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Deficits in memory are seen as a canonical sign of aging and a prodrome to dementia in older adults. However, our understanding of age-related cognition and brain morphology occurring throughout a broader spectrum of adulthood remains limited. We quantified the relationship between cognitive function and brain morphology (sulcal width, SW) using three cross-sectional observational datasets (PISA, AIBL, ADNI) from mid-life to older adulthood, assessing the influence of age, sex, amyloid (Aβ) and genetic risk for dementia. The data comprised cognitive, genetic and neuroimaging measures of a total of 1570 non-clinical mid-life and older adults (mean age 72, range 49-90 years, 1330 males) and 1365 age- and sex-matched adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). Among non-clinical adults, we found robust modes of co-variation between regional SW and multidomain cognitive function that differed between the mid-life and older age range. These cortical and cognitive profiles derived from healthy cohorts predicted out-of-sample AD and MCI. Furthermore, Aβ-deposition and educational attainment levels were associated with cognition but not SW. These findings underscoring the complex interplay between factors influencing cognition and brain structure from mid-life onwards, providing valuable insights for future research into neurodegeneration and the development of future screening algorithms., (© 2024. The Author(s).)

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.)

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases., Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories., Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points., Exposures: Individuals WODCI at baseline scan., Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed., Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7)., Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Importance: Physical health and chronic medical comorbidities are underestimated, inadequately treated, and often overlooked in psychiatry. A multiorgan, systemwide characterization of brain and body health in neuropsychiatric disorders may enable systematic evaluation of brain-body health status in patients and potentially identify new therapeutic targets., Objective: To evaluate the health status of the brain and 7 body systems across common neuropsychiatric disorders., Design, Setting, and Participants: Brain imaging phenotypes, physiological measures, and blood- and urine-based markers were harmonized across multiple population-based neuroimaging biobanks in the US, UK, and Australia, including UK Biobank; Australian Schizophrenia Research Bank; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer's Disease Neuroimaging Initiative; Prospective Imaging Study of Ageing; Human Connectome Project-Young Adult; and Human Connectome Project-Aging. Cross-sectional data acquired between March 2006 and December 2020 were used to study organ health. Data were analyzed from October 18, 2021, to July 21, 2022. Adults aged 18 to 95 years with a lifetime diagnosis of 1 or more common neuropsychiatric disorders, including schizophrenia, bipolar disorder, depression, generalized anxiety disorder, and a healthy comparison group were included., Main Outcomes and Measures: Deviations from normative reference ranges for composite health scores indexing the health and function of the brain and 7 body systems. Secondary outcomes included accuracy of classifying diagnoses (disease vs control) and differentiating between diagnoses (disease vs disease), measured using the area under the receiver operating characteristic curve (AUC)., Results: There were 85 748 participants with preselected neuropsychiatric disorders (36 324 male) and 87 420 healthy control individuals (40 560 male) included in this study. Body health, especially scores indexing metabolic, hepatic, and immune health, deviated from normative reference ranges for all 4 neuropsychiatric disorders studied. Poor body health was a more pronounced illness manifestation compared to brain changes in schizophrenia (AUC for body = 0.81 [95% CI, 0.79-0.82]; AUC for brain = 0.79 [95% CI, 0.79-0.79]), bipolar disorder (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.57-0.58]), depression (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.58-0.58]), and anxiety (AUC for body = 0.63 [95% CI, 0.63-0.63]; AUC for brain = 0.57 [95% CI, 0.57-0.58]). However, brain health enabled more accurate differentiation between distinct neuropsychiatric diagnoses than body health (schizophrenia-other: mean AUC for body = 0.70 [95% CI, 0.70-0.71] and mean AUC for brain = 0.79 [95% CI, 0.79-0.80]; bipolar disorder-other: mean AUC for body = 0.60 [95% CI, 0.59-0.60] and mean AUC for brain = 0.65 [95% CI, 0.65-0.65]; depression-other: mean AUC for body = 0.61 [95% CI, 0.60-0.63] and mean AUC for brain = 0.65 [95% CI, 0.65-0.66]; anxiety-other: mean AUC for body = 0.63 [95% CI, 0.62-0.63] and mean AUC for brain = 0.66 [95% CI, 0.65-0.66)., Conclusions and Relevance: In this cross-sectional study, neuropsychiatric disorders shared a substantial and largely overlapping imprint of poor body health. Routinely monitoring body health and integrated physical and mental health care may help reduce the adverse effect of physical comorbidity in people with mental illness.

Infants born very preterm face a range of neurodevelopmental challenges in cognitive, language, behavioural and/or motor domains. Early accurate identification of those at risk of adverse neurodevelopmental outcomes, through clinical assessment and Magnetic Resonance Imaging (MRI), enables prognostication of outcomes and the initiation of targeted early interventions. This study utilises a prospective cohort of 181 infants born <31 weeks gestation, who had 3T MRIs acquired at 29-35 weeks postmenstrual age and a comprehensive neurodevelopmental evaluation at 2 years corrected age (CA). Cognitive, language and motor outcomes were assessed using the Bayley Scales of Infant and Toddler Development - Third Edition and functional motor outcomes using the Neuro-sensory Motor Developmental Assessment. By leveraging advanced structural MRI pre-processing steps to standardise the data, and the state-of-the-art developing Human Connectome Pipeline, early MRI biomarkers of neurodevelopmental outcomes were identified. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, significant associations between brain structure on early MRIs with 2-year outcomes were obtained (r = 0.51 and 0.48 for motor and cognitive outcomes respectively) on an independent 25% of the data. Additionally, important brain biomarkers from early MRIs were identified, including cortical grey matter volumes, as well as cortical thickness and sulcal depth across the entire cortex. Adverse outcome on the Bayley-III motor and cognitive composite scores were accurately predicted, with an Area Under the Curve of 0.86 for both scores. These associations between 2-year outcomes and patient prognosis and early neonatal MRI measures demonstrate the utility of imaging prior to term equivalent age for providing earlier commencement of targeted interventions for infants born preterm., (Copyright © 2022. Published by Elsevier Inc.)

Background: Hearing loss is independently associated with a faster rate of cognitive decline in older adults and has been identified as a modifiable risk factor for dementia. The mechanism for this association is unknown, and there has been limited exploration of potential casual pathology., Objective: Our objective was to investigate whether there was an association between degree of audiometrically measured hearing loss (HL) and brain amyloid-β (Aβ) in a pre-clinical sample., Methods: Participants of the Australian Imaging and Biomarker Longitudinal Study (AIBL; n = 143) underwent positron emission tomography (PET) imaging and objective measurement of hearing thresholds within 5 years of imaging, as well as cognitive assessment within 2 years of imaging in this observational cohort study., Results: With one exception, study participants who had cognitive assessments within 2 years of their PET imaging (n = 113) were classified as having normal cognition. There was no association between cognitive scores and degree of hearing loss, or between cognitive scores and Aβ load. No association between HL and Aβ load was found once age was controlled for. As previously reported, positive Apolipoprotein E4 (APOE4) carrier status increased the risk of being Aβ positive (p = 0.002)., Conclusion: Degree of HL was not associated with positive Aβ status.

Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment., (© 2021. The Author(s).)

Objective: To determine the effect of β-amyloid (Aβ) level on progression risk to mild cognitive impairment (MCI) or dementia and longitudinal cognitive change in cognitively normal (CN) older individuals., Methods: All CN from the Australian Imaging Biomarkers and Lifestyle study with Aβ PET and ≥3 years follow-up were included (n = 534; age 72 ± 6 years; 27% Aβ positive; follow-up 5.3 ± 1.7 years). Aβ level was divided using the standardized 0-100 Centiloid scale: <15 CL negative, 15-25 CL uncertain, 26-50 CL moderate, 51-100 CL high, >100 CL very high, noting >25 CL approximates a positive scan. Cox proportional hazards analysis and linear mixed effect models were used to assess risk of progression and cognitive decline., Results: Aβ levels in 63% were negative, 10% uncertain, 10% moderate, 14% high, and 3% very high. Fifty-seven (11%) progressed to MCI or dementia. Compared to negative Aβ, the hazard ratio for progression for moderate Aβ was 3.2 (95% confidence interval [CI] 1.3-7.6; p < 0.05), for high was 7.0 (95% CI 3.7-13.3; p < 0.001), and for very high was 11.4 (95% CI 5.1-25.8; p < 0.001). Decline in cognitive composite score was minimal in the moderate group (-0.02 SD/year, p = 0.05), while the high and very high declined substantially (high -0.08 SD/year, p < 0.001; very high -0.35 SD/year, p < 0.001)., Conclusion: The risk of MCI or dementia over 5 years in older CN is related to Aβ level on PET, 5% if negative vs 25% if positive but ranging from 12% if 26-50 CL to 28% if 51-100 CL and 50% if >100 CL. This information may be useful for dementia risk counseling and aid design of preclinical AD trials., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Introduction: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects)., Methods: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD., Results: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD., Discussion: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium., (© 2020 the Alzheimer's Association.)

Background and Aims: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model., Methods: Seventy-seven very preterm infants (born <31 weeks gestational age (GA)) in a prospective longitudinal cohort underwent diffusion MR imaging (3T Siemens Trio; 64 directions, b ​= ​2000 ​s/mm 2 ). Motor outcome at 2 years corrected age (CA) was measured by Neuro-Sensory Motor Developmental Assessment (NSMDA). Scores were dichotomised into normal (functional score: 0, normal; n ​= ​48) and abnormal scores (functional score: 1-5, mild-profound; n ​= ​29). MRIs were pre-processed to reduce artefacts, upsampled to 1.25 ​mm isotropic resolution and maps of fractional anisotropy (FA) were estimated. Patches extracted from each image were used as inputs to train a CNN, wherein each image patch predicted either normal or abnormal outcome. In a postprocessing step, an image was classified as predicting abnormal outcome if at least 27% (determined by a grid search to maximise the model performance) of its patches predicted abnormal outcome. Otherwise, it was considered as normal. Ten-fold cross-validation was used to estimate performance. Finally, heatmaps of model predictions for patches in abnormal scans were generated to explore the locations associated with abnormal outcome., Results: For the identification of infants with abnormal motor outcome based on the FA data from early MRI, we achieved mean sensitivity 70% (standard deviation SD 19%), mean specificity 74% (SD 39%), mean AUC (area under the receiver operating characteristic curve) 72% (SD 14%), mean F1 score of 68% (SD 13%) and mean accuracy 73% (SD 19%) on an unseen test data set. Patch-based prediction heatmaps showed that the patches around the motor cortex and somatosensory regions were most frequently identified by the model with high precision (74%) as a location associated with abnormal outcome. Part of the cerebellum, and occipital and frontal lobes were also highly associated with abnormal NSMDA/motor outcome., Discussion/conclusion: This study established the potential of an early brain MRI-based deep learning CNN model to identify preterm infants at risk of a later motor impairment and to identify brain regions predictive of adverse outcome. Results suggest that predictions can be made from FA maps of diffusion MRIs well before term equivalent age (TEA) without any prior knowledge of which MRI features to extract and associated feature extraction steps. This method, therefore, is suitable for any case of brain condition/abnormality. Future studies should be conducted on a larger cohort to re-validate the robustness and effectiveness of these models., Competing Interests: Declaration of competing interest The authors declare they have no conflicts of interest to disclose., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Infants born very preterm are at risk of adverse neurodevelopmental outcomes, including cognitive deficits, motor impairments and cerebral palsy. Earlier identification enables targeted early interventions to be implemented with the aim of improving outcomes., Methods and Analysis: Protocol for 6-year follow-up of two cohorts of infants born <31 weeks gestational age (PPREMO: Prediction of Preterm Motor Outcomes; PREBO: Prediction of Preterm Brain Outcomes) and a small term-born reference sample in Brisbane, Australia. Both preterm cohorts underwent very early MRI and concurrent clinical assessment at 32 and 40 weeks postmenstrual age (PMA) and were followed up at 3, 12 and 24 months corrected age (CA). This study will perform MRI and electroencephalography (EEG). Primary outcomes include the Movement Assessment Battery for Children second edition and Full-Scale IQ score from the Wechsler Intelligence Scale for Children fifth edition (WISC-V). Secondary outcomes include the Gross Motor Function Classification System for children with cerebral palsy; executive function (Behavior Rating Inventory of Executive Function second edition, WISC-V Digit Span and Picture Span, Wisconsin Card Sorting Test 64 Card Version); attention (Test of Everyday Attention for Children second edition); language (Clinical Evaluation of Language Fundamentals fifth edition), academic achievement (Woodcock Johnson IV Tests of Achievement); mental health and quality of life (Development and Well-Being Assessment, Autism Spectrum Quotient-10 Items Child version and Child Health Utility-9D)., Aims: Examine the ability of early neonatal MRI, EEG and concurrent clinical measures at 32 weeks PMA to predict motor, cognitive, language, academic achievement and mental health outcomes at 6 years CA.Determine if early brain abnormalities persist and are evident on brain MRI at 6 years CA and the relationship to EEG and concurrent motor, cognitive, language, academic achievement and mental health outcomes., Ethics and Dissemination: Ethical approval has been obtained from Human Research Ethics Committees at Children's Health Queensland (HREC/19/QCHQ/49800) and The University of Queensland (2019000426). Study findings will be presented at national and international conferences and published in peer-reviewed journals., Trial Registration Number: ACTRN12619000155190p. WEB ADDRESS OF TRIAL: http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12619000155190p., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: The fluid and white matter suppression sequence (FLAWS) provides two T1-weighted co-registered datasets: a white matter (WM) suppressed contrast (FLAWS1) and a cerebrospinal fluid (CSF) suppressed contrast (FLAWS2). FLAWS has the potential to improve the contrast of the subcortical brain regions that are important for Deep Brain Stimulation surgery planning. However, to date FLAWS has not been optimized for 1.5 T. In this study, the FLAWS sequence was optimized for use at 1.5 T. In addition, the contrast-enhancement properties of FLAWS image combinations were investigated using two voxel-wise FLAWS combined images: the division (FLAWS-div) and the high contrast (FLAWS-hc) image., Methods: FLAWS sequence parameters were optimized for 1.5 T imaging using an approach based on the use of a profit function under constraints for brain tissue signal and contrast maximization. MR experiments were performed on eleven healthy volunteers (age 18-30). Contrast (CN) and contrast to noise ratio (CNR) between brain tissues were measured in each volunteer. Furthermore, a qualitative assessment was performed to ensure that the separation between the internal globus pallidus (GPi) and the external globus pallidus (GPe) is identifiable in FLAWS1., Results: The optimized set of sequence parameters for FLAWS at 1.5 T provided contrasts similar to those obtained in a previous study at 3 T. The separation between the GPi and the GPe was clearly identified in FLAWS1. The CN of FLAWS-hc was higher than that of FLAWS1 and FLAWS2, but was not different from the CN of FLAWS-div. The CNR of FLAWS-hc was higher than that of FLAWS-div., Conclusion: Both qualitative and quantitative assessments validated the optimization of the FLAWS sequence at 1.5 T. Quantitative assessments also showed that FLAWS-hc provides an enhanced contrast compared to FLAWS1 and FLAWS2, with a higher CNR than FLAWS-div., (Crown Copyright © 2019. Published by Elsevier Inc. All rights reserved.)

Introduction: 18 F-florbetaben is currently approved for the visual rule out of β-amyloid (Aβ) pathology. It is also used for recruitment and as an outcome measure in therapeutic trials, requiring accurate and reproducible quantification of Aβ burden in the brain., Methods: Data from eighty-eight subjects (52 male subjects, aged 79.8 ± 10.6 years) who underwent antemortem 18 F-florbetaben positron emission tomography scan and magnetic resonance imaging less than a year before neuropathological assessment at autopsy were evaluated. Image analysis was performed using the standard Centiloid (CL) statistical parametric mapping approach and CapAIBL ® . Imaging results were compared against autopsy data., Results: Against combined Bielschowsky silver staining and immunohistochemistry histopathological scores, statistical parametric mapping had 96% sensitivity, 96% specificity, and 95% accuracy, whereas magnetic resonance-less CapAIBL standardized uptake value ratio Whole Cerebellum had 94% sensitivity, 96% specificity, and 95% accuracy. Based on the combined histopathological scores, a CL threshold band of 19 ± 7 CL was determined., Discussion: Quantification of 18 F-florbetaben positron emission tomography scans using magnetic resonance-based and magnetic resonance-less CapAIBL ® approaches showed high agreement, establishing a pathology-based threshold in CL., (Copyright © 2019 the Alzheimer's Association. All rights reserved.)

The centiloid scale was recently proposed to provide a standard framework for the quantification of β-amyloid PET images, so that amyloid burden can be expressed on a standard scale. While the framework prescribes SPM8 as the standard analysis method for PET quantification, non-standard methods can be calibrated to produce centiloid values. We have previously developed a PET-only quantification: CapAIBL. In this study, we show how CapAIBL can be calibrated to the centiloid scale., Methods: Calibration images for 11 C-PiB, 18 F-NAV4694, 18 F-Florbetaben, 18 F-Flutemetamol and 18 F- Florbetapir were analysed using the standard method and CapAIBL. Using these images, both methods were calibrated to the centiloid scale. Centiloid values computed using CapAIBL were compared to those computed using standard method. For each tracer, a separate validation was performed using an independent dataset from the AIBL study., Results: Using the calibration images, there was a very strong agreement, and very little bias between the centiloid values computed using CapAIBL and those computed using the standard method with R 2  > 0.97 across all tracers. Using images from AIBL, the agreement was also high with R 2  > 0.96 across all tracers. In this dataset, there was a small underestimation of the centiloid values computed using CapAIBL of less than 0.8% in PiB, and a small over-estimation of 1.3% in Florbetapir, and 0.8% in Flutemetamol. There was a larger overestimation of 8% in NAV images, and 14% underestimation in Florbetaben images. However, some of these differences could be explained by the use of different scanners between the calibration scans and the ones used in AIBL., Conclusion: The PET-only quantification method, CapAIBL, can produce reliable centiloid values. The bias observed in the AIBL dataset for 18F-NAV4694 and 18 F-Florbetaben may indicate that using different scanners or reconstruction methods might require scanner-specific adjustments., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Often derived from partial correlations or many pairwise analyses, covariance networks represent the inter-relationships among regions and can reveal important topological structures in brain measures from healthy and pathological subjects. However both approaches are not consistent network estimators and are sensitive to the value of the tuning parameters. Here, we propose a consistent covariance network estimator by maximising the network likelihood (MNL) which is robust to the tuning parameter. We validate the consistency of our algorithm theoretically and via a simulation study, and contrast these results against two well-known approaches: the graphical LASSO (gLASSO) and Pearson pairwise correlations (PPC) over a range of tuning parameters. The MNL algorithm had a specificity equal to and greater than 0.94 for all sample sizes in the simulation study, and the sensitivity was shown to increase as the sample size increased. The gLASSO and PPC demonstrated a specificity-sensitivity trade-off over a range of values of tuning parameters highlighting the discrepancy in the results for misspecified values. Application of the MNL algorithm to the case study data showed a loss of connections between healthy and impaired groups, and improved ability to identify between lobe connectivity in contrast to gLASSO networks. In this work, we propose the MNL algorithm as an effective approach to find covariance brain networks, which can inform the organisational features in brain-wide analyses, particularly for large sample sizes., Competing Interests: MIC was funded by the Research Training Program (RTP) doctoral scholarship provided by the Australian Government and a top-up provided by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). JDD is the Biostatistics Team Leader of the Australian e-Health Research Centre and is funded by the CSIRO. JF is the Acting Group leader in the Biomedical Informatics team at the Australian e-Health Research Centre and is funded by the CSIRO. JM is funded by the Queensland University of Technology (QUT) and is affiliated with the Australian Research Council and Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). KM is jointly funded by QUT and the Australian Research Council (ARC) Laureate, project ID FL150100150 Bayesian Learning for Decision Making in the Big Data Era and is the Deputy Director of ACEMS. CCD is affiliated with QUT and ACEMS and was supported by an Australian Research Councils Discovery Early Career Researcher Award scheme DE160100741. The funders provided support in the form of salaries for the authors (MIC, JM, CCD, KM, JDD and JF), but did not have any additional role in the study design, decision to publish or preparation of the manuscript. There are no patents, products in development or marketed products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Aim: This study aimed to examine associations between structural MRI and concurrent motor, neurological and neurobehavioral measures at 30-32 weeks postmenstrual age (PMA; 'Early'), and at term equivalent age ('Term')., Method: In this prospective cohort study, infants underwent Early MRI (n = 119; 73 male; median 32 weeks 1 day PMA) and Term MRI (n = 102; 61 male; median 40 weeks 4 days PMA) at 3 T. Structural images were scored generating white matter (WM), cortical gray matter, deep gray matter, cerebellar and global brain abnormality scores. Clinical measures were General Movements Assessment (GMs), Hammersmith Neonatal Neurological Examination (HNNE) and NICU Neonatal Neurobehavioral Scale (NNNS). The Premie-Neuro was administered Early and the Test of Infant Motor Performance (TIMP) and a visual assessment at Term., Results: Early MRI cerebellar scores were strongly associated with neurological components of HNNE (reflexes), NNNS (Hypertonicity), the Premie-Neuro neurological subscale (regression coefficient β = -0.06; 95% confidence interval CI = -0.09, -0.04; p < .001) and cramped-synchronized GMs (β = 1.10; 95%CI = 0.57, 1.63; p < .001). Term MRI WM and global scores were strongly associated with the TIMP (WM β = -1.02; 95%CI = -1.67, -0.36; p = .002; global β = -1.59; 95%CI = -2.62, -0.56; p = .001)., Interpretation: Brain structure on Early and Term MRI was associated with concurrent motor, neurological and neurobehavioral function in very preterm infants., (Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.)

Preterm birth causes significant disruption in ongoing brain development, frequently resulting in adverse neurodevelopmental outcomes. Brain imaging using diffusion MRI may provide valuable insight into microstructural properties of the developing brain. The aim of this study was to establish whether the recently introduced fixel-based analysis method, with its associated measures of fibre density (FD), fibre bundle cross-section (FC), and fibre density and bundle cross-section (FDC), is suitable for the investigation of the preterm infant brain at term equivalent age. High-angular resolution diffusion weighted images (HARDI) of 55 preterm-born infants and 20 term-born infants, scanned around term-equivalent age, were included in this study (3 T, 64 directions, b = 2000 s/mm 2 ). Postmenstrual age at the time of MRI, and intracranial volume (FC and FDC only), were identified as confounding variables. Gestational age at birth was correlated with all fixel measures in the splenium of the corpus callosum. Compared to term-born infants, preterm infants showed reduced FD, FC, and FDC in a number of regions, including the corpus callosum, anterior commissure, cortico-spinal tract, optic radiations, and cingulum. Preterm infants with minimal macroscopic brain abnormality showed more extensive reductions than preterm infants without any macroscopic brain abnormality; however, little differences were observed between preterm infants with no and with minimal brain abnormality. FC showed significant reductions in preterm versus term infants outside regions identified with FD and FDC, highlighting the complementary role of these measures. Fixel-based analysis identified both microstructural and macrostructural abnormalities in preterm born infants, providing a more complete picture of early brain development than previous diffusion tensor imaging (DTI) based approaches.

Background and Purpose: The diagnostic and prognostic potential of brain MR imaging before term-equivalent age is limited until valid MR imaging scoring systems are available. This study aimed to validate an MR imaging scoring system of brain injury and impaired growth for use at 29 to 35 weeks postmenstrual age in infants born at <31 weeks gestational age., Materials and Methods: Eighty-three infants in a prospective cohort study underwent early 3T MR imaging between 29 and 35 weeks' postmenstrual age (mean, 32 +2 ± 1 +3 weeks; 49 males, born at median gestation of 28 +4 weeks; range, 23 +6 -30 +6 weeks; mean birthweight, 1068 ± 312 g). Seventy-seven infants had a second MR scan at term-equivalent age (mean, 40 +6 ± 1 +3 weeks). Structural images were scored using a modified scoring system which generated WM, cortical gray matter, deep gray matter, cerebellar, and global scores. Outcome at 12-months corrected age (mean, 12 months 4 days ± 1 +2 weeks) consisted of the Bayley Scales of Infant and Toddler Development, 3rd ed. (Bayley III), and the Neuro-Sensory Motor Developmental Assessment., Results: Early MR imaging global, WM, and deep gray matter scores were negatively associated with Bayley III motor (regression coefficient for global score β = -1.31; 95% CI, -2.39 to -0.23; P = .02), cognitive (β = -1.52; 95% CI, -2.39 to -0.65; P < .01) and the Neuro-Sensory Motor Developmental Assessment outcomes (β = -1.73; 95% CI, -3.19 to -0.28; P = .02). Early MR imaging cerebellar scores were negatively associated with the Neuro-Sensory Motor Developmental Assessment (β = -5.99; 95% CI, -11.82 to -0.16; P = .04). Results were reconfirmed at term-equivalent-age MR imaging., Conclusions: This clinically accessible MR imaging scoring system is valid for use at 29 to 35 weeks postmenstrual age in infants born very preterm. It enables identification of infants at risk of adverse outcomes before the current standard of term-equivalent age., (© 2017 by American Journal of Neuroradiology.)

Aims: To investigate the extent of white matter damage in children with unilateral cerebral palsy (UCP) caused by periventricular white matter lesions comparing between unilateral and bilateral lesions; and to investigate a relationship between white matter microstructure and hand function., Methods and Procedures: Diffusion MRI images from 46 children with UCP and 18 children with typical development (CTD) were included. Subjects were grouped by side of hemiparesis and unilateral or bilateral lesions. A voxel-wise white matter analysis was performed to identify regions where fractional anisotropy (FA) was significantly different between UCP groups and CTD; and where FA correlated with either dominant or impaired hand function (using Jebsen Taylor Hand Function Test)., Outcomes and Results: Children with unilateral lesions had reduced FA in the corticospinal tract of the affected hemisphere. Children with bilateral lesions had widespread reduced FA extending into all lobes. In children with left hemiparesis, impaired hand function correlated with FA in the contralateral corticospinal tract. Dominant hand function correlated with FA in the posterior thalamic radiations as well as multiple other regions in both left and right hemiparesis groups., Conclusions and Implications: Periventricular white matter lesions consist of focal and diffuse components. Focal lesions may cause direct motor fibre insult resulting in motor impairment. Diffuse white matter injury is heterogeneous, and may contribute to more global dysfunction., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated "direct" measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: -1.4% to -2.2% (AD) and -0.35% to -0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: -1.5% to -7.0% (AD) and -0.4% to -1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods., (Copyright © 2015. Published by Elsevier Inc.)

Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. Accurate diagnosis and prognosis, in terms of motor type and severity, is difficult to obtain due to the heterogeneous appearance of brain injury and large anatomical distortions commonly observed in children with CP. There is a need to optimise treatment strategies for individual patients in order to lead to lifelong improvements in function and capabilities. Magnetic resonance imaging (MRI) is critical to non-invasively visualizing brain lesions, and is currently used to assist the diagnosis and qualitative classification in CP patients. Although such qualitative approaches under-utilise available data, the quantification of MRIs is not automated and therefore not widely performed in clinical assessment. Automated brain lesion segmentation techniques are necessary to provide valid and reproducible quantifications of injury. Such techniques have been used to study other neurological disorders, however the technical challenges unique to CP mean that existing algorithms require modification to be sufficiently reliable, and therefore have not been widely applied to MRIs of children with CP. In this paper, we present a review of a subset of available brain injury segmentation approaches that could be applied to CP, including the detection of cortical malformations, white and grey matter lesions and ventricular enlargement. Following a discussion of strengths and weaknesses, we suggest areas of future research in applying segmentation techniques to the MRI of children with CP. Specifically, we identify atlas-based priors to be ineffective in regions of substantial malformations, instead propose relying on adaptive, spatially consistent algorithms, with fast initialisation mechanisms to provide additional robustness to injury. We also identify several cortical shape parameters that could be used to identify cortical injury, and shape modelling approaches to identify anatomical injury. The benefits of automatic segmentation in CP is important as it has the potential to elucidate the underlying relationship between image derived features and patient outcome, enabling better tailoring of therapy to individual patients., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Background: More than 50 percent of all infants born very preterm will experience significant motor and cognitive impairment. Provision of early intervention is dependent upon accurate, early identification of infants at risk of adverse outcomes. Magnetic resonance imaging at term equivalent age combined with General Movements assessment at 12 weeks corrected age is currently the most accurate method for early prediction of cerebral palsy at 12 months corrected age. To date no studies have compared the use of earlier magnetic resonance imaging combined with neuromotor and neurobehavioural assessments (at 30 weeks postmenstrual age) to predict later motor and neurodevelopmental outcomes including cerebral palsy (at 12-24 months corrected age). This study aims to investigate i) the relationship between earlier brain imaging and neuromotor/neurobehavioural assessments at 30 and 40 weeks postmenstrual age, and ii) their ability to predict motor and neurodevelopmental outcomes at 3 and 12 months corrected age., Methods/design: This prospective cohort study will recruit 80 preterm infants born ≤ 30 week's gestation and a reference group of 20 healthy term born infants from the Royal Brisbane & Women's Hospital in Brisbane, Australia. Infants will undergo brain magnetic resonance imaging at approximately 30 and 40 weeks postmenstrual age to develop our understanding of very early brain structure at 30 weeks and maturation that occurs between 30 and 40 weeks postmenstrual age. A combination of neurological (Hammersmith Neonatal Neurologic Examination), neuromotor (General Movements, Test of Infant Motor Performance), neurobehavioural (NICU Network Neurobehavioural Scale, Premie-Neuro) and visual assessments will be performed at 30 and 40 weeks postmenstrual age to improve our understanding of the relationship between brain structure and function. These data will be compared to motor assessments at 12 weeks corrected age and motor and neurodevelopmental outcomes at 12 months corrected age (neurological assessment by paediatrician, Bayley scales of Infant and Toddler Development, Alberta Infant Motor Scale, Neurosensory Motor Developmental Assessment) to differentiate atypical development (including cerebral palsy and/or motor delay)., Discussion: Earlier identification of those very preterm infants at risk of adverse neurodevelopmental and motor outcomes provides an additional period for intervention to optimise outcomes., Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12613000280707. Registered 8 March 2013.

Background: β-amyloid (Aβ) plaques in brain's grey matter (GM) are one of the pathological hallmarks of Alzheimer's disease (AD), and can be imaged in vivo using Positron Emission Tomography (PET) with (11)C or (18)F radiotracers. Estimating Aβ burden in cortical GM has been shown to improve diagnosis and monitoring of AD. However, lacking structural information in PET images requires such assessments to be performed with anatomical MRI scans, which may not be available at different clinical settings or being contraindicated for particular reasons. This study aimed to develop an MR-less Aβ imaging quantification method that requires only PET images for reliable Aβ burden estimations., Materials and Methods: The proposed method has been developed using a multi-atlas based approach on (11)C-PiB scans from 143 subjects (75 PiB+ and 68 PiB- subjects) in AIBL study. A subset of 20 subjects (PET and MRI) were used as atlases: 1) MRI images were co-registered with tissue segmentation; 2) 3D surface at the GM-WM interfacing was extracted and registered to a canonical space; 3) Mean PiB retention within GM was estimated and mapped to the surface. For other participants, each atlas PET image (and surface) was registered to the subject's PET image for PiB estimation within GM. The results are combined by subject-specific atlas selection and Bayesian fusion to generate estimated surface values., Results: All PiB+ subjects (N = 75) were highly correlated between the MR-dependent and the PET-only methods with Intraclass Correlation (ICC) of 0.94, and an average relative difference error of 13% (or 0.23 SUVR) per surface vertex. All PiB- subjects (N = 68) revealed visually akin patterns with a relative difference error of 16% (or 0.19 SUVR) per surface vertex., Conclusion: The demonstrated accuracy suggests that the proposed method could be an effective clinical inspection tool for Aβ imaging scans when MRI images are unavailable.

Due to physical limitations inherent in magnetic resonance imaging scanners, three dimensional volumetric scans are often acquired with anisotropic voxel resolution. We investigate several interpolation approaches to reduce the anisotropy and present a novel approach - constrained reverse diffusion for thick slice interpolation. This technique was compared to common methods: linear and cubic B-Spline interpolation and a technique based on non-rigid registration of neighboring slices. The methods were evaluated on artificial MR phantoms and real MR scans of human brain. The constrained reverse diffusion approach delivered promising results and provides an alternative for thick slice interpolation, especially for higher anisotropy factors., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

For clinical evaluation, assessing amyloid deposition with PiB-PET is desirable without requiring MR acquisition and associated fusion/segmentation techniques. A useful clinical tool is to estimate PiB-PET against the brain surface, which is however challenging using PET alone because of the lack of structural information. We propose a method to generate such estimate, where multiple atlases are selected and combined with local weights in a Bayesian framework. Qualitative and quantitative comparison with and without MRI are presented. Using PET only, the average error on the brain surface was around 13% compared to MRI-dependant method.

Accurate cortical thickness estimation is important for the study of many neurodegenerative diseases. Many approaches have been previously proposed, which can be broadly categorised as mesh-based and voxel-based. While the mesh-based approaches can potentially achieve subvoxel resolution, they usually lack the computational efficiency needed for clinical applications and large database studies. In contrast, voxel-based approaches, are computationally efficient, but lack accuracy. The aim of this paper is to propose a novel voxel-based method based upon the Laplacian definition of thickness that is both accurate and computationally efficient. A framework was developed to estimate and integrate the partial volume information within the thickness estimation process. Firstly, in a Lagrangian step, the boundaries are initialized using the partial volume information. Subsequently, in an Eulerian step, a pair of partial differential equations are solved on the remaining voxels to finally compute the thickness. Using partial volume information significantly improved the accuracy of the thickness estimation on synthetic phantoms, and improved reproducibility on real data. Significant differences in the hippocampus and temporal lobe between healthy controls (NC), mild cognitive impaired (MCI) and Alzheimer's disease (AD) patients were found on clinical data from the ADNI database. We compared our method in terms of precision, computational speed and statistical power against the Eulerian approach. With a slight increase in computation time, accuracy and precision were greatly improved. Power analysis demonstrated the ability of our method to yield statistically significant results when comparing AD and NC. Overall, with our method the number of samples is reduced by 25% to find significant differences between the two groups.

Rationale and Objectives: Radiotracers such as (11)C-PiB have enabled the in vivo imaging of amyloid-beta plaques in the brain, one of the histopathologic hallmarks of Alzheimer's disease (AD). Standardized uptake value ratio (SUVR) has become the most common normalization for (11)C-PiB as it does not require dynamic scans or blood sampling. Normalization is performed by computing the ratio of (11)C-PiB retention in the whole brain to that in cerebellar gray matter. However, SUVR is still conducted manually and is time consuming. An automated normalization algorithm is proposed., Materials and Methods: Sixty participants from the Australian Imaging Biomarkers and Lifestyle (AIBL) study were used to test the developed algorithm and compare it against manual SUVR. The cohort consisted of participants likely to have AD (n = 20), those with mild cognitive impairment (MCI; n = 20), and normal controls (NC; n = 20). The participants underwent (11)C-PiB PET scans. A subset (n = 15) also underwent magnetic resonance imaging scans. (11)C-PET scans were segmented using an expectation maximization approach with inhomogeneity correction using three-dimensional cubic B-Splines. A cerebellar region was propagated and constrained by segmentation. Comparisons were made between manual and automated SUVR using regional analysis. Receiver-operating characteristic curves were computed for the task of AD-NC classification. Positron emission tomographic segmentations were also compared to co-registered magnetic resonance images of the same patient., Results: Significant differences in regional means were observed between manual and automated SUVR. However, these changes were highly correlated (r > 0.8 for most regions). Significant differences (P < .05) in regional variances were also observed for the AD and NC subgroups. Area under the curve was 0.84 and 0.89 for manual and automated SUVR, respectively., Conclusions: The automated normalization technique results in less within-group variance and better discrimination between AD and NC participants.

Beta-amyloid (Abeta) plaques are one of the neuropathological hallmarks of Alzheimer's disease (AD) and can be quantified using the marker 11C PiB. As l1C PiB PET images have limited anatomical information, an Magnetic Resonance Image (MRI) is usually acquired to perform the spatial normalization needed for population analysis. We designed and evaluated a high dimensional spatial normalization approach that only uses the 11C PiB PET image. The non-rigid registration (NRR) is based on free form deformation (FFD) modelled using B-splines. To compensate for the limited anatomical information, the FFD is constrained to an allowable transform space using a model trained from MR registrations. Abeta deposition is dependent on disease staging, so a spatially normalized 11C PiB PET appearance model selects and refines the atlas. The approach was compared with MR NRR using data from healthy elderly, mild cognitive impaired and Alzheimer disease participants. Using segmentation propagation, an average Dice similarity coefficient of 0.64 and 0.73 was obtained for white and gray matter. The R-squared correlation between the uptake obtained in the frontal, parietal, occipital and temporal was 0.789, 0.843, 0.871 and 0.964. These are very promising results, considering the low resolution of 11C PiB PET images.