Using a Novel Digital Go/No-Go to Dissociate Intra-subject Temporal Fluctuations in Reaction Time and Accuracy.

Impulsivity can be a risk factor for serious complications for those with mood disorders. To understand intra-individual impulsivity variability, we analyzed longitudinal data of a novel gamified digital Go/No-Go (GNG) task in a clinical sample (n=43 mood disorder participants, n=17 healthy controls) and an open-science sample (n=121, self-reported diagnoses). With repeated measurements within-subject, we disentangled two aspects of GNG: reaction time and accuracy in response inhibition (i.e., incorrect No-Go trials) with respect to diurnal and potential learning effects. Mixed-effects models showed diurnal effects in reaction time but not accuracy, with a significant effect of hour on reaction time in the clinical sample and the open-science sample. Moreover, subjects improved on their response inhibition but not reaction time. Additionally, significant interactions emerged between depression symptom severity and time-of-day in both samples, supporting that repeated administration of our GNG task can yield mood-dependent circadian rhythm-aware biomarkers of neurocognitive function.

Temporal Alterations in White Matter in An App Knock-In Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) is the most common form of dementia and results in neurodegeneration and cognitive impairment. White matter (WM) is affected in AD and has implications for neural circuitry and cognitive function. The trajectory of these changes across age, however, is still not well understood, especially at earlier stages in life. To address this, we used the AppNL-G-F/NL-G-F knock-in (APPKI) mouse model that harbors a single copy knock-in of the human amyloid precursor protein (APP) gene with three familial AD mutations. We performed in vivo diffusion tensor imaging (DTI) to study how the structural properties of the brain change across age in the context of AD. In late age APPKI mice, we observed reduced fractional anisotropy (FA), a proxy of WM integrity, in multiple brain regions, including the hippocampus, anterior commissure (AC), neocortex, and hypothalamus. At the cellular level, we observed greater numbers of oligodendrocytes in middle age (prior to observations in DTI) in both the AC, a major interhemispheric WM tract, and the hippocampus, which is involved in memory and heavily affected in AD, prior to observations in DTI. Proteomics analysis of the hippocampus also revealed altered expression of oligodendrocyte-related proteins with age and in APPKI mice. Together, these results help to improve our understanding of the development of AD pathology with age, and imply that middle age may be an important temporal window for potential therapeutic intervention.

A Review on Smartphone Keystroke Dynamics as a Digital Biomarker for Understanding Neurocognitive Functioning.

Can digital technologies provide a passive unobtrusive means to observe and study cognition outside of the laboratory? Previously, cognitive assessments and monitoring were conducted in a laboratory or clinical setting, allowing for a cross-sectional glimpse of cognitive states. In the last decade, researchers have been utilizing technological advances and devices to explore ways of assessing cognition in the real world. We propose that the virtual keyboard of smartphones, an increasingly ubiquitous digital device, can provide the ideal conduit for passive data collection to study cognition. Passive data collection occurs without the active engagement of a participant and allows for near-continuous, objective data collection. Most importantly, this data collection can occur in the real world, capturing authentic datapoints. This method of data collection and its analyses provide a more comprehensive and potentially more suitable insight into cognitive states, as intra-individual cognitive fluctuations over time have shown to be an early manifestation of cognitive decline. We review different ways passive data, centered around keystroke dynamics, collected from smartphones, have been used to assess and evaluate cognition. We also discuss gaps in the literature where future directions of utilizing passive data can continue to provide inferences into cognition and elaborate on the importance of digital data privacy and consent.

A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity.

The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.

Signed graph representation learning for functional-to-structural brain network mapping.

MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory.

Being able to track and predict fluctuations in symptoms of mental health disorders such as bipolar disorder outside the clinic walls is critical for expanding access to care for the global population. To that end, we analyze a dataset of 291 individuals from a smartphone app targeted at bipolar disorder, which contains rich details about their smartphone interactions (including typing dynamics and accelerometer motion) collected everyday over several months, along with more traditional clinical features. The aim is to evaluate whether smartphone accelerometer data could serve as a proxy for traditional clinical data, either by itself or in combination with typing dynamics. Results show that accelerometer data improves the predictive performance of machine learning models by nearly 5% over those previously reported in the literature based only on clinical data and typing dynamics. This suggests it is possible to elicit essentially the same "information" about bipolar symptomology using different data sources, in a variety of settings.

A Hierarchical Graph Learning Model for Brain Network Regression Analysis.

Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.

Hippocampal functional connectivity across age in an App knock-in mouse model of Alzheimer's disease.

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disease. The early processes of AD, however, are not fully understood and likely begin years before symptoms manifest. Importantly, disruption of the default mode network, including the hippocampus, has been implicated in AD. Methods: To examine the role of functional network connectivity changes in the early stages of AD, we performed resting-state functional magnetic resonance imaging (rs-fMRI) using a mouse model harboring three familial AD mutations (App NL-G-F/NL-G-F knock-in, APPKI) in female mice in early, middle, and late age groups. The interhemispheric and intrahemispheric functional connectivity (FC) of the hippocampus was modeled across age. Results: We observed higher interhemispheric functional connectivity (FC) in the hippocampus across age. This was reduced, however, in APPKI mice in later age. Further, we observed loss of hemispheric asymmetry in FC in APPKI mice. Discussion: Together, this suggests that there are early changes in hippocampal FC prior to heavy onset of amyloid ß plaques, and which may be clinically relevant as an early biomarker of AD.

rest2vec: Vectorizing the resting-state functional connectome using graph embedding.

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used in connectomics for studying the functional relationships between regions of the human brain. rs-fMRI connectomics, however, has inherent analytical challenges, such as how to properly model negative correlations between BOLD time series. In addition, functional relationships between brain regions do not necessarily correspond to their anatomical distance, making the functional topology of the brain less well understood. Recent machine learning techniques, such as word2vec, have used embedding methods to map high-dimensional data into vector spaces, where words with more similar meanings are mapped closer to one another. Inspired by this approach, we have developed the graph embedding pipeline rest2vec for studying the vector space of functional connectomes. We demonstrate how rest2vec uses the phase angle spatial embedding (PhASE) method with dimensionality reduction to embed the connectome into lower dimensions, where the functional definition of a brain region is represented continuously in an intrinsic "functional space." Furthermore, we show how the "functional distance" between brain regions in this space can be applied to discover biologically-relevant connectivity gradients. Interestingly, rest2vec can be conceptualized in the context of the recently proposed maximum mean discrepancy (MMD) metric, followed by a double-centering approach seen in kernel PCA. In sum, rest2vec creates a low-dimensional representation of the rs-fMRI connectome where brain regions are mapped according to their functional relationships, giving a more informed understanding of the functional organization of the brain.

Real-Time Monitoring: A Key Element in Personalized Health and Precision Health.

Current management of psychiatric disorders relies heavily on retrospective, subjective reports provided by patients and their families. Consequently, psychiatric services are often provisioned inefficiently and with suboptimal outcomes. Recent advances in computing and sensor technologies have enabled the development of real-time monitoring systems for the diagnosis and management of psychiatric disorders. The state of these technologies is rapidly evolving, with passive monitoring and predictive modeling as two areas that have great potential to affect psychiatric care. Although outpatient psychiatry probably stands to benefit the most from the use of real-time monitoring technologies, there are also several ways in which inpatient psychiatry may also benefit. As the capabilities of these technologies increase and their use becomes more common, many ethical and legal issues will need to be considered. The role of governmental regulatory bodies and nongovernmental organizations in providing oversight of the implementation of these technologies is an active area of discussion.

A GENERALIZED FRAMEWORK OF PATHLENGTH ASSOCIATED COMMUNITY ESTIMATION FOR BRAIN STRUCTURAL NETWORK.

Diffusion MRI-derived brain structural network has been widely used in brain research and community or modular structure is one of popular network features, which can be extracted from network edge-derived pathlengths. Conceptually, brain structural network edges represent the connecting strength between pair of nodes, thus non-negative. The pathlength. Many studies have demonstrated that each brain network edge can be affected by many confounding factors (e.g. age, sex, etc.) and this influence varies on each edge. However, after applying generalized linear regression to remove those confounding's effects, some network edges may become negative, which leads to barriers in extracting the community structure. In this study, we propose a novel generalized framework to solve this negative edge issue in extracting the modular structure from brain structural network. We have compared our framework with traditional Q method. The results clearly demonstrated that our framework has significant advantages in both stability and sensitivity.

From Return of Information to Return of Value: Ethical Considerations when Sharing Individual-Level Research Data.

The implementation of digital health technologies into research studies for Alzheimer's disease and other clinical populations is on the rise. Digital tools and strategies create opportunities to further expand the framework for conducting research beyond the traditional medical research model. The combination of participatory and community-based research methods, electronic health records, and the creation of multi-dimensional, large-scale research platforms to support precision medicine, along with the Internet of Things era, have led to more engaged and informed research participants. Research participants increasingly possess an expectation they will play a critical role as partners in the design and conduct of research. Moreover, there is growing interest among research participants to have access to individual-level research data in real-time and/or at study completion. The traditional medical research model is largely one-directional where participants contribute data that is analyzed by researchers to yield generalizable knowledge. In this Ethics Review, we discuss a framework for a more nuanced intermediate research model, which is largely bidirectional and individually customized. Based on the seven ethical guidelines adopted by the National Institutes of Health, we speak to the ethical challenges of this intermediate type research. We also introduce a concept we are calling "MyTerms," in which prospective participants tailor the terms and conditions of informed consent to their personalized preferences for receiving information, including research results. Digital health technologies offer a convenient and flexible approach for researchers to develop protocols that make it possible for participants to obtain access to their study data in a personalized and meaningful way.

Differentiating weight-restored anorexia nervosa and body dysmorphic disorder using neuroimaging and psychometric markers.

Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are potentially life-threatening conditions whose partially overlapping phenomenology-distorted perception of appearance, obsessions/compulsions, and limited insight-can make diagnostic distinction difficult in some cases. Accurate diagnosis is crucial, as the effective treatments for AN and BDD differ. To improve diagnostic accuracy and clarify the contributions of each of the multiple underlying factors, we developed a two-stage machine learning model that uses multimodal, neurobiology-based, and symptom-based quantitative data as features: task-based functional magnetic resonance imaging data using body visual stimuli, graph theory metrics of white matter connectivity from diffusor tensor imaging, and anxiety, depression, and insight psychometric scores. In a sample of unmedicated adults with BDD (n = 29), unmedicated adults with weight-restored AN (n = 24), and healthy controls (n = 31), the resulting model labeled individuals with an accuracy of 76%, significantly better than the chance accuracy of 35% ([Formula: see text]). In the multivariate model, reduced white matter global efficiency and better insight were associated more with AN than with BDD. These results improve our understanding of the relative contributions of the neurobiological characteristics and symptoms of these disorders. Moreover, this approach has the potential to aid clinicians in diagnosis, thereby leading to more tailored therapy.

NeuroCave: A web-based immersive visualization platform for exploring connectome datasets.

We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome-either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)-in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms.

From Default Mode Network to the Basal Configuration: Sex Differences in the Resting-State Brain Connectivity as a Function of Age and Their Clinical Correlates.

Connectomics is a framework that models brain structure and function interconnectivity as a network, rather than narrowly focusing on select regions-of-interest. MRI-derived connectomes can be structural, usually based on diffusion-weighted MR imaging, or functional, usually formed by examining fMRI blood-oxygen-level-dependent (BOLD) signal correlations. Recently, we developed a novel method for assessing the hierarchical modularity of functional brain networks-the probability associated community estimation (PACE). PACE uniquely permits a dual formulation, thus yielding equivalent connectome modular structure regardless of whether positive or negative edges are considered. This method was rigorously validated using the 1,000 functional connectomes project data set (F1000, RRID:SCR_005361) (1) and the Human Connectome Project (HCP, RRID:SCR_006942) (2, 3) and we reported novel sex differences in resting-state connectivity not previously reported. (4) This study further examines sex differences in regard to hierarchical modularity as a function of age and clinical correlates, with findings supporting a basal configuration framework as a more nuanced and dynamic way of conceptualizing the resting-state connectome that is modulated by both age and sex. Our results showed that differences in connectivity between men and women in the 22-25 age range were not significantly different. However, these same non-significant differences attained significance in both the 26-30 age group (p = 0.003) and the 31-35 age group (p < 0.001). At the most global level, areas of diverging sex difference include parts of the prefrontal cortex and the temporal lobe, amygdala, hippocampus, inferior parietal lobule, posterior cingulate, and precuneus. Further, we identified statistically different self-reported summary scores of inattention, hyperactivity, and anxiety problems between men and women. These self-reports additionally divergently interact with age and the basal configuration between sexes.

Risk of Diabetes Hospitalization or Diabetes Drug Intensification in Patients With Depression and Diabetes Using Second-Generation Antipsychotics Compared to Other Depression Therapies.

OBJECTIVE: Use of second-generation antipsychotics (SGAs) for treatment of depression has increased, and patients with depression and comorbid diabetes or cardiovascular disease are more likely to use SGAs than those without these conditions. We compared SGA and non-SGA depression pharmacotherapies on the risk of diabetes hospitalization or treatment intensification in adults with depression and preexisting diabetes. METHODS: This was a retrospective cohort study of US commercially insured adults (2009-2015 Truven MarketScan Commercial Claims and Encounters Database) aged 18-64 years old with type 2 diabetes mellitus and unipolar depression previously treated with a selective serotonin reuptake inhibitor or serotonin-norepinephrine reuptake inhibitor. New users of SGAs versus non-SGAs, as well as specific treatments (aripiprazole, quetiapine, bupropion, mirtazapine, and tricyclic antidepressants [TCAs]) were matched on class/medication-specific high-dimensional propensity score. Cox proportional hazard models were used to compare the risk of diabetes-related hospitalization or treatment intensification. RESULTS: We identified 6,625 SGA (aripiprazole = 3,461; quetiapine = 1,977; other = 1,187) and 23,921 non-SGA patients for inclusion (bupropion = 15,511; mirtazapine = 1,837; TCAs = 5,989; other = 584) with a mean age of 51 years. In the matched cohort, the rate of diabetes-related hospitalization or drug intensification was 47.9 per 100 person-years in the SGA group and 43.5 per 100 person-years in the non-SGA group (adjusted hazard ratio [aHR] = 1.03; 95% CI, 0.96-1.11). When comparing treatment subgroups, the risk of events was lower for bupropion versus TCAs (aHR = 0.85; 95% CI, 0.76-0.98), quetiapine versus mirtazapine (aHR = 0.82; 95% CI, 0.67-0.99), and quetiapine versus TCAs (aHR = 0.84; 95% CI, 0.72-0.98). For other comparisons, differences were small and not statistically significant. CONCLUSIONS: While drug-specific effects on risk of diabetes hospitalization or treatment intensification most likely guide clinical decision making, we observed only modest differences in risk. The overall impact of SGAs on diabetes control depends not only on direct effects on glucose metabolism but also on effectiveness of depression symptom relief. Future studies evaluating other diabetes outcomes (glycosylated hemoglobin, diabetes complications) are needed.

The impact of depression medications on oral antidiabetic drug adherence in patients with diabetes and depression.

AIMS: To compare adherence and persistence to oral antidiabetic drugs (OAD) between patients who are new users of second generation antipsychotics (SGA) versus new users of other depression therapies in adults with type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD). METHODS: Adults 18-64 years with previously-treated T2DM and MDD (past OAD and SSRI/SNRI use) who are new users of SGA or non-SGA therapies (bupropion, lithium, mirtazapine, thyroid hormone, tricyclic antidepressant) were identified in the 2009-2015 MarketScan® Commercial Claims and Encounters database. Multivariate regression models were used to determine the odds of a ≥10% decline in OAD adherence over 180- and 365-days, and time to OAD discontinuation, adjusting for differences between groups. RESULTS: A total of 8664 (21.5% SGA), 8311 (22.1% SGA), and 17,524 (21.3% SGA) patients met inclusion criteria for the 180-day adherence, 365-day adherence, and persistence cohorts, respectively. Over 180-days, 16.6% of SGA and 13.3% of non-SGA initiators had a ≥10% decline in OAD adherence (adjusted odds ratio [OR] = 1.41, 95% CI 1.21-1.63). Over 365-days, 22.3% of SGA and 18.9% of non-SGA initiators had a ≥ 10% decline (OR = 1.34, 95% CI 1.17-1.53). Time to OAD discontinuation was similar between groups (adjusted hazard ratio = 1.03, 95% CI 0.94-1.12). CONCLUSION: Use of SGA was associated with a 1.3-1.4 times higher odds of a ≥10% decline in OAD adherence. Adherence to OAD is critical for optimal diabetes control and reductions in this magnitude may impact A1C. Close monitoring of OAD adherence after SGA initiation is warranted.

Baseline connectome modular abnormalities in the childhood phase of a longitudinal study on individuals with chromosome 22q11.2 deletion syndrome.

Occurring in at least 1 in 3,000 live births, chromosome 22q11.2 deletion syndrome (22q11DS) produces a complex phenotype that includes a constellation of medical complications such as congenital cardiac defects, immune deficiency, velopharyngeal dysfunction, and characteristic facial dysmorphic features. There is also an increased incidence of psychiatric diagnosis, especially intellectual disability and ADHD in childhood, lifelong anxiety, and a strikingly high rate of schizophrenia spectrum disorders, which occur in around 30% of adults with 22q11DS. Using innovative computational connectomics, we studied how 22q11DS affects high-level network signatures of hierarchical modularity and its intrinsic geometry in 55 children with confirmed 22q11DS and 27 Typically Developing (TD) children. Results identified 3 subgroups within our 22q11DS sample using a K-means clustering approach based on several midline structural measures-of-interests. Each subgroup exhibited distinct patterns of connectome abnormalities. Subtype 1, containing individuals with generally healthy-looking brains, exhibited no significant differences in either modularity or intrinsic geometry when compared with TD. By contrast, the more anomalous 22q11DS Subtypes 2 and 3 brains revealed significant modular differences in the right hemisphere, while Subtype 3 (the most anomalous anatomy) further exhibited significantly abnormal connectome intrinsic geometry in the form of left-right temporal disintegration. Taken together, our findings supported an overall picture of (a) anterior-posteriorly differential interlobar frontotemporal/frontoparietal dysconnectivity in Subtypes 2 and 3 and (b) differential intralobar dysconnectivity in Subtype 3. Our ongoing studies are focusing on whether these subtypes and their connnectome signatures might be valid biomarkers for predicting the degree of psychosis-proneness risk found in 22q11DS. Hum Brain Mapp 39:232-248, 2018. © 2017 Wiley Periodicals, Inc.

The significance of negative correlations in brain connectivity.

Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative. We validated this novel probability-based modularity approach on two independent publicly-available resting-state connectome data sets (the Human Connectome Project [HCP] and the 1,000 functional connectomes) and demonstrated that negative correlations alone are sufficient in understanding resting-state modularity. In fact, this approach (a) permits a dual formulation, leading to equivalent solutions regardless of whether one considers positive or negative edges; (b) is theoretically linked to the Ising model defined on the connectome, thus yielding modularity result that maximizes data likelihood. Additionally, we were able to detect novel and consistent sex differences in modularity in both data sets. As data sets like HCP become widely available for analysis by the neuroscience community at large, alternative and perhaps more advantageous computational tools to understand the neurobiological information of negative edges in fMRI connectomes are increasingly important.

Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer's disease deficits.

PURPOSE: In diffusion MRI (dMRI), fractional anisotropy derived from the single-tensor model (FADTI ) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors. METHODS: We compared the ability of standard FADTI and TDF-derived FA (FATDF ), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume. RESULTS: Across angular resolutions and statistical tests, FATDF showed larger effect sizes than FADTI , particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies. CONCLUSION: The TDF "corrected" form of FA may be a more sensitive and accurate alternative to the commonly used FADTI , even in clinical quality dMRI data. Magn Reson Med 78:2322-2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.