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Actigraphy, an objective measure of motor activity, reliably indexes increased movement levels in attention-deficit/hyperactivity disorder (ADHD) and may be useful for diagnosis and treatment-monitoring. However, actigraphy has not been examined in complex neurodevelopmental conditions. This study used actigraphy to objectively measure movement levels in individuals with a complex neurodevelopmental genetic disorder, tuberous sclerosis (TSC). Thirty participants with TSC (11–21 years, 20 females, IQ = 35–108) underwent brief (approximately 1 h) daytime actigraph assessment during two settings: movie viewing and cognitive testing. Multiple linear regressions were used to test associations between movement measurements and parent-rated ADHD symptoms. Correlations were used to examine associations between actigraph measures and parent-rated ADHD symptoms and other characteristics of TSC (symptoms of autism spectrum disorder (ASD), intellectual ability (IQ), epilepsy severity, cortical tuber count). Higher movement levels during movies were associated with higher parent-rated ADHD symptoms. Higher ADHD symptoms and actigraph-measured movement levels during movies were positively associated with ASD symptoms and negatively associated with IQ. Inter-individual variability of movement during movies was not associated with parent-rated hyperactivity or IQ but was negatively associated with ASD symptoms. There were no associations with tuber count or epilepsy. Our findings suggest that actigraph-measured movement provides a useful correlate of ADHD in TSC.

Background Often, imaging studies of Alzheimer's Disease (AD) use composite regions of interest (ROIs) to define global measures of AT(N) (amyloid/tau/neurodegeneration) pathology severity. Such composite ROIs necessarily span a relatively small part of the cortex, potentially omitting pathological signal in other parts of the brain. Here, we evaluated how composite AT(N) measures compare to ROIs spanning the whole brain in prediction of cognitive performance. Method We selected 473 individuals from ADNI who underwent cognitive testing, MRI, and PET (florbetapir and flortaucipir). Our target measure of interest was global cognitive performance (PHCGlobal: average of all domain scores from the Phenotype Harmonization Consortium). We selected composite (amyloid: Centiloid ROI SUVR, tau: meta‐temporal SUVR, neurodegeneration: meta‐temporal volume) and regional (SUVRs/volumes in 68 cortical gray matter ROIs) measures of AT(N) pathology. We trained support vector machines (SVMs) to predict PHCGlobal using either composite or regional features. In each case, we trained models with only one AT(N) category (unimodal) and all three (multimodal). All models also included age, sex, and APOE E4 carriership as features. A baseline model only included these demographic features. Models were trained using a repeated, nested cross‐validation scheme with 10 repeats and 10 outer folds. We used Nadeau‐Bengio t‐tests to compare the accuracy (root mean squared error, RMSE) of trained models in out‐of‐sample folds. For multimodal models, we derived feature importance estimates by computing the covariance of each feature with PHCGlobal. Result For both unimodal and multimodal models, SVMs trained on regional AT(N) information were consistently more accurate than versions trained on composite measures in predicting cognitive performance (Figure 1, Table 1). For regional models, the unimodal tau SVM and the multimodal SVM were more accurate than unimodal amyloid or neurodegeneration models. Visualizations of feature importance highlighted the relative extra weighting of tau for both biomarker and ROI SVMs (Figure 2). Conclusion We found that machine learning models which included ROIs spanning the whole brain were consistently more accurate than models including common global biomarker values when predicting cognitive performance. Our results indicate that imaging features outside of standard composite regions may be useful for assessing the biological progression of AD.

Background Application of the amyloid/tau/neurodegeneration (ATN) framework is varied, with some research relying on binary assessment of biomarkers and some using continuous or multidimensional measures. There are few investigations which directly evaluate how differing operationalizations of ATN affect prediction of cognitive impairment. Method We selected 473 individuals from ADNI who received PET imaging for amyloid‐beta (AV‐45) and tau (flortaucipir), as well as volumetric MRI. We evaluated a wide range of methods for deriving ATN biomarker assessments, spanning continuous, binary, and non‐binary categorical (i.e., staging models or quantiles of continuous values) measures (Table 1). We trained linear regression models to predict Preclinical Alzheimer Cognitive Composite (PACC) scores, combining different variations of ATN measures as predictors. We additionally used support vector machines (SVMs) to derive multivariate ATN measures; these models used regional PET uptakes or gray matter volumes (68 FreeSurfer regions) to predict PACC. A first set of experiments tested how addition of ATN improved prediction over a baseline model with just covariates (age, sex, APOE status), while a second set of experiments tested how models including non‐binary or SVM‐based ATN measures compared to ones using only binary measures. We used a repeated, nested, and cross‐validated design to evaluate the out‐of‐sample model performance as quantified by the root mean square error. Error estimates were compared using bias‐corrected t‐tests. Result Compared to models with standard covariates, addition of any ATN measure resulted in an increase in accuracy for predicting cognitive ability (Figure 1A). Benefits were largest for models which included tau or all three ATN assessments. SVM‐based models also outperformed baseline models. However, when compared to models using all‐binary ATN measures, addition of categorical, continuous, or SVM‐based ATN measures did not significantly improve prediction accuracy in any combination (Figure 1B). Feature importance analysis indicated that some ATN definitions consistently outperformed others (Figure 2). Conclusion Binary measures of ATN may be sufficient for prediction of cross‐sectional cognitive impairment. Further work is required to evaluate if these results hold in specific subpopulations (e.g., preclinical AD) or for predicting other measures of interest (e.g., longitudinal cognitive decline).

Background Application of the amyloid/tau/neurodegeneration (ATN) framework is varied, with some research relying on binary assessment of biomarkers and some using continuous or multidimensional measures. There are few investigations which directly evaluate how differing operationalizations of ATN affect prediction of cognitive impairment. Method We selected 473 individuals from ADNI who received PET imaging for amyloid‐beta (AV‐45) and tau (flortaucipir), as well as volumetric MRI. We evaluated a wide range of methods for deriving ATN biomarker assessments, spanning continuous, binary, and non‐binary categorical (i.e., staging models or quantiles of continuous values) measures (Table 1). We trained linear regression models to predict Preclinical Alzheimer Cognitive Composite (PACC) scores, combining different variations of ATN measures as predictors. We additionally used support vector machines (SVMs) to derive multivariate ATN measures; these models used regional PET uptakes or gray matter volumes (68 FreeSurfer regions) to predict PACC. A first set of experiments tested how addition of ATN improved prediction over a baseline model with just covariates (age, sex, APOE status), while a second set of experiments tested how models including non‐binary or SVM‐based ATN measures compared to ones using only binary measures. We used a repeated, nested, and cross‐validated design to evaluate the out‐of‐sample model performance as quantified by the root mean square error. Error estimates were compared using bias‐corrected t‐tests. Result Compared to models with standard covariates, addition of any ATN measure resulted in an increase in accuracy for predicting cognitive ability (Figure 1A). Benefits were largest for models which included tau or all three ATN assessments. SVM‐based models also outperformed baseline models. However, when compared to models using all‐binary ATN measures, addition of categorical, continuous, or SVM‐based ATN measures did not significantly improve prediction accuracy in any combination (Figure 1B). Feature importance analysis indicated that some ATN definitions consistently outperformed others (Figure 2). Conclusion Binary measures of ATN may be sufficient for prediction of cross‐sectional cognitive impairment. Further work is required to evaluate if these results hold in specific subpopulations (e.g., preclinical AD) or for predicting other measures of interest (e.g., longitudinal cognitive decline).

Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid‐β uptake estimates. Harmonization of tracer‐specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data‐driven harmonization model, for reducing tracer‐specific biases in regional amyloid PET measurements from [18F]‐florbetapir (FBP) and [11C]‐Pittsburgh compound‐B (PiB). One hundred thirteen head‐to‐head FBP‐PiB scan pairs, scanned from the same subject within 90 days, were selected from the Open Access Series of Imaging Studies 3 (OASIS‐3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM‐ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Variants of ComBat, including longitudinal ComBat and PEACE, were also tested. Intraclass correlation coefficient (ICC) and mean absolute error (MAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti‐amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates‐of‐change between simulated treatment and placebo groups were tested, and change in statistical power/Type‐I error after harmonization was quantified. In the head‐to‐head tracer comparison, ComBat with no covariates was the best at increasing ICC and decreasing MAE of both global summary and regional amyloid PET SUVRs between scan pairs of the same group of subjects. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate‐of‐change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FBP‐to‐PiB proportions. ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate‐of‐change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses. Using paired amyloid PET scans of the same subject using two different radiotracers, we showed that ComBat, a data‐driven harmonization technique, improves the absolute agreement of regional PET measurements across tracers. We also conducted simulations to demonstrate that ComBat may help increase statistical power in anti‐amyloid drugs clinical trials.

Background Differences in amyloid PET radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid uptake measurements, which may adversely affect the statistical power of clinical trials that utilize multiple tracers to track brain amyloid deposition. To address this, Centiloid was developed for standardizing global amyloid SUVRs across tracers to a common scale. Alternatively, ComBat is a technique for harmonizing batch effects while preserving variations from biologically‐relevant covariates. Unlike Centiloid, ComBat is entirely data‐driven, does not require a calibration process, and can be applied to regional SUVRs. However, it has not been validated for amyloid PET. Here, we evaluate whether ComBat improves detection of treatment effects in a simulated clinical trial. Method 365 amyloid‐positive subjects were identified from the OASIS‐3 dataset, from which 322 Pittsburgh compound B (PiB) and 260 18F‐Florbetapir (FBP) PET scans were selected. SUVRs of the global cortical region and 82 FreeSurfer regions were computed from each scan using the PET Unified Pipeline. Linear mixed effects (LME) models were fitted on each tracer separately and used to simulate longitudinal SUVRs of placebo and treatment groups in a hypothetical clinical trial (Figure 1). A negative rate‐of‐change was introduced to the treatment group to mimic a treatment effect. Additionally, tracer mixing proportions were varied within each group. Centiloid and ComBat were applied to simulated data to harmonize across tracers. A separate LME model was fitted to test for significant differences in SUVR rate‐of‐change between groups. Power was estimated as the proportion of significant findings across 1000 simulations. Result After harmonization with either Centiloid or ComBat with no covariates, an increase in power was observed in the presence of a treatment effect, and a decrease in Type‐I error was observed for no treatment effect (Figure 2). These changes were most prominent in cases where groups exhibited differing tracer mixing proportions. Similar patterns were observed for regional SUVRs (Figure 3). Conclusion We demonstrated that tracer harmonization is important for improving power in the presence of a treatment effect and reducing Type‐I error in its absence. ComBat performs comparably to Centiloid in harmonizing amyloid radiotracers in the context of a clinical trial.

Background Differences in amyloid PET radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid uptake measurements, which may adversely affect the statistical power of clinical trials that utilize multiple tracers to track brain amyloid deposition. To address this, Centiloid was developed for standardizing global amyloid SUVRs across tracers to a common scale. Alternatively, ComBat is a technique for harmonizing batch effects while preserving variations from biologically‐relevant covariates. Unlike Centiloid, ComBat is entirely data‐driven, does not require a calibration process, and can be applied to regional SUVRs. However, it has not been validated for amyloid PET. Here, we evaluate whether ComBat improves detection of treatment effects in a simulated clinical trial. Method 365 amyloid‐positive subjects were identified from the OASIS‐3 dataset, from which 322 Pittsburgh compound B (PiB) and 260 18F‐Florbetapir (FBP) PET scans were selected. SUVRs of the global cortical region and 82 FreeSurfer regions were computed from each scan using the PET Unified Pipeline. Linear mixed effects (LME) models were fitted on each tracer separately and used to simulate longitudinal SUVRs of placebo and treatment groups in a hypothetical clinical trial (Figure 1). A negative rate‐of‐change was introduced to the treatment group to mimic a treatment effect. Additionally, tracer mixing proportions were varied within each group. Centiloid and ComBat were applied to simulated data to harmonize across tracers. A separate LME model was fitted to test for significant differences in SUVR rate‐of‐change between groups. Power was estimated as the proportion of significant findings across 1000 simulations. Result After harmonization with either Centiloid or ComBat with no covariates, an increase in power was observed in the presence of a treatment effect, and a decrease in Type‐I error was observed for no treatment effect (Figure 2). These changes were most prominent in cases where groups exhibited differing tracer mixing proportions. Similar patterns were observed for regional SUVRs (Figure 3). Conclusion We demonstrated that tracer harmonization is important for improving power in the presence of a treatment effect and reducing Type‐I error in its absence. ComBat performs comparably to Centiloid in harmonizing amyloid radiotracers in the context of a clinical trial.

Background Predicting progression to mild cognitive impairment (MCI) and dementia in preclinical AD patients is crucial for proper recruitment into anti‐amyloid clinical trials. We evaluated the predictive ability of machine learning (ML) classifiers for distinguishing MCI‐progressors from non‐progressors using baseline amyloid positron emission tomography (PET) and magnetic resonance imaging (MRI) features as predictors. Method We selected cognitively‐normal, amyloid‐positive participants from ADNI (N = 86, 36 progressors), OASIS (N = 56, 12 progressors), and A4 (N = 210, 79 progressors). Subjects were classified as stable (remain CDR=0 at least 3 years from baseline) or progressor (convert to CDR>0 within 3 years of baseline). Each subject's first amyloid‐positive [18F]‐florbetapir PET scan and matching T1‐weighted MRI underwent standard PET‐MRI processing to obtain 86 regions‐of‐interest, from which regional standardized uptake value ratios and volume were computed. Principal component analysis was applied to reduce the number of amyloid and volume features. Age, sex, and APOE‐e4 carriership were also included as predictors. Three ML classifiers – logistic regression, support vector machine (SVM), and random forest – were trained to predict binary stable/progressor class. Data from two sites were used to train models and optimize hyperparameters using 5‐fold cross‐validation, while the third site was held out for testing. Performance was quantified using receiver operating characteristics area‐under‐the‐curve (AUC). To assess the importance of each predictor type (non‐imaging, amyloid, volume), nested models were trained by omitting one predictor type, and its AUC was compared to the model trained on all predictor types. Result Logistic regression with the full set of features performed the best with A4 (AUC=0.7391) and ADNI (AUC=0.8367) as the testing set, while SVM with either full features or with non‐imaging features omitted performed the best with OASIS as the testing set (AUC=0.8826) (Figure 1, Table 1). Omission of volumetric features generally resulted in the largest dip in AUC for A4 and OASIS testing sets, whereas omission of amyloid features resulted in the largest dip in AUC for ADNI (Table 1). Conclusion ML classifiers utilizing multimodal imaging are predictive of progression to MCI in preclinical AD individuals and are robust to external testing sites.

The nucleus accumbens shell (NAcSh) and the ventral pallidum (VP) are critical for reward processing, although the question of how coordinated activity within these nuclei orchestrates reward valuation and consumption remains unclear. Inhibition of NAcSh firing is necessary for reward consumption, but the source of this inhibition remains unknown. Here, we report that a subpopulation of VP neurons, the ventral arkypallidal (vArky) neurons, project back to the NAcSh, where they inhibit NAcSh neurons in vivo in mice. Consistent with this pathway driving reward consumption via inhibition of the NAcSh, calcium activity of vArky neurons scaled with reward palatability (which was dissociable from reward seeking) and predicted the subsequent drinking behavior during a free-access paradigm. Activation of the VP–NAcSh pathway increased ongoing reward consumption while amplifying hedonic reactions to reward. These results establish a pivotal role for vArky neurons in the promotion of reward consumption through modulation of NAcSh firing in a value-dependent manner. Inhibition of nucleus accumbens neurons is crucial for reward consumption. Vachez, Tooley et al. characterize arkypallidal neurons in the ventral pallidum that inhibit accumbal neurons to sustain reward consumption in a value-dependent manner.

INTRODUCTION Previous approaches pursuing in vivo staging of tau pathology in Alzheimer's disease (AD) have typically relied on neuropathologically defined criteria. In using predefined systems, these studies may miss spatial deposition patterns which are informative of disease progression. METHODS We selected discovery (n = 418) and replication (n = 132) cohorts with flortaucipir imaging. Non‐negative matrix factorization (NMF) was applied to learn tau covariance patterns and develop a tau staging system. Flortaucipir components were also validated by comparison with amyloid burden, gray matter loss, and the expression of AD‐related genes. RESULTS We found eight flortaucipir covariance patterns which were reproducible and overlapped with relevant gene expression maps. Tau stages were associated with AD severity as indexed by dementia status and neuropsychological performance. Comparisons of flortaucipir uptake with amyloid and atrophy also supported our model of tau progression. DISCUSSION Data‐driven decomposition of flortaucipir uptake provides a novel framework for tau staging which complements existing systems. Highlights NMF reveals patterns of tau deposition in AD. Data‐driven staging of flortaucipir tracks AD severity. Learned flortaucipir patterns overlap with AD‐related gene expression.