glucose metabolism. Similarly, atrophy was not observed in the caudate and pallidum despite marked amyloid accumulation. Finally, before hypometabolism, a hypermetabolic phase was identified for some cortical regions, including the precuneus and posterior cingulate. Additional analyses of individuals in which longitudinal data were available suggested that an accelerated appearance of volumetric declines approximately coincides with the onset of the symptomatic phase of the disease.

The apolipoprotein E (APOE) ε4 allele is strongly linked with cerebral β-amyloidosis, but its relationship with tauopathy is less established. We investigated the relationship between APOE ε4 carrier status, regional amyloid-β (Aβ), magnetic resonance imaging (MRI) volumetrics, tau positron emission tomography (PET), APOE messenger RNA (mRNA) expression maps, and cerebrospinal fluid phosphorylated tau (CSF ptau181). Three hundred fifty participants underwent imaging, and 270 had ptau181. We used computational models to evaluate the main effect of APOE ε4 carrier status on regional neuroimaging values and then the interaction of ε4 status and global Aβ on regional tau PET and brain volumes as well as CSF ptau181. Separately, we also examined the additional interactive influence of sex. We found that, for the same degree of Aβ burden, APOE ε4 carriers showed greater tau PET signal relative to noncarriers in temporal regions, but no interaction was present for MRI volumes or CSF ptau181. This potentiation of tau aggregation irrespective of sex occurred in brain regions with high APOE mRNA expression, suggesting local vulnerabilities to tauopathy. There were greater effects of APOE genotype in females, although the interactive sex effects did not strongly mirror mRNA expression. Pathology is not homogeneously expressed throughout the brain but mirrors underlying biological patterns such as gene expression. The genetic of tau pathology: APOE ε4 allele is associated with increased risk of developing late-onset Alzheimer's disease (AD). However, the relationship between the APOE ε4 allele and the major hallmarks of AD pathophysiology remains to be completely elucidated. Now, Dincer et al. used imaging data, cerebrospinal fluid, and mRNA from APOE ε4 carrier and noncarrier participants and showed that APOE ε4 influenced brain Aβ, tau, and CSF ptau181. Moreover, APOE ε4 was associated with increased tau burden in regions with high APOE mRNA expression. The results suggest that the severity of brain pathology might be driven by gene expression. APOE status should be considered when evaluating disease progression and clinical trials. [ABSTRACT FROM AUTHOR]

Background: Currently, brain tissue atrophy serves as an in vivo MRI biomarker of neurodegeneration in Alzheimer's disease (AD). However, postmortem histopathological studies show that neuronal loss in AD exceeds volumetric loss of tissue and that loss of memory in AD begins when neurons and synapses are lost. Therefore, in vivo detection of neuronal loss prior to detectable atrophy in MRI is essential for early AD diagnosis.Objective: To apply a recently developed quantitative Gradient Recalled Echo (qGRE) MRI technique for in vivo evaluation of neuronal loss in human hippocampus.Methods: Seventy participants were recruited from the Knight Alzheimer Disease Research Center, representing three groups: Healthy controls [Clinical Dementia Rating® (CDR®) = 0, amyloid β (Aβ)-negative, n = 34]; Preclinical AD (CDR = 0, Aβ-positive, n = 19); and mild AD (CDR = 0.5 or 1, Aβ-positive, n = 17).Results: In hippocampal tissue, qGRE identified two types of regions: one, practically devoid of neurons, we designate as "Dark Matter", and the other, with relatively preserved neurons, "Viable Tissue". Data showed a greater loss of neurons than defined by atrophy in the mild AD group compared with the healthy control group; neuronal loss ranged between 31% and 43%, while volume loss ranged only between 10% and 19%. The concept of Dark Matter was confirmed with histopathological study of one participant who underwent in vivo qGRE 14 months prior to expiration.Conclusion: In vivo qGRE method identifies neuronal loss that is associated with impaired AD-related cognition but is not recognized by MRI measurements of tissue atrophy, therefore providing new biomarkers for early AD detection. [ABSTRACT FROM AUTHOR]