Atrophy Measures Research Articles

Abstract WMP69: Brain Atrophy and White Matter Disease Agreement on NCCT and MRI in Ischemic Stroke

Background: NCCT is the most widely used parenchymal imaging for acute stroke. Evaluations of brain frailty measures like atrophy and white matter disease (WMD) are becoming increasingly relevant in stroke outcome prediction but are conventionally thought to be best seen on MRI. We assessed the agreement between baseline NCCT and follow-up MRI ratings of brain atrophy and WMD and compared their predictive validity in relation to 90-day functional outcomes in acute ischemic stroke. Methods: In this post-hoc analysis of baseline NCCT and follow-up MRI data from the Alteplase compared to Tenecteplase (AcT) randomised-controlled trial, expert readers (stroke neurologists and radiologists) assessed atrophy using the global cortical atrophy (GCA), Koedam, and medial temporal lobe atrophy scales. WMD was measured using the Fazekas scale. Binary agreement (none-mild vs. moderate-severe) and agreement across the full range of scores between atrophy and WMD measures on NCCT and MRI were calculated using Gwet’s agreement coefficient (AC1). Logistic regressions and Delong’s test were used to compare the area under the curve (AUC) for the prediction of 90-day modified Rankin score (mRS) 0-1 when using NCCT vs MRI-based ratings for each atrophy and WMD variable. Results: Of 1577 patients included in the AcT trial, 491 had interpretable NCCT+MRI. Binary agreement was substantial (AC1:0.68-0.80) for deep and total WMD scores, Koedam scale and frontal GCA. Almost perfect binary agreement (AC1:0.81-0.97) was found for all other measures. Deep white matter showed a significant difference between NCCT and MRI ratings for predicting 90-day mRS 0-1 (MRI AUC = 0.61 [0.56-0.66], CT AUC = 0.56 [0.50-0.60], p = 0.01, Delong p = 0.02) There was no significant difference in predicting 90-day mRS for the other variables (all p > 0.12, GCA: MRI AUC = 0.54 [0.49-0.60] CT AUC = 0.53 [0.47-0.58], p = 0.58, Delong p = 0.86). Conclusion: NCCT ratings of brain atrophy and WMD by experts have substantial to almost-perfect agreement with MRI atrophy and WMD ratings and achieve similar prediction of 90-day mRS 0-1 functional outcomes in acute stroke patients. This implies that it is reasonable to use NCCT scans to evaluate these brain frailty measures in clinical practice and in stroke trials.

Strong versus Weak Data Labeling for Artificial Intelligence Algorithms in the Measurement of Geographic Atrophy

PurposeTo gain an understanding of data labeling requirements to train deep learning models for measurement of geographic atrophy with autofluorescence (FAF) images. DesignEvaluation of AI algorithms SubjectsThe Age-Related Eye Disease Study 2 images were used for training and cross validation and GA clinical trial images were used for testing. MethodsTraining data consisted of two sets of FAF images; one with area measurements only and no indication of GA location (Weak-labeled) and the second with GA segmentation masks (Strong-labeled). Main outcome measuresBland Altman plots and scatter plots were used to compare GA area measurement between ground truth and AI measurements. Dice Coefficient was used to compare accuracy of segmentation of Strong model. ResultsIn the cross validation AREDS2 dataset (n = 601), mean area (SD) of GA measured by human grader, Weak-labeled and Strong-labeled AI models was 6.65 mm2 (6.3), 6.83 mm2 (6.29) and 6.58 mm2 (6.24). The mean difference between ground truth and AI was 0.18 mm2 (95% CI -7.57,7.92) for the Weak-labeled model and -0.07 mm2 (-1.61,1.47) for the Strong-labeled model. With GSK testing data (n = 156), the GA area (SD) was 9.79 mm2 (SD 5.6), 8.82 mm2 (4.61) and 9.55 mm2 (5.66) respectively. The mean difference between ground truth and AI for the 2 models was -0.97 mm2 (- 4.36,2.41) and -0.24 mm2 (- 4.98,4.49) respectively. The Dice Coefficient was 0.99 for intergrader agreement, 0.88 for the cross-validation data and 0.92 for the testing data. ConclusionsDeep learning models can achieve reasonable accuracy even with Weakly-labeled data. Training methods that integrate large volumes of Weak-labeled images with small number of Strong-labeled images offer a promising solution to overcome the burden of cost and time for data labeling. Words 3290

Exploring the effects of extended interval dosing of natalizumab and drug concentrations on brain atrophy in multiple sclerosis.

Extended interval dosing (EID) of natalizumab treatment is increasingly used in multiple sclerosis. Besides the clear anti-inflammatory effect, natalizumab is considered to have neuroprotective properties as well. This study aimed to study the longitudinal effects of EID compared to standard interval dosing (SID) and natalizumab drug concentrations on brain atrophy. Patients receiving EID or SID of natalizumab with a minimum radiological follow-up of 2 years were included. Changes in brain atrophy measures over time were derived from clinical routine 3D-Fluid Attenuated Inversion Recovery (FLAIR)-weighted magnetic resonance imaging (MRI) scans using SynthSeg. We found no differences between EID (n = 32) and SID (n = 50) for whole brain (-0.21% vs -0.16%, p = 0.42), ventricular (1.84% vs 1.13%, p = 0.24), and thalamic (-0.32% vs -0.32%, p = 0.97) annualized volume change over a median follow-up of 3.2 years. No associations between natalizumab drug concentration and brain atrophy rate were found. We found no clear evidence that EID compared to SID or lower natalizumab drug concentrations have a negative impact on the development of brain atrophy over time.

Skin autofluorescence, reflecting accumulation of advanced glycation end products, and the risk of dementia in a population-based cohort

Conditions such as hyperglycemia and oxidative stress lead to the formation of advanced glycation end products (AGEs), which are harmful compounds that have been implicated in dementia. Within the Rotterdam Study, we measured skin AGEs as skin autofluorescence, reflecting long-term accumulation of AGEs, and determined their association with the risk of dementia and with brain magnetic resonance imaging (MRI) measures. Skin autofluorescence was measured between 2013 and 2016 in 2922 participants without dementia. Of these, 1504 also underwent brain MRI, on which measures of brain atrophy and cerebral small vessel disease were assessed. All participants were followed for the incidence of dementia until 2020. Of 2922 participants (mean age 72.6 years, 57% women), 123 developed dementia. Higher skin autofluorescence (per standard deviation) was associated with an increased risk of dementia (hazard ratio 1.21 [95% confidence interval 1.01–1.46]) and Alzheimer’s disease (1.19 [0.97–1.47]), independently of age and other studied potential confounders. Stronger effects were seen in apolipoprotein E (APOE) ε4 carriers (1.34 [0.98–1.82]) and in participants with diabetes (1.35 [0.94–1.94]). Participants with higher skin autofluorescence levels also had smaller total brain volumes and smaller hippocampus volumes on MRI, and they had more often lacunes. These results suggest that AGEs may be involved in dementia pathophysiology.

CSF p-tau205: a biomarker of tau pathology in Alzheimer’s disease

Post-mortem staging of Alzheimer’s disease (AD) neurofibrillary pathology is commonly performed by immunohistochemistry using AT8 antibody for phosphorylated tau (p-tau) at positions 202/205. Thus, quantification of p-tau205 and p-tau202 in cerebrospinal fluid (CSF) should be more reflective of neurofibrillary tangles in AD than other p-tau epitopes. We developed two novel Simoa immunoassays for CSF p-tau205 and p-tau202 and measured these phosphorylations in three independent cohorts encompassing the AD continuum, non-AD cases and cognitively unimpaired participants: a discovery cohort (n = 47), an unselected clinical cohort (n = 212) and a research cohort well-characterized by fluid and imaging biomarkers (n = 262). CSF p-tau205 increased progressively across the AD continuum, while CSF p-tau202 was increased only in AD and amyloid (Aβ) and tau pathology positive (A+T+) cases (P < 0.01). In A+ cases, CSF p-tau205 and p-tau202 showed stronger associations with tau-PET (rSp205 = 0.67, rSp202 = 0.45) than Aβ-PET (rSp205 = 0.40, rSp202 = 0.09). CSF p-tau205 increased gradually across tau-PET Braak stages (P < 0.01), whereas p-tau202 only increased in Braak V–VI (P < 0.0001). Both showed stronger regional associations with tau-PET than with Aβ-PET, and CSF p-tau205 was significantly associated with Braak V–VI tau-PET regions. When assessing the contribution of Aβ and tau pathologies (indexed by PET) to CSF p-tau205 and p-tau202 variance, tau pathology was found to be the most prominent contributor in both cases (CSF p-tau205: R2 = 69.7%; CSF p-tau202: R2 = 85.6%) Both biomarkers associated with brain atrophy measurements globally (rSp205 = − 0.36, rSp202 = − 0.33) and regionally, and correlated with cognition (rSp205 = − 0.38/− 0.40, rSp202 = − 0.20/− 0.29). In conclusion, we report the first high-throughput CSF p-tau205 immunoassay for the in vivo quantification of tau pathology in AD, and a potentially cost-effective alternative to tau-PET in clinical settings and clinical trials.

Prenatal diagnosis of pontocerebellar hypoplasia with postnatal follow-up.

To describe the MR features enabling prenatal diagnosis of pontocerebellar hypoplasia (PCH). This was a retrospective single monocentre study. The inclusion criteria were decreased cerebellar biometry on dedicated neurosonography and available fetal Magnetic Resonance Imaging (MRI) with PCH diagnosis later confirmed either genetically or clinically on post-natal MRI or by autopsy. The exclusion criteria were non-available MRI and sonographic features suggestive of a known genetic or other pathologic diagnosis. The collected data were biometric or morphological imaging parameters, clinical outcome, termination of pregnancy (TOP), pathological findings and genetic analysis (karyotyping, chromosomal microarray, DNA sequencing targeted or exome). PCH was classified as classic, non-classic, chromosomal, or unknown type. Forty-two fetuses were diagnosed with PCH, of which 27 were referred for decreased transverse cerebellar diameter at screening ultrasound. Neurosonography and fetal MRI were performed at a mean gestational age of 29+4 and 31+0weeks, respectively. Termination of pregnancy occurred. Pregnancy was terminated in 24 cases. Neuropathological examination confirmed the diagnosis in 24 cases and genetic testing identified abnormalities in 29 cases (28 families, 14 chromosomal anomaly). Classic PCH is associated with pontine atrophy and small MR measurements decreasing with advancing gestation. This is the first large series of prenatally diagnosed PCHs. Our study shows the essential contribution of fetal MRI to the prenatal diagnosis of PCH. Classic PCHs are particularly severe and are associated with certain MR features.

Geodesic shape regression based deep learning segmentation for assessing longitudinal hippocampal atrophy in dementia progression

Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multiple independent scans. To accurately segment the hippocampal morphology from longitudinal 3T T1-weighted MR images, we propose a diffeomorphic geodesic guided deep learning method called the GeoLongSeg to mitigate the longitudinal variabilities that unrelated to diseases by enhancing intra-individual morphological consistency. Specifically, we integrate geodesic shape regression, an evolutional model that estimates smooth deformation process of anatomical shapes, into a two-stage segmentation network. We adopt a 3D U-Net in the first-stage network with an enhanced attention mechanism for independent segmentation. Then, a hippocampal shape evolutional trajectory is estimated by geodesic shape regression and fed into the second network to refine the independent segmentation. We verify that GeoLongSeg outperforms other four state-of-the-art segmentation pipelines in longitudinal morphological consistency evaluated by test–retest reliability, variance ratio and atrophy trajectories. When assessing hippocampal atrophy in longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), results based on GeoLongSeg exhibit spatial and temporal local atrophy in bilateral hippocampi of dementia patients. These features derived from GeoLongSeg segmentation exhibit the greatest discriminatory capability compared to the outcomes of other methods in distinguishing between patients and normal controls. Overall, GeoLongSeg provides an accurate and efficient segmentation network for extracting hippocampal morphology from longitudinal MR images, which assist precise atrophy measurement of the hippocampus in early stage of dementia.

PSYCHOSOCIAL RISK, SUBCLINICAL CVD, AND BRAIN VOLUMES: EXAMINING SEX DISPARITIES IN DEMENTIA RISK

Abstract The majority of the sex disparity in dementia remains unexplained. One potential risk factor for women’s higher incidence of dementia is subclinical cardiovascular disease (SCVD), including carotid atherosclerosis and arterial stiffness. To date, there is a significant gap in what is known about sex differences in the impact of psychosocial risk on pre-dementia risk factors both directly and via SCVD. Therefore, this study aimed to examine the mediating influence of SCVD on the relationship between psychosocial risk and brain volumes, as a proxy for pre-dementia risk, and whether the relationship is moderated by sex. The analysis included 136 older adults with a mean age of 68.04y who underwent neuropsychological, psychosocial, and vascular assessment (pulse wave velocity [PWV], carotid intima media thickness [IMT]), and 3T cranial magnetic resonance imaging. Moderated mediation analyses, adjusted for age and education, showed a mediating role of PWV in the relation of loneliness to total brain volumes (global measure of atrophy) was more pronounced among women (average causal mediation effect [ACME]=.44; p=.11). Unadjusted analyses trended toward moderated mediation, such that the mediating role of carotid IMT in the relation of anxiety to total WMLV (ACME=.001; p=.06) and total frontal WML volumes (ACME=.001; p=.05) was more pronounced among women than men. Findings suggest that in women, but not men, psychosocial risk factors may influence brain volumes via subclinical CVD. Additional research is needed to determine if these interrelations help to explain some of the sex disparity in dementia risk.

Abstract PR009: Effect of testosterone therapy on quantitative breast lobular atrophy in transmasculine individuals

Abstract Background. Most transmasculine (TM) individuals pursue gender-affirming testosterone therapy (TT). The health outcomes associated with long-term TT use remains unclear. We previously reported that TT is associated with breast lobular atrophy (i.e., shrinking) when assessed by pathologists. Breast lobules are the main sites whereby cancer develops, and there are ongoing research to clarify the association between lobular atrophy and breast cancer risk. As such, TT may influence breast cancer risk in TM individuals. This study investigated the quantitative relationship between length of TT use and lobular atrophy. Materials and Methods. Breast surgical specimens were obtained from 417 TM subjects who had gender affirming chest-contouring surgery. Clinical data were retrieved from medical records. Each subject had one to six H&amp;E slides that were reviewed by pathologists and digitized at 20× (n=1073 whole slide images (WSIs)). We applied our deep-learning computational pathology method that detects and quantifies acini and breast lobules to the WSIs. For each WSI, our method extracted total tissue area (mm2), fat area (mm2), number of breast lobules, area (mm2) and span (mm) of each lobule, and the number of acini in each lobule. Multiple WSIs for each subject were combined to compute four measures of lobular atrophy—1) median lobule size (mm2), 2) median acini counts per lobule, 3) number of lobules per mm2 of tissue area, and 4) % lobular area. We evaluated the relationship between quantitative measures of lobular atrophy for every six months of TT use with linear regression models, adjusting for age, year of surgery, race/ethnicity, BMI, chest binding, whether the subject removed their ovaries, and TT dose. Patients who did not use TT prior to surgery were assigned as zero months of TT. In order to improve the interpretability of the beta coefficient, we back transformed the natural log beta coefficients. Results. The size of breast lobules in TM individuals shrank by 3.9 % for every six months of TT use (adjusted p&amp;lt;0.001). The results of the other three measures were similar: the median number of acini in each breast lobule decreased by 5.5%, the number of lobules per mm2 of breast tissue decreased by 4.9%, and the % of lobular area in the breast was reduced by 10.0%, for every six months that a patient received TT (all adjusted p≤0.001). Conclusions. This is the first study to investigate the quantitative relationship between duration of TT and lobular atrophy. For every six months of TT, the breast lobules of TM subjects decreased in size and numbers at an approximate rate of 5%, after accounting for potential confounders. This work will support the understanding of breast cancer risk in the TM population, and contributes to reducing healthcare disparities in the transgender community. Citation Format: Gabrielle M. Baker, Yaileen D. Guzman-Arocho, Vanessa C. Bret-Mounet, Mitko Veta, Suzanne C. Wetstein, Brittany M. Charlton, Sarah S. Jackson, Gerburg M. Wulf, Yujing Jan Heng. Effect of testosterone therapy on quantitative breast lobular atrophy in transmasculine individuals [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr PR009.

The Sporadic Early‐Onset Alzheimer’s Disease Signature of Regional Atrophy

AbstractBackgroundIn EOAD, the localization of neurodegeneration is often more prominent in posterior lateral temporal, lateral and medial parietal, frontal, or occipital cortex relative to the medial and ventral temporal cortical localization of typical LOAD. Convergently, neuropathological investigations have identified a “hippocampal‐sparing” form of AD that is often present in younger patients. For these reasons, imaging biomarkers of neurodegeneration in EOAD, such as Magnetic Resonance Imaging (MRI) measures of atrophy, likely need to be different than those of neurodegeneration in typical LOAD. Although prior neuroimaging work has advanced our understanding of anatomical abnormalities in EOAD, these studies have included small samples, most composed of amnestic EOAD dementia and none focused on developing an MRI biomarker specific to EOAD. Such a disease‐signature MRI biomarker has been established and validated in LOAD dementia and has proven to be powerful in predicting progressive decline in people with MCI or who were cognitively unimpaired and in predicting molecular biomarker status. An a priori‐defined regional atrophy signature may also be helpful in constraining the analysis of MRI data in studies of putative disease‐modifying therapies.MethodLEADS is an observational study which includes annual MRI. In this analysis, we used FreeSurfer to define the sporadic EOAD atrophy signature in a small sample of MGH EOAD patients, and investigated its reproducibility in LEADS, as well as the magnitude of atrophy and its relationship to clinical measures.ResultThe EOAD signature is robustly reproducible across the two independent patient cohorts, with prominent atrophy in caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortex, with relative sparing of medial temporal lobe structures. Comparing patients to controls, Cohen’s d effect sizes within various regions ranged from 0.99 to 1.98.ConclusionThis study demonstrates the specific cortical regions with most prominent atrophy in patients with sporadic EOAD; this signature of regional atrophy will be used to examine longitudinal change.

Feature extraction by orthonormal projective non‐negative matrix factorization and longitudinal association between brain atrophy patterns and Alzheimer’s disease

AbstractBackgroundOrthonormal projective non‐negative matrix factorization (OPNMF) was a recently developed approach for feature extraction techniques of neuroimaging data. However, there has been few studies applying this to brain MRI to better characterize the anatomic changes seen in Alzheimer’s Disease (AD) and correlate these patterns with cognitive changes.MethodWe applied OPNMF with refinement to identify distinct spatial components of voxel‐wise volume loss in the structural MRI (3D T1 MPRAGE images) of subjects with AD (N = 109, mean age = 74.6 (7.9), ADNI2) relative to healthy young controls (N = 104, mean age = 24.0 (2.9), ABIDE1). Non‐negative least squares (NNLS) was then used to extract non‐negative coefficients representing subject‐specific quantitative measures of regional atrophy. This calculation of coefficients was extended to images not used for component discovery which also included Mild Cognitive Impairment (MCI) and controls. To investigate the cross‐sectional and longitudinal associations between cognitive assessments scores and regional atrophy severity in different diagnostic groups, we used multiple linear regression models and liner mixed‐effects models with random intercepts and slopes controlled for age, sex, educational level, APOE ε4 genotypes were utilized. A validation analysis was performed using ADNI3 images.ResultWe derived 6 atrophy components upon the same training set and there was a sharp decrease in the magnitude of the reconstruction error gradient when moving from 5 components to 6 components, and the components have meaningful interpretation. In the whole cohort, atrophy in 5 of the 6 regions was significantly associated with all three cognitive scores. There is evidence of overall positive trends between diagnosis group and atrophy severity changing rate in all 6 regions. The increased atrophy in all 6 regions was associated with decreases in MMSE and MOCA scores and increases in CDRSB scores in the whole cohort as well as in LMCI cohort. From the validation analysis, we showed that our result from OPNMF is stable in atrophy pattern discovery.ConclusionWe developed a robust pipeline for detecting spatially distinct atrophy components in AD and associated these atrophy patterns with cognitive changes which may help us better characterize the underlying pathology of types of AD and other dementias on MRI.

Evaluating the sensitivity of Centiloid quantification to pipeline design and image harmonization

AbstractBackgroundThe Centiloid scale is a well‐established standardized metric for estimating amyloid load in clinical and research settings. In this study, we evaluated the stability of Centiloid to pipeline design options, tracer, and image harmonization.MethodA total of 670 participants of the AMYPAD DPMS and PNHS multicenter studies with either [18F]flutemetamol or [18F]florbetaben PET scans and T1‐weighted MRI were included (Table1). PET images were harmonized to achieve an 8 mm Full‐Width‐at‐Half‐Maximum(FWHM) effective image resolution. Using SPM12, 32 Centiloid pipelines were created, calibrated and validated, based on combinations of four reference regions (Whole Cerebellum[WCB], Cerebellar Gray[CG], Whole Cerebellum+Brainstem[WCB+BSTM], Pons), two target VOI types (standard GAAIN vs subject‐based), two reference region types (standard GAAIN vs subject‐based) and two analysis spaces (native vs MNI). Generalized Estimating Equations (GEE) were used to evaluate the impact of the different factors on Centiloid. First, a base model including only the pipeline design factors was defined. Then, harmonization status (original vs harmonized PET images), tracer, and grey matter volume, as a measure of cerebral atrophy, were added to the base GEE model. Analyses were performed in the two cohorts separately and changes &gt;5 Centiloid were considered relevant.ResultQuantification space, target VOI, and reference region type had a small impact on Centiloid. Selection of reference region had the largest impact on Centiloid as using the Pons resulted in ∆Centiloid∼‐12.00 compared with using the standard reference region, WCB (Table2). Harmonization status affected Centiloid only for some reference regions: CG and Pons were sensitive to harmonization status (∆Centiloid∼ 5.45 and ‐5.70 respectively). In contrast, Centiloid was minimally affected by harmonization when using WCB and WCB+BSTM (Table3). Tracer and atrophy had a minimal effect on Centiloid for both DPMS and PNHS cohorts.ConclusionCentiloid quantification is robust against differences in analysis space, reference region type, target VOI, tracer and the presence of atrophy. However, choice of reference region can significantly influence Centiloid, with the Pons having the largest impact on Centiloid relative to other reference regions. Centiloid is stable against harmonization status and image resolution heterogeneities while using WCB as reference region.

Identifying Cognitive Resilience Related Genes Among Genetic Risk Loci of Alzheimer’s Disease

AbstractBackgroundCognitive resilience (CR) in the context of Alzheimer’s disease (AD) describes the ability to maintain cognitive function despite the presence of neuropathology (Hohman et al., 2016). While most studies have focused on investigating genetic risk factors for developing AD, we here examined the association of most robust risk loci (Bellenguez et al., 2021) with CR using a neuroimaging‐based approach that incorporates measures of brain atrophy and cognition into a quantitative CR metric (van Loenhoud, 2017).MethodWe performed magnetic resonance imaging (3T) and DNA testing on n = 440 amyloid‐positive individuals in different clinical stages of AD. Employing a global cognitive composite score, we predicted gray matter volume (GM) in each voxel, adjusted for sex, age, premorbid intracranial volume, disease stage, and scanner type. To operationalize CR, W‐scores were computed for each individual based on the standardized individual difference between predicted and observed GM within temporoparietal (TP) and whole brain (WB) masks. In this approach, positive W‐scores reflect low CR, while negative W‐scores reflect high CR. A total of 83 genetic risk variants for AD (Bellenguez et al., 2021) were each tested for their association with W‐scores in a linear regression model corrected for population substructure (PC1‐5).ResultWe found an association for 6 genetic loci, of which variants rs113706587 in gene RASGEF1c and rs6943429 in gene UMAD1 were associated with higher CR (TP: p&lt;0.01, p&lt;0.02; WB: p&lt;0.02, p&lt;0.01). As expected, APOEe4 was associated with low CR (TP: p&lt;0.01, WB: p&lt;0.01), as were genetic markers of genes CLU, MINDY2 (WB: p&lt;0.04, p&lt;0.04), and BCKDK (WB: p&lt;0.05, TP: p&lt;0.04). Importantly, no association was found between APOEe2 and CR (WB: p = 0.195, TP: p = 0.139).ConclusionAmong genetic risk‐loci for AD, we identified two genetic variants associated with cognitive resilience in amyloid‐positive individuals. Interestingly, APOEe4 associated with low CR, as did genes CLU, MINDY2, and BCKDK, indicating anti‐resilience – possibly through accelerating mechanisms. In addition, our results suggest that CR against AD is independent of the protective relationship found between APOEe2 and developing AD, highlighting the relevance of investigating the mechanisms behind CR.

Evaluating the sensitivity of Centiloid quantification to pipeline design and image harmonization

AbstractBackgroundThe Centiloid scale is a well‐established standardized metric for estimating amyloid load in clinical and research settings. In this study, we evaluated the stability of Centiloid to pipeline design options, tracer, and image harmonization.MethodA total of 670 participants of the AMYPAD DPMS and PNHS multicenter studies with either [18F]flutemetamol or [18F]florbetaben PET scans and T1‐weighted MRI were included (Table1). PET images were harmonized to achieve an 8 mm Full‐Width‐at‐Half‐Maximum(FWHM) effective image resolution. Using SPM12, 32 Centiloid pipelines were created, calibrated and validated, based on combinations of four reference regions (Whole Cerebellum[WCB], Cerebellar Gray[CG], Whole Cerebellum+Brainstem[WCB+BSTM], Pons), two target VOI types (standard GAAIN vs subject‐based), two reference region types (standard GAAIN vs subject‐based) and two analysis spaces (native vs MNI). Generalized Estimating Equations (GEE) were used to evaluate the impact of the different factors on Centiloid. First, a base model including only the pipeline design factors was defined. Then, harmonization status (original vs harmonized PET images), tracer, and grey matter volume, as a measure of cerebral atrophy, were added to the base GEE model. Analyses were performed in the two cohorts separately and changes &gt;5 Centiloid were considered relevant.ResultQuantification space, target VOI, and reference region type had a small impact on Centiloid. Selection of reference region had the largest impact on Centiloid as using the Pons resulted in ∆Centiloid∼‐12.00 compared with using the standard reference region, WCB (Table2). Harmonization status affected Centiloid only for some reference regions: CG and Pons were sensitive to harmonization status (∆Centiloid∼ 5.45 and ‐5.70 respectively). In contrast, Centiloid was minimally affected by harmonization when using WCB and WCB+BSTM (Table3). Tracer and atrophy had a minimal effect on Centiloid for both DPMS and PNHS cohorts.ConclusionCentiloid quantification is robust against differences in analysis space, reference region type, target VOI, tracer and the presence of atrophy. However, choice of reference region can significantly influence Centiloid, with the Pons having the largest impact on Centiloid relative to other reference regions. Centiloid is stable against harmonization status and image resolution heterogeneities while using WCB as reference region.

Significant dose‐dependent reduction in neurofilament light chain concentration in plasma with oral tau aggregation inhibitor hydromethylthionine mesylate

AbstractBackgroundHydromethylthionine mesylate (HMTM) is a potent oral tau aggregation inhibitor. Recent results from Study TRx‐237‐039 (LUCIDITY) suggest strong effects on cognitive and functional decline and progression of brain atrophy. We investigated the effect of HMTM on change in the plasma biomarker neurofilament light chain (NfL) in LUCIDITY. NfL is a clinically accessible and established biomarker for neurodegeneration that is known to be correlated with measures of clinical decline and brain atrophy in AD and MCI due to AD (MCI‐AD).MethodLUCIDITY is a double‐blind randomised controlled Phase 3 clinical trial comparing change over 12 months in cognitive, functional and brain atrophy outcomes at HMTM doses of 16 mg/day, 8 mg/day and methylthioninium chloride (MTC) at a dose of 4 mg twice weekly as a control in a 4:1:4 randomisation, with a subsequent 12 month blinded open‐label extension phase in which all participants received 16 mg/day.ResultBaseline and 12‐month NfL plasma levels were available as randomised in 161/185 participants receiving HMTM 16 mg/day, 38/48 receiving HMTM 8 mg/day and 136/185 receiving MTC 8 mg/week. The corresponding prespecified analysis model showed a 93% reduction in the change in NfL levels relative to control for HMTM 16 mg/day overall (p = 0.0278). Reduction in NfL change in the MCI‐AD subgroup was significant also at HMTM 8 mg/day (p = 0.0242). In the AD subgroup, there were directional dose‐dependent trends that did not reach statistical significance. Change in NfL level at 12 months was significantly correlated with change in ADAS‐cog11 (p = 0.0038) and whole brain volume (p = 0.0359) over 24 months.ConclusionThese results demonstrate that the orally administered tau aggregation‐inhibitor, HMTM, is able to produce a significant dose‐dependent reduction in neurodegeneration in AD as measured by change in plasma NfL levels.

A composite measure of atrophy using volumetric T1‐weighted and T2‐FLAIR imaging using the Boundary Shift Integral

AbstractBackgroundWhole‐brain volume loss using T1‐weighted imaging is an established measurement of neurodegeneration. The Boundary Shift Integral (BSI) produces reliable metrics of longitudinal volume loss, used in research studies and clinical trials. With the increased acquisition of volumetric T2‐FLAIR imaging in studies, there is opportunity to produce atrophy metrics using a composite atrophy measure (T1‐weighted and T2‐FLAIR) that is more precise than either modality alone.MethodData from Alzheimer’s Disease Neuroimaging Initiative (ADNI3) were used. Volumetric T1 and T2‐FLAIR weighted imaging were obtained (n = 269), for baseline and follow‐up; Table 1. T1‐weighted images were bias corrected and underwent Geodesic Information Flow (GIF), producing probabilistic brain masks. Masks were resampled to T2‐FLAIR space. The BSI was performed on serial T1‐weighted (gBSI), and T2‐FLAIR (fBSI) separately. We tested differences by diagnosis (control (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD)) between indirect volume change, gBSI, fBSI, and composite BSI, using linear regression and effect size. We further compared metric differences within diagnosis, using pairwise correlation, paired samples t‐test, and Pitman’s test of variance.ResultDemographic information is reported in Table 1. fBSI showed good Pearson’s correlation (r) with both gBSI (CN 0.7, p &lt; 0.001; MCI 0.8, p &lt; 0.001; AD 0.9, p &lt; 0.001), and indirect change (CN 0.6, p &lt; 0.001; MCI 0.6, &lt; 0.001; AD 0.7, &lt; 0.001). The composite BSI further showed good correlation with indirect change (CN 0.8, p &lt; 0.001; MCI 0.8, p &lt; 0.001; AD 0.8, 0.004). Mean values by diagnosis are shown in figure 1, along with paired samples t and variance tests. Pairwise comparisons reported in table 2, highlight good diagnostic group separation in all BSI metrics, with overall strong effect sizes for fBSI, composite BSI, and gBSI respectively.ConclusionCalculating atrophy using T2‐weighted imaging is possible using fBSI. This can be used in a composite atrophy measure using fBSI and gBSI. Both fBSI and composite BSI perform well with group separation, and with good correlations with classical atrophy metrics. Future work will explore the influence of White Matter Hyperintensities on BSI.

Separating Alzheimer’s disease from Dementia with Lewy bodies using a volumetric MRI classifier

AbstractBackgroundSeparating Alzheimer’s dementia (AD) from dementia with Lewy bodies (DLB) can be challenging, especially in DLB patients with concomitant AD pathology. It is imperative to distinguish the diseases for optimal patient management. To date, no single MRI feature can accurately separate the two diseases. This study examines whether a linear combination of regional atrophy measures can differentiate cognitively normal (CN), AD, and DLB patients.MethodWe computed volumes of 70 brain regions using an automatic segmentation tool. For each region, we derived age, sex, and head size‐dependent z‐scores. A linear classifier with all brain regions was used to predict if a patient belonged to CN, AD, or DLB. Diagnoses were based on international consensus criteria and CSF‐biomarkers. The classifier was tested in 327 CN (mean age 67±10y, 48%F), 501 AD (70±9y, 49%F), and 99 DLB (69±7y, 15%F) patients from three cohorts (ADNI, PredictND, ADC). The ADC cohort was assessed separately, as almost all DLB patients were from the ADC. In addition, we assessed classification performance in a subgroup of DLB patients with AD‐like CSF (DLB+, n = 57). Performance was evaluated using 10‐fold cross‐validation.ResultPerformance measures for the classifier are summarized in Table 1. The classifier accuracy was assessed in the three‐class classification task and in two‐class classifications where one of the groups was excluded from the analysis. The balanced accuracy in the three‐class classification was 67% for the DLB group and the DLB+ subgroup. For AD vs. DLB, the AUC was 0.77(95% CI:0.70‐0.82), for AD vs. DLB+ AUC was 0.72(0.64‐0.80). The classifier correctly classified CN and AD patients more often than DLB patients (Table 2). Most DLB+ patients were misclassified as AD. The hippocampus was the best single region for distinguishing AD vs. DLB, with an AUC of 0.70(0.64‐0.76).ConclusionThe joint classifier achieved high accuracy in separating AD vs. DLB patients. Although the accuracy was lower, the classifier could distinguish DLB patients with concomitant AD pathology from AD. Next, we will combine different brain regions to discern disease‐specific patterns and add clinical data to the classifier to improve differential diagnostic accuracy for DLB and DLB with concomitant AD pathology.

A composite measure of atrophy using volumetric T1‐weighted and T2‐FLAIR imaging using the Boundary Shift Integral

AbstractBackgroundWhole‐brain volume loss using T1‐weighted imaging is an established measurement of neurodegeneration. The Boundary Shift Integral (BSI) produces reliable metrics of longitudinal volume loss, used in research studies and clinical trials. With the increased acquisition of volumetric T2‐FLAIR imaging in studies, there is opportunity to produce atrophy metrics using a composite atrophy measure (T1‐weighted and T2‐FLAIR) that is more precise than either modality alone.MethodData from Alzheimer’s Disease Neuroimaging Initiative (ADNI3) were used. Volumetric T1 and T2‐FLAIR weighted imaging were obtained (n = 269), for baseline and follow‐up; Table 1. T1‐weighted images were bias corrected and underwent Geodesic Information Flow (GIF), producing probabilistic brain masks. Masks were resampled to T2‐FLAIR space. The BSI was performed on serial T1‐weighted (gBSI), and T2‐FLAIR (fBSI) separately. We tested differences by diagnosis (control (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD)) between indirect volume change, gBSI, fBSI, and composite BSI, using linear regression and effect size. We further compared metric differences within diagnosis, using pairwise correlation, paired samples t‐test, and Pitman’s test of variance.ResultDemographic information is reported in Table 1. fBSI showed good Pearson’s correlation (r) with both gBSI (CN 0.7, p &lt; 0.001; MCI 0.8, p &lt; 0.001; AD 0.9, p &lt; 0.001), and indirect change (CN 0.6, p &lt; 0.001; MCI 0.6, &lt; 0.001; AD 0.7, &lt; 0.001). The composite BSI further showed good correlation with indirect change (CN 0.8, p &lt; 0.001; MCI 0.8, p &lt; 0.001; AD 0.8, 0.004). Mean values by diagnosis are shown in figure 1, along with paired samples t and variance tests. Pairwise comparisons reported in table 2, highlight good diagnostic group separation in all BSI metrics, with overall strong effect sizes for fBSI, composite BSI, and gBSI respectively.ConclusionCalculating atrophy using T2‐weighted imaging is possible using fBSI. This can be used in a composite atrophy measure using fBSI and gBSI. Both fBSI and composite BSI perform well with group separation, and with good correlations with classical atrophy metrics. Future work will explore the influence of White Matter Hyperintensities on BSI.

Comparison of Focal Hippocampal T2‐Weighted MRI and Whole‐Brain T1‐Weighted MRI for Detection of Longitudinal Atrophy Using Deep Learning‐Based and Conventional Approaches

AbstractBackgroundLongitudinal measurement of medial temporal lobe (MTL) atrophy is shown to be effective in measuring Alzheimer’s disease (AD) progression and has been extensively studied on T1‐weighted MRI. However, there has been little work analyzing atrophy on high‐resolution T2‐weighted MRI of the hippocampal region, and it is unknown which MRI modality would yield a more effective biomarker of disease progression. We compare atrophy measurements derived from T1 and T2‐weighted MRI using a deep learning framework DeepAtrophy and a deformation‐based morphometry method ALOHA.MethodT2‐weighted MRI of 481 participants from ADNI2/3 are included in the study. DeepAtrophy was trained on 74 participants and evaluated on 407. During training, DeepAtrophy explicitly infers temporal information, such as the scan temporal order for a single scan pair and which image pair has a longer scan time interval for multiple scan pairs. During inference, DeepAtrophy predicts a “total progression score” for each scan pair. This score is expected to be higher (more progression) in individuals with more advanced AD. A similar experiment was conducted using T1‐weighted MRI from 481 ADNI2/GO participants (partially overlapping with T2‐weighted experiment). ALOHA pipelines tailored for T1‐weighted and hippocampal T2‐weighted MRI were evaluated on the same subjects and scan pairs as DeepAtrophy.ResultFor both T1 and T2‐weighted MRI, DeepAtrophy is more accurate, compared with ALOHA, in inferring the temporal order of a pair of longitudinal scans. In hypothetical one‐year and two‐years clinical trials scenarios simulated using ADNI data, DeepAtrophy has an improvement over ALOHA in detecting differences in progression between disease groups in both imaging modalities. The strongest effect for differentiating between Amyloid‐positive and Amyloid‐negative cognitively unimpaired group was obtained using DeepAtrophy progression measures on T2‐weighted MRI.ConclusionThis study supports the potential of high‐resolution focal T2‐weighted MRI of the hippocampus as a longitudinal biomarker of early AD progression suitable for future clinical trials.

Reliability of the global cortical atrophy visual rating scale applied to computed tomography versus magnetic resonance imaging scans in acute stroke

IntroductionResearch using magnetic resonance imaging (MRI) suggests regional cerebral atrophy measures (e.g., frontal lobe, temporal lobe) may predict post-stroke outcomes. Clinical CT scans have excellent potential for use in research but it is unclear whether regional atrophy measures from CT are reliable compared to MRI reference standards.MethodsWe used the Global Cortical Atrophy (GCA) scale to investigate reliability of atrophy measures on CT versus MRI scans from stroke patients originally recruited to the Oxford Cognitive Screening programme. Two raters provided standardised visual ratings at two timepoints. Weighted Kappa statistics assessed the reliability of regional atrophy scores. Spearman’s correlation and a two-way repeated measures ANOVA assessed the reliability of the total score.ResultsOn clinically acquired neuroimaging from 98 stroke patients (mean/SD age = 70.97/11.99, 42 female, 84 ischaemic stroke), regional GCA scores on CT versus MRI showed fair to almost perfect intra-rater agreement (κ = .50–.87), substantial to almost perfect intra-rater agreement on CT (κ = .67–.88), and moderate to almost perfect intra-rater reliability on MRI (κ = .50–.89). Regional GCA scores showed mostly moderate to substantial inter-rater reliability on both CT and MRI (κ = .43–.69), except the temporal horns and parieto-occipital region. There was a strong correlation between total GCA scores on CT and MRI (r (96) = .87–.88, p < .001).ConclusionsThese results support the use of cerebral atrophy measures from CT in clinical research, as visual ratings showed generally good agreement between CT and MRI, between raters, and between timepoints.