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White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.

The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual's progression through Alzheimer's disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend

In 2018, the Australian Dementia Network (ADNeT) was established to bring together Australia's leading dementia researchers, people with living experience and clinicians to transform research and clinical care in the field. To address dementia diagnosis, treatment, and care, ADNeT has established three core initiatives: the Clinical Quality Registry (CQR), Memory Clinics, and Screening for Trials. Collectively, the initiatives have developed an integrated clinical and research community, driving practice excellence in this field, leading to novel innovations in diagnostics, clinical care, professional development, quality and harmonization of healthcare, clinical trials, and translation of research into practice. Australia now has a national Registry for Mild Cognitive Impairment and dementia with 55 participating clinical sites, an extensive map of memory clinic services, national Memory and Cognition Clinic Guidelines and specialized screening for trials sites in five states. This paper provides an overview of ADNeT's achievements to date and future directions. With the increase in dementia cases expected over coming decades, and with recent advances in plasma biomarkers and amyloid lowering therapies, the nationally coordinated initiatives and partnerships ADNeT has established are critical for increased national prevention efforts, co-ordinated implementation of emerging treatments for Alzheimer's disease, innovation of early and accurate diagnosis, driving continuous improvements in clinical care and patient outcome and access to post-diagnostic support and clinical trials. For a heterogenous disorder such as dementia, which is now the second leading cause of death in Australia following cardiovascular disease, the case for adequate investment into research and development has grown even more compelling.

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

The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.

Background Cerebrospinal fluid (CSF) soluble biomarkers are useful at detecting pre‐clinical levels of Alzheimer’s disease (AD) biomarkers of b‐amyloid (Ab) and tau. Disease progression times for participants in longitudinal studies can be estimated for different biomarkers. Utilizing a new technique, this work compared the disease progression times between CSF and PET biomarkers. Methods Four hundred and ten participants from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) including participants form ACS/OASIS, ADNI and AIBL with three or more data points of longitudinal CSF Ab42 and pTau181 (pTau) and Ab PET were selected. PET results were expressed in Centiloid (CL), (299 cognitively unimpaired, 107 mild cognitively impaired, 4 AD dementia; aged 69±9; 216 females (NAIBL=30, NADNI=252, NOASIS=128). Disease trajectory curves for individual biomarkers and the pTau/Ab42 ratio were created by: 1) Fitting a function to the rates of change of the variable of interest versus its mean value), 2) integrating the fit to obtain longitudinal trajectory curves as a function of disease progression time for each of the variables. The participants’ disease progression time along each curve were estimated. Threshold values for Ab PET and pTau/Ab42 ratios were calculated using a gaussian mixture model. Estimates of age of onset were calculated using the progression times. The participants’ disease progression times for each of the different variables were compared using rank correlations. Results Rank correlations for the progression times were: r(Ab42, Ab PET) = 0.75, r(pTau, Ab PET)=0.62, and r(pTau/Ab42, Ab PET)=0.83. The estimated ages at which participants’ reach Ab PET and the pTau/Ab42 ratio thresholds are compared in Fig 1, the average age at which were estimated to reach the threshold values were 55 yr for pTau/Ab42 (threshold of 0.021) and 61 yr for Ab PET (threshold of 22 CL). Conclusions The high correlation between pTau/Ab42 and Ab PET

Background Longitudinal tau quantification may provide a useful outcome measure in disease‐specific therapeutic trials. Different tau PET tracers may have different sensitivity to longitudinal changes, but without a head‐to‐head comparison, equating results from different cohorts using different tracers can be biased. In this study, we aim to minimise this bias by matching participants in two cohorts imaged using 18F‐MK6240 and 18F‐Flortaucipir (FTP). Method A subset of 93 participants from AIBL and 93 from ADNI, imaged at baseline and 1 year later using 18F‐MK6240 and 18F‐FTP, respectively, were matched based on baseline clinical diagnosis, MMSE, age, and Centiloid value (CL). PET images were analysed with CapAIBL. Amyloid positivity (+/‐) was defined based on a threshold of 25CL. Subjects were grouped as 34 cognitively unimpaired amyloid negative (CU‐) and 24 positive (CU+), 18 mild cognitive impairment positive (MCI+) and 17 Alzheimer’s disease positive (AD+). Tracer retention was measured in the mesial temporal (Me), meta‐temporal (MT), temporoparietal (Te) and rest of the cortex (R). T‐tests were employed to assess group separation at baseline using SUVR and longitudinally using SUVR/Yr. Result As per selection criteria, there were no significant differences in age, MMSE or Centiloid between the cohorts using 18F‐MK6240 or 18F‐FTP in each subgroups. Baseline SUVR were significantly different between CU‐/CU+, CU+/MCI+ and CU+/AD+ in all regions for both tracers, except for CU‐/CU+ in R for 18F‐MK6240 (Figure 1). Using 18F‐MK6240, rate of change in CU+ was significantly higher than CU‐ in MT and Te, and both MCI+ and AD+ were higher than CU+ in R (Figure 2.Left). Using 18F‐FTP, rate of change in MCI+ was significantly higher than CU+ in Te, and AD+ higher than CU+ in MT, Te and R (Figure 2.Right). Conclusion In our matched cohorts using 18F‐MK6240 or 18F‐FTP, we found that, at baseline, both tracers can detect significant differences between clinical groups. Howe

In this article the affiliation details for Ulman Lindenberger were incorrectly given as ‘Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany’ but should have been ‘Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany’, ‘Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany’. The original article has been corrected.

BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. PURPOSE: To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. STUDY TYPE: Retrospective. POPULATION: Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. FIELD STRENGTH/SEQUENCE: Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. ASSESSMENT: StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. STATISTICAL TESTS: Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. RESULTS: Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. DATA CONCLUSION: While

BACKGROUND: Femoroacetabular impingement (FAI) cam morphology is routinely assessed using manual measurements of two-dimensional (2D) alpha angles which are prone to high rater variability and do not provide direct three-dimensional (3D) data on these osseous formations. We present CamMorph, a fully automated 3D pipeline for segmentation, statistical shape assessment and measurement of cam volume, surface area and height from clinical magnetic resonance (MR) images of the hip in FAI patients. METHODS: The novel CamMorph pipeline involves two components: (I) accurate proximal femur segmentation generated by combining the 3D U-net to identify both global (region) and local (edge) features in clinical MR images and focused shape modelling to generate a 3D anatomical model for creating patient-specific proximal femur models; (II) patient-specific anatomical information from 3D focused shape modelling to simulate 'healthy' femoral bone models with cam-affected region constraints applied to the anterosuperior femoral head-neck region to quantify cam morphology in FAI patients. The CamMorph pipeline, which generates patient-specific data within 5 min, was used to analyse multi-site clinical MR images of the hip to measure and assess cam morphology in male (n=56) and female (n=41) FAI patients. RESULTS: There was excellent agreement between manual and CamMorph segmentations of the proximal femur as demonstrated by the mean Dice similarity index (DSI; 0.964±0.006), 95% Hausdorff distance (HD; 2.123±0.876 mm) and average surface distance (ASD; 0.539±0.189 mm) values. Compared to female FAI patients, male patients had a significantly larger median cam volume (969.22 vs. 272.97 mm3, U=240.0, P<0.001), mean surface area [657.36 vs. 306.93 mm2, t(95)=8.79, P<0.001], median maximum-height (3.66 vs. 2.15 mm, U=407.0, P<0.001) and median average-height (1.70 vs. 0.86 mm, U=380.0, P<0.001). CONCLUSIONS: The fully automated 3D CamMorph pipeline developed in the present study successfu

PURPOSE: To obtain automated measurements of cam volume, surface area, and height from baseline (preintervention) and 12-month magnetic resonance (MR) images acquired from male and female patients allocated to physiotherapy (PT) or arthroscopic surgery (AS) management for femoroacetabular impingement (FAI) in the Australian FASHIoN trial. METHODS: An automated segmentation pipeline (CamMorph) was used to obtain cam morphology data from three-dimensional (3D) MR hip examinations in FAI patients classified with mild, moderate, or major cam volumes. Pairwise comparisons between baseline and 12-month cam volume, surface area, and height data were performed within the PT and AS patient groups using paired t-tests or Wilcoxon signed-rank tests. RESULTS: A total of 43 patients were included with 15 PT patients (9 males, 6 females) and 28 AS patients (18 males, 10 females) for premanagement and postmanagement cam morphology assessments. Within the PT male and female patient groups, there were no significant differences between baseline and 12-month mean cam volume (male: 1269 vs 1288 mm3, t[16] = -0.39; female: 545 vs 550 mm,3 t[10] = -0.78), surface area (male: 1525 vs 1491 mm2, t[16] = 0.92; female: 885 vs 925 mm,2 t[10] = -0.78), maximum height (male: 4.36 vs 4.32 mm, t[16] = 0.34; female: 3.05 vs 2.96 mm, t[10] = 1.05) and average height (male: 2.18 vs 2.18 mm, t[16] = 0.22; female: 1.4 vs 1.43 mm, t[10] = -0.38). In contrast, within the AS male and female patient groups, there were significant differences between baseline and 12-month cam volume (male: 1343 vs 718 mm3, W = 0.0; female: 499 vs 240 mm3, t[18] = 2.89), surface area (male: 1520 vs 1031 mm2, t(34) = 6.48; female: 782 vs 483 mm2, t(18) = 3.02), maximum-height (male: 4.3 vs 3.42 mm, W = 13.5; female: 2.85 vs 2.24 mm, t(18) = 3.04) and average height (male: 2.17 vs 1.52 mm, W = 3.0; female: 1.4 vs 0.94 mm, W = 3.0). In AS patients, 3D bone models provided good visualization of cam bone mass removal postostecto

Background Alzheimer’s disease (AD) clinical trials require cognitive test scores that assess change in cognitive function accurately. Here, we propose new composite cognitive test scores to detect earlier stages of AD accurately by using the full neuropsychological testing battery (in ADNI) and a manifold learning dimension reduction technique namely UMAP. Method Data for this study included N=1585 ADNI participants ([492 cognitively normal (CN), 804 mild cognitively impaired (MCI), 289 AD; aged 73.8±7.1; 708 females]; Table 1). Subjects with 3 or more follow‐up sessions were included. Cognitive test scores with more than 60% missing data were excluded. Missing data within included test scores were imputed using the MissForest algorithm. A linear mixed model using all follow‐up data was applied to calculate the random slope (rate of change) and random intercept for each cognitive score and for each subject. The scores and demographic measurements: age, gender, years of education and APOE‐ɛ4 status were used to inform the UMAP. Levels for the output variable were defined as: 1) stable CN, 2) CN who progressed to MCI or probable dementia due to AD, 3) stable MCI, 4) MCI who progressed to dementia AD and 5) dementia due to AD. The model calculated two composite scores. These cognitive stages were predicted using Support Vector Machine (SVM) analysis of both the new composite scores and the traditional clinical rating measures of Clinical Dementia Rating (CDR) and Mini‐Mental State Examination (MMSE). Result Predicting cognitive stages using the proposed composite scores show a highly significant improvement with a 0.981 accuracy and 0.976 reliability (evaluated by Cohen's kappa coefficient), compared to using the combination of CDR and MMSE scores covaried for demographics, which had 0.660 accuracy and 0.567 reliability. Individuals’ clinical and preclinical stages with regards to UMAP two‐dimensional embedding and the clinical rating measures, CDR and MMSE

Background The development of cognitive endpoints that can accurately assess changes in cognition over short time frames is crucial for clinical trials and research of Alzheimer’s disease (AD). Understanding the changing influence of contributing test scores on composites throughout the disease course provides the opportunity to optimise cognitive composite scores for different stages of AD. Method AIBL participants with declining cognitive performance were included in this study N=1275 [688 cognitively unimpaired (CU), 277 mild cognitively impaired (MCI), 310 AD; aged 73±9; 718 females]). Two cognitive composite scores (Episodic Memory (EM) and PACC) and their component test scores (California Verbal Learning Test‐II Delayed Recall (CVLT‐II DR), Logical Memory Delayed Recall (LMII), Rey Complex Figure Test 30 minute delayed recall (RCFT‐DR) and CVLT‐II DR, LMII, Digit Symbol Substitution Test (DS), MMSE, respectively) were evaluated. We first examined the relationship between each of component tests score for each composite. We then compared the extent to which longitudinal trajectories of each component test score and each cognitive composite score differed at each disease stage. Result CVLT‐II DR contributed the most to the EM composite followed by RCFT‐DR and LMII with the influence remaining unchanged across each disease stage. For PACC, CVLT‐II DR contributed the most to the initial decline, with MMSE and LMII contributing similar amounts and DS contributing the least. CVLT‐II DR contributed substantially to changes in PACC earlier in the disease course but MMSE drove the PACC change in later stages of disease. Initially, both composites follow similar longitudinal trajectories. However, the EM composite reaches a floor not observed for the PACC. Conclusion Understanding the temporal contribution of component tests scores on cognitive composites could provide improved cognitive endpoints tailored to use. For instance, MMSE is sensitive to change later

Background Previous studies have shown that Aß‐amyloid (Aß) likely promotes tau to spread beyond the medial temporal lobe. However, the Aß levels necessary for tau to spread in the neocortex is still unclear. Method 466 participants underwent tau imaging with [18F]MK6420 and Aß imaging with [18F]NAV4694 (Fig. 1). Aß scans were quantified on the Centiloid (CL) scale with a cut‐off of 25CL for abnormal levels of Aß (A+). Tau scans were quantified in three regions of interest (ROI) (mesial temporal (Me); temporoparietal neocortex (Te); and rest of neocortex (R)) and four mesial temporal region (entorhinal cortex, amygdala, hippocampus and parahippocampus) using the cerebellar cortex as reference region. Regional tau thresholds were established as the 95%ile of the cognitively unimpaired A‐ subjects. The prevalence of abnormal tau levels (T+) along the Centiloid continuum was determined. Result The plots of prevalence of T+ (Fig. 2) show earlier and greater increase along the Centiloid continuum in the medial temporal area compared to neocortex. Prevalence of T+ was low but associated with Aß level between 10‐40 CL reaching 23% in Me, 15% in Te and 11% in R. Between 40‐70 CL, the prevalence of T+ subjects per CL increased four‐fold faster and at 70 CL was 64% in Me, 51% in Te and 37% in R. In cognitively unimpaired (Fig. 3), there were no T+ in R below 50 CL. The highest prevalence of T+ was found in the entorhinal cortex, reaching 40% at 40 CL and 80% at 60 CL. Conclusion Outside the entorhinal cortex, abnormal levels of cortical tau on PET are rarely found with Aß levels below 40 CL. Above 40 CL prevalence of T+ accelerates in all areas. Moderate Aß levels are required before neocortical tau becomes detectable.

Background The introduction of the AT(N) framework raised several issues in regards to the definition of T+. What brain regions should be sampled? Based on one or on multiple tracers? In this work, we developed a “universal” cortical tau mask for the AD continuum derived from all the major tau ligands. This “universal” cortical mask will serve as the common tau area for all tracers over which several different regional sampling VOI or composites can be then applied. Guaranteeing sampling of the same common regions is the first step to develop a common scale for all tau tracers: the CenTauR. Method 464 participants underwent tau scans with either 18F‐AV1451 (CN=54/AD=24), 18F‐MK6240 (CN=157/AD=22), 18F‐PI2620 (CN=10/AD=21), 18F‐PM‐PBB3 (CN=30/AD=28), 18F‐GTP1 (CN=15/AD=38) or 18F‐RO948 (CN=35/AD=30). All CN were Aß‐ and all AD were Aß+. The tau scans were spatially normalized using CapAIBL and the cerebellar cortex was used as reference region. For each tracer, a difference image between the means of the Aß‐ CN and Aß+ AD patients was generated. Difference images were subsequently thresholded at 1/3 of the difference between Aß‐ CN and Aß+ AD in the inferior temporal lobe. A single tau specific mask was then constructed from the intersection of all the specific tau tracer masks. A MRI‐derived grey matter mask at PET resolution was applied to the composite mask only sampling grey matter regions. Finally, the mask was mirrored and fused to remove the hemispherical asymmetry of tau pathology. Agreement between masks was assessed by dice‐scores. Result Visually, all the tracer‐specific masks appeared very similar. None of the known off‐target binding regions were discernible in the resulting masks (Figure 1). There was good agreement between all masks, with dice‐scores of 0.60 and 0.66 for cortical regions. Conclusion We constructed an “universal” tau mask for the AD continuum based on all the commonly used tau tracers aiming at standardizing tau sampling and quantificat

Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer’s disease dementia (AD)) as an ‘Inception cohort’ who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an ‘Enrichment cohort’ (as of 10 April 2019). Objective: Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation. Methods: Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations. Results: AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression. Conclusion: This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.

Background: Previous research has identified a small subgroup of older adults that maintain a high level of cognitive functioning well into advanced age. Investigation of those with superior cognitive performance (SCP) for their age is important, as age-related decline has previously been thought to be inevitable. Objective: Preservation of cortical thickness and volume was evaluated in 76 older adults with SCP and 100 typical older adults (TOAs) assessed up to five times over six years. Methods: Regions of interest (ROIs) found to have been associated with super-aging status (a construct similar to SCP status) in previous literature were investigated, followed by a discovery phase analyses of additional regions. SCPs were aged 70 + at baseline, scoring at/above normative memory (CVLT-II) levels for demographically similar individuals aged 30–44 years old, and in the unimpaired range for all other cognitive domains over the course of the study. Results: In linear mixed models, following adjustment for multiple comparisons, there were no significant differences between rates of thinning or volume atrophy between SCPs and TOAs in previously identified ROIs, or the discovery phase analyses. With only amyloid-β negative individuals in the analyses, again there were no significant differences between SCPs and TOAs. Conclusion: The increased methodological rigor in classifying groups, together with the influence of cognitive reserve, are discussed as potential factors accounting for our findings as compared to the extant literature on those with superior cognitive performance for their age.

Introduction In cognitively normal (CN) adults, increased rates of amyloid beta (Aβ) accumulation can be detected in low Aβ (Aβ–) apolipoprotein E (APOE) ε4 carriers. We aimed to determine the effect of ε4 on the ability to benefit from experience (ie, learn) in Aβ– CNs. Methods Aβ– CNs (n = 333) underwent episodic memory assessments every 18 months for 108 months. A subset (n = 48) completed the Online Repeatable Cognitive Assessment‐Language Learning Test (ORCA‐LLT) over 6 days. Results Aβ– ε4 carriers showed significantly lower rates of improvement on episodic memory over 108 months compared to non‐carriers (d = 0.3). Rates of learning on the ORCA‐LLT were significantly slower in Aβ– ε4 carriers compared to non‐carriers (d = 1.2). Discussion In Aβ– CNs, ε4 is associated with a reduced ability to benefit from experience. This manifested as reduced practice effects (small to moderate in magnitude) over 108 months on the episodic memory composite, and a learning deficit (large in magnitude) over 6 days on the ORCA‐LLT. Alzheimer's disease (AD)–related cognitive abnormalities can manifest before preclinical AD thresholds.

BACKGROUND: Arthroscopic surgery for femoroacetabular impingement syndrome (FAI) is known to lead to self-reported symptom improvement. In the context of surgical interventions with known contextual effects and no true sham comparator trials, it is important to ascertain outcomes that are less susceptible to placebo effects. The primary aim of this trial was to determine if study participants with FAI who have hip arthroscopy demonstrate greater improvements in delayed gadolinium-enhanced magnetic resonance imaging (MRI) of cartilage (dGEMRIC) index between baseline and 12 months, compared to participants who undergo physiotherapist-led management. METHODS: Multi-centre, pragmatic, two-arm superiority randomised controlled trial comparing physiotherapist-led management to hip arthroscopy for FAI. FAI participants were recruited from participating orthopaedic surgeons clinics, and randomly allocated to receive either physiotherapist-led conservative care or surgery. The surgical intervention was arthroscopic FAI surgery. The physiotherapist-led conservative management was an individualised physiotherapy program, named Personalised Hip Therapy (PHT). The primary outcome measure was change in dGEMRIC score between baseline and 12 months. Secondary outcomes included a range of patient-reported outcomes and structural measures relevant to FAI pathoanatomy and hip osteoarthritis development. Interventions were compared by intention-to-treat analysis. RESULTS: Ninety-nine participants were recruited, of mean age 33 years and 58% male. Primary outcome data were available for 53 participants (27 in surgical group, 26 in PHT). The adjusted group difference in change at 12 months in dGEMRIC was -59 ms (95%CI - 137.9 to - 19.6) (p = 0.14) favouring PHT. Hip-related quality of life (iHOT-33) showed improvements in both groups with the adjusted between-group difference at 12 months showing a statistically and clinically important improvement in arthroscopy of 14 units (95% CI 5.6

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.

This prospective cohort study, "Prospective Imaging Study of Ageing: Genes, Brain and Behaviour" (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer's disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling over 3800 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for detailed functional, structural and molecular neuroimaging, lifestyle monitoring, detailed neurocognitive testing, plus blood sample donation. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.

Objective: To determine the extent to which deficits in learning over 6 days are associated with β-amyloid–positive (Aβ+) and hippocampal volume in cognitively normal (CN) adults. Methods: Eighty CN older adults who had undergone PET neuroimaging to determine Aβ status (n = 42 Aβ− and 38 Aβ+), MRI to determine hippocampal and ventricular volume, and repeated assessment of memory were recruited from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Participants completed the Online Repeatable Cognitive Assessment–Language Learning Test (ORCA-LLT), which required they learn associations between 50 Chinese characters and their English language equivalents over 6 days. ORCA-LLT assessments were supervised on the first day and were completed remotely online for all remaining days. Results: Learning curves in the Aβ+ CN participants were significantly worse than those in matched Aβ− CN participants, with the magnitude of this difference very large (d [95% confidence interval (CI)] 2.22 [1.64–2.75], p < 0.001), and greater than differences between these groups for memory decline since their enrollment in AIBL (d [95% CI] 0.52 [0.07–0.96], p = 0.021), or memory impairment at their most recent visit. In Aβ+ CN adults, slower rates of learning were associated with smaller hippocampal and larger ventricular volumes. Conclusions: These results suggest that in CN participants, Aβ+ is associated more strongly with a deficit in learning than any aspect of memory dysfunction. Slower rates of learning in Aβ+ CN participants were associated with hippocampal volume loss. Considered together, these data suggest that the primary cognitive consequence of Aβ+ is a failure to benefit from experience when exposed to novel stimuli, even over very short periods.

Background:Cerebrovascular disease often coexists with Alzheimer’s disease (AD). While both diseases share common risk factors, their interrelationship remains unclear. Increasing the understanding of how cerebrovascular changes interact with AD is essential to develop therapeutic strategies and refine biomarkers for early diagnosis. Objective:We investigate the prevalence and risk factors for the comorbidity of amyloid-β (Aβ) and cerebrovascular disease in the Australian Imaging, Biomarkers and Lifestyle Study of Ageing, and further examine their cross-sectional association. Methods:A total of 598 participants (422 cognitively normal, 89 with mild cognitive impairment, 87 with AD) underwent positron emission tomography and structural magnetic resonance imaging for assessment of Aβ deposition and cerebrovascular disease. Individuals were categorized based on the comorbidity status of Aβ and cerebrovascular disease (V) as Aβ–V–, Aβ–V+, Aβ+V–, or Aβ+V+. Results:Advancing age was associated with greater likelihood of cerebrovascular disease, high Aβ load and their comorbidity. Apolipoprotein E ɛ4 carriage was only associated with Aβ positivity. Greater total and regional WMH burden were observed in participants with AD. However, no association were observed between Aβ and WMH measures after stratification by clinical classification, suggesting that the observed association between AD and cerebrovascular disease was driven by the common risk factor of age. Conclusion:Our observations demonstrate common comorbid condition of Aβ and cerebrovascular disease in later life. While our study did not demonstrate a convincing cross-sectional association between Aβ and WMH burden, future longitudinal studies are required to further confirm this.

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.

Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.

Introduction Superior cognitive performance in older adults may reflect underlying resistance to age‐associated neurodegeneration. While elevated amyloid β (Aβ) deposition (Aβ+) has been associated with increased cortical atrophy, it remains unknown whether “SuperAgers” may be protected from Aβ‐associated neurodegeneration. Methods Neuropsychologically defined SuperAgers (n = 172) and cognitively normal for age (n = 172) older adults from the Australian Imaging, Biomarkers and Lifestyle study were case matched. Rates of cortical atrophy over 8 years were examined by SuperAger classification and Aβ status. Results Of the case‐matched SuperAgers and cognitively normal for age older adults, 40.7% and 40.1%, respectively, were Aβ+. Rates of age‐ and Aβ‐associated atrophy did not differ between the groups on any measure. Aβ− individuals displayed the slowest rates of atrophy. Discussion Maintenance of superior memory in late life does not reflect resistance to age‐ or Aβ‐associated atrophy. However, those individuals who reached old age without cognitive impairment nor elevated Aβ deposition (i.e. Aβ−) displayed reduced rates of cortical atrophy.